Proceedings of the 8th International Conference on Water Resource and Environment: Proceedings of WRE2022 (Lecture Notes in Civil Engineering, 341) 9819919185, 9789819919185

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Lecture Notes in Civil Engineering

Chih-Huang Weng   Editor

Proceedings of the 8th International Conference on Water Resource and Environment Proceedings of WRE2022

Lecture Notes in Civil Engineering Volume 341

Series Editors Marco di Prisco, Politecnico di Milano, Milano, Italy Sheng-Hong Chen, School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan, China Ioannis Vayas, Institute of Steel Structures, National Technical University of Athens, Athens, Greece Sanjay Kumar Shukla, School of Engineering, Edith Cowan University, Joondalup, WA, Australia Anuj Sharma, Iowa State University, Ames, IA, USA Nagesh Kumar, Department of Civil Engineering, Indian Institute of Science Bangalore, Bengaluru, Karnataka, India Chien Ming Wang, School of Civil Engineering, The University of Queensland, Brisbane, QLD, Australia

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Chih-Huang Weng Editor

Proceedings of the 8th International Conference on Water Resource and Environment Proceedings of WRE2022

Editor Chih-Huang Weng Department of Civil Engineering I-Shou University Kaohsiung, Taiwan

ISSN 2366-2557 ISSN 2366-2565 (electronic) Lecture Notes in Civil Engineering ISBN 978-981-99-1918-5 ISBN 978-981-99-1919-2 (eBook) https://doi.org/10.1007/978-981-99-1919-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023, corrected publication 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Organization

The 8th International Conference on Water Resource and Environment (WRE2022) Technical Program Committee Chair Dr. Chih-Huang Weng, Distinguished Professor, Department of Civil Engineering, I-Shou University, Taiwan

Technical Program Committee Dr. Azza Moustafa Abdelaty, Professor, National Research Center, Egypt Dr. AbdAllah Tharwat Abdelkhalik, Professor, Jazan University, Saudi Arabia Dr. Mohd Elmuntasir Ahmed, Research Scientist, Kuwait Institute for Scientific Research, Kuwait Dr. R. S. Ajin, Professor, Kerala State Emergency Operations Centre (KSEOC), India Dr. Pedro Antonio Guido Aldana, Mexican Institute of Water Technology, Mexico Dr. Chunjiang An, Concordia University, Canada Dr. Michael O. Angelidis, Professor, University of the Aegean, Greece Dr. Amelia Araujo, Centre for Environment Fisheries and Aquaculture Science (Cefas), UK Dr. Habib Bouzid, University of Mostaganem, Algeria Dr. Hi Ryong Byun, Professor, Pukyong National University, South Korea Dr. Marco Casini, Professor, Sapienza University of Rome, Italy Dr. Helder I. Chaminé, Professor, ISEP|Polytechnic of Porto, Portugal Dr. Jiongfeng Chen, University of California at Davis, USA Dr. Marcelo Enrique Conti, Professor, Sapienza University of Rome, Italy Dr. Jian Deng, Associate Professor, Lakehead University, Canada

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Organization

Dr. Sébastien Déon, Associate Professor, Université de Bourgogne Franche-Comté, France Dr Angela Dikou, Coral Reef Ecology, Greece Dr. Khac-Uan Do, Associate Professor, Hanoi University of Science and Technology, Vietnam Dr. Porowska Dorota, Associate Professor, University of Warsaw, Poland Dr. Christophe Douez, Associate Professor, Université d’Artois, France Dr. Ricardo Gomes, Professor, Polytechnic of Leiria, Portugal Dr. Angela Gorgoglione, Universidad de la Républica, Uruguay Dr. Abdelkader Hamlat, University of Laghouat, Algeria Dr. Dawei Han, Professor, University of Bristol, UK Dr. Hao Han, Institute of Earth Environment, Chinese Academy of Sciences, China Dr. Jianxun (Jennifer) He, University of Calgary, Canada Dr. Vlassios Hrissanthou, Professor Emeritus, Democritus University of Thrace, Greece Dr. Chin-Pao (C. P.) Huang, Professor, University of Delaware, USA Dr. Yassine Kadmi, Associate Professor, Université de Lille, France Dr. M. L. Kansal, Professor, Indian Institute of Technology Roorkee, India Dr. Komali Kantamaneni, University of Central Lancashire, UK Dr. Jamal Khatib, Professor, University of Wolverhampton, UK Dr. Ignacy Kitowski, Associate Professor, University of Life Science in Lublin, Poland Dr. Peiyue Li, Professor, Chang’an University, China Dr. Teik-Thye Lim, Professor, Nanyang Technological University (NTU), Singapore Dr. Zhi-Qing (ZQ) Lin, Professor, Southern Illinois University—Edwardsville, USA Dr. Oleg D. Linnikov, Russian Academy of Sciences, Russia Dr. Julia Lu, Professor, Ryerson University, Canada Dr. Gordana Medunic, Associate Professor, University of Zagreb, Croatia Dr. Bogusław Michalec, Professor, University of Agriculture in Krakow, Poland Dr. Seyedmehdi Mohammadizadeh, Universidade Estadual de Campinas (UNICAMP), Brazil Dr. Ashutosh Mohanty, Professor, Shoolini University, India Dr. Saad Mulahasan, University of Mustansiriyah, Iraq Dr. Shou-Qing Ni, Professor, Shandong University, China Dr. Giuseppe Oliveto, Professor, University of Basilicata, Italy Dr. Kaveh Ostad-Ali-Askari, Isfahan University of Technology (IUT), Iran Dr. Elena María Otazo-Sanchez, Professor, Hidalgo State Autonomous University, México Dr. Evan K. Paleologos, Professor, Abu Dhabi University, UAE Dr. Arkadiusz Piwowar, Associate Professor, Wroclaw University of Economics, Poland Dr. Endita Prima Ari Pratiwi, Universitas Gadjah Mada, Indonesia Dr. Fei Qi, Professor, Beijing Forestry University, China Dr. Mohammad Rahimi-Gorji, Ghent University, Belgium Dr. P. R. Rakhecha, Former Director, Indian Institute of Tropical Meteorology, India

Organization

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Dr. Anoop Kumar Shukla, Manipal Academy of Higher Education, India Dr. Dmitry Strunin, Associate Professor, University of Southern Queensland, Australia Dr. Narong Touch, Tokyo University of Agriculture, Japan Dr. Yiu Fai Tsang, Associate Professor, The Education University of Hong Kong, China Dr. Dongqi Wang, Professor, East China Normal University, China Dr. Foo Keng Yuen, Professor, Universiti Sains Malaysia, Malaysia Dr. George N. Zaimes, Professor, International Hellenic University, Greece Dr. Hua Zhong, Professor, Wuhan University, China Dr. Hui Zhu, Professor, Chinese Academy of Science, China

Preface

The 8th International Conference on Water Resource and Environment (WRE2022) was conducted virtually during November 1st–4th, 2022. COVID-19 is still out of control for many countries, and the conference is committed to reducing the risk of adverse health impacts to all of our participants. Therefore, after thoughtful consideration, WRE2022 was converted from onsite conference in Xi’an to a full online conference by using Microsoft Teams. It aims to provide a unique platform for researchers, scientists, engineers and professionals from all over the world to present their latest research results and new ideas in terms of water resource and environment. As an annually held conference, this conference has been successfully held Online in 2022, 2021 and 2020, and physically held in Beijing, Shanghai, Qingdao, Kaohsiung, and Macau in the past years. We received 195 submissions in 2022, 34 of them were accepted after an intense and strict peer review. The acceptance rate is about 18%. On behalf of the conference committees, I want to acknowledge the work of all those who have contributed in making the conference a success. We acknowledge steady support and encouragement by the Technical Program Committee, and are very grateful to the anonymous reviewers, whose invaluable expertise and efforts have led to the selection, out of 195 submissions. Last but not least, we thank all the authors and participants for contributing to the success of the conference with their hard work and commitment. It is hoped that the content of this book will lead to significant contributions to the knowledge in these up-to-date scientific fields. Kaohsiung, Taiwan

Chih-Huang Weng

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Contents

Water Resources and Hydrology A Literature Review on Machine Learning to Optimize Water Network Management Using Natural Language Processing . . . . . . . . . . . . Alicia Robles-Velasco, María Granados-Santos, and Luis Onieva

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The Prediction of Hydrometeorology Variables Using the Method of Recurrent Neuronal Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. A. Díaz León, M. A. Olarte Escobar, and M. Jara García

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“I Want to …Build”: Research on Dam Transshipment Facilities of Typical Hydropower Stations on the Right Bank of Jinsha River . . . . . Lingling Liu, Yongchang Wang, and Jianling Hou

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Exploration and Application of Identifying Displacement of Regional Rainfall Centers Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xintong Jiang, Xiaolong Liao, Xujian Quan, and Yixuan Zhong

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Adaptive Reservoir Operation Management Considering the Influence of Inter-Basin Water Transfer Project on Inflow . . . . . . . . . Xiaoqi Zhang, Yuan Yang, Yongqiang Wang, and Yinghai Li

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Method of Low-Pressure Pipe Layout for Peach Tree Irrigation in Hilly Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Azhong Dong, Miao Hou, Zhihuan Wang, Yan Ju, and Wenye Zhang

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Comprehensive Benefit Evaluation of River and Lake Connection Project in Western Jilin Province of China . . . . . . . . . . . . . . . . . . . . . . . . . . . Yan Wang

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Design of Reservoir’s Seasonal FLWLs Under the Influence of Its Upstream Cascade Reservoirs Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Yinghai Li, Yuan Yang, Yongqiang Wang, Qingqing Xia, Guo Yu, and Shan e hyder Soomro

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Multi-objective Flood Control Scheduling Study of the Suyukou Ditch Considering Flood Control Safety of the Downstream River . . . . . . 117 Yunke Xiao, Wan Liu, Yongqiang Wang, and Deyu Zhong Evolution Characteristics and Impact Evaluation of Meteorological and Hydrological Drought in the Jinsha River Basin . . . . . . . . . . . . . . . . . . 129 Yuanzhi Tang, Tailai Gao, Xiaoxuan Jiang, and Junjun Huo Mutation Analysis of Runoff–Sediment Combination and Profitable Frequency of Wetness–Dryness Encounter in the Middle Yellow River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Xinjie Li, Yuanjian Wang, Qiang Wang, Linlin Li, and Chunfeng Hao Feasibility Study on Air Film Rainwater Collection and Treatment Systems in Extreme Rainfalls Weather . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Shaohua Hu Rainfall-Runoff Modelling in the Kouilou-Niari Catchment Area in South-West of Congo-Brazzaville . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Christian Tathy, Christian Ngoma Mvoundou, Romain Richard Nière, and Harmel Obami-Ondon Analysis of Response Law of Rainstorm Under Different Microtopography Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Jun Guo and Li Li Research on Data Cleaning Method for Dispatching and Operation of Cascade Hydropower Stations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 Hang Lin, Shuai Xie, Zhengyang Tang, Yang Xu, and Yongqiang Wang Short-Term Downstream Water Level Prediction Model for Three Gorges–Gezhouba Cascade Reservoir Operation Based on LSTM Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 Sen Zhang, Zhilong Xiang, Yongqiang Wang, and Shuai Xie Water Supply System and Water Pollution Review of Water Quality Prediction Methods . . . . . . . . . . . . . . . . . . . . . . . . 237 Zhen Chen, Limin Liu, Yongsheng Wang, and Jing Gao Ultrasonic Disintegration as a Fast and Simple Method for Chemical Fractionation of Heavy Metals in Sewage Sludge: A Preliminary Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 Malwina Tytła Research Progress on Removal of Heavy Metal Ions in Water by Biological and Hydrogel Sorbent Materials . . . . . . . . . . . . . . . . . . . . . . . . 279 Weiwei Zhou, Yunwei Li, Kun You, Jingxin Hua, Fanhui Meng, Junling Zhao, Xuewu Zhu, and Daoji Wu

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Phytoremediation of Stormwater by Floating Treatment Wetland . . . . . . 295 Md Nuruzzaman, A. H. M. Faisal Anwar, and Ranjan Sarukkalige The Effect of Style and Scale of Information on Public Willingness to Conduct Water-Saving Behaviors in China . . . . . . . . . . . . . . . . . . . . . . . . 309 Tianze Xie Experimental Study on Isotopic Fractionation Factor and Evaporation Rate in Soil Water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Tao Wang, Haili Xu, and Ming Li The Impact of Hydraulic Hubs on the Spatial Variation of Water Quality in the Middle Reaches of the Hanjiang River and an Analysis of the Driving Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 Di Jia, Li Lin, Xiong Pan, Lei Dong, and Sheng Zhang Research on Model Reconstruction of Urban Water Supply and Drainage System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 Xinying Fan, Ming Cai, Xiaotao Gao, Jian Fu, Hao Ma, and Han Li Research on Pollution Tracing in Drinking Water Source by Space–Air–Ground Integrated System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357 Shaobo Wang, Mingzhan Wu, and Wanhua Yuan Aquatic Ecosystems and Environmental Sciences Experimental Study on Flocculation Effect of Waste Construction Mud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 Feifei Hu, Wenrui Tian, Qiaohuan Guo, Jiahuan Zhang, Li Niu, and Min Wang Experimental Study on Preparation of Unburned Ceramsite from Waste Mud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 Wenrui Tian, Feifei Hu, Qiaohuan Guo, Jiahuan Zhang, Qian Wang, and Min Wang Research on Development Trend of Ocean Energy at China and Abroad Based on Bibliometrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389 Xin Li, Haoxian Dong, Cailin Zhang, Changlei Ma, Yuxin Liu, and Haifeng Wang Prediction of Floating Recovery of Waste Plastics from Froth Flotation Using Artificial Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 Patricio Salvador R. Bicaldo and Alvin R. Caparanga Density, Distribution, and Chemical Composition of Microplastics in Qinghai Lake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415 Daye He, Xufeng Mao, Haipeng Gao, Ke Chen, Shanlei Bao, Huanhuan Zhang, and Xiaoyan Dou

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Research Progress on the Effects of Droughts and Floods on Nitrogen in Soil–Plant Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431 Wuxia Bi, Dawei Zhang, Baisha Weng, Zhaoyu Dong, and Denghua Yan Study on Fish Swimming Behavior Affected by Obstacles Based on Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443 Yi Zhou, Yuhui Yang, Yu Han, Huhu Liu, Yue Zhang, and Fengcong Jia Preliminarily Classified and Graded Prediction on River Ecological Water Demand: A Case of Xiongan New Area . . . . . . . . . . . . . . 461 Baodeng Hou, Ruixiang Yang, Xin Wang, Yuyan Zhou, and Yong Zhao Study on the Influence of Multiple Factors on Submerged Macrophyte Growth with Physics Experiments . . . . . . . . . . . . . . . . . . . . . . . 477 Chen Chen, Yanhao Zheng, Yu Deng, Yao Wu, Xiangxiang Zhang, Min Gan, and Xijun Lai Correction to: Ultrasonic Disintegration as a Fast and Simple Method for Chemical Fractionation of Heavy Metals in Sewage Sludge: A Preliminary Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Malwina Tytła

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Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489

About the Editor

Chih-Huang Weng is the Chairman of Department of Civil Engineering at I-Shou University, Taiwan. He also served as vice-president of North Kaohsiung Community University, Taiwan. He received his M.S. and Ph.D. degrees in 1990 and 1994, respectively, from the Department of Civil Engineering of the University of Delaware, USA. He is serving as the Associate Editor of Environmental Geochemistry and Health (Springer) and on the Editorial Board Panel Member of Coloration Technology (Wiley). He has also served as a Guest Editor of SCI journals, such as Agricultural Water Management (Elsevier) and Environmental Science and Pollution Research (Springer). He has also organized and chaired several international conferences. His main research interests focus on using advanced oxidation processes and adsorption for the treatment of wastewater and bacteria inactivation, ground water modeling, and application of electrokinetic technologies to soil remediation/sludge treatment/activated carbon regeneration.

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A Literature Review on Machine Learning to Optimize Water Network Management Using Natural Language Processing Alicia Robles-Velasco , María Granados-Santos, and Luis Onieva

Abstract In this study, natural language processing is proposed to automatize the extraction of information from an extensive number of scientific manuscripts on the use of machine learning in the water industry. Concretely, the articles’ search focuses on the literature published between 2013 and 2022 (both years included) that present or talk about the implementation of machine learning techniques to predict pipe failures in water distribution networks. For this purpose, it is a condition for the papers gathered to include the terms ‘pipe failure’, ‘water distribution or supply’ and ‘machine learning’, among others. The study discusses three aspects: (1) the use of different machine learning models; (2) some characteristics about the data processing, training, and validation phases; and (3) the variables or factors related to water distribution networks that are more popular according to the collected papers. This discussion is performed by analyzing the frequency of appearance of certain terms in the documents. Furthermore, the connection between the frequencies of a pair of terms is examined by using scatter plot graphs. Keywords Literature review · Machine learning · Natural language processing · Pipe failure · Water distribution networks · Water network management

1 Introduction According to the report recently published by The European Federation of National Water Services [1], the water supply network in Europe has approximately 4.3 million kilometers of pipes. Pipe failures, among other reasons such as pipe leaks and nonregister supplies, cause the loss of about 25% of treated water. This problem does A. Robles-Velasco (B) · M. Granados-Santos · L. Onieva Dpto. de Organización Industrial y Gestión de Empresas II, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Camino de los Descubrimientos S/N, 41092 Seville, Spain e-mail: [email protected] L. Onieva e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_1

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not only concern Europe. The water distribution system in USA contains 2.2 million miles of supply pipes, which corresponds to 3.5 million kilometers, and it is estimated that there are daily losses due to pipe breaks of about 6 billion gallons of water, approximately 22.7 billion liters [2]. This problem is compromising the human right to have access to drinking water. Moreover, it is reducing networks’ sustainability and increasing maintenance costs. Machine learning (ML) is considered a field of artificial intelligence that includes techniques and models that learn from data. These models can extract patterns and make predictions. The main steps to develop a ML system are: (1) definition of the system objective; (2) data collection; (3) data processing and exploration; (4) model selection; and (5) training and validation. A ML system cannot be built if data is not available. Thankfully, the tendency of water management companies to use geographic information systems and business intelligence programs is making the historical databases in this industry to significantly grow. All the aforementioned facts have led water management companies to promote the study and development of ML systems to solve or at least reduce the problem of pipe failures. For this reason, much scientific literature has been published on this topic in the last decade. As reviewing all the available literature is a slow and arduous work, this paper proposes the use of natural language processing techniques to automatize the extraction of information from a large number of scientific documents. Concretely, a total of 102 papers published between 2013 and 2022 (both years included) are considered. This approach has been previously used in other areas such as material science [3, 4], biodiversity science [5] or urban research [6], obtaining valuable results. To the best of our knowledge, this is the first attempt to use NLP to review papers on the topic of machine learning and water distribution networks.

2 Scientific Production on Machine Learning to Optimize Water Network Management Scopus is chosen as the database to search the articles. The filters introduced are: (‘machine learning’ OR ‘machine-learning’) AND (‘water supply’ OR ‘water distribution’) AND (‘pipe fail*’ OR ‘pipe break*’). The use of ‘*’ at the end of a word helps to represent all the words with this beginning. For instance, ‘pipe fail*’ includes both ‘pipe failure’ and ‘a pipe fails’. A total of 281 documents fulfill the requirements; however, those documents that are not articles, for example, conference papers or books, are excluded from the search. Moreover, the publishing years are limited to 2013–2022 (both years included) and the language of publications is constrained to English, having a final number of articles equals 102. Table 1 shows the number of articles according to the publication year. A significant increase in the number of published articles in the last five years is observed, which demonstrates that the use of ML in the water industry

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Table 1 Number of articles per publication year 2013

2014

2015

2016

2017

2018

2019

2020

2021

2

2

1

2

4

9

10

17

39

is a trendy topic. It needs to be taken into account that the search was done on April 30th, 2022; therefore, it does not cover the entire 2022. Given that 16 papers have been published in two months, it is expected that the number of publications in 2022 is much higher than in the previous year. In order to give the reader an idea about the documents obtained, some of them are presented below: . State-of-the-art review of water pipe failure prediction models and applicability to large-diameter mains [7]. . Artificial neural networks: Applications in the drinking water sector [8]. . Rehabilitation of an industrial water main using multicriteria decision analysis [9]. Unfortunately, there are some unrelated documents that fulfill the requirements of the search. As an example, there is a document about the use of machine learning in the e-commerce industry. . A machine learning based credit card fraud detection using the GA algorithm for feature selection [10]. These documents, which are a small percentage of the total, can be considered outliers of the dataset. The existence of outliers, also called atypical samples, is a frequent problem that can occur in any document search.

3 Methodology: Natural Language Processing Natural language processing (NLP) appeared in 1960 as a field of artificial intelligence that helps computers to understand, interpret and manipulate human language [11]. Nowadays, some uses of NLP are information extraction, data mining, machine translation, response search systems, summary generation and sentiment analysis. According to Khurana et al. [12], all these uses can be classified into natural language understanding and natural language generation. As the field gains more attention, applications of different models and techniques in conjunction with NLP are recurrently emerging; for instance, deep learning [13] and graph convolutional networks [14]. This work focuses on information extraction and retrieval, being a clear example of natural language understanding. The following list shows the main steps that are implemented in this work:

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Fig. 1 Scheme of the methodology

1. Data search: use a scientific database to perform a search according to certain criteria. Then, download the documents that fulfill those requirements. Finally, the original files, which usually are in pdf format, need to be transformed into txt files. 2. Data cleaning: this step includes the removal of punctuation, capitalization, stop words such as ‘it’, ‘this’ or ‘the’ among others. Moreover, a structure cleaning is also implemented in order to eliminate websites, usernames and other irrelevant information. 3. Data analysis: finally, the processed data is analyzed to extract information and find hidden patterns. In order to make it clearer, Fig. 1 shows a scheme of the aforementioned methodology. Both the main steps and the results of each step are shown in the scheme.

4 Results Python is the programming language used to develop the present study since it offers many libraries that are useful to implement NLP. For instance, the library NLTK [15] helps to clean texts and to prepare data for future analyzes. As a result of using this library, a matrix with as many lines as documents and as many columns as main words are obtained. Furthermore, the matrix contains the frequency of appearance of each word in each document. Figure 2 shows an

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Fig. 2 Word frequency matrix

example of the obtained matrix where the word ‘water’ appears eight times in the first document, twelve times in the second one and three times in the last one. Once the data have been cleaned, it is analyzed using Pandas library [16] as well as other graph libraries such as Matplotlib [17]. Figure 3 shows the most frequent words in the articles. On the one hand, in the image on the left, it can be observed that ‘water’, ‘network’, ‘learning’ and ‘prediction’ are the words that appear more times in the whole texts. Moreover, other words such as ‘datum’, ‘quality’, ‘neural’ or regression’ are also common. On the other hand, in the word cloud on the right, the most frequent words in the keyword lists of the papers are shown. As it was expected, the terms ‘water’, ‘network’ and ‘learning’ highlights because they were included in the search. Nevertheless, other terms such as ‘analysis’ or ‘management’ also appear. All these words inform about the main topic of the papers. Secondly, a more detailed analysis is performed by examining the frequency of the most relevant terms according to three different aspects: (1) ML models; (2)

Fig. 3 Word cloud visualization of the whole text (left) and the keywords (right) from all the revised documents

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data processing and exploration; and (3) water distribution networks. The next three sections are dedicated to one of the previous aspects.

4.1 Machine Learning Models As can be seen in Fig. 4, some ML models are more common than others in this field. For instance, ‘neural’ (from artificial neural networks), ‘vector’ (from support vector machines) and ‘tree’ (from decision trees) appear in most of the revised documents (red lines). On the contrary, ‘logistic’ (from logistic regression) and ‘forest’ (from random forest) are less frequent models. Nevertheless, it needs to be mentioned that some words, such as ‘vector’, are more general than others since they can be used to refer to different things related to the topic; for example, the model ‘support vector machine’ or the ‘weight vector’ of a model. Therefore, the results may be carefully analyzed. Additionally, it is interesting to analyze the frequency of appearance of the different terms (see Fig. 5). It is understood that if a term is repeated more than ten times, it means that this model is used in the study or at least the study is based on it. In this case, ‘neural’ and ‘tree’ are also the terms that appear with higher frequencies in the documents.

Fig. 4 Number of documents that contain (red) and not contain (blue) the given words related to ML models

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Fig. 5 Number of documents that contain the given words as many times as the given frequency

4.2 Data Processing, Training and Evaluation In this section, the terms analyzed are related to the data processing, the ML models’ training and their posterior evaluation using quality metrics. Concretely, the terms considered are obtained from a previous study developed by the authors where 13 articles on the use of machine learning for the prediction of pipe failures in water networks are reviewed [18]. Firstly, the terms related to data processing are: ‘missing’ (from missing values) and ‘outlier’. These two terms are common in machine learning applications since datasets usually include these two anomalies. Secondly, the terms related to the training phase are ‘sampling’, ‘undersampling’, ‘oversampling’ and ‘crossvalidation’. The first three terms are related to the fact that pipe failure databases are often unbalanced, since failures occur in a small percentage of the pipes that compose water distribution networks. The last term, ‘crossvalidation’, is a typical practice in ML to obtain reliable results regardless of the data split. In this case, it is assured that all possible ways to write compound terms are considered; for example, ‘undersampling’, ‘undersampling’ and ‘under sampling’. Thirdly, ML systems need to be evaluated and for this purpose, there exist many quality metrics. Some of them are examined in this work. Figures 6 and 7 show the number of documents that contain the aforementioned words against the number of documents that do not contain them. For instance, the term ‘sampling’ is mentioned in 39% of the revised manuscripts. Moreover, there is no difference in the use of under- or over-sampling. Figure 8 faces the times that each article mentions two words; specifically, the graph on the left faces the terms ‘sampling’ and ‘classification’, whereas the graph on the right faces ‘sampling’ and ‘regression’. As it was expected from a previous work carried out by the authors [18], it is more common to use sampling techniques when the authors are classifying the pipes instead of using a regression model. It can

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Fig. 6 Number of documents that contain (red) and not contain (blue) the given words related to data processing and exploration

Fig. 7 Number of documents that contain the given words as many times as the given frequency

be noticed in the three papers that use both ‘sampling’ and ‘classification’ more than 5 times. Regarding the quality metrics, it is observed that the RMSE (Root Mean Squared Error) is more frequent than the MSE (Mean Squared Error). Moreover, the confusion matrix and the ROC curve are also really common.

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Fig. 8 Scatter plot of the appearance of the words ‘sampling’ versus ‘classification’ and ‘regression’

4.3 Water Distribution Networks’ Variables In this section, the words analyzed are related to the variables or factors on the water distribution networks included in the studies. This is directly related to both: (1) the companies’ databases, and (2) the experts’ opinions and experience. On the one hand, Fig. 9 shows that the most usual variables are material, pressure, age and diameter. Moreover, it can be seen in Fig. 10 that all these terms are used many times in the documents that contain them. On the other hand, words such as ‘corrosion’ or ‘traffic’ are more uncommon. In the case of ‘corrosion’, it appears many times in the studies that use it. Consequently, these are studies that focus on this phenomenon, which can provoke serious problems in water distribution networks. Figure 11 faces the number of times that the documents employ two words; concretely, ‘material’, ‘diameter’ and ‘pressure’. The pipe material must be chosen

Fig. 9 Number of documents that contain (red) and not contain (blue) the given words related to water distribution networks’ variables

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Fig. 10 Number of documents that contain the given words as many times as the given frequency

from a commercial catalogue, and each material is only available for certain diameters, which is consistent with the results obtained. Likewise, the pressure inside the pipes is a factor that directly depends on the pipe diameter according to a mathematical formula. Finally, a tendency is also observed between ‘material’ and ‘pressure’, since there are several documents that use both words many times.

Fig. 11 Scatter plot of the appearance of the words ‘material’, ‘pressure’ and ‘diameter’

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In general, the analysis shows that some factors are more common than others and also the connections between some of them. If some researcher is interested in studying some specific phenomenon or factor, the use of the proposed NLP analysis can be very useful.

5 Conclusions In this study, Natural language processing is used to analyze a great number of scientific articles on the use of machine learning in the water industry. The study tries to facilitate the analysis of large amounts of text, as well as to extract patterns and discover relationships between the uses of different terms. Regarding the methodology, the present work has shown that it is possible, through the application of NLP techniques, to automatize the analysis of scientific literature. Besides, it allows establishing which are the main factors that must be considered according to a specific problem, as well as the most common models and processing techniques in the area. To implement the proposed methodology, a series of algorithms are developed, which have been demonstrated to work efficiently in terms of time and consumption of computer resources. Additionally, they are versatile and could be used for analyzing documents from other fields. It is important to highlight the significance of the cleaning phase since this affects the subsequent analysis of the results. Regarding the case study, the most interesting conclusions are obtained from the analysis of the variables or factors used in the different papers. The main factors used to study the appearance of pipe failures in water distribution networks, the results are very clear. As stated in Sect. 4.3 and according to the available literature, pipe failures mainly depend on the type of material, the diameter, the pressure, the age, and the temperature. Additionally, there are other factors that demonstrate to be more specific, for instance, the corrosion or the traffic. This may be because their collection is still emerging in the sector. Undoubtedly, this reveals gaps and niches for future research. As a future line of research, it would be interesting to divide the documents according to their similarity by using some intelligent technique such as clustering. Acknowledgements The authors wish to acknowledge to the Consejería de Economía, Conocimiento, Empresas y Universidad (Junta de Andalucía) and the European Regional Development Fund (ERDF) because of their financial support through the Actuaciones de Transferencia UNIVERSIDADES-CEI-RIS3 of the project “Gestión de redes de abastecimiento y saneamiento de agua mediante técnicas de Inteligencia Artificial (GRIAL)”.

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References 1. The European federation of national water services: Europe’s water in figures. An overview of the European drinking water and waste water sectors (2021) 2. Engineers A.S. of C. (2021) A comprehensive assessment of America’s infrastructure 3. Shetty P, Ramprasad R (2021) Automated knowledge extraction from polymer literature using natural language processing. iScience 24:101922 4. Venugopal V, Sahoo S, Zaki M, Agarwal M, Gosvami NN, Krishnan NMA (2021) Looking through glass: knowledge discovery from materials science literature using natural language processing. Patterns 2:100290 5. Thessen AE, Cui H, Mozzherin D (2012) Applications of natural language processing in biodiversity science. Adv Bioinform 6. Cai M (2021) Natural language processing for urban research: a systematic review. Heliyon 7 7. Wilson D, Filion Y, Moore I (2017) State-of-the-art review of water pipe failure prediction models and applicability to large-diameter mains. Urban Water J 14:173–184 8. O’Reilly G, Bezuidenhout CC, Bezuidenhout JJ (2018) Artificial neural networks: applications in the drinking water sector. Water Sci Technol Water Supply 18:1869–1887 9. Carriço N, Covas D, Almeida MDC (2021) Rehabilitation of an industrial water main using multicriteria decision analysis. Water (Switzerland) 13 10. Ileberi E, Sun Y, Wang Z (2022) A machine learning based credit card fraud detection using the GA algorithm for feature selection. J Big Data 9 11. Vásquez AC, Huerta HV, Quispe JP, Huayna AM (2009) Procesamiento de lenguaje natural. Rev Ing Sist e Informática 6:45–54 12. Khurana D, Koli A, Khatter K, Singh S (2022) Natural language processing: state of the art, current trends and challenges. Multimed Tools Appl 13. Aldunate Á, Maldonado S, Vairetti C, Armelini G (2022) Understanding customer satisfaction via deep learning and natural language processing. Expert Syst Appl 209 14. Ren H, Lu W, Xiao Y, Chang X, Wang X, Dong Z, Fang D (2022) Graph convolutional networks in language and vision: a survey. Knowl Based Syst 251:109250 15. Bird S, Klein E, Loper E (2009) Natural language processing with Python. O’Reilly Media, Inc. 16. The pandas development team: pandas library (2020). https://doi.org/10.5281/zenodo.3509134 17. Hunter JD (2007) Matplotlib: a 2D graphics environment. Comput Sci Eng 9:90–95 18. Robles-Velasco A, Muñuzuri J, Onieva L, Rodríguez-Palero M (2021) Trends and applications of machine learning in water supply networks management. J Ind Eng Manag 14:45–54

The Prediction of Hydrometeorology Variables Using the Method of Recurrent Neuronal Networks J. A. Díaz León , M. A. Olarte Escobar , and M. Jara García

Abstract This scientific article evaluates the prediction of hydrometeorological variables, which refer to temperature, precipitation, and flow. The applied methodology is long-term bidirectional recurrent neural networks (BRNN) in a series of 40 years of study for a better perspective on the climatological conditions in Metropolitan Lima until the year 2050. The BRNN model is formed by a single series of past observations, which means that the model analyzes one variable simultaneously to project the next value in the sequence, and unlike other LSTM models, the bidirectional model can model complex and long-time series of sequences efficiently. The purpose of the model is to divide the neurons of a regular RNN into 2 directions, one of them is for the positive time direction (forward states), and one is for the negative time direction (reverse states). In addition, mean square error (MSE), root mean square error (RMSE), and Nash Sutcliffe efficiency ratio (NSE) were used as error metrics to assess prediction performance. Regarding the results, the prediction of average temperatures tends to increase between the ranges of 0.30–0.90 °C with estimated maximum temperatures up to 27 °C. Keywords Temperature prediction · Precipitation prediction · Flow prediction · Long-term bidirectional recurrent neural network (BRNN) · Lima metropolitana

J. A. Díaz León (B) · M. A. Olarte Escobar · M. Jara García Facultad de Ingeniería Civil, Universidad Peruana de Ciencias Aplicadas, Prolongación Primavera 2390, Lima 15023, Perú e-mail: [email protected] M. A. Olarte Escobar e-mail: [email protected] M. Jara García e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_2

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1 Introduction Hydrometeorological variables such as temperature, precipitation, and flow, to mention the most important, directly influence water resource planning decisions. In addition, they serve as a source of information to estimate the supply of drinking water treatment plants, management of hydraulic resources, etc. According to the national aeronautics and space administration (NASA) and the national oceanic and atmospheric administration (NOAA), they mention that since 1980 the Earth has suffered an increase in average temperature, with the years 2016 and 2019 being the warmest [1]. Given this fact and considering the direct influence of temperature on the hydrological cycle, an alteration is inferred in the estimates of hydrometeorological variables such as temperature, precipitation, and flow. Therefore, the implementation of new technologies of good quality in the prediction of hydrometeorological variables is essential. Currently, artificial intelligence has had a great impact on its use of database study and prediction generation. This scientific article has the purpose of making hydrometeorological predictions using the methodology of recurrent neural networks. This means the use of artificial intelligence in the analysis of historical databases through automatic learning of the neural network that allows understanding and estimating predictions of hydrometeorological variables. A clear example is shown in the study carried out by Pinos et al. [2] who evaluated the performance of empirical models, artificial neural network models (ANNs) and multivariate adaptive regression spline models (MARS) to estimate the reference daily evapotranspiration (ETo). And compare it with the standard Penman–Monteith equation (FAO 56 P-M) to choose the best alternative to the standard method. The results showed that the improved ANNs are the most accurate to estimate the daily ETo. On the other hand, a study was carried out in 2020 at the John F. Kennedy airport to explore the potential of deep learning techniques for air temperature prediction. The models they used are multilayer perceptron (MLP), long-short-term memory (LSTM) network, and a combination of convolutional neural network and LSTM (CNN + LSTM), the latter showing the best prediction performance for day 1 and on day 10 with an accuracy of 97.42 and 71.58%, respectively. In both cases, they are superior to the other deep learning models MLP and LSTM [3].

2 Materials and Methods 2.1 Studio Area The study area is Metropolitan Lima, located in the province of Lima, department of Lima—Peru, Rimac River basin. The present research work uses the most representative hydrometeorological stations of Metropolitan Lima because they have a greater historical record such as the Campo de Marte Station and Ñaña, which are located at 118 and 553 m above sea level respectively.

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2.2 Methodology The methodology proposed in this scientific article is the use of recurrent neural network (RNN) of the long short-term memory bidirectional (LSTM bidirectional) type that will henceforth be called BRNN. BRNNs connect two hidden layers from opposite directions to the same output. In this way, the output layer can obtain information from past and future states simultaneously. It was invented by Schuster and Paliwal in 1997 to increase the amount of input information available to the network as most models such as MLP multilayer perceptron present limitations in input data flexibility and standard recurrent neural networks (RNN) present restrictions, as future input information cannot be accessed from the current state. The principle of BRNN is to divide the neurons of a regular RNN into two directions, one for the positive time direction (forward states) and one for the negative time direction (backward states). The output of both states is not connected to the inputs of the opposite direction states. So, the first direction learns the sequence of the input provided and the second learns the inverse of that sequence and then concatenates future predictions and gives more real values as result. Likewise, this methodology consists of univariate analysis, this implies that hydrometeorological variables such as temperature, precipitation and flow are analyzed individually. Thus, the BRNN model is composed solely of the historical record of recorded series for the prediction of variables in sequence. Unlike other types of LSTM models, the bidirectional can effectively model complex and extensive time series sequences thanks to its input learning flexibility.

3 Experiment 3.1 Data Preparation The preparation of the historical database is divided into three parts, due to the process of collecting information from the three variables: temperature, precipitation, and flow for the BRNN model. First, the input information of average temperatures was obtained from the CRUTEM extension of google earth because a limitation of data from the national service of meteorology and hydrology (SENAMHI) was found, which had historical data available only from the year 2015 to 2019. CRUTEM is a set of data derived from air temperatures near the earth’s surface recorded at weather stations on every continent on Earth. It has been developed and maintained by the climatic research unit since the early ‘80 s, with funding provided primarily by the U.S. department of energy. The latest version of CRUTEM is called CRUTEM4 and is available through google earth, for this, you must download a KML file and open it in Google Earth to download the desired information from anywhere in the world [4]. Then through this extension, it was possible to obtain information on average temperatures from the year 1901 to 2019 located in the geographical coordinates

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latitude 12° 15, 0,, S and longitude 77° 15, 00,, W compatible with the geographical coordinates of the campo de marte station, representative station of our study area. Second, the input information for the precipitation variable was obtained from the product PISCO (Peruvian interpolated data of the SENAMHI’s climatological and hydrological observations). The PISCO product was generated within the framework of the goal of development of applied research for disaster risk management— PREVAED executed by the directorate of hydrology of SENAMHI, and the authorship of this corresponds to the research group led by Dr. Waldo Lavado Casimiro [5]. The PISCO product is a complete gridded database of monthly precipitation that covers a time series that began on 1st January 1981 until 31st December 2016, these are obtained by typing the cartographic coordinates of the stations that are going to be studied through the R program. It was created in 1993 by Robert Gentleman and Ross Ihaka [6]. The station used for data collection is the Ñaña Station with an altitude of 553 m above sea level extracted from the PISCO product (version 1.1). Third, the access to information for the flow variable is from the national water authority’s (ANA) [7] station viewer records, which are Milloc, Río Blanco, Sheque, San Mateo, and Chosica hydrometric stations that belong to the río Rimac basin. Being Chosica is the one evaluated in this scientific article. Subsequently, we proceed with the compliance of missing data with the hec4 program. This software was developed by the center for hydrological engineering of the United States, this is used in the completion and extension of hydrometeorological historical records by analyzing parameters of adjacent stations establishing hypothetical sequences of high reliability that allow for completion and/or extend said records [8]. Once the historical data is completed, we proceed with the consistency analysis, where physical criteria and statistical methods are applied to correct possible systematic errors in data collection. Consistency analysis consists of graphical visual analysis, double mass analysis, and a statistical analysis evaluating the consistency of the mean and standard deviation. It begins with the graphical visual analysis, of the Chosica station between the period 1989–1994 register values below the average, generating uncertainty about the reliability of the data of that period. For the double mass analysis, the San Mateo hydrometric station is selected as the base station, because it has the least number of breaks. Finally, evaluating the homogeneity of the mean, the values of 0.76 for the “T” calculated and 2.02 for the “T” table are obtained, this implies that these values do not need correction of the mean. Likewise, for the evaluation of the consistency in the standard deviation, the values of 1.15 for the “F” calculated and 1.95 for the “F” table are obtained, so it is not necessary to correct the standard deviation. Figure 1 concluding that the historical data of the Chosica station are reliable and can be used in hydrological studies.

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Fig. 1 Statistical analysis of the Chosica hydrometric station

4 Model Training The proposed model essentially combines temporal and spatial information to predict future values of temperature, precipitation, and flow. Its structure includes a fully connected LSTM layer for better network training with a hidden layer and an output layer. The structure of BRNN as a hidden layer is as follows: at the beginning the gate “forget”, this cell is responsible for selecting what information should be forgotten, to have a selection of relevant data necessary for predictions. This information then passes the data through a second input gate, which tells us which parameters need to be updated in the LSTM cell. Finally, the third and final cell is the output gate. In this new cell, the memory works selectively from the new predicted information and internally identifies to what extent it will modify the hidden state to correctly process the information [9]. In general, the operation of this cell consists of receiving a data, generating a prediction and output to feed back to the hidden layer. This helps our network have a sense of what happened before. The scheme is represented by the input variables that are the “year” historical period observed, “month” the twelve months of each year and the data of the three hydrometeorological variables of the study. The outputs variables are prediction results for the year 2050 of the average temperature in degrees Celsius (°C), average precipitation in millimeters (mm) and flow in cubic meters per second (m3 /s) (Fig. 2). The BRNN modeling process in Python is shown in Fig. 3. It should be noted that this process is the same for each variable analyzed particularly.

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Fig. 2 Schematic of the BRNN recurrent neural network

4.1 Import Libraries There are countless deep learning frameworks available today. The libraries used in this research paper were Pandas written in Python for data manipulation and analysis, NumPy for working with arrays and keras also written in python and able to run with TensorFlow for fast and flexible experimentation of the LSTM bidirectional model.

4.2 Create the Feature BRNN learns a function that maps a sequence of past observations as input to an output observation. This sequence of observations is transformed into multiple training data from which the LSTM can learn. For example, to obtain the prediction of the average temperatures of the campo de marte Station in Metropolitan Lima to the year 2050, the sequence will be divided into multiple input/output patterns called samples through the split_sequence function.

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Fig. 3 Programming procedure for predicting monthly average temperatures with BRNN recurrent neural networks

4.3 Import the Base The import of the base consisted of an excel file with the complete and analyzed historical information of the three input variables: “year”, “month”, “average temperature”, “average precipitation” and “average flow”.

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4.4 Creating Sequences The second step is that the BRNN learns a function that maps a sequence of observations passed as input to an observation of output. This sequence of observations is transformed into multiple training data that the LSTM can learn from called: “samples” through the split_sequence function. In the BRNN modeling for the temperature variable, 5-time steps were used as input for the prediction of an output step being learned. In addition, the sequence model has 50 LSTM neurons within each hidden layer as shown in the code below written in python. #Select the number of steps back that will be considered for the prediction n_steps = 5 #Create sequences of X and Y X, y = split_sequence(data_aux[‘Tem.media’].tolist() , numero_pasos) #Define the model model = Sequential() model.add(Bidirectional(LSTM(50, return_sequences = True,activation= ‘relu’))) model.add(Bidirectional(LSTM(50, return_sequences = False,activation= ‘relu’))) model.add(Dense(1)) model.compile(optimizer=’adam’, loss=’mse’ , metrics = [‘mse’]) model.summary() #fit model model.fit(X, y, epochs=100, verbose=0)

Similarly, the following code written in python shows the structure of the BRNN modeling for the precipitation variable. In it, unlike the temperature variable, 24 steps were used because the fact of having precipitation values close to zero in most of the observations in the historical series, few training steps are not enough to better fit the data to the model and show a more reliable MSE and NSE value. Therefore, with 24 steps it showed the best adjustment result. The sequential model has 50 LSTM neurons within each hidden layer as in the modeling of the temperature variable.

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#Select the number of steps back that will be considered for the prediction n_steps = 24 #Create sequences of X and Y X, y = split_sequence(data_aux[‘Pp.media’].tolist() , numero_pasos) #Define the model model = Sequential() model.add(Bidirectional(LSTM(50, return_sequences = True,activation= ‘relu’))) model.add(Bidirectional(LSTM(50, return_sequences = False,activation= ‘relu’))) model.add(Dense(1)) model.compile(optimizer=’adam’, loss=’mse’ , metrics = [‘mse’]) model.summary() #fit model model.fit(X, y, epochs=100, verbose=0)

Finally, the following code written in python shows the structure of the BRNN modeling for the flow variable is shown. In it, unlike the variable of temperature and precipitation, 12 steps were used which gave the best adjustment result and most reliable MSE and NSE values. The sequential model has 60 LSTM neurons within each hidden layer as in the modeling of the temperature and precipitation variable. #Select the number of steps back that will be considered for the prediction n_steps = 12 #Create sequences of X and Y X, y = split_sequence(data_aux[‘Q.medio’].tolist() , numero_pasos) #Define the model model = Sequential() model.add(Bidirectional(LSTM(60, return_sequences = True,activation= ‘relu’))) model.add(Bidirectional(LSTM(60, return_sequences = False,activation= ‘relu’))) model.add(Dense(1)) model.compile(optimizer=’adam’, loss=’mse’ , metrics = [‘mse’]) model.summary() #fit model model.fit(X, y, epochs=100, verbose=0)

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4.5 Prediction of Hydrometeorological Variables In this section, the “predict” model was used to generate the predictions of temperature, precipitation and average monthly flow indicating that the final period of analysis is the last year until which historical information is counted, and the final prediction period is the year 2050.

5 Metrics In the present study, we used some of the most used metrics within mean error square (MSE) and root mean square error (RMSE) machine learning to evaluate and validate the quality of the model. Additionally, one of the community approved metrics was used to evaluate the efficiency of runoff simulations: Nash Sutcliffe. The MSE is an error metric that measures the difference between the values predicted by a model and its actual values, which helps to improve the level of confidence that the model has and the RMSE becomes the square root of the MSE that indicates the range of variation in which the predicted value of the variable under study would be found. The Nash Sutcliffe criterion (NSE) identifies the relative value of the residual variance. The simulation of model runoff can be considered “very good” if NSE is between 0.6 and 0.8, “excellent” if NSE > 0.8 and “perfect” if NSE = 1 [10].

6 Results and Discussion 6.1 Temperature Prediction The temperature dataset was initially validated by back testing Fig. 4 which shows the behavior modeled by the neural networks of the set of historical data and the new models for the prediction of temperatures in the BRNN to the year 2050 in Metropolitan Lima. As a result, it is obtained that the data modeled during the back testing are quite close to the real historical/observed values in the field represented with blue lines and the red ones are predicted based on learning for the same historical series between the years 1905 and 2019. The results obtained from the calibration and validation of the BRNN model for the historical period (January 1905 to December 2019) reached an MSE value of 0.28768 and RMSE of 0.5364. This RMSE value indicates that if the actual average temperature is 20 °C, then the new value of the projected average temperature would be in a range of 20 °C ± 0.54 °C. In this sense, the lower this RMSE value, the more reliable the model will present, demonstrating an adequate learning of the BRNN. Additionally, the Nash Sutcliffe efficiency index was calculated for the calibration of

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Fig. 4 Comparison of the historical series of observed versus calculated temperatures (back testing)

the BRNN model and was promising. The NSE reached a value of 0.928 interpreted as excellent. The results of the prediction of monthly average temperatures in Metropolitan Lima to the year 2050 using BRNN indicate a variation. This variation estimates an increase in a range of 0.30–0.90 °C reaching average temperatures of 20.19 °C with respect to the year 2019 that had average temperatures of 19.32 °C. Likewise, there is a peak value of 28.49 °C in the year 2050, noting an increase of 5.39 °C with respect to the year 2019. A minimum temperature of 14.97 °C with respect to the year 2019 perceiving a reduction in the minimum temperature of 1.33 °C (Table 1). As far as we know, the use of machine learning proposed in this research paper was as satisfactory as the climate risk analysis (CRA) study developed by the Municipality of Lima in 2020 [11]. In which it reflects maximum temperature values of up to 27.00 °C. Similarly, the results obtained in this paper for minimum temperatures are within the range of 10.00 and 19.00 °C that also agrees with the study developed by the municipality of Lima by an extensive engineering technique, demonstrating that both results are reliable. Table 1 Climatological variation of temperatures of the year 2050 with respect to the year 2019

Year 2019

2050

Description

Temperature (°C)

Maximum temperature

23.10

Minimum temperature

16.30

Average temperature

19.32

Maximum temperature

28.49

Minimum temperature

14.97

Average temperature

20.19

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Fig. 5 Average monthly temperatures according to five-year periods from 2021 to 2050

This table shows the comparison between the maximum, minimum and mean temperature values of the end period years used for both historical data (2019) and predicted data (2050). Figure 5 represents the development of the temperature prediction according to five-year periods beginning in the year 2021 to 2050. Where it is highlighted that the months of February, April and November present the greatest increases in temperatures and the months of August and September represent a decrease. These results show the high value of using machine learning techniques for modeling hydrometeorological variables, which are substantially important and practical.

6.2 Precipitation Prediction As in the temperature assessment, the precipitation data set was validated initially by means of a back testing Fig. 6. This shows the comparison of the historical series of observed and calculated precipitations where it can be noted that there is a good fit between the observed data represented by the blue color (historical data) and those calculated represented by the red color by the modeling of the BRNN. The results obtained from the BRNN model for the historical period (January 1981 to December 2016) reached an MSE value of 0.3046 and RMSE of 0.5519. This RMSE value indicates that if the average actual precipitation is 3 mm, then

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Fig. 6 Comparison of the historical series of observed versus calculated precipitation (back testing)

the new value of the projected average precipitation would be in the range of 3 mm ± 0.55 mm. In this sense, the lower this RMSE value, the more reliable the model will be, demonstrating an adequate learning of the BRNN. Additionally, the Nash– Sutcliffe efficiency index was calculated for the calibration of the BRNN model and was promising. The NSE reached a value of 0.712 interpreting it as “very good” in the table of reference values according to the criteria by Nash Sutcliffe developed by Molnar. Finally, the years that comprise the historical and forecast period are analyzed to have an analysis faithful to reality, since there may be recent factors that did not happen before. Where it is highlighted that during the historical period 1991–2020, the five-year period 2011–2015 in the months of January, February and March present rainfall with values that exceed the average, considered 0.575 mm, with average rainfall greater than 2.00 mm being the five-year period which presents the peak values in this phase (Fig. 7). In comparison, analyzing the prediction of the period 2021–2050, in the five-year period 2036–2040 there are rains during the months of January, February, March and even April that exceed the considered average of 0.512 mm, as happened previously in the five-year period 2011–2015, with average rainfall greater than 2.50 mm (Fig. 8). It is also observed in the year 2002 there is a maximum with an average rainfall of 8.86 mm followed by rainfall of 5.62 and 6.20 mm in the years 1993 and 2017 respectively for the historical period 1991–2020. On the other hand, in Fig. 9 in 2040, there is a maximum with an average rainfall of 7–8 mm and another in the year 2037 with an average rainfall in a range from 5 to 7 mm for the prediction period. Demonstrating that the project values are consistent with respect to the historical newspaper used as the basis of the study.

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Fig. 7 Average monthly rainfall accumulated according to five-year periods (1991–2020)

Fig. 8 Average monthly rainfall accumulated according to five-year periods (2021–2050)

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Fig. 9 Predicted rainfall for the year 2050 by BRNN model

6.3 Flow Prediction Once again, the backtesting detailed in Fig. 10 is carried out, where the blue trace represents the historical values and the red trace the predicted values. In addition, in much of the graph, it is observed that both traces overlap, which represents a slight variation between the current and predicted values. For the verification and validation of the flow predictions under the BRNN methodology, the MSE and RMSE metrics were used, to which the values of 1.4005 and 1.185 respectively correspond. The latter being the range of uncertainty in which it oscillates, it is the true value. Additionally, the Nash Sutcliffe efficiency index was applied for the calibration of the BRNN model, resulting in 0.995. Based on Molnar’s Nash Sutcliffe Criterion reference values, this model is excellent. As a result, it was achieved that the years 2025, 2030, 2038, 2040, 2042, 2045 and 2050 present higher flow records, reaching values of up to 104.12 m3 /s (Fig. 11). On the other hand, these maximum values have been recorded between the months of February, March and April due to the rainy season called peak flow records. By analyzing the volume of water transit in the Hm3 unit, a reduction in annual water traffic is obtained. Figure 12 shows the analysis of the prediction of flows according to five years from 2021 to 2050, where: the five-year period 2031–2035 presents a reduction of 2% compared to the previous five-year period, represented at 19.368 Hm3 /year; the five-year period 2036–2040 presents a reduction of 1% with respect to the previous five-year period, represented by 10.415 Hm3 /year and the

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Fig. 10 Comparative histogram between series of observed flows versus calculated by back testing

Fig. 11 Index of predicted flows of the Chosica hydrometric station

five-year period 2046–2050 presents a reduction of 7% with respect to the previous five-year period, represented at 69.025 Hm3 /year.

The Prediction of Hydrometeorology Variables Using the Method …

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Fig. 12 Record of flow traffic at the Chosica station in Hm3 /year

7 Conclusions It’s concluded that the results obtained by the BRNN model are very close to the Climate Risk Analysis (CRA) study developed by the Municipality of Lima, validating the reliability of the results obtained in this research project. Under the BRNN model for the precipitation variable, it was observed that there are average peak precipitation values for the years 2037 and 2040 in a range of 5– 7 mm and 7–8 mm, respectively. These values remain within the range of standard precipitation records at the Ñaña station. There was a significant reduction in precipitation during the five-year periods 1991–1995, 1996–2000, 2001–2005, 2006–2010 and 2016–2020, generating similar cycles in future precipitation during the five-year periods 2021–2025, 2026–2030, 2031–2035, 2041–2045 and 2046–2050 concluding that there is not much variation and uncertainty in the proposed methodology since it is not far from historical reality. The article concludes that the water predictions detail a future water imbalance in the transition of the river of the Chosica station, detailing that the period of avenues is reduced but with higher peak records than the conventional ones, and the drought period has a longer duration. The calibration of the BRNN model carried out additionally with the Nash Sutcliffe efficiency index presents NSE values of 0.93, 0.71, and 0.99 respectively for the variables of temperature, precipitation and flow, placing the results of the models as excellent, very good, and excellent respectively in the table of reference values according to the Nash Sutcliffe criterion developed by Molnar. Therefore, it is concluded that by presenting a good efficiency performance the data projected for the year 2050, will be reliable and safe.

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It is concluded that the BRNN model is the most suitable for the estimation of peak data values since it fits very well with the input and training values for the prediction of output values as seen during the development of the temperature, precipitation, and flow variables. Acknowledgements Special gratitude to our parents. Also, to the master’s in engineering Edgar David Valcárcel Trujillo for his time provided and help as a guide for our insertion into the wide world of Python programming using artificial intelligence during the present research project.

References 1. Ciencia de la NASA Homepage. https://ciencia.nasa.gov/an%C3%A1lisis-de-nasa-y-noaarevelan-que-2019-fue-el-segundo-a%C3%B1o-m%C3%A1s-c%C3%A1lido-registrado. Accessed 2021/10/15 2. Pinos J, Chacón G, Feyen J (2020) Comparative analysis of daily reference evapotranspiration models with application to the wet Andean páramo ecosystem in Southern Ecuador. Meteorologica 45(1):25–45 3. Anjali T, Chandini K, Anoop K, Lajish VL (2019) Temperature prediction using machine learning approaches. In: 2019 2nd international conference on intelligent computing, instrumentation and control technologies, (ICICICT), vol 1. IEEE, New Jersey, pp 1264–1268 4. Harris I, Osborn TJ, Jones P, Lister D (2020) Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci Data 7(109):1–18 5. Aybar C, Lavado W, Huerta A, Fernandez C, Vega F, Sabino E, Felipe O Uso del Producto Grillado “PISCO” de precipitación en Estudios, Investigaciones y Sistemas Operacionales de Monitoreo y Pronóstico Hidrometeorológico. SENAMHI-DHI-2017, Lima-Perú 6. Rodríguez J (2019) ¿Qué puede hacer el software R para resolver tus problemas? Revista Digital Universitaria (RDU) 20(3):1–10 7. Autoridad Nacional del Agua (ANA) Homepage. https://snirh.ana.gob.pe/Observatorio SNIRH/. Accessed 2022/02/07 8. US Army Corps of Engineers Homepage. https://www.hec.usace.army.mil/publications/Com puterProgramDocumentation/HEC-4_UsersManual_(CPD-4).pdf. Accessed 2021/10/20 9. Guridi G (2017) Modelos de redes neuronales recurrentes en clasificación de patentes. https:// repositorio.uam.es/handle/10486/679893 10. Molnar P (2011) Calibration. Watershed Modelling, SS 2011. Institute of Environmental Engineering, Chair of Hydrology and Water Resources Management, ETH Zürich, Switzerland 11. Sistema metropolitano de información ambiental (SMIA) Homepage. https://smia.munlima. gob.pe/documentos-publicacion/detalle/586. Accessed 04/06/2022

“I Want to …Build”: Research on Dam Transshipment Facilities of Typical Hydropower Stations on the Right Bank of Jinsha River Lingling Liu, Yongchang Wang, and Jianling Hou

Abstract At present, with the increase of waterway cargo transportation volume, the advantages of waterway transportation cannot be brought into full play because some hydropower stations are constructed without considering the construction of navigation facilities and dam transshipment facilities, or without considering the currency capacity of built navigation buildings. In view of this, this paper takes the typical hydropower station on the right bank of Jinsha River as the research object. According to the hydrological conditions of the reservoir area and the topographical and geological conditions of the upstream and downstream of the power station, the location of the dam transshipment wharf and the dam transshipment highway transportation mode of the typical hydropower stations are summarized by combining quantitative and qualitative analysis. Taking coal transportation as an example, we have studied the transportation modes of water-highway-water transport over dam from Huize to Shuifu Port for three times, from Qiaojia to Shuifu Port transport over dam for two times, and from Yongshan to Shuifu Port transport over dam for one time by using the economic comparative analysis method. The results show that the new mode of bulk cargo transportation proposed in this paper can meet the high-quality development needs of Jinsha River navigation in terms of safety and efficiency; based on the high mountains and valleys and complex geological conditions, the “Z” shape general layout scheme of the grading platform is innovatively proposed; waterhighway-water transport over dam has certain advantages in transportation cost, and the transportation cost is the lowest when the dam is overturned twice; during the water-highway-water transport over dam, the highway transportation distance should be shortened as far as possible to improve the economic benefits of dam turnover transportation. L. Liu · Y. Wang · J. Hou (B) China Waterborne Transport Research Institute, Beijing, China e-mail: [email protected] L. Liu e-mail: [email protected] Y. Wang e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_3

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Keywords Jinsha River downstream · Bulk cargo · Transshipment · Water-highway-water · Highway-water · Transportation cost comparison · Economy

1 Introduction At this stage, due to a variety of reasons, some Jinsha River hydropower stations are not constructed with navigation facilities and transport over dam facilities at the same time, resulting in poor waterway transportation and unable to meet the current waterway transportation needs. Relevant scholars have carried out extensive research on the above problems. Zeng put forward the composite transportation mode of highway-water-highway through quantitative research on the contribution of ro-ro transportation at home and abroad and ro-ro transportation to Chongqing’s economic and social development [1]; Liu and Lin compared ro-ro transportation with highway, railway and other transportation modes, and explained the socio-economic value of ro-ro transportation [2]; Liu studied the transportation scheme of rolling on and rolling off of coal in Longtan reservoir area of Hung-shui Ho, and came to the conclusion that the transportation scheme of 59 parking spaces of rolling on and rolling off ships is the best [3]; Cao et al. by comparing the water-highway-water dam overturning transportation mode of ro-ro vehicles with the water-highway-water short-distance transportation mode, they concluded that the less the times of dam overturning and transportation, the more advantageous the transportation cost of the water-highway-water transportation scheme [4]; Liu et al. proposed a variety of transportation modes, mainly highway dam overturning transportation, supplemented by belt dam overturning transportation, and supplemented by highway and chute dam transshipment [5]. The research results at this stage show that the bulk cargo transportation volume is large and the cargo value is low, which is generally not suitable for dam transshipment However, the lower reaches of the Jinsha River are rich in mineral resources, and a large number of mining construction materials need to be transported to the lower reaches of the Yangtze River. The water-highway-water transport over dam transportation cost and the highway-water transportation cost have more cost advantages. The change law of the water-highway-water transport over dam times and transportation cost of bulk cargo needs to be further studied. To sum up, there is a lack of relevant research on the efficient method of dam transshipment of typical hydropower stations on the Jinsha River at home and abroad. In view of this, this paper takes Baihetan, Xiluodu and Xiangjiaba hydropower stations in the lower reaches of the Jinsha River as examples to analyze and study the mode of bulk cargo dam transshipment, and studies and compares the cost of water-highwaywater transport over dam for three times, two times and once and highway-water highway transportation.

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35

2 Project Overview and Research Methods 2.1 Page Setup Baihetan, Xiluodu and Xiangjiaba are located in the lower reaches of the Jinsha River downstream. They are the second, third and fourth rungs of the four hydropower stations in the lower reaches of the Jinsha River. The specific location is shown in Fig. 1. It can be concluded from Fig. 1 that the Jinsha River has many beaches, steep slopes, rapids and other characteristics, and with the completion of four large hydropower stations in Wudongde, Baihetan, Xiluodu and Xiangjiaba, the Jinsha River has the conditions for the construction of deep-water waterway (deep-water waterway: waterway that exceeds the largest ships (3000 t) of inland navigation standards). However, due to various reasons, when the hydropower stations in Wudongde, Baihetan and Xiluodu are constructed, Navigation facilities and transport over dam facilities were not constructed synchronously, resulting in poor waterway transportation. Although Xiangjiaba hydropower station has built a ship lift, its designed annual carrying capacity is 1.12 million tons. In 2013, the transportation volume of overturned goods of the dam waterway transportation exceeded 3 million tons [6], which can no longer meet the waterway transportation demand at this stage. Therefore, it

Xiluodu hydropower station Baihetan hydropower station

Xiluodu hydropower station

Fig. 1 Location map of Baihetan, Xiluodu, and Xiangjiaba hydropower stations

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is necessary to carry out a comparative study of various transportation modes for shipping in the lower reaches of the Jinsha River.

2.2 The Research Methods The research methods used in this paper mainly include quantitative and qualitative analysis and economic comparative analysis methods. Quantitative and Qualitative Analysis Method. When selecting the route, we adopt a combination of quantitative and qualitative analysis methods, comprehensively consider the factors such as goods type, transportation mode and transportation cost savings, and combine the reservoir operation mode of the three hydropower stations, the geological and hydrological conditions around the reservoir and other influencing factors to select the optimal route [5]. Economic Comparative Analysis Method. When calculating the transportation cost, we consider the waterway transportation, highway transportation and port loading and unloading expenses, etc. and compare the calculation results with the estimated cost of typical route transportation of Jinsha River, and obtain the change law of the number of water-highway-water transport over dam and transportation cost of bulk cargo, and compare it with the highway-water highway transportation cost.

3 Transshipment Facilities Construction Scheme 3.1 Baihetan Transshipment Facilities Construction Scheme Upstream Transshipment Wharf (Santan Transshipment Wharf). The Santan transshipment wharf is located on the right bank of Jinsha River, in Santan village, Qiaojia county, about 3.3 km away from the dam of Baihetan hydropower station. The wharf needs to build one 3000 t bulk berth and three 1000 t bulk berths, which need to occupy 356.0 m of the shoreline. The pontoon is used for the berthing platform, and the size of 3000 t bulk berth is 70 × 15 × 2.5 m (length × width × draft, the same as below). It can take into account the berthing of 5000 t ships; the pontoons are used for the berthing platform, and the size of 1000 t bulk berths are 60 × 13 × 2.0 m. It can take into account the berthing of 3000 t ships. Each pontoon is equipped with a floating crane. Downstream Transshipment Wharf (Tuogu Transshipment Wharf). The Tuogu transshipment wharf is located on the right bank of Jinsha River, in Tuogu village, Qiaojia county, 165.3 km away from the dam of Xiluodu hydropower station. The

“I Want to …Build”: Research on Dam Transshipment Facilities …

37

wharf is located in the perennial backwater area of Xiluodu reservoir. The water level in the reservoir area ranges from 540.0 to 600.0 m, and the water level amplitude is 60.0 m. The wharf adopts the scheme of vertical grading platform and ramp. According to the reservoir operation mode of Xiluodu hydropower station, threelevel wharf platforms are arranged respectively. The platform is arranged in a “V” shape. Five 1000 t bulk berths are arranged along the bank of each level of platform (the structure is reserved according to 3000 t level). The platforms are connected by ramp. And a total of 1130.0 m of shoreline is occupied. Each berth is equipped with two 50 t tire cranes to load and unload cargo. The Transshipment Highway. The transshipment highway of Baihetan hydropower station is 33.6 km in total, of which the special highway for dam turning of Baihetan hydropower station is 32.5 km long (including tunnel 17.0 km), and a new 0.4 km port road at Santan transshipment wharf is connected with the special highway for transshipment of Baihetan hydropower station. The port road and transshipment special highway are constructed according to class B highway, with a road width of 10.0 m. At the same time, a new 0.7 km port road at Tuogu transshipment wharf is built. The transshipment facilities of Baihetan are shown in Fig. 2.

3.2 Xiluodu Transshipment Facilities Construction Scheme The Upstream Transshipment Wharf (Majiaheba Transshipment Wharf). The Majiaheba transshipment wharf is located on the right bank of Jinsha River, 3.9 km away from the dam site of Xiluodu hydropower station, with a planned shoreline of 1320.0 m. The construction of the wharf requires the demolition of the original 1 # and 2 # working berth infrastructure, and the unified planning and construction with the bulk cargo berth. 16 berths are arranged along the bank (one working ship berth, thirteen 1000 t bulk cargo berths and two 3000 t bulk cargo berths). The Downstream Transshipment Wharf (Fotan Transshipment Wharf). The Fotan transshipment wharf is 145.0 km away from the dam site of Xiangjiaba hydropower station and close to the original Gaolin wharf. After the shoreline of Fotan transshipment wharf and Labatan wharf are uniformly arranged, the total length of the shoreline is 1386.0 m, and a total of seventeen berths and one working ship berth can be arranged. The Transshipment Highway. The dam transshipment highway of Xiluodu hydropower station is 13.5 km long, and a 3.5 km long tunnel needs to be built to connect with the dam overturning highway of Xiluodu hydropower station. The tunnel is designed according to the standard of class I highway tunnel, and the dam transshipment highway of Xiluodu hydropower station is constructed according to the standard of class I highway, with a total length of 10.0 km. The transshipment facilities of Baihetan are shown in Fig. 3.

