River Sand Mining Modelling and Sustainable Practice: The Kangsabati River, India (Environmental Science and Engineering) 3030722953, 9783030722951

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Environmental Science and Engineering

Raj Kumar Bhattacharya Nilanjana Das Chatterjee

River Sand Mining Modelling and Sustainable Practice The Kangsabati River, India

Environmental Science and Engineering Series Editors Ulrich Förstner, Technical University of Hamburg-Harburg, Hamburg, Germany Wim H. Rulkens, Department of Environmental Technology, Wageningen, The Netherlands Wim Salomons, Institute for Environmental Studies, University of Amsterdam, Haren, The Netherlands

The ultimate goal of this series is to contribute to the protection of our environment, which calls for both profound research and the ongoing development of solutions and measurements by experts in the field. Accordingly, the series promotes not only a deeper understanding of environmental processes and the evaluation of management strategies, but also design and technology aimed at improving environmental quality. Books focusing on the former are published in the subseries Environmental Science, those focusing on the latter in the subseries Environmental Engineering.

More information about this series at http://www.springer.com/series/7487

Raj Kumar Bhattacharya Nilanjana Das Chatterjee

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River Sand Mining Modelling and Sustainable Practice The Kangsabati River, India

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Raj Kumar Bhattacharya Department of Geography Vidyasagar University Midnapore, West Bengal, India

Nilanjana Das Chatterjee Department of Geography Vidyasagar University Midnapore, West Bengal, India

ISSN 1863-5520 ISSN 1863-5539 (electronic) Environmental Science and Engineering ISBN 978-3-030-72295-1 ISBN 978-3-030-72296-8 (eBook) https://doi.org/10.1007/978-3-030-72296-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 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 Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

To my Grandmother, Late Uma Bhattacharya

Foreword

Healthy ecosystems are the most critical components of the natural environment that are indispensable for human wellbeing and sustainable development. However, the ever-expanding human aspirations, economic developments and urbanization have imposed immense pressure on the natural resources. Indiscriminate extraction of natural resources, especially the building materials, for meeting the rising demand in the construction sector has imposed dire concern to the environment. The river ecosystems are severely impacted by the environmental consequences as they are the first to hit the adversities of economic developments. Among the various kinds of human interventions, mining for aggregate materials like sand and gravel is the most disastrous as the activity threatens the very existence of the river ecosystems. At the same time, the continued supply of aggregate materials like sand and gravel is to be ensured to maintain the pace of developments and economic activity. Such continued human–environment interaction invokes the need for a balanced approach between sand and gravel extraction, and environmental protection. In this context, the effort of Dr. Rajkumar Bhattacharya and Prof. Nilanjana Das Chatterjee in bringing out the book “River Sand Mining, Modelling and Sustainable Practice—The Kangsabati River, India” receives considerable significance and relevance. The book offers a wide spectrum of subject components covering almost all the essential aspects of river sand mining practice, by considering the case study of Kansabati River in India. Various chapters in this book are grouped under three parts. The first part comprises three chapters dealing with the global scenario, geomorphic threshold of sand mining and seiment budget assessment. The second part embodies four chapters delineating the sediment grain size characteristics, hydraulic variables of flow and sediment regime, channel morphology and ecology. The third part includes sand resources estimation, optimum utilization and identification of sustainable mining sites. This book provides a compelling evidence on the need of environmental conservation and sustainable resource extraction for developmental requirements.

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I am sure the book will be very useful for academicians, researchers and students, and also a valuable source material for the decision/policymakers at different levels and the people at large. I congratulate the authors for bringing this crucial geo-environmental aspect to the focus and wish them all the very best.

Dr. D. Padmalal Scientist-G and Head, Hydrology Group, ESSO-National Centre for Earth Science Studies (NCESS) Ministry of Earth Science Government of India, Thiruvananthapuram, India

Preface

In this era of urbanization, worldwide demand for sand and gravel are increasing day by day to meet huge requirement of construction sector, land filling and transportation sector based infrastructural project etc. It results in over extraction of sand from channel bed, and hampers the natural renewal of sediment, geological setup and morphological processes of the riverine system. Many researchers have addressed that irrational sand and gravel mining are associated with channel hydraulics, morphology and riverine biota especially in alluvial channel. In contrary, optimum sand mining (SM) must be needed for the continuation of rational economic activity. But some crucial research questions are raised: (1) what is the optimal amount of SM in respect to resilience of stream hydraulics, morphological and river ecosystem variables, (2) how to determine the river health response in between pre and post mining stages or sites, and (3) how to propose sustainable SM sites following healthy premises of riverine process-response system (RPRS). After the critical analysis between geomorphic threshold and geo-environmental consequences of instream SM, sediment budget (SB) is a crucial requirement for the determination of under, optimum and over SM with respect to natural sediment replenishment and sediment extraction. On the other hand, several validated geospatial models are adopted to find out the various responses of instream SM in accordance to pre-mining or sandbar, mining and post-mining stages or sites. Optimization models (Ops) of annual SM rate and environmental impact assessment (EIA) of mining consequences both are final assessment techniques for the determination of overall interrelationship between response factors and responding variables in upper, middle and lower reach, respectively. All of the applied methodologies predicted fruitful results that are summarized from channel geomorphological threshold to sustainable SM based proposed mining sites in this book. In India, illegal SM (alluvial channel) and gravel mining (perennial channel) are one of the important anthropogenic issues that hamper the sustainable drainage system. SM consequences are more serious and disturbing in an alluvial reach of the Kangsabati River. Construction of Mukutmonipur dam (1958) on the river causes huge sediment deposition along the middle and downstream due to abruptly ix