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Tuogu transshipment wharf

Baihetan hydropower station

Santan transshipment wharf

Fig. 2 The transshipment facilities of Baihetan

3.3 Xiangjiaba Transshipment Facilities Construction Scheme The Upstream Transshipment Wharf (Xintan Transshipment Wharf). The Xintan transshipment wharf is located on the right bank of Jinsha River, 2.0 km upstream of Xiangjiaba hydropower station dam site. The length of the planned wharf shoreline is 1057.0 m, and 15 1000 t berths are arranged, including two 1000 t berths with a length of 200.0 m; thirteen 1000 t bulk cargo berths will be built. The wharf is arranged in two broken lines. Seven 1000 t bulk cargo berths are arranged

“I Want to …Build”: Research on Dam Transshipment Facilities …

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Xiluodu hydropower station

Majiaheba transshipment wharf

Fotan transshipment wharf

Fig. 3 The transshipment facilities of Xiluodu

on the shoreline of section I and six 1000 t bulk cargo berths are arranged on the shoreline of section II. The Transshipment Scheme. The total length of the dam transshipment highway of Xintan transshipment wharf is 5.96 km, including 1.2 km new port dredging road, 1.6 km old tunnel, 2.66 km new road and 0.5 km old road. The dam transshipment highway of Xintan transshipment wharf is connected with the existing tunnel mouth through the port dredging road, which is designed according to the standard of class II highway. The transshipment facilities of Xiangjiaba are shown in Fig. 4.

4 Comparative Analysis of Transportation Economy [7] In order to facilitate the calculation of transportation costs, we assume that: (1) 1000 t ships pass through the lower reaches of Jinsha River, and the cargo weight is 1000 t; (2) highway freight rate is calculated in tons and kilometers; (3) the bulk cargo transportation cost is calculated by the bulk carrier transportation price, which is about 0.10 ¥/t km. See Table 1 for freight rate table.

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Xiangjiaba hydropower station

Majiaheba transshipment wharf

Fig. 4 The transshipment facilities of Xiangjiaba Table 1 Table of basic data of freight rate The mode of transportation Highway

The distance (km)

The freight rate (¥/t)

Note Refer to the information price of Yunnan transportation engineering materials and equipment [8]

≤10

9.83

10 < L ≤ 150

0.67

L > 150

0.59

Bulk carrier transport

0.10 ¥/t km

Refer to the typical route transportation cost of Jinsha River basin

Expressway toll standard

0.08 ¥/t km

Refer to Yunnan expressway weight toll standard

Bulk handling charges

17.0 ¥/t (ship-car (ship) straight)

Refer to the price survey form of domestic trade of bulk and miscellaneous cargo in the middle and upper reaches of the Yangtze River (February 2015)

“I Want to …Build”: Research on Dam Transshipment Facilities …

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4.1 Calculation of Three Times Transportation Cost of Turning Dam Transfer Schematic diagram of transportation mode of coal from Huize the source of cargoes, to Shuifu Port. The coal starts from Huize, gets on the ship through Menggu wharf, and then arrives at Shuifu port through waterway and is transported over dam (three times of dam turning and transshipment) (Table 2). The total transportation distance is 513.16 km, including waterway transportation distance of about 387.1 km and highway transportation distance of 126.06 km. Transportation mode from Huize to Shuifu port is shown in Fig. 5. The coal arrives at Shuifu Port from Qiaojia and is transported to Shuifu Port twice through water-highway-water and highway-water dams. The transportation cost calculation results are shown in Table 3. The transportation cost of waterhighway-water is 0.41 ¥/t km, which is lower than the short-distance transportation cost of Jinsha River highway-water by 0.67 ¥/t km. Water transportation cost 18.63% Table 2 Table of three times over dam transport cost calculation Starting and ending points

From Huize to Shuifu Port Highway-Water

Water-highway-water

Use Huize highway to Shuifu Port

Use Huize highway to Menggu The mode of transportation wharf, tum waterway, and then through Baihetan, Xiluodu and Xiangjiaba transshipment facilities to Shui fu Port

–

387.1

Waterway distance (km)

355

126.06

Highway distance (km)

355

513.16

Total distance (km)

–

38,710

Water transport cost (¥)

237,850

84,460.2

Highway transport cost (¥)

–

102,000

Stevedorage (¥)

237,850

225,170.2

Total freight (¥)

0.67

0.44

Freight (/t km)

–

17.2

Waterway transport as a percentage of the total freight (%)

100

37.5

Highway transport as a percentage of the total freight (%)

–

45.3

Steve do rage as a percentage of the total freight (%)

Transport over dam 3 times, loading and unloading 6 times

Note

Plan

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Highway 73km The source of cargo in Huize County Highway 370 km

Menggu Waterway 78 .8 km wharf

The Santan transshipment wharf

Highway

42 .6 km

The Baihetan hydropower station

The Tuogu Waterway transshipment 165 .3km wharf

The Majiaheba transshipm ent wharf

Highway 5. 96km

13 .5 km

The Xiluodu hydropower station

The Fotan transship Waterway ment 143 .0 km wharf

The Xintan transship ment wharf

The Xiangjiaba hydropow er station

The Shuifu Port

The Shuifu Port

Fig. 5 Transportation mode from Huize to Shuifu port

of the total cost, road transportation cost 40.3% of the total cost, and loading and unloading cost 41.1% of the total cost. The transportation cost of water-highwaywater dam overturning is 39.43% lower than that of highway-water short-distance transportation. Table 3 Table of two times over dam transport cost calculation From Huize to Shuifu Port Highway-water

Starting and ending points Water-highway-water

Plan

Use Qiaojia highway to Shuifu Use Qiaojia highway to Tuogu The mode of transportation Port wharf, turn waterway and then through Baihetan, Xiluodu and Xiangjiaba transshipment facilities to Shuifu Port –

308.3

Waterway distance (km)

385

99.46

Highway distance (km)

385

407.76

Total distance (km)

–

30,830

Water transport cost (¥)

257,950

66,638.2

Highway transport cost (¥)

–

68,000

Stevedorage (¥)

257,950

165,468.2

Total freight (¥)

0.67

0.41

Freight (¥/t km)

–

18.63

Waterway transport as a percentage of the total freight (%)

100

40.3

Highway transport as a percentage of the total freight (%)

–

41.1

Stevedorage as a percentage of the total freight (%)

Transport over dam 2 times, loading and unloading 4 times

Note

“I Want to …Build”: Research on Dam Transshipment Facilities … Highway 5.96km

Highway 13.5km The

The source of cargo in Qiaojia County

Highway Tuogu Waterway The Majiaheba transship 80km 165.3km transshipment ment wharf

Highway 385km

wharf

The Xiluodu hydropower station

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The Fotan Waterway The Xintan transshipme 143.0km transshipm nt wharf ent wharf

The Xiangjiaba hydropower station

The Shuifu Port

The Shuifu Port

Fig. 6 Transportation mode from Qiaojia to Shuifu Port

4.2 Calculation of Two Times Transportation Cost of Turning Dam Transfer Schematic diagram of transportation mode of coal from Qiaojia, the source of cargoes, to Shuifu Port. The coal starts from Qiaojia, gets on the ship through Tuogu transshipment wharf, and then arrives at Shuifu port through waterway and is transported over dam (two times of dam turning and transshipment). The total transportation distance is 407.76 km, including waterway transportation distance of about 308.3 km and highway transportation distance of 99.46 km. The coal arrives at Shuifu Port from Qiaojia and is transported to Shuifu Port twice through water-highway-water and highway-water dams. The transportation cost calculation results are shown in Table 3. The transportation cost of waterhighway-water is 0.41 ¥/t km, which is lower than the short-distance transportation cost of Jinsha River highway-water by 0.67 ¥/t km. Water transportation cost 18.63% of the total cost, road transportation cost 40.3% of the total cost, and loading and unloading cost 41.1% of the total cost. The transportation cost of water-highwaywater dam overturning is 39.43% lower than that of highway-water short-distance transportation. Transportation mode from Qiaojia county to Shuifu port is shown in Fig. 6.

4.3 Calculation of One Times Transportation Cost of Turning Dam Transfer Schematic diagram of transportation mode of coal from Yongshan, the source of cargoes, to Shuifu Port. The coal starts from Yongshan, gets on the ship through Yongshan transshipment wharf, and then arrives at Shuifu Port through waterway and is transported over dam (one times of dam turning and transshipment). The total transportation distance

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The Fotan

The source of cargo in Yongsh an County

Highway transship Waterway ment 20km 143.0km wharf

Highway 165km

The Xintan transshipment wharf

The Xiangjiaba hydropower station

The Shuifu port

The Shuifu port

Fig. 7 Transportation mode from Yongshan to Shuifu Port

is 168.96 m, including waterway transportation distance of about 143.0 km and highway transportation distance of 25.96 km. Transportation mode from Yongshan county to Shuifu port is shown in Fig. 7. The coal from Yongshan to Shuifu Port needs to be transported through waterhighway-water and highway-water dams twice to reach Shuifu Port. The calculation results of transportation costs are shown in Table 4. The transportation cost of waterhighway-water is 0.42 ¥/t km, which is lower than the short-distance transportation cost of Jinsha River highway-water by 0.67 ¥/t km. Water transportation cost accounts for 32.48% of the total cost, road transportation cost accounts for 32.5% of the total cost, and loading and unloading cost accounts for 47.5% of the total cost. The transportation cost of water-highway-water dam overturning is 36.81% lower than the short-distance transportation cost of highway-water.

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Table 4 Table of one time over dam transport cost calculation Starting and ending points

From Huize to Shuifu Port Highway-water

Water-highway-water

Plan

Use Yongshan highway to Shuifu Port

Use Yongshan highway to Fotan wharf, turn waterway, and then through Xiangjiaba transshipment facilities to Shuifu Port

The mode of transportation

–

143

Waterway distance (km)

198

25.96

Highway distance (km)

198

168.96

Total distance (km)

–

14,300

Water transport cost (¥)

132,660

23,230

Highway transport cost (¥)

–

34,000

Stevedorage (¥)

132,660

71,530

Total freight (¥)

0.67

0.42

Freight (¥/t km)

–

32.48

Waterway transport as a percentage of the total freight (%)

100

32.5

Highway transport as a percentage of the total freight (%)

–

47.5

Steve do rage as a percentage of the total freight (%)

Transport over dam 2 times, loading and unloading 4 times

Note

4.4 Comprehensive Comparison In conclusion, it can be seen from Tables 2, 3 and 4 that, first of all, the transportation cost of highway-water dam turnover for three times, twice and once is lower than that of highway-water transportation, indicating that water-highway-water dam turnover has certain advantages in transportation cost; Secondly, the transportation cost of highway-water dam turnover twice is lower than that of dam turnover three times and dam turnover once. The transportation cost of dam turnover twice is more advantageous in terms of transportation cost, which is inconsistent with the conclusion that the less the number of dam turnover is, the more advantageous the transportation cost of water-highway-water transportation scheme is [4]. The transportation cost of dam turnover is not only related to the number of dam turnover, but also related to the highway transportation distance, which should be comprehensively considered analysis and calculated factors such as waterway transportation distance and dam turnover times. Finally, under the condition of the same loading and unloading costs, the highway transportation distance should be shortened as far as possible,

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the highway transportation cost should be reduced, the economic benefits of the water-highway-water transportation mode should be improved, and the advantages of waterway transportation should be brought into full play.

5 Conclusions In this paper, we take the dam transshipment facilities system of a typical hydropower station on the right bank of the Jinsha River as the research object, and study the dam turnover and transfer facility scheme and transportation mode by using the combination of quantitative and qualitative analysis methods and economic comparative analysis methods, and draw the following main conclusions: (1) Based on the comparative analysis of the existing transshipment transportation mode and the transportation economy, safety and timeliness, this paper breaks through the limitations of the existing Ro-Ro transshipment and container transshipment, studies and puts forward a new model of bulk transshipment transportation, and constructs a safe, reliable and efficient belt conveyor transshipment transportation scheme to help the high-quality development of Jinsha River shipping and the construction of integrated transport network. (2) Based on the topographic and geomorphic characteristics of high mountains and valleys on both sides of Jinsha River, such as complex geological conditions, exposed bedrock on both sides, deep valley and “V”-shaped or “U”-shaped, the general layout of the wharf is arranged in the scheme, and the “Z” general layout scheme of the grading platform is innovatively put forward. (3) The transportation cost of water-highway-water transshipment for three times, twice and once is lower than that of highway-water transportation, indicating that water-highway-water dam overturning transportation has certain advantages in transportation cost. (4) The transportation cost of water-highway-water transshipment is the lowest when the dam is overturned twice. The transportation cost of transshipment is not only related to the number of transshipment, but also related to the highway transportation distance, loading and unloading costs, etc., which should be analyzed and calculated based on the local actual situation. (5) Under the condition of the same loading and unloading costs, the highway transportation distance should be shortened as far as possible, the highway transportation cost should be reduced, the economic benefits of the water-highwaywater transportation mode should be improved, and the advantages of waterway transportation should be brought into full play.

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References 1. Zeng XL (2016) Research on development of Chongqing Ro-Ro ship transportation. Chongqing Jiaotong University 2. Lin GG, Liu QZ (2009) Research on the demand and factors of Ro-Ro transportation for continuous dam turning in Hongshui River. West China Commun Sci Technol 30(12):105–110+116 3. Liu Y (2010) Research on transportation mode of Hung-shui Ho Ro-Ro transshipment. Wuhan University of Technology 4. Cao MX, Zhang ZH, Xie ZZ, Wang XH (2017) Shipping conditions in cascade development of Jinsha River. Port Waterway Eng 525(02):57–66 5. Liu G, Liu MW, He LL, Luo H (2017) Discussion on long-term d transshipment transportation scheme of Bulk in Jinsha River Project. J Chongqing Jiaotong Univ (Nat Sci Ed) 189(10):83–90 6. Zhang JL (2015) Adaptability analysis of dam turning Highway and dam turning transportation system in Wujiang River. Transp Sci Technol 268(01):82–84 7. Cao MX (2012) Research on development condition and key technology of shipping conditions in cascade development of Jinsha River. Nanjing Institute of Water Resources 8. Cargo China Ports (2015) Refer to the information price of Yunnan transportation engineering materials and equipment survey on the price of domestic trade of bulk and miscellaneous. 272(02):55

Exploration and Application of Identifying Displacement of Regional Rainfall Centers Method Xintong Jiang, Xiaolong Liao, Xujian Quan, and Yixuan Zhong

Abstract Under the influence of climate change and human activities, the hydrological cycle of catchments has changed significantly, and hydrological extreme events such as flood disasters are becoming a prominent issue nowadays. This paper proposes a method of identifying the regional rainfall center and the displacement of interperiodic rainfall centers. Based on the Thiessen polygon method commonly used in hydrology, this paper divides the area into multiple irregular polygons and uses the rainfall intensity of a unique weather station contained within each polygon to represent the rainfall intensity within this polygon area. Referring to the centroid calculation formula of the object, the rainfall intensity of the time scale will be studied to replace the mass density of the centroid formula, and the coordinates of the regional rainfall center of the corresponding time scale for each research period will be calculated. The coordinates of the rainfall center in each period are formed into two-dimensional vectors to quantitatively describe the changes in rainfall center position in different periods. In this paper, Xijiang River Basin is taken as an application case to verify the calculation method. The results show that the calculation method of rainfall centers and their displacement is reasonable, credible, and operable, which can be applied to the study of regional rainfall centers and provide a scientific basis for flood control as well as the joint dispatch of reservoir groups. Keywords Rainfall center · Displacement · The spatial distribution · Xijiang river basin

X. Jiang · X. Liao (B) · X. Quan · Y. Zhong China Water Resources Pearl River Planning Survey and Design Co. LTD, Guangzhou, China e-mail: [email protected] X. Jiang e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_4

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1 Introduction Changes in climate and human activities can alter significantly the hydrological cycle of catchments [1]. Associating with extremes in rainfall (from tropical storms, thunderstorms, orographic rainfall, widespread extratropical cyclones, etc.), hydrological extreme events such as flood disasters, temporal and spatial variation of watershed hydrological factors in watersheds are becoming very prominent. As one of the most important links in the hydrological cycle, rainfall varies from year to year and over decades, and changes in the amount, intensity, frequency, and type affect the environment and society [2]. The study of temporal and spatial variability of rainfall is the core issue in exploring the response of water resources to climate change and the hydrological effects of climate change and human activities. The indicators of rainfall intensity, max\min\total rainfall, duration, frequency, coverage, etc. are usually used to analyze rainfall behavior [3–5]. Previous analyzes of rainfall are mainly about temporal evolution analysis and spatial distribution analysis, and most of the temporal analyzes focus on frequency analysis, cycle analysis, trend analysis and mutation analysis [6–8], while from the perspective of spatial distribution, the researches mainly focus on the rainfall spatial difference [9–11], but less consideration has been given to the most concentrated part of rainfall intensity in the region. Many researchers [12, 13] based on probability statistics, principal component analysis, Euclidean distance, and other methods to study the regional rainfall characteristics by using the evaluation indexes of rainfall intensity, duration, and range of single-station or regional rainfall. Their conclusion would classify the rainfall process into comprehensive intensity grades, but could not accurately calculate the location of the regional rainfall center. At present, there is no clear definition of rainfall center and unified method to determine rainfall center. Neil McIntyre [14] took Rd as the relative rainfall center which was calculated by using two indexes, total rainfall at the rain stations and the straight line distance from the drainage basin outlet to the rain stations. The larger the Rd value is, the farther the rainstorm is from the drainage outlet. YAIR GOLDREICH [15] defined the rainfall center as the area with the rainfall maximum and established the location of the rainfall center by applying the Distance Correlation Matrix technique. Chen [16, 17] studied the interannual variation of the summer and cold season rainfall center in the South China Sea, which is defined over the area covered by a threshold value of rainfall above 10mm day-1 in the SCS. Liu [18] defined the largest annual flood-causing rainstorm as the largest annual flood center and studied the regional distribution and the moving trajectory of the flood-causing annual maximum storm center over 100 years in China. The above studies used the single-station method to determine a single rainfall center, which means the rainfall of multiple stations in the region is compared, and the station whose rainfall intensity and frequency reach a certain level would be defined as the rainfall center in the region. However, the spatial and temporal characteristics of rainfall in different regions vary greatly, so this research method cannot be applied to any region in China. Moreover, the meteorological stations have a small record range

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and rare distribution, and point data cannot reflect the rainfall situation in the whole region. Despite the researchers [19, 20] used spatial interpolation of rainfall data set to calculate rainfall gravity center point based on gravity center ellipse technique in ArcGIS, given the longitude and latitude, terrain, slope height, the slope of the meteorological sites, the regional rainfall results vary according to the interpolation model, therefor such calculations of rainfall centers tend to result in large errors also. In this paper, the Thiessen polygon method, which is commonly used in the field of hydrology, is used to calculate station rainfall as surface rainfall. The Thiessen polygon method can divide the area into multiple irregular polygons and use the rainfall intensity of a unique weather station contained within each polygon to represent the rainfall intensity within this polygon area. As a result, the whole region becomes composed of multiple irregular polygons with inhomogeneous rainfall intensity. This study proposes a method to calculate the rainfall center of a region based on the equation for calculating the center of mass of an inhomogeneous density object, combined with the Thiessen polygon method. By calculating the coordinates of the location of the regional rainfall center on the corresponding time scale for each study period, the displacement of the regional rainfall center can be obtained, and the changes in the location of the regional rainfall center can thus be identified for a more in-depth understanding and analysis of the regional rainfall characteristics. Given the imprecise determination method of rainfall center, which is not conducive to the analysis of regional precipitation characteristics, the identification method of rainfall center and its displacement path is first proposed in this paper innovatively. Based on the precipitation intensity corresponding to each Thiessen polygon region in each specified period, the method can accurately calculate the location of the rainfall center in the study area for each specified period. The displacement path of the rainfall center in the study area can be quantitatively described according to the location changes of the rainfall center corresponding to two adjacent specified periods so that we can accurately identify the variation of the spatial distribution of precipitation in the region, and better understand its precipitation characteristics, which can provide technical support for effective flood control and reduction of natural disasters caused by precipitation.

2 Identifying Displacement of Rainfall Centers Method 2.1 Determination of Rainfall Centers Firstly, representative rain stations were selected according to the research area, where the area would be divided into several irregular polygons according to The Thiessen polygon method. The rainfall intensity of a single rain station contained in each polygon was used to represent the rainfall intensity within the polygon area. According to the research needs, the research period can be divided into different time scales, such as daily, monthly, ten-day, annual, flood season and non-flood season

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rainfall, etc. A certain research time scale was selected to calculate the total\mean rainfall of each Thiessen polygon region within each time scale, and then the centroid position of each Thiessen polygon region was calculated according to the Eq. (1). ˜ ˜ xd xdy yd xdy , T Yi = ˜ T Xi = ˜ d xd y d xd y

(1)

where represents the abscissa of the position of the centroid of the region of the i’th Thiessen polygon, represents the ordinate of the position of the centroid of the region of the i’th Thiessen polygon, x and y represent the abscissa value, and the ordinate value respectively. The centroid position, area, and rainfall intensity of each Theissen polygon are used to research rainfall center position in a selected time scale. The calculation principle refers to the centroid calculation formula of the object and replaces the mass density in the centroid formula with the rainfall intensity. The coordinates of the rainfall center are calculated by Eq. (2): Σ xn =

T X i · pi,n · Ai Σ , yn = pi,n · Ai

Σ

T Yi · pi,n · Ai Σ (n = 1, 2, . . .) pi,n · Ai

(2)

where is the abscissa of regional rainfall center in the n’th research period, is the ordinate of regional rainfall center in the n’th research period, is the rainfall intensity of the i’th Thiessen polygon in the n’th research period, and is the area of the i’th Thiessen polygon.

2.2 Displacement of Rainfall Centers To study the interannual displacement of regional rainfall centers, the study time would be divided into different periods. The location coordinates of the rainfall center in each period were calculated according to the method for determining the rainfall center, and the coordinates of the rainfall center in each period were formed into a two-dimensional vector, and the changes in the location of the rainfall center in different periods were quantitatively described according to the Eq. (3). L = (xn+1 , yn+1 ) − (xn , yn ) (n = 1, 2, . . .)

(3)

where, (xn+1 , yn+1 ) and (xn , yn ) represent the location coordinates of the regional rainfall center in the n + 1 and n’th research period respectively.

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3 Application Case 3.1 Study Area The Pearl River Basin is a first-level water resources division in China, consisting of the Xijiang, Beijiang, Dongjiang, and Pearl River Delta rivers, covering an area of 453,700 square kilometers, of which 442,100 square kilometers are in China. Xijiang River is the main trunk stream of the Pearl River, originating from Maxiong Mountain in Zhanyi County, Qujing City, Yunnan Province. From upstream to downstream, the Xijiang River is divided into Nanpan River, Hongshui River, Qianjiang River, Xunjiang River and other river segments. Its main tributaries are Beipan River, Liujiang River, Yujiang River, Guijiang River and Hejiang River, etc. It flows into the South China Sea at Modao Gate of Zhuhai City in Guangdong Province with a total length of 2214 km (Fig. 1). Xijiang River Basin, as a major channel from southwest to sea, plays an important role in China’s "One Belt and One Road" Initiative. As it is located in the subtropical monsoon climate zone, most of the rainfall in the basin is concentrated in summer, accounting for about 65% of the annual rainfall, which is more prone to flood disasters [21–23]. Taking Guangxi province as an example [24] 90% of the region was affected by heavy rainfall from June to July 1994, resulting in economic losses of nearly ten billion yuan. In 1998, the flood peak flow of the Wuzhou hydrological station exceeded 100 years, resulting in the disaster of more than 1 million people and more

Fig. 1 The study area

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than 1 million acres of farmland. Considering that the Xijiang River basin is located in the monsoon region and the terrain of the basin is complex, the annual flood control task is heavy, and the current research on the Xijiang River basin mainly focuses on the change of runoff [25, 26], rainstorm and flood [27, 28], so it is of great significance to study the rainfall center and its displacement of the Xijiang River, which could provide technical support and advice reference for the flood control command.

3.2 Data Processing According to the above calculation method of rainfall center and its displacement, the flow chart is shown in Fig. 2. In this paper, A total of 86 rain stations in and around the Xijiang River basin were selected for Thiessen polygon analysis to make the calculation more accurate (Fig. 3). The rainfall data from 1951 to 2015 were selected and divided into 7 research Fig. 2 The flow chart

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Fig. 3 Selected rainfall station to make ThiessenPolygons

periods (1951–1960, 1961–1970, 1971–1980, 1981–1990, 1991–2000, 2001–2010, 2011–2015). The average rainfall in the flood season (from April to October) in each research period of each station was taken as the research data, and the rainfall intensity corresponding to the Thiessen polygon grid of each station in each division period was calculated respectively. According to the position of centroid points in each Thiessen Polygons as shown in Fig. 4, the location of rainfall centers in the flood season in each research period was calculated according to the above determination method of rainfall centers, and the displacement of intergenerational rainfall centers was analyzed.

3.3 Analysis and Discussion Interdecadal average rainfall in flood season. The distribution diagram of average rainfall in the flood season in each research period is shown in Fig. 5a–g. The spatial distribution of rainfall in the Xijiang River basin shows that the average rainfall in flood season is higher in the southeast and lower in the northwest. The average rainfall was more than 1000 mm in most parts of the southeast and less than 1000 mm in most parts of the northwest. In most of the periods, the Liujiang and Hejiang regions in the northeast formed a high-value area with a rainfall of more than 1200 mm. In addition, the middle and lower reaches of Hongshui River also had a high-value area with a rainfall of more than 1400 mm.

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Fig. 4 The centroid points of ThiessenPolygons

Rainfall center during the flood season in Xijiang River Basin. From 1951 to 2015, the rainfall centers in the flood season of each decade were located in the middle reaches of the Xijiang River and the lower reaches of the Hongshui River (Fig. 6). The location of the rainfall centers in different interdecadal flood seasons was relatively stable, concentrated in the Hongshui River region, close to the Liujiang River region, and located between Yantan, Letan and Dahua Reservoirs. The result is consistent with the location of heavy rain in the Xijiang River basin in recent decades [29]. The displacement of rainfall centers in the flood season in the Xijiang River Basin. In the past 70 years, the rainfall center moved west (including northwest) twice and east four times (including northeast twice and southeast twice) (Fig. 7). From the 1950s to the 1980s, the rainfall center moved to the upper reaches of Hongshui River, However, from the 1980s to the present, during the flood seasons, the rainfall center tends to migrate to the middle and lower reaches. The trend analysis of average rainfall in flood seasons of different interdecadal (Fig. 8) shows that in the past 70 years, in the Xijiang River Basin, the middle and lower reaches of the Hongshui River and the Guijiang and Hejiang River area showed a trend of increasing rainfall during the flood season, the growth slope of which is 0–20 mm/10a. While most of the remaining areas showed a decreasing trend in the flood season, the growth slope of which is 0 to −20 mm/10a or more than −20 mm/10a, which also well explains why the rainfall center moved to the lower reaches after the 1980s. Research shows that Hongshuihe River and Liujiang River are the rainstorm centers of the Xijiang River Basin, and more than ten disastrous rainstorms have

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(a) 1951-1960 Average Rainfall in Flood Season(mm)

(b) 1961-1970 Average Rainfall in Flood Season(mm)

(c) 1971-1980 Average Rainfall in Flood Season (mm)

(d) 1981-1990Average Rainfall in Flood Season(mm)

(e) 1991-2000 Average Rainfall in Flood Season(mm)

(f) 2001-2010Average Rainfall in Flood Season(mm)

(g) 2011-2015 Average Rainfall in Flood Season(mm)

Fig. 5 Intergeneration average rainfall in flood season

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Fig. 6 Rainfall center in the flood season in Xijiang River Basin

Fig. 7 The displacement of rainfall center in the flood season in Xijiang River Basin

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Fig. 8 Trend analysis of intergeneration average rainfall in flood season

occurred since the 1960s, and the rainfall is mainly concentrated in the middle and lower reaches of Hongshuihe River and Liujiang River, among which the probability of falling on Hongshuihe River is 18.1%, and the probability of falling on Liujiang River is 24.1% [30]. Therefore, the rainfall center in the flood season of the Xijiang River basin calculated in this paper is located in the middle and lower reaches of the Hongshui River, close to the Liujiang River, which is consistent with the conclusions obtained from relevant studies; There are six main weather systems affecting rainfall in the Pearl River Basin [31]: subtropical high, trough, front, shear line, southwest vortex and tropical cyclone. In the weather system with subtropical high as the main factor, the rainstorm center has the highest probability of falling over the Hongshui River, while in the weather system with a trough, front, shear line and southwest vortex as the main factor, the rainstorm center has the highest probability of falling over Liujiang River [30]. Under the influence of subtropical high, heavy rain occurred in the 1960s and 1970s were mainly located in the Hongshui River, while under the influence of southwest vortex, shear line, stationary front and other factors, several heavy drops of rain mainly occurred in the Liujiang area after 1990s [30], which is consistent with the displacement track of rainfall center obtained in this paper.

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4 Conclusion Based on the hydrologic statistical analysis of the Thiessen polygon method and irregular polygon centroid calculation formula, this paper proposes a method to determine the regional rainfall center. Firstly, representative rain stations were selected according to the study area, where the area was divided into several Thiessen polygons according to the Thiessen polygon method; Then, the rainfall data of each station were obtained according to the research time scale (flood season from April to October), divided into several analysis periods according to the research needs, and the rainfall intensity of each station corresponding to the research time scale was calculated; Then referring to the centroid calculation formula of the object, the rainfall intensity of the time scale will be studied to replace the mass density of the centroid formula, and the coordinates of the regional rainfall center of the corresponding time scale for each research period will be calculated; Finally, the coordinates of rainfall center in each period are formed into two-dimensional vectors to quantitatively describe the changes of rainfall center position in different periods. This paper applies this method to the Xijiang River Basin which is the mainstream of the Pearl River, and draws the following conclusions: (1) The spatial distribution of rainfall in The Xijiang River Basin presents a pattern of high rainfall in the southeast and low rainfall in the northwest. There are high rainfall areas in the Liujiang River, Guijiang River, Hejiang River and the middle and lower reaches of the Hongshui River; (2) The location of the rainfall center in the flood season in each decade is relatively stable, which is located in the area near Hongshui River and Liujiang River and between Yantan, Letan and Dahua Reservoir; (3) From the 1950s to the 1980s, the rainfall center moved to the upper reaches of the Hongshui River, and from the 1980s to the present, the rainfall center moved to the Liujiang River; The rainfall center determination method and its displacement calculation method proposed in this study are applied to the Xijiang River Basin, and the results are quite consistent with the actual situation along with the conclusions drawn by most researchers. Therefore, the method proposed in this study is reasonable, credible and operable, which can be applied to the study of regional rainfall centers and provide a scientific basis for flood control as well as the joint dispatch of reservoir groups.

References 1. Bouraoui F, Grizzetti B, Granlund K et al (2004) Impact of climate change on the water cycle and nutrient losses in a Finnish catchment. Clim Change 66(1):109–126 2. Trenberth KE (2005) The impact of climate change and variability on heavy precipitation, floods, and droughts. Encycl Hydrol Sci 17 3. Murawski A, Zimmer J, Merz B (2016) High spatial and temporal organization of changes in precipitation over Germany for 1951–2006. Int J Climatol 36(6):2582–2597

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Adaptive Reservoir Operation Management Considering the Influence of Inter-Basin Water Transfer Project on Inflow Xiaoqi Zhang, Yuan Yang, Yongqiang Wang, and Yinghai Li

Abstract The seasonal flood limited water level (FLWL) is one of the important ways for the reservoir to efficiently use flood resources under the premise of meeting flood control requirements in the flood season. This paper established the joint distribution of summer and autumn maximum 7-day flood volume based on the Copula theory by considering the impact of inter-basin water transfer projects, and then deduced the most probable combination form for seasonal floods with the help of the Lagrangian multiplier method. Finally, the optimal design of the seasonal FLWL scheme was obtained by building an optimal model with the objective function of maximizing the available water supply, where the joint distribution probability value was regarded as the main flood control constraint. Selecting China’s Danjiangkou Reservoir as a case study, the results show that: (1) The general form of the most likely combination of seasonal floods considering the impact of inter-basin water X. Zhang · Y. Yang · Y. Wang (B) Changjiang River Scientific Research Institute, Changjiang Water Resources Commission of the Ministry of Water Resources of China, Wuhan 430010, China e-mail: [email protected] X. Zhang e-mail: [email protected] Y. Yang e-mail: [email protected] Hubei Key Laboratory of Water Resources and Eco-Environmental Sciences, Changjiang River Scientific Research Institute, Wuhan 430010, China X. Zhang · Y. Wang Research Center on the Yangtze River Economic Belt Protection and Development Strategy, Hubei, Wuhan 430010, China Y. Yang · Y. Li College of Hydraulic and Environmental Engineering, China Three Gorges University, Yichang 443002, China e-mail: [email protected] Y. Li Engineering Research Center of Eco-Environment in Three Gorges Reservoir Region, Ministry of Education, China Three Gorges University, Yichang 443002, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_5

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transfer projects is derived. (2) The optimal reservoir FLWLs in summer and autumn seasons are, respectively, 161.9 and 164.8 m. (3) The relative increment of the annual average diversion volume has increased by 1.8%, and the assurance probability of downstream water has increased by 0.79%, indicating that the optimized FLWLs have significantly improved the water supply. These research results are helpful to improve the flood resource utilization rate of the reservoir in the flood season. Keywords Seasonal flood limited water level · Most likely combination · Joint distribution · Water supply · Danjiangkou reservoir

1 Introduction With the rapid development of the economy and society, the problems of seasonal floods and droughts, scarcity of water resources and unreasonable distribution of time and space are becoming increasingly prominent, resulting in higher requirements for flood control and waterlogging elimination and benefit guarantee of reservoirs, and also highlights the contradiction between flood risk control and benefit promotion of reservoirs. Reservoirs play an essential role in flood risk control and comprehensive development of water resources [1, 2]. Therefore, the reservoir can be used to reasonably adjust the flood limited water level (FLWL) and alter water storage to solve the comprehensive contradiction between flood risk control and profit promotion of the reservoir, so as to change the contradiction that most reservoirs have a large amount of spilled water before and during the main flood season and no water can be stored after the flood season. The FLWL is the most important parameter for balancing flood risk control and protection, and should not be exceeded during the flood season, so as to provide sufficient water storage for flood control [3]. Through the research on the characteristics of the reservoir in the flood season, the scientific utilization of the reservoir capacity is conducive to improving the reservoir’s utilization rate in flood resources. However, the traditional static control of single FLWL will lead to the situation that the incoming water does not dare to be over stored in the flood season, and there is no water to be kept after the flood season, resulting in a large amount of waste and unconventional full utilization of water resources. Therefore, it is necessary to formulate a reasonable phased FLWL scheme to change this adverse situation. Xiong et al. [4] proposed an entropy-based method to divide the flood season and then partition the entire flood season into multiple sub-seasons to calculate the seasonal flood control water level. Xie et al. [5] built a multi-objective model to deduce the optimal seasonal FLWL by considering the joint operation of the reservoir and downstream floodplains. Chang et al. [6] do not think the seasonal flood control limit level of cascade reservoirs is isolated, and an increase in the FLWL of the upstream reservoir may bring about flood risk in the downstream reservoir. Two methods are proposed—multiple duration limited water level and dynamic limited

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water level—with the purpose of increasing water storage of a reservoir while maintaining its security for flood control. Guo et al. [7] considered the influence of the regulation and storage of reservoirs in the upper reaches of the Yangtze River on the design flood process of the Three Gorges Reservoir (TGR), and discussed for the first time the design flood of the TGR in the operation period and the FLWL in the flood season, which provided a theoretical basis for the study of the adaptive operation under the influence of the regulation and storage of large cascade reservoirs in the operation period. Aiming at the problem of the influence of upstream reservoir regulation and storage on the FLWL of downstream reservoirs after the completion of cascade reservoirs. Li et al. [8] proposed the seasonal FLWL of Xiangjiaba Reservoir considering the influence of Xiluodu Reservoir’ operation. These studies can provide theoretical support for the determination of FLWLs of the cascade reservoir. Zhou et al. [9] extended the dynamic control models of FLWL for a complicated multi-reservoir system. Ouyang et al. [10] proposed FLWL optimization design of cascade reservoirs considering both flood risk loss and economic benefit. For reservoir adaptive operation management, Yang et al. [11] proposed an adaptive reservoir operation framework, where the framework provided a robust method for identifying adaptive reservoir operation strategies to address streamflow non-stationarity. Kompor et al. [12] update the previous studies, which proposed various reservoir operations for flood mitigation, using ECMWF prediction information to support adaptive reservoir operation decision-making. However, inter-basin water transfer project (IBWTP) is seldom considered in reservoir operation. Therefore, the aim of this study is to establish the joint distribution function between the summer and autumn floods of the reservoir by considering the influence of IBWTP, derive an optimal seasonal FLWL, so as to provide suggestions provide suggestions for the adaptive reservoir operation management.

2 Study Area and Methodology 2.1 Study Area and Data Danjiangkou reservoir is located in the upper and middle reaches of Hanjiang River Basin (106° 12, –111° 26, E, 31° 24, –34° 11, N) (see Fig. 1). The construction of Danjiangkou reservoir was started in 1958. The main water source comes from the Hanjiang River and its tributary Danjiang River, the annual average water volume is 39.48 billion m3 , the normal water level is 170 m, the water area can reach 1050 km2 , and the corresponding reservoir capacity can reach 29.05 billion m3 . It is a first-class water source protection area in China and also an important water source for the Middle Route Project of China’s South-to-North Water Transfer Project. Danjiangkou reservoir, as one of the most fully functional large reservoirs in China, has a variety of ecological functions, such as power generation, irrigation, flood control, shipping, aquaculture and tourism.

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Fig. 1 A schematic diagram of the study area

The statistical characteristics of the data in the study area are given, and changes in the study data can be more visually observed [13]. Information on inflow from the Danjiangkou Reservoir from 1954 to 2012 was used as the basis for the study, the statistical characteristics of the Danjiangkou Reservoir inflow are given in Table 1. The following steps are set to deduce the reservoir’s optimal seasonal FLWL combination scheme, which are shown in Fig. 2. Table 1 Statistical characteristics of inflow in Danjiangkou Reservoir

Characteristics

Danjiangkou Reservoir Summer flood season

Autumn flood season

Mean value (108 m3 ) 45

35

Cv

0.61

0.75

Cv/Cs

2.3

2.0

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Fig. 2 Flowchart of the method for the optimal design of reservoir seasonal FLWL

2.2 Deduction of Reservoir Inflow Considering the Influence of IBWTP It is assumed that the entire flood season be divided into M sub-seasons, i.e., Di represents flood season staging i (i = 1, . . . , M), and Q Dj,ti is the observed reservoir inflow during the period Di in the jth year (N represents number of years for the observed streamflow, and j = 1, . . . , N ). The reservoir inflow during flood seasons Di is obtained as follows: considering the effect of the IBWTP I j,t Di I j,t = Q Dj,ti − Q Y

(1)

where Ti is the length of the flood season stage Di , and t = 1, . . . , Ti , Q Y represents the water diversion capacity in front of the dam of the IBWTP in flood season, which is given according to the water supply operating rules and the current reservoir operating status (e.g., reservoir water level, inflow magnitude). The characteristic parameters of flood magnitude are calculated according to Di in Eq. (1), which is uniformly recorded as the variable X aDi = the term I j,t

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 Di Di Di X a,1 , . . . , X a, j , . . . , X a,N (where X = {Q, W } and a = { p, 3d, 7d, 15d, 30d}).

A note was added as follows: the variable X aDi can be characterized as the flood Di , the maximum 7peak value Q Dp i , the maximum 3-day flood volume value W3d Di Di , or day flood volume value W7d , the maximum 15-day flood volume value W15d Di the maximum 30-day flood volume value W30d , all of which are the conventional parameters describing flood magnitude characteristics in China.

2.3 Copula-Based Correlation Analysis for Seasonal Floods This section must be in two columns. That different seasonal floods have a slight correlation is the assumption of this study, which is also assumed by some hydrologists when designing of seasonal floods [3]. The Copula method has been commonly applied in analyzing the correlation of hydrological elements by constructing a joint distribution for multiple variables, and consequently, it is selected as the analytical method in this study to identifying the relationship between several seasonal floods. The Copula function for M seasonal floods is built as follows:        F xaD1 , . . . , xaD M = Cλ FX aD1 xaD1 , . . . , FX aD M xaD M , i = 1, . . . , M

(2)

  where FX aDi xaDi is the marginal distribution function of variable X aDi ,   F xaD1 , . . . , xaD M is the joint distribution function of variables X aD1 , . . . , X aD M , Cλ (·) is the Copula connection function, λ is the parameter of the Copula function, which is obtained from its relationship with the Kendall rank correlation coefficient. The commonly used Copula functions include Clayton, Frank, Gumbel, Gaussian in the field of hydrological analysis [14, 15]. The ordinary least squares (OLS) method (as in Eq. (3)) is used to evaluate the effectiveness of the Copula method, and the Copula function form with the smallest OLS value is selected as the joint distribution function. ⎡ | n |1 Σ (3) OLS = √ (Pei − Pi )2 n i=1 where n is the number of samples, Pei and Pi are empirical frequency and theoretical frequency, respectively.

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2.4 Derivation of the Most Probable Combination Form for Seasonal Floods The seasonal floods combination form is characterized by X aD1 , . . . , X aD M , and the possibility of which  can be evaluated   by the joint probability density function value f xaD1 , . . . , xaD M . The value of f xaD1 , . . . , xaD M is greater, the possibility of the combination X aD1 , . . . , X aD M is greater. Therefore, the derivation of the most probable combination form for seasonal floods can be represented as follows: ⎧ M  ⊓ ⎪   ⎪ ⎨ max f x D1 , . . . , x D M  = cλ F D1 x D1 , . . . , F D M x D M  f X aDi xaDi a a a a Xa Xa (4) i=1 ⎪ ⎪   D1  ⎩ DM s.t T = 1/ 1 − F xa , . . . , xa      where cλ FX aD1 xaD1 , . . . , FX aD M xaD M is the density function of the copula func  tion; f X aDi xaDi is the probability density function for X aDi ; T is the joint return period, the value of which can be determined based on the flood control standard, and the concept of OR return period is selected in this study to describe the   term T because of it is usually concerned by hydrologists [16]. The function f xaD1 , . . . , xaD M is continuous in the domain of M-dimensional space, thus it must have a minimum or a maximum. The Lagrange multiplier method is used to solve the most probable combination of X aD1 , . . . , X aD M , and the Lagrange function is constructed as follows:        L xaD1 , . . . , xaD M , μ = cλ FX aD1 xaD1 , . . . , FX aD M xaD M M ⊓

         f X aDi xaDi + μ Cλ FX aD1 xaD1 , . . . , FX aD M xaD M − 1 + 1/T

(5)

i=1

A necessary condition for  the existence of extreme values is that the derivative of the Lagrange function L xaD1 , . . . , xaD M , μ with respect to the independent variables (i.e., X aDi (i = 1, . . . , M) and μ) are equal to zero. Therefore, the most probable combination solution, namely X aD1 ∗ , . . . , X aD M ∗ , can be obtained by solving the following equation set: ⎧   M  D  ⊓  D   D  ⎪ ∂L ∂cλ 2  D1  ∂Cλ ⎪ ⎪ = f D xa f Di xa i + μ f + cλ f , D xa 1 x 1 =0 ⎪ ⎪ D1 D1 D1 X D1 a 1 1 ⎪ X ⎪ X X a a ∂ xa ∂ xa ∂ xa a a ⎪ i=2 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪··· ⎨    D   D  M−1  D   D  ⊓ ⎪ ∂cλ ∂Cλ ∂L ⎪ 2 M +c f, M ⎪ = f f Di xa i + μ f x x xa M = 0 ⎪ λ a a D D ⎪ DM DM DM X DM M M ⎪ X X X ⎪ ∂ xa a a ∂ xa ∂ xa a a i=1 ⎪ ⎪ ⎪  ⎪  D   D  ⎪ ∂L ⎪ ⎪ ⎩ = C λ F D1 x a 1 , . . . , F D M x a M − 1 + 1/T = 0 ∂μ Xa Xa

(6)

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2.5 Optimization Model A multi-objective optimization model is established to derive the optimal seasonal FLWL combination scheme.

2.5.1

Objective Function

Maximizing mean annual water supply:   Max S Z D1 , . . . , Z D M

(7)

where Z Di is decision vector (i = 1, . . . , M), representing the reservoir FLWL during the flood season stage Di , S(·) are reflecting the benefits of hydropower generation and water supply, respectively.

2.5.2

Constraints

(1) Joint distribution probability value:

Coptimal ≤ Cconventional

(8)

where Coptimal is the optimized joint distribution probability value, and Cconventional is the conventional joint distribution probability value. (2) Reservoir water balance equation:   Vt+1 = Vt + It − Q out,s − Q out,t · Δt

(9)

where Δt is the time interval, Vt is the reservoir storage at time t, It and Q out,t are respectively the reservoir inflow and release during time period Δt, and Q out,s is the diversion water from the reservoir during time period Δt. (3) Reservoir storage limits:

VL ≤ Vt ≤ VU

(10)

where VL and VU are respectively the minimum and maximum allowable reservoir storages during the flood season.

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(4) Release capacity limits:

Q out,L ≤ Q out,t ≤ Q out,U

(11)

where Q out,L and Q out,U are respectively the minimum and maximum reservoir release during the flood season. (5) Hydropower generation limits:

N L ≤ N t ≤ NU

(12)

where Nt is the output at time t (kW), N L and NU are respectively the minimum and maximum outputs at time t.

3 Results and Discussions 3.1 Establishment of Joint Distribution Based on Copula Method On the basis of the water supply operating rules of Danjiangkou Reservoir, the water diversion capacity in front of dam of the IBWTP Q Y during the flood seasons (as in Eq. (1)) contains two parts: (i) maximum design water diversion capacity of Qingquangou is 420 m3 /s; (ii) maximum design water diversion capacity of Taocha is 100 m3 /s. The Pearson’s correlation test was performed to verify the correlation between the summer and autumn flood seasons, and the specific calculation results are as follows: The flood peak values Q Dp 1 , Q Dp 2 and the maximum 7-day flood volumes D1 D2 W7d , W7d during summer and autumn flood seasons are respectively combined into joint observation sequences, and then the Pearson correlation coefficients for Q Dp i Di (i = 1, 2) and W7d (i = 1, 2) are estimated as 0.2337 and 0.3042, respectively, both of which are bigger than the statistic value ra = 0.218. Since the correlation D1 D2 and W7d is stronger than that between Q Dp 1 and Q Dp 2 , the copulabetween W7d based correlation analysis for the maximum 7-day flood volumes during summer and autumn flood seasons is selected as the result display. The Copula function for seasonal floods of the Danjiangkou Reservoir was built as follows:        D1 D2 D1 D2 = Cλ FW D1 w7d , FW D2 w7d (13) F w7d , w7d 7d

7d

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Table 2 Estimation results for the parameter λ values and OLS values of different forms of Copula function Function forms

Clayton

Frank

Gumbel

Gaussian

λ

0.4363

1.8824

1.2333

0.3126

OLS value

0.0160

0.0156

0.0190

0.0165

D1 D2 where W7d and W7d represent the maximum 7-dayflood volumes during summer and 

D1 D2 , w7d represents the joint distribution autumn flood seasons, respectively, F w7d     D1 D2 D1 D2 function of variables W7d and W7d , FW D1 w7d and FW D2 w7d are the marginal 7d

7d

D1 D2 distribution function of W7d and W7d , and the form of which usually choses the Person-III distribution in China, Cλ (·) is the Copula connection function, λ is the parameter of the Copula function. Table 2 shows the results for the OLS values of different forms of Copula function which were estimated according to Eq. (3). Thus, the Frank-type Copula function was chosen as the joint distribution function for the D1 D2 , W7d during summer and autumn flood seasons, maximum 7-day flood volumes W7d and the fitting results of the theoretical distribution and empirical distribution of the maximum 7-day flood volumes are shown in Fig. 3.

3.2 Determination for the Most Probable Seasonal Flood Combination In this study, the joint distribution function value for the Danjiangkou Reservoir is      evaluated by combining Eqs. (2) and (5), Cλ FX aD1 xaD1 , FX aD2 xaD2 = 1 − Pλ = 1 − 1/T = 0.999, according to the flood control standard requirements of the Danjiangkou Reservoir (i.e., the return period T is set at 1000 year.). As described in Sect. 2.4, the most probable combination of X aD1 , X aD2 can be determined through solving the Lagrange multiplier equations (as in Eq. (15)).   ⎧  D1      ∂L ∂Cλ ∂cλ 2  D1  , ⎪ ⎪ x + c x f X aD2 xaD2 + μ D1 f X aD1 xaD1 = 0 = f f D D ⎪ λ 1 a a D1 D1 X a 1 ⎪ X a ⎪ ∂ xa ∂x ∂ xa ⎪ ⎪   a ⎨         ∂L ∂Cλ ∂cλ 2 , = f D2 xaD2 + cλ f D2 xaD2 f X aD1 xaD1 + μ D2 f X aD2 xaD2 = 0 D D 2 2 Xa ⎪ ∂ xa ∂ xa X a ∂ xa ⎪ ⎪ ⎪   ⎪     ∂ L ⎪ ⎪ ⎩ = Cλ FX aD1 xaD1 , FX aD2 xaD2 − 1 + 1/T = 0 ∂μ (14) where the type of the probability density function is selected as Pearson-III, which

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Fig. 3 Comparison results for Copula distribution functions: a Theoretical Copula curve (Franktype Copula); b Empirical Copula curve 



 α1 −1 −β x D1 −g 1 D 1 · xa 1 − g1 e 1 a D1 Xa ⎡(α1 )       α2 −1 −β x D2 −g     1 D2 D D D2 α2 2 a 2 · xa − g2 = β2 · f D2 x a e , C λ F D1 x a 1 , F D2 x a 2 Xa Xa Xa ⎡(α2 ) 

f

D1

xa



α

= β1 1 ·

(15)

Solution formula, xaD1 = 16.2 billion m3 , xaD2 = 18.4 billion m3 .

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3.3 Deduction of Optimal Seasonal FLWL Combination Scheme Derivation for Seasonal FLWL based on Optimization Objective Function. Objective function. Considering the constraint of standard for flood control, an optimization model for the most likely combination of seasonal floods is established. The FLWLs in summer and autumn are two decision variables. The most likely combination scheme is derived by solving the optimal model given in Sect. 2.5. Finally, the optimal FLWLs in summer and autumn are respectively 161.9 and 164.8 m. Comparison of Conventional and Optimal Schemes. Table 3 gives the comparison results of the conventional and optimal schemes. Compared with the conventional scheme, the power generation guarantee rate deduced from the optimization scheme has increased by 1.1%, which means that the optimization scheme can generate more benefits without increasing flood risk. In terms of flood control, the increments of seasonal FLWLs in summer and autumn flood seasons are respectively 1.9 and 1.3 m. Finally, the water supply has increased by 1.8% and the downstream water assurance rate has increased by 0.79% under the condition of the optimal scheme, indicating that the optimal design for the reservoir seasonal FLWL has significantly improved the water supply, which is the priority operation task for Danjiangkou Reservoir besides flood control. Table 3 Comparison of the conventional and optimal schemes Content

Unit

Conventional scheme

Optimal scheme

FLWL in summer

m

160.0 [5]

161.9

FLWL in autumn

m

163.5 [5]

164.8

Annual average hydropower generation

Billion kWh

3.60

3.40

−0.2

Annual average spilled water

Billion m3

2.91

2.89

−0.02

Annual average water diversion

Billion m3

9.25

9.42

0.17

Assurance probability of downstream water

%

96.64

97.43

0.79

Assurance % probability of power generation

95.74

96.84

1.1

Variation in value 1.9 1.3

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4 Conclusions In this study, the joint distribution of the maximum 7-day flood volume of the reservoir in summer and autumn is derived by considering the influence of IBWTP on reservoir inflow, and then the most probable combination form for seasonal floods is solved by the Lagrange multiplier method. Finally, the optimization model is established with the objective function of maximizing the available water supply. The main conclusions are drawn as follows. The general form of the most likely combination of seasonal floods considering the impact of IBWTP is derived. The optimal seasonal FLWLs of Danjiangkou Reservoir in summer and autumn are then derived as 161.9 and 164.8 m, respectively. Under the optimal seasonal FLWLs, the relative increment of the annual average water diversion volume is 1.8%, and the assurance probability of power generation and downstream water increases by 1.1 and 0.79%. Acknowledgements This work is funded by National Natural Science Foundation of China (52109003), and Major Scientific and Technological Project of the Ministry of Water Resources (China) (SKR-2022006).

References 1. Deng C, Liu P, Liu Y, Wu Z, Wang D (2015) Integrated hydrologic and reservoir routing model for real-time water level forecasts. J Hydrol Eng 20(9):05014032 2. Ming B, Liu P, Bai T, Tang R, Feng M (2017) Improving optimization efficiency for reservoir operation using a search space reduction method. Water Resour Manag 31(4):1173–1190 3. Liu P, Li L, Guo S, Xiong L, Zhang W, Zhang J, Xu CY (2015) Optimal design of seasonal flood limited water levels and its application for the Three Gorges Reservoir. J Hydrol 527:1045–1053 4. Xiong F, Guo S, Chen L, Chang FJ, Zhong Y, Liu P (2018) Identification of flood seasonality using an entropy-based method. Stoch Env Res Risk Assess 32(11):3021–3035 5. Xie A, Liu P, Guo S, Zhang X, Jiang H, Yang G (2018) Optimal design of seasonal flood limited water levels by jointing operation of the reservoir and floodplains. Water Resour Manag 32(1):179–193 6. Chang J, Guo A, Du H, Wang Y (2017) Floodwater utilization for cascade reservoirs based on dynamic control of seasonal flood control limit levels. Environ Earth Sci 76(6):1–12 7. Guo S, Xiong F, Wang J, Zhong Y, Tian J, Yin J (2019) Preliminary exploration of design flood and control water level of Three Gorges Reservoir in operation period. J Hydrol Eng 50:1311–1317 8. Li Y, Xia Q, Zhang H, Wang Y, Tu Y, Nie P (2021) Study on segmentation of flood control level on Xiangjiaba reservoir considering the influence of Xiluodu reservoir regulation. J China Hydrol 03:32–37 9. Zhou Y, Guo S, Liu P, Xu C (2014) Joint operation and dynamic control of flood limiting water levels for mixed cascade reservoir systems. J Hydrol 519:248–257 10. Ouyang S, Zhou J, Li C, Liao X, Wang H (2015) Optimal design for flood limit water level of cascade reservoirs. Water Resour Manag 29(2):445–457 11. Yang G, Zaitchik B, Badr H, Block P (2021) A Bayesian adaptive reservoir operation framework incorporating streamflow non-stationarity. J Hydrol 594:125959

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12. Kompor W, Yoshikawa S, Kanae S (2020) Use of seasonal streamflow forecasts for flood mitigation with adaptive reservoir operation: a case study of the Chao Phraya River Basin, Thailand, in 2011. Water 12(11):3210 13. Burgan HI, Vaheddoost B, Aksoy H (2017) Frequency analysis of monthly runoff in intermittent rivers. In: World environmental and water resources congress 2017, pp 327–334 14. Chen L, Huang K, Zhou J, Duan HF, Zhang J, Wang D, Qiu H (2020) Multiple-risk assessment of water supply, hydropower and environment nexus in the water resources system. J Clean Prod 268:122057 15. Volpi E, Fiori A (2012) Design event selection in bivariate hydrological frequency analysis. Hydrol Sci J 57(8):1506–1515 16. Yin J, Guo S, Gu L, He S, Ba H, Tian J, Chen J (2020) Projected changes of bivariate flood quantiles and estimation uncertainty based on multi-model ensembles over China. J Hydrol 585:124760

Method of Low-Pressure Pipe Layout for Peach Tree Irrigation in Hilly Area Azhong Dong , Miao Hou , Zhihuan Wang , Yan Ju , and Wenye Zhang

Abstract Compared with traditional agricultural irrigation, water-saving irrigation technology (WSIT) can save water and improve water utilization rate. Therefore, WSIT is important for the sustainable development of agriculture. In China, lowpressure tube irrigation (LPPIT), as an effective WSIT, has been gradually applied to crop irrigation. Pipe layout is important in LPPIT, and it has a significant impact on conveyance efficiency, water saving, construction investment, land occupation, and so on. However, there are few studies on pipe layout in hilly area where LPPIT is suitable and necessary. For this, the paper proposes a method of low-pressure pipe layout in the hilly area. Taking the peach tree irrigation in the hilly area as an example, the proposed method is applied. Keywords Agriculture irrigation · Hilly area · Low-pressure pipe irrigation technology · Peach tree · Water saving

1 Introduction To overcome water scarcity, water-saving irrigation technologies (WSITs) are actively adopted around the world. Currently, WSITs mainly include low-pressure pipe irrigation technology (LPPIT), sprinkler irrigation technology (SIT), drip irrigation technology (DIT), micro-irrigation technology (MIT), cocoon water-saving technology (CWST) [1–5]. In some countries, WSITs are applied depending on the crop types and climatic conditions. For example, drip irrigation technology (DIT) is widely used for soybean irrigation in a low rainfall region in Japan [6]. In Lebanon, micro-irrigation technology (MIT), which uses mini-sprinklers to spray water into the root zone of the crops, has been widely used to irrigate potato crops [7]. During the

A. Dong · M. Hou (B) · Z. Wang · Y. Ju · W. Zhang Jiangsu Hydraulic Research Institute, 97 Nanhu Road, Nanjing 210017, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_6

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dry season in northern Ethiopia, mangoes are irrigated with the cocoon-style watersaving technology (CWST), which uses a small water storage measure to deliver water to the soil at a constant, slow and unchanging rate [2]. As a big agricultural country, canal irrigation system (CIS) is a traditional irrigation agriculture system in China. It has the advantage of low investments. However, the CIS has a serious water waste problem due to water leakage and evaporation. According to annual water consumption statistics, agriculture irrigation water consumption accounted for 80% in China [8]. A large amount of irrigated water consumption has seriously restricted the efficient development of agriculture in China. To alleviate water stress, China began to develop WSITs since the 1970s. By the end of 2020, the water-saving irrigation area in China has reached 567 million mu, of which the irrigated area using high-efficiency WSITs such as LPPIT, SIT and MIT accounts for 62% (350 million mu). At present, LPPIT is currently widely used in irrigation due to its low investment, convenient management, and easy to meet agricultural water needs. The basic feature of LPPIT is the use of low-pressure pipelines to transport water from the water source to the farmland [9]. It releases water from the outlet to the farmland for surface irrigation. Compared with the CIS, LPPIT can reduce water leakage and land use [10]. China’s terrain is complex and diverse, including plains, hills, mountains, plateaus and basins, among which hilly areas account for 66.67% of the country’s land area. It is of great importance for water conservation to develop WSITs in hilly area. Liu demonstrated that it is necessary and suitable to develop WSITs, especially LPPIT in hilly areas [11, 12]. The complete implementation process of LPPIT comprises the following steps: (i) laying out the pipes, (ii) making irrigation scheduling, (iii) choosing the pipe material, (iv) calculating the pipe diameter, and (v) selecting the pump. Pipe layout is the first step, which is significant for LPPIT, but not easy to achieve. At present, studies related to LPPIT mainly focus on the hydraulic simulation methods [9] and design methods [13, 14]. Fewer studies have focused on the impact of the pipe layout method on irrigation effectiveness, particularly in the hilly area. Due to the large elevation difference in hilly area, it is more critical to lay out the pipe reasonably. Therefore, the paper takes peach tree irrigation in the hilly area as an example to study the pipeline layout, which can be used as a reference for similar areas.

2 Study Area and Way of Irrigation Liyang city, between Maoshan Mountain and Taihu Lake, is located in the southwest of the Jiangsu Province. The city has jurisdiction over 3 subdistricts and 9 towns, with a permanent population of 790,000 and a total area of 1,535 km2 , of which the hilly area accounts for 50%. It has the east Asian subtropical monsoon climate with an average annual temperature of 16 °C, and an average annual rainfall of 1141 mm. Local soils include two major soil types (i.e. red and yellow–brown soil). The peach tree is one of the main cash crops cultivated in the city. The paper selects the peach

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Fig. 1 Location and topographic map of the study area

orchard located in Jiandong Village, and Shangxing Town in this city as the study case (Fig. 1). The terrain elevation of the study area increases from southeast to northwest, and the slopes on both sides are between 1/200 and 1/400. There is a Guantang reservoir in the study area, with a catchment area of 0.78 km2 and a total storage capacity of 0.935 million m3 .

3 Method and Basic Design Conditions of Pipe Layout in Hilly Area A typical low-pressure pipe irrigation system (LPPIS) ordinary includes a water source system (WSS) (e.g. rivers, wells, reservoirs, pump stations, sluices), a water conveyance irrigation pipeline system (WCIPS) (e.g. main pipes, branch pipes, subbranch pipes), farmland irrigation system (e.g. flexible pipes, field ditches). Guo et al. showed that the investment of WCIPS was highest in LPPIS, accounting for about 70% of the total investment [15]. Among the WCIPS, the pipe layout directly impacts the investment. Figure 2 shows the process of pipe layout based on some guidelines in the hilly area. According to Fig. 2, firstly, it is necessary to prepare the topographic map of the hilly area. Then, the pipes are arranged according to the principles of the pipe layout in the hilly area. Finally, the pipe layout is determined and the pipe layout diagram is drawn up. As shown in Table 1, the basic design parameters for the

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selected case are composed of irrigation water resources, irrigation guarantee rate, field water capacity, peach planting area, etc.

Fig. 2 The flow chart of pipe layout in hilly area

Table 1 The main design conditions of study case Parameters

Data (m3 )

Sources of data

0.935 million

Design report

Irrigation guarantee rate (%)

90

Irrigation water quota for farmland irrigation (DB32/T3817-2020)

Field water capacity (%)

25

Specification for agrometeorological observation (the soil volume)

Irrigation water resources

Peach planting area (km2 )

0.27

Design report

Planting row space (m)

5

Design report

Design irrigation volume for peach trees (m3 /d)

0.3

Design report

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4 Results and Discussion According to the planting plan, peach trees were mainly planted along the slope, with a few planted along the contour line. Based on the proposed pipe layout method, the pipe layout of the study area is shown in Fig. 3. According to the design conditions in Sect. 3, the irrigation area was divided into 6 irrigation groups. The main pipe with a total length of 241 m was connected to the pump, and laid along the main road. Then, three branch pipes branching from the main pipe were arranged along the main road, and their lengths are 162 m, 244 m, and 577 m, respectively. To facilitate pipe construction and save costs, the 17 sub-branch pipes with a total length of 3505 m were arranged perpendicular to the branch pipes. Proper pipe spacing can reduce pipe length and pipe investment, so as to reduce the total project cost. In this case, the maximum distance between each branch pipe is 115 m, and the minimum distance is 88 m, which is between the common layout spacing of other similar projects in China. The layout of pipes seems appropriate and the irrigation system is easy to manage. Because the pipes are underground, the project takes up less plantable land. More importantly, the irrigation system can fully meet the irrigation needs of crops. After completing the design of pipe layout, the next step is to carry out the pipe construction. It is the key part of the implementation of LPPIT. Pipe construction affects its cost and irrigation efficiency. Figure 4 shows the common pipe construction process of LPPIT, which includes (i) preparation for the construction, (ii) surveying on the construction, (iii) digging the channels, (iv) foundation treatment and laying

Fig. 3 Proposed pipe layout in hilly area

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bedding layer, (v) installing the pipes, (vi) handing the connections of pipes, (vii) testing the pressure of pipes, and (viii) earth filling. Currently, there are only few studies for the pipe construction process in hilly areas. Therefore, the construction process shown in Fig. 4 is quite common in LPPIT construction in hilly areas. The subsequent research studies combined with the characteristics of hilly area pipe construction are also worth continuing. In order to better manage water resources and integrate water-saving technologies and project management measures, China has implemented the integrated agricultural water pricing reform (IAWPR). As the policy guarantee to promote water conservation in agriculture, the IAWPR is the most comprehensive and systematic innovation in farmland and water conservancy department currently in China [8]. The Central government carried out the pre-reform pilot in 2014. Then, the IAWPR began in 2016 officially and is planned to take ten years to complete this reform. Agriculture-related government departments participating in IAWPR include the

Fig. 4 The process of pipe construction

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Fig. 5 The key tasks for the IAWPR

national development and reform commission, water resources department, financial department and agriculture department. The main reform objectives include: (i) promote water-saving projects in agricultural; (ii) improve the integrity and farmland irrigation efficiency of irrigation system via establishing the operation and maintenance (O&M) system. Then, in order to achieve the above reform goals, the national government had given the local governments a number of specific reform tasks, as Fig. 5 shown. There are eight major reform tasks in IAWPR, (i) setting the irrigation water requirements of main crops according to their growing needs; (ii) building the irrigation water saving and water incentive reward mechanism with promoting to adopt the water-saving irrigation technologies; (iii) encouraging to establish the agricultural water trading markets; (iv) encouraging the development of water-saving measures and projects related to farmland irrigation (e.g. drop irrigation, micro-irrigation, lowpressure pipeline irrigation, etc.), and by agricultural training to improve farmers’ scientific knowledge of water use; (v) rationally determining the agricultural irrigation prices in irrigation districts; (vi) installing metrological facilities in irrigation districts; (vii) establishing engineering management organizations which are non-government and based on farmers; (viii) formulating water fees reasonably and establishing an agricultural water fee subsidy mechanism.

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It can be seen from Fig. 5 that there are eight key tasks in IAWPR with three involving water-saving irrigation technology. Promoting water-saving irrigation technologies is to achieve the objective requirements of agricultural and water conservancy modernization. According to the document of “Suggestions on carrying out the integrated agricultural water pricing reform” which was posted by General Office of the State Council of the People’s Republic of China, the universal application of advanced agricultural water conservation technology measures is of great importance to accomplish the reform goals. Taking Jiangsu as an example, it completed its set reform task by the end of 2020. The city’s arable land area is about 4.539 million hectares, of which 92.3% is irrigated. With the completion of IAWPR, its water-saving irrigation project area reached 2.946 million hectares in the province, accounting for 65%.

5 Conclusions Water saving is the eternal theme of China’s water conservancy construction. To develop water saving in agriculture is an inevitable choice to accelerate the process of water-saving society, agriculture modernization and economic-social development. The integrated agricultural water pricing reform is the major strategic policy of prioritizing water conservation and water management in China. Through IAWPR, it can improve the efficiency of irrigation agriculture water-saving, promote water saving measures and to a large extent in increasing the integrity and matching the rate of the irrigation systems. In China, LPPIT is one of the main measures of farmland water saving irrigation technology. It helps irrigation districts solve irrigation problems effectively. And it has the advantage of being low-cost and easy to operate and is widely used in small irrigation and water conservancy constructions in China. Pipe layout is the most basic design in LPPIT and it directly affects the project investment and operation cost. The paper proposed a method of pipe layout for lowpressure pipe irrigation technology in the hilly area. The results showed that, (i) the main pipe was laid along the main road, the branch pipe was arranged vertically with the main pipe, and sub-branch pipe was arranged essentially along the contour line, (ii) the length of main pipe and three branch pipes were 241, 162, 244, 577 m respectively. There were 17 sub-branch pipes with a total length of 3 505 m, (iii) proper pipe layout reduced the length of the pipes thereby minimizing the project cost of study case, and (iv) LPPIT can greatly reduce the amount of plantable land occupied by irrigation facilities. In general, the proposed method is feasible, and the research results can provide reference for the development of low-pressure pipeline layout in similar areas. Acknowledgements The paper is funded by the Water Resources Science and Technology Project of Jiangsu Province (Grant No. 2019043 and No. 2022006).

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References 1. Chen XZ, Thorp KR, Ouyang ZY et al (2019) Energy consumption due to groundwater pumping for irrigation in the North China Plain. Sci Tot Environ 669:1033–1042 2. Wang Y, Li F, Xu H et al (2022) The practice on water-saving irrigation system in the Northwest China. Int Core J Eng 8(3):140–143 3. Zou XX, Li YE, Roger C et al (2013) Cost-effectiveness analysis of water-saving irrigation technologies based on climate change response: a case study of China. Agric Water Manag 129:9–20 4. Sun XM, Mu ZZ (2021) Research progress of water-saving irrigation measures. Sci J Intell Syst Res 3(12):104–108 5. Chen XJ (2022) Influence of high-efficiency water-saving irrigation measures on the quality of cultivated land in high-standard farmland construction projects. Sci J Intell Syst Res 4(3):244– 246 6. Chomsang K, Morokuma M, Agarie S et al (2021) Effect of using drip irrigation on the growth, yield and its components of soybean grown in a low rainfall region in Japan. Plant Prod Sci (41):1–15 7. Sabbagh M, Gutierrez L (2022) Micro-irrigation technology adoption in the Bekaa Valley of Lebanon: a behavioural model. Sustainability 14:7685 8. Yang X, Hou M, Wang J et al (2022) Integrated agricultural water pricing reform (IAWPR) in China: a state-of-the-art review with focus on strategic significance, policy design, reform process and case reform effect. Water Policy 24(2):242–260 9. Gong ZH, Jiang XH (2021) Hydraulic simulation based on PSO for irrigation projects with low-pressure pipe conveyance. J Drain Irrig Mach Eng 39(10):1046–1050 10. Smout IK (1999) Use of low-pressure pipe systems for greater efficiency. Agric Water Manag 40(1):107–110 11. Ye HL, Chen ZG, Jia TT et al (2021) Response of different organic mulch treatments on yield and quality of Camellia oleifera. Agric Water Manag 245:106654 12. Fan Y, Luo Y, Wei C (2010) On level terrace engineering design of slope land in the hilly and mountainous, Southwestern China. J Mt Sci 28(5):560–565 13. Li DX, Sheng ZH (2018) Research progress in optimal design of irrigation pipe network. China Rural Water Hydropower 2:23–27 14. Gajghate PW, Mirajkar A, Shaikh U et al (2021) Optimization of layout and pipe sizes for irrigation pipe distribution network using steiner point concept. Math Probl Eng 2:1–12 15. Guo XP, Wang M, Chen S et al (2019) Study on suitable control scale of low-pressure pipeline irrigation in rice growing area of Jiangsu province. J Irrig Drain Eng 38(11):28–35

Comprehensive Benefit Evaluation of River and Lake Connection Project in Western Jilin Province of China Yan Wang

Abstract A comprehensive benefit evaluation system of the river and lake connectivity project was established to study influence on the Jilin Province of China. Taking the comprehensive benefit as the general goal, four criteria such as social equity, economic development, ecological maintenance and risk aversion were selected as the criterion level, and 18 index points were selected as the indicator level, after which the evaluation results were calculated. Engineering comprehensive benefit is calculated by catastrophe theory. The results show that the comprehensive benefit of the project is good in the west of Jilin province, and the ecological benefit is the best. Keywords Rivers and lakes connected · Mutation theory · The Gini coefficient

1 Introduction Jilin Province of China is a big agricultural province which is short of water resources and the form of water shortage is severe. The provincial party committee and the provincial government put forward the western river and lake connection project based on the existing water system backbone connection project and many natural lake bobs in view of the coexistence of resource and engineering water shortage in the west of Jilin province [1]. The project can realize the restoration and benign cycle of ecological environment in the west and guarantee the safety of agricultural production by utilizing the passing storm flood resources. After the completion of the project, it can realize for many years an average of 708 million m3 , water diversion and existing bubble marsh and with the recent construction of water storage project, the west lake bubble water storage volume can be maintained all the year round in 3.72 billion m3 that can promote the development of regional economy, and water for industrial and agricultural production and life provides a reliable guarantee, bringing significant economic benefits and social benefits. Y. Wang (B) Changchun Institute of Technology, Changchun 130021, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_7

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In order to reasonably evaluate the project benefits and improve the comprehensive management level of river and lake connectivity, it is of positive significance to establish a suitable evaluation index system and evaluation method for the comprehensive benefits of river and lake connectivity. In the comprehensive evaluation of the water system connectivity project, the comprehensive benefits can be divided into economic benefits, social benefits and ecological and environmental benefits. For example, Li and Hao constructed a classification system for river and lake system connectivity and classified typical cases in China [2]. Lu and Zhao put forward several basic criteria that should be generally followed for the connectivity of river and lake systems, and preliminarily constructed an evaluation index system for the connectivity of river and lake systems, and analyzed and discussed the meaning and selection principles of the evaluation index [3]. Li et al. established the connectivity evaluation model of the river and lake systems based on the number of connections, and used the confidence criterion evaluation method of attribute mathematics and the equal division principle evaluation method of the number of connections to complement each other to ensure the reliability of the evaluation results [4]. Gao and Qiu built a comprehensive benefit evaluation index system of watershed ecological compensation, and took the Tingjiang River Basin as an example to conduct cost–benefit analysis [5]. In terms of evaluation methods, at present, the comprehensive evaluation method of river and lake connectivity is still in the exploratory research stage, and most of the methods are mainly a qualitative description, such as hierarchical analysis, fuzzy comprehensive evaluation, principal component analysis and so on. For example, Ru and Lu in order to study the comprehensive benefits of prefabricated buildings, used the Delphi method to conduct weight research, analytic hierarchy process to process data, and finally determined the weight of indicators, and established a fuzzy comprehensive evaluation model through consistency test [6]. In order to study the comprehensive benefits of prefabricated buildings, Zhou and Zhang constructed a gray comprehensive benefit evaluation model from the four aspects of environment, economy, society and safety, and calculated the indicators using the expert scoring method [7]. Ji et al. used the principal component analysis method to reduce the dimension of indicators, and evaluated the comprehensive benefits of water-saving agriculture based on the TOPSIS method [8]. Wang established the comprehensive evaluation index system of soil and water loss benefits from the social, ecological and economic levels, and evaluated the comprehensive benefits of soil and water loss control in the small watershed of Fuxin region by combining the hierarchical entropy model [9]. Lu et al. used the analytic hierarchy process and TOPSIS method to evaluate the comprehensive benefits of water resources in Zhengzhou [10]. The investment of the river and lake connectivity project in the Jilin Province is huge, but there is no comprehensive benefit evaluation of the relevant literature; this article is based on previous studies, constructed for rivers connecting the engineering comprehensive benefit evaluation system of the Jilin province, through the selection criterion layer and index layer, based on the catastrophe theory to complete the comprehensive benefit evaluation, and provide the theoretical basis for engineering evaluation.

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2 Overview of the Study Area The project scope is mainly the right bank of Nenjiang River and the west plain area of the left bank of Songhua River of China, the administrative division includes Zhenlai County, Da’an, Tao Nan City, Tongyu County, Taobbei District in Baicheng area, Qian’an County, Qianguo County, Changling County, Hadashan Demonstration Area in Songyuan area, and the west Nongan County in Changchun area, a total of 10 counties (cities, districts), with a total area of 44,600 km2 . It accounts for 23.8% of the Jilin Province. The center of the study area is located at 124.041 139°E and 45.003 774°N. The study area is located in the Songhua River system, including Songhua River (above Sancha River), Nen River, Taoer River and Huolin River. There are many tributaries on the right bank of the Nenjiang River, and few tributaries on the left bank. The average annual precipitation in the region is about 400 mm, which is the lowest in the province. The annual average evaporation is 1600–1800 mm, located in the Songnen Basin, the study area is rich in groundwater, which is supplied by precipitation infiltration, river leakage, irrigation infiltration and lateral underground runoff. The amount of groundwater resources in the study area is 3.113 billion m3 , including the repeated calculation amount of groundwater resources in the hilly area of 0.25 billion m3 , and the recoverable amount of groundwater is 2.481 billion m3 . The sunshine duration in the region is relatively long, with the average sunshine duration reaching 2800–3000 h per year, which is conducive to the growth of crops.

3 Construction of Criterion Layer The establishment of the index system is the basis of evaluation. Whether the index system is scientific and reasonable directly affects the correctness of the evaluation results. There are many and complex factors affecting the comprehensive benefits of river and lake connectivity projects, which are an organic system with multiple quantities, multi-level, overlapping and feedback. Therefore, emphasis should be given to the establishment of evaluation indicators. The comprehensive benefit evaluation of river and lake connection project involves many aspects of the society, economy and ecological environment. Most of the evaluation factors can be quantitatively described by precise numerical values through actual measurement, investigation and calculation, while some factors can only be described qualitatively because of the uncertainty of their connotation and extension, which is difficult to be expressed or calculated by exact numerical or mathematical methods. The establishment of the index system follows the following principles: (1) Social equity criterion: The social criterion for the connecting river and lake systems is to promote the social development of the connected places on the premise of ensuring equity and maintaining stability. It mainly includes the following two points: first, the development and utilization of urban and rural

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water resources should be coordinated to promote the coordinated development of urban and rural areas; Second, we need to consider the demand for water in the upstream and downstream areas, on the left and right banks, and between the areas where water resources are transferred and the areas where water resources are transferred, so as to promote the coordinated development of different regions. (2) Economic development criteria: one is to promote efficient utilization of water resources through the connectivity of the river and lake systems; Second, the scale of investment should match its economic and social benefits and ecological and environmental benefits; Third, according to the different stages of regional economic and social development and its economic bearing capacity, scientifically and reasonably determine the implementation plan and financing plan of the river and lake water system connectivity project. (3) Ecological maintenance criteria: namely, the ecological service function (or ecological value) minus the ecological cost of the connected water system should be greater than the ecological service function of the connected water system before the connected, and ecological damage caused by the connected water system should be avoided. (4) Risk avoidance criteria: after connectivity, the risk of drought and flood disasters of the connected places decrease compared with that before connectivity; The ecological and environmental risks associated with the changes of water cycle elements of the connected two places after the connection are reduced compared with those before the connection. The engineering safety and economic risks of the connecting rivers, lakes and reservoirs should be minimized.

4 Construction of Indicator Layer According to the criterion layer, the evaluation indexes are refined, and 18 indexes are selected to form the basic index layer according to the principle of index determination. Some indexes related to social equity are evaluated by Gini coefficient, which is an index proposed by Italian economist Gini in the early twentieth century according to Lorenz curve to judge the average degree of distribution. The more unequal the distribution is, the greater will be the radian of Lorenz curve, and the greater will be the Gini coefficient. The calculation formula is as follows: G=

n  i=1

Wi Yi + 2

n−1 

Wi (1 − Vi ) − 1

(1)

i=1

where W i —when calculating the matching Gini coefficient of water resources and cultivated land—is the proportion of the cultivated land area of each group after grouping the water resources to the total cultivated land area; When calculating the

Comprehensive Benefit Evaluation of River and Lake Connection … Table 1 Gini coefficient evaluation criteria [11]

The Gini coefficient

Distribution state

Less than 0.2

Absolute average

0.2–0.3

Comparing the average

0.3–0.4

Relatively reasonable

0.4–0.5

Gap is larger

More than 0.5

Wide disparity

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Gini coefficient matching water resources and GDP, it is the proportion of GDP of each group after water resources grouping to GDP; When calculating the Gini coefficient matching water resources and population, it refers to the proportion of the population of each group after grouping the water resources to the total population. Y i —the proportion of water resources owned by each population in the total water resources after grouping by water resources; V i —The cumulative number of Y i from I equals 1 to I. According to the calculation, the Gini coefficient of matching water resources and cultivated land is 0.15, according to the Gini coefficient evaluation standard in Table 1 [11], it is absolutely average, and the allocation of the water resources is reasonable. The Gini coefficient of the water resources and cultivated land matching was 0.34, indicating that the water resources and GDP matching was relatively reasonable. The Gini coefficient of the water resources and population matching is 0.16, indicating the absolute average of the water resources and population matching. The evaluation results of comprehensive benefit evaluation index system and other indexes of the river and lake connectivity project in Jilin Province are shown in Table 2.

5 Comprehensive Benefit Evaluation of River and Lake Connection Project 5.1 Conversion of Evaluation Results There are both qualitative and quantitative indexes in the comprehensive benefit evaluation of the river and lake connectivity projects. For the positive index, the larger the index value is, the better is the evaluation result. For the negative index, the smaller the index value, the worse the evaluation result. In order to make each index have comparability in the whole system, it is necessary to carry out technical treatment for each index before the catastrophe model solves the comprehensive evaluation result value. For the quantitative index in the evaluation index system, the measured value of the index must be normalized to a dimensionless interval according to some membership function before comprehensive evaluation. For qualitative indicators, the method of membership degree of evaluation grade can be used to determine,

0.75

Poor

Poor

Gini coefficient 1 of water resource and population matching B3

Resettlement and Bad project occupation B4

Degree of compensation measures in place B5

Bad

0.75

1

Water resources and GDP match Gini coefficient B2

0.75

1

Poor

Norms of social Gini coefficient equity matching water resources and A1 cultivated land B1

Bad

Comprehensive benefit of connecting river and lake project in western Jilin Province

Index layer

Rule layer

The target layer

Medium

Medium

0.5

0.5

0.5

Medium

Good

Good

0.25

0.25

0.25

Good

Table 2 Comprehensive benefit evaluation index system of river and lake connectivity project in western Jilin Province

Optimal

Optimal

0

0

0

Optimal

(continued)

Medium

Medium

0.16

0.34

0.15

The evaluation results

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The target layer

Extreme adverse

Bad

Bad

Degree of improvement in biodiversity B10

Improvement of wetland and nature reserve B11

7

Economic internal rate of return B8

Degree of aquatic ecological impact B9

0

Economic net present value B7

Environmental improvement criteria A3

1

Economic benefit cost ratio B6

Criteria for economic development A2

Bad

Index layer

Rule layer

Table 2 (continued)

Poor

Poor

Very bad

8

100,000

1.5

Poor

Medium

Medium

Moderate adverse

9

200,000

2

Medium

Good

Good

Mild adverse

10

300,000

2.5

Good

Very good

Very good

Weak disadvantage

12

400,000

10

Optimal

Good

(continued)

Medium

Weak disadvantage

8.99%

151,974

1.51

The evaluation results

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The target layer

Risk avoidance criterion A4

Rule layer

Table 2 (continued)

Most likely

Most likely

Operational Almost certainly management risk B17

Risk of arable land immersion B18

Almost certainly

Most likely

Almost certainly

Ecological environmental risk B16

Most likely

Almost certainly

Risk during construction B15

Most likely

Almost certainly

Poor

Improvement Bad degree of saline-alkali land B13

Risks to social stability B14

Poor

Bad

Land desertification improvement degree B12

Poor

Bad

Index layer

May

May

May

May

May

Medium

Medium

Medium

Low

Low

Low

Low

Low

Good

Good

Good

Very low

Very low

Very low

Very low

Very low

Very good

Very good

Optimal

May

May

May

Low

Low

Good

Good

The evaluation results

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such as 0, 0.25, 0.5, 0.75 and 1 respectively representing poor, poor, medium, good and excellent, so that all indicators are comparable. Index of smaller and better type: ⎧ 1, 0 ≤ X ≤ a1 ⎪ ⎪ ⎪ ⎨a −X 2 Y = , a1 < X < a2 ⎪ a − a1 2 ⎪ ⎪ ⎩ 0, a2 ≤ X

(2)

Index of larger and better type: ⎧ 1, X ≥ a2 ⎪ ⎪ ⎪ ⎨ X −a 1 Y = , a1 < X < a2 ⎪ a − a 2 1 ⎪ ⎪ ⎩ 0, 0 ≤ X ≤ a1

(3)

where a2 and a1 are the upper and lower bounds of the function. Formula (3) is used to transform the original index values and other standard values of river and lake connectivity engineering by catastrophe progression, and the results are shown in Table 3.

5.2 Application of Catastrophe Theory in Comprehensive Evaluation According to the transformation value of the comprehensive index in Table 3, the multi-criteria evaluation is adopted according to mutation theory method using mutation system normalization formula up gradually integrated computation (Fig. 1), if an index is decomposed into two indicators, can think the system is sharp point mutation, if an index can be broken down into three index, can be thought of as the system is dovetail mutations, If an indicator can be broken down into four sub-indicators, the system can be considered a butterfly mutation, and if an indicator can be broken down into five sub-indicators, the system can be considered an Indian hut mutation. Since each index is irreplaceable, the complementary criterion is adopted to calculate the mean value of each variable after normalization. In this way, the comprehensive benefit grade standard (Table 4) and the comprehensive benefit evaluation value of the project can be obtained. The evaluation value is between 0 and 1, where 0 represents the worst state of comprehensive benefit, and 1 represents the best state of comprehensive benefit (Fig. 2). The detailed calculation steps are as follows: (1) Calculation of A1 (social equity) index system

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Table 3 Index value of comprehensive benefit evaluation of river and lake connectivity project Index layer

Original index value

Index conversion value

Gini coefficient matching water resources and cultivated land

0.15

0.85

Water resources and GDP match Gini coefficient

0.34

0.66

Gini coefficient of water resource and population matching

0.16

0.84

Resettlement and project occupation

General

0.5

Degree of compensation measures in place

General

0.5

Economic benefit cost ratio

1.51

0.15

Economic net present value

151974

0.38

Economic internal rate of return

8.99%

0.5

Degree of aquatic ecological impact

Weak disadvantage

1

Degree of improvement in biodiversity

General

0.5

Improvement of wetland and nature reserve

Good

0.75

Land desertification improvement degree

Good

0.75

Improvement degree of saline-alkali land

Good

0.75

Risks to social stability

Low

0.75

Risk during construction

Low

0.75

Ecological environmental risk

May

0.5

Operational management risk

May

0.5

Risk of arable land immersion

May

0.5

A1 system includes B1 , B2 , B3 , B4 and B5 . Indicators B1 , B2 , B3 , B4 and B5 constitute the Indian hut mutation, because indicators B1 , B2 , B3 , B4 and B5 conform to the complementary criterion, according to the mean value principle, there are √ √ √ √ √ 5 6 4 3 x A1 = ( 0.85 + 0.66 + 0.84 + 0.5 + 0.5)/5 = 0.9026 (2) Calculation of A2 (economic benefit) index system The A2 system includes B6 , B7 and B8 . Index B6 , B7 and B8 constitute swallowtail type mutations, and according to the mean value principle, there are √ √ √ 4 3 x A2 = ( 0.15 + 0.38 + 0.50)/3 = 0.6516 (3) Calculation of A3 (social benefit) index system A3 system includes B9 , B10 , B11 , B12 , B13 . Index B9 , B10 , B11 , B12 , B13 constitute the Indian hut mutation, according to the average principle, there are √ √ √ √ √ 3 4 5 6 x A3 = ( 1 + 0.5 + 0.75 + 0.75 + 0.75)/5 = 0.9247

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Fig. 1 Scope of project planning area

Table 4 Correspondence between comprehensive evaluation value and comprehensive benefit grade of catastrophe model evaluation method Level

Bad

Poor

Medium

Good

Optimal

Evaluate the index value

0.8731

Complementary dovetail model A1

A3

A2

Complementary cusp model

Complementary dovetail model

B1

B3

B2

B4

B5

Complementary B6 B 7 B8

butterfly B9

Fig. 2 Catastrophe model of comprehensive benefit evaluation of river and lake connectivity project

(4) Calculation of A4 (risk aversion) index system A4 system includes B14 , B15 , B16 , B17 and B18 . Index B14 , B15 , B16 , B17 and B18 constitute the Indian hut mutation, according to the average principle, there are √ √ √ √ √ 3 4 5 6 x A3 = ( 0.75 + 0.75 + 0.5 + 0.5 + 0.5)/5 = 0.8756

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(5) Calculation of the comprehensive evaluation index value of the overall target layer The general target layer is composed of A1 , A2 , A3 and A4 , which constitute butterfly mutation. It conforms to the complementary criterion. According to the average value principle, there are √ √ √ √ 3 5 4 x I = ( 0.9026 + 0.6516 + 0.9247 + 0.8756)/4 = 0.9431 That is, the comprehensive benefit evaluation value of the river and lake connection project is 0.9431. According to Table 4 comprehensive benefit grade reference table, the comprehensive benefit of the river and lake connection project in the western Jilin Province is superior.

6 Conclusions The most significant benefit brought by the west river and lake connection project in the Jilin Province is ecological benefit, followed by social equity benefit. The river and lake connection project plays a role in promoting the rational allocation of local ecology and resources, while good ecology and harmonious society can also promote the improvement of economic benefits, thus promoting the improvement of comprehensive benefits of river and lake connection project in central and western China.

References 1. Song JL (2014) Construction of river and lake connectivity project in western Jilin province to enhance regional water conservancy support and support capacity. Water Resour Plann Design 4:1–4 2. Li ZL, Hao XP (2011) Discussion on the classification system of river and lake system connectivity. J Nat Resour 11:1975–1982 3. Lu F, Zhao J (2013) Basic criteria and evaluation indexes for river and lake system connectivity. China Water Resour 9:17–20 4. Li L, Xu W et al (2017) Connectivity grade evaluation model of river and lake systems based on connection number. China Rural Water Resour Hydropower 9:93–99 5. Gao YH, Qiu SL (2022) Evaluation of comprehensive benefits of ecological compensation in river basins: a case study of Tingjiang River Basin. J Agric Univ Hebei 24(2):25–33 6. Ru QJ, Lu Q (2021) Comprehensive benefit evaluation of prefabricated buildings using fuzzy comprehensive evaluation. J Shanxi Architect 48(5):194–198 7. Zhou HT, Zhang H (2021) Comprehensive benefit evaluation of prefabricated buildings. Build Technol 52(9):1115–1118 8. Ji HH, Hu ZH et al (2019) Comprehensive benefit evaluation of water-saving agriculture based on multi-objective evaluation and Topsis method: a case study of Heping Irrigation area in Heilongjiang Province. Water Saving Irrig 4:41–45

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9. Wang HL (2021) Application of hierarchy-entropy combined assessment principle in comprehensive benefit assessment of soil and water control in Fuxin. Water Sci Eng Technol 5:14–17 10. Lu JH, Wang XS et al (2021) Comprehensive benefit evaluation of water resources in Zhengzhou based on TOPSIS 42(6):49–54 11. Wei MR, Huo JJ et al (2022) Analysis of water resource balance in the Yangtze River Economic Belt 22(15):6291–6298

Design of Reservoir’s Seasonal FLWLs Under the Influence of Its Upstream Cascade Reservoirs Regulation Yinghai Li, Yuan Yang, Yongqiang Wang, Qingqing Xia, Guo Yu, and Shan e hyder Soomro

Abstract In the cascade reservoirs, the interception and storage of upstream reservoirs led to significant changes in the inflow of downstream reservoir, which result in deviation of flood design standards and unreasonable flood limited water level (FLWL). This paper proposes a framework to design the reservoir’s seasonal FLWLs considering the influence of its upstream cascade reservoirs regulation. Firstly, the natural runoff of the reservoir is reduction calculated to the operation period which includes the discharge process calculation of upstream cascade reservoirs and interval flood encounter analysis. Further, an entropy weight-based improved Fisher optimal segmentation method is proposed for flood season staging, and then flood regulation calculation for the seasonal design flood process is conducted to determine the seasonal FLWL. Finally, the flood control risk and benefit analysis of the seasonal FLWLs is carried out. Taking China’s Three Gorges reservoir as a case, the flood season can be divided into three stages and the seasonal FLWL can rise to a certain extent compared with the original value. Compared to the original design standard, Y. Li (B) · Y. Yang · G. Yu Hubei Key Laboratory of Hydropower Engineering Construction and Management, China Three Gorges University, Yichang 443002, China e-mail: [email protected] Y. Yang e-mail: [email protected] G. Yu e-mail: [email protected] Y. Li · Y. Yang · Q. Xia · G. Yu · S. Soomro College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang 443002, China e-mail: [email protected] Y. Wang Water Resources Department, Yangtze River Scientific Research Institute, Wuhan 430010, China e-mail: [email protected] Q. Xia Survey Team of Jingzhou Yangtze River Administration, Jingzhou 434000, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_8

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the average annual reservoir storage and power generation can be increased without increasing the risk of flood control. Keywords Cascade reservoirs · Flood control operation · Flood encounter analysis · Flood season staging · Risk and benefit analysis · Seasonal flood limited water level

1 Introduction The flood limited water level (FLWL) is the upper limit water level for the reservoir to obtain more economic benefits without lowering the flood control standards during the flood season. Most reservoirs in China are mainly designed with a single limit flood level, which can meet the flood control safety during the whole flood season, but sacrifices part of power generation and other benefits. The seasonal FLWL is one of the important ways for the reservoir to efficiently use flood resources by meeting flood control requirements during the flood season. At present, the common method for determining seasonal FLWL is based on the consistency of the hydrological regime. However, with the construction of cascade reservoirs, the flood peak, peak time and flood volume of the downstream reservoir’s flood inflow have changed significantly due to the influence of the upstream reservoirs operation, which also influences the flood season staging and the seasonal FLWL. The international research related to seasonal FLWL focuses on the seasonal variation of flood and flood control operation. Beurton and Thieken [1] analyzed and calculated the measured data of the maximum flood using the Cluster Analysis method, showing the characteristics of different seasonal floods. Ahmadi et al. [2] studied the multi-objective optimization of reservoir real-time operation under climate change. Kompor et al. [3] used ECMWF prediction information to support adaptive reservoir operation decision-making and proposed various reservoir flood control measures. In China, the relative research mainly focuses on the design of floods and FLWLs of cascade reservoirs. Xiong et al. [4] developed an entropy-based method that divides the whole flood season into multiple sub-seasons to calculate seasonal floods. Chang et al. [5] considered that the seasonal FLWL of cascade reservoirs is not isolated. In order to increase the reservoir’s water storage while maintaining its flood control security, a graded FLWL approach and a dynamic FLWL approach were proposed. Yun and Singh [6] proposed a dynamic flood control limit level based on the conditional probability of heavy rain. Xiong et al. [7] studied the design of flood control water level calculation theory and method of cascade reservoirs in the operation period. Pan et al. [8] studied the real-time dynamic control of reservoir FLWL in flood season based on the analysis of the availability of reservoir prediction information. Mo et al. [9] used the fractal method to segment the flood season of multi-functional reservoir.

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Therefore, based on deep analysis of the spatial–temporal variation of the reservoir’s inflow and its’ regional composition, this study presents a complete framework for calculating the seasonal FLWLs of the reservoir considering the influence of its upstream cascade reservoirs regulation. In addition, an entropy weight-based improved Fisher optimal segmentation method is proposed for flood season staging. It is expected to provide theoretical guidance for the secure and reliable operation of the cascade reservoirs in flood season under changing conditions. The rest of this paper is organized as follows: Sect. 2 presents the framework and methodology of reservoir’s design seasonal FLWLs. Section 3 applies the proposed methodology to calculate Three Gorges reservoir seasonal FLWLs and analysis the flood control risk and benefits. Section 4 concludes the paper.

2 Methodology A framework is proposed to calculate reservoir seasonal FLWLs under the influence of its upstream cascade reservoirs regulation. The flowchart of the framework is shown in Fig. 1.

2.1 Flood Encounter Analysis The annual maximum flood peak and volume can well follow the P-III distribution [10], Von Mises distribution function [11] can accurately describe the distribution process of flood occurrence time. Therefore, to analyze of interval flood encounter between the discharge of upstream cascade reservoirs and runoff of the tributaries, the P-III distribution function and Von Mises distribution function are used to fit the flood peak and occurrence time respectively. Then, the symmetric multi-dimensional copula function is adopted to analyze the flood encounter risk. Copula function is a multi-dimensional joint distribution function defined on [0, 1] with uniform distribution. It can connect the edge distribution of multiple random variables to construct joint distribution. Assuming that the marginal distribution functions of a random variable X i (i = 1, 2, . . . , n) are FX i (x) = PX i (X i ≤ xi ), where n is the number of random variables, xi is the value of X i . So, there is a unique copula function as given by (1): H (x1 , x2 , . . . , xn ) = C(FX 1 (x1 ), FX 2 (x2 ), . . . , FX n (xn )) = C(u 1 , u 2 , . . . , u n ) (1) where Fk (xk ) = u k , k = 1, . . . , n, u k is continuous in interval [0, 1]. Common multi-dimensional Copula functions include Frank Copula, Clayton Copula, and Gumbel Copula [10]. The fitting effect of the Copula function can be tested by calculating the root mean square error (RMSE) between measured and theoretical values of the flood, and then the most suitable function can be selected.

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Fig. 1 Flowchart of reservoir’s seasonal FLWL design method

2.2 Design of Seasonal FLWLs Improved Fisher Optimal Segmentation Method for Flood Season Staging. Fisher optimal segmentation method is one of the ordered clustering methods. This method can ensure the time-ordered of sample sequence, which is consistent with the characteristic of the natural time-ordered flood season. When the Fisher optimal segmentation method is used for flood season staging, how determining the weight of each index will directly affect the accuracy of staging. The entropy weight-method is based on the information entropy implied by variables. In the flood season staging,

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the corresponding weight can be calculated according to the information entropy of the index, to eliminate the subjective influence on the weights as far as possible. The main steps of the entropy weight-based Fisher optimal segmentation method are as follows. (a) Data processing Suppose  sequence and each sample has m indices, i.e.  there is an ordered sample X i = xi1 , xi2 , . . . , xi j , . . . , xim . Since the physical dimensions of each index are different, the indices need to be standardized to obtain the standardized matrix X , =   xi, j at first. n×m

According to the standardized matrix X , , the entropy value H j of the index j is calculated first [12].   n 1  f i j ln f i j Hj = − ln n i=1

(2)

Calculate the entropy weight w ,j according to the entropy value H j of the index j. wi, =

1 − Hi m m − i=1 Hi

(3)

where 0 ≤ w ,j ≤ 1, mj=1 w ,j = 1. Then, the vector Y in the Fisher optimal segmentation method is determined by the entropy weight wi, . ⎞ ⎡ , x11 · · · y1 ⎜ .. ⎟ ⎢ .. Y =⎝ . ⎠=⎣ . ⎛

y2

, xn1

···

⎤⎡ , ⎤ , x1m w1 .. ⎥⎢ .. ⎥ . ⎦⎣ . ⎦

, xnm

(4)

wm,

(b) Define class diameter and objective function   Suppose that the sample of a class P is yi , yi+1 , . . . , y j (i < j ), then the mean value y p of the class is as (5).  1 yt j − i + 1 t=1 j

yp =

(5)

If D(i, j ) is used to represent the class diameter of a certain class P, it can be expressed as (6).

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D(i, j ) =

j  

yt − y p

T 

yt − y p



(6)

t=1

According to the principle of optimal segmentation, the objective function is defined as (7). The segmentation that minimizes the objective function value is the Fisher optimal segmentation. B(n, k) =

k 

D(i 1 , i t+1 − 1)

(7)

t=1

(c) The derivation of optimal segmentation The optimal k segmentation of the ordered sample {y1 , y2 , . . . , yn } is formed by adding another segment after the optimal segmentation of its truncated sub-segments B ∗ (n, k − 1). If classification k(1 < k < n) is known, find the point i k so that B ∗ (n, k) minimizes. B ∗ (n, k) = B ∗ (i k − 1, k − 1) + D(i k , n)

(8)

where class k is Pk = {i k , i k + 1, . . . , n}. According to the same method, all classifications p1 , p2 , . . . , pk can be found and the optimal k classification can be obtained. (d) Determination of the optimal segments number k According to the classification results, there are two methods to determine the optimal segments number. a. Draw the curve B ∗ (n, k) − k of the objective function changing with the classification number k, and take the k value corresponding to the objective function at the obvious bend of the curve as the optimal classification.    B(n,k)−B(n,k−1)  b. Calculate the non-negative slope f (k) =   . Make the k with the k−(k−1) maximum f (k) as the optimal segments number. Calculation of Seasonal FLWLs. Design flood refers to the flooding process of a certain frequency that may happen following the requirements of flood control design standards. According to the selection principle of typical flood, the seasonal typical flood processes detrimental to the reservoir flood control which conforms to the flood characteristics of the basin are selected. After that, using the same frequency amplification method to calculate the seasonal design flood process. Further, starting from the original FLWL, with 0.1 m as the step length, gradually raise the beginning water level to conduct flood regulation calculation for each seasonal design flood until the maximum water level (Z m ) exceeds the design flood level (Z st ). Figure 2 is the main steps of the determination of FLWL.

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Fig. 2 Flowchart of reservoir’s seasonal FLWL calculation method

2.3 Flood Control Risk and Benefit Analysis In this study, the frequency analysis method is used to analyze the flood control risk of the seasonal FLWL [13]. When the reservoir encounters different frequency floods under a certain FLWL Z i scheme, if the maximum water level Z m (Z i ) of a certain frequency design flood reaches the limit risk water level index Z f (namely the characteristic water level of the reservoir, such as the design flood level or checking flood level), the frequency is taken as the flood control risk rate P f of the FLWL Z i . P f = P (Z m ≥ Z f )

(9)

With the rise of seasonal FLWL in flood season, the increased water storage can be used for water supply. Besides that, the raised FLWL can increase the water head for power generation in flood season. Where the increased water storage and power generation can be calculated by (10) and (11) respectively. ΔW = W pr e − Wori

(10)

where Wori and W pr e represent the storage capacity of the reservoir before and after seasonal FLWL adjusted during the flood season respectively.

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ΔE = E pr e − E ori

(11)

where E pr e and E ori represent the power generation before and after seasonal FLWL adjusted during the flood season respectively. The power generation E is calculated by E = AQ H Δt, A is the output coefficient, Q the average generating discharge of each seasonal stage, H the average power generation net head of each seasonal stage, Δt is the period of each seasonal stage.

3 Case Study 3.1 Research Area The Yangtze River is the biggest river in China. The Three Gorges (TG) reservoir, as the control core water conservancy project in the upper reaches of it, plays a pivotal role in the flood control system of the whole upper reaches of the Yangtze River. Jinsha River is another name for the upper main stream of the Yangtze River. Xiluodu (XLD) reservoir and Xiangjiaba (XJB) reservoir are located in the lower reaches of the Jinsha River, which have been built and put into normal operation at present [14]. They form a huge cascade; the operation modes have a great impact on the flood into the TG reservoir. However, the TG reservoir still adopts a single FLWL based on natural historical runoff to guide flood control operations. In the actual dispatching operation, it will inevitably cause the deviation of flood design standards, and lead to the unreasonable setting of FLWL. Figure 3 shows the location of these three reservoirs on the map.

3.2 Interval Flood Encounter Analysis Between XJB and TG The inflow of the TG reservoir is composed of the main stream of the lower reaches of the Jinsha River and the tributaries between the XJB reservoir and the TG reservoir. The first-level tributaries in the interval include the Minjiang River, Jialingjiang River, and Wujiang River. The topological map is shown in Fig. 4. In the study, the synchronous daily discharge data of Xiangjiaba gauging station in Jinsha River, Gaochang gauging station in Minjiang River, Beibei gauging station in Jialingjiang River and Wulong gauging station in Wujiang River from 1950 to 2010 were selected for analysis. As it involves the main stream and three tributaries, therefore, the symmetric 4dimensional Copula function is selected for flood encounter analysis. By calculation and comparison, the Gumbel copula function and Clayton copula function are used to analyze the flood peak and encounter time of the main and tributaries respectively.

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Fig. 3 Map of the study area

Fig. 4 Topological map of interval and the gauging stations between XJB and TG

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The encounter probability p of flood peak occurrence time for Xiangjiaba gauging station and Gaochang gauging station, Beibei gauging station and Wulong gauging station in flood season can be calculated as (12): P4 =

N 

P(tk < Tx ≤ tk+1 , tk + τ X G < TG ≤ tk+1 + τ X G , tk

k=τ X B +dt

+ τ X B < TB ≤ tk+1 + τ X B , tk + τ X W < TW ≤ tk+1 + τ X W )

(12)

where tk means the kth day of the flood season; Tx , TG , TB and TW mean the flood peak occurrence time at Xiangjiaba gauging station, Gaochang gauging station, Beibei gauging station and Wulong gauging station respectively; τ X G means flood propagation time from the Xiangjiaba gauging station to the junction of the Jinsha River and the Minjiang River; τ X B means the flood propagation time from XiangJiaba gauging station to the junction of Jinsha River and Jialingjiang River; τ X W represents flood propagation time from the Xiangjiaba gauging station the junction of Jinsha River and Wujiang River. After obtaining the marginal distribution of flood occurrence time and the optimal Copula function, according to Eq. (12), the occurrence probability of floods at the main stream and three tributaries of the flood peak time are shown in Fig. 5. According to Fig. 5, flood encounter time of the main stream and three tributaries is mainly concentrated from mid-July to mid-August. Compared with natural runoff, after being regulated by XLD-XJB cascade reservoirs, the encounter probability decreased from 3.0 × 10–8 to 2.6 × 10–8 . Since the encounter probability is very small, in the general case, there will be no simultaneous flooding of the main stream and tributaries. However, considering the safety of flood control, the main flood season should be set from mid-July to mid-August as much as possible.

Fig. 5 Flood occurrence time and occurrence probability of 4 stations in trunk and tributaries

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After obtaining the marginal distribution of the flood peak and the optimal Copula function, the probability P4T of simultaneous flood encountering of greater than a certain return period T in the Jinsha River mainstream and tributaries of Minjiang River, Jialingjiang River, and Wujiang River is calculated as (13).   T P4T = P Q X > qxT , Q G > qGT , Q B > q BT , Q W > qW

(13)

where Qx , QG , QB , QW represent flood peaks of the Jinsha River, Minjiang River, T are the designed flood peak Jialingjiang River, and Wujiang River, qxT , qGT , q BT , qW of the T-year return period. According to Eq. (13), the probability of simultaneous 10-year floods in mainstream and tributaries of Minjiang River, Jialingjiang River, and Wujiang River is only 1.6 × 10–10 , and the probability of simultaneous larger floods is much lower. It can be viewed as an impossible event.

3.3 Calculation of Seasonal FLWLs of TG Reservoir Through the analysis of flood peak and occurrence, time encounter probability of Jinsha River, Minjiang River, Jialing River, and Wujiang River, it is almost impossible to happen simultaneous huge floods in the main stream and tributaries. The inflow process of the TG reservoir affected by the regulation of XLD-XJB cascade reservoirs can be calculated by summing up the flood from XJB and each tributary gauging station according to the confluence time. Reduction calculation of TG reservoir inflow process (1950–2010) affected by XLD-XJB cascade reservoirs regulation, the different frequencies flood peak and volume of the TG reservoir inflow are shown in Table 1, after regulation of XLDXJB cascade reservoirs at different frequencies, the flood peak and flood volume of TG reservoir inflow runoff are smaller than natural runoff. The flood season of the TG Reservoir is divided by using the improved Fisher optimal segmentation method. The mathematical statistics method is used to compare. The results of the two methods are consistent. Therefore, the flood season can be divided into three stages. The pre-flood season is from June 10th to 30th, the main flood season is from July 1st to August 31st, and the post-flood season is from September 1st to 30th. For different types of floods, the regulation results will be obviously different, so the impact of different flood peak types needs to be considered. Therefore, choosing 2 typical seasonal floods process of each stage in Fig. 6, then taking 145 m as the initial water level, according to the seasonal FLWLs calculation method mentioned above to determine the seasonal FLWLs. Where TFP means the typical flood process, DFP means the design flood process of different frequencies. The calculation results of seasonal FLWLs of the TG reservoir after regulation of XLD-XJB cascade reservoirs are shown in Table 2. Compared with the initial single FLWL, the former flood season

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Table 1 Flood peak and volume of the TG Reservoir before and after the XLD-XJB cascade regulation Type Qm

(m3 /s)

W3d

W7d

(108 m3 )

(108 m3 )

W15d

(108 m3 )

Frequency (%)

0.01

0.1

1

10

Natural runoff

95,389

85,244

73,742

59,433

After regulation

92,580

82,457

71,111

57,231

Reduction rate (%)

2.9

3.3

3.6

3.7

Natural runoff

207.6

186.5

162.6

132.8

After regulation

200.5

181.2

159.2

131.3

Reduction rate (%)

3.4

2.8

2.1

1.1

Natural runoff

407.6

369.5

325.9

270.8

After regulation

395.1

354.6

309.7

255.8

Reduction rate (%)

3.1

4.0

5.0

5.5

Natural runoff

792.7

711.2

620.4

510.4

After regulation

763.3

675.2

581.1

474.7

Reduction rate (%)

3.7

5.1

6.3

7.0

can be increased by 14.1 m, the main flood season can be increased by 3.7 m, and the post-flood season can be increased by 14.2 m.

3.4 Flood Control Risk and Benefit According to the methods mentioned in Sect. 2, the flood control risk and benefits results of the TG are displayed in Table 3. It can be seen from Table 3 that under the control of seasonal FLWLs, the risk rates of over-design and check water levels in the pre-flood season, main flood season, and post-flood season of the TG reservoir are lower than the design standard of 0.1% and check standard 0.01% of the reservoir. Compared with the original FLWL, the water storage increased by 84.17 × 108 m3 in the pre-flood season, 11.16 × 108 m3 in the main flood season, 84.87 × 108 m3 in the post-flood season, and 180.20 × 108 m3 in the whole flood season under the seasonal FLWLs conditions. If all the increased water storage is used for power generation, the power generation can be increased by 2.84 × 108 , 3.85 × 108 and 3.65 × 108 KWh in the pre, main, and post-flood seasons respectively. The power generation can be increased by 10.33 × 108 KWh in the whole flood season. Thus, considering the influence of XLD-XJB reservoirs regulation, the benefits of the TG reservoir can be further improved without increasing flood control risk.

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Fig. 6 Three Gorges dam site seasonal design flood process Table 2 Calculation results of the Three-Gorge reservoir’s seasonal FLWLs

Project

Pre-flood (day/month)

Main flood (day/month)

Post-flood (day/month)

The starting and ending time

10/6–30/6

1/7–31/8

1/9–30/9

Seasonal FLWL (m)

159.1

148.7

159.2

Initial FLWL (m)

145

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Table 3 Flood control risk and benefit of seasonal FLWLs of TG reservoir Pre-flood Main flood Post-flood Sum

Project Risks

Over design water level risk (%) Over check water level risk (%)

Benefits Water storage increment (billion m3 ) Power generation increase (billion KWh)

0.00062 0.00077

0.00254 0.00988

0.00129 0.00976

/ /

84.17

11.16

84.87

180.2

2.84

3.85

3.65

10.33

4 Conclusions This study presents a complete framework for calculating and analyzing the seasonal FLWLs of reservoirs considering the influence of its upstream cascade reservoirs regulation. This framework has three steps which include reduction calculation of reservoir inflow, calculation of seasonal FLWLs, and analysis of flood control risk and benefits. The proposed framework is applied to redesign the seasonal FLWLs of the TG reservoir affected by the regulation of its upstream XLD-XJB cascade reservoirs. The calculation results show that the flood season can be divided into three stages without increasing the flood control risk of the TG Reservoir, and the corresponding FLWLs can rise to 159.1, 148.7, and 159.2 m respectively. The water storage and power generation can be further improved by 180.20 × 108 m3 and 10.33 × 108 KWh. This study can provide theoretical guidance for the secure and stable operation of the cascade reservoirs in flood season under changing conditions. Acknowledgements This work is jointly supported by the CRSRI Open Research Program (CKWV2021889/KY) and the National Natural Science Foundation of China (52179018).

References 1. Beurton S, Thieken A (2009) Seasonality of floods in Germany. Hydrol Sci J 54(1):62–76 2. Ahmadi M, Haddad O, Loáiciga H (2015) Adaptive reservoir operation rules under climatic change. Water Resour Manage 29(4):1247–1266 3. Kompor W, Yoshikawa S, Kanae S (2020) Use of seasonal streamflow forecasts for flood mitigation with adaptive reservoir operation: a case study of the Chao Phraya River Basin, Thailand, in 2011. Water 12(11):3210 4. Xiong F, Guo S, Chen L, Chang FJ, Zhong Y, Liu P (2018) Identification of flood seasonality using an entropy-based method. Stoch Env Res Risk Assess 32(11):3021–3035 5. Chang J, Guo A, Du H, Wang Y (2017) Floodwater utilization for cascade reservoirs based on dynamic control of seasonal flood control limit levels. Environ Earth Sci 76(6):1–12 6. Yun R, Singh V (2008) Multiple duration limited water level and dynamic limited water level for flood control, with implications on water supply. J Hydrol 354(1–4):160–170 7. Xiong F, Guo S, Chen K, Yin J, Liu Z, Zhong Y, Hong X (2019) Design flood and control water levels for cascade reservoirs during operation period in the downstream Jinsha River. Adv Water Sci 30(03):401–410 8. Pan J, Xie Y, Liu M, Gao Z, Gao Z, Xue B (2022) Dynamic control of water level in flood limited reservoir based on intelligent calculation. Math Prob Eng 2022

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9. Mo C, Deng J, Lei X, Ruan Y, Lai S, Sun G, Xing Z (2022) Flood season staging and adjustment of limited water level for a multi-purpose reservoir. Water 14(5):775 10. Chen L, Guo S, Zhang H, Yan B, Liu X (2011) Flood coincidence probability analysis for the upstream Yangtze River and its tributaries. Adv Water Sci 22(03):323–330 11. Huang K, Chen L, Zhou J, Zhang J, Singh V (2018) Flood hydrograph coincidence analysis for mainstream and its tributaries. J Hydrol 565:341–353 12. Huang S, Chang J, Leng G, Huang Q (2015) Integrated index for drought assessment based on variable fuzzy set theory: a case study in the Yellow River basin, China. J Hydrol 527:608–618 13. Zhao W, Li R, Zi L (2020) Discussion on risk flood control operation of reservoir group in Changjiang River Basin. Yangtze River 51(12):135–140+178 14. Zhou Y, Guo S, Chen J (2015) Multi-objective decision and joint refill schemes of XiluoduXiangjiaba-Three Gorges cascade reservoirs. J Hydraul Eng 46(10):1135–1144

Multi-Objective Flood Control Scheduling Study of the Suyukou Ditch Considering Flood Control Safety of the Downstream River Yunke Xiao, Wan Liu, Yongqiang Wang, and Deyu Zhong

Abstract As an important flood control project, a flood control reservoir is charged with the dual tasks of flood control safety and rainwater utilization. This paper establishes a multi-constrained optimal flood control scheduling model based on a differential evolutionary algorithm with the objective of the lowest maximum water level in front of the dam and minimum maximum discharge flow and gives the steps of solving the DE algorithm for optimal flood control scheduling of the interceptor reservoir. The algorithm is applied to solve the flood control optimization scheduling example problem of the Jinshan flood control reservoir, and the solution result is compared with the conventional scheduling situation. The result shows that the algorithm can generate a scheduling solution in a short time and provide a reference for the multi-objective flood control scheduling decision of the flood control reservoir. Keywords Crossover variation · Differential evolution algorithm · Flood control scheduling · Multi-constraint · Multi-objective · Optimal solution · Suyukou ditch

Y. Xiao · W. Liu · Y. Wang (B) Changjiang River Scientific Research Institute of Changjiang Water Resources Commission, 23 Huangpu Street, Wuhan 430010, China e-mail: [email protected] Y. Xiao e-mail: [email protected] W. Liu e-mail: [email protected] Hubei Provincial Key Laboratory of Basin Water Resources and Ecological Environment, 23 Huangpu Street, Wuhan 430010, China Research Center On the Yangtze River Economic Belt Protection and Development Strategy, 23 Huangpu Street, Wuhan 430010, China D. Zhong State Key Laboratory of Hydroscience and Hydraulic Engineering, Tsinghua University, Beijing 100084, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_9

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1 Introduction The optimal scheduling of reservoir flood control is crucial to reduce flood losses [1]. It is a large multi-stage, multi-constrained nonlinear optimization problem. This issue should not only take into account the safety of the reservoir dam itself and the upstream and downstream flood control requirements [2], but also need to consider numerous constraints such as reservoir capacity, water balance, and uniform downstream flow to reduce downstream flood control pressure. For such problems, domestic and foreign scholars used dynamic programming, linear programming [3], stepwise optimization, and particle swarm algorithms to solve them. However, dynamic programming is based on the optimization principle to decompose the complex multi-stage decision problem into a series of sub-problems to solve recursively [4, 5]. With the number of problem dimensions increasing; Linear programming provides a linear treatment of the actual problem, but is no longer applicable when handling non-linear optimization problems. Progressive optimization algorithm is based on the principle of stepwise optimization in solving the reservoir flood control problem. The initial solution of reservoir discharge is often constructed based on the principle of “how much is coming and how much is being released”, which is likely to lead to a large amount of incoming water when the initial solution is not feasible. The particle swarm optimization algorithm is derived from the study of bird feeding behavior. The flock searches the area around the nearest bird, which has the advantage of fast and efficient search in the early stage [6], but all the particles approach the optimal particles in the later stage so that the overall population loses its original diversity and the particles are easily caught in the local optimal solution. In contrast, the principle of differential evolution (DE) is simple and easy to implement and has a high capability in population and collaborative search, which obtained good results in many optimization problems [7]. In this paper, the differential evolution (DE) algorithm is applied to the optimal flood control scheduling of Jinshan Barrage Reservoir, and the results show that it can give full play to the flood control regulation capacity of the reservoir, ensure the safety of the reservoir itself as well as the downstream flood control objects, and provide a reasonable and effective optimal scheduling solution for the reservoir.

2 Study Area and Methodology 2.1 Study Area The Jinshan area in Hongguang Town, Helan County is a flood-prone area and the focus of flood control work in Helan County. Through years of construction, Jinshan flood control project has formed a preliminary “guide, guide, stop, storage (stagnation), retreat” flood control system, stopping and storing the floods of four big

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mountain flood channels between Suayukou and Kaili Kou ditch in Helan Mountain (shown in Fig. 1), protecting the West Main Canal, the second farm canal, He Nuan Highway, Baolan Railway and other transportation and irrigation facilities and Hongguang Town, Heilan County, Changxin township, Nuanquan farm 3 townships, about 78,300 people’s lives and property safety [8]. Jinshan flood control reservoir project started in June 1977, took five years to build a parallel to the West Canal flood control dike, the project was completed in 1982, after several times to strengthen the transformation. Stop flood bank is located at the eastern foot of the Helan Mountains, the end of the silver west section, is the main flood control project in the western part of Helan County, mainly to stop the Su Yu Kou, Helan Kou, Pan Gou and plug the mouth of four large mountain flood ditch flood. Jinshan flood control reservoir control confluence area of 299.6 km2 , of which 168.3 km2 mountain, slope 131.3 km2 , south dike to Baiyang Dun, north dike to Jinshan Forest, the main dam parallel to the west dry canal, is north–south direction, from the west dry canal distance of about 1.3 km. The area is a local sudden rainstorm flood-prone area, flood storm, peak type water prone area, small flood is diffuse flow loss in flood accumulation Slopes, large floods are converging into channels, drains

Fig. 1 A schematic diagram of the study area

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and lakes, pools, depressions, etc. in the lower part of the slopes, and the flash floods are more dangerous. At present, there are four flood gates on the levee, He Shan highway from the middle of the flood control reservoir through, at 3 + 850 will be divided into two parts of the South and North reservoirs, the South reservoir with 1 #, 2 # flood gates, the North reservoir with 3 #, 4 # flood gates, the design flood flow are 10 m3 /s. The four flood drains will be transferred after the flood water into the West Main Canal. At present, the Jinshan flood control reservoir flood control scheduling method is: when the flood season comes, the flood control reservoir empty reservoir to meet the flood, the design standard flood, Jinshan flood control reservoir to enable 2 #, 3 # flood gates into the west dry canal, the total control of the amount of discharge is less than 20 m3 /s, until the amount of empty reservoir flood. When encountering super-standard flooding, the spillway is activated to ensure the safety of the dam, and after the water level of the reservoir returns, the floodgate is still used to control the amount of flood discharge evenly into the West Trunk Canal until the reservoir is empty. In terms of flood control scheduling, there is a lack of a multi-objective scheduling plan that takes into account flood control safety and flood utilization [9, 10].

2.2 Differential Evolutionary Algorithm Differential evolution algorithm is a class of population-based adaptive global optimization algorithms, which mainly includes three basic operations: variation, crossover, and selection. The DE algorithm first uses a floating-point vector for encoding to generate a population of individuals. In the optimization process of the DE algorithm, two individuals are selected from among the parent individuals to make vector differences to generate the difference vector, and then another individual is selected to sum with the difference vector to generate the experimental individuals. Then, the crossover operation is performed between the parent individuals and the corresponding experimental individuals to generate new child individuals; finally, the selection operation is performed between the parent individuals and the child individuals, and the individuals that meet the requirements are saved to the next generation population. Assuming that D is the dimension of the problem to be solved and N is the population size, the evolutionary process of the DE algorithm is as follows: Population initialization. The initial solution should cover the search domain = range  g as much as possible, so generate the initialized solution set P(X, G) g |i = 1, 2 . . . . . . N ; g = 1, 2 . . . . . . g max} , where each element X = X i   i g

g

g

xi,1 , xi,2 , xi, j (i = 1, 2 … D) using Eq. (1):

  g xi, j = xi,L j + rand(0, 1) ∗ xi,Uj − xi,L j

(1)

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U L where x0,i and x0,i denote the upper and lower limits of the i-th individual of the first generation, respectively, and rand (0, 1) denotes the random number satisfying uniform distribution in the (0, 1) interval.

Mutation. The mutation process is actually the superposition of the difference vectors on top of the individual vectors. Specifically, the mutation operation is applied to each g individual xi in the gth generation to obtain the corresponding mutated individual g+1 vi : g+1

vi

  = xrg1 + F xrg2 − xrg3 i = 1, 2, 3 . . . N

(2)

If the variant individual crosses the boundary, the boundary value is taken directly. g

Crossover. After completing the mutation operation, the parent individual xi and the g+1 mutant individual vi are crossed over to generate a new experimental individual g+1 ui :  g+1 u i, j

=

g+1

vi, j , i f (rand( j) < CR or j = rand(1, N ) g xi, j , otherwise

(3)

where rand(j) is the uniform random number between [0, 1] and CR is the crossover probability; rand(1, N) denotes the random number satisfying uniform distribution in the interval [1, N]. g+1

Selection. The child solution vector u i generated after the mutation and crossover g operation process is compared with the parent solution vector xi , and a greedy algorithm is used to select the next generation of population individuals. When the target value of the child solution vector is better than that of the parent solution vector, it can replace the parent solution vector into the next generation; otherwise, g xi will be directly used as the child (Fig. 2).

3 Multi-Objective Flood Control Scheduling Model for Reservoirs 3.1 Objective Function In this paper, the maximum water level in front of the dam is used as the optimization target for the safety of the dam itself and upstream flood control, and the maximum discharge flow is used as the optimization target for downstream flood control to establish a multi-objective reservoir flood control optimization scheduling model [11, 12].

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Initializing the population

Whether termination conditions are met

Yes

Output optimal individuals

No Mutation operation

Stop

Crossover operation

Boundary condition processing

Calculate the objective function

Select operation

Fig. 2 Differential evolution algorithm process

min F1 = min{max qt } t = 1, 2, . . . T

(4)

min F2 = min{max Z t } t = 1, 2, . . . T

(5)

where Z t is the water level in front of the dam at time t; qt is the discharge flow at time t; T is the total number of scheduling periods. In this paper, F 1 is used as the primary objective of optimization, and F 2 is used as the secondary objective. When comparing the dominance of the two scheduling schemes, F 1 is compared first, and if the values of F 1 of the two schemes are equal, the dominance of the two schemes is determined according to the value of F 2 . The dominance of the two solutions is determined based on the value of F 2 .

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3.2 Binding Conditions The constraints are the following three items [13, 14]: Reservoir water balance constraint Vt = Vt−1 + (Q t − qt )Δt

(6)

Where V t , V t-1 are the reservoir capacity at the end and beginning of the period, respectively; Qt and qt are the incoming flow and outgoing flow of the reservoir at the end of t time period, respectively. Drainage capacity constraint qΔt ≤ q(Z t , Bt )

(7)

where qΔt is the average downstream flow rate during the time period; q(Z t , Bt ) is the relationship between the discharge capacity of the floodgate and the spillway. Water level constraint Z min ≤ Z t ≤ Z max

(8)

where Z t for t time reservoir water level; Z min , Z max for t time reservoir allowable minimum and maximum water level, respectively.

4 Results and Discussions The flooding process of the Jinshan Flood Control Reservoir is obtained according to the same magnification of the “20160821” and “20180722” flooding process of the Suyuokou hydrological station [15], as shown in the figure [16] (Fig. 3). The starting water level of the reservoir is the dead water level of 1125.46 m, and the static storage capacity is used in the calculation. At the beginning of the scheduling, the inflow is less, and the reservoir continues to discharge floods according to the principle of inflow balance; when the flood inflow continues to rise, the reservoir discharges according to the maximum discharge capacity (Table 1). The flood of “20160822” lasted for a long time, the flood peak showed a compound peak, and the flooding process was relatively gentle, the results of its conventional and optimal scheduling were analyzed: the conventional scheduling took the water balance to discharge before 19:40 on the 21st, maintaining the reservoir water level at the dead level, after which the incoming flow became larger and discharged according

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(a) 20160821

(b) 20180722 Fig. 3 Flood process diagram Table 1 Comparison of scheduling results Flood number 20160816 20180722

Scheduling method

Maximum outflow (m3 /s)

Maximum reservoir level after flood regulation (m)

Regular scheduling

20

1127.013

Optimized scheduling

19.85

1127.015

Regular scheduling

18.72

1126.671

Optimized scheduling

16.15

1126.741

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to the discharge capacity. The maximum discharge limit of 20 m3 /s is reached at 01:30 on the 22nd, after which it is maintained at that level until the end of the flood. After optimal scheduling and peak regulation, the maximum downstream flow is 19.85 m3 /s at the highest reservoir level of only 0.002 m, which is beneficial to the safety of downstream protection objects. The flood of “20180722” is characterized by fast rise, fierce onset, multiple flood peaks, sharp and thin peaks, steep rise and fall, and the analysis of the results of its regular and optimal scheduling: the regular scheduling is similar to that of the 2016 flood, with the incoming flow discharged before 20:05 on the 22nd and the maximum discharge capacity discharged afterwards. In the optimized scheduling, water is stored and not discharged at the beginning when the incoming water level is slow, and a higher discharge level is maintained at the period of higher incoming water, and then only water is stored and not discharged again at the period of falling flood. The maximum discharge flow rate of optimized scheduling is 2.57 m3 /s smaller than that of conventional scheduling, which effectively ensures downstream safety (Figs. 4 and 5).

5 Conclusions Multi-objective optimal scheduling of reservoirs is a complex multi-stage, multiconstraint and multi-objective optimization problem. In order to solve this problem, a multi-objective optimal scheduling model for flood control reservoirs is established with the objectives of minimum maximum reservoir level and minimum maximum discharge flow. To solve the model effectively, a differential evolutionary algorithm is used to solve the problem. In the case study of Jinshan flood control reservoir, it is shown that the multi-objective optimal scheduling model established in this paper is more effective in guaranteeing downstream safety than the conventional scheduling model when encountering oversized large floods, and the convergence speed of the adopted DE algorithm is fast, which can quickly generate non-inferior scheduling methods for reservoir scheduling decision makers to choose. It provides a new approach and method for reservoir multi-objective flood control scheduling.

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(a) regular scheduling

(b) optimized scheduling Fig. 4 The process of conventional and optimal scheduling operation for the “20160822” flood

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(a) regular scheduling

(b) optimized scheduling Fig. 5 The process of conventional and optimal scheduling operation for the “20180722” flood

Acknowledgements This work is supported by the Special Coordinated Key Projects of Tsinghua University-Ningxia Yinchuan Water Network Digital Water Management Joint Research Institute (SKL-IOW-2019TC1904).

References 1. Xie W, Chang-Ming JI et al (2010) Particle swarm optimization based on cultural algorithm for flood optimal scheduling of hydropower reservoir. J Hydraul Eng 2. Zhan S, Tang Z (2010) Discussion on flood regulation and operation modes of xiajiang hydrojunction. Yangtze River

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3. Needham JT, Watkins DW, Lund JR et al (2000) linear programming for flood control in the Iowa and Des Moines Rivers. J Water Resour Plan Manag 126(3):118–127 4. Zeng X, Hu T, Cai X et al (2019) Improved dynamic programming for parallel reservoir system operation optimization. Adv Water Resour 131:103373 5. Ahmadianfar I, Khajeh Z, Asghari-Pari SA et al (2019) Developing optimal policies for reservoir systems using a multi-strategy optimization algorithm. Appl Soft Comput J 80:888–903 6. Samani NN, Sheybani M, Zlatanova S (2021) Detection of safe areas in flood as emergency evacuation stations using modified particle swarm optimization with local search. Appl Soft Comput 111:107681 7. Gao Y, Liu J (2009) Parameter study of differential evolution algorithm. J Nat Sci Heilongjiang Univ 26(1):81–85 8. Yang K, Ji X, Mao L et al (2020) Numerical simulation of two extreme floods and torrential rains in Helan Mountains and comparison of terrain effects. Arid Meteorol 04:581–590 9. Zhang X, Liu P, Xu C et al (2019) Real-time reservoir flood control operation for cascade reservoirs using a two-stage flood risk analysis method. J Hydrol 577:123954 10. Liu X, Luo J (2019) A dynamic multi-objective optimization model with interactivity and uncertainty for real-time reservoir flood control operation. Appl Math Model 74:606–620 11. Lu Q, Zhong P, Xu B et al (2022) Multi-objective risk analysis for flood control operation of a complex reservoir system under multiple time-space correlated uncertainties. J Hydrol 606:127419 12. Liu X, Zhu Y, Guo X et al (2015) Comparative study on multi-objective optimization and dispatching technologies of reservoirs. J Yangtze River Acad Sci 07:9–14 13. Qin H, Zhou J, Wang G et al (2009) Research on multi-objective flood control scheduling of reservoirs based on multi-objective differential evolution algorithm. J Hydraul Eng 05:513–519 14. Huang X, Xu B, Zhong P et al (2022) Robust multiobjective reservoir operation and risk decision-making model for real-time flood control coping with forecast uncertainty. J Hydrol 605:127334 15. Chen Y, Chen N, Ren X et al (2018) Forecast deviation and predictability analysis of rare and extremely heavy rain in the foothills of Helan Mountain. Meteorology 01:159–169 16. Burgan HI, Icaga Y (2019) Flood analysis using Adaptive Hydraulics (AdH) model in Akarcay Basin. Teknik Dergi 30(2):9029–9051

Evolution Characteristics and Impact Evaluation of Meteorological and Hydrological Drought in the Jinsha River Basin Yuanzhi Tang, Tailai Gao, Xiaoxuan Jiang, and Junjun Huo

Abstract The Jinsha River Basin (JRB) is situated on the eastern edge of the Tibetan Plateau and is a significant water source for the Yangtze River. An in-depth understanding of its drought evolution and propagation is a crucial guide for drought identification and risk assessment in the plateau and the entire Yangtze River. Hence, this paper analyzed the trend pattern of meteorological elements in the JRB and explored the dynamics of meteorological and hydrological drought. The results indicated that precipitation in the JRB fluctuated and increased at a rate of 8.01 mm/10a on average, and the average temperature increased by around 1.82 °C over the past 61 years, with both indicating a higher value downstream than upstream. Meteorological droughts were mainly focused in the 1970s, 1980s, and mid-1990s, with an overall reduction in meteorological droughts on a 12-month time scales. There were differences in the hydrological drought trends at different stations, Zhimenda and Shigu stations decreased and Pingshan station increased. In addition, the JRB was dominated by mild drought, with an average frequency of 13% for meteorological drought and 30% for hydrological drought, and the correlation between SRI and SPI was most significant in the upstream area where human activities were less frequent. The hydrological drought was about 9–10 months later than the meteorological drought due to climate change, subsurface change and human activity. The findings could be utilized as a reference for regional water resource management and basin drought hazard warning. Y. Tang · T. Gao · X. Jiang · J. Huo (B) Changjiang River Scientific Research Institute, Wuhan 430010, China e-mail: [email protected] Y. Tang e-mail: [email protected] T. Gao e-mail: [email protected] X. Jiang e-mail: [email protected] J. Huo Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_10

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Keywords Jinsha River Basin · Hydrometeorological drought · Trend analysis · Standardized drought index

1 Introduction Drought from water deficit has become one of the most prevalent and severe natural disasters today as a result of global warming [1, 2]. The alteration of the subsurface by human activities has affected the intensity of regional evapotranspiration [3], the destabilization of atmospheric water content, and land use. Increasing frequency of extreme weather has also raised the risk of regional drought disasters. Widespread drought events have far-reaching consequences for eco-environmental systems [4, 5], agricultural production security [6], and socio-economic development [7]. Therefore, exploring the spatial and temporal distribution characteristics and trends of regional droughts is of practical significance for revealing hydrological cycle change patterns and developing water security planning and management policies. Droughts can be subdivided into meteorological, hydrological, agricultural, and socio-economic drought events based on the distinct hydrological processes of the water cycle and the differences in the indicator objects [8], but the same is that persistent precipitation deficiency is the direct causative factor for these four types of drought [9]. In order to assess the features of different forms of droughts and predict the drought evolution, several reliable drought indices, such as the streamflow drought index (SDI), the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), the soil moisture drought index (SMDI), and the standardized runoff index (SRI), have been developed in recent years and received wide attention from academics [10, 11]. Among them, the SPI was recommended by the World Meteorological Organization (WMO) for its simplicity, robustness, and multiple time scales [12], and the SRI based on flow data can characterize the hydrological processes and has demonstrated good applicability in the field of drought research [13, 14]. For example, Xu et al. [15] calculated the SPI and discovered that extreme drought conditions exist in non-arid regions of China, whereas large-scale droughts were typically concentrated in the area from the North China Plain to the lower Yangtze River; Li et al. [16] succeed in identifying the hydrological drought in the Dongting Lake basin using the SRI probability distribution based on Copula function. In the joint research of SPI and SRI, Zhao et al. [17] found that the hydrological drought was more severe than the meteorological drought in the Jing River basin; Huang et al. [18] estimated the hydro-meteorological drought index and believed that the propagation time from meteorological to hydrological drought had noticeably seasonal characteristics; Van et al. [19] showed that groundwater systems were strong controls of the hydrological aridity of the equatorial and temperate climate types; while Zhou et al. [20] used time-lag analysis to find that land use change in the eastern Qilian Mountains significantly influenced meteorological and hydrological drought. It can be seen from these previous studies that the combination of multiple drought indices can demonstrate the impact and propagation of meteorological and

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hydrological drought in terms of water balance and energy balance [21], which is of guidance for the study of uncertainty in the spatial and temporal evolution of regional droughts under dynamic environments. The Jinsha River Basin (JRB) is the largest hydroelectric power generation base in China due to its significant altitude difference and an abundance of hydropower resources, but it is also particularly vulnerable to climate change of the distinctive natural environment and delicate ecological balance [22]. Particularly, the recent droughts have worsened regional water disputes and had a significant impact on the spatial pattern of water resources in the entire Yangtze River. It has been noted that the overall average annual runoff of the JRB showed a non-significant decreasing trend over the past three decades [23], leading to recurrent drought issues. Additionally, due to the influence of the monsoon belt, the precipitation characteristics of the JRB were strongly spatially heterogeneous [24], and had a significant impact on local agriculture. For example, the average drought area in the middle and lower reaches accounted for 35.4% of the total arable land area during 2001–2011 [25]. The majority of recent research on the JRB drought characteristics has concentrated on discussions of a single component, such as meteorological, agricultural, or hydrological drought [26, 27]. However, the specific distinctions between various drought indices and their intrinsic drivers are not yet well comprehended, which may lead to uncertainty in the conclusions [9, 28]. Given the multidimensional and stochastic nature of drought, studies cannot rely on individual variables or indices (e.g., precipitation, soil moisture, and runoff). Hence, the joint effect researches of multiple drought indices in the JRB and their response relationships are still attractive. The main objectives of this study are: (1) to calculate two widely used drought indices (SPI and SRI) and inspect the characteristics of the spatio-temporal dynamics of drought in the JRB at different time scales over 61 years; (2) to investigate the intrinsic link between SPI and SRI and explore the lag effect of hydrological drought on meteorological drought. The results will further enhance the comprehension of drought patterns in the JRB and provide reference for basin drought hazard warning and regional water resources management.