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break of slope. Over extraction of instream and floodplain SM can be especially seen in Mohanpur and Kapastikri (middle and downstream) with a rate of extraction 474926.59 cu ft. out of 588155.6 cu ft. of Kangsabati River (2012–2016, DLRO Paschim Midnapore and Bankura, West Bengal). Objective of SB in this work is to understand the stability status of channel segments through the assigning of sediment source and sink. Revised universal soil loss equation, sediment delivery distributed model, sediment extraction record datasets (2002–2016) are used to estimate the SB throughout the channel. SB revealed that instream mining leads to interruption of sediment grain size deposition processes along the channel bed incorporate with shear stress which is needed for particle movement. G-STAT, Grad-Stat, Sedlog and linear discrimination function are used to determine the mean, shorting, peakness and skewness of sediment grain size distribution. DuBoys equation and Shields formula are applied for assessment of shear stress and critical shear stress in threshold range between erosion and deposition in mining and sandbar sites. As a result, three different disruption or consequences are occurred i.e. hydrological, morphological and ecological consequences, respectively. In term of stream hydrological consequences, well known established hydraulic equations along with Acker-White (1973) and Meyer-Peter-Muller methods (1948) are used to derive the hydraulic response on bedload transport and mining intensity, and also tries to determine the effects of mining intensity on bedload sediment transport and pit migration with the presence of instream shear force from sandbar to mining sites. In term of morphological consequences, digital shoreline analysis system based statistical models of end point rate and linear regression rate for estimating the riverbank shifting and resultant erosion-accretion rate, bank erosion hazard index for prediction of bank erosion vulnerability zone, geometrical indices for estimating of channel planform change, are used to compare geomorphic responses in mining and sandbar sites. In terms of ecological consequences, water quality index and habitat suitable index integrated with multiple logistic regressions are applied for the detection of water quality deterioration, three tier habitat transformation and degradation caused by instream SM. Ops and EIA both have find out the over, optimum and under mining sites as well as to propose potential mining sites with the respect of threshold values of several variables. Based on field experience and scientific analysis, sustainable mining sites have been suggested following resilience state of river dynamic variables, assessed by Ops and EIA. This book demonstrates the geospatial models along with Ops and EIA techniques for better understanding the resilience state of stream hydraulics, morphological and river ecosystem variables during pre-mining and post-mining using of micro-level datasets. In this context, this book attempts to apply many established models with real datasets in the case study of Kangsabati River. The pragmatic training of utilizing geospatial techniques would be helpful for the students, researchers, academicians, decision makers and practitioners to using those techniques for their own purpose at large scale.

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The exceptionality of this volume is its style of presenting the separate methodologies and models are adopted to validate the issue for each chapter along with citing case studies, which will grow up the interests of the scientific reader community. These modern techniques could be facilitated for that community due to present of detail models clarification along with analysis of enough comprehensive algorithms; as a result, they could apply those models as per their choices for the present of lucid writing style. This book proposed specific practicable measures to minimize the environmental consequences of instream mining in respect to optimum SM. We will discuss how the threshold limits of each variable in stream hydraulics, morphological and river ecological regime, as well as find out the most affected variables. Consequently, all outputs will be very useful for the readers to create their own model in respect to RPRS. Midnapore, India

Raj Kumar Bhattacharya Nilanjana Das Chatterjee

Acknowledgements

We are taking this opportunity to pay respect to the teachers in the Department of Geography, Vidyasagar University. We are highly obliged to Dr. D. Padmalal, Scientist-G and Head, Hydrology Group, ESSO-National Centre for Earth Science Studies (NCESS), Ministry of Earth Science, Government of India, who helped immeasurably towards the completion of this book. We do express gratitude to A. C. Dinesh, Geologist, Marine wing, GSI, Mangalore for providing many technical supports and useful suggestions. We are obligated to the Irrigation Department of West Bengal, D.L & L.R.O of Paschim Medinipur, and Bankura for their continuous co-operation and support. We are especially thankful to the administrative authorities of our sole institutions for extending their supports to access USIC laboratory, Departmental laboratory and library. We are grateful to Mr. Kousik Das, UGC Junior research fellow, Vidyasagar University, for his constant technical support in preparing this research work. We also thank students of Geography department for their rigorous efforts during field visits. Valuable editorial advice including thorough guidance from Doris Bleier, Publishing Editor of Springer Nature continuously helped us to enrich the content and improve the quality of this book. We are indebted to Mr. Chandra Sekaran Arjunan, Project Coordinator, Books, Springer, who took the responsibility of the project coordination and supervised the entire production process. Last, but not least, we would like to thank our families for their continuous support, understanding the importance of the work and encouragement during the entire work. Raj Kumar Bhattacharya Nilanjana Das Chatterjee

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Contents

1 River Sand Mining and its Management: A Global Challenge 1.1 River Sand Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Past Work on River Sand Mining . . . . . . . . . . . . . . . . . . 1.2.1 Sand Mining and Channel Hydrology . . . . . . . . . . 1.2.2 Sand Mining and Channel Morphological . . . . . . . 1.2.3 Sand Mining and Riverine Ecology . . . . . . . . . . . 1.3 Past Work on River Sand Mining in India . . . . . . . . . . . . 1.4 Sand: Mineralogical Structure, Origin and Types . . . . . . . 1.5 Environmental Sensitivity of Sand . . . . . . . . . . . . . . . . . . 1.6 Economic Significance of Sand . . . . . . . . . . . . . . . . . . . . 1.7 Global Challenge for Sustainable Sand Mining During Twenty-First Century . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.8 Scope of the Present Study . . . . . . . . . . . . . . . . . . . . . . . 1.9 Selection of the Study Area . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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2 Geomorphic Threshold and Sand Mining: A Geo-environmental Study in Kangsabati River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Geomorphic Threshold and Instream Sand Mining in Alluvial Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Alluvial River Sand as Geomorphic Product . . . . . . . 2.2.2 Sand Mining Exceeding Threshold Limits . . . . . . . . . 2.2.3 Sand Mining Process and Consequences . . . . . . . . . . 2.3 An Alluvial Quarried Reach in Kangsabati River . . . . . . . . . 2.3.1 Geo-environmental Setting of Kangsabati Catchment Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Selection of the Channel Segments Along the Kangsabati River . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Sand Mining Crossed the Threshold Limit and Lower Reach of Kangsabati River . . . 2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . .

in Middle ................. ................. .................

3 Fluvial Sediment Budget and Mining Impact Assessment: Use of RUSLE, SDR and Hydraulic Models . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Estimation of Sediment Source . . . . . . . . . . . . . . . . . . . . . . . 3.3 Soil Loss Assessment Using of RUSLE . . . . . . . . . . . . . . . . . 3.4 RUSLE Model Set Up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Case Study: Estimation of Mean Annual Soil Erosion at Sub Basin Level of Kangsabati Basin Using RUSLE—A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.1 Estimation of RULE Factors . . . . . . . . . . . . . . . . . . . . 3.5.2 Delineation of Potential MASE . . . . . . . . . . . . . . . . . . 3.5.3 Relation Between Soil Erosion with Land Use/Land Covers (LULC) and Basin Area . . . . . . . . . . . . . . . . . 3.6 Sediment Delivery Ratio (SDR) and Sediment Yield (SY) . . . 3.7 Case Study: Assessing of Sediment Delivery Ratio (SDR) and Sediment Yield (SY) at Sub Basin Level of Kangsabati Basin—A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7.1 Estimation of SDR Factors . . . . . . . . . . . . . . . . . . . . . 3.7.2 Delineation of Sediment Delivery Ratio (SDR) . . . . . . 3.7.3 Potential Annual SDR at Sub-basin Level . . . . . . . . . . 3.7.4 Validation of SDR . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7.5 Delineation of SY . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7.6 Potential Annual SY at Sub-basin Level . . . . . . . . . . . 3.8 Sink of Sediment Budget . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.9 Case Study: Assessing of Sediment Sink and Sediment Budget in Kangsabati River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.9.1 River Sand Mining in Kangsabati River . . . . . . . . . . . 3.9.2 Estimation of Sediment Transport (QT) . . . . . . . . . . . . 3.9.3 Estimation of Sediment Concentration (X) . . . . . . . . . . 3.9.4 Estimation of Sediment Budget in Eight Segments of Kangsabati River . . . . . . . . . . . . . . . . . . . . . . . . . . 3.10 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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4 Sediment Grain Size Analysis and Mining Intensity: Estimation by GRADISTAT, G-STAT and LDF Techniques . . . . . . 105 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 4.2 Sand Mining Response on SGD . . . . . . . . . . . . . . . . . . . . . . . . 106

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Sediment Grain Size Analysis . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Application of GRADISTAT Software for Measuring the SGD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Case Study: Accessing the Relationship Between Stream Energy and Sediment Grain Size Distribution in Kangsabati River Using GRAD Stat . . . . . . . . . . . . . . . . . 4.4.1 Preparation of Sampling Process . . . . . . . . . . . . . . . . . 4.4.2 Textural Characterization . . . . . . . . . . . . . . . . . . . . . . 4.4.3 Bivariate Scatter Graphs of Grain Parameters . . . . . . . 4.5 Case Study: Estimation the Transporting Mechanism and Depositional Environment in Kangsabati River Using G-STAT (Grainsize Statistics) Software . . . . . . . . . . . . . . . . . 4.5.1 Cumulative Weight Percentage Diagrams of Sediment Textural Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.2 Analysis of Granulometric Properties Using Triangular Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.3 Analysis of Transport Mechanism and Mode of Deposition Using CM Diagram . . . . . . . . . . . . . . . . . . 4.5.4 Estimation of Tractive Current Deposits at Course Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Linear Discriminate Function (LDF) . . . . . . . . . . . . . . . . . . . 4.6.1 Case Study: Derivation of Sediment Depositional Environment in Kangsabati River Using LDF . . . . . . . 4.6.2 Bivariate Graph of Sediment Depositional Environment During Pre Monsoon and Monsoon . . . . . . . . . . . . . . 4.7 Grain Size Related to Bed Shear Stress (s0) and Critical Shear Stress (U*) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7.1 Case Study: Erosion and Deposition Process in Relation to Mining Intensity During Pre Monsoon and Monsoon in Kangsabati River . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7.2 Erosion and Deposition Process in Relation to SGD . . 4.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Supplementary Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5 Mining Response on Alluvial Channel Flow and Sediment Transport: Application of Hydro-Morphological Techniques and Principal Component Analysis (PCA) . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Mining Genesis Turbulence Flow and Its Affected Hydraulic Variables of Sediment Transport . . . . . . . . . . . . . . . . . . . . . 5.2.1 Measure of Hydraulic Variables of the Flow Regime . 5.2.2 Measure of Hydraulic Variables of the Sediment Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Case Study: Sand Mining Affected Interruption of Hydraulic Variables in Flow Regime of Kangsabati River . . . . . . . . . . . 5.3.1 Hydraulic Variables of Flow Regime and Mining Intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Hydraulic Variables of Sediment Transport and Mining Intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.3 Bivariate Relation Between Hydraulic Variables of Flow Regime with Mining Intensity . . . . . . . . . . . . . . . . . . 5.3.4 Bivariate Relation Between Hydraulic Variables of Sediment Regime with Mining Intensity . . . . . . . . . . . 5.4 Comparatively Supremacy of Hydraulic Variables of Bedload Transport and Their Clustering Using Principal Component Analysis (PCA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Principle of PCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Hydraulic Variables Set up for PCA . . . . . . . . . . . . . . 5.4.3 Supremacy Execution Amongst the Hydraulic Variables of Sediment Transport in Quarried River Kangsabati Using PCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Deformation of Hydrodynamic Regime . . . . . . . . . . . . . . . . . 5.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Supplementary Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Contents

5.3

6 Sand Mining Consequences on Channel Morphology: Practical Use of Digital Shoreline Analysis System (DSAS), Geometrical Indices and Compound Factor (CF) . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Application of Hydro-Morphological Techniques to Measure the Mining Induced Geomorphic Responses (GRs) . . . . . . . . . 6.3 Case Study: Mining Induced Geomorphic Responses and Riverine Land Cover Changes in Kangsabati River . . . . . . . . . 6.3.1 Estimation and Prediction of Mining Affected River Bank Erosion Using Digital Shoreline Analysis System (DSAS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 BLS and Erosion/Accretion Process . . . . . . . . . . . . . . 6.3.3 Others Mining Induced GRs . . . . . . . . . . . . . . . . . . . . 6.3.4 Channel Planform Change . . . . . . . . . . . . . . . . . . . . . 6.3.5 RLCs Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Prioritization of Mining Induced Geomorphic Consequences Using Compound Factor (CF) . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Case Study: Mining Affected Geomorphic Prioritization at Segment Level in Kangsabati River . . . . . . . . . . . . . . . . . . . . 6.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contents