2 Study Area and Data 2.1 Study Area Originating from the southern glacier at Jianggendiru peak of the Goaltending Snowy Mountain in the middle of the Tanggula Mountains, Jinsha River Basin (JRB) is located at the western margin of the Tibetan Plateau, the Yunnan-Guizhou Plateau (YGP) and the Sichuan Basin (SB) of China. The geographical position is at 90°23, – 104°37, E, 24°28, –35°46, N, with an estimated area of 540,000 km2 . The river’s total length from the source of Jinsha River to Yibin City along the mainstream is 3500 km, with a total difference in altitude of 5100 m, accounting for 55% of the length and

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95% of the total altitude difference of the Yangtze River. The special topographic and geomorphological conditions lead to strong solar radiation, complex and variable temperature, and distinct dry and wet seasons in the basin. Despite having abundant hydropower assets, the problem of JRB drought is becoming more prominent due to global warming and human activity that is alternating the spatial dispersion of water resources.

2.2 Data Sets The meteorological and hydrological data used for this study mainly include daily average temperature, daily maximum temperature, daily minimum temperature, daily precipitation and daily runoff. Time series data with quality control for 61 years (1960–2020) from 39 meteorological stations with good completeness were selected based on the differences in time periods recorded at meteorological sites in the JRB, of which 30 stations were located within the basin. Data were obtained from National Meteorological Information Center (http://www.nmic.cn/). Temperature data were standardized to remove incorrect and invalid values. To address the issue of missing precipitation data, correlation analysis was performed based on station location and record years, and interpolation was carried out using adjacent stations and similar years with correction for singular values. The hydrological stations chosen for this analysis were Zhimenda, Shigu, and Pingshan stations, which have time series from 1960 to 2016, 1960 to 2017, and 1960 to 2012, respectively, and runoff data at Ping Shan Station used Xiangjiaba station from 2013 to 2020. Some of the missing measurements were fitted to the data using the water level flow relationship curve. The hydrological drought in the areas upstream, midstream, and downstream of JRB were characterised using 3 hydrological stations. Figure 1 depicts each meteorological and hydrological station’s geographical location.

3 Methodology 3.1 Standardized Precipitation Index The standardized precipitation index (SPI) was first proposed by McKee et al. [29] in 1993 and used to characterize the probability of precipitation over a period of time [30]. It could be utilized to determine the severity of drought by the magnitude of the value (smaller values represented more severe drought and vice versa, wetter), and had been successfully applied in many studies [31, 32]. The variation of precipitation was often fitted with G distribution in drought analysis and evaluation, and subsequently, its cumulative probability was found and transformed into a standard normal distribution to calculate the SPI value. The main calculation formula was:

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Fig. 1 Geographical location of meteorological and hydrological stations in the JRB

⎧   ⎨ − t − C0 +C1 ∗t+C2 2 ∗t 2 3 , 0 < p ≤ 0.5 1+d1 ∗t+d2 ∗t +d3 ∗t   . SPI = ⎩ + t − C0 +C1 ∗t+C2 2 ∗t 2 3 , 0.5 < p < 1.0 1+d1 ∗t+d2 ∗t +d3 ∗t ⎧ /

⎪ ⎨ ln 1/ p 2 , 0 < p ≤ 0.5  t= /  ⎪ ⎩ ln 1−1p2 , 0.5 < p < 1.0

(1)

(2)

where p is the cumulative probability, C and d are constants: C 0 = 2.5155, C 1 = 0.8029, C 2 = 0.0103, d 1 = 1.4328, d 2 = 0.1893, d 3 = 0.0013.

3.2 Standardized Runoff Index Similar to the rationale of SPI, Shukla et al. [33] proposed a standardized runoff index (SRI) to express the probability of occurrence of runoff in a basin, focusing on the study of surface water resource abundance and deficit. For visual comparison

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Table 1 Categories of SPI and SRI to drought [34]

Grade

Category

SPI/SRI

1

No drought

>−0.5

2

Mild drought

(−1.0, −0.5]

3

Moderate drought

(−1.5, −1.0]

4

Severe drought

(−2.0, −1.5]

5

Extreme drought

≤−2.0

and discussion of the drought index, the same G distribution as the SPI was chosen to calculate the SRI, with a time scale of January to December, and the various time scales were calculated in the form of cumulative flow summaries. The monthly and annual drought indexes were divided into five categories (Table 1).

3.3 Trend Analysis The Mann–Kendall (M–K) test, a reliable nonparametric statistical test, is commonly used to analyze the outcomes of trend analyzes of meteorological elements, hydrological variables, and other time series [35]. In this study, the null hypothesis H0 was defined as the lengthy time series being independent identically distributed random sample values with the following estimates of the statistical variable S: S=

n n−1 Σ Σ



sgn x j − xi

(3)

i=1 j=i+1



sgn x j − xi



⎧ ⎨ 1, x j − xi > 0 = 0, x j − xi = 0 ⎩ −1, x j − xi < 0

(4)

When the sample size n > 10, the variance of the statistic S was defined as: var(S) =

Σ 1 [n(n − 1)(n − 5) − f t ( f t − 1)(2 f t + 5) 18 t ⎧ S−1 ⎪ ⎨ var(S) , S > 0 Z= 0, S = 0 ⎪ ⎩ S+1 , S < 0 var(S)

(5)

(6)

where ft is the number of samples per group and t is the number of groups of identical samples, Z is the standard normally distributed statistical variable. |Z| ≥ 1.28, 1.96, or 2.32 indicates that the time series is significant at the 90, 95, or 100% confidence level, respectively.

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The Sen slope (β) and moving average approach were used to estimate linear trends of precipitation and temperature in the JRB to reduce the impact of time series outliers. Kriging interpolation was performed to evaluate the spatial pattern of the meteorological and hydrological drought. Additionally, we introduced correlation coefficients (confidence level defined as 99%) to investigate the response of hydrological drought to meteorological drought.

4 Results and Discussion 4.1 Characteristics of Meteorological Elements and Trends Analysis Based on the Tyson polygon method, the annual average precipitation and temperature changes of JRB were determined in Fig. 2. As can be seen, JRB experienced an average annual precipitation of 617.05 mm from 1960 to 2020 with variations between 519 and 728.2 mm. Sen slope trend analysis showed that precipitation had a generally fluctuating increasing tendency (β = 8.01 mm/10a). The precipitation series exhibited a more pronounced downward trend around 1972, 1996, and 2011, with the average precipitation below 600 mm, whereas it has been slowly increasing over the past ten years, according to the 5-year sliding average curve. The JRB’s annual mean temperature, on the other hand, fluctuated within a 0.5 °C range from the 1960s to the 1980s, when it was more stable, then increased steadily after the 1990s. Sen slope estimation revealed the JRB’s yearly mean temperature increased by almost 1.82 °C throughout the study period. From the spatial distribution perspective, the JRB precipitation generally exhibited a feature that the upstream was smaller than the downstream (Fig. 3a). There were 18 sites with precipitation greater than 800 mm, which were mainly located near the downstream valley of JRB, where water resources were abundant and the main area for hydroelectric power generation. The precipitation in areas farther away from the river decreased to some extent. The spatial pattern of temperature change was essentially similar to that of precipitation.

Fig. 2 Variations of precipitation (a) and temperature (b) over JRB from 1960 to 2020

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Fig. 3 Spatial distribution and trend changes of precipitation (a/c) and temperature (b/d) over JRB from 1960 to 2020

To assess the change trends of precipitation and temperature in the JRB, the annual average data from meteorological stations were examined by the M–K method. It has been discovered that there were considerable regional disparities in the distribution of precipitation trends: 63% of the stations indicated an increasing trend in annual mean precipitation during the study period, with 7 sites having a significant increasing trend (α = 5%), mostly in the upstream and midstream areas (Fig. 3c). However, the precipitation trends in the downstream areas were primarily decreasing, which suggested that the downstream areas were drying out despite the large precipitation values. Meanwhile, only one station in the basin demonstrated a declining trend in temperature, while 97% of the sites showed an increase and almost all of them reached the significance level (α = 5%), with the most significant station being Deqin in the midstream (β = 0.43 °C/10a). This shows that the continued temperature increase and precipitation decline in the JRB downstream region poses a significant challenge to drought.

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4.2 Analysis of Drought Indexes and Characteristics The analysis above revealed that the variation of meteorological variables in the JRB had significant spatio-temporal characteristics, which further contributed to the difference in regional drought patterns. Figure 4 depicts the intensity and frequency of meteorological and hydrological droughts at different time scales (1 month, 3 months, 6 months, 12 months) during the study period. According to the drought index on a one-month time scale, the JRB experienced 81 months of mild drought, 10 months of moderate drought, and 1 month of severe drought, with frequencies of 11.1, 1.37, and 0.14%, respectively, indicating an average of nearly two months of drought per year. Comparing the SPI-3, it was found that the frequency of seasonal drought increased (12.2% for mild and 2.2% for moderate), most likely as a result of the area water resources’ seasonal uneven distribution, which concentrated dryness. However, the frequency of droughts reduced to 4.7%, and all of them were mild, as the time scale was 12 months and the basin’s capacity to regulate interannual water levels rose. In addition, the frequency of hydrological drought in the three hydrological stations essentially stayed at 30%, with the difference that Zhimenda station was dominated by mild and moderate drought, while low-frequency extreme drought existed in Shigu and Pingshan stations. In general, the drought index on a smaller time scale typically swings more dramatically over time, and a 12-month time scale index is rather stable and can be utilized to depict the interannual variability of drought in the basin. Figure 5 shows the change of SRI values at Zhimenda, Shigu, and Pingshan stations and SPI values under the control area of the hydrographic sites on the 12-month time scales. It was noticeable that the meteorological droughts in the basin were mainly concentrated in the 1970s–1980s and mid-1990s (Fig. 5c). The SPI values exhibited an overall

Fig. 4 Drought intensity and frequency distribution at different time scales in the JRB

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Fig. 5 The variation of SPI and SRI values at Zhimenda (a), Shigu (b), and Pingshan (c) stations on the 12-month time scales in the JRB

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significant increasing trend from 1960 to 2020, according to the M–K test, demonstrating that the meteorological drought has decreased over the past 61 years, which was consistent with the trend of precipitation. Related studies have noted that the YGP and the SB in the JRB experienced a significant drought trend from 1961 to 2012 [15], which was not at odds with the results. This was primarily because of the overall difference in precipitation brought on by the expansion of the regional extent. The hydrological drought trend, however, varied among stations, with increasing SRI values at Zhimenda and Shigu sites and decreasing trends at Pingshan sites, primarily from 2009 to 2016. Another interesting finding was that SRI and SPI values had essentially the same trajectory at various regional scales, and the correlation coefficients were significant at the 99% confidence level, with Zhimenda station having the maximum correlation coefficient of 0.81 (Fig. 5a). But as the basin grew, the association between SRI and SPI in the middle and lower reaches of the JRB steadily declined (Fig. 5b/c). This might be a reason that the headwaters of rivers upstream of the JRB were largely natural rivers with little human interference and the influence of precipitation on runoff was more direct, leading to a more sensitive response of hydrological drought to meteorological drought. On the other hand, the original runoff pattern have changed in recent years due to the construction of numerous hydropower stations downstream of JRB, and human activities like reservoir scheduling and inter-regional water transfer have optimized the allocation of water resources, reducing the correlation between meteorological drought and hydrological drought. For instance, the SRI and SPI values near the Ping Shan station in 2013 showed an opposite trend (Fig. 5c). To investigate the temporal variation of seasonal meteorological and hydrological drought in different regions of the JRB, the SPI and SRI were calculated for three monthly time scales (SPI-3, SRI-3), and the frequency of droughts in different seasons were recorded for February to May, June to August, September to November and December to February (Fig. 6). It can be seen that the frequency of meteorological droughts (Fmd) was lower than the frequency of hydrological droughts (Fhd) in all seasons, and regional differences in seasonal fluctuation were significant. According to the change of Fmd, the summer meteorological drought within the Shigu and Pingshan stations’ control area had the same frequency (Fmd = 13.12%), while the spring, autumn, and winter seasons showed a declining tendency from upstream to downstream. These results indicate that the upstream of JRB is more susceptible to drought, whereas the middle and lower reaches of the JRB experience some relief from the drought for an increase in precipitation, and the summer precipitation accumulation becomes a controlling factor for the meteorological drought in the middle and lower reaches. On the other hand, Fmd was highest in the upstream of JRB in the fall, with an opposite relationship to the downstream, while meteorological drought in the midstream mostly occurred in the spring and winter. By contrast, the difference between Fmd and Fhd was the smallest in the upper reaches, demonstrating a strong association between meteorological and hydrological droughts (Fig. 4a). Fhd showed little seasonal variation from region to region, remaining at about 30%. It suggests that in addition to the influence of meteorological drought, other factors,

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Fig. 6 Seasonal variations in the frequency of drought in the JRB’s different regions

such as land use, vegetation changes and inter-basin water transfers may also cause changes in hydrological drought [36]. This should be taken into account in future research.

4.3 The Response and Propagation of Hydrological Drought to Meteorological Drought As two important processes in the surface water cycle, precipitation and runoff have significant effects on the propagation from meteorological drought to hydrological drought in terms of their trends and response relationships, and hydrological drought lags behind meteorological drought as a result of the accumulation of meteorological drought effects in time. Hence, we correlated SPI values at different time scales and SRI-1 at three hydrological stations (Table 2). As can be seen, the correlation coefficients between SRI-1 and SPI at Shigu station were greater than those at Zhimenda and Pingshan stations in the time scale of 1–6 months, and when the time scale was greater than 6 months, the correlation coefficient increased to the maximum at Zhimenda station. Overall, the correlation coefficients between SRI-1 and SPI at 3 stations showed an increasing trend, and reached the maximum at 9–10 months with coefficients of 0.71, 0.66 and 0.55 (significant at 99% confidence level), respectively, and gradually declined after more than 10 months. These results suggested that the hydrological drought in the JRB lagged behind meteorological drought by about 9–10 months, and became less sensitive when the time scale was greater than 10 months. The SPI and SRI were closely associated under numerous time scale comparisons but not synchronously coupled. The propagation of meteorological drought to hydrological drought is a complex process influenced by multiple effects of

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Table 2 Correlation coefficients of SPI and SRI-1 at different time scales Month

1

2

3

4

5

6

7

8

9

10

11

12

SPI-n 0.241 0.358 0.411 0.460 0.515 0.594 0.661 0.678 0.709 0.710 0.699 0.679 and SRI-zmd SPI-n and SRI-sg

0.258 0.406 0.446 0.505 0.564 0.600 0.631 0.640 0.657 0.642 0.635 0.621

SPI-n and SRI-ps

0.237 0.379 0.400 0.423 0.446 0.474 0.502 0.521 0.546 0.541 0.533 0.505

Note All correlation coefficients were significant at the 99% confidence level

climate changes, subsurface changes and human activities. In general, meteorological drought has a longer propagation time in dry areas compared to wet areas. The JRB’s climate gradually changes from arid to wet from upstream to downstream, and an increase in temperature can hasten the rate at which runoff reacts to precipitation. Therefore, the time-lag effect of hydrological drought is more remarkable in Zhimenda station. Overall, nevertheless, the differences in hydrological drought lag times among hydrological stations were small. It has been shown that watershed structural (soil, groundwater system, etc.) characteristics can modify the propagation of meteorological drought [19]. There was a positive correlation between runoff and soil water content in the JRB [37], and the groundwater system recharged surface water by draining to the soil layer when river runoff decreased, which might prolong the propagation time. In addition, the hysteresis effect of land use change on hydrological drought has a significant impact [18]. For instance, the development of large hydropower plants in the JRB has produced numerous terraced reservoirs in the main rivers, changing the land use type from forests and grasslands to water bodies, increasing the evaporation loss from the watershed [20] and, to some extent, raising the risk of hydrological drought. However, the reservoir regulation of terraced hydropower plants and inter-basin water transfer made the river flow stable, which in turn largely prolonged the propagation of drought and reduced the frequency of hydrological drought. It is true that improving the deployment management of watershed runoff and enhancing the drought disaster warning capability are effective measures to ensure the controllability of hydrological drought, but for further examining drought impact mechanism in the JRB, how to precisely and quantitatively assess the contribution of meteorological drivers such as precipitation, temperature and evaporation and subsurface drivers such as soil type, vegetation cover and land use to the propagation time of meteorological drought is the focus of research in the next stage.

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5 Conclusions In this study, we analyzed the climate trends and spatial patterns in the JRB from 1960 to 2020 and investigated the characteristics and connection between meteorological and hydrological droughts by calculating the SPI and SRI values on the time scales of 1–12 months. The main conclusions are as follows: (1) The overall precipitation in the JRB was higher downstream than upstream, with more than 60% of the sites indicating a rising trend and the average precipitation fluctuating increasing at a rate of 8.01 mm/10a. The annual mean temperature in the basin increased by around 1.82 °C over the past 61 years, most significantly in the middle and upper reaches. (2) Meteorological droughts in the JRB were mainly focused in the 1970s, 1980s, and mid-1990s, and were dominated by spring and winter droughts, with an overall reduction on a 12-month time scale. There were differences in the trends at different stations, with a slowdown at Zhimenda and Shigu stations and an increase at Pingshan station. (3) The JRB was dominated by mild drought, with an average frequency of 13% for meteorological drought and 30% for hydrological drought. The correlation between SRI and SPI on a 12-month time scale was most significant in the upstream area where human activities were less frequent, while the optimum propagation time from meteorological to hydrological drought in the JRB was 9–10 months. Acknowledgements This research was supported by grants from the National Key Research and Development Project (No. 2021YFC3000202).

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Mutation Analysis of Runoff–Sediment Combination and Profitable Frequency of Wetness–Dryness Encounter in the Middle Yellow River Xinjie Li, Yuanjian Wang, Qiang Wang, Linlin Li, and Chunfeng Hao

Abstract By using the long series data of annual Runoff-Sediment (R-S) recorded at the Tongguan hydrological stations in the period 1961–2019 to clarify the variation rules in the mid & up reaches of Yellow River, the dynamic changes and the runoff– sediment relationship trend was analyzed. The findings were as follows: (1) The long sequence have been declining, with an abrupt discontinuity point in 2003 and a remarkable decrease, particularly beyond 2003. A more obviously reduced tendency in the sediment data is also observed. (2) The joint distribution of the R-S load changes significantly after a mutation of the R-S load relationship occurs. When the probability is lower than 90%, both the R-S load decrease, and the reduction in the sediment load is more obvious. (3) The analysis revealed that the probability of encountering an asynchronous runoff decrease and a sediment load increase is higher than that of having a synchronous runoff reduction and an increasing sediment load. The eigenvalues changed significantly at the design frequency. Detailed suggestions and basic data for the engineering design, as well as a flood mitigation and prevention design based on a joint distribution model are presented. Keywords Yellow River · Hurst index · Mann–Kendall · Trend test · Frequency curve · Copula function

X. Li · Y. Wang (B) · Q. Wang · L. Li Key Laboratory of Yellow River Sediment of the Ministry of Water Resources, Yellow River Institute of Hydraulic Research, Zhengzhou 450003, China e-mail: [email protected] X. Li · L. Li College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China C. Hao State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_11

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1 Introduction As a typically heavily sediment laden river, the sand transporting quantity of Yellow River is the highest in the world, and the water and sediments originate from different sources. The imbalance of the R-S relationship is remarkable. The runoff of Yellow River originates mainly from natural precipitation; that is, melted water from snow and groundwater from the upper reaches [1, 2]. In contrast, the sediments originate mostly from the Loess Plateau and some sandy areas. A great quantity of sediment is transported and deposited on the lower river by wind and water. The imbalance between the water and sediment combination causes serious silting of the reservoirs as well as the riverbed. As a result, the riverbed of the downstream channel rises, and the main river channels are less effective at reducing flooding and transporting sediments. With water conservancy construction and the implementation of water and soil project as well as irrigation project, environmental conditions, such as complex boundary conditions would fundamentally change the water and sand relationship. In the last few years, the measured runoff mainstream decreased markedly in the Yellow River basin, affecting the sustainable development of agricultural production, the social and economic, and local residents in the region [3]. The annual quantity of sediment transport observed at the Tongguan hydrometric station from the beginning of the twenty-first century has decreased from 1.6 × 109 t in 1919–1959 to 1.5 × 108 t in 2000–2019. This sharp reduction of up to 85% has attracted extensive attention. The annual runoff decreased by 43% from 4.26 × 109 t in 1919–1959 to 2.44 × 109 t in 2000–2019. The sharp reduction produced a negative impact on the estuarine ecological environment. The major bottleneck in managing the Yellow River is the unharmonious relationship between annual R-S. Annual sediment takes on a synchronously-decreasing trend, which not only affects the evolution of the river system itself, but is also closely related to utilizing and protecting the local water resources [4, 5]. The phenomenon in which the rivers are higher than the ground level becomes aggravated, threatening the lives and properties of people living along the river [6]. Therefore, studies on the characteristics, transformation regulation, and development trends of the R-S relationship in the Yellow River have attracted continuing concern [7–9]. Because of the importance of the evolution trends and laws of runoff extreme, the evolutionary law of large river basins has been studied from different perspectives in China. The variability of the erosion process of Yellow River is studied by the M–K test and cumulative curves of hydrological parameters using the prototype observation data during 1950–2015 [10]. To study the trends of runoff in the Shiwang River, and to reveal the intrinsic connection between rainfall with runoff and sediment yields, Xu et al. analyzed measured data from hydrological stations in the period 1970–1989 using double cumulative curves [11]. Bao et al. used the data observed in the Liaohe River from 1985 to 2013, including riverbed cross-sections and hydrological and sediment transport data, to examine the R-S characteristics of the region. In addition [12]. Wei et al. used the data of R-S from the Datong Hydrological Station in the Yangtze River in 1980–2000 to establish a new type of frequency curve that transforms the frequency expression based on probability

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theory [13]. Furthermore, the feasibility of establishing sediment frequency curves was discussed [14]. A runoff and sediment distribution function were theoretically derived based on the Gaussian copula function at two hydrological stations [15]. Similarly, the copula function is employed to build a joint probability of R-S to exhibit the variation patterns in the Yellow River from a stochastic perspective [16]. Lin et al. quantitatively investigated different combinations of flow-sediment variation of Songliao Basin using sediment frequency distribution curves [17]. Chu and Li also used double cumulative curves to analyze the variability of the flow–sediment relationship of Yellow River mainstream; they used the Mann–Kendall test and the wavelet theory to examine variations in the flow-sediment at different time scales [18]. Mo et al. adopted the copula function to construct a distribution model for the annual runoff of the Weihe [19]. To study the relationship between the R-S frequencies, Jin established theoretical frequency curves for the R-S series of the middle reaches of the Yellow River [20]. Yan et al. conducted a Mann–Kendall test to identify mutation rules in the R-S data measured at the Jing River Basin in the period 1958– 2013, and examined the scale effects of R-S through a comparative analysis [21]. Cui et al. examined the hydrological and sediment transport processes at 16 hydrological stations in the Jinghe to obtain the spatial scale rule between the sediment transport rate and the catchment area [22]. Guo et al. applied the copula theory to analyze the evolution of the R-S load relationship at the Zhangjiashan Station along the Jing he [23]. Yao et al. adopted Pearson type III curves to construct the marginal distributions of hydrological and sediment transport in the Jinghe. Moreover, the authors adopted the copula function to establish a distribution model of R-S load in the Jinghe and analyzed the frequencies based on a multidimensional copula function [24]. Huang et al. also used the copula function to identify mutation rules in the R-S data measured in the Weihe and studied the monthly and annual evolution of these series [25]. Chen et al. established the equilibrium relationship of flow-sediment transport in the Huanghe [26]. Tian et al. determined the mineral composition and particle size distribution of the samples in Yellow River, and analyzed the relationship between the physical features of Loess deposits and river sediments [27]. Chen et al. evaluated the effect of sediment allocation in the Yellow River in the period 1960– 2015 using a proposed comprehensive evaluation index system and a comprehensive evaluation method [28]. Many efforts have been directed toward predicting the future tendency of sediment transport, but only few studies have focused on the profitable frequency of wetness– dryness encounter. In this study, the rule of distribution and trends of the hydrological and sediment transportation data series were analyzed and measured at the Tongguan during the period 1961–2019, and their various combinations in the runoff and sediment inflows were examined. According to the catastrophe rule, the joint distribution model of flow-sediment was set up by the copula function, and the probability of encountering synchronous variations was determined. The findings provide theoretical basis and technical support for flood control and disaster mitigation, and ecological protection of the beach areas and reservoirs in the multi-sand river.

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2 Data Used for Analysis The Tongguan Hydrological Station, located 114 km from the Sanmenxia Dam, is the key control station along the middle Yellow River, which changes its route to the east after flowing from the north to the south past the Shanxi and Shaanxi provinces. It also marks the end of the backwater where the water level of the Sanmenxia Dam is normal [29], as shown in Fig. 1. Floods through the Tongguan Hydrological Station are mainly attributed to water from the upstream areas of the Longmen along the mainstream of the Yellow River and water from tributaries such as the Jinghe, Weihe, and Luohe. Meanwhile, the sediments transported through the station come mainly from the Loess Plateau, Longmen of the Yellow River, Xianyang of the Weihe River, Zhangjiashan of the Jinghe, Hejin of the Fenhe, and Zhuangtuo of the North Luohe (33°40, 19,, –40°35, 1,, N, 103°57, 43,, –112°39, 43,, E). These sediments travel through the lower north mainstream of the Huanghe and the lower reaches stream of the Weihe, which experience scouring and silting, before eventually reaching the Tongguan Hydrological Station. The station controls 91% of the river basin, 90% of the runoff, and approximately 100% of the sediment load. With an arid and semiarid climate, the region receives little but concentrated precipitation, mostly in the form of rainstorms. Sediments arrive at the station mostly during the flood season from June to September. This research mainly used the daily R-S data from a survey conducted by the Hydrological Yearbook of the Yellow River basin over a 59-year period (1961–2019). The data reflects the R-S entering the mid and up reaches of the Yellow River.

Fig. 1 Yellow River basin

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3 Methods This paper analyzes the evolution and mutation rules of the runoff-sediment in the last few years with M-A (Moving average) method, M–K trend test method, Hurst index method, Sliding T-test method, Double Cumulative Curve Analysis method and Two-dimensional Copula theory.

3.1 M-A Method The M-A method of trend extrapolation was used to analyze and discuss the status and future evolution of inflow sequences. Its calculation formula is as follows [30]: yi =

k Σ 1 x j+i , i > k 2k + 1 j=−k

(1)

where yi denotes the moving average of point i, x j+i means the sequential value of point j + i, k is the unilateral moving time interval, and 2k + 1 is the smoothing length. An appropriate value of k was assigned based on the sample size. The highfrequency oscillations in the original data series were averaged to obtain a long-term variation pattern of the series.

3.2 M–K Trend Test Method M–K test developed by Mann and Kendall performs trend analysis [31, 32]. The null hypothesis is denoted as H 0 . It is assumed that there exists a series (x 1 ,…, x n ) with n independent samples and the same distribution for random variables. In the alternative hypothesis denoted as H 1 , there is a two-sided test, and for all k, j ≤ n, and k /= j, the distributions of x k and x j are different. First, the statistic S of the Mann–Kendall test is computed as follows: S=

n−1 Σ n Σ

  Sgn x j − xk , (k = 2, 3, · · · , n)

(2)

⎧ ⎨ +1 x j − xk > 0 = 0 x j − xk = 0 . ⎩ −1 x j − xk < 0

(3)

k=1 j=k+1

where 

Sgn x j − xk



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When S is normally distributed, the expected value and variance are defined as follows: E(sk ) = n(n + 1)/4.

(4)

Var(Sk ) = n(n − 1)(2n + 5)/72.

(5)

The trend statistic is defined as follows: Sk − E(Sk ) , (k = 2, 3, . . . , n). U Fk = √ Var(Sk )

(6)

When UF 1 = 0 and the significance level of A = 0.05, its critical value U α (0.05) = ±1.96, and U α (0.001) = ±2.58. If UF k > 0, the series shows an increasing trend. In contrast, if UF k < 0, it indicates that the series exhibits a decreasing trend. In particular, if |UF k | < 1.96, the trend is insignificant, and if 1.96 < |UF k | < 2.56, the trend is significant. Finally, if |UF k | > 2.56, a highly significant trend is suggested [33]. UF k follows a standard normal distribution. Next, the time series is reversed, and the above calculations are repeated such that UBk = −UF k , where UBk is the reverse series of UF k . Both series are plotted using the same coordinate system. Furthermore, confidence curves corresponding to the assigned significance levels are drawn. If UBk and UF k intersect within the confidence curves, the year corresponding to the point of intersection is the period in which a mutation occurs. If n > 10, the statistics for the samples exhibiting standard normal distributions are calculated as follows: ⎧ S−1 ⎪ ⎨ √Var(S) , S > 0 Z= (7) 0, S = 0 . ⎪ ⎩ √ S+1 , S < 0 Var(S) Here, Z is the statistic that obeys the normal distribution, Var(S) is the variance Z > 0 indicating an increasing precipitation for that specific time series, otherwise, the precipitation is decreasing. In addition, when |Z| ≥ Z 1-α/ 2 the null hypothesis is rejected, and the higher the numbers of |Z|, the bigger pronounced the upward (downward) trend becomes.

3.3 Hurst Index Hurst exponent H effectively and quantitatively describes the self-similarity and long-range dependence of a time series. The value of H lies within [0, 1]. When H = 0.5, the data is a random series with finite variance and mutually independent data. In contrast, when 0.5 < H < 1, the data shows persistent changes, and future changes

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are consistent with past variation trends. Furthermore, when 0 < H < 0.5, the data is unsustainable; that is, past changes are unsustainable. The R/S method is used to calculate H, as demonstrated below [34]. For a time sequence {ζ (t)}, t = 1, 2, …, N, there is a series τ = 1, 2, …, N. For τ, the average value of the series is defined as follows: ⟨ζ ⟩ τ =

τ 1Σ ζ (t). τ t=1

(8)

For all moments within the series τ, the cumulative difference of time t is X (t, τ ) =

t Σ   ζ (u) − ⟨ζ ⟩ τ , 1 ≤ t ≤ τ

(9)

u=1

The extreme difference series R is R(τ ) = max X (t, τ ) − min X (t, τ ). 1≤T ≤τ

1≤T ≤τ

(10)

and the standard deviation series S(τ ) is ⎡ | τ |1 Σ 2 ζ (t) − ⟨ζ ⟩ τ . S(τ ) = √ τ t=1

(11)

As a result, the following is obtained: R(τ ) = (cτ ) H . S(τ )

(12)

Here, H is the Hurst exponent, c is a constant, and τ is the length of the time series. The Hurst exponent H was obtained by applying the least-squares method to the double logarithmic plot between τ and R/S using measured data.

3.4 Sliding T-Test Method The sliding T-test method was used for verification because all the special points are not variants. Before and after the sliding point, the distribution functions of the two sequence populations F 1 (x) and F2 (x) are set, and two samples of capacities n1 and n2 are selected from the populations F 1 (x) and F 2 (x), respectively, and the null hypothesis is tested. If F 1 (x) = F 2 (x), then

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T =

x1 − x2

21 Sw n11 + n12

(13)

where n1 n 1 +n 2 1 Σ 1 Σ xt , x2 = xt n 1 t=1 n 2 t=n +1

(14)

(n 1 − 1)S1 2 + (n 2 − 1)S2 2 n1 + n2 − 2

(15)

n1 nΣ 1 +n 2 1 Σ 1 (xt − x1 )2 , S2 2 = (xt − x1 )2 n 1 − 1 t=1 n 2 − 1 t=n +1

(16)

x1 =

1

Sw 2 = S1 2 =

1

If the sliding T-test method obeys the t(n1 + n2 -2) distribution, the significance level a is selected, and the value of the analyte-based cutoffs t a/ 2 is determined by checking against the distribution table. It rejects the null hypothesis when T > t a/ 2 , and concludes that there is a significant difference. The hypothesis is accepted when T < t a/ 2 , and there is no significant difference.

3.5 Double Mass Curve Analysis Method The double mass curve method is the most widely used method for consistency analysis of hydrological factors and the evolution trend of hydrological series. It draws the continuous cumulative curves of two variables at the same time. The average slope of the cumulative curves changes suddenly at the break point if there is a sudden change in the runoff series.

3.6 Two-Dimensional Copula Theory Definitions and Properties of the Copula Function. The copula function was first suggested by Sklar, which is theoretically based on the two-dimensional Sklar’s theorem [35]. If H (x, y) is a joint distribution function with marginal distributions F 1 (x) and F 2 (y), and there exists a two-dimensional copula function C, then, H (x, y) = Cθ (F1 (x), F2 (y)). If F 1 (x) and F 2 (y) are continuous, then a copula function exists.

(17)

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In view of the above theorem, the correlation of random variables reflected by the copula function isn’t affected by the marginal distribution of the random variables because a joint distribution contains all information about the variables, and the information will not be distorted during conversion. In general, copula functions are divided into three types: elliptical, quadratic, and Archimedean. In hydrological event studies, Archimedean copulas are mostly used. The specific expressions and parameter relationships as in Table 1, where “T” is the Kendall rank correlation coefficient, copula function parameter, x and  “theta” is the  θ ∫ t y are the time series T = 1 + θ4 θ1 exp(t)−1 dt − 1 . 0

Marginal Distribution. The marginal distribution of each variable must be determined before constructing the copula function. When a hydrological data series variable of a hydrological event is studied, it is often assumed that the hydrological data sequence is the Pearson type III distribution. Subsequently, the statistical parameters of the frequency distribution of the hydrological variable are estimated through optimized curve fitting. The Gringorten empirical frequency formula is employed to calculate the empirical cumulative frequency of the marginal distribution of the variable [36]: H (x) = P(X ≤ xm ) =

i − 0.44 , N + 0.12

(18)

where P is the empirical probability of X < x m , i denotes the sequence number of x m , and N is the size. Copula Function Determination Two-Dimensional Empirical Joint Distribution. The annual runoff (X) and annual sediment load (Y ) are reorganized in ascending order. The empirical frequency of the joint distribution is then obtained by selecting data pairs from the rearranged series, where x i ≤ x j and yi ≤ yj (i < j = 1, …, n), as illustrated below: Table 1 Three types of commonly used Archimedean copula functions Copula Clayton Frank

Gumbel

H (x, y) H (x, y) =



xθ

+

yθ

1/θ −1

Relationship between and τ=

θ 2+θ , θ

∈ (0, ∞)  ∫θ 4 1 t

H (x, y) =   −θ x  −θ y   −1 e −1 e − θ1 1 + (e−θ −1)

τ =1+

H (x, y) =   1/θ  exp − (−lnx)θ + (−lny)θ

τ = 1 − θ1 , θ ∈ [ 1, ∞)

θ

θ

0

exp(t)−1 dt

 − 1 ,θ ∈ R

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Σi H (xi , yi ) = P(X ≤ xi , Y ≤ yi ) =

Σj

m=1

Nmn − 0.44 , N + 0.12 n=1

(19)

where P is the two-dimensional joint probability of having X ≤ x i , Y ≤ yj , N mn is the sequence number of the data pair, and N is the total number of data pairs. Joint Distribution Modeling. Once the marginal distributions of the R-S have been determined, it is necessary to determine the parameters of the copula function. For a two-variable one-parameter Archimedean copula function, the non-parametric estimation method is often used to estimate the parameters involved [36]. This is achieved based on the relationship between parameter τ and θ. The relationship between the two parameters is estimated as follows: ∫ ∫ τ (x, y) = 4

C(X, Y )dC(X, Y ) − 1

(20)

where C (X, Y ) represents a two-variable copula function. The two-variable Kendall rank correlation coefficient τ is estimated as follows:  −1 n τ= 2

Σ

   sign xi − x j yi − y j

(21)

1≤i≤ j≤N

where (x i , yi ) denotes the sample, N is the number of samples, and sign(n) is the sign function computed as: ⎧ ⎨ 1 x >0 sign(x) = 0 x =0. ⎩ −1 x < 0

(22)

Parameter Estimation. Non-parametric estimation, curve fitting methods, and approximate maximum likelihood methods are commonly used to estimate the copula parameters. In this study, a non-parametric estimation was used to calculate the parameter θ of the Archimedean copula function. The relationship between τ and θ of the copula was adopted for the estimation, as presented in Table 1. Evaluation Indicators. The goodness-of-fit of the frequency curves can be assessed using the root mean square error (RSME) and the minimum Nash–Sutcliffe efficiency (NSE). The RSME is calculated as follows: ⎡ | N |1 Σ √ RSME = MSE = √ [xc (i) − x0 (i )]2 N i=1

(23)

where MSE is the mean variance, N is the sample size, x c (i) is the frequency, x 0 (i) is the empirical frequency, and i is the sample number.

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The NSE is obtained as follows:   N 1 1 Σ 2 NSE = 1 − 2 [xc (i ) − x0 (i)] σ0 N i=1

(24)

where σ 2 is the variance of the actual observation data, and the NSE lies within the variation range of (−∞, 1). An NSE value close to 1 indicates a good result. When the NSE = 1, the calculation result of the model matches the observation.

4 Discussion 4.1 Evolution Analysis The variation rules between variations in the annual R-S, is shown in Fig. 2. In addition, the annual runoff in the basin has been decreasing gradually since the mid-1990s. A more significant reduction in annual sediment load was also noted. Variations in the R-S relationship were examined using the moving correlation coefficient method. The moving average diagram of the R-S measured at the Tongguan Station was obtained (see Fig. 2). The R-S have been decreasing. Before 2003, variations in the R-S were in phase, and their decreases were almost the same. The 7year moving average curves show similar patterns. Since 2003, the R-S have changed considerably. The 7-year moving average curves demonstrate remarkably less similar

Fig. 2 Evolution rule curve of the annual R-S

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variations. Reductions in the runoff and sediment load were no longer synchronized. The average annual sediment load continued to decrease, whereas the average annual runoff increased and decreased, and showed an increasing trend since 2003.

4.2 Mutation Analysis Trend analysis and abrupt tests of annual R-S were carried out by the Mann–Kendall test method. Figures 3 and 4 show the Mann–Kendall (M–K) plots of the annual R-S sequences. AS is observed in Fig. 3, the annual average runoff decreased significantly since 1983, and the annual runoff UF and UB curves intersected in 1987; therefore, the mutation point of the annual runoff is in 1987. The trend of the reduction exceeded the 0.05 significance level (±1.96) since 1994. As is shown in Fig. 4, the annual sediment load decreased significantly since 1966. The trend of the decrease significantly exceeded the 0.05 threshold since 1986. The annual curves between UF and UB meet in 1999. Therefore, the mutation point is in 1999. The M–K trend statistic Z and the Hurst exponent H (persistence coefficient) of the annual R-S at the Tongguan Station are presented in Table 2. The statistic Z of the annual runoff at the Tongguan Station is −4.92, as shown in Table 2, exceeding the significance threshold of 90%, that is, Z α/2 = ±1.96. This indicates that the annual runoff has been decreasing. Meanwhile, the persistence

Fig. 3 The mutation analysis by M–K test in annual runoff

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Fig. 4 The mutation analysis by M–K test in annual sediment

Table 2 Statistical table of trend and persistency coefficient Z

Trend

H

Property

Annual runoff

−4.92

Decreased

0.31

Unsustainability

Annual sediment load

−7.26

Decreased

0.85

Sustainability

coefficient H = 0.31, it means that the downward law will not be significant in the future. In contrast, the Z of the annual sediment load is −7.26, which is much higher than the significance threshold of Z α/2 = ±2.32. Furthermore, H = 0.85, indicating that the sediment load reduction is relatively persistent. Hence, the annual sediment load sequence shows a noticeable decrease, and the decreasing law is expected to continue in the future. To pinpoint the exact time when abrupt changes occurred, we used the sliding T statistic curve method to test the results, as shown in Fig. 5. Figure 5 shows the moving t curve. Given a level of significance, a = 0.01, the T-test shows that the annual runoff in 1969, 1986, 1997, and 2003 changed abruptly, whereas the annual sediment load suddenly changed in 1997 and 2004–2007. These findings demonstrate that the law of variation between annual R-S exhibits significant changes after 2003.

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Fig. 5 Statistic changing trend diagram of the overlapping average method

4.3 Diagnosis of Variation in Water–Sediment Relationship To further study the variability of the annual R-S relationship at the Tongguan Station, we used the double cumulative curve method for analysis, as shown in Fig. 6. The slope of the double cumulative curve for 1961–2003 is steeper than that for 2004– 2019, it means a remarkable change in the R-S relationship between the two time periods and that the relationship changed considerably in 2003. Although the trend of R-S sequences dropped significantly, the sediment load is less affected by runoff variations because the runoff–sediment load correction has become less significant. The moving correlation coefficient method and the double cumulative curve method provide a relatively consistent runoff–sediment load relationship variability, as shown by the moving average. The R-S relationship changed considerably in 2003. The correlation of R-S sequences before 2003 is more significant than afterwards. From the results obtained by the moving correlation coefficient method and the double cumulative curve method, the R-S load relationship exhibited a sudden and significant change in 2003. The R-S series were, therefore, divided into two phases: 1961–2003 and 2004–2019.

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Fig. 6 Double mass curve of water and sediment data in 1961–2019

4.4 Variation Feature Analysis Table 3 presents the kurtosis and skewness of the annual R-S series before and after 2003 calculated using MATLAB. The frequency histograms, kurtosis, and skewness of the R-S series before and after 2003 reveal that the skewness of R-S series are positive. This means that both series obey right-skewed distributions (in which the left tail of the probability density function is short and the right tail is a long vertex skewed to the right). The overall probability density curves before and after 2003 differ considerably. The curve is less symmetrical, and the distribution becomes flattened. The kurtosis of both series exceeds 3.0, regardless of the sudden change in 2003, indicating that the overall probability density curve is steeper than normal distributions near its peak. The same can be observed in the frequency histograms. Both curves show sharp peaks and thick tails. Table 3 Parameter statistics of water and sediment series

Time series 1961–2003 2004–2019

Skewness

Kurtosis

Runoff

0.7014

3.0958

Sediment

0.9235

3.2287

Runoff

1.1064

3.2631

Sediment

1.1970

4.3662

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It is assumed that the annual R-S series follows the Pearson type III distributions. The optimized curve fitting method was adopted to obtain the statistical parameters of the frequency distribution curves, as shown in Figs. 7 and 8.

Fig. 7 P-III frequency curve of the runoff series

Fig. 8 P-III frequency curve of the sediment load series

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Table 4 Frequency analysis of annual R-S series for 1961–2019 Eigenvalue Annual runoff

Variation Annual sediment load Variation 1961–2003 2004–2019 amplitude (%) 1961–2003 2004–2019 amplitude (%) 341.29

254.56

−25.41

10.20

1.97

CV

0.36

0.27

−25.00

0.52

0.51

−1.92

CS

0.76

1.16

52.63

0.99

0.32

−67.68

1%

746.03

510.09

−31.63

31.51

5.65

−82.07

10%

527.51

375.16

−28.88

18.77

3.65

−80.55

37.50%

367.1

273.45

−25.51

10.86

2.28

−79.01

50%

322.16

244.32

−24.16

8.97

1.91

−78.71

62.50%

281.06

217.35

−22.67

7.42

1.59

−78.57

90%

178.81

148.24

−17.10

4.41

0.84

−80.95

99%

101.11

92.46

−8.56

3.16

0.36

−88.61

Mean

−80.69

The differences between the eigenvalues of the R-S series at different frequencies before and after 2003 were analyzed and presented in Table 4. It was observed that the annual average R-S decreased considerably since 2003 as the R-S relationship changed. In particular, the runoff decreased by 25.41%. The decrease in the sediment load reached 80.69%, which is much higher than that in the runoff. Meanwhile, the variation coefficients C v of both the annual runoff and sediment load decreased. The annual sediment load fluctuated more significantly than the runoff. The skewness coefficients C s vary significantly. The skewness coefficient of the annual runoff series in 2004–2019 increased by 52.63% compared with that in 1961–2003. In contrast, the skewness coefficient of the annual sediment load decreased by 67.68%. It was discovered that after the change in the R-S relationship (2004–2019), both the runoff and sediment load decreased when 1% ≤ P ≤ 99%. At the design frequencies, the reduction in the runoff is smaller than that in the sediment load.

4.5 Water Sediment Joint Distribution Model The non-parametric estimation method was used for the frequency analysis [37]. The parameters of the two-variable, one-parameter Archimedean copula function were estimated by the relationship between the Kendall rank correlation coefficient τ and the copula function parameter θ, as presented in Table 5. The calculated Kendall rank correlation coefficients before and after 2003 are 0.413 and 0.324, respectively. Three common copula functions, Clayton, Frank, and Gumbel, were adopted for parameter analysis. In the meantime, the RSME and NSE were adopted to evaluate the goodness-of-fit of the models (Table 5). According to the RMSE and NSE in Table 5, the Clayton copula function provides the highest goodness-of-fit for the joint distribution model of the R-S series measured

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Table 5 Parameter estimates and goodness-of fit tests of copula function Copula function

Time series

Kendall correlation coefficients

RMSE

NSE

Clayton

1961–2003

0.403

0.1579

0.9901

2004–2019

0.367

0.2205

0.9478

Frank

1961–2003

0.431

0.2095

0.9826

2004–2019

0.367

0.2426

0.9368

1961–2003

0.431

0.2363

0.9779

2004–2019

0.367

0.2700

0.9216

Gumbel

at the Tongguan Station before and after 2003. The values of the parameter θ are 1.403 before 2003, and 0.939 after 2003. The results are shown in the following equations: In 1961–2003:  −1/1.403 H (x, y) = F(x)−1.403 + F(y)−1.403 − 1 .

(25)

In 2004–2019:  −1/0.939 H (x, y) = F(x)−0.939 + F(y)−0.939 − 1 .

(26)

The Archimedean copula function was applied to construct joint distribution models of the R-S series at the station in 1961–2003 and 2004–2019. Episodes with high and low R-S series were divided by the frequency, as presented in Table 6. The criteria adopted were pr = 37.5% and pp = 62.5%. The dividing standard of wet, normal, and dry years in the Tongguan Hydrological Station are listed in Table 7. The years with high and low R-S series at the station in 1961–2003 based on the copula function differ significantly from those in 2004–2019. This also validates the reliability of the copula-based method to determine variations in the annual R-S series. Table 6 Dividing standard of high normal and low years

Runoff for high year

Sediment load for high Sediment load for normal Sediment load for low year year year       P X ≥ x pr ; Y ≥ y pp P X ≥ x pr ; y pp ≤ Y ≤ y pr P X ≥ x pp\r ; Y ≥ y pp

Runoff for P normal year Runoff for low year

x pp ≤X ≤x pr ; Y ≥y pp

P

x pp ≤X ≤x pr ; y pp ≤Y ≥y pr

P

x pp ≤X ≤x pr ; Y ≤y pp

      P X ≤ x pp ; Y ≥ y pr P X ≤ x pp ; y pp ≤ Y ≤ y pr P X ≤ x pp ; Y ≤ y pp

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Table 7 Dividing standard of wet, normal, and dry years at the Tongguan Hydrological Station Runoff and sediment series

Probability 37.5% ≤ P ≤ 62.5%

P < 37.5%

Runoff (low)

Sediment (low)

Runoff (normal)

Sediment (normal)

Runoff (high)

Sediment (high)

1961–2002

≤281.06

≤7.42

(281.06–367.1)

(7.42–10.86)

≥367.1

≥10.86

2003–2019

≤217.35

≤1.59

(217.35–273.45)

(1.59–2.28)

≥273.45

≥2.28

P > 62.5%

4.6 Probability of Encountering Synchronous Variations Table 8 presents the profitable frequency of wetness-dryness encountered before and after 2003 as the R-S series relationship changes. (1) The probability of encountering synchronous variations in the R-S series was lower than that of having asynchronous variations during all the periods. In particular, for synchronous variations, a low runoff is often accompanied by a low sediment load regardless of variations in the R-S series relationship. For asynchronous variations, the runoff was more frequently unchanged, whereas the sediment load increased regardless of variations in the R-S series relationship. (2) When asynchronous variations occur in the river basin, it is equally likely to have a high runoff with a low sediment load and a low runoff with a high sediment load owing to the symmetry of the copula function. Similarly, it is equally likely to have an unchanged runoff with a low sediment load and an unchanged sediment load with a low runoff. The probability of having an unchanged sediment load Table 8 The profitable frequency of wetness-dryness encountered at the Tongguan Hydrological Station Type

The combination

Frequency (%) 1961–2003

Same frequency

Different frequency

2004–2019

High flow and sediment year

16.67

11.76

Normal flow and sediment year

7.14

5.88

Low flow and sediment year

21.43

29.42

Total

45.24

47.06

High flow and normal sediment year

14.29

11.76

High flow and low sediment year

0

0

Normal flow and high sediment year

19.05

23.54

Normal flow and low sediment year

11.9

11.76

Low flow and high sediment year

2.38

5.88

Low flow and normal sediment year

7.14

0

Total

54.76

52.94

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Fig. 9 Contour maps of the runoff-sediment joint distribution at different return periods in 1961– 2003

with a high runoff is comparable to that of having an unchanged runoff with a high sediment load. Although the probability of having a high runoff but a low sediment load or having a low runoff but a high sediment load is the smallest, there is a strong frequency correlation between the two. Hence, completely different variations are least likely. (3) After the runoff–sediment load relationship changed (2004–2019), the probability of synchronous variations was 47.06%. It increased by 1.82% compared with 1961–2003 (45.24%). Meanwhile, the probability of having asynchronous variations was smaller (52.94%). The probability distributions of all combinations remain essentially the same. The probabilities of encountering a low runoff and a sediment load, having a low runoff but a high sediment load, and having an unchanged runoff with a high sediment load increase, whereas those of encountering other combinations decrease. Figures 9 and 10 show the strain contour of the joint distribution of the R-S series in 1961–2003 and 2004–2019, respectively with different recurrence rates. The annual R-S series at the Tongguan Station in 1961–2003 and 2004–2019 changed significantly. Given the same probability, the runoff and the sediment loads in 2004– 2019 were smaller than those before the variation. As shown in Figs. 9 and 10, the probability of encountering different combinations of runoff and sediment load variations at the station under any recurrence rate can be obtained, and the probability of encountering an annual R-S series of different magnitudes can be estimated. Future water and sediment load entering downstream channels can be determined. This

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Fig. 10 Contour maps of the runoff-sediment joint distribution at different return periods in 2004– 2019

work provides guidance for R-S control and monitoring, as well as flood control and mitigation in the lower reaches of the Yellow River.

5 Conclusions It is meaningful to muster the variation discipline of R-S under rapid environmental changes. This enables further grasping of the evolvement mechanisms of runoff and sediment yield. So, this study determined the variation trends of annual RS series at the Tongguan Station in 1961–2019. Joint distribution models of RS relationship changes were constructed based on the copula function to examine the probabilities of having different combinations of high and low R-S discharge episodes. The conclusions go as follows: (1) Both the runoff and sediment load entering the lower reaches of the Yellow River through the Tongguan Station have been decreasing. The decrease in the two load types was similar before 2003, and the variation patterns changed dramatically after 2003. The sediment load decreased, whereas the runoff fluctuated considerably. (2) The moving correlation coefficient and the double cumulative curve method reveal that the R-S relationship at the Tongguan Station changed remarkably in 2003. The double accumulation curves of the R-S in 1960–2003 and 2004–2019 differ considerably. When P < 90%, both the runoff and sediment load decreased,

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and the decrease in the sediment load was more obvious. This further confirms that the runoff–sediment load relationship changed abruptly. The decrease noted in the sediment load series was more significant. (3) The probability analysis shows that the probability of encountering complete opposite variations in the R-S series for all time periods was the smallest. In addition, the probability of synchronous variations was lower than that of encountering asynchronous variations. In the period 1961–2003, the probability of synchronously unchanged R-S was the highest, followed by that of having synchronously high R-S series. In the period 2004–2019, the probability of synchronous unchanged runoff and sediment load was the highest, followed by that of encountering synchronously low R-S series. Moreover, the probability distributions of the different combinations of variations were relatively uniform. (4) The copula function was adopted to establish a joint distribution model of the RS series obtained at the Tongguan Station. This allows us to further understand the R-S relationship at the downstream channels and provide guidance for R-S control as well as flood prevention and mitigation in the multi-sand river. Acknowledgements This research was financially supported by the National Natural Science Foundation of China (Grant No. 51879115, 42041004, 52009140), the Natural Science Youth Foundation of Henan Province (Grant No. 222300420235). Raw data supporting the conclusion of this article will be made available by the authors, without undue reservation. Declaration No conflict of interest exits in the submission of this paper, the work described was original research that has not been published previously, and paper is approved by all authors for publication.

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Feasibility Study on Air Film Rainwater Collection and Treatment Systems in Extreme Rainfalls Weather Shaohua Hu

Abstract Given the frequency of extreme rainfall events, the capacity of rainwater collection and treatment systems must be improved urgently to cope with extreme and heavy rainfall in the future. Due to the current rainwater collection and treatment systems (CRCTSs) have had difficulty coping with extreme precipitation in a short time for their ability lack of elasticity and low mobility character. This article discussed the possibility of a new system for collecting and treating rainwater by air film (AFRCTs) to cope with extreme rainfall. The new system should have features like current systems, and the feasibility of queries like mobility, respond quickly and effectively, flexible capacity and less negative impacts should also be analyzed. Results demonstrated that against extreme rainfalls was urgent that initiative should be placed in the core position of study, a new system should focus on the target of exceeding rainfalls during extreme rainfalls and less negative impacts also should be obeyed. Additionally, countermeasures were explored for possible disadvantages of new systems, such as safety, capacity pressure, deviation, practical experience and costs. The Conclusion section exhibited that new systems not only can greatly improve processing capacity of extreme rainfalls, but also existing potential multiple benefits, such as irrigation, Green Power Generation and air carbon capture. Keywords Air film · Extreme rainfalls · Rainwater collection · Processing system · Feasibility analysis · Plastic

S. Hu (B) School of Economics and Management, Hubei University of Science and Technology, Xianning 437000, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_12

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1 Introduction 1.1 Extreme Rainfalls Become Difficult to Cope with In 2021, from July 17 to July 20, Zhengzhou, the capital city of Henan Province in China, recorded a historic high of 617.1 mm in the process of rainfalls, and the rain falls in three days is equivalent to the total rainfall in a year. In Germany, from July 13 to 18, 150 mm of rain fell in its affected areas in 24 h, which was the total rainfall of more than a month in a single day. Additionally, on July 24, the Maharashtra of India suffered extreme rainfalls, which reached the highest in the same month in 40 years. On July 25, London was hit by extremely heavy rainfall, eight subway stations of London had to be closed. On July 28, floods caused by heavy rainfalls in Islamabad flooded the streets and washed away cars on the streets. The extreme rainfalls in July, which had large coverage and historical rainfalls that hit several countries located in Eastern Asia, Central Asia and Western Europe. The current urban rainwater collection and treatment systems (CRCTSs) in above mentioned countries are powerless. Evidently, if there is no system, which has the ability to solve the high rainwater, extreme rainfalls weather will become a major security problem in the future.

1.2 The Ability of Rainwater Treatment and Collection Need Increasing Rainwater collecting and treating abilities have been increasing throughout human history, in 2,000 BC, middle-class families in the Middle East were able to build a system for rainwater collection and utilization [1, 2]. The United States has developed rainwater infiltration technology to restore the natural rainwater [3]. Japan also designed and implemented the rainwater retention and infiltration plan [4]. Germany integrated a series of functions of collection, storage, interception, infiltration and ecological as a system for rainwater collection and treatment [5]. Nevertheless, so many countries like China, Germany, Belgium, The Netherlands, Austria, India and Pakistan suffered heavy loss of human and property due to the extreme rainfalls in July 2021, it means current rainfall collecting and treating systems (CRCTSs) can’t correspondent to extreme rainfalls. With the possibility of extreme rainfalls greatly increasing in the future, designing a new system for increasing the ability of rainwater collection and treatment against extreme rainfalls is the key need.

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1.3 Air Film Building Air film is a new product integrating architecture, structural mechanics, new materials, automatic control and mature practical technical. It is formed by special building fiber membrane materials, which are used as the “shell” that is supported by automatic pressurization and fresh air system [6]. A facility made by air film can meet temporary placement needs, such as entertainment, industry, workshops, research facilities for agriculture, commerce and temporary production. A series of advantages of an air film facility can be concluded that fast construction and easy movement [7], strong plasticity [8], low cost [9], shape variability [10, 11], especially suitable for long-span and large-span place needs [12]. While before inflation, the air film volume limits for convenient storage, the safety of the air film installation after inflation is high as the fall to earth will not be quick but slow. With the above advantages to meet the failures of CRCTSs in coping with extreme rainfall is the objection of this manuscript. Hence, analyzing the feasibility of air film rainwater collection and treatment collection system is necessary.

2 Methods 2.1 Study Methodology The methodology used in this study is based on air film advantages in response to the failure of CRCTSs facing extreme rainfalls that developed under the plan “The current rainwater collection and treatment systems’ failure-meeting map for extreme rainfalls (Fig. 1). The methodology plan is based on strategy and the results from previous air film facility related study.

Fig. 1 The current rainwater collection and treatment systems’ failure-meeting map for extreme rainfalls

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2.2 Failure of Current Rainwater System Identification In July 2021, the short-term extreme rainfalls of the mentioned countries exceeded the historical extreme rainfall values was the main reason. The rainfalls in China, Pakistan, India, Germany and the UK was too high that exceeding the upper limit of CRCTSs’ capacity, which was the important reason for the flood. Huge amount of precipitation brought by extreme rainfalls made the pressure of CRCTSs in above mentioned countries suddenly increasing that the risks of flood, debris flow, and other secondary disasters rise sharply. Thus, unable to deal with the excess rainfalls is the failure of CRCTSs that we have to face and focus on in the future.

2.3 Scheme Option Decision and Problem Analysis According to the above analysis of the cause of recent floods in the above countries, limited capacity of CRCTSs can’t correspond to excess rainfalls. How to deal with excess rainfalls is the key point, the following two scheme options should be considered. Scheme one, increasing the capacity of CRCTSs. While enhancing the capacity of CRCTSs, it is difficult to make sure the amount of capacity should be increased. Nevertheless, advanced CRCTSs in Western Europe are also unable to deal with extreme rainfalls. Additionally, for improving CRCTSs, much time and money should be invested, but the result is likely to be unsatisfactory. Thus, enhancing the capacity of CRCTSs is not feasible. Scheme two, designing a new system to focus on the exceeding rainfalls. For the sake of dealing with exceeding rainfalls a new system as a supplement to CRCTSs is necessary to be designed. Because not focus on all but exceed rainfalls in extreme rainfalls, Scheme two is more feasible than Scheme one. However, the new system will have to face problems like CRCTSs in extreme rainfalls as follow: Problem one, lack of mobility. Once CRCTSs are completed, most of them are basically fixed in the located area where their rainwater collection and treatment range are fixed. Obviously, it is hard to take account of areas far from CRCTSs with weak or no rainwater collection and treatment capacity in extreme rainfalls, which are prone to flood disasters caused by downstream. Mobility should be considered in designing the new system. Problem two, CRCTSs are unable to allocate capacity effectively and rapidly. With lack of mobility, most CRCTSs are difficult to change locations allocating their capacity rationally that capacity is unable to be configured effectively between areas, most importantly the amount of rainwater in extreme rainfalls can increase rapidly in a very short time. If the new system like CRCTSs lacks the ability to allocate capacity effectively and rapidly, huge downstream from extreme rainfalls will occur quickly.

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Problem three, rainwater processing capacity is limited and not flexible. Based on the huge rainwater from extreme rainfalls, the ability of CRCTSs against extreme rainfalls is mainly based on their maximal rainwater processing capacity. If the local capacity condition can’t cope with the huge amount of rainwater, then it is necessary to take measures for changing the capacity flexibly that corresponds to extreme rainfalls within the area, otherwise, flood disaster will be inevitable. Obviously, the flexible rainwater processing capacity for focusing on excessive rainwater is the key point of adapting to extreme rainfalls, it should be considered in studying new systems. Problem four, avoiding negative impacts on nature or society is necessary. With the aim of avoiding intervening in nature and harm or inconvenience to public or private interests [13, 14], a series of laws and regulations should be obeyed. While CRCTS is improving, its construction on the ground is hard to avoid negative impacts in land use. Now turning to the new system, it is the same that a set of negative impact assessments should be investigated to ensure moderately suitable for nature or society, especially in land use [15].

2.4 Query Analysis Based on the above problems analysis, if designing a new system against extreme rainfalls, the queries that CRCTSs need improving should be answered. An overview of the decision tree which was tested in the study is presented in Fig. 2. Combining with above two schemes analysis, it has been found that most CRCTSs are unable to deal with extreme rainfalls mainly due to the problems of mobility, rapid and effective response, capacity flexibility and negative impacts on nature or society. Based on the above all, the following five queries were identified: Q1: Does the new system have the functions of CRCTSs? Q2: Does the new system have mobility? Q3: Does the new system respond rapidly and effectively? Q4: Does the new system have a flexible capacity? Q5: Does the new system have any negative impact on nature and society?

Fig. 2 A decision tree for identifying the need for and feasibility of implementing a new system against extreme rainfalls

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2.5 Feasibility Analysis Queries’ Feasibility Analysis. On the basis of the characteristics of air film, this paper highlights the idea of establishing a rainwater collection and treatment system. The new system, air film rainwater treatment and collection systems (AFRTCS), with the help of air film characteristics can moderately meet the queries in functions, mobility, rapidly and effectively respond, flexible capacity and less negative impact on nature or society. Feasibility analysis of the above aspects can be identified as follow: Rainwater Collection and Treatment Functions’ Feasibility Analysis. The main material of AFRTCS is PVC, which is woven into the fabric substrate by fiber. Its characteristics of high strength and good flexibility that made air film facilities can be shaped into various shapes, based on the functions of CRCTSs like collection, storage, discharge and transmission to produce AFRTCSs’ facilities for the same functions is feasible and should be considered. Mobile Feasibility Analysis. Before Air film rainwater collection and treatment systems (AFRCTSs) inflation, with the small occupation and lightweight characteristic, AFRCTSs will become very convenient in moving that rainwater collection and treatment functions won’t be tied in a fixed and limited range. It means mobility contributes to AFRTCs’ active response to extreme rainfalls, now even far away from CRCTSs’ areas may have the ability of rainwater collection and treatment to cope with extreme rainfalls. Obviously, unlike passive CRCTSs against extreme rainfalls, with active mobility establishing AFRCTSs in no CRCTSs areas, the coverage of rainwater collection and treatment will be enlarged. Mobility should be investigated in AFRCTSs further study. Feasibility Analysis for Rapidly and Effectively Response. While AFRCTSs can actively against extreme rainfalls in areas, huge rainwater falls so quickly in extreme water that rapidly and effectively respond is important. If inflation made AFRTCS can fly in the sky [16, 17], we can send it to the area for the sake of focusing on exceeding extreme rainfalls rapidly, and according to allocating capacity effectively and rapidly, AFRTCS will become more powerful in facing extreme rainfalls. Feasibility Analysis for Flexible Capacity. Even though AFRTCS become a response to extreme rainfalls initiatives and positive with mobile, rapid and effective configuration, unable to accurately predict extreme rainfalls is still a challenge that the exceed rainfalls for AFRTCS focusing on also become a difficulty. Based on the extreme rainfalls changing AFRTCS capacity are necessary and indispensable, the total amount of capacity is based on the number of AFRCTSs that we can simply change the amount plus or delete AFRCTSs number. In practice, with inflation and floating characters, establishing a number of AFRCTSs fly in the sky in advance, once the current capacity is unable to burden extreme rainfalls, adopting the capacity of AFRCTSs in advance to cope with exceed rainfalls is rapidly that construction time will be greatly reduced. Feasibility Analysis for Less Negative Impact on Nature and Society. Above feasibility analysis demonstrated that AFRCTSs is a temporary facility that installing and

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uninstalling is easy. With the very small volume character that the storage occupation is limited after exhausting gas, compared with CRCTSs on the ground, it will generate less pollutants and wastes during floating in the sky. Based on these, AFRCTSs can better avoid negative impacts on nature and society.