7 Sand Mining Consequences on Habitat Ecology, Water Quality and Species Diversity: Implementing of HSI, MLR, WQI and ANN Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Three Tier Habitat (TTH) Degradation or Alternation and Sand Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Establishment of Habitat Suitability Index (HSI) for TTH Degradation or Alternation . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Application of Multiple Logistic Regression (MLR) for Assessment of Sand Mining Impact . . . . . . . . . . . . . . . . . 7.4.1 MLR Model Set Up . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.2 Basic Principle of MLR . . . . . . . . . . . . . . . . . . . . . . . 7.4.3 MLR Set Up for TTH Alteration or Degradation . . . . . 7.5 Case Study: Multi Habitat Suitable Parameters Based TTH Alteration or Degradation in Quarried Kangsabati River . . . . . 7.5.1 Factor Affecting on Habitat Suitability . . . . . . . . . . . . 7.5.2 Validation of Habitat Suitability Model . . . . . . . . . . . . 7.5.3 Result of Habitat Suitable Parameters . . . . . . . . . . . . . 7.5.4 HSI of Koeleria Macrantha During Pre Mining and Post Mining Phase . . . . . . . . . . . . . . . . . . . . . . . . 7.5.5 HSI of Cynodon Dactylon During Pre Mining and Post Mining Phase . . . . . . . . . . . . . . . . . . . . . . . . 7.5.6 Validation of HSI of Koeleria Macrantha and Cynodon Dactylon Species . . . . . . . . . . . . . . . . . 7.6 Water Quality Deterioration . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6.1 Determination of Water Quality in Mined River Using Water Quality Index (WQI) . . . . . . . . . . . . . . . . . . . . 7.6.2 Relative Weighted Arithmetic WQI Set Up . . . . . . . . . 7.6.3 Application of Artificial Neural Network (ANN) Model and MLR to Explain the Impact of Sand Mining on Water Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6.4 Case Study: Water Quality Assessment in Quarried Kangsabati River . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7 Assessment of Sand Mining Impact on Instream Biota Using Biodiversity Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7.1 Case Study: Assessment of Instream Biota in Kangsabati River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7.2 Correlations of Estimated Water Quality Parameters and Instream Biota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xix

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254 255 255 257

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258 258 261 262

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8 Sand Resource Estimation, Optimum Utilization and Proposed Sustainable Sand Mining: Recommending Sand Auditing, Optimization Model and EIA . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Audit of River Sand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Adopted Methodology . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Case Study: Utilizing Sand Audit Report to Estimate the Amount of River Sand Resource for Mining Plan of the Kangsabati River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.1 Estimation of Sand Resources in Possible Mining Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.2 Allocation of Mineable Sand in Possible Mining Sites . 8.3.3 Bed Level Lowering Estimates the Recorded and Non-recorded Sand Mining . . . . . . . . . . . . . . . . . 8.4 Optimal Sand Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.1 Optimal Model Related Theories . . . . . . . . . . . . . . . . 8.4.2 Optimal Model Premises Hypothesis . . . . . . . . . . . . . . 8.4.3 Optimization Model Establishment . . . . . . . . . . . . . . . 8.5 Case Study: Optimal Sand Extraction or Sand Mining Plan for Kangsabati River . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6 Environmental Impact Assessment (EIA) for Propose Sand Mining Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6.1 Methodological Set Up for EIA Through Analytical Hierarchy Process (AHP) . . . . . . . . . . . . . . . . . . . . . . 8.6.2 Methodological Set Up for EIA Through Rapid Impact Assessment Matrix (RIAM) . . . . . . . . . . . . . . . . . . . . 8.7 Case Study: EIA of Instream Sand Mining for Allocating of Sustainable Sand Mining Sites of the Kangsabati River . . . 8.7.1 Impact on Riverine Environment . . . . . . . . . . . . . . . . 8.7.2 EIA for Proposing of Sustainable Sand Mining Sites in Upper Course . . . . . . . . . . . . . . . . . . . . . . . . 8.7.3 EIA for Proposing of Sustainable Sand Mining Sites in Middle Course . . . . . . . . . . . . . . . . . . . . . . . . 8.7.4 EIA for Proposing of Sustainable Sand Mining Sites in Lower Course . . . . . . . . . . . . . . . . . . . . . . . . 8.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.9 Key Suggestions for Sustainable Sand Mining . . . . . . . . . . . . 8.10 General Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Supplementary Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Contents

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313 313 314 315

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322 323 323 324 325

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352 358 360 361 362 371

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375

List of Figures

Fig. 1.1

Fig. 1.2 Fig. 1.3 Fig. 1.4

Fig. 1.5

Fig. 1.6

Fig. 2.1

Fig. 2.2

Sand mining consequences in several aspects. Sources Prepared by author, based on the ideas of Victor and Ampofo (2013) and Kamboj et al. (2017). . . . . . . . . . . . . . . . . . . . . . . Hierarchical mineral stability series under weathering process. Source Modified by the authors, based on Goldich (1938) . . . Sand classification flowcharts. Source Modified by the authors, based on Gavriletea (2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . Sand sensibility: a breeding, feeding, hiding and spawning sufficiency of fish community. Source Modified by the authors, based on Hauer et al. (2018), b supply of nutrients into channel bed hyporheic zone. Source Modified by the authors, based on McClain et al. (1998), c alteration of hyporheic zone. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Worldwide sediment discharge loads (1993, 2011). Source Prepared by the authors, based on Gleick (1993) and Milliman and Farnsworth (2011) . . . . . . . . . . . . . . . . . . . . . . Global sand market (1995–2018): a export and import trade value in six continents (Million $), b trade forecasts of sand ($). Source Prepared by the authors, based on distributor map and trade forecasts preparing by Observatory of Economic Complexity (http://oec.world/emn/profile/hs92/sand) . . . . . . . . Geomorphic threshold and erosion-deposition process: a threshold related with sediment load, b threshold related with slope. Source Prepared by the authors, based on Lane’s balance relationship amongst sediment load, grain size, channel slope and discharge (1955) . . . . . . . . . . . . . . . . . . . . Sand mining induced geomorphic threshold limit in alluvial channel. Source Prepared by the authors following Rinaldi et al. (2005), Rovira et al. (2005) . . . . . . . . . . . . . . . . . . . . . .

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Fig. 2.3

Fig. 2.4 Fig. 2.5

Fig. 2.6 Fig. 2.7 Fig. 2.8 Fig. 2.9

Fig. 2.10 Fig. 2.11

Fig. 2.12

Fig. 2.13 Fig. 3.1

Fig. 3.2 Fig. 3.3

Fig. 3.4

Fig. 3.5

List of Figures

Crossing the geomorphic threshold limits: a huge sedimentation (sand extraction < natural replenishment), b intensive sand mining (sand extraction > natural replenishment). Source Prepared by the authors based on the ideas of Lane (1955), Kondolf (1994, 1997) . . . . . . . . . . . . . . a Kangsabati basin area. Source Authors. b Entire sub-basins in Kangsabati basin. Source Authors . . . . . . . . . . . . . . . . . . . Geological set up. Source Authors are prepared from Geological Survey of India map sheet 73I, J and N (http://www.portal.gsi.gov.in) . . . . . . . . . . . . . . . . . . . . . . . . . Geomorphic set up of the Kangsabati basin. Source Authors are prepared from morphological map West Bengal . . . . . . . . Eight different channel segments in Kangsabati River. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Geometric characteristics and land cover along with different cross sectional position in Khatra segment. Source Authors . . Geometric characteristics and land cover along with different cross sectional position: a Raipur segment, b Lalgarh segment. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Geometric characteristics and land cover along with different cross sectional position in Dherua segment. Source Authors. . Geometric characteristics and land cover along with different cross sectional position: a Mohanpur segment, b Kapastikri segment. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . Geometric characteristics and land cover along with different cross sectional position: a Panskura segment, b Rajnagar segment. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . Threshold limit of sand mining consequences in eight segments. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . Schematic diagrams of sediment budget with the following of source to sink system of riverine sediment. Source Prepared by the authors, based on Rhine sediment budget (Frings et al. 2014; Grimaud et al. 2018) . . . . . . . . . . . . . . . . . . . . . . . . . . . RUSLE methodological flow chart. Source Modified by authors, based on Bhattacharya et al. (2020b) . . . . . . . . . . . . . Isohyets map and rainfall erosivity factor in the study area: a 2002, b 2016. Source Modified by authors, based on Bhattacharya et al. (2020a, b) . . . . . . . . . . . . . . . . . . . . . . . . . Soil parameter: a spatial distribution map, b K factor. Source Modified by authors, based on Bhattacharya et al. (2020a, b) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Estimation slope parameter in Kangsabati basin: a LS factor, b flow accumulation, c flow direction, d m factor and e ß factor. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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List of Figures

Fig. 3.6

Fig. 3.7 Fig. 3.8 Fig. 3.9 Fig. 3.10 Fig. 3.11 Fig. 3.12

Fig. 3.13

Fig. 3.14 Fig. 3.15

Fig. 3.16 Fig. 3.17 Fig. 3.18

Fig. 3.19

Fig. 3.20

Fig. 3.21

Fig. 3.22

LULC pattern and estimation of c factor: a LULC during 2002, b C factor during 2002, c LULC during 2016 and d C factor during 2016. Source Modified by authors, based on Bhattacharya et al. (2020b) . . . . . . . . . . . . . . . . . . . . . . . . . . . Potentiality of MASE distribution in Kangsabati basin: 3.6a MASE in 2002 and 3.6b MASE in 2016. Source Authors . . . Spatial distribution of soil loss in different LULC at sub-basin level: a during 2002, b during 2016. Source Authors . . . . . . . Relationship between basin area and soil loss: a 2002, b 2016. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Flow chart of SDR model. Source Modified by authors, based on Bhattacharya et al. (2020b) . . . . . . . . . . . . . . . . . . . . . . . . Travel time in Kangsabati basin. Source Modified by authors, based on Bhattacharya et al. (2020a) . . . . . . . . . . . . . . . . . . . Surface roughness in Kangsabati basin: a 2002; b 2016. Source Modified by authors, based on Bhattacharya et al. (2020a) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Flow velocity in the entire basin: a 2002 and b 2016. Source Modified by authors, based on Bhattacharya et al. (2020a) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Flow length of all sub tributaries. Source Modified by authors, based on Bhattacharya et al. (2020a) . . . . . . . . . . . . . . . . . . . Sediment delivery ratio (SDR): a 2002, b 2016. Source Modified by authors, based on Bhattacharya et al. (2020a) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial distribution of SDR in different LULC at sub-basin level: a during 2002, b during 2016. Source Authors . . . . . . . Validation of SDR following model results: a SDR validated in 2002, b SDR validated in 2016. Source Authors . . . . . . . . SY distribution in study area: a SY during 2002, b SY during 2016. Source Modified by authors, based on Bhattacharya et al. (2020a) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sand mining sites in different segments along the Kangsabati river: a Raipur, b Lalgarh, c Dherua, d Rajnagar and e Panskura segment. Source Prepared by authors, based on DL & DLR, 2010–2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sand mining sites along the Kangsabati river: a Mohanpur, b Kapastikri. Source Prepared by authors, based on DL & DLR, 2010–2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shifted of registered long term sand mining sites (2002–2016). Source Prepared by authors, based on DL & DLR, 2010–2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Entire sediment budget analysis in Kangsabati basin. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xxiii