3 Results 3.1 Assessment Although the goal of the air film rainwater collection and treatment system is to collect and treat excess precipitation, it is also important to evaluate and compare it with the current rainwater collection and treatment system. Based on the characters of the air film, the evaluation compares the five queries’ feasibility of the two different systems in collecting and treating extreme rainfall and gives appropriate ratings (see Table 1). The assessment criteria are affirmation (+) or negation (0). The above table shows that among the five needs to be analyzed, the CRCTSs are one less functional need than the AFRCTSs, but with the advantages of the air film characters, the AFRCTSs is feasible to the five needs in extreme rainfall. In addition to the need for functional feasibility, the CRCTSs is not afraid of the feasibility of the other four needs, including mobility, rapidly and effectively respond, flexible capacity and less negative impact. To sum up, the air film rainwater collection and treatment system constructed with the characteristics of air film creatively solves a series of needs faced by the current system when dealing with extreme rainfall, which can also be the biggest uniqueness of this study. Table 1 The assessment of the two different systems shows the assessed feasibility of the five queries under extreme rainfall events Assessment system

Assessment index

Functions

Mobility

Rapid and effective response

Flexible capacity

Less negative impact

CRCTSs

Needs for analysis

0

+

+

+

+

Feasibility

+

0

0

0

0

Needs for analysis

+

+

+

+

+

Feasibility

+

+

+

+

+

AFRCTSs

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3.2 Response to Extreme Rainfalls is Urgent AFRCTSs are not instead CRCTSs but deal with the exceeded rainfalls that CRCTSs are unable to bear under extreme rainfall events. Based on this idea, not only a series of feasibility analysis above are indispensable, but also focusing on exceeding rainfalls to alleviate the capacity pressure of CRCTSs for achieving the purpose of comping with historical extreme rainfalls is more significant. It means only the areas of low capacity and large amount of rainwater are the objective areas that should be cared by AFRCTSs, based on this target to carry out will make AFRCTSs become more efficient in coping with extreme rainfalls. However, extreme rainfalls’ intensity and frequency are likely to continue increasing before global climate condition gets better and coping with extreme rainfalls constantly refreshing the historical extreme value is indeed urgent and necessary.

3.3 Focus on the Exceed Rainfalls is the Target With much human-caused global warming would occur over the coming century [18], we have to keep the increase in global average temperature to well below 2°C above pre-industrial levels and to pursue efforts to limit the temperature increase to 1. 5°Cabove pre-industrial levels [19]. In this situation, extreme rainfalls’ intensity and frequency are likely to continue increasing before global climate condition get better. At present, coping with extreme rainfalls of constantly refreshing the historical extreme value is indeed urgent and necessary.

3.4 The Initiative is the Study Core From ancient times to the present, the honor brought by the technology to deal with natural disasters will become a memory of the past. Because human beings who have entered modern civilization through long-term and arduous struggles will have to face the threat of global extreme climate. In order to prevent human civilization from floating and sinking with the waves in the extreme climate, we should persist in dealing with the global extreme climate no matter how hard it is, just for those loved ones, our country and nation. If initiative exists in our study, hope will exist forever. The initiative that responds to extreme rainfalls positively is the core of AFRCTSs study. Under the initiative leading, a series of functions like moving, flying and shaping can be taken into consideration, superiors of mobility, capacity, rapidly, effectively, efficiently are the best embodiment of initiative. With the above superiors, AFRCTSs will possess the benefits such as establishing AFRCTSs in the sky in advance for capacity need, fast moving to the areas needing enhancing capacity and adjusting capacity actively in a wide range. The initiative as a core contributes to

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us the possibility that transforming current passive CRCTSs to active AFRCTSs, it also demonstrated oppositely that lack or no initiative was the critical reason for CRCTSs unable to cope with huge rainwater from extreme rainfalls, and as a result of deadly floods and landslides. As the core, the degree of initiative decides suffering or well in extreme rainfalls in the future.

3.5 Ecological Environmental Protection as the Premise As a symptom of extreme precipitation event, the root of extreme rainfalls is the global extreme climate caused by the deterioration of the global ecological environment. Only by the way of protecting and improving the ecological environment, can we eliminate the breeding soil for extreme rainfalls. It’s necessary to abide by the ecological environment laws and regulations for avoiding negative nature impacts, such as minimizing the impact on the ecosystem when establishing facilities against extreme rainfalls, with floating, mobility and small storage characters that AFRCTSs is a fine option for protecting ecological environment. What’s more, pollutants and wastes that may be produced in the process of AFRCTSs making or working also should be taken into consideration. Plastic, as the main material source of AFRCTSs, is an important pollutant. Thus, reprocessing recycled waste plastics as material of AFRCTSs is not only helpful against extreme rainfalls, but also benefits for reducing plastic pollutants. Different from establishing CRCTSs caused a great construction waste polluting environment, with inflation installing or exhausting gas uninstalling and storage made AFRCTSs have no pollutants left.

4 Discussion 4.1 Possible Difficulties in Realizing AFRCTSs Under practical extreme rainfalls condition, there may be difficulties in realizing AFRCTSs that should be identified as follow: Safety. The exceed rainfalls for AFRCTSs focusing on may be increasing so rapidly that the weight of AFRCTSs becomes very heavy in a short time. Once this case occurs, because floating in the sky, and the collected rainwater is unable to discharge quickly, AFRCTSs will fall and cause a safety accident. How to deal with such a large amount of rainwater quickly for avoiding safety accidents should be taken into consideration. Capacity Pressure. With excess rainfalls increasing rapidly the capacity pressure of AFRCTSs will rise quickly. It means capacity of AFRCTSs not only should be flexible, but also can change greatly in a very short time, otherwise capacity pressure

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from exceeding rainfalls will make AFRCTSs can’t realizing the goal of collecting and treating enough exceed rainfall. Once the above situation occurs, the probability of flood will rise rapidly, and it is very likely to cause ecological disasters and loss of personnel and public property. Taking measures to alleviate the capacity pressure of AFRCTSs should be analyzed. Deviation. Thunderstorms and waves caused by extreme precipitation can cause facilities in the air to leave the predetermined zone, measures should be taken to avoid the deviation, otherwise, AFRCTSs will be out of the range and control. Based on the deviation case, predetermined areas will have to burden more pressure caused by extra rainfalls that their CRCTSs are unable to cope with. Obviously, the result of deviation is catastrophic and should explore countermeasures. Practical. As a new system response to extreme rainfalls, AFRCTSs lacks practical application experience in extreme rainfalls weather. The research in this new field will have to face many uncertain difficulties. Only by practical experience, unpredictable difficulties can be located and studied. What’s more, with more practical experience getting, AFRCTSs will become more perfect in practice, and the ability to deal with extreme rainfalls can hold in our hands.

4.2 Countermeasures for Possible Difficulties Countermeasure for Safety. For the sake of avoiding AFRCTSs falling on land, countermeasures for safety should be studied from bearing capacity, rainwater and discharging. Analysis can be identified as follow: First, for the sake of keeping bearing capacity within the safe range as far as possible, calculating different shapes of air film bearing capacity is necessary, choosing high bearing capacity shapes as alternatives of AFRCTSs facilities shape will contribute to the AFRCTSs greater safety factor. Moreover, AFRCTSs facilities don’t crash, but slowly falling on land will greatly decrease damage, based on this idea, adopting high bearing capacity shapes to design air film additives that afford bearing capacity quickly and installed on AFRCTSs in the case should be studied. Secondly, the design of an air-film drainage system to discharge rainwater can also reduce the weight of AFRCSs in a safe range. With the air film character of long-distance and long-span, air film drainage can transform the rainwater out of safe range to the locations that are unaffected or less affected by extreme rainfalls, such as reservoirs, wetlands, lakes, swamps and other places with strong water storage ability. Moreover, air film drainage can float in the sky also should be considered, because as an independent drainage system floating in the sky, it will not only contribute to establishing fast enough to transform rainwater to the far areas where urban drainage is hard to reach, but also can effectively reduce the pressure of urban drainage systems, and it has high safety to nature and society on the ground. Countermeasure for Capacity Pressure. AFRCTSs focusing on the rainwater is also huge and increasing rapidly that AFRCTSs should greatly and rapidly raise capacity, which is decided by the number of AFRCTSs. Under this concept, spaces

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for many AFRCTSs affording enough capacity should be supplied at first, and then they must be established in a very short time. Unlike CRCTSs on the ground, the air is three-dimensional, not two-dimensional, there is much space for many AFRCTSs floating in the sky to occupy, and then we can establish them in advance for preparing enhance capacity during extreme rainfalls. With the character of floating, we can send AFRCTSs facilities to the places in a short time, according to this way increasing or decreasing capacity between different regions that artificially and actively dispersing or accumulating the capacity in a wide range is practicable. What’s more, in the aim of maximizing the use of limited space, dynamic deployment behavior and self-erection for accommodating more AFRCTSs floating in the sky also should be studied [20]. Once successful, limited spaces will supply more capacity, because before dynamic deployment behavior and self-erection, the volume of AFRCTSs is limited, and AFRCTSs will become more powerful against extreme rainfalls. Countermeasure for Deviation. On the premise of the safe capacity bearing, AFRCTSs can be stabilized by the gravity itself and collected rainwater. Additionally, air film has the character of resisting strong winds, which is helpful for fixing AFRCTSs [21]. Moreover, with the shape variability character designing air film shapes that can resist winds and rainfalls, such as rotating shape that by the rotation eliminating the influence of wind or rain to avoid deviation. At last, connecting AFRCTSs with the firm ground attachment for avoiding deviation is also a feasible countermeasure. Countermeasure for Practical Experience. A series indicator of AFRCTSs lacks practical experience, such as shape, size, wind resistance, bearing level, and many uncertain difficulties. Getting the above information is quite difficult under extreme rainfalls weather, countermeasures were identified as follows: First, based on the intensity of rainfalls identifying rainfalls as weak, medium, high and extreme levels. Experiments for obtaining practical experience can be exercised from easiest weak rainfalls, and then to easier medium rainfalls, then to harder high rainfalls, then to extreme rainfalls finally. With information from the above experiments establishes a model that can explain the relationships between rainfall intensities and AFRCTSs indicators. Second, with the model to predict the above indicators in extreme rainfalls weather, according to the results from model to establishing and testing AFRCTSs. Based on testing indicators in extreme rainfalls, we can improve indicators of AFRCTSs for the sake of practical adaptability. Finally, with extreme rainfalls practical experience continuously accumulated, such as processing rainfalls, wave speed and other uncertain difficulties information under extreme rainfalls weather. Based on the provided practical experience increasing, from a solid foundation for improving the design of AFRCTSs is available in the future, and then not only the AFRCTSs is more adaptive in dealing with extreme rainfalls, but also beneficial to the improvement of ability for extreme rainfalls collection and treatment.

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It is believed that one day just only obtaining general situation of extreme rainfalls, a corresponding AFRCTSs plan can be quickly established for avoiding the negative impact or secondary disasters from extreme rainfalls. Countermeasure for Costs. In the context of the new coronavirus spreading the global world, it will undoubtedly make things worse to global financial systems for investing large amounts of money against extreme rainfalls. Based on the low costs, taking recycled plastics as the main material to establish AFRCTSs for reducing costs is particularly necessary. Unlike CRCTSs need high expenses for maintenance and repairing, costs for AFRCTSs are less of the air film characters of convenient storage, cheaper maintenance skills and reusing many times. Minimizing costs in designing AFRCTSs is significant for the tight finance countries all over the world.

5 Conclusion Based on the five problems faced by the current rainwater collection and treatment systems in dealing with extreme rainfall, this study developed a rainwater collection and treatment feasibility system made by air film. The purpose of the design system is to achieve rapid and effective response to extreme rainfall through the new rainwater collection and treatment system’s mobility and flexibility capacity. The above is the main conclusion of this paper. The ability for extreme rainfalls collection and treatment needs analysis results demonstrated that it is necessary to effectively against extreme rainfalls across large landscape areas. The study identified air film facility characteristics that contribute to faster construction, easy movement, strong plasticity, convenient storage, low cost and can be shaped into various shapes. In addition, a series of long-span and longspan large air film facilities may be able to discharge large amounts of rainfalls far away that avoiding extreme rainfalls damage. This valuable opportunity should continue investigating in practical studies. After the identification failure of CRCTSs and a scheme decision assessment, a new system, AFRCTSs, was developed to deal with five problems in the incompetence of CRCTSs in extreme rainfalls. The scheme decision analysis concluded that focusing on excess rainfalls is the key for establishing AFRCTSs, it can be also challenging to build AFRCTSs for not violating the laws and regulations of nature and society. Based on the five problems of CRCTSs, the feasibility study of queries was performed in five analyses to demonstrate the assessment scheme. As well as queries feasibility studies like active, rapidly respond and floating in the sky were also key considerations for choosing appropriate measures in the analysis. The queries analysis results demonstrated against extreme rainfalls are urgent and that initiative should be placed in the core position of study. The target was concluded that AFRCTSs should focus on the exceeding rainfalls of CRCTSs are unable to bear. Analysis of environmental protection materials for building AFRCTSs is also considered, and it is concluded that while taking plastics as a material for

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the AFRCTSs building, reprocessing recycled waste plastics and reducing plastic pollutants should be also undertaken. The results also concluded that the material cost is also an important assessment criterion for AFRCTSs building. Following this study, countermeasures for possible difficulties in AFRCTSs should be considered. It is recommended that high bearing capacity shape, additives and air film drainage should be considered in AFRCTSs designing for reducing the risk of falling, and then the gravity of AFRCTSs facilities and collected rainwater used for avoiding deviation should be studied, in addition, connecting AFRCTSs with the firm ground attachments for avoiding deviation was also considered. Based on the volume of inactive AFRCTSs being very limited, another recommendation for enhancing capacity against exceeding rainfalls pressure was greatly increasing the numbers of AFRCTSs in the air in advance. Finally, starting a step by step project to gain practical experience with AFRCTSs in different intensities of rainfalls is also needed. It is concluded that AFRCTSs could be a capacity-effective and environmentally friendly extreme rainfalls risk reduction measure with multiple potential benefits, such as irrigation, Green Power Generation and air carbon capture. These benefits should be further investigated to identify the role that AFRCTSs in the integrated process of turning extreme rainfalls into benefits. In addition, the cheap material and operating costs also should be concerned in AFRCTSs designing. While the study of AFRCTSs is not collecting and treating all but exceed extreme rainfalls, it is expected that providing a sustainable development path to deal with unpredictable and increasingly severe extreme climate change in the future.

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Rainfall-Runoff Modelling in the Kouilou-Niari Catchment Area in South-West of Congo-Brazzaville Christian Tathy, Christian Ngoma Mvoundou, Romain Richard Nière, and Harmel Obami-Ondon

Abstract Located in the south-west of the Republic of Congo, and endowed with important agricultural and industrial activities, the Kouilou-Niari catchment area is characterized by the closure of all its hydrological stations since 1983, in spite of its increasing vulnerability to climatic hazards. Thus, this work is the first approach to rainfall-runoff modelling in this watershed in order to contribute to the management of its water resources. The objective is to calibrate and validate the GR2M rainfall-runoff hydrological model with monthly time steps and two parameters, at one hydrological station (Sounda station) closest to its outlet over the period 1952– 1953. Subsequently, a projection of the discharge simulations is made for the period 1983–2013 when no hydrological data are available. Finally, some estimates of the water levels are obtained through the determination of a rating curve at the Sounda station, and then they are compared to the altimetric heights extracted from the ENVISAT mission over the period 1995–2010. The results obtained show that the calibration and validation of the GR2M model at the Sounda station are satisfactory, with a Nash criterion of 80.5% in validation. The similarity of the projected water level variations with the altimetry heights seems satisfactory. Keywords Kouilou-Niari watershed · Rainfall-Runoff modelling · GR2M model · Projected flows · Rating curve · Satellite altimetry water heights

C. Tathy (B) · C. Ngoma Mvoundou · R. R. Nière · H. Obami-Ondon Laboratory of Mechanics, Energetics and Engineering, Ecole Nationale Supérieure Polytechnique, Université Marien Ngouabi, B.P. 69, Brazzaville, Congo e-mail: [email protected] C. Ngoma Mvoundou e-mail: [email protected] R. R. Nière e-mail: [email protected] H. Obami-Ondon e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_13

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1 Introduction The Kouilou-Niari watershed is located in the south-west of the Republic of Congo. It covers over a sixth of the country’s area and shelters nearly 19.5% of the national population according to the last general population census organized by the National Institute of Statistics in 2007. This watershed is one of the most active economic zones in Congo, hosting numerous industrial, forestry, and agricultural activities. Unfortunately, it is vulnerable to numerous climatic hazards, such as floods and low water levels, which have caused a lot of harm recently, especially to agriculture. Faced with this situation, the administrative authorities are not in a position to provide satisfactory solutions to the population, although agriculture has been retained as a major axis of economic development in the various strategic development plans since the 1980s. The absence of operational hydrological stations throughout the watershed area since 1983 is undoubtedly one of the factors contributing to the difficulty in proposing effective assistance from the population in the field of water resource management. Indeed, since the last studies carried out on this watershed by French electricity (EDF) in 1958 [1] on the project to develop a hydroelectric dam in the Sounda gorges, and the various works carried out by ORSTOM (former IRD) [2–6], the hydrological functioning of the Kouilou-Niari watershed has not been studied for more than 30 years, and no rainfall-runoff modelling has ever been carried out there. However, several studies conducted in tropical regions and in North Africa have shown the efficiency of the simple model with a monthly time step and two GR2M parameters in proposing satisfactory rainfall-runoff models. We can name among them Kouassi [7] in the N’zi Bandama watershed in Ivory Coast, Ouédraogo [8] in the Iradougou watershed in Niger, Ardoin [9] in the Sassandra watershed in Ivory Coast, Amiar, Bouanani and Baba-Hamed [10] in the Oued Touil watershed in Algeria and Bouanani, Baba-Hamed and Bouanani [11] in the Oued Sikkak watershed in Algeria. Therefore, due to a lack of recent hydrological data, this work aimed to propose a calibration and validation of the GR2M model at Sounda station over the period from 1952 to 1982 of availability of in situ hydrological data, then to compare through a rating curve, the projections of water heights obtained thanks to GR2M and the projected discharge, with the satellite altimetry measurements extracted from the data of the Environmental Satellite mission (ENVISAT) on a virtual station near Sounda, between the years 1995 and 2010.

2 Data and Methods 2.1 Study Area The study area is the Kouilou-Niari watershed, located in the south-western part of the Republic of Congo Brazzaville (Figs. 1 and 2), between 2° and 5° latitude south

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Fig. 1 Map of the Kouilou-Niari watershed

and 11°45 and 15° longitude east, with a surface area of about 60,000 km2 out of the 342,000 km2 of the country. Its main watercourse, the Kouilou-Niari River, is 690 km long and changes name according to three different zones: N’douo in the Batékés Plateaux (upper course: northern zone), Niari (middle course: central zone) in the Niari valley where it is reinforced by its tributaries: the Loukouni, Comba, Loudima, Louessé and Bouenza. Crossing the Mayombe massif through a succession of narrow gorges in its lower course (southern zone), the river becomes the Kouilou, a name it retains until its mouth on the Atlantic Ocean. The watershed occupies nearly 17% of the country’s surface area, and the river’s flow reaches an average of 930 m3 /s at the Sounda hydrological station near its outlet. Thus, the study area has three distinct hydrological sectors (sub-basins): in the north, the N’douo sub-basin (upper course), in the centre the Niari sub-basin (middle course) and in the south the Kouilou sub-basin (lower course). Between 1952 and 1982, the N’douo sub-basin had two hydrological stations (Moukomo and Lekana), the Niari sub-basin had nine (Makaka, Bac Miambou, Bac de la Safel, Kayes, Loudima I, Loudima II, Biyamba I, Makabana and Kibangou) and finally, the Kouilou sub-basin had 1 station (Sounda). All these stations were closed in 1983. The longitudinal profile of the Kouilou-Niari basin runs from N’douo (z = 650 m of altitude) to the Mayombe (z = 0 m of altitude) at Atlantic Ocean. The climate of the Kouilou-Niari watershed belongs to the humid tropical domain, characterized by two rainy seasons alternating with two dry seasons of unequal length.

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Fig. 2 Map of hydrological and climatic stations of the Kouilou-Niari watershed

The volume of rainfall in the March–April–May season (MAM) is, however, slightly higher than in the October–November–December season (OND). The watershed has seven climatic stations to which we have added the three neighbouring stations of Djambala, Divénié and Pointe-Noire (Fig. 2). The temperatures show a certain thermal homogeneity over the period 1952–2013. They vary between 28.4 °C at Pointe-Noire and 19.8 °C at Mouyondzi. On the whole, this watershed has experienced fairly high rainfall averaging 1,846 mm with a maximum of 2,234 mm in 1961 at Djambala, and a minimum of 551 mm in 1958 at Pointe-Noire. The soils of the basin are classified as moderately saturated ferrallitic soils, made up of sandy to sandy-clay materials of the cirque series, rejuvenated on slopes, suitable for traditional agriculture over a large area. The Mayombe has well-developed ancient soils in the parts of the landscape that are sheltered from erosion, and soils whose youthful characteristics are linked to the slopes and active erosion even under forest. The physical characteristics of these soils (structure, porosity and cohesion) are generally satisfactory, except for the sandier soils and those that are heavily depleted at the surface, even those with a heavier texture (from crystalline rocks). From a particle size point of view, these soils are essentially sandy clay (20–40% of silts), clay sandy (30–50% clay) and dominantly sandy (60–75% sand); clay and silt contents are very low [12].

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The Niari sub-basin separates the limestone-dolomitic terrain in the north from the marl formations in the south and includes the Chaillu massif. The Mayombe forest is located in the Kouilou sub-basin.

2.2 Data Used Climatological data were obtained from hydroclimatological stations in the KouilouNiari watershed and were provided by the meteorological department of the Agence Nationale de l’Aviation Civile (A.N.A.C) in Brazzaville]. They are monthly time steps and cover the period from 1952 to 2013. The hydrometric data are also monthly and cover a hydrological cycle from 1952 to 1982. They come from the archives of the Institut de Recherche pour le Développement (IRD in France, formerly ORSTOM), from Molinier et al. [5] and from the hydrological service of the Institut de Recherche en Sciences Exactes et Naturelles (IRSEN) in Brazzaville. Finally, the satellite data (altimetry water heights) are from the ENVironmental SATellite (ENVISAT) mission, which operated from 2002 to 2010 with a revisit period of 35 days. The water level series, referenced to the EIGEN6 geoid, was extracted manually at a virtual station (point of intersection between the satellite ground track and a river) near the Sounda in situ station. Its coordinates are 4° 03 S, 12° 19 E.

2.3 The GR2M Model The Agricultural Engineering model with two parameters and with a monthly time step, known as the GR2M model (Fig. 3), is a global rainfall-runoff model developed at Cemagref (in France) at the end of the 1980s, with the aim of applying in the field of water resources. This version, which appears to be the most successful, is that of Mouelhi et al. [13]. Its structure brings it closer to conceptual models with reservoirs and a process for monitoring the basin’s hygrometric status appears to be the most effective technique to account for earlier conditions and guarantee the model’s continued operation. The structure of GR2M model consists of two major functions: a production function associated with a production store and a transfer function represented by a routing store. The production function reflects the actual transformation of the rain into a sheet of water available for runoff, on the other hand, the transfer function reflects the movement of this sheet of water, accumulated on the ground during precipitation towards the outlet of the watershed. Thus, to these two functions is added a function of opening on the outside of the subsoil other than the atmospheric environment. These three functions make it possible to simulate the hydrological behaviour of the watershed. The GR2M model thus restores the flows calculated through a series of

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Fig. 3 Structure of GR2M model [13]

reservoirs from the initial data of the catchment area at the beginning of the simulated period. The input variables are the rain P, the observed discharge Qobs, and the evapotranspiration E. The output variable is the simulated discharge noted Qsim. The use of the GR2M model then requires the calibration of two parameters X1 and X2 (Fig. 3). X1 represents the maximum production store capacity or production function (mm), and X2 represents the underground exchange coefficient or transfer function (−). These were optimized using the Nash criterion to compare the monthly discharge observed with those simulated by the hydrological model. The two input variables, rainfall (P) and potential evapotranspiration (E), are modulated in the same proportion and simultaneously by multiplying their values by the coefficient X1 . The optimization criterion in the context of our study is based on the Nash criterion (Mouelhi, [14]; Perrin, [15]), which is defined as follows: 

 2  − Q Q obs,i cal,i Nash(Q) = 100 × 1 − i=1  2 n i=1 Qobs,i − Qobs n

where: Qobs,i and Qcal, : observed and calculated discharge at time step i (m3 /s); Qobs : average discharge observed over the period considered (m3 /s); n : total number of time steps of the simulation period.

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The Nash criterion can also be applied to the square roots of the discharge and the logarithms of the discharge. The combined use of these three criteria makes it possible to emphasize several hydrological situations: the Nash criterion on discharge is more sensitive to periods of flooding, the Nash criterion on logarithms of discharge is more sensitive to periods of low water levels and that on square roots is more sensitive to the average of annual discharges. Koffi et al. [16] have recalled the evaluation of the performance of the model: – – – –

Nash ≥ 90%: the model is excellent; 80% < Nash < 90%: the model is very satisfactory; 60% < Nash < 80%: the model is satisfactory; Nash < 60%: the model is bad. One can see that the Nash is not lower bound.

2.3.1

Methodology

Once the climatic (P and T ) and hydrological (Qobs) data have been collected, the first step of this work consisted in estimating the potential evapotranspiration E. Indeed, the GR2M model using the input variables P, E, and Qobs, the estimation of potential evapotranspiration was made over the entire period of climate data availability from 1952 to 2013. Due to the lack of sufficient data on relative humidity and sunshine, we used Thornthwaite method, which is essentially based on temperatures. Its validity is justified by the fact that the average temperature of the watershed is below 26.5 °C. The second stage of the study consisted in calibrating and validating automatically the parameters X1 and X2 of the GR2M model at the Sounda station over the period from 1952 to 1982, of the availability of in situ hydrological data. Remember that for this, the model only uses part of the first months to carry out the calibration, then part of the following months to carry out the validation of this adjustment, and finally it carries out for the remaining months, a calculation of simulated discharge Qsim which he compares to the Qobs observed in situ. The third step consisted in extending or projecting the previously simulated discharge Qsim into Qproj over the period from 1983 to 2013. Indeed, since we no longer have discharge measured in situ between 1983 and 2013, we use the parameters X1 and X2 obtained previously, as well as the climatic data available over this period to simulate the projected discharge Qproj using GR2M. The fourth stage consisted in determining a rating curve at the Sounda station using gaugings carried out between 1952 and 1982 at this station. We assume that the geomorphological conditions at this station are stable enough for the rating curve determined to be usable for this study. This rating curve was determined by an inverse method using the Shuffled Complex Evolution Metropolis (SCEM-UA) optimization algorithm who is based on the Bayesian framework and Markov Chain Monte Carlo method (Vrugt et al. [17], Paris et al. [18]).

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Finally, the fifth step consisted, of using the rating curve as well as the previously projected discharge, in comparing the projected heights and the altimetric heights extracted at a virtual station near Sounda over the period from 1995 to 2010.

3 Results and Discussion 3.1 Results The optimization procedure of the GR2M model was initialized as advised in its operating mode by the parameters X1 = 6.00 mm and X2 = 1.00. Table 1 summarizes the results obtained after the calibration and automatic validation of GR2M at the Sounda station. It can be seen that over the 31 years (1952–1982), i.e. 372 months of the period of availability of the hydrological data, the calibration period was carried out over the first 30 months, from January 1952 to June 1957, and the validation period was carried out over the following 36 months, from July 1954 to June 1957. The optimal parameters obtained are X1 = 8.46 mm and X2 = 1.04. The values of the Nash criterion on the discharge, the square roots of the discharge and the logarithms of the discharge obtained in the validation period are, respectively, 80.5%, 8303% and 84.9%. They are all above 80%, meaning that the model is very satisfactory. The hydrographs of rainfall, observed and simulated streamflows are shown in Fig. 4. It can be seen that the evolutions of observed and simulated streamflows are similar, with variations in accordance with the evolution of rainfall. Figure 5 shows an acceptable linear correlation of these streamflows with a correlation coefficient R = 0.86. Similarly, Fig. 6 shows, on the one hand, the annual evolutions of the standardized values of the simulated rainfall and discharge between 1952 and 1982, and on the other hand, the projected rainfall and discharge between 1983 and 2013, thanks to the optimized parameters X1 and X2 , the observed rainfall and the calculated evapotranspiration. Overall, the evolutions are similar, except between the years 1982 and 1983, where there is a peak between the simulated and projected discharge. Table 1 Calibration and validation results of GR2M in the Kouilou-Niari watershed Length of periods

Optimised parameters

Calibration period

Validation period

Inputs

From January 1952 to June 1954 (30 months)

From July 1954 to June 1957 (36 months)

Outputs

Nash criteria √ Q

X1

X2

X1

X2

Floods Q

6.00

1.00

8.46

1.04

80.5%

83.3%

Low water lnQ 84.9%

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500

200

400

400 600

300

Rainfalls Qsimulated

Qobserved 200

800

100

1000

Streamflow (mm/month)

Rainfall (mm/month)

Sounda station (1952-1982) 0

0

Months

Fig. 5 Simulated streamflows and observed streamflows after validation period (1952–1982) [19]

Simulated streamflows (mm/month)

Fig. 4 Hydrographs of rainfall, observed streamflow and simulated streamflows at Sounda station (1952–1982), [19]

Sounda station (1952-1982)

200

y = 2,6746x0,7628 R² = 0,7448

150

100

50

0

0

30

60

90

120

150

Observed streamflows (mm/month)

This peak undoubtedly represents a rupture in the new time series of discharge (1952–2013) due to the change in the method of measurement, in situ until 1982 and altimetric from 1983 onwards. The rating curve determined at the Sounda station, together with the projected discharges, has made it possible to estimate the water levels over the period from 1983 to 2013. Figure 7 shows the evolution of these projected water levels as well as the altimetric heights at a virtual station near Sounda between 1995 and 2010. The agreement of the variations of the two curves seems acceptable.

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Fig. 6 Annual evolution of rainfall (1952–2013), observed discharge (1952–1982) and projected discharge (1983–2013) at Sounda station

Fig. 7 Comparison of altimetry water heights and projected water heights at Sounda station

3.2 Discussion This work mainly concerns the calibration and validation of the GR2M model on the Kouilou-Niari basin and the evaluation of its potential to simulate flows over long periods of unavailable data. The quality of the results obtained can be appreciated through the values of the Nash criterion which are higher than 80%, thus suggesting a very satisfactory modelling [16]. This study confirms the effectiveness of the GR2M model on watersheds located in tropical Africa, and for which the gauging is carried out at fairly large time steps. It completes the work done by Ngoma Mvoundou et al. [19], on the same watershed, which showed that the performances obtained with GR2M are comparable to those of Kouasi in Ivory Coast [7], Ouédraogo in Niger [8], Ardoin in Ivory Coast [9] and many other authors. In the particular case of this study, Table 1 also shows us that GR2M models low water levels better than floods in this watershed. Several works have been carried out on the recent use of altimetry in hydrology, and in particular in Africa. Most of the time, satellite altimetry and rainfall data

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are used. The study made by Becker et al. [20] shows the interest of satellite altimetry for poorly gauged rivers such as the Kouilou-Niari. The methodology has also been applied to the Tsiribihina river basin in Madagascar by Andriamihaja Andriambeloson et al. [21]. On the other hand, the GR2M model was calibrated on the Kouilou-Niari basin using historical flow measurements to simulate future flows distributed in the basin. The calibration curve determined made it possible to validate the altimetric information with the projected water levels. This curve allows water levels and discharge to be converted interchangeably. Thus, the discharge of the Kouilou-Niari River can be estimated with any update of the satellite data of water levels obtained by the currently operational altimetry missions. Thus, one can conclude that GR2M model is reliable in the Kouilou-Niari watershed.

4 Conclusion This study consisted in calibrating and validating the GR2M model on the KouilouNiari watershed in the context of a lack of data over the period from 1952 to 2013. As in several studies carried out in the African tropics, the model produced satisfactory results during the validation period for the period for which hydrological data were available, i.e. from 1952 to 1982, with a Nash criterion exceeding 80% at the Sounda station, which is the closest to the outlet. The use of satellite altimetry at a virtual station close to Sounda allowed us, through a rating curve, to confirm that the flow projections made over the period from 1995 to 2010 of unavailability of hydrological data have similar variations to those of the altimetric water heights. The results obtained show the ability of the GR2M model to simulate flow projections over long periods of unavailable hydrological data. While waiting for the decision-makers to reopen some hydrological stations, we hope to use the optimized X1 and X2 parameters for future water resource management plans in this watershed.

References 1. Energie Electrique d’AEF (1958) Fleuve Kouilou-Niari, Aménagement de Sounda, avantprojet. Tome III. EDF-IGUFE, France 2. Rodier J (1964) 1964: Régimes hydrologiques de l’Afrique noire à l’Ouest du Congo. PhD thesis, University of Toulouse, France 3. Olivry JC, Hiez G (1967) Dossier des stations limnimétriques de la République du Congo. 2 ème partie: les stations limnimétriques du bassin versant du Kouilou-Niari. Hydrologie ORSTOM, Brazzaville 4. Anonymous (1967) Étude des resources en eau de la vallée du Niari. Rapport FAO. Topographie, hydrologie et hydrogéologie, Brazzaville

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Analysis of Response Law of Rainstorm Under Different Microtopography Conditions Jun Guo and Li Li

Abstract In recent years, with the global climate change, extreme rainstorm disasters occur frequently, and rainstorm has become an important disaster affecting the national economic security. Taking the precipitation data of Hunan Province as an example, this paper analyzes the shortage of missing precipitation measurement in conventional rainfall observation stations. The main reason is the enhancement effect of local precipitation due to the influence of terrain. At the same time, combined with the multi-source fusion precipitation data and digital elevation data with a resolution of 30 m in Hunan Province, the analysis found that the correlation coefficient between local precipitation and elevation reached 0.93, indicating that precipitation would significantly increase with the increase of elevation. Furthermore, the physical mechanism of rainstorm enhancement under different terrain conditions is analyzed through four typical topographic features, namely, windward slope type, trumpet mouth type, valley type, basin type. It provides an effective technical means for accurate calculation of rainstorm magnitude and also provides a theoretical basis for subsequent fine numerical prediction of rainstorm in microtopography. Keywords Rainstorm · Microtopography · Physical mechanism of rainstorm · Association analysis

J. Guo (B) School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan 430074, China e-mail: [email protected] ChangJiang River Scientific Research Institute of ChangJiang Resources Commission, Huangpu Street 23, Wuhan 430010, China L. Li State Grid Hunan Electric Power Company Limited Disaster Prevention and Reduction Center, Longhua Road, Changsha 410129, China e-mail: [email protected] State Key Laboratory of Disaster Prevention and Reduction for Power Grid Transmission and Distribution Equipment, Longhua Road, Changsha 410129, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_14

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1 Introduction In recent years, with the global climate change, extreme rainstorm disasters occur frequently, and rainstorm has become an important disaster affecting the national economic security. In 2021, Henan will suffer from a rare rainstorm disaster in the world. The main reason is that a large amount of water vapor will be transported to the inland areas of China under the guidance of the easterly airflow around Typhoon 6 “Yanhua” and the southern side of the subtropical high, providing abundant water vapor sources for heavy rainfall in Henan. The terrain of Henan is high in the west and low in the east. Influenced by the special terrain of Taihang Mountain in the north and Funiu Mountain in the west, the easterly airflow has strengthened the lifting convergence effect when encountering mountains. The strong precipitation area is stable and less dynamic in the western and northwest mountain areas of Henan Province, and the precipitation increases significantly in front of the windward slope. This also reflects that the terrain has an obvious influence on the rainstorm. However, the location of the current rainfall observation stations cannot completely cover the special terrain areas prone to rainstorm. According to statistics, the control area of each hydrological station in the country is about 150–10,000 km2 , especially in some micro terrain areas where rainstorm and flood disasters occur frequently and the environment is bad, there are fewer hydrological observation stations and observation data [1]. This may easily lead to missed measurement of local extreme rainstorm. It is not conducive to the prevention and control of rainstorm disasters. Especially, for the reservoir basin area, if the rainstorm observation data are inaccurate, the reservoir inflow will be smaller, which is not conducive to the reservoir flood control and optimal operation of power generation. Usually, the spatial interpolation of stations is used to compensate for the precipitation, temperature, evaporation and other factors in the area lacking observation data. The commonly used spatial interpolation algorithms are: inverse distance interpolation, Tyson polygon interpolation, Kriging interpolation, etc. Zhou et al. [2] used the inverse distance square method and the Tyson polygon interpolation method to carry out the space–time distribution according to the rainfall at large-scale stations, to supplement the lack of precipitation observation in large-scale watersheds. Hu et al. [3] applied the Kriging algorithm to spatial interpolation of precipitation in a large range. Wang et al. [4] proposed a spatial interpolation method of precipitation based on PER Kriging, which is better than Kriging algorithm in application. At the same time, related scholars have also proposed other improved spatial interpolation algorithms. Gan et al. [5] proposed a spatial interpolation method of precipitation considering the influence of geography and topography. Liu et al. [6] proposed an improved method for the spatial interpolation model of HM Bayes network precipitation. Huang et al. [7] proposed a spatial interpolation method of rainfall based on information diffusion theory. Zhang and Zhang [8] proposed the spatial interpolation model of precipitation with elliptic exponential function. These improved methods take into account the influence of geography, topography and other factors, but due to the small density of existing observation stations, the analysis scale is often tens

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to hundreds of kilometers. However, the physical impact mechanism of local microtopography on precipitation and its related meteorological elements has not been analyzed, and the reliability of interpolated precipitation data increases sharply with the decrease of the density of sparse stations. It is impossible to analyze the enhancement effect of microtopography area on precipitation, and it is difficult to effectively calibrate the accuracy of precipitation in areas with insufficient data. Therefore, this study analyzes the deficiencies of the existing rainfall observation station data. The relationship between rainstorm and terrain is analyzed. At the same time, the physical mechanism of rainstorm response under different terrain conditions is carried out. It provides an effective technical means for accurate calculation of rainstorm magnitude and also provides a theoretical basis for subsequent fine numerical prediction of rainstorm in microtopography.

2 Analysis of the Deficiency of the Existing Rainfall Observation Station Data Rainstorm disasters are generally generated by small-scale convective weather systems, which are very sudden. It also has obvious spatial heterogeneity characteristics and obvious differences in successive precipitation processes. In this research, Zhexi reservoir basin in Hunan Province is selected as an example for analysis. There are 32 rainfall observation stations in Zhexi reservoir basin. Based on the observation data of 32 rainfall observation stations that have been built and 3000 telemetry stations that have been built by the meteorological department, a comparative analysis is made. The specific analysis is as follows: (1) Analysis of rainstorm process from April 24 to 30, 2013 The observation of rainstorm process from April 24 to 30, 2013 is shown in Fig. 1. There is a 130 mm local rainstorm center in Ellipse A, while the rainfall of the nearest rainfall observation station is only 91 mm, 30.8% smaller. There is a local rainstorm center of 147 mm in the area of ellipse B, while the rainfall of the nearest rainfall observation station is only 103 mm, 29.9% smaller. There is a local rainstorm center of 146 mm in Ellipse C, while the rainfall of the nearest rainfall observation station is only 123 mm, 15.7% smaller. (2) Analysis of rainstorm process from May 5 to 15, 2013 The observation of rainstorm process from May 5 to 15, 2013 is shown in Fig. 2. There is a 101 mm local rainstorm center in Ellipse A, while the rainfall of the nearest rainfall observation station is only 66 mm, 34.7% smaller. There is a 90 mm local rainstorm center in the area of ellipse B, while the rainfall of the nearest rainfall observation station is only 64 mm, which is 28.9% smaller. There is a 127 mm local rainstorm center in the elliptical C area, while the rainfall of the nearest rainfall observation station is only 76 mm, which is 40.2% smaller. There is a 106 mm local

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Fig. 1 Rainstorm observation from April 24 to 30, 2013

rainstorm center in the elliptical D area, while the rainfall of the nearest rainfall observation station is only 52 mm, which is 50.9% smaller. (3) Analysis of rainstorm process from June 19 to 25, 2014 The observation of rainstorm process from June 19 to 25, 2014 is shown in Fig. 3. There is a 133 mm local rainstorm center in the area of ellipse A, while the rainfall of the nearest rainfall observation station is only 68 mm, 48.9% smaller. There is a 89 mm local rainstorm center in the area of ellipse B, while the rainfall of the nearest rainfall observation station is only 70 mm, 21.3% smaller. There is a 234 mm local rainstorm center in the elliptical C area, while the rainfall of the nearest rainfall observation station is only 131 mm, 44.1% smaller. There is a 80 mm local rainstorm center in the elliptical D area, while the rainfall of the nearest rainfall observation station is only 65 mm, 18.8% smaller. It can be seen from the above analysis that the conventional rainfall observation stations can not completely cover the micro terrain area prone to heavy rain, which is easy to cause missed measurement of local heavy rain. Therefore, when analyzing

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Fig. 2 Rainstorm observation from May 5 to 15, 2013

the precipitation in the basin, we can not only rely on the data of a few conventional rainfall observation stations but also need to combine satellite, radar and other observation methods.

3 Analysis of Correlation Between Rainstorm and Terrain This study takes Hunan Province as the research area. The precipitation data are the average annual total precipitation of nearly 30 years, with a resolution of 30 m. The data combine the precipitation data retrieved from conventional rainfall observation stations and satellites. The terrain elevation data are SRTM1 v3.0 data with 30 m resolution.

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Fig. 3 Rainstorm observation from June 19 to 25, 2014

3.1 Correlation Analysis of Rainstorm and Terrain in the Whole Province The precipitation and elevation of each grid point in Hunan Province are extracted. Scatter plot of elevation and annual average precipitation is drawn, as shown in Fig. 4. It can be seen from Fig. 4 that the precipitation tends to increase with the increase of elevation, but the correlation is not particularly strong. The correlation coefficient is 0.57. The main reasons may be: (1) Due to different terrain structures in different regions, the enhancement effect of terrain on precipitation may be significantly different. Therefore, the correlation between the overall terrain elevation and precipitation in the scatter map is reduced. (2) There are significant differences in meteorological conditions in different regions, and the enhancement effect of terrain on precipitation is different under different meteorological conditions. Therefore, it may also reduce the correlation between the overall terrain elevation and precipitation in the scatter map.

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Fig. 4 Scatter map of elevation and precipitation in Hunan Province

3.2 Correlation Analysis of Rainstorm and Terrain in Local Small Area Take the downstream part of Zishui River Basin in Hunan Province as an example. The specific location is the rectangular area shown in Fig. 5. The precipitation and elevation of each grid point in the region are extracted. Scatter plot of elevation and annual average precipitation is drawn, as shown in the following Fig. 6. It can be seen from Fig. 6 that the precipitation increases significantly with the increase of elevation, and the correlation between them is very high. The correlation coefficient is 0.93. At the same time, there is also a characteristic in the figure that the dispersion degree at low elevation is higher than that at high elevation, and the precipitation at high elevation tends to be saturated. The main reason may be that: with the increase of elevation from the foot of the mountain, the topographic rain caused by the elevation of the terrain gradually increases the precipitation. After reaching a certain height, the water vapor in the air decreases due to a large amount of precipitation, and the growth rate of precipitation will slow down with the continuous rise of elevation.

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Fig. 5 Geographical location of the downstream area of Zishui River Basin

4 Physical Mechanism Analysis of Rainstorm Response Under Different Terrain Conditions According to the joint analysis of precipitation data and terrain, it is found that the terrain features of rainstorm mainly include: windward slope type, trumpet mouth type, valley type, basin type. The physical mechanism is analyzed as follows: (1) In the windward slope terrain area (as Fig. 7), on the top of the mountain and on the side of the windward slope, the air mass containing a large amount of water vapor is forced to rise along the mountain slope under the action of wind to expand adiabatic. Make the content of water droplets increase, leading to the increase of precipitation. (2) In the trumpet mouth terrain area (as Fig. 8), due to the convergence and acceleration of airflow, it is easier to form convective unstable structures. A strong upward movement is generated, resulting in enhanced precipitation. (3) In the valley terrain area (as Fig. 9), large wind speed is generated through narrow pipe effect, which is also easier to form convective unstable structure. A strong upward movement will result in a substantial increase in precipitation.

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Fig. 6 Scatter map of elevation and precipitation in Hunan province

Fig. 7 Windward slope type

air climbing air sinking

(4) In the topographic area of the basin (as Fig. 10), due to the complex terrain, the airflow climbing and lifting are complex, and there will be a large precipitation center locally.

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Fig. 8 Trumpet mouth type

mountaintop

mountaintop

Fig. 9 Valley type

Fig. 10 Basin type

5 Conclusions In recent years, with the global climate change, extreme rainstorm disasters occur frequently, and rainstorm has become an important disaster affecting the national economic security. Therefore, accurate calculation of storm size has become the primary task to prevent and control storm disasters. Taking the precipitation data of Hunan Province as an example, this paper analyzes the shortage of missing precipitation measurement at conventional rainfall observation stations, which is mainly due to the enhancement effect of local precipitation due to the influence of topography. At the same time, combining the multi-source fusion precipitation data and digital elevation data with a resolution of 30 m in Hunan Province, it is found that the correlation coefficient between local precipitation and elevation reaches 0.93. It shows that the

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precipitation will increase significantly with the increase of elevation. Furthermore, the physical mechanism of rainstorm enhancement under different topographical conditions is analyzed through four typical topographic features, namely, windward slope type, trumpet mouth type, valley type and basin type. It provides an effective technical means for accurate calculation of rainstorm magnitude and also provides a theoretical basis for subsequent fine numerical prediction of rainstorm in microtopography. Next, the physical calculation formula of rainstorm enhancement under different terrain and meteorological conditions will be summarized according to the research conclusions of this paper. It provides important technical support for improving the accuracy of rainstorm numerical prediction and correcting the accuracy of conventional rainstorm observation. At the same time, the analysis conclusions of this paper will be verified in other basins, which is also the next research work to be carried out. Acknowledgements This work was supported by National Natural Science Foundation of China— Research on response mechanism of micro terrain rainstorm and adaptive rainstorm flood forecasting method in areas with lack of data (52109004), and Scientific research project of State Grid Hunan Electric Power Company (5216AF21N00L).

References 1. Li H (2009) Runoff prediction in ungauged catchments. Dalian University of Technology 2. Zhou Z, Jia Y, Wang H et al (2006) Interpolating precipitation in space and time in large-scale basin based on rain gauges. Hydrology 26(1):6–11 3. Hu Q, Hu Y, Yang D et al (2014) A Matlab based ordinary kriging model developed for rainfall spatial interpolation over macro regions and a case application. J Basic Sci Eng 22(1):106–117 4. Wang S, Yang D, Qin T et al (2011) Spatial interpolation of precipitation using the PER-Kriging method. Adv Water Sci 22(6):756–763 5. Gan W, Chen X, Cai X et al (2010) Spatial interpolation of precipitation considering geographic and topographic influences: a case study in the Poyang Lake Watershed, China. In: Geoscience and remote sensing symposium (IGARSS). Honolulu, Hawaii, pp 3972–3975 6. Liu H, Wang H, Sun Q et al (2016) Improvement of the HM-Bayes networks based precipitation interpolation model and application. Syst Eng Theory Pract 36(11):2964–2976 7. Huang H, Liang Z, Ren L et al (2017) Spatial rainfall interpolation method based on information diffusion theory. Int J Hydroelectr Energy 35(11):1–5 8. Zhang S, Zhang K (2015) Interpolation model of precipitation distribution with elliptic exponential function. South-to-North Water Transf Water Sci Technol 13(3):530–533,542

Research on Data Cleaning Method for Dispatching and Operation of Cascade Hydropower Stations Hang Lin, Shuai Xie, Zhengyang Tang, Yang Xu, and Yongqiang Wang

Abstract High-quality datasets are the basis for refined scheduling and operation of cascade hydropower stations, but the data quality is often affected by many factors, including data collection and transmission equipment failures, data storage system defects, and so on. Consequently, useless or wrong data often appear in the datasets and hurt the operations of cascade hydropower stations. Data cleaning is a key method to improve data quality. In this study, a two-stage data cleaning framework is used to detect and correct abnormal data. Firstly, many unsupervised machine learning methods are applied to detect abnormal data and abnormal data variation in the dataset. Secondly, the abnormal data are corrected by the proposed univariate data interpolation method. This data cleaning framework is applied in the water level datasets of Three Gorges–Gezhouba cascade hydropower stations. The results demonstrate that the two-stage data cleaning framework based on the isolation H. Lin · S. Xie (B) · Y. Wang Water Resources Department, Changjiang River Scientific Research Institute, Wuhan 430010, China e-mail: [email protected] H. Lin e-mail: [email protected] Y. Wang e-mail: [email protected] S. Xie · Z. Tang · Y. Xu Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science, Yichang 443000, China e-mail: [email protected] Y. Xu e-mail: [email protected] Z. Tang · Y. Xu Three Gorges Cascade Dispatch & Communication Center, Yichang 443000, China Y. Wang Hubei Key Laboratory of Water Resources & Eco-Environmental Sciences, Wuhan 430010, China Research Center On the Yangtze River Economic Belt Protection and Development Strategy, CWRC, Wuhan 430010, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_15

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forest algorithm and random forest regression can effectively detect and correct the abnormal data in the water level datasets. The false detection rate and recall rate of the abnormal data detection method are 0.003 and 0.98, respectively, and the mean square error and goodness of fit R2 of the correction model in the test, sets are 0.092 and 0.998, respectively, which can meet the data quality requirements of cascade hydropower stations. Keywords Data cleaning · Cascade hydropower station · Isolated forest · Random forest

1 Introduction Cascade hydropower stations play an important role in many fields, such as flood controlling and drought defying, water resource management, and electricity supply [1, 2]. To maximize the comprehensive benefits, the cascade hydropower stations should be fine-regulated and controlled, which are based on accurate data. However, generator set failure, monitoring equipment failure, electromagnetic signal interference, transmission equipment damage, and other reasons have resulted in many vacancies and abnormal data in the original dispatching operation data of cascade hydropower stations [3, 4]. The existence of these “dirty data” affected the analysis of the response law of dispatching elements of cascade hydropower stations, the generation scheduling, and the preparation of dispatching schemes, which will further influence the comprehensive benefits of hydropower stations. Therefore, constructing high-quality dispatching operation datasets through data cleaning methods is of great significance for improving the accuracy of cascade hydropower station regulation and operation. Data cleaning refers to the process of detecting inconsistent, missing, or erroneous data through relevant technical methods and removing erroneous and inconsistent data [5]. As the main technical method to improve data quality [6], data cleaning has attracted more and more attention, and various data cleaning methods have gradually developed and matured through the application and practice of scholars in many related industries in their respective professional fields. For example, in the detection of abnormal data, Rai Ajeet [7] proposed a technique to identify abnormal data points in time series using the Isolation Forest (IF) algorithm, which can effectively monitor the operation of hydroelectric units using data generated by sensors. In terms of abnormal data correction, Xie [8] preprocessed the electromagnetic real-time monitoring data of hydraulic fracturing based on the random forest (RF) interpolation method, which improved the correctness and effectiveness of the geophysical data of fracturing monitoring. Though many methods have been proposed for the data detection and correction of hydropower units and performed well, these methods may be not suitable for the dispatching and operation datasets of cascade hydropower stations because of the inherent relationships among the datasets. For example, the fluctuation of water level

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should follow the natural law [9], which means the fluctuation amplitude should be in a specific range. In addition, there are strong connections among water levels in different gauging stations [10]. In general, high-quality datasets are the basis of fine dispatching and operation of cascade hydropower stations, but many factors will reduce the data quality and further affect the hydropower station operations. To improve the data quality, a data cleaning framework, which is developed based on a variety of abnormal data detection methods and the characteristics of dispatching and operation data, is applied in the water level datasets of Three Gorges–Gezhouba cascade hydropower stations in this study. In the framework, five abnormal data detection methods based on unsupervised learning are used to detect abnormal data and a data interpolation method based on random forest regression is proposed to correct data. The remainder of this study is organized as follows. The case study, data, and methods are introduced in Sect. 2. In Sects. 3 and 4, the results of data detection and data correction are demonstrated and discussed. Finally, the main conclusions are summarized in Sect. 5.

2 Case Study, Data, and Methods 2.1 Case Study and Data China has always placed the development and utilization of hydropower resources in a high-priority strategic position of energy development [11] and completed the construction of Xiluodu-Xiangjiaba, Three Gorges–Gezhouba, and other cascade hydropower stations. Three Gorges–Gezhouba cascade hydropower station is the core project of protecting and harnessing the Yangtze River, shouldering heavy comprehensive tasks such as flood control, power generation, water supply, shipping, and ecological protection [12]. In this study, the abnormal data of water level data of different hydrological stations in the historical dispatching operation data of Three Gorges–Gezhouba cascade hydropower station from 2015 to 2020 were detected and corrected. Table 1 shows an overview of the original data used. Table 1 Overview of original data Data type

Stations

Temporal sequence

Temporal scale

Water level (m)

Baishatuo, Zhongxian, Shibaozhai, Wanxian, Yunyang, Fengjie, Wushan, Padong, Zigui, Fenghuangshan

2015–2020

111 h

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2.2 Methods Overall Approach Framework. In this study, a two-stage data cleaning framework is applied to detect and correct abnormal data in the water level dataset. In the first stage, the abnormal data and data variation is detected by five data detection methods. Generally, the water level at a specific station and time is closely linked to water levels at other stations and previous times. Therefore, the data detection methods are not only applied to detect abnormal data directly but also applied to detect abnormal temporal variation and abnormal spatial variation. In the second stage, the data interpolation method is used to fill the eliminated abnormal data in the dataset. The overall flowchart of this study is shown in Fig. 1. Data Detection Methods. According to the characteristics of dispatching and operation of cascade hydropower stations, this study is based on the hourly value, time change, and space change of water level of each station and adopts the abnormal data detection method based on unsupervised learning to detect the abnormal water

Fig. 1 Flowchart of overall approach framework

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level data of abnormal station. The detection results of all methods are evaluated by performance indicators, the final abnormal data detection results are formed. The unsupervised learning-based abnormal detection methods used in this study include histogram-based anomaly detection method (HBOS), K-nearest neighborbased anomaly detection method (KNN, AvgKNN), isolated forest method (IF), and clustering-based local outlier factor method (CBLOF). The HBOS method is an outlier detection method proposed by Markus Goldstein and Andreas Dengel in 2012. This method mainly divides the data into multiple intervals according to the characteristics through the histogram. Then, the score factor of different data is calculated by the scoring model, and each sample is scored abnormally by the score factor. The higher the score, the more likely it is an abnormal point, thereby identifying abnormal data [13]. The KNN method is a detection method for identifying outliers proposed by Fabrizio Angiulli and Clara Pizzuti in 2002. This method identifies the outliers by calculating the statistical value of the distance from the K-nearest neighbors to the current sample [14]. In this method, the maximum, average, and median distances can be used as the discriminant basis. In this study, the maximum value and the average value are used as the discriminant basis respectively, and the formed methods are recorded as KNN and AvgKNN respectively. The IF method is an isolation-based abnormal data diagnosis method proposed by Liu Fei et al. at the 8th International Conference on Data Mining in 2008. This method uses an isolation mechanism to detect abnormal data instead of analyzing normal points [15]. The CBLOF method is a clustering-based local abnormal factor detection method proposed by He Zengyou et al. in 2003 [16]. This method can not only diagnose abnormal data but also give a measure of the outlier degree of abnormal data. Data Correction Method. Based on the abnormal data detection results, the abnormal data in the dataset are eliminated. Considering the cause of abnormal data for cascade hydropower stations is complex, multiple data anomaly methods coexist, causal, this study adopts the univariate data interpolation method based on random forest regression to interpolate the vacancy values in the dataset after eliminating the abnormal data, that is, to correct the abnormal data. The random forest regression algorithm is a combination strategy developed by Breiman in 2001 to deal with classification problems [17]. The essence of the algorithm is a classifier ensemble algorithm based on decision trees. The principle adopted by the decision tree is the minimum mean square error (MSE). Evaluation Indicators. The performance evaluation of abnormal detection methods can be regarded as the binary classification of a sample. In machine learning, a confusion matrix is usually used to record the classification of samples for binary classification problems [18]. Each column of the confusion matrix corresponds to a predicted category, and each row corresponds to the true label of the sample. A 2 ×2order matrix is used for binary classification problems. In the confusion matrix, the true positive class (TP) represents the abnormal data and the detection result is also abnormal data, and the false negative class (FN) represents the abnormal data missed

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Table 2 Confusion matrix of the binary classification problem

Forecast as abnormal

Forecast as normal

The actual abnormal

TP

FN

The actual normal

FP

TN

detection as normal data. The false positive class (FP) represents that the normal data are misdetected as abnormal data, and the true negative class (TN) represents that the sample is normal data and the detection result is normal data. The binary confusion matrix is shown in Table 2. In this study, recall rate and false detection rate are used as the performance evaluation indexes of each abnormal data detection method. Among them, recall rate represents the proportion of actually abnormal data detected, and false detection rate represents the ratio of the amount of normal data incorrectly detected as abnormal to the actual amount of normal data. It is generally believed that the method with a higher recall rate and lower false detection rate has better performance. The expression of recall rate and false detection rate is as follows: R=

TP TP + FN

(1)

F=

FP FP + TN

(2)

where, R and F represent the recall rate and false detection rate, respectively, TP represents the number of true positive class data, FN represents the number of false negative class data, FP represents the number of false positive class data, and TN represents the number of true negative class data. Mean square error (MSE) and goodness of fit (R2 ) can be used as indexes to evaluate the performance of random forest regression in abnormal data correction. MSE represents the accuracy of abnormal data correction results. The more accurate the results are, the smaller the MSE is. R2 represents the goodness of fit, the greater the goodness of fit, the better the fitting effect of abnormal data correction. The calculation formula is: Σn (yi − f (xi ))2 MSE = i=1 (3) n 2 ΣN  Y − Y t t t=1 (4) R2 = 1 − Σ  2 N t=1 Yt − Y Δ

where, n is the number of samples, yi and f (xi ) represent the true and predicted values of the ith sample, respectively, Yt is the actual value at t moment, Y t is the predicted value at t moment, and Y is the average value of the actual value. Δ

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Fig. 2 Boxplots of water level values at a 1-h time scale at different stations

3 Abnormal Data Detection 3.1 Overall Abnormal Situation of Each Station In this study, the water levels of different stations are taken as examples for data cleaning. Figure 2 shows the distribution of water level values at the 1-h time scale of each station by box plot. It can be seen from Fig. 2 that there are obvious outliers in the water level data of Zigui Station, which deviates significantly from the normal water level range, but on the whole, the water level data of most stations are distributed within the normal range. Based on the hourly water level data of Zhongxian Station, the time-varying relationship and spatial variation relationship of water level of Zhongxian Station can be obtained by making the difference hour by hour along the time series and by making the difference step by step with the adjacent stations along the route. The temporal and spatial distribution of water level data is shown in Figs. 3 and 4 respectively. It can be seen from Figs. 3 and 4 that the change in water level is prone to abnormal situations. For example, in Zhongxian Station, the water level varies from 134 to 195 m, but the water level can change as much as 33 m.

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Therefore, based on the fluctuation of water level, the abnormal value of water level data of Zhongxian Station is detected and corrected.

Fig. 3 Boxplots of time-varying water levels at a 1-h time scale at different stations

Fig. 4 Boxplots of spatial variation of water level at a 1-h time scale at different stations

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The water level data of Zhongxian Station is divided into the following two situations for abnormal data detection: (1) only according to the absolute value of water level at each station and (2) according to the absolute value, time, and space change of water level at each station.

3.2 Anomaly Data Detection Considering Only Absolute Value of Water Level The above five abnormal data detection methods are used to detect the abnormal value of hourly water level data, and the influence of different abnormal data detection methods on the abnormal data detection effect is studied. Figure 5 shows the abnormal detection results of water level data of Zhongxian Station. Since there are no data labels that are abnormal and normal in the original data, the detection results obtained by the abnormal data detection method are assumed to be actual data in this study, and 80% of the data are brought into the logistic regression model for model training, and the trained model is used to predict the remaining data. The recall rate and false detection rate of the logistic regression model corresponding to each method are calculated by using the prediction results of the model and the detection results assumed to be actual data, which can replace the recall rate and false detection rate of the abnormal data detection method. Table 3 shows the recall rate and false detection rate of the five abnormal data detection methods.

3.3 Anomaly Data Detection Considering Temporal and Spatial Variation of Water Level The above five abnormal data detection methods based on unsupervised learning are used to detect the abnormality of the temporal and spatial relationship of water level data. Figures 6 and 7 show the results of abnormal detection of the temporal and spatial changes of water level data in Zhongxian Station, respectively.

3.4 Comparison and Analysis of Results It can be seen from Figs. 5, 6, and 7 that there are large differences between the detection results generated by different abnormal data detection methods, that is, different abnormal data detection methods have different characteristics. The results of the CBLOF method, KNN method, and AvgKNN method are similar, and these three methods tend to detect large outliers. On the other hand, HBOS and IF methods

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Fig. 5 Abnormal detection results of water level in Zhongxian Station

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Table 3 Outlier data detection method performance evaluation results Evaluation indicator

Anomaly data detection method CBLOF

HBOS

IF

KNN

AvgKNN

False detection rate

0.02

0.019

0.003

0.01

0.008

Recall

0.98

0.98

0.98

0.99

0.99

are different, both of which tend to label data with higher outlier scores as outliers. On the whole, all outliers can be detected through the scoring of the five models. From the performance evaluation results of the abnormal data detection methods in Table 3, the false detection rate of the IF method is 0.003 and the recall rate is 0.98, so the use of the IF method can effectively identify abnormal data in the dataset. According to the principle of choosing the method with the lowest false detection rate and the highest recall rate, the detection result of the IF method is selected as the optimal abnormal data detection result, and the abnormal data in the dataset is eliminated based on the abnormal data detection result of the IF method. Abnormal data detection results based on the IF method eliminate abnormal data in the dataset. The histogram of the water level of Zhongxian Station before and after eliminating abnormal data is shown in Fig. 8. Gaussian distribution was used to fit the water level data of the Zhongxian station before and after eliminating the abnormal data to estimate the parameters of statistical distribution. Before eliminating abnormal data, the mean is μ = 163.05, variance is σ = 9.64, 0.95 confidence interval of mean is [162.97, 163.13], and 0.95 confidence interval of variance is [9.59, 9.7]. After eliminating abnormal data, the mean is μ = 163.16, the variance is σ = 9.54, the 0.95 confidence interval of the mean is [162.38, 163.94], and the 0.95 confidence interval of the variance is [9.48, 9.6]. According to the frequency distribution before and after eliminating abnormal data, the quality of water level data after treatment is improved to a certain extent compared with that before treatment.

4 Abnormal Data Correction Taking the water level data of Zhongxian station from 2015 to 2020 after excluding abnormal data as the research object, a total of 51,556 groups of data were collected. The first 38,667 data were used for model training, and the last 12,888 data were used for model verification. The performance indexes of the water level fitting model based on random forest regression are calculated using the measured data, and the results are shown in Table 4. According to Table 4, Figs. 9 and 10, the MSE of the algorithm is less than 0.1, indicating that the model has a small error in the training process and high prediction accuracy, which can meet the prediction requirements of cascade hydropower stations. The goodness-of-fit R2 of each fitting model, the category is above 0.99,

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Fig. 6 Abnormal detection results of hourly water level change in Zhongxian Station

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Fig. 7 Abnormal detection results of spatial variation of water level in Zhongxian Station

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Fig. 8 Statistical distribution of water level before and after removing abnormal data

Table 4 Water level fitting model performance indicators

Fit model category

Performance indicators MSE

R2

Water level training model

0.006

1.000

Water level test model

0.092

0.998

Water level training model

0.006

1.000

Fig. 9 Water level fitting model training results

which is close to 1, indicating that the fitting effect of the model is good. By analyzing the performance evaluation index, the results show that the data interpolation method based on random forest regression can effectively correct the abnormal data of

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Fig. 10 Water level fitting model test results

cascade hydropower stations based on protecting the original data information from being destroyed.

5 Conclusions This study introduces the available methods for cleaning the operation data of cascade hydropower stations. Aiming at the abnormal data existing in the massive historical operation data of cascade hydropower station, an unsupervised learning method is proposed to detect the abnormal data, and a random forest regression method is proposed to correct the abnormal data. The actual data on water level is used to verify the feasibility and effectiveness of the proposed method, and the following conclusions can be drawn: (1) IF method is used to detect abnormal data of cascade hydropower station dispatching operation data, the false detection rate is 0.003, and the recall rate can reach 98%, which can effectively detect abnormal data with high outlier scores. (2) Using the random forest regression model to impute the data gaps in the water level data after removing the abnormal data, the MSE of the data training model and the validation model is both no more than 0.1, and the goodness of fit R2 is above 0.99, indicating that the fitting accuracy is high, the prediction accuracy is high, and the recovered data are close to the real data.

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Acknowledgments This work is funded by Natural Science Foundation of Hubei Province of China (2022CFD164), National Key Research and Development Program of China (2022YFC3202302), and Natural Science Foundation of Hubei Province of China (2022CFD027).

References 1. Wang L, Han X, Chen G, Ding L, Luo B (2019) Research & prospect of cascade hydrophotovoltaic-pumped storage hybrid power generation technology In: 2019 IEEE innovative smart grid technologies—Asia (ISGT Asia) 2. He S, Guo S, Yin J, Liao Z, Liu Z (2022) A novel impoundment framework for a mega reservoir system in the upper Yangtze River basin. Appl Energy 305(12):117792 3. Hou W, Wei H, Zhu R (2019) Data-driven multi-time scale robust scheduling framework of hydrothermal power system considering cascade hydropower station and wind penetration. IET Gener Transm Distrib 13(6):896–904 4. Duan R, Zhou J, Cai Y, Wang T, Yue L (2022) Construction method of variable working condition state index of hydropower unit based on low-quality data. Hydropower Energy Sci 40(6):5 5. Wang Y, Zhang C, Zhang B, Wu T (2007) A survey of data cleaning research. Mod Libr Inf Technol 12:50–56 6. Han J, Xu L, Dong Y (2008) A survey of data quality research. Comput Sci 35(2):6 7. Rai A (2021) Unsupervised learning algorithms for hydropower’s sensor data, Singapore. Springer Singapore, pp 89–94 8. Xie W, Jiang Q, Li D, Zhang Q, Pei J (2020) Interpolation analysis of hydraulic fracturing electromagnetic real-time monitoring data based on random forest. China Sci Technol Inf 12:2 9. Wahyuni S, Oishi S, Sunada K (2009) Analysis of water level fluctuation in Aydarkul-ArnasayTuzkan Lake system and its impacts on the surrounding groundwater level. Annu J Hydraul Eng JSCE 53:37–42 10. Cai X, Gan W, Wei J (2017) Optimizing remote sensing-based level-area modeling of large lake wetlands: case study of poyang lake. IEEE J Select Top Appl Earth Obs Remote Sens 8(2):471–479 11. Zhou J, Du X, Zhou X (2021) Analysis, forecast and countermeasures of hydropower development situation during the 14th Five-Year Plan. Hydropower Pumped Storage 7(01):1–5 12. Li S, Gao J, Fang G (2022) Review and prospect of watershed cascade hydropower development in China. Hydropower Pumped Storage 8(02):1–6 13. Goldstein M, Dengel A (2012) Histogram-based outlier score (HBOS): a fast unsupervised anomaly detection algorithm. In: KI-2012: poster and demo track 14. Angiulli F, Pizzuti C (2002) Fast outlier detection in high dimensional spaces. In: European conference on principles of data mining & knowledge discovery 15. Liu FT, Ting KM, Zhou ZH (2008) Isolation forest. In: IEEE data mining 16. He Z, Xu X, Deng S (2003) Discovering cluster-based local outliers. Pattern Recogn Lett 24(9–10):1641–1650 17. Breiman (2001) Random forests. Mach Learn 45(1):5–32 18. Elmrabit N, Zhou F, Li F, Zhou H (2020) Evaluation of machine learning algorithms for anomaly detection. In: 2020 international conference on cyber security and protection of digital services (Cyber Security)

Short-Term Downstream Water Level Prediction Model for Three Gorges–Gezhouba Cascade Reservoir Operation Based on LSTM Algorithm Sen Zhang, Zhilong Xiang, Yongqiang Wang, and Shuai Xie

Abstract In this study, LSTM, long-term and short-term memory model, is applied to short-term downstream water level prediction of cascade hydropower stations. LSTM is an artificial neural network model that combines multiple regression ideas and time series ideas, so it is different from the traditional model based on physical model and empirical formula. By inputting the time series of downstream water level and physical factors that affect the downstream water level, the model completes training, and the purpose of accurately predicting the downstream water level is achieved. Taking the Three Gorges and Gezhouba cascade hydropower station as an example, this study shows that the neural network algorithm can increase the accuracy of short-term water level prediction. Keywords Cascade hydropower station · Intelligent algorithm · Water level prediction

S. Zhang · Z. Xiang · Y. Wang · S. Xie (B) Changjiang Water Resources Commission of the Ministry of Water Resources of China, Changjiang River Scientific Research Institute, Wuhan 430010, China e-mail: [email protected] S. Zhang e-mail: [email protected] Z. Xiang e-mail: [email protected] Y. Wang e-mail: [email protected] Hubei Key Laboratory of Water Resources & Eco-Environmental Sciences, Changjiang River Scientific Research Institute, Wuhan 430010, China Research Center On the Yangtze River Economic Belt Protection and Development Strategy, Wuhan 430010, Hubei, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_16

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1 Introduction Xiluodu-Xiangjiaba cascade hydropower station and Three Gorges–Gezhouba cascade hydropower station undertake heavy comprehensive tasks such as flood control, power generation, navigation, sediment and ecological protection. As the core project of Yangtze River protection and management, its safe, efficient and stable operation, is very important. However, due to the influence of the existing observation technology and dispatching calculation system, as well as the constraints of short dispatching period, dynamic reservoir capacity and unsteady flow, the ultra-shortterm (2 h and less than 2 h) dispatching of cascade hydropower stations faces many technical problems, which restrict the economic benefits of cascade hydropower stations. At present, the using method of ultra-short-term water level forecasting is still the traditional physical formula forecasting method. That method builds a model to calculate downstream water level according to the water balance formula, water level-flow relationship and empirical formula. However, the above relation curves and formulas are constructed on the basis of the above-day scale. When it is applied to short-term forecast, lag of downstream water level change, downstream jacking and peak regulation of power station can not be considered, and there is a large forecast error [1]. Based on this reality, this paper chooses to use the depth learning algorithm to achieve the accurate prediction of water level in cascade reservoir operation [2]. The water level data has obvious time series characteristics; therefore, this study believes that the ideal way is to combine the ideas of multiple regression and time series to make the water level prediction more accurate and perfect [3]. However, the traditional multiple regression modeling method can not consider the time series relationship, and the time series analysis method is not good for nonlinear data processing [4]. Therefore, this paper chooses the long and short-term memory model (LSTM) that combines the time series idea and regression idea to achieve the prediction goal of the model, and programs and calculates the medium and long-term runoff forecast of the LSTM model based on Python Keras database. The optimization algorithm is Adam optimization algorithm [5]. The application of artificial neural network model in the field of water resources optimal allocation has been practiced by many scholars. At present, there are many examples of applying neural network model to the direction of water resources in China. For example, the research team of Taiyuan University of Technology used BP-neural network to study the optimal allocation of water resources in Jinzhong City, and in the joint research team of China Yangtze Power Co and Three Gorges University, Xu Gang and other scholars used deep learning model to construct the real-time flood control dispatching model of Three Gorges Reservoir. The combination improvement of neural network model and time series has also appeared in other research fields. Liu Feng combined clustering analysis with neural network to forecast time series. Experiments prove that the radial basis neural network model with clustering analysis can improve the accuracy of continuous prediction and reduce

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the complexity of the model. Tang Weiming of Wuhan University and Ma Senbiao of Zhongrui Network Company put forward the idea of applying the particle swarm LSTM-attention composite model to the water level prediction of a reservoir in Fuzhou City by using the advantages of LSTM in dealing with long time series problems and the advantages of adaptive global search of particle swarm optimization algorithm [6].

2 Methodology In this paper, the LSTM model is used to predict the short-term downstream water level of cascade reservoirs operation, which can combine the idea of multiple regression and time series, and can obtain more accurate fitting results for the short-term regression problems such as the short-term water level prediction with multivariable effects [7].

2.1 Model Introduction LSTM model is a special recurrent neural network model (RNN) of artificial neural network model Figure 1. Its key is cell state, expressed as C t , which stores the variable information of the current period. The current LSTM model receives the cell state C t −1 from the previous cycle and calculates it with the input signal Xt to obtain the cell state C t of the current period. In order to solve the problem of gradient vanishing and gradient explosion in long sequence training, LSTM model constructs forgetting gate, memory gate and output gate. The function of the gate is to introduce or remove information from the cell state C t . The gates used include a Sigmod neural network layer and a bitwise multiplication operation [7]. In the follow pictures, we use the yellow square to represent the Sigmod neural network layer. The pink circle represents the bitwise multiplication operation. The

Fig. 1 The forgetting gate and the memory gate

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function of Sigmad is to convert the input signal to a value between 0 and 1 to determine how much input information can pass through. 0 means ‘no quantity is allowed to pass’ and 1 means ‘all quantities are allowed to pass’. The four steps of information transmission are as follows [8]: • Step1: The Forgetting Gate First, the forgetting gate f t is calculated. F t is used to select or reject the information of the previous period. The calculation formula is as follows:   f t = σ U f xt + W f h t−1 + b f

(1)

In the formula, U f , W f and bf are adjustable parameter matrices or vectors of forgetting gate, which will be optimized in the training process; σ activate the function for Sigmod. • Step 2: The Memory Gate The function of the memory gate is opposite to that of the forgetting gate. It will determine which part in the newly input information of x t and ht −1 will be held back. The memory gate consists of two parts, including Sigmod neural network layer and Tanh neural network layer. The calculation formulas are as follows: ic = σ (Ui xt + Wi h t−1 + bi )

(2)

C˜ t = tanh(UC xt + Wc h t−1 + bc )

(3)

The function of the Tanh neural network layer is to integrate the input x t and ht −1 , and then create a new state candidate vector through the Tanh neural network layer. The value range is between −1 and 1. • Step 3: Update Cell State In this step, Fig. 2, the output f t of the forgetting gate multiplies the previous cell state C t −1 to select to forget or retain some information. The output of the memory gate is added with the information selected from the forgetting gate to obtain a new cell state C t . This means that the cell state C t at time t already contains the information transmitted at time t − 1 that needs to be discarded at this time and the information i t · C˜ t that needs to be newly added obtained from the input signal at time t. C t will continue to be delivered to the LSTM network at time t + 1 and will be delivered as a new cell state. The formula is as follow: Ct = f t × Ct−1 + i t × C˜ t • Step 4: Output Gate

(4)

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Fig. 2 Update cell state and output gate

Now we have the cell state C t −1 , the output signal ht −1 at the previous moment and the input signal x t at the moment t. The output gate is to integrate them as the output signal at the current moment. X t and ht-1 go through a Sigmod neural network layer to output a value ot between 0 and 1. C t is passed through Tanh to a value between −1 and 1 then multiplied by ot to obtain an output information H t . H t will be passed to the next stage as an input signal at the next time. ot = σ (Uo xt + Wo h t−1 + bo )

(5)

h t = ot × tanh(Ct )

(6)

2.2 Calculation Method LSTM model is a supervised learning model. When used, the data should be divided into training set and test set. The calculation steps are as follows. • Step 1: Filtering and Preprocessing Data The data required for construction of the model can be obtained from the corresponding hydropower station. After obtaining data, data filtering and data cleaning should be carried out first. The median filtering method is used to filter and denoise data to avoid the fluctuation of high-time precision data. Normalization method was used for data preprocessing. Assuming that X max is the maximum value in the data and X min is the minimum value in the data, the calculation method is as follows [4]: X i = (X i − X min )/(X max − X min ) • Step 2: Sample Format Conversion

(7)

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The long short-term memory network model requires the input data to be threedimensional tensor, and the format is [samples, time steps, features]. The ‘samples’ refers to data samples. The observation record at each time point is a data sample, and the length of this dimension is the number of observation records; ‘Time steps’ represents time scale and process, and one-time step represents one observation time point in the sample; ‘Features’ represent data features observed at the time step. The length of this dimension is determined by the number of variables in each observation. In brief, the first dimension of the specified three-dimensional tensor corresponds to the rows of the two-dimensional input data matrix, and the length of the dimension is the number of rows of the matrix. The third dimension of the three-dimensional tensor corresponds to a column of the two-dimensional input data matrix, and the length of the dimension is equal to the number of columns of the matrix. Thus, the relationship between the two-dimensional input data matrix and the three-dimensional tensor is established, and the data can be transformed into the three-dimensional tensor. • Step 3: Construct and Train Model The three-door calculation process of LSTM model has been shown in the previous paper. When constructing the water level prediction model of LSTM cascade hydropower stations, a layer of LSTM network is used as the hidden layer, and a dense layer is used as the output layer, which is directly connected with the hidden layer. The number of samples input and the number of training times in the LSTM layer depend on the situation, and the time step is slightly longer than the corresponding time of the downstream water level to the upstream water. The output of the model is the predicted value of the water level of the cascade hydropower station, and the mean square error is used to evaluate the error between the predicted value and the actual value. During the training, the model uses the mean absolute error (MAE) and the maximum error (MAXE) as the evaluation indexes, wherein the mean absolute error measures the stability of the model, and the maximum error in the prediction period measures the prediction accuracy of the model. MAE =

m 1  |ymeasure (i) − yestimate (i )| m i=1

MAXE = max(|ymeasure (i ) − yestimate (i)|)

(8) (9)

The idea of model training is that if the actual value is constant, then the error is a univariate function of the independent variables of the predicted value. By using optimization methods such as the steepest descent method and Newton method, the optimization direction of the predicted value can be obtained, and then the optimal direction of the predicted value is passed to the weighting parameters, so as to gradually optimize the weight of the parameter model and make the model more accurate in training (Fig. 3).

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Fig. 3 LSTM calculation flow

∂H ∂H ∂H ∂H ∂H → → → → ∂t j ∂u j ∂w jk ∂z j ∂v j k Finally, the basic flowchart is as follows in Fig. 4.

Fig. 4 LSTM model construction flowchart

(10)

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3 Case Study 3.1 Parameter Setting This study is based on the application of cascade reservoir of Three Gorges– Gezhouba. When applied, the input and output variables of the model are shown in Tables 1 and 2. The data input format of LSTM model is a three-dimensional tensor, so after data normalization, it needs to be converted into three-dimensional tensor. Each time period in the input data is 1 hour, ranging from 1 January 2015 to 1 October 2019. 75% of the data is used as a training set and 25% as a verification set. The forecasted time span is 24 h, so the second dimension of three-dimensional tensor is 24. The third dimension of a three-dimensional tensor corresponds to a column of a twodimensional input data matrix whose length is equal to the number of columns of the matrix (varying from 5 to 13 depending on the predicted data input). In the LSTM layer of our example, the number of samples per input is set to 64, the number of training is set to 60, and the activation function is set by default. Table 1 Prediction model of water level in the lower reaches of the Three Gorges Project Output data

Input data

Time

Water level of Sandouping (t-23– t)

Water level at the upstream of Gezhouba Dam; Water level at Fenghuang Mountain; Water level at the downstream of the Three Gorges

t-47–t-24

Three Gorges Reservoir abandon flow; Three Gorges Reservoir plant output; Output of Gezhouba Power Plant; Gezhouba Reservoir plant abandon flow

t-23–t

Table 2 Prediction model of water level in the lower reaches of Gezhouba Dam Output data

Input data

Time

Gezhouba 7 # unit downstream water level (t-23–t)

Gezhouba upstream water level; Gezhouba 7 # unit downstream water level

t-47–t-24

Three Gorges Reservoir abandon flow; Three Gorges Reservoir plant output; Output of Gezhouba Power Plant; Gezhouba Reservoir plant abandon flow

t-23–t

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3.2 Result Discussions The prediction results of the model are shown in Figs. 5 and 7. Compared with the short-term dispatching water level prediction under the existing method, the prediction accuracy of the model is significantly improved.

Fig. 5 Predicted water level and measured water level of Sandouping (all year of 2018)

Fig. 6 Predicted water level and measured water level of Sandouping (24 h in different dispatching periods)

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Fig. 7 Predicted water level and measured water level of Gezhouba Dam plant unit 7 (all year of 2018)

In the figures, the abscissa represents time, and the vertical coordinate represents water level. The red line is the estimated water level, and the blue line is the real water level. It can be seen from the prediction result chart, Tables 3 and 4, that the predicted water level at Sandoping Station at the downstream of the Three Gorges has a high degree of fit with the actual water level. The root mean square error of the rolling prediction for the whole year of 2018 is 0.07 m. The model captures the characteristics

Fig. 8 Predicted water level and measured water level of Gezhouba Dam plant unit 7 (24 h in different dispatching periods)

Short-Term Downstream Water Level Prediction Model for Three … Table 3 Comparison of different model prediction effect (Sandouping)

Table 4 Comparison of different model prediction effect (Gezhouba Dam plant unit 7)

233

Mean absolute error (m)

Maximum absolute error (m)

Traditional prediction model error

0.27

1.21

LSTM prediction model error

0.067

0.17

Mean absolute error (m)

Maximum absolute error (m)

Traditional prediction model error

0.45

1.42

LSTM prediction model error

0.15

0.52

of water level changes in each dispatching period and shows good fitting accuracy in the process of increasing the discharge volume during the ebb and flow period and peak shaving of the power station during the flood season. From left to right, Fig. 6 shows the prediction results of Sanduping water level in the Three Gorges flood falling period (June 8th), flood season (August 8th), impounding period (October 31st) in 2016. The rolling prediction of the downstream water level of Unit 7 shows that the root mean square error of the water level for the whole year of 2018 is 0.15 m. From left to right, the prediction chart in Fig. 8 shows the prediction results of the downstream water level of Unit 7 of Gezhouba Dam during the flood falling period (June 9th), flow period (September 9th), storage period (October 31st) in 2018. Compared with the traditional methods, the water level prediction accuracy of the intelligent algorithm has been greatly improved. The mean absolute error and maximum absolute error in different periods are shown in Table 5.

4 Conclusion The calculation results of this paper can provide some support for the application of LSTM model in the short-term water level prediction of hydropower station. The model of Three Gorges–Gezhouba cascade reservoirs in this paper has achieved good prediction results, compared with the traditional model used in hydropower stations. But the prediction of Gezhouba still has a large error, because the inflow of Gezhouba is calculated by the outflow of Three Gorges Reservoir, the accuracy of this calculation process needs to be improved. This is also the improvement direction of this model [9, 10].

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Table 5 Prediction effects of different period Three Gorges

Gezhouba Dam

Periods

Mean absolute error (m)

Maximum absolute error (m)

Flood falling period (June 8th)

0.06

0.09

Flow period (August 8th)

0.13

0.17

Storage period 0.05 (October 31st)

0.08

Flood falling period (June 9th)

0.14

0.20

Flow period (September 9th)

0.37

0.52

Storage period 0.28 (October 31st)

0.39

Acknowledgements This work is funded by Natural Science Foundation of Hubei Province of China (2022CFD164), National Natural Science Foundation of China (42271044), National Public Research Institutes for Basic R&D Operating Expenses Special Project (Number CKSF2021486), Natural Science Foundation of Hubei Province of China (2022CFD027).

References 1. Chong KL, Lai SH, Ahmed AN et al (2021) Optimization of hydropower reservoir operation based on hedging policy using Jaya algorithm. Appl Soft Comput J 106 2. Afshar A et al (2007) Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation—science direct. J Franklin Inst 344(5):452–462 3. Castelletti AS et al (2010) Tree-based reinforcement learning for optimal water reservoir operation. Water Resour Res 46(9) 4. Liu P, Cai X, Guo S (2011) Deriving multiple near-optimal solutions to deterministic reservoir operation problems. Water Resour Res 47(8):2168–2174 5. Yang MH, Nam WH, Kim HJ et al (2021) Anomaly detection in reservoir water level data using the LSTM model based on deep learning. Korean Soc Hazard Mitig (1) 6. Ma SB, Tang WM, Chen CQ (2022) Study on reservoir water level prediction based on LSTM optimization model. Fujian Comput 38(05):1–8 7. Dong Y, Zhang Y, Liu F et al (2021) Reservoir production prediction model based on a stacked LSTM network and transfer learning 8. Loganathan GV, Bhattacharya D (1990) Goal-programming techniques for optimal reservoir operations. J Water Resour Plan Manag 116(6):820–838 9. Zhou Y, Guo S, Liu P, Xu C (2014) Joint operation and dynamic control of flood limiting water levels for mixed cascade reservoir systems. J Hydrol 519:248–257 10. Mohammad H, Faridah O, Kourosh Q (2015) Developing optimal reservoir operation for multiple and multipurpose reservoirs using mathematical programming. Math Probl Eng 2015(pt.2):1–11

Water Supply System and Water Pollution

Review of Water Quality Prediction Methods Zhen Chen, Limin Liu, Yongsheng Wang, and Jing Gao

Abstract Water quality prediction plays a crucial role in environmental monitoring, ecosystem sustainability, and aquaculture, and it plays an important role in both economic and ecological benefits. For many years, researchers have been working on how to improve the accuracy of water quality prediction. However, at this stage, with the increase in external climate change, external noise, precipitation, and many other uncertainties, water quality prediction is facing the problem of insufficient accuracy. This paper analyzes the research methods of water quality prediction at home and abroad in recent years and summarizes the introduction through two aspects: mechanical water quality prediction methods and non-mechanical water quality prediction methods. Firstly, the mechanism of water quality prediction method is introduced, which uses various hydrological data information such as initial water head, bottom slope, hydraulic radius, etc. to predict water quality; Secondly, it introduces the non-mechanical water quality prediction method that uses historical water quality index data to analyze and mine to predict water quality. Finally, after introducing the existing prediction methods, the development direction of water quality prediction is analyzed and summarized. Z. Chen · L. Liu · Y. Wang (B) College of Data Science and Application, Inner Mongolia University of Technology, Hohhot, China e-mail: [email protected] Z. Chen e-mail: [email protected] L. Liu e-mail: [email protected] Z. Chen · L. Liu · Y. Wang · Y. Wang Inner Mongolia Autonomous Region Software Service Engineering Technology Research Center Based on Big Data Inner Mongolia Autonomous Region, Hohhot, China J. Gao School of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_17

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Keywords Water quality prediction · Data analysis · Mechanistic water quality prediction · Non-mechanistic water quality prediction

1 Introduction In any ecosystem, the quality of the water is vital, for example, it can represent the degree of water contamination and have a direct impact on the development of aquatic creatures [1]. Predictive modeling of surface water quality-related variables is now considered an efficient and feasible approach for water conservation. It can be used to predict the trend of water quality at a certain point in the future so that appropriate measures can be taken at the right time to achieve the purpose of water ecology protection and can reduce a lot of human and material resources. To safeguard the environment, sustain ecosystems, advance socio-economic development, and promote human health, accurate water quality forecasting is essential. Therefore, predicting water quality is very useful in real life [2]. Over the years, researchers have worked to increase the reliability of water quality predictions by developing new techniques and refining those that already exist. The existing models can be broadly categorized into two groups: mechanistic water quality prediction methods and non-mechanistic water quality prediction methods. Next, each of the two types of methods involved in the existing literature is analyzed and presented, and Fig. 1 shows the classification strategy and the linear structure of this paper. To strongly illustrate the effectiveness and usability of these models, a number of papers from recent years are cited for demonstration.

Fig. 1 Classification of water quality prediction methods

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2 Mechanistic Water Quality Prediction Methods The mechanistic water quality prediction model mainly uses the principle of conservation and energy conservation and derives the mathematical model according to fluid mechanics. The process of establishing the model for the physical, chemical, biological and interactions among components that affect the change of water quality is fully considered [3] and then predicts the trend of water quality. Mechanistic water quality prediction methods mainly include the S-P model, QUAL model, WASP model, SWAT, etc. [4], in this regard, foreign countries have developed more perfect, and the updating speed of the model is relatively fast.

2.1 S-P Water Quality Prediction Model In 1925, two American scholars, H.W. Streeter and E.B. Phelps, proposed the Streeter-Phelps (S-P) model, which primarily examined the connection between dissolved oxygen (DO) and biochemical oxygen demand (BOD). This model has been widely used because of its excellent characteristics and interpretability in simulating and predicting water quality [5]. S-P model, also known as the BOD-DO model (biochemical oxygen demand dissolved oxygen), is a relatively simple onedimensional model, which is mainly used to describe the change law of BOD and DO in rivers [6] Through the research, it was found that because S-P parameters are complex but extremely important [7], the selection of parameters became a very important task for the S-P model in simulating water quality, and the researchers used various methods to accurately determine the parameters of the model so that the S-P model could meet the water quality prediction requirements. For example, when studying the water quality of the Nanfei River, to obtain optimal parameters, good experimental results can be obtained. He [8] used the least squares method, the graphical method, and the two-point method to estimate the carbonized BOD attenuation coefficient K1, and used the formula estimation method to estimate the atmospheric reoxygenation coefficient K2. Similarly, to determine the parameters of the S-P model, Zhou applied an improved genetic algorithm to the S-P model to obtain good experimental results [7]. Liu [9] found that with the improvement of the water quality model, its structure became more complex, there were more and more variable parameters, and the accuracy of the parameters themselves became more and more important for the simulation and prediction effect of the model. They used the S-P model in the process of simulating and predicting the BOD and DO in water quality but found that the S-P model contains many parameters. To improve the model’s prediction output accuracy, the parameter calibration of the S-P model was improved. An improved genetic algorithm and parameter estimation method are used to determine the parameters. Experiments show that the method has high efficiency and is easy to implement by computer. To study the organic pollution in small watershed rivers, Li and Li simulated and predicted the water quality

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of rivers for three indicators: chemical oxygen demand (COD), DO, and BOD. It considered the realistic model parameters such as BOD removal rate, human factors such as river flow, natural factors, and uncertainties such as detection means, and considered further optimization based on the traditional water management optimization model, using interval parameters to characterize the uncertain parameters in the model, and obtained an improved S-P model, and obtained good experimental results through experiments [10]. To compute the hydrodynamic process, Wu and Yu [11] employed the shallow water equation (SWE), and to assess the complicated gas, they further linked the modified S-P model with the SWE model. The model was applied to analyze mass transportation and re-aeration in the river, and the effect of sudden changes in channel cross-section on dissolved oxygen distribution and aeration capacity was determined.

2.2 QUAL Water Quality Prediction Model After the S-P model, with the scholars’ continuous research on one-dimensional models and the successful development of QUAL series software, certain conditions were created for the proposed QUAL2E model, etc. QUAL2E is one of the QUAL series models. 1977, the U.S. Environmental Protection Agency proposed the QUAL2E water quality steady-state model, which can simulate the change process of water quality and is used in the simulation of surface water [12]. Bárbara [13] investigated the hydraulics and water quality characteristics of the middle channel of the Araguari River in the state of Amapá, Brazil, using the QUAL2E model, and studied the impact of the UHECN hydroelectric power plant on river water quality, which showed that the main water characteristics changed by the hydroelectric power plant were hydraulics and water quality (PH, conductivity, water temperature, BOD, etc.) Knapik [14] modeled and analyzed the water quality of the Iguazu River in the Curitiba metropolitan area through QUAL2E and QUAL2K to determine, in their paper, highlighted that the differences between the two models are mainly related to the mass balance equations in the simulation of dissolved oxygen, nitrogen, phosphorus, and organic matter concentrations. Zhao [15] found that the most important thing for both QUAL2E and QUAL2K models is the selection of the best parameters. A modified version of the QUAL2E model, the QUAL2K model is a one-dimensional steady-state river water quality model. The QUAL2K model considers the effects of multiple factors, including hydrology and temperature, with dissolved oxygen. QUAL2E and QUAL2K have their own excellent characteristics in water quality simulation and prediction. QUAL2K has the same basic principle as QUAL2E, but on the basis of the QUAL2E model, the interaction between some elements is added to make up for the shortage of the QUAL2E model, and QUAL2K model has a more friendly interface. However, the QUAL2K model is complex and has many parameters, so the selection of parameters is a complex task, which has been studied by related scholars. To address the problem that the parameters of the QUAL2E model

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are difficult to determine, Gong proposes to combining Bayesian theory and MCMC method to propose an effective method for parameter determination. Parameter values related to COBODf were determined by Bayesian-MCMC using a section of Boulder Creek River as the experimental object, and the validity of the method was verified based on the experimental results [16]. Similarly, Chen [17] performed improved operations on the QUAL2K model in terms of parameter optimization and used the improved Morris screening method to perform sensitivity analysis with DO as the predictor to optimize the relevant parameters of the model, and then determined the highly sensitive parameters, sensitive parameters, and low sensitive parameters, and finally achieved the expected experimental results.

2.3 WASP Water Quality Prediction Model At the same time as the QUAL model development, in 1983, the U.S. Environmental Protection Agency proposed a model that can reflect multiple dimensions of water quality pollutant changes, namely, the Water Quality Analysis Simulation Program (WASP) model [18]. WASP is a box dynamics model, which requires different segments of the water body to achieve better water quality simulation and prediction. At the same time, it can simulate one-dimensional, two-dimensional, and threedimensional water quality problems that have been more widely used in foreign countries [19]. WASP model includes two modules: hydrodynamic module (HYNHYD) and water quality module; and the water quality module can be divided into two parts: eutrophication module (EUTRO) and toxic chemical module Mn(TOXI), chemical substances, such as organic compounds, metal and sediment, etc. [20] TOXI is mainly used to simulate toxic chemicals such as organic compounds, metal and sediment. This model has excellent performance and can be used to explain, simulate and predict a variety of water quality changes, so it is often used for water quality control and management. Liu combined the WASP water quality prediction model with the water quality characteristics of Baiyangdian to find a way to improve the purification capacity of Baiyangdian and found that the selection of model parameters is very critical for the WASP model, which is related to whether the model can accurately respond to the water quality change process [21]. To solve the problem that the riverbed of the simulated mountain river changes drastically, and the water level and flow velocity of the river are different in different reaches, Zhao and Yao [22] used the WASP model to simulate the water quality of the Gulin River in the southeastern mountainous area of Sichuan Province, and obtained the reduction of COD and ammonia nitrogen in each section of the Gulin River is to achieve the purpose of protecting the Gulin River. WASP is known as the “universal water quality model”, and its accuracy has been recognized by many scholars; however, it also has certain shortcomings, that is, although WASP is a hydrodynamic model, it is relatively weak in the hydrodynamic simulation. Therefore, in the process of simulating and predicting water quality, other models need to be introduced to drive the WASP model for accurate simulation

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prediction [23]. To address this issue, scholars in China have also conducted some exploration and achieved some results. He [24] coupled EFDC (Environmental Fluid Dynamics Code) and WASP when simulating and predicting the water quality of the Three Gorges of the Yangtze River, and used ESDC as the hydrodynamic model of WASP to compensate for the insufficient hydrodynamic effect of WASP, and finally obtained a hybrid model for analysis. Simulate the water quality of the Yangtze RiverJialing River in the main urban section of Chongqing after the implementation of water resource management measures. Jia [25] coupling EFDC and WASP was used to predict and simulate the water quality changes of Nansha River after using three different wastewater treatment methods in the Nansha River Wastewater Treatment Plant, and to develop a conservation plan for Nansha River based on this. WASP can be simulated for most water quality applications because of its excellent characteristics. However, it has certain disadvantages of its own. For example, WASP does not take into account all the processes and zooplankton dynamics that occur in the water eutrophication phenomenon. In addition, WASP includes oversimplified sediment flux calculations, and sediment transport processes are independent of shear stress. Finally, WASP cannot handle calculations for mixing zones, and sinkable materials, and does not consider some variables such as macroalgae or epiphytes.

2.4 SWAT Water Quality Prediction Model In 1994, the Soil and Water Assessment Tool (SWAT) model was developed by the U.S. Department of Agriculture to effectively simulate water and soil transformation and transport processes in watersheds, including the hydrology model, soil erosion model, and pollution load model [26]. SWAT borrows the QUAL2E method when performing nutrient calculations. Andrade [27] applied SWAT to the Mundaú River Basin in northern Brazil to simulate soil moisture and flow and finally found that the river flow and soil moisture were in a good state, and found that although the SWAT model existed to a certain extent uncertainty, supplementary data such as soil moisture can be used to calibrate and validate the SWAT model. Pradhan [28] combined SWAT and artificial neural networks to simulate daily runoff and compared the results with observed data for performance analysis. Their study was conducted in three different watersheds with three different climatic characteristics, namely, the Siseti River basin in the subtropical (partially humid) climate zone, the Srebok River basin in the tropical (humid) climate zone, and the Harroid River basin in the semi-arid (dry) climate zone. Although SWAT has superior performance in predicting water quality simulation, its various submodels have some shortcomings in dealing with different problems. Giles [29] found that the SWAT model uses a submodel involving a linear regression of water temperature (WT) on observed air temperature to simulate river temperature. However, using constant generalized regression coefficients in this submodel can significantly limit the ability of the model to estimate river network temperatures across the continental United States, especially when water temperatures are altered by climate, topography, land use, and hydrology processes.

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They propose a method to improve the default river temperature submodel of SWAT by optimizing the geographically specific values of the coefficients in the regression model to achieve better predictive simulation. Kalcic [30] improved the hydrology response unit (HRU) of the SWAT model, which found that it is often advantageous to define the HRU as a specific spatial location bounded by building redlines or field boundaries, and therefore defined the HRU by field boundaries to achieve optimal simulations by adding uniquely named soils to the SWAT user soil database and creating field boundary layers with most land use and soil properties results. As the model is increasingly used in the United States and its results are feasible, it is gradually being used in other Western countries. However, because of the late start in water quality in China, the relevant hydrological information is insufficient, resulting in its relatively late application in China. Aiming at the problems of uncertainty in the spatial distribution of the calibration parameters of SWAT and its sub-models, Li [31] used the vegetation module to calibrate the SWAT model with multiple objectives to improve the simulation quality of the entire model. For example, taking the Meichuan River Basin as an example, the parameters are calibrated considering a variety of objectives, and the uncertainty of the parameters is reduced, to achieve the best model effect.

2.5 Summary of the Mechanistic Approach S-P, QUAL2K, WASP, and SWAT all belong to mechanistic models, which are widely used because of their excellent characteristics in prediction models, and the summary of mechanistic prediction methods is shown in Table 1. However, as the research progresses, the complexity within the model is gradually increased to obtain a more detailed representation of the migration transformation process of pollutants in water, and the selection of the corresponding parameters becomes more difficult [32]. The parameters are the key to the accuracy of the whole model. Therefore, the selection of parameters has become a difficult and important task. To solve the problem of difficult parameter selection, a lot of research work has been put into it, and the more common parameter rate determination methods include genetic algorithm, particle swarm optimization algorithm, Bayesian inference method, simulated annealing method, Markov Chain Monte Carlo (MCMC) method, etc. [32]. The mechanistic water quality prediction method is more fully considered a variety of factors of water quality changes; therefore, the model’s prediction simulation effect is relatively good. Through the above study, it can be found that mechanistic reason has more accurate results in water quality prediction, and the range of predictions that can be simulated is expanding as the relevant institutions improve the corresponding model methods. For example, the WASP model can now simulate and predict the transformation of substances in the water and substrate. However, the mechanistic water quality prediction method has some other shortcomings in addition to the rate of the relevant parameters. For example, when using the WASP model to make predictions requires a certain understanding of hydrodynamics and environmental toxicology, and requires

Dissolved oxygen as the core includes: temperature, ammonia nitrogen, nitrite nitrogen, E. coli and other water quality indicators in 13 Mainly including conventional pollutants, organic pollutants, water temperature, E. coli, hard-to-degrade pollutants, etc

One dimension

One-dimensional, two-dimensional, three-dimensional

QUAL

WASP

The study mainly focuses on oxygen balance but also involves some non-oxygen-consuming substances

One dimension

S-P

Main research content

Dimensionality

Predictive models

Flexible simulation of virtually any type of water body, modular structure-based, sediment-forming model for remineralization

The effect of wastewater discharge on river water quality can be studied, and the kinetic parameters can be referred to the values of simpler models

More accurate simulation prediction for BOD and DO

Advantages

Table 1 Comparison of research methods of mechanistic water quality prediction River

Main application areas

(continued)

It cannot handle mixing All water ecosystems zones, calculations for sinkable materials, and does not take into account variables such as macroalgae or epiphytes, it requires external hydrodynamic files to solve advection, oversimplified sediment flux calculations, and sediment transport processes are independent of shear stress

The number of river Rivers, outfalls, water segments is limited, and the intakes process of material migration is considered simpler

Only biochemical oxygen demand and dissolved oxygen were predicted, and the predictions were relatively limited

Disadvantages

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Dimensionality

Multidimensional

Predictive models

SWAT

Table 1 (continued) Study a variety of different hydrological physicochemical processes, such as water quantity, water quality, and the transport and transformation of pesticides

Main research content It can be used for watersheds without monitoring data, can simulate a variety of different hydrological and physical processes in complex large watersheds, and has high computing efficiency

Advantages Non-linear relationship between hydrological response (output) and hydrological characteristics (input), large number of model parameters need to be calibrated

Disadvantages

Rivers, lakes, reservoirs

Main application areas

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some knowledge of other fields besides water quality. This raises the requirements of the personnel involved.

3 Non-mechanical Water Quality Prediction Although the effect of mechanistic water quality prediction is good and has reached the level of application in foreign countries, there are problems such as high modeling difficulty, poor practicality, and portability; and its need for relatively complete information on the water environment, and China’s information in hydrology is relatively less than perfect, this is an important factor limiting its development in China. Therefore, there is an urgent need for a predictive simulation method that does not rely on the basic water quality data information, in this context, the non-mechanical method was born. The non-mechanical water quality prediction method analyzes and models the historical data information related to water quality, finds the implicit relationship, deduces the changing trend of water quality based on this, and then predicts the water quality. Non-mechanical water quality prediction compared to mechanical water quality prediction has its unique advantages, such as non-mechanical water quality prediction method on the hydrological data requirements is not high, the modeling difficulty is low, so it is easier to promote. The existing literature on nonmechanical methods mainly includes regression prediction methods, grey system methods, artificial intelligence-based methods, etc. [33].

3.1 Regression Analysis Water Quality Prediction Method Based on Multiple Linear Regression. Most scholars are familiar with regression analysis methods since they are often utilized in simulation and prediction applications. The Multivariable Linear Regression model (MLR) is based on the least squares approach, in which the model is fitted so that the sum of squares of the discrepancies between observed and predicted values is minimized. In many cases, MLR methods are applied to water quality prediction. East China Jiaotong University Xiang [34] proposed a linear regression-based model to predict the groundwater flow rate because the groundwater system is a complex stochastic system and there is a complex correlation between groundwater flow and its influencing factors predict. In Chen [35], the improved principal component analysis method and multiple linear regression model are fused to evaluate and predict water quality, which first uses the improved principal component analysis method to analyze the water quality data, and then inputs the linear regression model to obtain the water quality evaluation and prediction results.

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The main advantage of this technique is its relative simplicity and ease of implementation. However, its application has some limitations. The main ones include: Weight consistency. Least squares method assumes that the sample data on the prediction points are equal weight, but in practice, a variety of factors affect the importance of water quality is different. For example, nitrate is an important chemical element that affects water quality, and ammonia nitrogen can be converted to nitrate by consuming dissolved oxygen in water [36]. Therefore, the influence of ammonia and dissolved oxygen on nitrate has a higher weight, while dissolved oxygen receives the influence of water temperature and pH, which indirectly affects the concentration of nitrate, the weight is relatively low, but the prediction method of multiple linear regression can not take this situation into account. Linear internality. MLR cannot detect non-linear relationships between water quality parameters [37]. There are a variety of indicators affecting water quality, and there is a complex nonlinear relationship between them, therefore, based on multiple linear regression analysis is not a good prediction of water quality data. Moreover, sometimes in the water quality prediction, the choice of the factor and the factor using what expression is only a speculation, which affects the unpredictability of some factors. Water Quality Prediction Methods Based on Autoregressive Methods. One of the techniques for time series forecasting analysis is the Difference Integrated Moving Average Autoregressive Model (ARIMA) or ARIMA(p,d,q) model. Where p denotes the autoregressive term, q denotes the quantity of moving average terms, and d denotes the number of differences that are created when the time series smooths out. The method is based on stochastic process and mathematical statistics, using the historical testing data of water quality, studying the statistical mathematical laws of data series changes, analyzing the time change relationship among them, and then making certain predictions on the future changes of water quality. Liu [38] used the ARIMA model to predict the DO and ammonia nitrogen (NH4 + -N) content of water quality indexes in Changsha and Yiyang sections of the more polluted Xiangjiang River basin, and according to the experimental results, we can find that the ARIMA model can make feasible predictions for the water quality of Xiangjiang River basin. Melesse [39] applied the ARIMA method to make comparative experimental predictions of daily and weekly sand production in three rivers in the United States. Their experimental finding shows that the ARIMA approach and the ANN method both achieved accurate prediction results for the daily simulation of suspended sediment load (SSL) on the Mississippi River. However, the ARIMA model also presents several drawbacks that prevent further advancement in the area of water quality. Its drawbacks mainly include: The linear nature of the input data is presupposed [40]. Its functionality is not as good as the neural network approach in terms of the prediction of linear problems. The time series data must be checked to see if they are smooth during the model identification step because building an ARIMA model depends on this. In reality, conventional ARIMA models cannot capture the non-linearity of water quality data

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because of the complexity and temporal dependence of the components in water quality [41] and non-smoothness [42]. Amplify the high-frequency noise in the data [43]. In the modeling process of ARIMA, to change the water quality data set into a smooth time series, data from the water quality time series must be varied numerous times, but in the process of differencing, the high-frequency noise in the data is amplified, and the data are difference several times, which will inevitably cause the loss of time series information [44].

3.2 Grey System Method Professor Deng Julong first suggested the Grey System Theory in 1982, which combines two mathematical methods, automatic control and operations research, and is mainly used to solve the problem of modeling grey systems with unclear information [45]. The method is computationally efficient and has good generalization [46]. Hu [47] improved the traditional grey system model to establish a grey system dynamic model group, smoothed the basic water quality data to reduce the impact of data fluctuation problems, and carried out permanganate analysis on the Qinhuai River. In Delgado [48], the grey clustering method was improved by using the triangular whitening weight function (CTWF) to make the model more focused on the indicators that have a greater impact on water quality, and they used the monitoring data from the Peruvian National Water Agency (ANA) in the Santa River basin. The water quality prediction model was developed and analyzed for 21 monitoring points, and good prediction results were achieved in each testing point. The traditional twoparameter grey model in the original series is always accompanied by the influence of extreme values, affecting the accuracy of grey model simulation and prediction, Li [49] solved the problem of unreasonable distribution of parameter background values by increasing the number of parameters in the background values and then improved the smoothness of the background values to enhance the prediction accuracy of water consumption. Although the proposed grey system theory has solved the problem of unclear water quality index factors and incomplete understanding of the principle of action, there are certain shortcomings [50]. The grey system parameter estimates are calculated based on the basic form (difference equation), but the time response function of the model is based on the whitening equation (differential equation), so there must be a certain error between the differential equation to the differential equation, resulting in poor prediction accuracy. Additionally, the grey theory approach can only be used to approximate exponential functions; it cannot be used to approximate complicated nonlinear functions [2].

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3.3 Neural Network Approach Artificial Neural Networks (ANN) have gained more attention recently in water resources research [48]. It is a robust computational algorithm for modeling complex nonlinear relationships, especially when the explicit form of the relationship between variables is unknown, ANN can better capture the complex nonlinear information between data [51]. A mathematical system called a neural network converts a set of input data into a matching number of output data [52]. There are many different ways to classify ANNs, but in the topic of forecasting water quality, there are two basic groups to take into account: feedforward neural networks (FNN) and recurrent neural networks (RNN) [53]. Water Quality Prediction Method Based on Feedforward Neural Network. Feedforward Neural Network (FNN) is a neural network in which neuronal connections exist only between the input layer, the hidden layer, and the output layer. It is often used to learn the relationship between the independent variables as network input and the dependent variables specified as network outputs, making it the most popular and widely used model for many practical applications, and its network structure is shown in Fig. 2; Back Propagation Neural Network (BPNN) [54] and Radial Basis Function Neural Network (RBFNN) [55]. BPNN is a back propagation neural network trained by the BP algorithm, which can calculate the input and activation values of each layer and backbreaking to calculate the error term of each layer and finally calculate the partial derivatives of the parameters of each layer to achieve parameter update [56]. With the issue that the conventional BP method is simple to slip into local minima, Yu [57] introduced an enhanced BP neural network algorithm based on particle swarm intelligence for forecasting the concentration of chlorophyll a (Chl-a) in water. The weights of the neural network are modified to optimize the structure of the neural

x1 x2 Water quality monitoring data

x3

… xn–1

xn Fig. 2 Architecture of FNN

Forecast results

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network so that the entire prediction model can reach the global optimum. Ding [58] used a genetic algorithm to update and optimize the parameters of the BPNN network to achieve good water quality prediction. After experiments revealed that the hybrid optimized BP model has higher prediction accuracy than the traditional BP neural network, PSO optimized BP neural network, and GA optimized BP neural network, Yan [59] proposed a hybrid optimization algorithm of BP neural network combining particle swarm optimization (PSO) and genetic algorithm (GA), which can predict the water quality of Beihai Lake in Beijing on time series with good performance. The tests demonstrate that compared to regular BP neural network, PSO optimized BP neural network, and GA optimized BP neural network, the hybrid optimized BP model has stronger prediction ability and improved robustness of water quality indicators. RBFNN is an FNN with a radial basis function as the activation function, which is a local approximation neural network [60] RBFNN is a local approximation neural network with a unique best approximation point, which can effectively solve the local optimum problem of BPNN [61]. The topology is compact and the learning speed is relatively fast, many obvious advantages make RBF have a strong potential for application in more and more fields [62]. To create forecasting models for the adequate and reliable acquisition of wastewater BOD and wastewater TN, Meng [63] developed an adaptive radial basis function (ATO-RBF) network, which demonstrated greater prediction accuracy when compared with the traditional method. To enhance the conjugate gradient method’s learning rate and search orientation, Yang [64] proposed a hybrid conjugate gradient method, i.e., ICG-RBF, which combines the FR method and PRP method to optimize the search direction. Simulation results of the ICG-RBF model are more accurate, require fewer iterations, and have faster convergence than those of the conventional RBF neural network model. Similarly, Ren [65] also used to improve the RBF for water quality prediction, also achieved more promising prediction results. Ghose [66] prediction of groundwater table depth in western Orissa using BPNN and RBFNN found that both BPNN and RBFN predicted the depth of groundwater table within very good tolerances, both networks fitted the data collected at Rengali station well and showed minimum variation error in predicting the depth of groundwater table. However, the two methods also show different properties, with the BPNN showing better prediction accuracy compared to the RBFN, but faster convergence compared to the BPNN. Radial basis function neural networks (RBFNN) and back propagation neural networks (BPNN) were also employed by Suen and Eheart [67] to forecast nitrate concentrations in the Upper Sangamon River Basin, Illinois, USA. In contrast with conventional regression techniques, results of BPNN and RBFNN were evaluated. According to the findings, RBFNN outperformed BPNN and regression techniques in terms of performance. Hong [68] invented a hybrid RBF-based model (GRA-RBF) to predict trihalomethanes (THMs) in tap water and also achieved better results after experimental analysis. Although BP neural networks and RBF neural networks have the advantages of not requiring a priori knowledge and parallel computation, they also have some disadvantages and shortcomings.

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BP neural networks frequently experience the issue of the local optimal solution and slow down the learning rate [69]. The development of BP neural networks in the field of prediction is also constrained by the fact that the traditional BP neural network with a learning rate will slow down the learning rate and probably run into the problem of a local minimum. To address this problem, we can start with some optimization algorithms, such as the ant colony algorithm, to optimize the classical BP neural network so that it can avoid the problem of local optimum. The theory and learning algorithm need to be further improved. the RBF processing of nonlinear data is mainly reflected in the basic function of its hidden layer, however, the center of the kernel function mostly controls the basis function’s properties, and the effect of mapping out from arbitrarily selected centers in the data is not optimal. Convolutional Neural Networks (CNNs) are feedforward neural networks that include convolutional computation and have a deep structure. These features allow CNNs to reduce the complexity of the network and provide more powerful feature mapping capabilities [70]. With the help of these characteristics, CNNs can simplify networks and offer more effective feature mapping capabilities. Jia [71] found that the current research on COD prediction is mainly several on the pre-processing of spectral data and extraction of spectral features, and few scholars consider the method of spectral data modeling, causing them to propose the use of CNN to model the prediction of spectral data notation. The experimental results show that CNN has a strong prediction ability for COD in water, high prediction accuracy, and a good fit of the regression curve. The minimum MAE and RMSE were obtained compared to other models, and the MAE and RMSE were reduced by 25.5% and 21.33% compared to the traditional CNN prediction model. Pyo [72] used a 3D water quality model to generate synthetic spatio-temporal water quality data for river cross-sections and used to investigate the ability of convolutional neural networks (CNN) to predict harmful cyanobacterial blooms with a Nash–Sutcliffe efficiency (NSE) of 0.87, which is still acceptable even at 10% noise amplitude with NSE = 0.76. Wang [73] aimed at the problem that the eutrophication degree of lakes cannot be determined by manual calculation and an accurate prediction model cannot be built because of the inaccurate determination of data, a 3D fractal network CNN was proposed to automatically extract the features in remote sensing images, aiming to achieve scientific prediction of the eutrophication status of lakes. High accuracy was achieved through experimental validation. Song and Kim [74] used hydrological images and convolutional neural networks to predict the biochemical oxygen demand and total phosphorus load in agricultural areas with good prediction results. Water Quality Prediction Method Based on Recurrent Neural Network. Unlike feedforward neural networks, the neurons within the layers of a Recurrent Neural Network (RNN) are interconnected and allow feedback [75]. It is a recurrent neural network. It is mainly effective in predicting data with time series characteristics and can find the time series information implied in the data. Therefore, it is currently used in time series prediction, natural language processing, and machine translation. However, it also has some shortcomings, namely, gradient explosion and gradient

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xt input gate

xt

it

output gat

ot

cell

xt

ct

ft

ht

forget gate

xt Fig. 3 Architecture of LSTM

disappearance problems that occur easily in the training phase. The RNN’s hidden layer of Long Short-Term Memory Networks (LSTM) adds a processor termed “memory cell state” to determine if the information is valuable or not [76]. The more intricate internal structure of the LSTM allows it to get the state, recall information that must be retained for a long period and forgets irrelevant information [76]. This allows it to selectively change the transmitted information. The network structure is shown in Fig. 3. Gated Recurrent Unit networks (GRU) are also proposed to address this problem. GRU is better to train than LSTM and can produce results that are comparable to LSTM, which can greatly enhance the effectiveness of training. Cao [77] according to the characteristics of dissolved oxygen in water, firstly, by improving the clustering method, the time series of dissolved oxygen concentration is clustered, and then GRU is used to predict and analyze the time series data of water quality after clustering. Compared with the traditional water quality prediction method, whose method has higher accuracy and timeliness. The ARIMA model can better reflect the linearity of the time series data, but the non-linear variation of the water quality series is not well handled. The ARIMA model can better reflect the linear characteristics of time series data, but it cannot handle the nonlinear changes in the water quality series well. Hu [78] combined the nonlinear characteristics of LSTM in processing time series data, and proposed ARIMA. Combined with LSTM, it can predict the changing trend of river water quality analysis. The experimental results show that this method has indeed achieved the expected effect. Eze and Ajmal [79] proposed to use the EEMD algorithm to decompose the raw sensor data into multiple eigenmode functions (IMFs) for the problems of low accuracy and poor generalization ability of traditional prediction methods; then, feature selection was used to carefully select IMFs strongly correlated with raw sensor data and integrated into the input of LSTM; finally, construct a hybrid EEMD-based LSTM prediction model. In response to the complexity of water quality data and susceptibility to noise, Yang [80] proposed a water quality prediction method based on Convolutional

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Neural Networks (CNN) and LSTM to predict pH and NH3 -N. Experimental results showed that this model outperformed other models and could predict different time lags stably. Similarly, Zhou [81] also proposed a hybrid model using a combination of CNN and LSTM to accurately predict the dissolved oxygen concentration in water. This model was found to be better than the single LSTM model and the BPNN model through experiments. Although LSTM and GRU have improved RNN to some extent, there are some shortcomings: (1) Inability to consider multiple correlations among water quality data indicators. Although multiple indicators are considered in the process of prediction using LSTM, it is not possible to consider the analysis of the correlation between these indicators. Therefore, some scholars have recently started to focus on correlation analysis [76, 82, 83]. The correlations of the data are first analyzed, and then predictions are made. However, only direct interrelationships between multiple variables can be analyzed, and indirect interactions that exist with intermediate variables as bridge are not well captured for analysis. (2) Assuming that the data are sequence-dependent, that parallel operations are not possible, that hardware resources cannot be fully utilized, and that the gradient still vanishes after the sequence length exceeds a certain limit. And when the time is large and the network is deep, it will be more time-consuming. Water Quality Dataset. An approach to anticipate the trend of changes in water quality using neural networks is based mostly on historical time series analysis mining of data related to water quality. Therefore, analysis and pre-processing of water quality data sets are very important, which are related to the accuracy of the prediction results, and whether it can meet the expected requirements. Input water quality elements. Based on their source and character, the three primary categories of water quality factors are chemical, physical, and biological. A variety of water quality characteristics have been modeled in the research. Algae, nutrients, or nutrient-related chemical indicators like DO and BOD, and surface and groundwater quality factors like water temperature, permanganate index, total nitrogen, total phosphorus, and pH are few examples. The three variables (chemical, physical, and biological) are considered by the review papers that were chosen for this paper. The reason why neural network-based water quality prediction methods focus so much on these variables may be because in many water ecosystems when analyzing water quality environments, these factors are always used. Specific input indicators and predictors are shown in Table 2. Data normalization. The original data for the variables should be normalized to a range of the adjustment to comply with the neural network algorithm’s criteria. To match the distribution of the estimated output, the transformation adjusts the distribution of the input variables [84]. Because water quality indicators have different quantitative ranges, for example, conductivity is always measured in thousands, while total nitrogen levels are generally maintained within a range of 10. The reason for data sample scaling is the initial equalization of variable importance and the improvement of interpretability of network weight [85]. In the artificial neural network

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Table 2 Input water quality index and prediction index in neural network method Author

Method

Input Indicators

Predictors

Area of use

Han et al. [1]

RBF

NH, TN, suspended solids concentration (TSS), BOD5, COD, DO

DO

Sewage treatment plant

Wang et al. LSTM [2]

DO, TP

DO, TP

Taihu

Chen et al. [35]

PCA-MLR

SD, WT, pH, TN, TP, DO, TSS, NH3 -N, Nitrate–N, IMn

Chl-a

Hanfeng Lake

Nourani et al. [37]

ANN

Groundwater level (GL), EC, TDS

EC, TDS

Groundwater in the Ardabil Plain

Liu and MCMC-ARIMA Wang. [38]

NH4 + -N, DO

NH4 + -N, DO

Xiangjiang River

Melesse et al. [39]

BP

Precipitation, flow, pre-flow, pre-sedimentation

Sediment load

Mississippi River, Missouri River, Rio Grande River

Zare et al. [40]

ANN

pH, EC, Mg2+ , Cl−, Na+, K+, bicarbonate, sulfate, Ca2+ , TDS, Th

Nitrate

Arak Groundwater

Huo et al. [41]

BP

NH3 -N, SD, Chl-a, season, water color, turbidity, DO, pH, Chl-a, NH-N, BOD, TN

DO, TN, Chl-a, SD

Fuxian Lake

Chang et al. [42]

BP-NARX

pH, EC, DO, BOD, COD, SS, E. coli colonies, NH3 -N, WT, TP

TP

Dahan River

Zhang [46] EEMD-LSTM

pH, DO, NH3 -N, CODMn, temperature, rainfall, wind speed, barometric pressure

pH, DO, NH3 -N, CODMn

Taihu

Khani and Rajaee, [52]

WR-WANN

DO

DO

Clackamas River

Yu et al. [57]

PSO-BP

WT, SD, CODMn, DO, Chl-a TP, TN, Chl-a, pH

Ming Lake (continued)

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Table 2 (continued) Author

Method

Input Indicators

Ding et al. [58]

BPNN

Ph, total nitrogen, total Water quality Taihu phosphorus, pollution situation permanganate index, COD, NH3 -N, pH, TN, CODMn, and other 23 water quality parameters

Predictors

Yan et al. [59]

BP

pH, Chl-a, DO, BOD, EC, NH4 H

DO

Beihai Lake

Deng et al. [60]

BP, RBF

pH, WT, UVA254, DOC, Br–, NH4 + -N, NO2 -N, halogenated ketones

HKs

Running water

Lin et al. [62]

RBF

WT, pH, DOC, DCAA, TCAA, NH4 + -N, Cl2, BCAA, HAA5, HAA9 UVA254, DCAA, TCAA, BCAA, HAA5, HAA9

Running water

Meng et al. RBF [63]

WT, NH4 -H, TN, TP, oil and grease, sludge volume, suspended matter in mixed liquor

Sewage Treatment Plant

Yang et al. [64]

RBF

WT, pH, salinity, redox DO potential

Aquaculture base

Suen and Eheart, [67]

BPNN, RBFNN

Daily precipitation, temperature

Upper Sangamon River

Hong et al. [68]

RBF, GRA

Trihalomethanes, Trihalomethanes bromodhalomethanes, total THMs, WT, pH, UVA254, dissolved organic carbon, bromides, nitrites, NH3

Running water

Jia et al. [71]

CNN

Spectral water quality data

COD

–

Pyo et al. [72]

CNN

Cyanobacterial biomass, water quality variables, and environmental variables

Cyanobacteria

Luo Dongjiang

Lim et al. [75]

RNN, LSTM

Water level, flow rate, BOD, COD, SS average wind speed, average temperature, BOD, COD, suspended solids (SS)

BOD, TN

Nitrate

Area of use

Jinjiang

(continued)

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Table 2 (continued) Author

Method

Input Indicators

Predictors

Area of use

Hu et al. [76]

LSTM

WT, Chl-a, pH, DO, conductivity, salinity, turbidity

pH, WT

Xinchun town mariculture base

Cao et al. [77]

GRU

DO, WT, pH, and meteorological data such as temperature, barometric pressure, and solar radiation

DO

Fenghua Farm Base

Hu et al. [78]

LSTM

COD, NH4 + -N

COD, NH4 + -N

Xihe

Eze and LSTM Ajmal, [79]

DO, pH, WT, salinity, ammonia, nitrogen

DO

Laizhou Mingbo Marine Aquaculture Base

Yang et al. [80]

CNN-LSTM

Ph, DO, BOD, NH4 + -N Ph, NH4 + -N

Beilunhekou

Zhou et al. [81]

CNN-LSTM

DO

DO

Bengbu Gate

Zhou et al. [82]

IGRA-LSTM

TN, TP, NH4 + -N, SS, WT, DO, pH, transparency, chloride (CL)

DO

Taihu Lake, Victoria Bay

Hu et al. [76]

LSTM

WT, Chl, pH, DO, conductivity, salinity, turbidity

WT, pH

Xinchun town mariculture base

Li et al. [83]

LSTM

DO, WT, NH4 + -N, pH, atmospheric temperature, atmospheric humidity, barometric pressure, wind speed

DO

Shrimp pond

Singh et al. BPNN [84]

Ph, BOD, NH4 + -N, TP, DO, BOD COD, and other 13 kinds of water quality indicators

Gomti River

model, the disintegrated data range is used to sequentially transform the data, and the sequence is realized to a range that corresponds with the output transfer function, to confirm that all training variable data of the neural network receive proper attention during the training process and speed up network convergence [86]. The method of normalization chosen varies depending on the method and the predictor. Partitioning of the data set. It is vital to utilize various data to evaluate the performance of the prediction model to optimize the water quality prediction model and

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get the best possible forecast impact. This is to ensure that even in different water quality data sets, the prediction model can still have almost the same performance. The whole water quality data set is usually divided into training set, validation set and the trial set. The partitioning of the training and validation sets is extremely important for the training of the entire model, and improper partitioning will prevent the model from achieving optimal prediction performance. For this reason, various authors have used manual or randomized procedures to partition these datasets. The complete data set required to create the calibration model is split into two subsets: the training set and the validation set, which are used to build the calibration model and check its dependability, respectively. The complete dataset was split into subgroups for odd and even years by Suen and Eheart [67]. They are trained with data from odd years and validated with data from even years. K-means sorting was used by Cao [77] to produce k datasets, and 10% of the data were chosen as the test set and the remaining 90% as the training set. To get the best model prediction, Yang [80] utilized the first 80% of the data as the training set and the remaining 20% as the test set. The KennardStone (KS) approach was utilized by Singh [84] to divide the whole data into training, validation, and test sets. The model dataset is created using the KS technique so that items are evenly dispersed throughout the training domain. Therefore, the training model takes into account all potential causes of data volatility. Evaluation indicators of the model. To evaluate the prediction performance of the model is good or bad, need to use a quantitative evaluation index. The most popular assessment metrics for using neural networks to predict water quality is mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE). MSE is a global statistic that is used to measure the average variance between the output and the target but offers no insight into the error distribution; RMSE is MSE open a root, and the essence of MSE is the same, except that it is used for a better description of the data; MAE represents the mean of the absolute values of the errors between the observed and true values. There isn’t a verified index that is accepted globally yet. The efficiency of various model approaches may not be comparable when considering only MSE, RMSE, and MAE [87]. Different evaluation indicators can be selected according to the difference between the prediction indicators and the prediction indicators. MAE measures the series change in the level predicted by its model and is reported in the original series units [88]. Both RMSE and MAE are mathematical measures that assess the overall model output error and gauge how well the model simulates the actual values. Different criteria can be used for various models to judge how well they work. In addition to MSE, RMSE, and MAE, the following are commonly used: sum of squared errors (SSE), mean absolute percentage error (MAPE), root mean square error (MRSE), consistency index (AO), combined mean relative underestimation and overestimation error (PARE), mean absolute relative error (AARE), relative error (RE) and dimensionless error index (NDEI), etc. The summary of non-mechanistic prediction methods is shown in Table 3.

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Table 3 Comparison of non-mechanical water quality prediction research methods Predictive models

Prediction method

Advantages

Disadvantages

Regression analysis

Multiple linear regression

The principle is relatively simple and easy to implement

Weight consistency, linear intricacy, inability to capture non-linear relationships between water quality parameters

Since the return

The model is very flexible and can represent several different types of time series, and can take into account the impact of seasonality on water quality data

The linear form of the model is presupposed, a linear correlation structure is assumed between the time series values, and non-linearity and non-smoothness between the data cannot be captured

Grey system

–

Mining complex systems for information about the patterns they contain

Insufficient adaptability for approximate non-simultaneous exponential sequences

Neural networks

Feed-forward neural networks

Solving nonlinear problems, overcoming local minimal value problems, insensitivity to missing data

Without considering the time lag effect between output and input, the computation process is slower and convergence is slower

Recurrent Neural Networks

Solving long-term dependency problems that cannot be captured by feedforward networks,

Assuming that the data is sequence-dependent, that parallel operations are not possible, that hardware resources cannot be fully utilized, and that the gradient disappears after the sequence length exceeds a certain limit

4 Summary The water quality simulation prediction method of research is a hot spot in the field of water quality, according to the mechanism used in the simulation prediction model, this paper from the mechanistic water quality prediction method and nonmechanical water quality prediction method as two aspects of the current water quality simulation prediction method is summarized and introduced, mechanical water quality prediction method and non-mechanical water quality prediction method comparison is shown in Table 4.

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Table 4 Comparison of mechanistic and non-mechanistic approaches Mode

Advantages

Disadvantages

Predictive models Commonly used methods

Mechanistic water quality prediction

Accurate simulation results

The model is Water quality complex and has simulation model high requirements for hydrological data

S-P, QUAL, WASP, SWAT

Non-mechanical water quality prediction

The requirements for hydrological data are not high, and the modeling difficulty is relatively low

Inadequate consideration of implicit relationships between water quality data

Grey system theory

Grey prediction model

Time series model Multiple linear regression, autoregression Machine learning methods

Feed-forward neural networks, feedback neural networks

(1) It can be found from the cited literature that more and more factors are considered in the water quality prediction in academic circles, and the scope of consideration is wider. From the perspective of the research space, the evolution of water quality has gradually shifted to the evolution of water quality in the entire basin; from the perspective of the research mechanism, it has gradually shifted from the hydrodynamic mechanism to the multi-process water cycle in the basin; from the perspective of influencing factors, gradually From single-factor prediction to multi-factor transformation; from the perspective of prediction methods, gradually from a single prediction method to multi-method comprehensive prediction, and more attention is paid to obtaining key parameters based on prototype observations and control experiments. (2) Mechanism water quality prediction method: The mechanism water quality prediction method analyzes many factors in the water environment, including physics, chemistry, biology, etc. and finds the changing relationship between the various factors, to predict the change of water quality. Mechanism-based water quality prediction methods have been widely used in foreign countries because of their accurate prediction results and excellent characteristics of simulating water quality changes. However, due to the imperfect hydrological data in our country, and the mechanism-based water quality prediction method has high requirements for hydrological data, the mechanism-based water quality prediction method cannot be well promoted in my country. In contrast, non-mechanical water quality prediction methods are easier to generalize because they do not require high hydrological data and are less difficult to model. (3) Non-mechanical water quality prediction method: Non-mechanical water quality prediction is not as complicated as the mechanism-based water quality prediction method, but only based on historical water quality data, mining and

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analyzing the data in the form of mathematical statistics, and establishing a water quality prediction model to predict water quality. Case. The non-mechanical prediction methods especially the prediction methods based on the artificial neural network have better prediction performance. It can be found through the cited papers that, among various modeling methods, the artificial neural network has been proved to be an acceptable and reliable modeling method. This model is more widely used to simulate different kinds of parameters of water when compared to other AI models. Over the past decade, to increase the precision of water quality forecasting, many researchers have experimented with various techniques. The findings indicate that practically all water quality characteristics may be accurately predicted using artificial neural networks. However, artificial neural network-based methods also have some disadvantages. The method is sensitive to input data. Hence the artificial intelligence model’s performance for various kinds of water quality metrics from different water might be lower. Researchers usually often highlight this fact. Other kinds of physical, chemical, and biological variables haven’t been modeled using some of these models. There is a lack of a thorough investigation of its applicability.

5 Outlook The actual water environment is very complex, which contains a variety of physical, chemical, biological and other effects, full of many contingencies and uncertainties, and the impact of various factors is random and uncertain, many factors can lead to instability of water quality prediction results. Therefore, how to scientifically and effectively overcome or reduce the impact of this uncertainty is also a focus of water quality prediction. With the increase in awareness of environmental protection and the importance attached to environmental protection in countries around the world, water quality testing information has gradually become abundant. The continuous accumulation of water quality data provides strong data support for data-based prediction methods. However, different prediction methods have their advantages and disadvantages. Therefore, when choosing a water quality prediction method, an appropriate water quality prediction method should be selected according to specific water quality data and needs to achieve the best prediction effect. Because of the current situation in my country, future water quality prediction research work can be carried out in the following aspects: (1) Increase the attention to hydrological remote sensing images. With the gradual development of image processing technology, the combination of GIS images and water quality index data for prediction is also a prediction development direction. The use of deep learning for GIS remote sensing images for training should gradually change from supervised training to supervise training-based, unsupervised training as a supplement, and the use of GPU to further accelerate, and thus improve the accuracy and efficiency of water quality prediction.

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(2) Focus on the impact of climate change, and seasonality on water quality prediction. Climate change is an important factor affecting water quality, but the current neural network approach does not take into account such characteristics. By adding the evolution process of each element of the basin water cycle in the context of climate change on the water quality of pollutants and nutrients in the mechanism of observation and experiment, the impact of climate change on the water environment mechanism simulation prediction, multi-climate change factors multi-climate change factors on the impact of composite pollution of water bodies, etc., and some areas in the south can take into account the factors of abundant water and dry period. (3) Focus on the relationship between water quality data for indicators. Water quality time series is a multivariate time series data, and in the study of water quality, you need to select the factors affecting water quality according to specific scenarios, for example, in the study of dissolved oxygen in water, we only need to focus on the prediction of dissolved oxygen, in the study of ammonia nitrogen in the water, we only need to focus on the prediction of ammonia nitrogen, but there is a certain correlation between the various elements of the water environment due to the complex relationship. Indirect interactions using middle variables as bridges exist in addition to direct interactions between multiple factors. Therefore, more attention to the complex relationship between water quality elements, for example, through the characteristic attention mechanism for the important elements of the weight distribution, is also a feasible prediction direction.