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Fig. 3.23 Fig. 4.1

Fig. 4.2 Fig. 4.3

Fig. 4.4 Fig. 4.5 Fig. 4.6 Fig. 4.7

Fig. 4.8

Fig. 4.9

Fig. 4.10

Fig. 4.11 Fig. 4.12 Fig. 4.13

Fig. 4.14 Fig. 4.15 Fig. 4.16 Fig. 4.17

List of Figures

Sediment budget status in the eight segments of Kangsabati River. Source Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sediment grain size related with erosion/deposition. Source Authors are digitised from Hjulström-Sundborg diagram (1935) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sediment sample sites in Kangsabati River. Source Prepared by the authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SGD in every class weight during pre monsoon and monsoon: a upper course, b middle course, c lower course. Source Prepared by the authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Grain size versus sorting: a Pre monsoon, b monsoon season. Source Prepared by the authors. . . . . . . . . . . . . . . . . . . . . . . . Grain size versus skewness: a Pre monsoon, b monsoon season. Source Prepared by the authors . . . . . . . . . . . . . . . . . Grain size versus kurtosis: a Pre monsoon, b monsoon season. Source Prepared by the authors. . . . . . . . . . . . . . . . . . . . . . . . Sediment distributions (phi) in upper course sample sites of Kangsabati River: a pre monsoon, b monsoon. Source Prepared by the authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sediment distributions (phi) in middle course sample sites of Kangsabati River: a pre monsoon, b monsoon. Source Prepared by the authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sediment distributions (phi) in lower course sample sites of Kangsabati River: a pre monsoon, b monsoon. Source Prepared by the authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Triangular diagrams at course level: a upper, b middle, c lower, d trend of textural distribution. Source Prepared by the authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CM diagrams predict mode of sediment transport in upper course: a pre monsoon, b monsoon. Source Authors . . . . . . . CM diagrams predict mode of sediment transport in middle course: a pre monsoon, b monsoon. Source Authors . . . . . . . CM diagrams predict mode of sediment transport in lower course: a pre monsoon, b monsoon. Source Prepared by the authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tractive current deposits in upper course: a pre monsoon, b monsoon. Source Prepared by the authors . . . . . . . . . . . . . . Tractive current deposits in middle course: a pre monsoon, b monsoon. Source Prepared by the authors . . . . . . . . . . . . . . Tractive current deposits in lower course: a pre monsoon, b monsoon. Source Prepared by the authors . . . . . . . . . . . . . . Relationship between discriminate functions of Y1 and Y2: a pre monsoon, b monsoon. Source Prepared by the authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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. . 116 . . 120 . . 121 . . 123

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. . 130 . . 130 . . 131 . . 131

. . 133

List of Figures

Fig. 4.18

Fig. 4.19

Fig. 4.20 Fig. 5.1 Fig. 5.2

Fig. 5.3

Fig. 5.4

Fig. 5.5

Fig. 5.6

Fig. 5.7

Fig. 5.8

Relationship between discriminate functions of Y2 and Y3: a pre monsoon, b monsoon. Source Prepared by the authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Relationship between discriminate functions of Y3 and Y4: a pre monsoon, b monsoon. Source Prepared by the authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Available and critical shear stress at course level: a pre monsoon, b monsoon. Source Prepared by the authors . . . . . . Mining pit induced turbulent flow near Rangamati (middle course). Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nature of sediment transport along the upper course: a mode of sediment transport in sandbar site near Lalgarh (upper course), b huge bed extraction reduces sediment transport in mining sites near Sarenga, c trapping of sediment and nutrients in pits near Bikampur. Source Authors . . . . . . . . . . . . . . . . . . . . . . . Hydrodynamic interruption along the middle course: a transitional flow based ripple mark near Mohanpur (middle course), b mining induced anarbrancing flow creates numerous braid channels near Kankabati, c sediment traps in recirculation zone of pit sites near Debangai (middle course). Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interruption of bedload transport along the lower course: a particle fall velocity based sediment accumulation near Kapastikri bifurcation point, b mining induced pool sites near Singhaghai ghat, c turbulent flow affected bank erosion in pit sites near Narajole. Source Authors . . . . . . . . . . . . . . . . . . . . Bivariate correlation among hydraulic variables of flow regime in sandbar, mining and pit sites: a discharge versus channel flow, b velocity versus channel flow, c Manning coefficient versus channel flow, d velocity versus roughness coefficient. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bivariate correlation among hydraulic variables of sediment transport in sandbar, mining and pit sites: a particle diameter versus Sediment transport, b shear stress versus Bedload transport, c shear velocity versus Bedload transport, d sediment concentration versus Sediment transport. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Class wise categorization of stream hydraulics using the Z scores of PC1 and PC2 Prinsscore along the upper course. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Class wise categorization of stream hydraulics using the Z scores of PC1 and PC2 Prinsscore along the middle course. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xxv

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xxvi

Fig. 5.9

Fig. 5.10

Fig. 5.11

Fig. 5.12

Fig. 5.13

Fig. 5.14

Fig. 6.1

Fig. 6.2

Fig. 6.3

Fig. 6.4

Fig. 6.5 Fig. 6.6 Fig. 6.7

List of Figures

Class wise categorization of stream hydraulics using the Z scores of PC1 and PC2 Prinsscore along the lower course. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Channel bed disruption from Anicut dam to Debangai of Kapastikri segment in 2012: a river bed disruption, b longitudinal bed slope. Source Authors . . . . . . . . . . . . . . . . Channel bed disruption from Anicut dam to Debangai of Kapastikri segment in 2016: a river bed disruption, b longitudinal bed slope. Source Authors . . . . . . . . . . . . . . . . Channel bed disruption from Lalgarh Govt. College to Lalgarh bridge of Lalgarh segment during 2012: a river bed disruption, b longitudinal bed slope. Source Authors . . . . . . . . . . . . . . . . Channel bed disruption from Lalgarh Govt. College to Lalgarh bridge of Lalgarh segment in 2016: a river bed disruption, b longitudinal bed slope. Source Authors . . . . . . . . . . . . . . . . River bed lowering in respect of mining intensity and replenishment rate: a Kapastikri Segment, b Lalgarh segment. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conceptual schematic diagrams of mining induced channel hydromorphic responses. Source Modified by the authors, based on Calle et al. (2017) . . . . . . . . . . . . . . . . . . . . . . . . . . Eight different segments are demarcated for the micro-level estimation of riverbank shifting in Kangsabati River. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bank line shifting derived by LRR model: a distribution of intersection positions at both side from the common baseline, b the spatial trend and magnitude of bank line shifting along the transect (No. 101) from the baseline toward left and right sides of river banks at Mohanpur segment. Source Authors . . EPR and LRR model predicted long term (2000–2020) lateral bank line shifting including photographs snapped during the field survey depicting bank shifting driven river bank erosion in eight segments: a Khatra, b Raipur, c Lalgarh, d Dherua, e Mohanpur, f Kapastikri, g Panskura, h Rajnagar. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LRR model based bank line shifting in eight segments during 2000–2020: a left bank, b right bank. Source Authors . . . . . . EPR based bank line shifting in eight segments during 2000–2020: a left bank, b right bank. Source Authors . . . . . . Year wise intensity of shifting in predicted backlines (2020, 2030) and actual bank lines (2000, 2006, 2010, 2016 and 2020) at left bank: a Khatra, b Raipur, c Lalgarh, d Dherua, e Mohanpur, f Kapastikri, g Panskura, h Rajnagar. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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. . 192