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Ultrasonic Disintegration as a Fast and Simple Method for Chemical Fractionation of Heavy Metals in Sewage Sludge: A Preliminary Study Malwina Tytła

Abstract The aim of this study was to investigate whether ultrasonic disintegration can be used for chemical fractionation of heavy metals (HMs) in sewage sludge. For this purpose, the conventional BCR sequential extraction (CSE) and ultrasound assisted-extraction (USE) procedures, proposed by the Community Bureau of Reference (now the Standards, Measurements and Testing Programme), were compared. One of the main objectives of this study was to investigate the effect of the material type of the extraction tubes are made, i.e., polypropylene (PP) or glass (GL), on the fractionation results. The research was carried out using ERM-CC144 (Joint Research Center; JRC), a certified reference material (CRM). The process of ultrasonic disintegration (UD) was conducted in the ultrasonic bath. The temperature and sonication time during the process were maintained at constant levels, i.e., 30 ± 5 °C and 1 h, respectively. The content of HMs (Cd, Cr, Cu, Ni, Pb, and Zn) in the certified reference material and extracts were analyzed with inductively coupled plasma optical spectrometry. The conducted research revealed that ultrasound treatment, instead of multi-hour shaking, significantly reduces the operation time of conventional extraction, regardless of the extraction tube material. However, the present study does not include examination of different sonication times, power, or frequency. Therefore, further research is needed to investigate this topic in far more detail. Keywords Sewage sludge · Heavy metals · Sequential extraction · Ultrasonic disintegration · Extraction tube

The original version of this chapter was revised: The figures 1 and 2 have been updated. The correction to this chapter is available at https://doi.org/10.1007/978-981-99-1919-2_35 M. Tytła (B) Institute of Environmental Engineering, Polish Academy of Sciences, 34 M. Skłodowskiej Curie St., 41-819 Zabrze, Poland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023, corrected publication 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_18

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1 Introduction One of the biggest concerns of agricultural application of sewage sludge is the presence of heavy metals (HMs). The use or disposal of this biosolid waste causes not only environmental issues but also health issues. Moreover, the traditional processes applied to remove heavy metals from sewage sludge are still insufficient [1]. The situation becomes more difficult due to the fact that the most substantive tool for estimating the potential ecological risk associated with the introduction of stabilized and dewatered sewage sludge into the environment is the knowledge of its chemical forms, while the current standards in Poland (J. L. 2015, Item. 257) [2] and the European Union (EU) (86/278/EEC) [3] include only the total metal content. However, it is precisely the chemical form in which the metal occurs that provides information about its mobility, bioavailability, and toxicity [4–6]. One of the most common methods used for the determination of the chemical forms of heavy metals in various environmental samples (soils, sediments, sewage sludge, etc.) is the three-step BCR sequential extraction procedure (the fourth additional step is optional) proposed by the Community Bureau of Reference (now the Standards, Measurements and Testing Programme) [7, 8]. Unfortunately, this method is very time consuming, due to the many hours of shaking the sample after each extraction step [9, 10]. Therefore, there is a need to search for other techniques, which allow for a shortened extraction time. One of them is ultrasonic disintegration (UD), which is based on the cavitation phenomenon [11]. The main effect of the UD process is the particle fragmentation and microcrack formation, which may facilitate and accelerate various processes, such as dissolution, digestion, extraction, or leaching [12]. Another way to shorten and streamline the extraction is to conduct the process in one extraction tube. This approach eliminates the need to transfer a sample from one extraction tube to another and also reduces the resulting losses. The majority of centrifuge manufacturers available on the market offer tubes made of polypropylene (PP), instead of glass (GL). This allows us to conduct the whole process in one tube. PP is one of the most consumed resins; it is a semicrystalline thermoplastic material, which is easy to process and relatively cheap. However, there are studies that show some properties of PP may be subject to change or deterioration as a result of the impact of an ultrasonic wave. For example, it was found that the intrinsic viscosity and molecular weight of PP decreases with increasing sonication time and tends to reach a limiting value. Moreover, it was also revealed that the complex viscosity, storage modulus, and loss modulus of PP decrease with the introduction of ultrasound oscillations [13]. This means that ultrasound waves have a degrading effect on PP properties and structure, which may also have a negative influence on the extraction efficiency. Therefore, it is necessary to check whether the extraction tube material has an influence on the obtained results. Ultrasonic treatment is successfully used in the extraction of metals from soils or bottom sediments [12, 14]. With regards to sewage sludge, only a few studies have been described [9, 10, 15]. However, the results presented so far suggest that

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ultrasound-assisted BCR sequential extraction can be also successfully applied to sewage sludge. The aim of this work was to investigate (a) whether ultrasonic disintegration can be used for chemical fractionation of HMs (Cd, Cr, Cu, Ni, Pb, and Zn) in sewage sludge and; (b) whether the material of the extraction tubes (PP or GL) has an impact on the results of ultrasound treatment in the process of BCR sequential extraction.

2 Materials and Methods 2.1 Materials The research was carried out using ERM-CC144 (Joint Research Center; JRC), a certified reference material (CRM). The material used for the production of the ERM-CC144 is a sewage sludge of domestic origin, which was collected in Italy and Amsterdam. According to the Origin certificate, the material was dried, sieved, and homogenized [16]. The chemical reagents and extractants for the mineralization and extraction processes as well as for quantitative analysis were of high purity. All solutions were prepared in deionized water (first degree of purity), which meets the parameters of PN-EN 3696:1999 [17].

2.2 Sequential Extraction Methods The conventional (CSE) and ultrasound-assisted (USE) sequential extraction procedures were used for chemical fractionation of selected HMs (Cd, Cr, Cu, Ni, Pb, and Zn) in the sewage sludge (ERM-CC144). The conventional BCR extraction method is the three-step procedure proposed by the Community Bureau of Reference (now the Standards, Measurements and Testing Programme). There is also one additional step, which is optional. The ultrasound-assisted extraction differs from the conventional by including the usage of ultrasound waves instead of multi-hour shaking. The two methods for sequential extraction of heavy metals are shown in Table 1, whereas the flowchart of sequential extraction methods is shown in Fig. 1. The working parameters of the ultrasonic bath (Sonic 5, Polsonic) used in this study were 2 × 320 W and 40 kHz. During the process, the extraction tubes were placed in a special basket and were covered with a plate that was made of stainless steel with laser cut holes, to keep them in one position and prevent dislocation. The water temperature in the ultrasonic bath (30 ± 5 °C) and the sonication time (1 h) were kept constant. The laboratory scale setup for ultrasound treatment is shown in Fig. 2.

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Fig. 1 A flowchart of sequential extraction methods (UD—ultrasonic disintegration) Table 1 The BCR sequential extraction methods [7, 8, this study] Step

Fraction

Reagents and procedure

CSE

I

Acid soluble/exchangeable fraction; bound to carbonates (F1)

Add 20 ml of CH3 COOH (0.11 M) to 0.5 g of sample

16 h of shaking

1 h of ultrasound treatment

II

Reducible fraction; bound to Mn and Fe oxides (F2)

Add 20 ml of NH2 OH·HCl (0.1 M, pH = 2) to residue from stage I

16 h of shaking

1 h of ultrasound treatment

USE

Extraction conditions

(continued)

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Table 1 (continued) Step

Fraction

Reagents and procedure

CSE

USE

III

Oxidizable fraction; bound to organic matter and sulfides (F3)

Add 5 ml of H2 O2 (8.8 M, pH = 2) to residue from stage II; heat to 85 °C for 1 h (repeat twice); and 25 ml of CH3 COONH4 (1 M, pH = 2)

16 h of shaking

1 h of ultrasound treatment

IV

Residual fraction (F4) (optional)

Add 15 ml of HCl and 5 ml of HNO3 (3:1) to residue from stage III and mineralize

–

–

Fig. 2 The laboratory scale setup for ultrasound treatment

2.3 Heavy Metals Determination The content of selected HMs (Cd, Cr, Cu, Ni, Pb, and Zn) in ERM-CC144 and obtained extracts were analyzed with inductively coupled plasma optical spectrometry (Avio200 ICP-OES, PerkinElmer Inc.). The determination of total heavy metals content (THMC) in the solid samples was preceded by wet mineralization in an open system. For this purpose, 1 g of sample was mineralized with 5 ml of HNO3 (65%) and 15 ml of HCl (35–38%). The obtained mixture was placed in a glass flask and was heated on an electric hot plate. The solutions were filtered through fine filters diluted with 5% HNO3 to a volume of 50 ml. The same procedure was used for the samples from the fourth extraction stage. All samples were stored at 4 °C prior to quantitative analysis on ICP-OES.

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2.4 Percentage Recovery of the Sequential Extraction Methods The recovery of sequential extraction procedures was calculated as follows (Eq. 1) [18]: ( RM =

F1 + F2 + F3 + F4 T H MC

) × 100; %

(1)

where: F1–F4 represents the concentration of heavy metal in the individual chemical fractions, and THMC represents the total metal content in the analyzed sample.

2.5 Quality Control and Total Heavy Metal Concentrations The quality control was conducted using ERM-CC144 (JRC). The analysis was carried out in triplicate with a reagent blank, and the amount of sample used was 1 g. The recovery rates (R) for analysed heavy metals in ERM-CC144 were in the range of 87.9–107.3% (Eq. 2) [19]. The obtained results indicate that the conducted analysis was under control. The results of the total heavy metals concentrations are presented in Table 2. ( R=

THMC CRM

) × 100; %

(2)

where: THMC represents the total content of heavy metal in analyzed sample, and CRM represents the content of heavy metal given in accordance with the certificate of ERM-CC144. Table 2 Heavy metals concentrations in ERM-CC144

Metal

This study

ERM-CC144

mg/kg

R %

Cd

12.9 ± 0.4

14.5

Cr

147.7 ± 3.6

168.0

87.9

Cu

370.4 ± 5.7

348.0

107.3

89.0

Ni

79.7 ± 1.3

91.0

87.6

Pb

144.7 ± 3.5

157.0

92.9

Zn

959.2 ± 13.5

980.0

98.0

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3 Results and Discussion The analysis results indicate that analyzed sewage sludge (ERM-CC144) exhibited the highest concentration for Zn (959.2 mg/kg) and the lowest for Cd (12.9 mg/kg). The mean concentrations of HMs in the sludge were in the following order: Zn > Cu > Cr > Pb > Ni > Cd (Table 2). According to the literature data, the content of particular heavy metals in sewage sludge can be ordered as follows: Zn > Cu > Cr > Ni > Pb > Cd [20]. Therefore, the obtained results are in good agreement with the literature data. Taking into account that sewage sludge in ERM-CC144 was collected in Italy and Amsterdam, the concentrations of heavy metals were reference to attachment I B of Council Directive of 12 June 1986 (86/278/EEC) [3]. According to the EU standards, the concentrations of analyzed heavy metals in the discussed sewage sludge do not exceed the limit values of these elements in the sludge used in agriculture. However, to identify the major sources of heavy metal pollution, it is necessary to carry out a multivariate statistical analysis. Unfortunately, to do this, it is necessary to collect an appropriate amount of data (dataset). The comparison of HMs concentrations in the present study with similar studies is shown in Table 3. Table 3 Concentrations of heavy metals in sewage sludge of various countries of the world Country

Cd

Cr

Cu

Ni

Pb

Zn

References

mg/kg Italy and Amsterdam (ERM-CC144) 12.9 147.7 370.4 79.7

144.7

959.2 This study

Poland

4.1

70.7

252.9 126.1

China

3.0

–

–

49.9

119.7

764.2 [21]

France

0.6

27.6

149.0 26.4

19.7

548.0 [22]

Spain

–

24.1

37.1

26.4

544.0 [23]

India

0.6

65.0

346.0 –

Egypt

4.0

–

538.0 81.0

8.0

59.9 1660.0 [19]

37.0 1674.0 [24] 750.0 1204.0 [25]

3.1 Comparison of the Result of Sequential Extraction Methods The BCR sequential extraction method was used to characterize the contribution of each chemical fraction in selected heavy metals. The two procedures of sequential extraction were investigated, i.e., the conventional and ultrasound-assisted. The main difference between the discussed methods was the replacement of the extended shaking of samples with ultrasound treatment (1 h sonication in ultrasonic bath). Similar procedures were presented by Kazi et al. [9] and Gwebu et al. [10], who

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applied 30 and 60 min of ultrasound treatment (ultrasonic bath) instead of mechanical shaking, respectively. The conducted experiment was also aimed at providing an answer for the question of whether the type of material from which the extraction tube is made has an impact on the ultrasound wave propagation in the medium and the results of the ultrasound treatment? For this purpose, the reference material in PP and GL extraction tubes was subjected to 1-h ultrasound treatment in the ultrasonic bath. The percentage distribution of chemical fraction of heavy metals in the sewage sludge is presented in Fig. 3. Among all of the chemical fractions, the mobile fractions (F1 and F2) are dominant in Cd and Zn, with proportions of nearly 60–80%, while the immobile fractions (F3 and F4) account only about 20–40%, regardless of method used and the type of extraction tubes. The results are in accordance with author’s previous study [5, 18, 19]. However, in relation to Zn, the differences in fractionation patterns between the conventional and ultrasound-assisted extraction procedures were revealed. The percentage of Zn in each fraction is ranked as follows: F2 > F1 > F3 > F4 and F3 > F1 > F2 > F4 and F1 > F3 > F2 > F4 for the conventional (CSE_PP) and modified extraction (USE_PP and USE_GL) methods, respectively. After ultrasound treatment, part of the Zn in fraction F2 was observed to pass into fraction F3. However, these differences were not very significant, and the concentration of Zn in fractions F1–F3 was comparable for both types of extraction tubes. Other researchers also have reported that the differences in the fractionation pattern of Zn extracted from sewage sludge were observed in CSE and USE BCR sequential extraction [10]. However, the present study has a preliminary character and does not include examination of different sonication times, frequency or power of the ultrasonic bath. Generally, the conducted research has shown that even if ultrasonic waves may degrade the extraction tubes made of PP [13], the ultrasound treatment still significantly reduces the operation time of conventional extraction. In addition, GL tubes can deteriorate over time as well. Therefore, this simple experiment revealed that the type of material from which the extraction tube is made has no effect on the obtained results when the process is conducted at a constant temperature, frequency, and power. Of course, it is necessary to control the condition of the extraction tubes, as well as their periodic replacement.

3.2 Recovery Rates for Heavy Metals Table 4 shows the percentage recovery (RM ) values for the heavy metals under the experimental conditions. The recoveries of HMs in the samples extracted by the conventional method were between 93.1 and 121.1%, and recoveries of 96.3–128.7 and 88.4–125.2% were observed for the samples extracted by ultrasound-assisted method using PP and GL tubes, respectively. Regardless of the material from which the tubes are made, the percentage recovery for all HMs was within an acceptable range for both conventional and modified extraction methods. Therefore, the obtained

Ultrasonic Disintegration as a Fast and Simple Method for Chemical … Fig. 3 The distribution of the chemical fractions of heavy metals in the sewage sludge (ERM-CC144)

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Table 4 The percentage recovery for heavy metals RM

Cd

Cr

Cu

Ni

Pb

Zn

% CSE_PP

121.1

93.1

101.3

95.2

100.3

100.4

USE_PP

128.7

96.3

116.1

101.6

104.7

106.8

USE_GL

125.2

88.4

108.5

94.3

98.7

105.4

data demonstrated good reliability in the present study. This is also in line with the recoveries reported by other scientists [26–28].

4 Conclusions In this study, the fractionation of six heavy metals (Cd, Cr, Cu, Ni, Pb, and Zn) in sewage sludge (ERM-CC144–produced from sewage sludge of domestic origin) was investigated. The conducted experiment revealed that ultrasonic disintegration successfully shortened the time of conventional BCR sequential extraction, with the exception of Zn. The research indicated that after ultrasound treatment, part of the Zn in fraction F2 was noted to pass into fraction F3. The observed differences were not very significant, and the concentrations of zinc in fractions F1–F3 were comparable. Moreover, the conducted research also revealed that the type of material (PP or GL) from which the extraction tube is made has no effect on the obtained results, provided, that the BCR sequential extraction process is conducted at a constant temperature, frequency, and power. Acknowledgements The author gratefully acknowledges Zuzanna Berna´s for her technical support during research. Funding This research was funded by the National Science Centre, Poland, under grant number 2019/35/D/ST10/02575 (“The way of metals binding in sewage sludge and the ecological risk”).

References 1. Yesil H, Molaey R, Calli B, Tugtas AE (2021) Removal and recovery of heavy metals from sewage sludge via three-stage integrated process. Chemosphere 280:130650 2. Regulation of the Minister of Environment of 6th February 2015 on the Municipal Sewage Sludge (J. L. 2015, Item. 257). https://isap.sejm.gov.pl. Accessed 2022/04/04 3. Council Directive of 12th June 1986 on the Protection of the Environment, and in Particular of the Soil, When Sewage Sludge Is Used in Agriculture (86/278/EEC). https://eur-lex.europa. eu/legal-content/PL/TXT/PDF/?uri=CELEX:31986L0278&from=EN. Accessed 2022/04/04

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4. Nkinahamiraa F, Suanona F, Chia Q, Lia Y, Fengd M, Huangd X, Yua C-P, Suna Q (2019) Occurrence, geochemical fractionation, and environmental risk assessment of major and trace elements in sewage sludge. J Environ Manag 249:109427 5. Tytła M (2019) Assessment of heavy metal pollution and potential ecological risk in sewage sludge from municipal wastewater treatment plant located in the most industrialized region in Poland-Case. Study Int J Environ Res Public Health 16:2430 6. Tytła M, Widziewicz-Rzo´nca K (2021) Heavy metals in municipal sewage sludge—a brief characteristic of potential threats and methods used to assess the ecological risk. Environ Earth Ecol 5:18–25 7. Ure AM, Quevauviller P, Mantau H, Griepink B (1993) Speciation of heavy metals in soils and sediments. An account of the improvement and harmonization of extraction techniques undertaken under the auspices of the BCR of the Commission of the European Communities. Int J Environ Anal Chem 51(1–4):135–151 8. Álvarez EA, Callejón Mochón M, Jiménez Sánchez JC, Ternero Rodríguez M (2002) Heavy metal extractable forms in sludge from wastewater treatment plants. Chemosphere 47(7):765– 775 9. Kazi TG, Jamali MK, Siddiqui A, Kazi GH, Arain MB, Afridi HI (2006) An ultrasonic assisted extraction method to release heavy metals from untreated sewage sludge samples. Chemosphere 63(3):411–420 10. Gwebu S, Tavengwa NT, Klink MJ, Mtunzi FM, Modise SJ, Pakade VE (2017) Quantification of Cd, Cu, Pb and Zn from sewage sludge by modified-BCR and ultrasound assisted-modified BCR sequential extraction methods. Afr J Pure Appl Chem 11(2):9–18 11. Tytła M (2018) The effects of ultrasonic disintegration as a function of waste activated sludge characteristics and technical conditions of conducting the process—comprehensive analysis. Int J Environ Res Public Health 15:2311 ´ 12. Le´sniewska B, Krymska M, Swierad E, Wiater J (2016) An ultrasound-assisted procedure for fast screening of mobile fractions of Cd, Pb and Ni in soil. Insight into method optimization and validation. Environ Sci Pollut Res 23(24):25093–25104 13. Liu Y, Xie LS, Ma YL, Xue K, Qiu W, Shan T, Gao G (2015) The effects of sonication time and frequencies on degradation, crystallization behavior, and mechanical properties of polypropylene. Polym Eng Sci 55:2566–2575 14. Kovács K, Halász G, Takács A, Heltai G, Széles E, Gy˝oric Z, Horváth M (2018) Study of ultrasound-assisted sequential extraction procedure for potentially toxic element content of soils and sediments. Microchem J 138:80–84 15. Pérez-Cid B, Lavilla I, Bendicho C (1998) Speeding up of a three-stage sequential extraction method for metal speciation using focused ultrasound. Anal Chim Acta 360(1–3):35–41 16. Origin certificate ERM-CC144 https://crm.jrc.ec.europa.eu/p/40455/40459/By-material-mat rix/Soils-sludges-sediment-dust/ERM-CC144-SEWAGE-SLUDGE-elements/ERM-CC144 17. PN-EN 3696 (1999) Water used in analytical laboratories—requirements and test methods (in Polish: Woda stosowana w laboratoriach analitycznych—Wymagania i metody bada´n) 18. Tytła M, Widziewicz K, Zielewicz Z (2016) Heavy metals and its chemical speciation in sewage sludge at different stages of processing. Environ Technol 37(7):899–908 19. Tytła M (2020) Identification of the chemical forms of heavy metals in municipal sewage sludge as a critical element of ecological risk assessment in terms of its agricultural or natural use. Int J Environ Res Public Health 17:4640 20. Shrivastava SK, Banerjee DK (2004) Speciation of metals in sewage sludge and sludgeamended soils. Water Air Soil Pollut 152:219–232 21. Huang Z, Lu Q, Wang J, Chen X, Mao X, He Z (2017) Inhibition of the bioavailability of heavy metals in sewage sludge biochar by adding two stabilizers. PLoS One 12:e0183617 22. Tella M, Doelsch E, Letourmy P, Chataing S, Cuoq F, Bravin MN, Saint MH (2013) Investigation of potentially toxic heavy metals in different organic wastes used to fertilize market garden crops. Waste Manag 33(1):184–192 23. Sánchez CH, Gutiérrez A, Galindo JM, González-Weller D, Rubio C, Revert C, Burgos A, Hardisson A (2017) Heavy metal content in sewage sludge: a management strategy for an ocean island. Revista de Salud Ambiental 17(1):3–9

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24. Qayoom U, Bhat SU, Ahmad I, Kumar A (2022) Assessment of potential risks of heavy metals from wastewater treatment plants of Srinagar city, Kashmir. Int J Environ Sci Technol 19:9027–9046 25. Ashmawy AM, Ibrahim HS, Moniem SMA, Saleh TS (2012) Immobilization of some metals in contaminated by zeolite prepared from local materials. Toxicol Environ Chem 94(9):1657–1669 26. Łukowski A (2017) Fractionation of heavy metals (Pb, Cr and Cd) in municipal sewage sludge from Podlasie Province. J Ecol Eng 18(1):132–138 27. Tu J, Huang H, Zhao Q, Wei L, Yang Q (2012) Heavy metal concentration and speciation of seven representative municipal sludges from wastewater treatment plants in Northeast China. Environ Monit Assess 184:1645–1655 28. Tytła M, Widziewicz-Rzo´nca K, Berna´s Z (2022) A comparison of conventional and ultrasoundassisted BCR sequential extraction methods for the fractionation of heavy metals in sewage sludge of different characteristics. Molecules 27:4947

Research Progress on Removal of Heavy Metal Ions in Water by Biological and Hydrogel Sorbent Materials Weiwei Zhou, Yunwei Li, Kun You, Jingxin Hua, Fanhui Meng, Junling Zhao, Xuewu Zhu, and Daoji Wu

Abstract Due to severe health issues, heavy metal ions (HMI) in water bodies have posed significant challenges to environmental remediation. Compared with mainstream treating technologies for HMI removal, such as chemical precipitation, electrochemistry, ion exchange, and membrane separation, the adsorption-based method has been considered a promising approach for its advantages of wide application range, fast reaction speed, adaptability to different reaction conditions, and low energy consumption. Biological- and hydrogel-based materials provide costeffective alternatives to satisfy the demand for low-cost and highly efficient adsorbents for HMI removal. This review summarizes the adsorption mechanisms and W. Zhou · X. Zhu · D. Wu (B) School of Municipal and Environmental Engineering, Shandong Jianzhu University, Jinan 250101, China e-mail: [email protected] W. Zhou e-mail: [email protected] X. Zhu e-mail: [email protected] W. Zhou · Y. Li Shandong ShuiFa Environmental Technology Co., Ltd., Jining 272352, China e-mail: [email protected] K. You School of Municipal and Environmental Engineering, Shenyang Jianzhu University, Shenyang 110168, China e-mail: [email protected] W. Zhou · J. Hua · F. Meng Shandong Urban Construction Vocational College, Jinan 250103, China e-mail: [email protected] F. Meng e-mail: [email protected] J. Zhao Beijing Vocational College of Labour and Social Security, Beijing 101199, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_19

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fabrication of biological- and hydrogel-based adsorbents for eliminating HMI from water. In addition, the removal efficiency and factors affecting HMI removal have been discussed in detail. The adsorption–desorption of HMI and reuse of the two adsorbents are analyzed as well. This review is expected to provide novel insights into the application and promotion of biological- and hydrogel-based adsorbents for robust removal of HMI in water treatment. Keywords Heavy metal · Biological absorbent · Hydrogel absorbent · Ion concentration · Functional groups

1 Introduction Heavy metals are defined as the metals with densities bigger than 4.5 g/cm3 , like iron, copper, silver, gold, mercury, lead, cadmium, etc. With regard to environmental pollution, heavy metals are mainly defined as the substances with obvious biological toxicity, including cadmium, mercury, chromium, lead and metal-like arsenic. Heavy metals are not easily biodegradable, but can be bioamplified and enriched thousands of times through the food chain, and ultimately enter the body. Heavy metals can affect enzymes and proteins in the human body, and deposit in some organs, resulting in chronic toxicity. In 1953, mercury-containing wastewater has been discharged into a bay by a nitrogen fertilizer company in Minamata, Kumamoto Prefecture, Japan. Mercury causes compounds such as methyl mercury to form in fish and shellfish in nearby bays, which move up the food chain into animals and humans, causing severe damage to the nervous system. In 2000, a gold mine in Romania overflowed with sewage containing cyanide, copper, lead, etc., which washed into the Tissa River, a tributary of the Danube, and spread downstream, killing a large number of fish in the river and making the water undrinkable. Since then, frequently “arsenic poisoning”, “cadmium rice”, and other heavy metal pollution incident in the water. Faced with the high incidence of heavy metal pollution in water, a series of problems caused by heavy metal pollution have caused serious threats to environmental safety and public health and become a major environmental problem related to human health and survival. Domestic and foreign scholars have carried out a lot of studies on heavy metal pollution in water. Some progress has been made in mechanism analysis, preparation of removal materials, and analysis of removal process. At present, the traditional methods used in removing heavy metal ions (HMI) from water include chemical precipitation [1–3], electrochemical [4], ion exchange [5–7], adsorption [8], membrane separation [2, 9]. The chemical precipitation method has the advantages of simple operation, high selectivity, and relatively cheap precipitant, but it requires large amount of chemically stable precipitant, the method is costly, and it may cause secondary pollution. The advantage of the electrochemical method is that it can achieve a fast and good control process, requiring fewer chemicals. The disadvantage is that the initial investment and ongoing operation are costly. The advantage of the ion exchange method is its high adsorption capacity. The resin can

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be regenerated, but it is highly sensitive to changes in pH. In addition, regeneration of resin requires chemicals, easily causes secondary pollution, and is expensive [10]. Membrane separation has the advantages of high selectivity and high efficiency for removing HMI. However, it requires external pressure during operation and the membrane maintenance is costly [11]. In summary, traditional methods have some problems, such as high treatment costs, easy secondary pollution, complex processes, and poor selectivity for HMI, especially for the low concentration of heavy metal wastewater, so treatment outcomes are not ideal. In comparison, the adsorption method has been highly concerned by researchers because of its advantages such as wide application range, rapid reaction rates, adaptability to different reaction conditions, low energy consumption, and availability of different adsorbents [8]. Researchers are trying to find materials that have a good affinity with different HMI, large adsorption capacity and high selectivity. Biosorbents and hydrogel sorbents, which offer efficient removal of HMI, have attracted particular attention due to their sustainability and recyclability. Todorova et al. fabricated a novel biosorbent from Bacillus cereus for the removal of HMI in aqueous solutions [12]. The result showed that 92.13% removal efficiency of Pb(II) was reached at pH 5.0, dosage of 2 g/L, and adsorption time of 120 min. Bulgariu et al. functionalized soy water biomass (SCA-SWB) using a chelating agent for removing HMI [13]. Compared with the original soy waste biomass, the removal efficiency of SCA-SWB for Ni(II), Cu(II), and Pb(II) increased by 128%, 131%, and 196%, respectively. Tang et al. found that the crosslinked chitosan (CTS)/sodium alginate/calcium ion double-network hydrogel could adsorb 81.25, 70.83 and 176.50 mg/g of Cd2+ , Cu2+ and Pb2+ , respectively [14]. Arshad et al. demonstrated that polyethylenimine modified graphene oxide hydrogel could uptake 181, 374, and 602 mg/g of Cd(II), Hg(II), and Pb(II) ions, respectively [15]. These results suggest that biological and hydrogel adsorbents have highly efficient removals of HMI from water. Due to high sorption capacity, functionality, nontoxicity, and regeneration abilities, the biological and hydrogel adsorbents are considered as promising candidates for HMI removal. Therefore, this paper reviews the adsorption principles, preparation methods, influencing factors, desorption, and reuse of the two adsorbents. The descriptions and discussions will provide significant insights into the removal of HMI from waters using the two kinds of adsorbents.

2 Removal of HMI from Water by Biological Absorbents Biological adsorption refers to the high attraction between inactive biomass and removal substances like compounds and metal ions, which is derived from a variety of functional groups in the cell wall of biomaterials. The strong attraction can effectively eliminate the pollution and harm caused by HMI to the environment [16]. Compared with traditional methods, the biological adsorption has the advantages of rich raw material sources, low operation costs, fast removal rates, and high removal

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efficiencies [17]. Algae [18] and fungi [19] have all been effectively and successfully used in heavy metal removal studies.

2.1 Principle of Biological Adsorption of HMI in Water Due to the complexity of biological structure, the biological adsorption mechanism is often different with different species and metal ions. Inactive organisms depend mainly on surface adsorption, whereas active organisms rely on both active and surface adsorption. Because biological cells can adsorb HMI from the solution to the cell surface, HMI can be removed [20]. Kapoor et al. [20] proved through experiments that amino, phosphoric acid, carboxyl, and other chemical functional groups in biological cell walls can absorb heavy metals, and the HMI enter the cells through cell transport. (1) Through the porous structure of the cell, HMI diffused through a high concentration to low concentration areas in cells. The mode of transportation is not dependent on the metabolic activity of biological cells, called “biological removal” or “passive transport”. This is a non-directional mode of transport, which can transport HMI, nucleic acids and other substances related to the physiological metabolism of cells. (2) In physiological metabolism, active transport means that HMI can be transported to the body through cell membrane. Both “active transport” and “passive transport” can be referred to as the accumulation of HMI in living organisms. Yin et al. [21] proved experimentally that there is another important reason for the biological removal of HMI, that is, some HMI participate in physiological metabolic activities of microorganisms. Related transport enzymes are involved in the adsorption process of heavy metals. After enzymes bind to these transport proteins, nickel and chromium ions, rather than trace metal ions, are transported and preserved in the cell through associated ion channels. This also leads to changes in the structure and permeability of cell membranes and accelerates the deposition of HMI in cells. Yetis et al. [22] confirmed through experiments that the removal of HMI by microorganisms is divided into two stages. The first stage is the binding process between HMI in solution and functional groups (–COOH, –NH2 , =NH, –SH, –OH, etc.) of the microbial cell wall. The reaction in the modification process is rapid and requires a short time. The second stage is the diffusion process of HMI bound to functional groups in the cell membrane, which is slow and takes a long time.

2.2 Preparation of Biological Absorbents Researchers have carried out different in-depth studies on biosorbents, in order to find biosorbents with structural specificity, controllable morphology, high adsorption capacity, and removal rate, so that it can become a reliable and sustainable water treatment resource that is easy to large-scale production. Xu et al. [23] prepared

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Fig. 1 Schematic picture of fabrication of mesoporous lignin-based biosorbent (MLBB) from rice straw through a SO3 micro-thermal explosion process

mesoporous lignin-based biosorbents (MLBB). The unique 3D structure and the rich array of hydroxyl groups make them ideal precursors for mesoporous biosorbents. Rice straw was used as the raw material and SO3 micro-thermal was used. The preparation of MLBB was shown in Fig. 1. Nematidil et al. [24] extracted B-type gelatin from black tilapia dung and in a uniform medium, butyl methacrylate (BuMc) and acrylic acid (AcA) and were grafted and copolymerized with butyl radical on the backbone of gelatin to synthesize gelatin-based nanocomposite biological adsorbent. The preparation process was as follows. A device with the capacity of 100 mL installed with a mechanical stirrer was used for this experiment. 0.4– 1.2 g of gelatin was mixed with 30 mL distilled water under argon gas pressure. The reactor temperature is stable at 65 °C in the thermostatic water bath. After the gel was completely dissolved, 0.1–0.3 g of 20 nm montmorillonite powder was mixed with the gelatin solution and stirred at 300 rpm for 15 min. Ammonium persulfate was mixed with the original mixture and stirred for 15 min. Subsequently, 70% neutralized AcA with a concentration of 0.73–1.82 mol L−1 , 0.15–0.4 mol L−1 of BuMc, 0.003–0.016 mol L−1 of N, N-methylenebisacrylamide and 0.003–0.008 mol L−1 of N,N,N, ,N, -tetramethylethylenediamine were simultaneously added to the reaction mixture. The original mixture was stirred continuously at 65 °C and 300 rpm for 35 min. After completing the reaction, the gelatine products were poured into ethanol with 300 mL for dehydration for another 24 h. The products were then filtered and rinsed with ethanol (100 mL). The filtered products were dried in an oven for 24 h at 50 °C. Under the optimal adsorption conditions of pH of 6.0, temperature of 328 K, time of 60 min, adsorbent mass of 0.1 g and initial ion concentration of 200 ppm, the maximum adsorption capacities of Cu2+ and Cd2+ by single ion system were 192.4 and 172.3 mg g−1 , respectively. The chemical adsorption process and physical adsorption process are spontaneous, and the prepared nanocomposite material has a high recyclable-removal rate, which could be used as an efficient adsorbent to remove and recover HMI in water.

2.3 Influence of Biological Adsorption of HMI in Water There are many factors that affect the removal of HMI in biological adsorption experiments, and these are discussed below. pH. Fourest et al. showed [25] that the changes in solution pH value directly affected the degree of HMI dissolution in aqueous solution and the degree of dissociation

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of functional groups (–COOH, –NH2 , =NH, –SH, and –OH) on the biological cell wall, thus affecting the heavy metal removal by living organisms. Nematidil et al. [24] found that as the pH value of the solution was elevated from 3 to 6, the adsorption capacity and removal rates for Cu2+ and Cd2+ metal ions by biological adsorbents increased sharply. When the pH exceeded 6, the adsorption capacity of metal ions decreases due to the presence of metal precipitation. With the accumulation of metal ions, the adsorbent performance deteriorated. On the other hand, under strongly acidic conditions, the COO– group of AcA on the surface was protonated, and the adsorption capacity of metal ions decreased. With the increasing acid concentrations, the competition between metal ions and H+ was enhanced by the negative charge on the biological adsorbent surface, and metal adsorption decreased accordingly. Therefore, for AcA-co-BuMc/MMT biological adsorbents, pH 6 was the best. Yakup Arıca et al. [26] found through experiments that the biological adsorption of metals relies on protonation/deprotonation from carboxyl groups on the cell wall. With increasing pH, more carboxyl groups on the cell wall components were deprotonated and carried a negative charge. This meant that the negative charge attracted the positively charged Cd2+ , which combined with more cadmium. When the pH value was >6.0, the biosorption capacity decreased, probably because the formation of cadmium hydroxide complex blocked the biosorption of metals. The optimal pH was 6. Ion Concentration. The ion concentration is also an important factor affecting the removal of HMI by biological adsorbents. Generally speaking, the removal rate for HMI by biological adsorbents increases with increasing HMI concentration. The higher the concentration, the better the initial removal rate, but the lower the overall removal rate. The lower the concentration is, the lower the initial removal rate, but the higher the removal rate. Nematidil et al. [24] found that when the initial concentration of HMI was changed of 75–300 mg L−1 at 298 K, the adsorption capacity of Cd2+ and Cu2+ metal ions were changed of 70.3–172.4 mg g−1 and 62.1–148.0 mg g−1 , respectively. The increased capacity of adsorption may be attributed to the fact that adsorption on the adsorbent is a diffusion-based process. The diffusion phenomenon provides good mass transfer and improves the metal adsorption capacity of Cd2+ and Cu2+ . Amini et al. [27] found that with the pH and ion concentration of 6.04 and 89.86 mg L−1 , respectively, the maximum removal rate of Cd2+ by Aspergillus Niger was 99.75%. When the pH and ion concentration were 6.00 and 89.7 mg L−1 , respectively, the removal efficiency for Ni2+ was the highest. When the pH and ion concentration were 6.43 and 89.55 mg L−1 , respectively, the highest removal rate of Pb2+ was 100%. The study of L. M. Barros Junior showed that the efficiency for removal of Cd2+ was approximately 84% at pH 4.75 and with an ion concentration of 10 mg L−1 [28]. The former was more effective, possibly due to a saturation of adsorption sites and the increased number of ions available for binding sites in the biomass where Cd2+ would form complexes. Absorbent Amount. The amount of adsorbent affects the removal of HMI by biological adsorbents. Generally, with an increase in the amount of biosorbent, the removal rate increases.

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Nematidil et al. [24] found that when the adsorbent mass increase of 0.05– 0.15 g, the capacity of the adsorbent reduced. The total surface area of the adsorbent decreased and the length of the diffusion path increased due to the increase in biosorption capacity, and this was due to the aggregation of adsorbent particles. Thus, the removal rates of Cu2+ and Cd2+ increase from 63.2% and 67.5% to 89.6% and 98.7%, respectively, with the increasing biological adsorbent concentration. These increases in removal efficiency may be related to an increase in effective binding sites for HMI in the solution. Amini [27] studied the influence of adsorbent amount on the biosorption of Cd2+ , Ni2+ , and Pb2+ in the range of 24–90 mg L−1 . When the biomass dose was 5.45 g L−1 and the initial ion concentration was 89.8 mg L−1 , the removal rate of Cd2+ reached 100%. When the biomass dose was 6.0 g L−1 and the initial ion concentration was 89.9 mg L−1 , the maximum removal rate of Ni2+ was 90%. When the initial ion concentration was 89.9 mg L−1 and the biomass dose was 5.7 g L−1 , the maximum Pb2+ removal rate could reach 100%. Aspergillus Niger can be used as a suitable biological adsorbent. Other Factors. Some researchers have also found that other factors may affect the removal of HMI. These factors include temperature, contact time, pretreatment of biological adsorbents, etc. Nematidil [24] found that temperature and contact time also influenced the removal efficiency of HMI. The removal rates of Cu2+ and Cd2+ metal ions in the aqueous solution were improved with increasing solution temperature. With increasing temperature, the amount of adsorption increased, which proved that the adsorption process is endothermic. The removal rate increased rapidly within 50 min of contact time and remained almost constant thereafter. The removal efficiencies for Cd2+ and Cu2+ were 96% and 86%, respectively. The high removal rates are possible because of the external sites of the biological adsorbent that support diffusion into the porous structure of the adsorbent and to the high content of active chelating sites. Amini [29] found that sodium hydroxide pretreatment with Aspergillus Niger biosorbent enhanced the removal rate of Cd2+ . When the biomass dose was 5.8 mg L−1 and the initial cadmium concentration was 25.8 mg L−1 , the maximum Cd2+ removal rate was 96.7%, but the absorption capacity of Cd2+ was 10.14 g biomass−1 . NaOH-treated fungi provided pore structures with carboxyl groups on the biological adsorbent’s surface. In fact, sodium hydroxide treatment removed most of the cell wall materials containing amorphous polysaccharides, phosphate groups and –COOH, and produced a clean surface morphology and more accessible spaces in the glucan-chitin skeleton. This allowed more HMI to chelate on the surface.

2.4 Desorption and Reuse of Biological Absorbents Desorption and reuse of adsorbents are very important for removal of HMI from wastewater with biological adsorbents. Repeated and sustainable use of biological adsorbents without affecting adsorption capacity is the ideal situation. Arıca [26] studied the desorption of Cd2+ by biological adsorbents with the intermittent

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method. More than 90% of Cd2+ adsorbed on biological adsorbents were subsequently desorbed by 10 mM HCl. To test the reusability of biological adsorbents, Cd(II) ions were adsorbed and desorption cycles were performed three time. During the repeated adsorption and desorption processes, none of the adsorption capacity of the biological adsorbents changed significantly, and the maximum change was 3%. These findings indicated that Alginate beads and both fungus-entrapped biological adsorbents can be used repeatedly to remove HMI. The researchers also noted that HMI should be treated not only for removal but also for recovery. Ajao [30] found that when extracellular polymeric substance (EPS) produced from nitrogen-limited glycerol/ethanol wastewater was recovered from the aqueous solution, 99.9% of the influent metals were adsorbed. The average recoveries of Cu2+ and Pb2+ were 86% and 90%, respectively, when the metal was desorbed with 0.1M HCl.

3 Studies on Adsorption of HMI in Water by Hydrogels Hydrogels are polymeric materials with a 3D network structure, and they expand significantly in water and maintain their original structure and properties. They also swell and contract when experiencing small changes in external factors, such as temperature and pH. Hydrogels exhibit large adsorption capacities, high removal efficiencies, fast adsorption, easy desorption, and environmental friendliness and are suitable for enrichment and separation of HMI. Hydrogels can be divided into three categories: CTS, acrylamide, and natural polymer graft. The process of HMI adsorption by hydrogels is mainly chemical adsorption, and it is supplemented by physical adsorption. Chemisorption mainly relies on functional groups (e.g., carboxyl, hydroxyl, amino and sulfonic groups) in hydrogels, which can adsorb, exchange, and chelate with heavy metals [31]. Physical adsorption mainly depends on the 3D network structure of hydrogel [32].

3.1 Principle of Hydrogel Adsorption of HMI in Water CTS hydrogels contain a large number of amino and hydroxyl groups and have a strong chelating effect on metal ions. Due to the shortcomings of CTS itself, such as a narrow pH adaptation range, poor adsorption selectivity, and long adsorption equilibrium time, researchers generally prepare CTS gels with a three-dimensional network structure through crosslinking reaction. In this way, the controllable cross-linking process, structure and mechanical properties of the hydrogel, the specific physical– chemical properties and potential reactivity of CTS can be comprehensively utilized to achieve the organic unification of the structure and the adsorbent functionality. The acrylamide polymer in the hydrogel is a homopolymer, the polymer chains have sufficient effective length, and the material contains strong adsorption sites involving ether groups and amide groups. The amide groups in polyacrylamide molecule form

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hydrogen bonds, which can easily introduce various anionic groups, cationic groups, and hydrophobic groups. The relative molecular weight of the synthesized copolymer can reach more than 106 and can form stable chelates by combining with many HMI. Natural polymer-grafted hydrogels are natural polymers with skeletal molecules, such as cellulose-grafted AcA/acrylamide molecules. The molecular structures of grafted hydrogels are relatively stable and have a wide variety of raw material sources and low prices. In nature, there are biodegradable, easily stripped and suitable for combination with HMI to form stable chelates. Hydrogels show good sensitivity to low concentrations of HMI.

3.2 Preparation of Hydrogel Adsorbents To improve the selective adsorption and renewable utilization of hydrogel adsorbents, researchers have studied new hydrogel adsorbent materials with enhanced mechanical strength of materials and improved the cross-linking technologies and preparation processes. Wang et al. [32] prepared a CTS-polyvinyl alcohol (CTSPVA) hydrogel with a 3D network structure by glutaraldehyde crosslinking method and alternate freeze–thaw method. The preparation process was as follows. In order to obtain a homogeneous solution, 42 mL of 10% PVA solution was mechanically mixed with 490 mL of 2% CTS solution for 2 h at room temperature. 4 mL of 25% glutaraldehyde aqueous solution was then stirred continuously at room temperature for 4 h. The gel was then frozen in a −18 °C refrigerator for 24 h and then thawed for 6 h at room temperature. Following 3 times alternate freeze–thaw cycles, the hydrogel was gained. After overnight immersion in a diluted NaOH solution, the hydrogel was rinsed several times with Millipore water until the pH of the suspension approached 7.0. Ethanol was used as a dehydrating agent to remove the neutralized product three times. After drying overnight in an oven at 80 °C, the dehydrated product was sieved through an 80-mesh screen for further adsorption study. It is found that the unique feature of CTS-PVA hydrogel adsorbent was its swelling property. Before drying, the expansion ratio of the hydrogel expanded well in distilled water was about 200 g/g. However, after drying, the value was changed to 4.66 g/g, which can only expand slightly. It was found that the adsorbent had good adsorption capacity for Hg2+ (585.90 mg/g), and its selectivity coefficients for Hg2+ were 36,642, 284,298, and 488 times that of Pb2+ , Cd2+ , and Cu2+ , respectively. Its adsorption mechanism for Hg2+ is as follows: (1) The gel adsorbents have a 3D network structure. (2) The coexistence of free pendant –NHCOCH3 , –NH2 , and C=N groups. These all N-containing functional groups participated in the adsorption of Hg2+ . Mao et al. [33] mixed AcA of 3.60 g, neutralized with NaOH, N,N, -methylenebis-acrylamide of 0.15 g and 45 mL water in a 250-mL beaker at 600 rpm for 30 min. After that, sodium alginate of 0.3 g was added and stirred for another 4 h. When the sodium alginate was completely dissolved, attapulgite and ammonium persulfate of 0.50 g was added into the suspension. The stirring was discontinued and the temperature was increased up to 45 °C, and the reaction time was 1.0 h. The hydrogel

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was rinsed in ethanol and water for 15 min, dried at room temperature, cut into small pieces and made into a network-structured hydrogel. It is found that the hydrogel containing 10% attapulgite has a compressive stress of 1.23 MPa, which was 4 times than that of pure hydrogel material, and the adsorption capacities of Pb(II) and Cu(II) were 392 and 273 mg/g, respectively. Zhao and co-workers [34] prepared modified cellulose hydrogels (MCC-g-(AA-co-AM)) by blending acrylamide and AcA. The preparation method was shown as follows. 1.5 g microcrystalline cellulose (MCC) was dispersed by magnetic stirring at 200 rpm in 30 mL distilled water and placed in a 250 mL flask connected with a reflux condenser, nitrogen pipe, and thermometer, and the utilization of nitrogen was to remove the dissolved oxygen. After stirring at 50 °C for 15 min, 0.3 g of ammonium persulfate (APS) was added to generate hydroxyl radicals. Then, the mixture of 0.3 g acrylamide (AM), 9 mL AcA and 0.1 g N,N, -methylene bisacrylamide (MBA) was slowly added into a three-neck flask and stirred continuously for 15 min. Then, the polymerization was completed by stirring at 50 °C for 2 h. After the reaction, the samples were washed with anhydrous ethanol to remove some substances which were not taken part in the reaction, and then dried in a 60 °C vacuum oven. Then the required hydrogel adsorbent was obtained. Under the optimized conditions, the adsorption capacities of Cu2+ , Pb2+ , and Cd2+ were up to 158, 393, and 290 mg/g, respectively.

3.3 Factors Influencing Hydrogel Adsorption of HMI in Water In the experimental study of hydrogel adsorption of HMI in water, different scholars analyzed various experimental factors and summarized below. pH. Wang et al. [32] prepared CTS-PVA hydrogel adsorbent via the glutaraldehyde crosslinking method. The isoelectric point of the adsorbent was measured to be 7.85 using the solid addition method [35]. Therefore, in the solution with pH below 7.85, the particle surface of hydrogel was positive, which was not conducive to the adsorption of HMI by electrostatic interaction. With the increase of pH from 2.00 to 5.85, the adsorption capacity of the adsorbent for Hg2+ was increased. Even in mercury acetate solution at pH 2.00, the adsorption capacity was 401.2 mg/g. Therefore, the adsorption mechanism of Hg2+ is likely the chelation between functional groups and Hg2+ , rather than the electrostatic interactions. The pH increased after adsorption of Hg2+ , which may be attributed to the hydrolysis of acetic acid by the dissociated mercuric acetate. Vieira et al. [36] used N,N, -methylene bisacrylamide, free-radical CTS and AcA as raw materials, CTS/acid-activated montmorillonite composite hydrogel and CTS-based hydrogel were prepared through copolymerization of the AcA, radical CTS and N,N, -methylenebisacrylamide. The pH of the aqueous solution changed between Fickian and non-Fickian transfer as well as Fischier and non-Fischier transfer, which had a great influence on the mechanism of water absorption. In the pH range of 5.5–3.5, the adsorption capacities for Ni(II) of

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CTS hydrogels and CTS/acid-activated montmorillonite composite hydrogels were 42.4–36.5 and 37.2–42.0 mg/g, respectively. The adsorption capacities for Pb(II) of CTS hydrogels and CTS/acid-activated montmorillonite composite hydrogels were 41.1–30.2 and 35.22–26.11 mg/g, respectively. Ion Concentration. Generally speaking, the removal rate increases with the increase in ion concentration. Lian et al. [37] prepared a hemicellulose hydrogel with the grafting copolymerization method. The influences of concentration and temperature on Pb2+ removal rate were studied. The findings demonstrated that the adsorption capacity of hemicellulose hydrogels for Pb2+ increased with the increasing of ion concentration, and the adsorption capacity of Pb2+ can reach 5.08 mg/g when the ion concentration of Pb2+ was 20 ppm and the temperature was 50 °C. These results indicated that the cellulose-based hydrogel was an ideal Pb2+ adsorbent. Vieira [36] found that the initial metal concentration was increased from 100 to 400 mg dm−3 . The adsorption capacities of Ni2+ on CTS hydrogels and the composite hydrogels were 30.3–100.7 mg/g and 26.30–71.12 mg/g, respectively. The adsorption capacities of Pb2+ on CTS hydrogels and the composite hydrogels were 40.5–177.9 mg/g and 35.11–107.86 mg/g, respectively. When the initial concentration was higher than 400 mg dm−3 , the adsorption capacity of the composite hydrogel appeared to be stable because the active sites in the composite hydrogel network were saturated. However, the similar situation was not observed in CTS-based hydrogels due to the presence of additional active sites and the absence of montmorillonite. Temperature. Hashem et al. [38] took sunflower stem (SFS) and acrylonitrile as raw materials and potassium permanganate-citric acid as an initiator to form graft SFS. Amidoximated sunflower stalks (ASFS) was prepared by reaction with hydroxylamine hydrochloride under alkaline conditions. ASFS showed effective adsorption of Cu2+ in water, and the maximum adsorption capacity was 0.620 mmol/g. It was found that when the polymerization temperature was elevated from 40 to 60 °C, the nitrogen content of the grafted samples increased. Above 60 °C, the nitrogen content was almost constant. Increasing the temperature to 60 °C can increase the nitrogen content by enhancing the diffusion and migration of monomers within the cellulose structure and forming of the free radicals necessary for grafting. Ali and colleagues [39] fabricated chelating polyvinylpyrrolidone/acrylic copolymer hydrogel by radiation-induced copolymerization polyvinylpyrrolidone/AcA (PVP/AcA) copolymer hydrogels, and they used a 25–600 °C temperature range with different compositions of the copolymer hydrogel and metal ion-chelating hydrogel to determine relative thermal stability. It was found that the pure PVP exhibited high thermal stability and began to decompose at 400 °C, and the pure AcA exhibited four characteristic degradation steps at 450, 400, 250, and 100 °C, which corresponded to the backbone degradation, decarboxylation, anhydride formation, and loss of associated water, respectively [40, 41]. The thermal stability of the prepared PVP/AcA copolymer hydrogel increased with the increasing number of stable groups and PVP content. The chelated metal Mn and Fe ions did not affect the thermal stabilities of copolymer hydrogels. The presence of Cu ions reduced the thermal stability of copolymers. The decreased thermal stability

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of copper-chelated hydrogels may be attributed to the loss of water in coordination with copper ions. The loss of water molecules coordinated with the Cu ions resulted in slightly higher weight losses in the early stages of decomposition [42]. Other Factors. Lian et al. [37] found experimentally that with mixed solutions containing Cu2+ , Cd2+ , Pb2+ , and Hg2+ , the adsorption performance of hydrogel adsorbent for Hg2+ ions was still good. The adsorption capacities of hydrogel to HMI increased in the order Cd2+ < Pb2+ < Cu2+ < Hg2+ . Vieira et al. [36] also found that the weight of CTS-based hydrogels increased from 50 to 400 mg/g. The adsorption capacities of Pb(II) and Ni(II) decreased from 277.9 to 32.5 mg/g and 87.7 to 20.1 mg/g, respectively. These variations in adsorption capacity with hydrogel mass can be explained by single/multisite adsorption and adsorption kinetics [36]. The higher the mass of the hydrogel is, the lower the metal adsorption capacity, because polysaccharide hydrogels adsorb water firstly and heavy metals secondly [43]. Therefore, the concentration of metals remaining in the aqueous solution was increased after the rapid absorption of water, resulting in a low adsorption capacity. Other physicochemical properties such as the metal ion radius and electron/electrochemical affinity also affected the adsorption capacity of the hydrogels [44]. Hashem et al. [38] observed the effects of adsorbent concentration on Cu(II) ion removal. It was found that when the concentration of adsorbent was 2–6 g/L, the removal rate increased with increasing adsorbent concentration, which may be due to the increased availability of adsorbent exchange sites. However, when the concentration of adsorbent is 7–10 g/L, the removal effect remains stable, which may be caused by the blockage of the effective active sites on the surface of the adsorbent. Competition among Cu2+ for surface sites creates overlapping areas [45].

3.4 Desorption and Reuse of Hydrogel Adsorbents Sustainable hydrogel adsorption materials with high desorption rates and retention of desorption rate after reuse are the ideal materials to be sought. Wang et al. [46] used o-carboxymethylated CTS to cross-link a thiourea and glutaraldehyde synthesized a new resin, which had excellent adsorption capacity and high selectivity for Ag+ . At pH of 4.0, the maximum Ag+ uptake was 3.8 mmol/g. The adsorption process was exothermic and conformed to the quasi-second-order kinetic model. The desorption rate of Ag+ by thiourea (0.5 M)-HCl solution (2.0 M) reached 99.23%, and there was no obvious decrease after five repetitions, indicating that this method can be used for Ag+ recovery. Wu et al. [47] successfully synthesized P(HEA/MALA), a new copolymer hydrogel, using hydroxyethyl acrylate (HEA) and malamic acid (MALA) as copolymers by using low-temperature radiation 60 Co-γ-ray irradiation for 24 h at −78 °C technology. The findings demonstrated that the adsorption capacity of hydrogel elevated with increasing pH until they reached the adsorption saturation. This phenomenon was caused by the electrostatic actions of functional groups, carboxyl ion exchange, and amino chelation. The competitive adsorption experiment

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showed that the competitive preference decreased in the order of Pb2+ > Cu2+ > Ni2+ > Zn2+ > Cd2+ for a mixed metal solution, and the desorption efficiency of hydrogel with disodium EDTA as desorption agent was higher than 92%. It could be reused three times.

4 Conclusions Biosorption and hydrogel sorption have provided a new way to solve the environmental pollution and recover HMI, but the research in this area has not been widely used in industrial production. Biosorption has some disadvantages such as insufficient removal capacity and a long equilibration times required for the removal process. Hydrogel adsorbents can introduce a large number of effective groups through grafting and other methods. They have great potential insensitivity and adsorption selectivity, but the high preparation cost also restricts the promotion of this technology. In the future, biosorption technology should be focused on cultivation of new strains with large removal capacities for HMI and short equilibration times for industrial production. More attention should be paid to development and preparation of cross-linking technology for hydrogel adsorbents, reducing the preparation cost of hydrogels, enhancing the mechanical strengths and adsorption capacities of hydrogels, and developing specific functional hydrogel materials with different functional groups and high sensitivity to HMI.

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Phytoremediation of Stormwater by Floating Treatment Wetland Md Nuruzzaman , A. H. M. Faisal Anwar , and Ranjan Sarukkalige

Abstract Floating treatment wetland (FTW) is a promising technology for nutrient and metal removal from stormwater. Plant is the key component of an FTW, facilitating pollutant removal through plant uptake and microbial actions. A careful selection of plant species is essential for an efficient FTW. This paper reviews available literature focusing on the role of plants in FTWs to identify research gaps and provide future research directions. From field-scale research, it was identified that Baumea articulata, Phragmites australis, Chrysopogon zizanoidses and Carex appressa were high-performing plants for nitrogen and phosphorus removal. It was found that the presence of microbial community largely depends upon the plant species. Microbial species and abundance are also limited by environmental factors such as pH, dissolved oxygen and nutrient concentration. Multi-species plantation is widely adopted in field-scale FTWs, but its effectiveness is not proven even though it has the potential for enhanced treatment under the right condition. Development of plant harvesting strategies for permanent removal of pollutants from the FTW system was found to depend on the season and nutrient distribution in plant tissue. This review paper provides critical insights into plant selection, role of microbes, multi-species plantation and harvesting strategies for permanent removal of pollutants from an FTW system. Keywords Constructed floating wetland · Stormwater · Plant uptake · Phytoremediation

Md Nuruzzaman (B) · A. H. M. F. Anwar · R. Sarukkalige School of Civil and Mechanical Engineering, Curtin University, GPO Box U1987, Perth, WA 6845, Australia e-mail: [email protected] A. H. M. F. Anwar e-mail: [email protected] R. Sarukkalige e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_20

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1 Introduction Stormwater pollution is one of the key sources of pollution to the receiving waterbodies such as rivers, lakes and estuaries [1]. Agricultural lands where fertilizers are applied contribute nutrients, such as nitrogen and phosphorus to the stormwater [2]. Urban runoff typically contains heavy metals, such as copper, zinc, lead, nickel, cadmium, etc. and hydrocarbons such as oil and grease [3]. An excessive influx of nutrients (nitrogen, phosphorus) triggers algal bloom causing dissolved oxygen (DO) depletion at the end of the algae life cycle due to dead algae decomposition [4]. Metal influx causes toxicity to the aquatic animals and harms the aquatic ecosystem [5]. As such, treatment of stormwater is paramount to protect aquatic habitats. Floating treatment wetland (FTW), also known as constructed floating wetland (CFW) or floating treatment island (FTI), is recently gaining worldwide popularity for its effectiveness, low cost and convenience in retrofitting FTW in existing stormwater ponds [6, 7]. In an FTW system, water-tolerant plant species are floated on the water with their soilremoved roots extending into the water column and the upper part (leaves) being in the air [8]. In other words, FTW employs phytoremediation to purify polluted water. Direct plant uptake of nutrients and metals plays a major role in pollutant removal from stormwater [9, 10]. The root matrix provides a large surface area for microbial growth and biofilm production [11]. Microbial endophytes and microbes attached to the root matrix convert toxic forms of pollutants into less toxic forms (e.g., ammonia nitrogen to nitrate nitrogen) and remove pollutants (e.g., denitrification) [12]. Microbes help converting pollutants into more readily bioavailable form to the plants [13]. The root matrix also acts as a physical filter and traps sediment and sediment-bound pollutants from stormwater [14]. FTWs offer advantages that traditional constructed wetlands (CWs) cannot afford. For example, traditional CWs have little flexibility on the depth of water flow and extra flow needs to be diverted away from it to protect the plants. In contrast, FTW can handle variable depth without any damage as it floats on the water. FTW also removes the additional land requirements as opposed to CWs, making FTW a very cheap solution [15]. The maintenance requirement is also very low for FTW. All these advantages are shooting the popularity of FTW high across the world. Field-scale and pilot-scale FTWs have been installed across the globe including USA, Australia, China, India, Brazil, New Zealand, Italy, Mexico and Singapore for stormwater, river water and wastewater treatment [14, 16–23]. Microcosm and mesocosm studies were conducted to improve its treatment performance. Many of the published articles focused on the plant performance of pollutant removal. For instance, Luca et al. investigated Typha domingensis for nitrogen and phosphorus removal [24]. White and Cousins utilized Canna flacida and Juncus effusus for nutrient removal from agricultural runoff [25]. Ladislas et al. experimented Cd, Ni and Zn removal by J. effusus and Carex riparia [26]. Selection of plant species plays a key role in achieving high treatment efficiency since plant physiology determines the need for nutrients and thus nutrient uptake. Despite the existence of numerous research articles on various plant performance, not much

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information is available on future research direction and developing a guideline to select high-performing plant species, maintenance and utilization of different plant species to optimize pollutant removal. As such, this review paper aims to review currently available information and provide direction to future research and layout the foundation on the development of guidelines on using plant species in FTW.

2 Plant Bio-accumulation of Pollutants Comparison of plant bio-accumulation of pollutants (nutrients and heavy metals) within the plant tissue for plants used in FTWs is a difficult task due to variation in reporting. There were three main approaches to reporting bio-accumulation in plant tissue: (1) gm of pollutants per unit area of FTW [27], (2) gm of pollutants per unit dry weight of plants [26] and (3) gm of pollutants per plant [28]. This variability in reporting, especially when other crucial information, e.g., plant density, total number of plants and total biomass of plants are absent, makes it harder to compare plant performance. Furthermore, the use of plants for treating different types of water induces additional errors for comparison. As such, we present bio-accumulation of pollutants from field-scale studies only and in mg per gm dry weight (Table 1). It is important to note that just measuring the pollutant concentration within plant tissue is not so accurate in comparing plant performance. This is because a species can have a high concentration within its tissue, but if it fails to produce enough biomass so that total accumulation is less, it may lead to an inefficient treatment by the FTW system compared to a species that can produce a high amount of biomass even concentration within plant tissue is low. The importance of this factor was also highlighted by Vymazal [29]. For example, by only observing the N concentration in Alisma subcordatum (24.1 mg/gm) and Carex stricta (11 mg/gm) in Table 1, one may conclude that the latter is an inferior plant compared to the former one, which would be a wrong conclusion. By observing the dry weight of the two plants, it can be understood that due to high biomass production by C. stricta (221 gm), it will outweigh the total N removal by A. subcordatum per plant, which has only 1.29 gm of dry biomass. From Table 1, it seems that Baumea articulata is the highest performing plant out of the 16 unique plants. This high accumulation and plant growth could be fuelled by high nutrient concentration (9 mg/L N and 5 mg/L P). It can be observed that N accumulation in Carex appressa used by Huth et al. [30] and Schwammberger et al. [27] were 26 mg/gm and 10.57 mg/gm, respectively, marking a stark difference. P concentration of the same plant was 3.3 mg/gm and 1.01 mg/gm by the two studies, respectively. This difference in nutrient accumulation in the same plant species occurred due to their exposure concentration mainly. N and P concentration in Schwammberger et al. [27] study was 1.8 and 0.08 mg/L, respectively, compared to 9 mg/L N and 5 mg/L P in the Huth et al. [30] study. N concentration in J. effusus by Tharp et al. and Winston et al. were both around 15 mg/gm [11, 31]. In both of the studies, urban runoff was the source of nutrients and thus nutrient concentrations were similar. B. articulata was the top performing plant

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Table 1 Pollutant bio-accumulation in plant tissue from field-scale studies References

[30]

[27] [31]

Plant

Maximum bio-accumulation (mg/g)

[26]

[34] [11]

Maximum root length (m)

Water type

Sewage

B. articulata

N-25, P-3.5

920

–

P. australis

N-20, P-3.2

393

–

C. appressa

N-26, P-3.3

98

–

C. zizaninides

N-22.5, P-3.3

209

–

C. appressa

N-10.57, P-1.01, K-11.45, Ca-3.08

227

2.1

Urban runoff Suburban runoff

J. effusus

P-2.1

9

0.18

Schoenoplectus tabernaemontani

P-3.5

7

0.23

Carex comosa

P-4.8

Pontederia cordata P-5.72 [33]

Dry biomass per plant (gm)

38

0.42

3

0.14

A. subcordatum

N-24.1

1.29

–

C. stricta

N-18.2

4.31

–

Iris versicolor

N-16.2

2.7

–

Urban runoff

J. effusus

N-14.6

4.47

–

P. cordata

N-20.4

2.24

–

J. effusus

Cd < 0.0001, Ni-0.04, Zn-0.093

12

–

C. riparia

Cd < 0.0001, 56 Ni-0.063, Zn-0.094

–

Carex virgata

P-1.79

–

Highway runoff Urban runoff

82.4

J. effusus

N-15, P-1.5, K-11

45

–

C. stricta

N-11, P-0.7, K-9

221

0.75

Spartina pectinate

N-11.5, P-1, K-7.5

66

–

Hibiscus moscheutos

N-16, P-1.1, K-6

74

–

P. cordata

N-14, 0.9, K-30

58

–

Highway runoff

in terms of total N and P accumulation per plant, followed by Phragmites australis, Chrysopogon zizaninides and C. appressa, all of which are characterized by high nutrient concentration accumulation capacity and biomass production. Lai et al. investigated 35 wetland plants in microcosm-constructed wetlands and identified unique characteristics of plants that have a higher potential to uptake nutrients [32]. It was found that plants having fibrous root matrix (individual root diameter. D < 1 mm) can uptake a higher amount of nutrients (N and P) into their tissue compared to thick root plants (D > 1 mm). Information regarding the root characteristics of any of the plants in Table 1 were not reported in the respective literature.

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3 Role of Microbes Attached with Root Matrix Microbial communities play an important role in the phytoremediation mechanisms [35], but it is not a well-understood phenomenon and one of the least explored aspects of FTW [36]. Microbes are known to break down complex compounds into simple nutrients readily available to plants [13]. After investigating three plants, e.g., Iris pseudacorus, Thalia dealbata and Typha orientalis, Zhang et al. concluded that plant species play a vital role in the abundance of ammonia-oxidizing micro-organisms [37]. Zhang et al. studied the response of the functional genes available in the root zone and floating bed of FTW system [38]. Among the functional genes, anammox, amoA, narG, nirK, nirS and nosZ were identified from the quantitative polymerase chain reaction (qPCR) analysis. It was observed that the presence of aerobic ammonia oxidation gene (amoA) increased with the increase of NH3 -N concentration in the water. The presence of annamox was much higher than amoA due to low DO concentration in the water. It was also found that denitrifying genes (narG, nirK, nirS, nosZ) were less in the root zone than in the floating bed due to the micro-environment created by oxygen released by the root in the rhizosphere. It was concluded that plant uptake of nitrogen and phosphorus contributed less than the microbial removal in N and P removal. In contrast, Keizer-Vlek et al. demonstrated that plant uptake explained 74% N and 60% P removal in their respective study [9]. The remaining removal could be attributed to microbial activities, which implies that plant uptake was the major source of nutrient removal. Water environmental conditions such as nutrient concentration, temperature, DO and pH have a significant impact on pollutant removal by microbial activities [13], which was also iterated by another study [39]. It is possible that the differences in the studies by Zhang et al. and Keizer-Vlek et al. were due to water environmental conditions [9, 38]. Bacteria augmented FTW successfully removed oil, organic and inorganic compounds from crude oil-contaminated water, as demonstrated by Rehman et al. [40]. Metal removal was also reported to be accelerated in bacterial-assisted FTW to treat river water [41]. Different other studies also demonstrated that microbial presence depends on the plant species, environmental factors such as DO, pH and nutrient availability and the correlation between loss of nutrient species and responsible microbial gene copy numbers as outlined in Table 2 [12, 36, 42–44]. Future research can focus on resolving the issue with the contribution of microbes attached with root matrix in pollutant removal. Most of the microbial studies are laboratory-scale. As such, field-scale studies are necessary to understand their true contribution. Furthermore, the feasibility of bacteria inoculation in field-scale FTWs and its effectiveness can be explored for higher treatment efficiency.

NO3 -N, DO

Vallisneria natans, Potamogeton malaianus, Ceratophyllum demersum, Elodea nuttallii

Canna indica

C. indica, I. pseudacorus

Unplanted floating bed

[12]

[38]

[39]

[36]

COD, NH3 -N, NO3 -N, TN

COD, NH3 -N, TN, TP

TN, TP

Water quality parameters

Plant

References

PCR-DGGE analysis

PCR-DGGE analysis

qPCR analysis

qPCR analysis

Molecular methods applied

Table 2 Features and findings of studies on microbes attached with root matrix of FTWs

(continued)

Water depth is vital for denitrifying communities, aeration did not impact nitrifying and denitrifying communities after establishment period

Bacterial abundance changes with temperature, pollutant concentration and plant growth

β-Proteobacteria, α-Proteobacteria

amoA, nirS, nirK

narG and nirK were the most abundant genes among the nitrogen functional genes

Denitrifying genes are positively correlated with nitrate concentration but negatively correlated with DO

Key findings

amoA, anammox, narG, nirK, nirS and nosZ

narG, napA, nirS, nirK, norB and nosZ

Identified bacteria/gene

300 Md Nuruzzaman et al.

Water quality parameters

NH3 -N, NO3 -N

Plant

Eichhornia crassipes

I. pseudacorus, T. dealbata, NH3 -N, NO3 -N, TN, TP T. orientalis

E. crassipes, Pistia – stratiotes, Ipomoea aquatic

References

[43]

[37]

[42]

Table 2 (continued)

qPCR analysis

qPCR analysis

PCR-DGGE and qPCR analysis

Molecular methods applied

amoA

amoA

nirK, nirS and nosZ

Identified bacteria/gene

Ammonia oxidizing bacteria (AOB) amoA gene copies were more abundant than ammonia oxidizing archaea (AOA) amoA gene copies

Plant type affected the copy number of nitrifying communities

Nitrogen loss was significantly correlated with denitrifying gene copy numbers

Key findings

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4 Multi-species Plantation Many field-scale FTW studies adopted mono species plantation. However, there are abundant examples of multi-species plantation as well. For instance, Ladislas et al. used C. riparia and J. effusus at a 50:50 proportion in a stormwater pond receiving highway runoff in France [26]. Pontederia sagittata and Cyperus papyrus were planted at a 20:80 ratio in a eutrophic urban pond in Mexico [23]. Numerous other studies also reported the use of multi-species plantations using up to 18 different species at different proportions [11, 30, 45, 46]. However, none of the field studies has tested how multi-species plantation affects treatment efficiency. Only a few studies have reported investigating the effect of multi-species plantation in microcosm and mesocosm experiments [47, 48]. Multi-species plantations affect treatment efficiency either by enhancing or decreasing the overall efficiency. Enhanced treatment efficiency is obtained when the plants have a synergistic effect, i.e., the overall efficiency is higher than the sum of individual species contributions [47]. Conversely, when the overall efficiency is less than the sum of individual contributions, it is known as an antagonistic effect. An additive effect occurs when the overall efficiency equals the individual contributions. Chance et al. investigated multi-species plantation by Iris ensata, Canna × generalis, Agrostis alba, C. stricta and Panicum virgatum in different combinations and found an additive effect for nitrogen and phosphorus removal [47]. It implies multispecies plantation was neither of any benefit nor having any negative effect. Han et al. studied Ca, Mg and K removal by a combination of Oenanthe javanica, Rumex japonicas, Phalaris arundinacea and Reineckia carnea and concluded that plant diversity had no significant effect on treatment efficiency [48]. However, it was also noted that multi-species plantations altered the plant uptake of heavy metals. It was also stated that the selection of the right plant species with the right combinations might enhance treatment efficiency. Geng et al. investigated multi-species plantation by experimenting 15 combinations of Oenanthe hookeri, R. japonicas, P. arundinacea and R. carnea, including bi, tri and tetra species combinations for P removal from wastewater [49]. It was observed that bi and tri species combinations that included O. hookeri removed the highest amount of P. However, this result was not statistically significant. It was concluded that species richness has an overall positive impact on P removal given that the most important species is present in the combination. It has been demonstrated in case of CWs that multi-species plantation positively influences treatment efficiency [50, 51]. It is reasonable to assume that multi-species plantation will also benefit FTW systems. But the problem that needs to be solved is the right kind of species with the right combination. As such, future research can concentrate on finding out the synergistic effect between plant species to facilitate informed selection of multi-species combination rather than mere arbitrary selection for FTW applications.

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5 Plant Harvesting Literature on developing harvesting strategies of FTW plants for the permanent removal of pollutants from the system is scarce. Developing a harvesting strategy requires analyzing plant tissue samples throughout the year to understand the peak pollutant accumulation season of the plant species in use and pollutant distribution in the roots and shoots [25, 52]. It also requires observation of plant senescing period when accumulated pollutants may return back to the water column due to nutrient relocation from shoots to roots [53]. Plant senescing may be driven by environmental factors such as temperature and season [54]. Ge et al. recommended that T. dealbata and C. indica shoots should be harvested during late October and early November in Jiaxing city, China, based on the peak nutrient accumulation season [52]. For Lythrum salicaria, harvesting was recommended during September. Wang et al. recommended whole-plant harvesting of P. cordata during September in Virginia, USA [28]. However, it was also noted that whole plant harvesting is more aggressive and sometimes may pose difficulty, especially when plant roots are entangled with the floating bed. Shoot harvesting of S. tabernaemontani was found suitable during October. Chua et al. harvested Typha angustifolia and C. zizanioides multiple times in a single year in Singapore and found that the plants could mature again within 80–100 days [18]. Multiple harvesting did not pose any performance issue to the plant species. However, it was reported that harvesting shoots prior to its peak season may reduce nutrient removal capacity in subsequent years [55]. Wang et al. suggested that any adverse effects from multiple harvesting of plant leaves need to be investigated [28]. The study conducted by Xu et al. in Jinan, China observed maximum nutrient accumulation in I. pseudacorus and T. dealbata in September [56]. The authors recommended only shoot harvesting due to its low cost and operational simplicity. It is to be noted that peak nutrient accumulation within plant tissue and senescing events are driven by seasonal changes. Since, seasons typically vary from one geographic location to another, the harvesting period of the same plant species will be different in different geographic location unless there is no variation in seasons between the concerned regions. As such, investigation at a local level is required for optimum results. It is important to note that harvesting only plant roots is not feasible as it can pose threats to the survival of plants. Therefore, development of harvesting strategies should focus mainly on the shoot harvesting. Plants that can translocate a significant amount of nutrients from their roots to shoots will be more effective in the permanent removal of nutrients from the system due to simplicity of plant shoot harvesting. As such, future research should endeavour identifying these kinds of plants and their capacity to store nutrients. Plants that can produce a higher amount of shoots will have an advantage in storing a higher amount of nutrients. The harvested plant biomass enriched with N and P can be composted and used as fertilizers in the agricultural land and gardens. However, caution must be taken if the plants have accumulated a significant amount of heavy metal, especially cadmium and lead within its tissue as that would have toxic heavy metals entering into the food chain.

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6 Conclusions We reviewed the literature on the role of plants in FTW systems and identified some key research gaps. We also discussed how these gaps should be addressed in future research. The following are the key conclusions drawn based on the arguments made throughout the paper. . The main role in removing pollutants in an FTW system is played by the plants. Other components like microbial removal are largely controlled by plant species, i.e., microbial removal is passively supported by the plant. In some cases, microbial removal may have a significant role to play, but it is not a well-understood removal pathway. Plant bio-accumulation of nutrients and plant growth is influenced by environmental factors such as nutrient concentration in water. Generally, plants capable of producing a higher amount of biomass will tend to remove a higher amount of total nutrient and metals from the system. As such, plant biomass production should be factored in when comparing different plants instead of just measuring pollutant concentration in plant tissue. . The presence of microbial communities in the plant root matrix largely depends on the plant species. Different plant species tend to create different microenvironment due to plant secretion of exudates, organic acid and oxygen release, which controls the type and abundance of microbial communities. Bacteria inoculation has proven to enhance treatment efficiency, but studies are limited. As such, further investigations are required to truly understand the overall benefit of different types of bacteria inoculation and their effectiveness in field-scale FTWs. . Multi-species plantation is widely practised in FTW systems, but little attention is given to the careful selection of plant species to attain a synergistic effect. Thus, it is highly likely that the true potential of multi-species plantation is not utilized and, in some cases, may even have negative outcomes due to antagonistic effects. Therefore, scientific investigation on multi-species plantation practice is of high importance. . Despite a plethora of literature on FTW phytoremediation studies, little attention is paid to developing harvesting strategies for the permanent removal of nutrients from the system. Maintenance negligence is highly likely to undermine the effort to improve water quality by an FTW. Furthermore, harvesting strategies should be developed under local conditions as studies from other geographic regions may not be applicable anywhere else due to climatic variations, even if the same plant species is used.

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The Effect of Style and Scale of Information on Public Willingness to Conduct Water-Saving Behaviors in China Tianze Xie

Abstract In order to alleviate water scarcity and its negative impacts, it is important to reduce household water consumption, which largely depends on people’s willingness to conduct water-saving behaviors. The study tests how the public’s willingness to conduct water-saving behaviors is influenced by the scale (i.e., national versus regional level) and style (i.e., emotional versus abstract) of information. The results show that when emotional information is provided, regional information leads to a higher willingness to conduct water-saving behaviors than national information; when national information is provided, abstract information leads to a higher willingness to conduct water-saving behaviors than emotional information. These results provide critical insights for policymakers and organizations aiming to promote the public’s willingness to conduct water-saving behaviors. Keywords Water-saving behaviors · Self-efficacy · China · National · Regional · Emotional · Abstract

1 Introduction Many countries now face severe water scarcity due to human activities (e.g., overexploitation) and natural reasons (e.g., uneven spatial water distribution) [1–3]. Responding to global water challenges, governments worldwide and international organizations are taking measures to strengthen the protection and sustainable management of water [4, 5]. For example, China has begun to move toward demandside management and plays an active role in establishing a water market [6, 7]. Yet, for many countries, there are still a variety of challenges in solving the problem of water scarcity. Among those challenges, one key challenge is the public’s willingness to reduce household water consumption. Increasing prosperity in developing countries leads T. Xie (B) Nanjing Foreign Language School, 30 Bei Jing Dong Lu, Nanjing 210008, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_21

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to a growing number of consumers, which is likely to offset any future savings in water that might be achieved from leveling growth or technology innovations [8, 9]. Therefore, promoting water-saving behavior at the household level will play an important role in addressing the water shortage problem [9–11]. However, not many people show strong willingness or take actual measures to save water in daily life [12]. Previous research has studied the effects of some factors on the willingness to conduct water-saving behaviors, including information, moral norms, policy incentives, participants’ region, and so on [13–15]. However, only a few studies focus on the effect of the information’s style, and to the best of my knowledge, no research has been carried out to study the combined effect of the information’s style and scale. This study aims to further the understanding of effective strategies that can motivate people to be more willing to conduct water-saving behaviors. Specifically, this study tests whether and the extent to which (a) scales (i.e., national versus regional level) and (b) styles of the information (i.e., emotional versus abstract information) influence the public’s willingness to conduct water-saving behaviors.

1.1 Scale of the Information One key factor that is likely to influence the public’s willingness to conduct watersaving behaviors is the scale of the information. The study distinguishes two scales of the information, namely, macroscopic information about a nation’s water scarcity (i.e., at a country scale) and microscopic information about a region’s water scarcity (i.e., at a county scale). Different scales of information are likely to influence the public’s belief about whether they can mobilize effectively to succeed in achieving a goal (i.e., self-efficacy [16]). This paper argues that when people read microscopic information about water scarcity, they are more likely to think that the condition can be improved with their own efforts compared to when people read macroscopic information about water scarcity. Thus, their actions can play a role in ameliorating the situation there. Indeed, evidence suggests that self-efficacy may help encourage engagement in more challenging pro-environmental behaviors [17]. Therefore, this paper proposes that people are more likely to conduct water-saving behaviors after reading regional information than national information (Hypothesis 1).

1.2 Style of the Information Another key factor is the style of the information, namely, whether the information is abstract (i.e., using numbers to show the water scarcity situation) or emotional (i.e., using words indicating strong emotions), as defined in this paper. Emotional information usually can reduce the cognitive separation between participants and the instance of water scarcity (i.e., psychological distance [18]). Abstract information, on the contrary, is less likely to reduce the psychological distance between readers and

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the instance. This study further argues that people are more likely to conduct watersaving behaviors when their psychological distance from the event is reduced than increased. Taken together, this paper proposes that emotional information is more likely to lead to a higher willingness to conduct water-saving behaviors compared to abstract information (Hypothesis 2). When organizations try to encourage water-saving behaviors, they usually utilize different strategies to remind people of water scarcity in a certain area or country. Hence, it is important to study how different strategies jointly influence people’s willingness to conduct water-saving behaviors. However, very little research has been conducted to study how the combination of scale and style of the information jointly influence people’s willingness to conduct water-saving behaviors. On the one hand, both regional and emotional information may be necessary to reach the optimal effect—namely higher willingness to conduct water-saving behaviors; on the other hand, providing one of the information may already be enough to lead to a higher willingness to conduct water-saving behaviors. This paper proposes that the combination of emotional and regional information works optimally, compared to providing only one of the information and compared to other combinations (Hypothesis 3). Arguably, regional information may be able to strengthen the impact of emotional information on reducing psychological distance. Specifically, in addition to emotional information, with information pointing out the exact region that lacks water, the cognitive separation between readers and the instance of water scarcity may further decrease, which in turn may enhance people’s willingness to conduct water-saving behaviors. In sum, this study tests the following hypothesizes: (1) regional information leads to more water-saving behaviors than national information (H1); (2) emotional information leads to more water-saving behaviors than abstract information (H2); (3) the combination of emotional and regional information works optimally, compared to providing of only one of the information and compared to other combinations (H3).

2 Methods 2.1 Procedure and Design This study tested our reasoning via an online survey with respondents from a pre-recruited Chinese panel. Participants received an invitation from the panel to complete an online study about water-saving behaviors [19]. At the end of the study, the participants were informed that some of the information provided in the survey was fictitious according to the study’s needs. They were then thanked and offered a token amount of money. The questionnaire was written in Chinese, and the respondents were all in China. 219 responses were received from the panel. Considering the time required to read materials to guarantee the quality of the study, responses that were finished in less than 60 s were removed. As a result, there were 90 valid responses, of which 38 were

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male and 52 were female, with a mean age of 33 years old and a standard deviation of 7 years. Respondents were diverse in terms of income and region [20]. More detailed demographic information can be found in the supplementary information. This study followed a 2 × 2 experimental between-subjects design to test our hypotheses. Participants were asked to read a text about water scarcity. Next, the scale and style of the information were manipulated. The scale of the Text. In the regional text, participants read about water scarcity in Shiquan County, Shaanxi Province. Specifically, participants read that “The water shortage problem is relatively serious in Shiquan County, Shaanxi Province.” In the national text, participants read about water scarcity in China. Specifically, participants read that “The water shortage problem is relatively serious in China.” The style of the Text. In the emotional text, participants read about people’s discontent toward troubles caused by frequent water shutoffs. Specifically, they read that “Shiquan/Chinese people affected by water shortage will experience water cuts from time to time, and they can only avoid the peak of water use and get up at night to take a bath. Some people need to keep a large pot filled with water at home in case of emergency. In addition to the helplessness, strong discontent gradually emerged among those affected.” In the abstract text, participants read about data showing the water scarcity situation. Specifically, they read that “Shiquan/China’s water resources per capita are 2239 m3 , which is approximately 1/3 of the global water resources per capita. In 2020, the water consumption per capita was 411.9 m3 , the household water consumption per capita was 61 m3 , and the agricultural water consumption per capita was 258 m3 .”

2.2 Measures After reading the text, participants were asked to indicate their willingness to conduct water-saving behaviors. They also answered some demographic questions about gender, age, education level, and income. Willingness to Conduct Water-Saving Behaviors. Participants were asked to indicate to what extent, on a 7-point scale ranging from 1 (not at all) to 7 (very much) [19], they would shorten shower time, reduce shower frequency, and start the washing machine only when it is full. Then the mean score of the three items was computed to reflect the participant’s willingness to conduct water-saving behaviors (M = 5.289, SD = 1.234, α = 0.743). Self-efficacy. Participants were asked to indicate to what extent, on a 7-point scale ranging from 1 (not at all) to 7 (very much), they thought their water-saving behaviors could help those affected by water scarcity (M = 4.420, SD = 1.445).

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Psychological Distance. Participants were asked to indicate to what extent, on a 7-point scale ranging from 1 (not at all) to 7 (very much), they thought water scarcity could affect their life (M = 6.290, SD = 1.274).

3 Results This study used ANOVA to test the hypotheses. The study included the main effect of the scale of the information, the mean effect of the style of the information, and the interaction between scale and style in the model. The results showed that there were no significant main effects of the scale (F(1,90) = 0.405, p = 0.526, η2 = 0.005) and style (F(1,90) = 1.515, p = 0.222, η2 = 0.017) of information on the willingness to conduct water-saving behaviors. The results suggest that providing national or regional information may not have an influence on the public’s willingness to conduct water-saving behaviors. Similarly, providing abstract or emotional information may not influence the public’s willingness to conduct water-saving behaviors. Importantly, the interaction between scale and style on the willingness to conduct water-saving behaviors was significant, F(1,90) = 5.676, p = 0.019, η2 = 0.062). As depicted in Fig. 1, post-hoc tests revealed that when emotional information was provided, regional information would lead to a higher willingness to conduct watersaving behaviors than national information (p = 0.032, 95% CI (0.0671, 1.4726)); when national information was provided, abstract information would lead to a higher willingness to conduct water-saving behaviors than emotional information (p = 0.014, 95% CI (0.1906, 1.6521)). The main effect of the scale on self-efficacy was not significant (F(1,90) = 0.209, p = 0.649, η2 = 0.002). The influence of the interaction between scale and style over self-efficacy was not significant either (F(1,90) = 1.686, p = 0.198, η2 = 0.019). But the main effect of the style of the information on self-efficacy was significant (F(1,90) Fig. 1 The effect of the style and scale of information on the willingness to conduct water-saving behaviors. Note Means of bars with different letters (a, b) significantly differ from each other (α < 0.05, Bonferroni-Holm corrected), those with the same letter do not significantly differ from each other

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= 7.342, p = 0.008, η2 = 0.079). When the scale was provided, abstract information would lead to higher self-efficacy. The main effects of the scale (F(1,90) = 0.314, p = 0.577, η2 = 0.004) and the style(F(1,90) = 1.466, p = 0.229, η2 = 0.017) and the interaction between them (F(1,90) = 1.751, p = 0.189, η2 = 0.020) on psychological distance were all insignificant.

4 Discussions This paper studied the effect of style (i.e., emotional versus abstract) and scale (i.e., regional versus national) of information on public’s willingness to conduct watersaving behaviors in China. This paper extended previous research by (1) conducting an experimental study to test the causal effects of the style and scale of information on public willingness to conduct water-saving behaviors, (2) testing the combined effects of the scale and style of information on public willingness. This research has important theoretical implications. Specifically speaking, among different factors, the information’s scale and style have rarely been studied [13, 15]. Most importantly, the combined effect of these two factors has never been studied. Hence, this study not only tests the independent main effects of the two factors on people’s willingness to conduct water-saving behaviors separately but also theorizes and tests their interaction effect. In this way, this research provides crucial insights on how different strategies could be applied together in promoting water-saving behaviors. Practically speaking, these two factors have been commonly applied in real-life interventions in promoting water-saving behaviors in China. For example, policies are often designed and implemented both at national and regional levels across different regions in China [21]. In addition, emotional information is often provided in the news about water shortages, and sometimes policy makers and reports tend to use abstract numbers to describe the stress and severity of water shortage [22]—such strategies concern the style of information. However, as stated above, despite its widespread use in real life, its effects in doing so remain unclear. This highlights the practical importance and urgency of studying both factors. The results of this paper showed that when emotional information (i.e., being helpless and discontent) is provided, regional information (i.e., county level) will promote people’s willingness to conduct water-saving behaviors more than national information. And if national information is provided, abstract information (i.e., concrete numbers) will promote water-saving behaviors more than emotional information. Similar to the results of previous research [14], the main effect of the scale of information did not have a significant effect on people’s willingness to conduct water-saving behaviors. One possible explanation for this insignificant result could be that people in China today may perceive water shortage as a national problem instead of a regional one, even when regional water shortage information is provided (cf. [14]).

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Unexpectedly, our results showed that providing abstract information was more effective in enhancing self-efficacy compared to providing emotional information. This may be due to the participants’ high education level in this research, leading to a stronger understanding of concrete numbers among all participants. Future research could test this possibility. This study has important practical implications. For policymakers or the central government, providing abstract information may be a better choice to call on the public’s willingness to conduct water-saving behaviors. For journalists or authors, if they want to write a touching or emotional story, it is better to focus on a small region. And for campus newspapers or activities organizers, providing abstract information may contribute to participants’ perceived self-ability in addressing environmental problems. The study also has some limitations. Based on the fact that participants in China’s online panel generally have higher educational backgrounds, most of the respondents in this research are undergraduate students. Also, the sample size is a little small due to the limited time and size of the study. But every scenario received more than 20 responses, which meets the basic requirements of a 2 × 2 experimental betweensubjects design, and the effect size is strong enough to support the results. Future research could use a more representative sample with bigger sample sizes to test the pattern of results in order to firmer the generalizability of the results. In the emotional text, this study uses the emotions “helplessness” and “discontent.” Future research may further test whether different types of emotions [23–25], such as positive versus negative emotions, in the text, have different effects on people’s willingness to conduct pro-environmental behaviors. To conclude, this study validates the causal effects of the style and scale of information on public willingness to conduct water-saving behaviors. The results showed a significant interaction between the scale and style of the information on people’s willingness to conduct water-saving behaviors. Specifically, when the information is on the national scale, abstract information is more likely to lead to a higher willingness to conduct water-saving behaviors than emotional information. When the information is emotional, regional information is more likely to lead to a higher willingness to conduct water-saving behaviors than national information.

Appendix 1 See Table 1.

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Table 1 Demographics of respondents

China Gender

Male

38

Female

52

Mean age (SD) Education

0

Junior school

0

High school

13

University

77

I do not want to say

0

Family income per month/Yuan

Total

33 (7)

Primary school

10,000

40

I do not want to say

2

90

Appendix 2 Questionnaire Scenarios Regional and Emotional 1 The water shortage problem is relatively serious in Shiquan County, Shaanxi Province. Shiquan people affected by water shortage will experience water cuts from time to time, and they can only avoid the peak of water use and get up at night to take a bath. Some people need to keep a large pot filled with water at home in case of emergency. In addition to the helplessness, strong discontent gradually emerged among those affected. National and Emotional 2 The water shortage problem is relatively serious in China. Chinese people affected by water shortage will experience water cuts from time to time, and they can only avoid the peak of water use and get up at night to take a bath. Some people need to keep a large pot filled with water at home in case of emergency. In addition to the helplessness, strong discontent gradually emerged among those affected. Regional and Abstract 3 The water shortage problem is relatively serious in Shiquan County, Shaanxi Province. Shiquan’s water resources per capita are 2239 m3 , which is approximately 1/3 of the global water resources per capita. In 2020, the water consumption per

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capita was 411.9 m3 , the household water consumption per capita was 61 m3 , and the agricultural water consumption per capita was 258 m3 . National and Abstract 4 The water shortage problem is relatively serious in China. China’s water resources per capita are 2239 m3 , which is approximately 1/3 of the global water resources per capita. In 2020, the water consumption per capita was 411.9 m3 , the household water consumption per capita was 61 m3 , and the agricultural water consumption per capita was 258 m3 . Questions Following the Scenarios 1. To what extent do you think your behaviors can change the situation of water scarcity? (1: not at all, 7: very much) 2. To what extent do you consider water shortage will influence your life? (1: not at all, 7: very much) 3. Given the situation mentioned in the text, to what extent will you shorten your bath time? (1: not at all, 7: very much) 4. Given the situation mentioned in the text, to what extent will you reduce your bathing frequency? (1: not at all, 7: very much) 5. Given the situation mentioned in the text, to what extent will you run your washing machine on a full load? (1: not at all, 7: very much) 6. To what extent will you reuse or recycle used water bottles? (1: not at all, 7: very much) Note: some of the information provided in the survey was fictitious according to the study’s needs.

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6. Guo H, Chen X, Liu J, Zhang H, Svensson J (2020) Joint analysis of water rights trading and water-saving management contracts in China. Int J Water Resour Dev 36(4):716–737 7. Wang J, Li Y, Huang J, Yan T, Sun T (2017) Growing water scarcity, food security and government responses in China. Global Food Secur 14:9–17 8. Myers N, Kent J (2003) New consumers: the influence of affluence on the environment. Proc Natl Acad Sci USA 100(8):4963–4968 9. Harlan SL, Yabiku ST, Larsen L, Brazel AJ (2009) Household water consumption in an arid city: affluence, affordance, and attitudes. Soc Nat Resour 22(8):691–709 10. Mainieri T, Barnett EG, Valdero TR, Unipan JB, Oskamp S (1997) Green buying: the influence of environmental concern on consumer behavior. J Soc Psychol 137(2):189–204 11. Berk RA, Cooley TF, LaCivita CJ, Parker S, Sredl K, Brewer M (1980) Reducing consumption in periods of acute scarcity: the case of water. Soc Sci Res 9(2):99–120 12. Su H, Zhao X, Wang W, Jiang L, Xue B (2021) What factors affect the water saving behaviors of farmers in the Loess Hilly Region of China? J Environ Manag 292 13. Han H, Chua BL, Hyun SS (2020) Eliciting customers’ waste reduction and water saving behaviors at a hotel. Int J Hosp Manag 87 14. Shwom R, Dan A, Dietz T (2008) The effects of information and state of residence on climate change policy preferences. Clim Change 90(4):343–358 15. Zhu J, Zhao X, Zhu T, Li L (2021) Which factors determine students’ water-saving behaviors? Evidence from China colleges. Urban Water J 18(10):860–872 16. Capron Puozzo I, Audrin C (2021) Improving self-efficacy and creative self-efficacy to foster creativity and learning in schools. Think Skills Creat 42 17. Lauren N, Fielding KS, Smith L, Louis WR (2016) You did, so you can and you will: selfefficacy as a mediator of spillover from easy to more difficult pro-environmental behaviour. J Environ Psychol 48:191–199 18. Baltatescu S (2014) Psychological distance. Encyclopedia of quality of life and well-being research 19. Wang Z (2022) Study of psychological and behavioral factors influencing public acceptability of sustainable energy transition in China. Adv Energy Res Dev 1:217–226 20. Liu L, Bouman T, Perlaviciute G, Steg L (2021) The more public influence, the better? The effects of full versus shared influence on public acceptability of energy projects in the Netherlands and China. Energy Res Soc Sci 81 21. Lv F (2019) The interactions of the multi-agencies on the policy-making process in China: an example from public culture service policy. CASS J Polit Sci 22. Lei G (2021) Focus on wetlands and conserve water resources-World Wetland Day 2021 theme interpretation 23. Cherry K (2019) The 6 types of basic emotions and their effect on human behavior. VeryWellMind 24. Calder AJ, Burton AM, Miller P, Young AW, Akamatsu S (2001) A principal component analysis of facial expressions. Vis Res 41(9) 25. Wang X, Zong C (2021) Distributed representations of emotion categories in emotion space. In: ACL-IJCNLP 2021—59th annual meeting of the Association for Computational Linguistics and the 11th international joint conference on natural language processing, proceedings of the conference, pp 2364–2375

Experimental Study on Isotopic Fractionation Factor and Evaporation Rate in Soil Water Tao Wang, Haili Xu, and Ming Li

Abstract Evaporation is an important component of the hydrologic cycle. The isotopic tracer method is widely used to study the evaporation process, whereas isotopic fractionation is the foundation of application. Isotopic fractionation is affected by atmospheric factors. The evaporation rate could reveal all effects of atmospheric factors. Therefore, there must be a certain relation existing between isotopic fractionation and evaporation rate. To reveal the isotopic differences between evaporation vapor and residual water with time intervals of days, an average isotopic fractionation factor was introduced. A field bare soil evaporation experiment was carried out under initial uniform isotopic profiles and no inflow during evaporation conditions. The experimental results showed that the isotopic compositions of soil water at topsoil became enrichment with time; isotopic compositions of soil water below 15-cm-depth were almost not affected by evaporation. There were function relations between the average isotopic fractionation factor and evaporation rate with the time interval of one day and seven days. Soil cracks on topsoil surface and errors in isotopic measured analysis, soil water extracted by vacuum method, and evaporate rate measurement influenced the calculation of average isotopic fractionation factor. Keywords Average isotopic fractionation factor · Evaporation rate · Hydrogen and oxygen isotopes

1 Introduction Isotopic fractionation of hydrogen and oxygen often occurs in phase changes in condensation and evaporation processes, resulting in different isotopic compositions in different phases. It is the foundation for widely using hydrogen and oxygen stable isotopes to trace the hydrologic cycle. Evaporation and mixing processes in the unsaturated zone are two important effect factors in calculating groundwater recharge T. Wang (B) · H. Xu · M. Li POWERCHINA Chengdu Engineering Corporation Limited, Chengdu 610072, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_22

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by applying the isotopic technique. Therefore, isotopic fractionation in evaporation should be understood. Evaporation is an important component of the water cycle. The isotopic study of evaporation was carried out by many researchers, including evaporation from free water and soil water. Craig et al. found that hydrogen and oxygen-18 of free water evaporation did not obey the simple batch distillation equation in nonzero air humidity conditions, and a stationary isotopic state would reach as water decreased to zero [1, 2]. Wu et al. analyzed the variations of isotope compositions of lake Qinghai and found that evaporative enrichment had a significant impact on isotopic contents in lake water [3]. Gibson et al. combined the most recent equations required for the estimation of evaporative losses based on the revised Craig-Gordon model [4]. It indicated that the major factor contributing to the overall uncertainty in evaporative loss calculations is uncertainty in the estimation of the isotope composition of ambient air moisture. In the isotopic study of free water evaporation, the isotopic fractionation factor was a critical indispensable parameter for estimating evaporation rate and isotopic variations with time. And isotopic fractionation factor, such as kinetic fractionation, was hard to obtain leading to hindering the field application of the theory of Craig and Gordon [2]. Isotopic variations of soil water in the evaporation process are more complex than free water. Barnes and Allison developed a mathematical model describing the isotopic movements in soil profiles as soil water evaporated from dry soil under quasi-steady state and isothermal conditions [5]. In this literature, three independent methods were introduced to estimate the evaporation rate from dry soils and indicated that the calculation values of the evaporation rate were affected by the isotopic fractionation factor. A series of soil water evaporation experiments showed that the slopes of δD versus δ 18 O decreased as the thickness of the mulch layer increased at the soil surface. Barnes and Allison further developed their theory to describe the shape of isotopic profiles from soil water evaporation under non-isothermal and quasi-steady-state conditions [6]. The isotopic profiles were similar to those under isothermal conditions, but a minimum isotope value could be observed beneath the evaporation front due to the vapor flux as opposed to the evaporation flux and depleted vapor resulting in the depletion of the liquid phase. Walker et al. established equations describing isotopic profile variations from a bare soil surface under nonsteady evaporation conditions [7]. The analytical solutions of equations are rather difficult to obtain, so the method of numerical solutions is widely used for isotopic variations in soil water evaporation despite of instability of numerical solutions. Shurbaji and Phillips introduced a numerical model describing movements of heat, liquid water, vapor water, and isotopic species [8]. A transition factor was taken into account for the isotopic movement equation. The study of isotopic profiles varying with time in vertical soil layers and evaporation rate requires calculating the values of the isotopic fractionation factor. Isotopic fractionation is composed of equilibrium fractionation and kinetic fractionation. Equilibrium fractionation is mainly affected by temperature [9, 10]. Kinetic fractionation caused by different diffusivities in different molecules depends on relative

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humidity and dry superficial layer thickness [1, 2, 11]. Diurnal variations of atmospheric factors are greatly drastic, resulting in isotopic fractionation factors greatly varying with time. The evaporation rate is a comprehensive reflection of atmospheric factors. Therefore, there must be a certain relationship between evaporation rate and isotopic fractionation factor. Hydrologic data, including evaporation rate, discharge, and precipitation, were recorded with time intervals of hours or days, while isotopic fractionation was instantaneous and impossible to measure in the field. In this paper, an average isotopic fractionation factor was defined to describe the isotopic differences between vapor and liquid phases in evaporation with the time intervals of hours or days. The objectives of this paper were to study isotopic variations of soil water evaporation from bare soil and try to find the function relationship between evaporation rate and the average isotope fractionation factor through evaporation experiment in field conditions.

2 Experimental Study The evaporation experiment was conducted in bare clay soil. The experimental time was from 4th April to 5th May 2009. The study area was 2.5 m in length and 1.92 m in width with an area of 4.8 m2 . There was a 20 cm of height of soil wall around the study area used for ponding water infiltrating. Grass growing in the evaporation area was cleaned before the experiment. Thirteen probes of Time-Domain Reflector (TDR) with a length of 20 cm were horizontally inserted in soil profiles used for continuously measuring the volume water content of different depths at 1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110 cm. Another three probes of TDR were vertically inserted in surface soil with triangle form at a different place in the study area for observing water content at depths of 0–20 cm. Two thermometers were also vertically inserted to measure the soil temperature at depths of 2 and 20 cm. The dry bulk density of soil was measured at 10 cm of depth intervals from 0 to 110 cm (Fig. 1), with a mean value of 1.51 g/cm3 . A canopy was used for preventing rainfall from infiltrating into soil profiles during the evaporation experiment. A water pool with 2.45 m in length, 1.96 m in width, and 3 m in depth was used to store infiltration water. To obtain an initial uniform isotopic profile in soil, a total amount volume of 9.03 m3 of water had infiltrated into the soil profile. The process of water infiltration continuously last four days from 8:00 am to 8:00 pm. Two pieces of transparent polyethylene mulch were covered on the soil surface and the surface of the water pool during the water infiltration period. The δ 18 O measured values of water infiltrating were −7.31‰ and −7.35‰, at the beginning and the end of the infiltration period, respectively. The volumetric water content of the soil profile before the water infiltrating and evaporation experiment began were shown in Fig. 2. It was found that infiltration water had percolated to the depth of 110 cm. The isotopic compositions of soil water above the depth of 110 cm were uniform according to the similar soil column experiment results of Araguas-Araguas et al. [12].

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Fig. 1 The dry bulk density in the soil profile

Fig. 2 The volumetric water content profiles of the soil

There was an evaporation pan of 50 cm in diameter and height beside the study area. Air temperature, soil temperature, relative humidity, and evaporation rate from the pan were recorded every day at predetermined time intervals. Soil samples were collected using a soil drill with a diameter of 5 cm at time intervals of 1, 2, 3, 4, 5, 6, 7, 8, 15, 16, 23, 24, 31, 32 d. The sampling depths were respectively 0–15 cm, 15–30 cm, 30–45 cm, 45–60 cm, 60–75 cm, 75–90 cm, 90–100 cm, and 100–115 cm. The collected soil samples were immediately sealed in transparent polyethylene bags and stored at a shaded place with a temperature maintained at around 25 °C. The holes on the soil surface after collecting the soil sample were filled with 1-cm-thick paper plates. Thus, it could reduce the effects of evaporation on isotopic compositions of soil water nearby the holes.

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The soil water was extracted by vacuum method for 5 h at 100 °C. As a limitation of experimental conditions, only two soil samples at depths of 0–30 cm could be extracted for isotopic analysis on the day of collecting soil samples. In a comparison of δ 18 O values between extracted soil water and infiltrating water, the maximum difference value was −0.7‰, and all extracted soil water showed an isotopic depletion trend. The extracted water samples were collected using 30-ml plastic bottles. Before isotopic analysis, the bottles were tightly sealed using adhesive tape for reducing the effect of water evaporation. Oxygen isotopic compositions of water samples were measured using a MAT-253 mass spectrometer in the isotopic laboratory of Hohai University in Nanjing, China. The measured results were expressed as δ-values relative to the international standard VSMOW. Analytical precision was ±0.2‰ for oxygen isotope analyses.

3 Results 3.1 Temperature and Relative Humidity The temperature data of air and soil surface at depths of 2 and 20 cm were shown in Fig. 3. The daily mean temperature of air and soil surface at depths of 2 and 20 cm during the experiment ranged from 9.1 to 25.9 °C, 7.6 to 29.8 °C, 7.5 to 23.9 °C, with arithmetic mean values of 20.4, 23.4, and 19.1 °C. It was found that the daily temperature of soil surface at 2 cm was higher than that of air, whereas daily soil temperature at depth of 20 cm was lower than that of air. There was an increasing trend in the temperature with time. The daily mean relative humidity was shown in Fig. 4. The value of relative humidity ranged from 42 to 92%, with a mean value of 62%. Fig. 3 The temperature of air and soil surface at depths of 2 and 20 cm

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Fig. 4 The daily mean relative humidity

3.2 Evaporation Rate of Evaporation Pan and Soil Water The evaporation rate from the evaporation pans indirectly revealed the potential evaporation from bare soil. The observed daily evaporation rate ranged from 1.0 to 7.5 mm/d, with a mean value of 3.9 mm/d (Fig. 5). The total amount of pan evaporation was 121.5 mm. The evaporation rate from the evaporation pan was primarily affected by atmospheric factors. There were nine rainfall events during the experiment with a total amount of 69.6 mm. The values of evaporation rate greatly declined, and the slope of cumulative evaporation with time decreased in rain events (Fig. 5). Since a canopy was set up during rainfall events, no rain fell into the study area and evaporation pan. The evaporation rate from bare soil depends on the atmospheric factors and soil characteristics. Figure 6 showed the evaporation rate from bare soil varies with time above the depth of 110 cm. By comparing Figs. 5 and 6, it was indicated that the

Fig. 5 Evaporation rate and cumulative evaporation from pan

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Fig. 6 Evaporation rate from bare soil

evaporation rate between 1 and 3 d was mainly determined by atmospheric factors, belonging to the first drying stage. The soil surface was wet and soil water in the soil profile was lost easily. The evaporation rate increased rapidly at this stage, with a maximum value of 4.4 mm/d. A thin drying soil layer slowly appeared after the third day, and some soil cracks were observed on the ninth day. It belonged to the second drying stage. The evaporation rate was restricted by both soil texture and atmospheric factors and decreased to constant at end of the experiment. A total amount of 42.4 mm cumulative evaporation was measured during the experiment, with a mean evaporation rate of 1.4 mm/d. The evaporation rate and total evaporation amount from bare soil were lower than those from the evaporation pan. There was an obvious fluctuation in evaporation rate at the second drying stage due to the measurement errors of water content and the effects of soil cracks on the soil surface.

3.3 Isotopic Variations of Soil Water Figure 7 showed the isotopic variations in soil water undergoing evaporation at different depths of soil layers. The results showed that the oxygen isotopic compositions of soil water at depths from 0 to 15 cm isotopically enriched with time. The δ 18 O values of soil water varied from −8.03 to −5.12 ‰, with a mean value of −6.49‰. Evaporation was the main effect factor causing isotopic enrichment of soil water at the topsoil [13]. It was found that there was a slight difference in the δ 18 O values of soil water between two continuous days. The δ 18 O values of soil water from the depths of 15–30 cm ranged from −7.85 to −7.25 ‰, fluctuating slightly with a mean value of −7.58 ‰. Table 1 showed the isotopic characteristic values of soil water under evaporation conditions in different soil layers. The maximum, minimum, and mean values of δ 18 O in soil water of different soil layers below the depth of 15 cm showed similar varying trends. Although there were some tree roots non-uniformly distributing in the soil profiles, no effects on isotopic profiles occurred by these roots [14]. The results of isotopic variation

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Fig. 7 Isotopic variations in soil water at different soil layers

Table 1 Isotopic characteristic values of soil water Depth (cm)

Sample amount

δ 18 O (‰) Max.

Min.

Mean

0–15

14

−5.12

−8.03

−6.49

15–30

14

−7.25

−7.85

−7.58

30–45

14

−7.06

−8.02

−7.60

45–60

14

−7.20

−8.11

−7.65

100–115

14

−7.15

−8.06

−7.11

in soil water in Fig. 7 and Table 1, indicated that the isotopic compositions of soil water were almost not affected by evaporation below the depths of 15 cm, notwithstanding the variations of water content observed. Therefore, the isotopic compositions below the 15 cm depth could be considered constant. The isotopic error of soil water extraction has a great influence on the analysis of isotopic data.

4 Discussions 4.1 Evaporation Rate and Isotopic Fractionation Factor with Time Interval of One Day The average isotopic fractionation with a time interval of one day is defined as: α1d =

RE R SW

(1)

where α 1d is the average isotopic fractionation factor with a time interval of one day, R E and RSW are the corresponding isotopic ratios of evaporation vapor and

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residual soil water undergoing evaporation in one day, respectively. Equation (1) was often expressed as δ-values. The isotopic compositions of soil water in topsoil would increase without the influence of input water in evaporation conditions [5, 6]. To reduce the effects of errors in isotopic measurement and soil water extraction, a cubic polynomial function was used to fit the isotopic data from 0 to 15 cm in depth. The fitting equation was δ 18 O = 2 × 10−4 t 3 − 0.0112 t 2 + 0.2428 t − 7.9174 (R2 = 0.9246), where t was the time of evaporation. The relationship between evaporation rate and the average isotopic fractionation factor with a time interval of one day estimated by Eq. (1) was shown in Fig. 8. From Fig. 8A, it was found that the isotopic fractionation factor increased as the evaporation rate increased. Isotopic fractionation factors at the beginning of evaporation were lower than those at the end of evaporation under the evaporation rates were close conditions. For example, the value of α 1d was 0.9785 corresponding to 0.47 mm/d of evaporation rate on the first day, whereas 0.9914 of α 1d corresponds to 0.48 mm/d of evaporation rate on the 31st day. It indicated that isotopic compositions of soil water at topsoil varied greatly at lower evaporation rates, and isotopically enriched as topsoil drying. A logarithmic function, α 1d = 0.004Ln(E) + 0.9939 (R2 = 0.1929), could be used to reveal the variation trend of average isotopic fractionation factors with evaporation rate. The evaporation rate between 0 and 15 cm occupied a high proportion of the total evaporation rate. Below the depths of 15 cm, the δ 18 O values of soil water varied around the mean value of −7.58‰. The process of evaporation and isotopic variations mainly took place above the depth of 15 cm. Therefore, the δ 18 O values of soil water below the depth of 15 cm could be considered as a constant of −7.58‰ ignoring the errors in isotopic measurement and soil water extraction. On the other hand, the evaporation had not affected the isotopic compositions of soil water below 15-cm-depth. The average isotopic fractionation factors increased slowly with the calculative depths of evaporation rate increasing. The logarithmic function relationship between evaporation rate and average isotopic fractionation factor became unobvious from (A) to (E) of Fig. 8. The variation trend of evaporation rate and average isotopic fractionation factor from 0 to 110 cm was different from other conditions (Fig. 8E). The results showed that evaporation rate has an important impact on average isotopic fractionation factors which slowly approached the value of 1 at the end of the experiment with the low evaporation rate. The measured errors of evaporation at low values could result in large variations in the calculation of average isotopic fractionation factors.

4.2 Evaporation Rate and Isotopic Fractionation Factor with Time Interval of Seven days Figure 9 showed the relationship between evaporation rate and average isotopic fractionation factor with a time interval of seven days. The results showed that there was

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Fig. 8 Evaporation rate and average isotopic fractionation with a time interval of one day

a functional relationship existing between the evaporation rate and average isotopic fractionation factors, especially in depths of 0–15 cm. The values of average isotopic fractionation factors obviously increased with the increasing of evaporation rate from 0 to 15 cm in depth, with a logarithmic function relationship α 7d = 0.0067Ln(E) + 0.9807 (R2 = 0.5182). The degree of isotopic fractionation causing by soil evaporation was rather larger at a lower evaporation rate. The isotopic enrichment mainly occurred in the topsoil from 0 to 15 cm. The variations of observed water content profiles showed that the evaporation front developed to a depth of less than 5 cm.

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As the calculative depths of the evaporation rate increased, the average isotopic fractionation factors increased. The higher average isotopic fractionation factors corresponding to lower evaporation rates showed a great variation as the calculative evaporation rate increased with depths. The α 7d with the value of 0.9908 was corresponding to the 1.72 mm/7d evaporation rate between depths of 0 and 15 cm, while 0.9979 of α 7d at 5.32 mm/7d of evaporation rate between depths of 0 and 110 cm. According to the isotopic variations with time, the isotopic profiles had not reached a steady state. The time for isotopic profiles to reach a steady state after a rain event typically needs 1 year or more [7].

4.3 The Effect Factors of Calculation Isotopic Fractionation Factors There are two main factors affecting the calculative results of the average isotopic fractionation factor, namely, the errors in measured evaporation and isotopic data resulting from soil water extraction and isotopic analysis. The evaporation rate of soil water was estimated from the water content profiles in the soil. The volume water content profiles at different depths were recorded using the Time-Domain Reflector (TDR) with a precision of ±0.2%. When the evaporation rate was low, the measured errors of evaporation took a large proportion in evaporation and resulted in large errors for calculating average isotopic fractionation factors. Comparison of the results of average isotopic fractionation factors using the evaporation rate with time intervals of one day and seven days, it was found that the effect of measurement errors in evaporation decreased obviously with the calculative time interval increases. The errors of isotopic data mainly resulted from the soil water extraction. The maximum isotopic difference between soil water extracted and input water was −0.7 ‰, and isotopic compositions of soil water extracted were more depleted than those of input water. The isotopic differences between two continuous days sometimes were small (Fig. 7), and easily covered up by the errors of soil water extraction. The dry soil layer played an important role in soil evaporation [15]. The thin dry soil layer appeared after the third day leading to the evaporation rate greatly decreasing. There were a lot of different size soil cracks occurring on the soil surface undergoing evaporation, with a maximum width of 5 mm and depth of 2.5 cm. These cracks greatly influence on evaporation rate which may make the isotopic profiles non-uniform at the same depth of surface soil at different places. Figure 10 showed the evaporation rate above 20-cm-depth measured by three probes of TDR at different places. The results showed that the evaporation rate measured by the three probes of TDR were different, and that soil cracks could result in non-uniform evaporation despite a small evaporation area with a value of 4.8 m2 .

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Fig. 9 Evaporation rate and average isotopic fractionation factors with a time interval of seven days

5 Conclusions This paper studies the relationship between evaporation rate and average isotopic fractionation factors with the time intervals of one day and seven days through soil evaporation experiment under the initial uniform isotopic profile and without rainfall input conditions. The average isotopic fractionation factors increase as the

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Fig. 10 The evaporation rate above 20-cm-depth at different places

evaporation rate of soil water increases, especially in depths from 0 to 15 cm. A logarithmic function could be used to describe the relationship between evaporation rate and average isotopic fractionation factors with time intervals of one day and seven days. The variation trend of average isotopic fractionation factors with the time interval of seven days was more obvious than that with the time interval of one day. The isotopic enrichment of soil water with time was observed at topsoil from 0 to 15 cm, and the values of δ18 O varied from −8.03 to −5.12‰ with a fluctuation amplitude of 36.2%. Isotopic compositions of soil water were not affected by soil evaporation below the depth of 15 cm. The errors of isotopic analysis and measured evaporation played an important role in calculating average isotopic fractionation factors. The isotopic analysis errors mainly resulted from the soil water extraction by the vacuum method and isotopic measurement. The measured evaporation errors occurred in the measurement of water content profiles using TDR equipment. These errors were difficult to avoid, but the effects on calculating average isotopic fractionation factors could be reduced using cumulative evaporation rate and fitting the isotopic data. Soil cracks in the soil drying process indirectly affected the isotopic profiles.

References 1. Craig H, Gordon LI, Horibe Y (1963) Isotopic exchange effects in the evaporation of water: 1. Low-temperature experimental results. J Geophys Res Atmos 68:5079–5087 2. Craig H, Gordon LI (1965) Deuterium and oxygen-18 variations in the ocean and marine atmosphere. In: Tongiorgi E (ed) Stable isotopes in oceanographic studies and paleotemperatures. Lab. Geologica Nucleare, Pisa, pp 9–130 3. Wu H, Li X, Li J et al (2015) Evaporative enrichment of stable isotopes (δ18 O and δD) in lake water and the relation to lake-level change of Lake Qinghai, Northeast Tibetan Plateau of China. J Arid Land 7(5):1–13 4. Gibson JJ, Birks SJ, Yi Y (2016) Stable isotope mass balance of lakes: a contemporary perspective. Quatern Sci Rev 131:316–328 5. Barnes CJ, Allison GB (1983) The distribution of deuterium and oxygen-18 in dry soils: 1. Theory. J Hydrol 60:141–156

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6. Barnes CJ, Allison GB (1984) The distribution of deuterium and oxygen-18 in dry soils: 3. Theory for non-isothermal water movement. J Hydrol 74:119–135 7. Walker GR, Hughes MW, Allison GB, Barnes CJ (1988) The movement of isotopes of water during evaporation from a bare soil surface. J Hydrol 97:181–197 8. Shurbaji ARM, Phillips FM (1995) A numerical model for the movement of H2 O, H2 18 O, and 2 HHO in the unsaturated zone. J Hydrol 171:125–142 9. Ellehoj MD, Steen-Larsen HC, Johnsen SJ et al (2013) Ice-vapor equilibrium fractionation factor of hydrogen and oxygen isotopes: experimental investigations and implications for stable water isotope studies. Rapid Commun Mass Spectrom 27(19):2149–2158 10. Koeniger P, Gaj M, Beyer M et al (2015) Review on soil water isotope based groundwater recharge estimations. Hydrol Process 10775 11. Rothfuss Y, Merz S, Vanderborght J et al (2015) Long-term and high frequency non-destructive monitoring of water stable isotope profiles in an evaporating soil column. Hydrol Earth Syst Sci 19(4):4067–4080 12. Araguas-Araguas L, Rozanski K, Gonfiantini R, Louvat D (1995) Isotope effects accompanying vacuum extraction of soil water for stable isotope analyses. J Hydrol 168:159–171 13. Yidana SM, Fynn OF, Adomako D et al (2016) Estimation of evapotranspiration losses in the vadose zone using stable isotopes and chloride mass balance method. Environ Earth Sci 75(3):1–18 14. Lin Y, Wang GX, Guo JY et al (2012) Quantifying evapotranspiration and its components in a coniferous subalpine forest in Southwest China. Hydrol Process 26(20):3032–3040 15. Wang X (2015) Vapor flow resistance of dry soil layer to soil water evaporation in arid environment: an overview. Water 7(8):4552–4574

The Impact of Hydraulic Hubs on the Spatial Variation of Water Quality in the Middle Reaches of the Hanjiang River and an Analysis of the Driving Factors Di Jia, Li Lin, Xiong Pan, Lei Dong, and Sheng Zhang Abstract The Hanjiang River occupies a very important position in China’s water resource allocation and is a typical dam-influenced river in China. To monitor the water quality and heavy metal content of the river, and to investigate the influence of water hubs on the changes of both, sampling sites were set up in October 2020 upstream, before, and below the dams of three typical water hubs in the middle reaches of the Hanjiang River for analysis and investigation. The results showed that the concentrations of TP, TN, NH4-N, and Fe exceeded the standard; the concentrations of conventional water quality indicators changed significantly (P < 0.01) along and before and after the dams, and generally showed an increasing trend before and after the Wangfuzhou and Cuijiaying water conservancy hubs; the concentrations of Cr differed after the dams (P < 0.05); the distribution of TP in the vertical direction was significantly different (P < 0.05), the bottom layer concentration before the dam is 2–3 times higher than the surface and middle layer concentration; Fe concentration in the water conservancy hub before the accumulation of the bottom layer, the rest

D. Jia · L. Lin (B) · X. Pan · L. Dong Basin Water Environmental Research Department, Changjiang River Scientific Research Institute, Wuhan 430010, China e-mail: [email protected] D. Jia e-mail: [email protected] X. Pan e-mail: [email protected] L. Dong e-mail: [email protected] Key Lab of Basin Water Resource and Eco-Environmental Science in Hubei Province, Wuhan 430010, China S. Zhang State Key Laboratory of Pollution Control and Resources Reuse, School of Environment, Nanjing University, Nanjing 210023, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C.-H. Weng (ed.), Proceedings of the 8th International Conference on Water Resource and Environment, Lecture Notes in Civil Engineering 341, https://doi.org/10.1007/978-981-99-1919-2_23

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of the heavy metal concentrations in the horizontal and vertical distribution are not significantly different (P > 0.05). Keywords Dams · Spatial distribution · Terraced hydro-junctions · Water quality variation

1 Introduction The construction of hydraulic hubs, on the one hand, provides enormous economic benefits to human society and, on the other hand, has many irreversible effects on the ecological environment. The construction of multiple hydro-junctions on rivers has led to the artificial division of the natural river channel into many sections subject to human regulation and scheduling. The size of the water flow, the transport and transformation of nutrients, the growth, and reproduction of aquatic organisms, and the composition of the community are all affected to varying degrees. More than 60% of river systems worldwide are affected by dam construction, e.g., the Ganga Buhang dam was built and water quality downstream deteriorated [1]. Luo et al. found that the construction of dams harmed water quality, as they interfered with the mobility of water bodies, resulting in downstream pollution becoming severe, nutrient enrichment of watershed rivers, and moderate eutrophication; at the same time, the aquatic communities upstream and downstream of dams differed significantly, indicating that dam regulation has a greater impact on water ecology [2]. The Hanjiang River is a typical dam-affected river in China. Compared to single dams, the ecological effects of step dams on river ecosystems are more complex and cumulative [3]. They create discontinuities that alter the hydrological conditions, morphology, and habitat of the river. Large volumes of water and suspended particulate sand sediments are intercepted by step dams, resulting in changes in river regulation, water circulation, and nutrient loading [4, 5]. As the water source of the South-North Water Transfer Project comes from the Hanjiang River, it is extremely important to monitor its water quality, explore the impact of the water hubs on water quality changes, and manage and maintain it on time.

2 Materials and Methods 2.1 Study Area Overview The Hanjiang River is one of the major tributaries of the Yangtze River in China and ranks first in the Yangtze River system in terms of the watershed area. The mainstream of the Hanjiang River is divided by the Danjiangkou, which is the upper reaches;

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the Danjiangkou to Zhongxiang City is the middle reach, passing through Shiyan City, Xiangyang City, and Jingmen City, with a length of about 223 km, and four tributaries (Beihe River, Nanhe River, Qinghe River, and Tangbai River) converge in the middle reaches; the Zhongxiang to Hankou is the lower reaches, with a length of about 382 km. The river’s middle reaches are wider than the upper valleys, and the flood discharge capacity has become worse. The average annual temperature is about 15–17 °C, and the average annual precipitation is 800–900 mm. Since 1950, many water conservancy hubs have been built in the Hanjiang River basin to regulate water resources. The construction of three water conservancy hubs in the middle reaches of the Hanjiang River basin, Danjiangkou, Wangfuzhou, and Cuijiaying, began in 1958, 1995, and 2005 and was completed in 1973, 2003, and 2010, respectively. The main function of these three water conservancy hubs is shipping and power generation, and their operation impacts water quality and the ecological environment. In addition, the Danjiangkou Reservoir is the core location of the South-North Water Diversion, and the impact on the ecological environment of the cross-regional large-scale water transfer project team is extremely complex.

2.2 Selection of Study Sites Since the Xinglong water conservancy hub in the lower reaches of the Hanjiang River is a plain river-type low head gate and dam hub with two tasks: south-tonorth water diversion and Yangtze River-to-Hanjiang River diversion, therefore, the research wasn’t done on it, and only the three midstream water conservancy hubs were analyzed. Three sampling locations were chosen for each of these hubs: 1. upstream of the hubs; 2. at the front end of the hubs; 3. near the downstream end of the hubs. The sampling points were set according to the distribution of the midstream hydro-junctions in the Hanjiang River and considering the feasibility of sampling: S1 was taken from the densely populated village of Wangjiawan in Danjiangkou; S2 was taken from upstream of Laohekou, which is densely populated; S3 was located at the hydrological station of Huangjiagang in Danjiangkou; S4 was densely populated in Xiangyang. S5 has villages and some factories nearby; S6 is taken from Yujiahu hydrological station in Xiangyang City. The information on each sampling point is shown in Table 1, the schematic diagram is shown in Fig. 1, and the sampling time is October 2020.

2.3 Data Collection and Pre-processing Hydrological Data. Hydrological data for the Hanjiang River and its tributaries and hydrological hubs were obtained from the Yangtze River Sediment Bulletin and Hydrological Bureau of the Yangtze River Water Resources Commission, the Hubei Provincial Hydrological and Water Resources Center, the Cuijiaying Navigation

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Table 1 Conditions of each sampling site Section number Section name

Sampling location

Water depth (m)

S1

Danjiangkou City Wangjiawan Village

25–30

Upper Danjiangkou

D1

Danjiangkou Dam Front

Danjiangkou reservoir area

55–60

S2

Below Danjiangkou Dam

0.5 km below Danjiangkou Dam, Danjiangkou City

1–2

S3

Upstream of Wang Fuzhou

Upstream of Laohekou, 1–2 Huangjiagang Hydrological Station, Danjiangkou City

D2

In front of the Wangfu Zhou Laohekou City Bridge in Dam Laohekou City

S4

Below the Wangfu Zhou Dam

0.5 km below Wangfu Zhou 6–7 Dam, in front of South River

S5

Upstream of Cuijiaying

Niushou Township, Xiangyang City

6–7

D3

In front of Cuijiaying Dam

Cuijiaying Depot, Xiangyang City

5–6

S6

Below Cuijiaying Dam

Xiangyang Yujiahu Hydrological Station

4–5

Fig. 1 Sampling section distribution diagram

7–8

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Table 2 Water conditions in each reservoir in October 2020 Reservoirs

Water level (m)

Water storage (m3 )

Inbound flow (m/s)

Outbound flow (m/s)

Danjiangkou Reservoir

164.6

2.19 × 1010

1210

783

Wangfuju Reservoir

86.1

1.44 × 108

–

–

Cuijiaying Reservoir

62.6

–

532.2

532.8

and Power Hub Management Office, the Hanjiang River Basin Water Resources Bulletin, and the Hubei Provincial Water Resources Bulletin 2020 of the Hubei Provincial Water Resources Department, etc. Each data survey was conducted using state-of-the-art instruments at fixed sampling points, and water discharge (WD) and suspended sediment discharge (SSD) are measured by an in-situ acoustic Doppler current profiler, and the water level is measured by a hydrological gauge. In October 2020, the water level of Danjiangkou Reservoir is 164.6 m, the inlet flow is 1210 m/s, the outlet flow is 783 m/s, and the storage volume is 2.19 × 1010 m3 . The water level of Wangfuzhou Reservoir is 86.1 m, and the storage volume is 1.44 × 108 m3 . The upstream water level of Cuijiaying is about 62.6 m, the downstream water level is 54.2 m, the inlet flow is 532.2 m/s, the outlet flow is 532.8 m/s, and the power generation volume is 3774.48 million degrees. The water conditions of each reservoir in October 2020 are as follows in Table 2. Water Quality Data. The water quality was determined by the Institute of Basin Water Environment, Yangtze River Academy of Water Resources Commission, and the samples were collected and tested in October 2020. Surface, middle, and bottom waters were collected in polyethylene bottles at each sampling site. Water samples were collected and immediately added to concentrated sulfuric acid to pH < 2 and transported back to the laboratory on the same day, placed in a dark environment at 4 °C for storage, and the required indicators were determined as soon as possible according to the national standard Water and Wastewater Monitoring and Analysis Methods (Fourth Edition) [6]. The monitored water quality indicators include ammonia nitrogen (NH4 -N), total phosphorus (TP), total nitrogen (TN), nitrate nitrogen (NO3 -N), orthophosphate (PO4 3− ), permanganate index (CODMn ), total organic carbon (TOC), copper (Cu), zinc (Zn), selenium (Se), arsenic (As), cadmium (Cd), lead (Pb), iron (Fe), manganese (Mn), chromium (Cr), nickel (Ni). Analysis Method. Correlation analysis of water quality physical and chemical indicators to explore the relationship between the water quality indicators. Combined with each water hub upstream and downstream points and water samples in the depth of the two-factor ANOVA, to explore the spatial variation of water quality and the relationship between the gradient water hub, and water depth. The analytical and computational mapping software is EXCEL 2019, SPSS 23.0 (Statistical Product and Service Solutions, USA), and Origin 2021 for Windows.

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3 Results 3.1 Water Quality Condition The water quality target for the middle reaches of the Hanjiang River Water Function Area Plan is Case II surface water, and overall, the water quality in the middle reaches of the Hanjiang River is relatively good. Heavy Metals. The sampling did not detect Cd, Pb; Se detection rate was 51.85%; Cu, Zn, As, Fe, Mn, and Cr, although widely present in the water, except for Fe concentration exceeded the standard, the rest of the detected heavy metal content all meet the standard, the specific detection values are shown in Table 3. General Water Quality Indexes. Among the conventional water quality indicators, TN exceeds the standard more seriously, of which NO3 -N is the main form of dissolved nitrogen in the water body, accounting for 81.2%, and NH4 -N accounts for 12.9%; the concentration of TN in the tested water bodies exceeds the water quality standard of Case II, the exceedance rate reaches 100%, and the maximum concentration exceeds the water quality standard of Case II by 3.87 times; NH4 -N slightly exceeds the standard, and the exceedance occurs in the middle water body under Wangfuzhou Dam. TP concentration exceeded the standard in the bottom water body in front of the dam, PO4 3− accounted for 41.2% of the TP; see the details of the test in Table 4. Table 3 Detection status of heavy metals in the water body Monitoring projects

Max. value (µg/L)

Min. value (µg/L)

Mean value (µg/L)

Detection rate (%)

Case II water quality indexes (µg/L)

Compliance rate

Cu

17.75

0.00

1.04

92.59

1000

100.0

Zn

13.85

0.27

4.03

100.00

1000

100.0

Se

2.72

0.00

0.61

51.85

10

100.0

As

2.29

0.85

1.26

100.00

50

100.0

Cd

0.05L

0.05L

0.05L

5

100.0

10

100.0

0.00

Pb

0.09L

0.09L

0.09L

0.00

Fe

591.62

16.99

79.24

100.00

300*

96.3

Mn

20.19

0.74

3.70

100.00

100*

100.0

Cr

1.87

0.87

1.20

100.00

50

100.0

*

Standard limit value

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Table 4 The detection of conventional water quality indicators in the water body Monitoring projects

Max. value (mg/L)

Min. value (mg/L)

Mean value (mg/L)

Case II water quality indexes (mg/L)

Exceedance rate

Compliance rate (%)

Max. exceedance multiple

TP

0.15

0.02

0.05

0.1

11.1%

88.9

0.54

TN

2.44

1.17

1.58

0.5

100%

0

3.87

NH4 -N

0.56

0.05

0.20

0.5

3.7%

96.3

0.12

NO3 -N

1.87

0.99

1.28

5

0

100

0

PO4 3−

0.07

0.01

0.02

–

–

–

–

CODMn

2.93

2.02

2.37

4

0

100

0

TOC

4.68

0.62

3.30

5*

0

100

0

*

Standard limit value

3.2 Correlation Analysis of Water Quality Indicators The correlations of water quality physical and chemical indicators were analyzed, and the obtained correlation analysis matrix is shown in Fig. 2. The matrix shows a strong correlation between Fe, Mn, and Cr, mainly because these three heavy metals are associated and homologous, and the geochemical reactions are similar. The correlation between TP concentration and As, Fe, Mn, and Cr concentrations also existed to some extent, which is presumed to be related to human activities. TN concentrations correlated strongly with NO3 -N, and TP concentrations correlated strongly and positively with PO4 3− , stemming from the fact that the main forms of nitrogen and phosphorus present in the water column are nitrate and PO4 3− , respectively. The concentration of PO4 3− had a strong positive correlation with the CODMn and a strong negative correlation with TOC.

3.3 Spatial Variation Characteristics of Water Quality From a macroscopic point of view, the concentrations of conventional water quality indicators have a large change along the process (P < 0.01), and the difference in the distribution of phosphorus upward is more significant (P < 0.05); according to the analysis of the three separate processes through the water conservancy hub, it can be found that, except for TP, PO4 3− , the concentrations of the other conventional water quality indicators before and after passing through the Wangfuzhou and Cuijiaying water conservancy hubs have changed significantly (P < 0.01). TP, TN, TOC, NH4 N, NO3 -N, and CODMn showed an overall increasing trend after passing through the dam. There was no significant difference (P > 0.05) in the distribution of heavy metals across the dam and the vertical direction, except for the difference in the concentration

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Cu

Cu

Zn

0.032 -0.056 0.0056 0.11 -0.044 0.12 Zn

Se

-0.21

0.13

0.13 -0.021 -0.026 -0.052 -0.13 -0.089 -0.12 -0.085 -0.16 -0.072 0.029

0.30

Se

0.47

0.57

0.44

0.12

0.19

0.21

-0.12

-0.16

-0.21

-0.13

-0.17

-0.31

0.13

0.22

0.26

0.25

0.63

0.20

0.17

0.18

0.54

0.54

-0.32

Fe

Fe

0.93

0.82

0.59 -0.021 -0.036 0.028

0.12

0.13

-0.10

Mn

***

Mn

0.79

0.65

0.063 -0.059 0.14

0.22

0.19

-0.28

Cr

***

***

Cr

0.59

0.087 0.099

0.12

0.12 -0.024 -0.29

**

***

**

TP

0.37

0.25

0.38

0.69

0.51

0.81

0.95

0.47

0.039 -0.31

As

-0.080 0.14

0.10

As

0.8 0.6 0.4 0.2 0

TP

***

TN

*

TN

NH4-N

**

*** NH4-N 0.70

NO3-N

*

***

*** NO3-N 0.52

*

**

**

***

CODMn

**

**

TOC

0.25 -0.057 0.068

*

PO43-

-0.61

*

-0.4

0.019 -0.41

PO43-

0.62

-0.71

*** CODMn -0.36

***

-0.2

***

-0.6 -0.8

TOC

C

M n

TO

4 3-

D

CO

3 -N PO

4 -N

O

H N

* p