. . 200

. . 204

. . 206

. . 211 . . 216 . . 218

. . 220

List of Figures

Fig. 6.8

Fig. 6.9

Fig. 6.10

Fig. 6.11 Fig. 6.12 Fig. 7.1

Fig. 7.2 Fig. 7.3

Fig. 7.4

Fig. 7.5

Fig. 7.6

Fig. 7.7 Fig. 7.8 Fig. 7.9 Fig. 7.10

Year wise intensity of shifting in predicted backlines (2020, 2030) and actual bank lines (2000, 2006, 2010, 2016 and 2020) at right bank: a Khatra, b Raipur, c Lalgarh, d Dherua, e Mohanpur, f Kapastikri, g Panskura, h Rajnagar. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mining responses and its induced land cover dynamics in eight segments: a Khatra, b Raipur, c Lalgarh, d Dherua, e Mohanpur, f Kapastikri, g Panskura, h Rajnagar. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sand mining induced pool-riffle alteration: a pool-riffle sequence during mining, b alteration of pool- riffle sequences during post mining. Source Authors are prepared from pool riffle sequences giving by Dey (2014) . . . . . . . . . . . . . . . . . . Mining induced bed level lowering: a Lalgarh, b Mohanpur, c Kapastikri. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . Mining vulnerable segments based on compound values. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Schematic diagram of sand mining induced direct and indirect impact on river ecology. Source modified by the authors, based on Koehnken et al. (2020) . . . . . . . . . . . . . . . . . . . . . . . . . . . Conceptual evaluation of sand mining induced three tier habitat destruction/alteration. Source authors . . . . . . . . . . . . . Habitat suitability of Koeleria macrantha during pre and post mining: a Khatra, b Raipur, c Lalgarh, d Dherua, e Mohanpur, f Kapastikri, g Panskura and h Rajnagar. Source Authors . . . Habitat suitability of Cynodon dactylon during pre and post mining: a Khatra, b Raipur, c Lalgarh, d Dherua, e Mohanpur, f Kapastikri, g Panskura and h Rajnagar. Source Authors . . . ROC for habitat suitability of Koeleria macrantha a Khatra, b Raipur, c Lalgarh, d Dherua, e Mohanpur, f Kapastikri, g Panskura and h Rajnagar. Source authors . . . . . . . . . . . . . . ROC for habitat suitability of Cynodon dactylon a Khatra, b Raipur, c Lalgarh, d Dherua, e Mohanpur, f Kapastikri, g Panskura and h Rajnagar. Source authors . . . . . . . . . . . . . . Frame work of basic principle of ANN. Source modified by the authors, based on Bisht et al. (2013). . . . . . . . . . . . . . . . . . . . Sample sites from sandbar, mined and pits of Kangsabati River. Source authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ANN structure: a sandbar, b mined sites, c pits. Source authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pearson correlation matrix between WQI and PP: a sandbar, b mining, c pits. Source authors . . . . . . . . . . . . . . . . . . . . . . .

xxvii

. . 225

. . 233

. . 238 . . 239 . . 245

. . 252 . . 253

. . 265

. . 273

. . 280

. . 281 . . 286 . . 289 . . 297 . . 299

xxviii

Fig. 8.1

Fig. 8.2

Fig. 8.3

Fig. 8.4

Fig. 8.5

Fig. 8.6

Fig. 8.7 Fig. 8.8 Fig. 8.9 Fig. 8.10

List of Figures

Schematic diagrams of sediment storage zone in the particular stretch of a channel. Source Modified by the authors, based on Padmalal and Maya (2014) . . . . . . . . . . . . . . . . . . . . . . . . . . . Linear function in between quantity and price of aggregate sand resources relationship. Source Modified by the authors, based on Zhai et al. (2020) . . . . . . . . . . . . . . . . . . . . . . . . . . . Inverse proportion function relationship in between quantity and price of aggregate sand resources relationship. Source Modified by the authors, based on Zhai et al. (2020) . . . . . . . General functional relationship in between quantity and price of aggregate sand resources relationship. Source Modified by the authors, based on Zhai et al. (2020) . . . . . . . . . . . . . . . . . Relationship in between demand and supply amount of river sand resource: a q
i [ vmaximum , b q
i ¼ vmaximum , c q
i \vmaximum . Source Modified by the authors, based on Zhai et al. (2020) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sand mining statuses during 2011–2014: a Lohatikri, b Relapal, c Gumripal, d Lilukhola, e Kankabati, f Debangai. Source Authors are prepared from Kangsabati sand mining database, DL and LRO of Paschim Mednipore, Bankura (2010–2014) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hierarchical set up of criteria and alternatives. Source Modified by the authors, based on Saaty (2008) . . . . . . . . . . . Proposed sustainable mining sites in upper course: a Khatra, b Raipur, c Lalgarh. Source Authors . . . . . . . . . . . . . . . . . . . Proposed sustainable mining sites in middle course: a Dherua, b Mohanpur. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . Proposed sustainable mining sites along the lower course: a Kapastikri, b Panskura, c Rajnagar. Source Authors . . . . . .

. . 317

. . 328

. . 328

. . 329

. . 331

. . 334 . . 337 . . 351 . . 355 . . 359

List of Tables

Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 3.1 Table 3.2 Table 3.3 Table 4.1 Table 4.2 Table 4.3

Table 4.4

Table 4.5 Table 4.6 Table 4.7 Table 5.1

Geological succession beds in the Kangsabati basin . . . . . . . Characteristics of eight different channel segments . . . . . . . . Average width-depth ratio and maximum depth distribution in different segments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Land cover patterns in different segments of Kangsabati River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Estimation of soil erodibility factor or K factor using soil taxonomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . RUSLE parameter and soil loss in twenty seven sub basin during 2002 and 2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SDR and SY in twenty seven sub basin during 2002 and 2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive statistical analysis for the SGD during pre monsoon season . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive statistical analysis for the SGD during monsoon season . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of grain size statistical parameters (in percentage of the total number at each location) and sediment type during pre monsoon season . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of grain size statistical parameters (in percentage of the total number at each location) and sediment type during monsoon season . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of estimated environments using discriminate functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive statistic of shear stress and critical shear stress during pre-monsoon season . . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive statistic of shear stress and critical shear stress during monsoon season . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hydraulic variables of flow and sediment regime during pre monsoon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

.. ..

33 36

..

39

..

47

..

60

..

70

..

72

. . 114 . . 115

. . 117

. . 118 . . 136 . . 139 . . 139 . . 162

xxix

xxx

List of Tables

Hydraulic variables of flow and sediment regime during monsoon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Table 5.3 Hydraulic variables of flow and sediment regime during post monsoon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Table 5.4 Correlation coefficient and significance level of dependent and independent hydraulic variables of flow and sediment regime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Table 5.5 Database arrangement for executing PCA . . . . . . . . . . . . . . . Table 5.6 Flow and sediment regime hydraulic factors loadings of Principal Components for upper course dataset . . . . . . . . . . . Table 5.7 Flow and sediment regime hydraulic factors loadings of Principal Components for middle course dataset . . . . . . . . . . Table 5.8 Flow and sediment regime hydraulic factors loadings of Principal Components for lower course dataset . . . . . . . . . . . Table 6.1 Image used in estimation of bank line shifting, erosion and accretion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Table 6.2 LRR predicted left bank line shifting rate (erosion and accretion) during 2000–2016 in eight different segments . . . . Table 6.3 LRR predicted right bank line shifting rate (erosion and accretion) during 2000–2016 in eight different segments . . . . Table 6.4 EPR model based average rate (m/year) of periodic shifting of left and right banks at eight different segments . . . . . . . . . . . Table 6.5 Prediction of erosion and accretion across the left bank . . . . . Table 6.6 Prediction of erosion and accretion across the right bank. . . . Table 6.7 DSAS model based bank line shifting results of RMSE and student’s t test for different segments during 2000–2016 . . . . Table 6.8 Riverine land cover patterns at segment level during 2002 . . Table 6.9 Riverine land cover patterns at segment level during 2016 . . Table 6.10 Channel planform change (sinuosity index, braiding index, braid channel ratio) and pool-riffle alteration during 2002–2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Table 6.11 Mining induced geomorphic consequence prioritization in eight different segments using CF value. . . . . . . . . . . . . . . . . Table 7.1 Derivation of coefficient with significance values of input variables layer on Koeleria macrantha’ and Cynodon dactylon species dominance in different segments using MLR . . . . . . . Table 7.2 Sand mining affected areas in habitat suitability of Koeleria macrantha’ and Cynodon dactylon . . . . . . . . . . . . . . . . . . . . . Table 7.3 Classification accuracy of HSI on Koeleria macrantha’ and Cynodon dactylon in three segments . . . . . . . . . . . . . . . . Table 7.4 Model summary of binary multiple logistic regression analysis in three different segments . . . . . . . . . . . . . . . . . . . . . . . . . . . Table 7.5 Physiochemical information of instream water in sandbar sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Table 5.2

. . 163 . . 164

. . 177 . . 182 . . 183 . . 183 . . 184 . . 203 . . 209 . . 210 . . 217 . . 224 . . 229 . . 230 . . 232 . . 232

. . 238 . . 244

. . 263 . . 271 . . 279 . . 282 . . 290

List of Tables

Physiochemical information of instream water in mining sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Table 7.7 Physiochemical information of instream water in pit sites . . . Table 7.8 Iteration results of the proposed ANN model . . . . . . . . . . . . . Table 7.9 Coefficient of the MLR in sandbar, mined and pits sites . . . . Table 7.10 Site wise distribution of phytoplankton and zooplankton . . . . Table 7.11 Correlation between physiochemical properties and instream biota in sandbar, mined, and pit sites . . . . . . . . . . . . . . . . . . . Table 8.1 Prospective mining areas in Lalgarh segment based on KSMP 2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Table 8.2 Prospective mining areas in Mohanpur segment based on KSMP 2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Table 8.3 Computation of expected of bed lowering and non-recorded sand mining over the years in different mining segments . . . . Table 8.4 Computation of sand mining demand, optimum volume and maximum profit in selected mining sites of Kangsabati River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Table 8.5 Pair wise comparison matrix . . . . . . . . . . . . . . . . . . . . . . . . . Table 8.6 Final priority in hierarchical structure of sand mining criteria and sub-alternatives for EIA . . . . . . . . . . . . . . . . . . . . . . . . . Table 8.7 Environmental aspects/sub-components and impact categories of sand mining from upper course mining sites (after Pastakia 1998; Resmi et al. 2011) . . . . . . . . . . . . . . . . . . . . . . . . . . . . Table 8.8 Environmental aspects/sub-components and impact categories of sand mining from middle course mining sites (after Pastakia 1998; Resmi et al. 2011) . . . . . . . . . . . . . . . . . . . . . . . . . . . . Table 8.9 Environmental aspects/sub-components and impact categories of sand mining from lower course mining sites (after Pastakia 1998; Resmi et al. 2011) . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xxxi

Table 7.6

. . . . .

. . . . .

291 292 295 301 303

. . 304 . . 319 . . 320 . . 322

. . 333 . . 342 . . 343

. . 348

. . 353

. . 356

List of Plates

Plate 1.1 Plate 2.1

Plate 2.2 Plate 2.3

Plate 3.1 Plate 3.2

Plate 3.3 Plate 3.4 Plate 4.1

Plate 4.2

Various type of river sand: a gravel size, b coarser sand, c medium sand, d finer sand. Source Authors . . . . . . . . . . . . . Instream sand mining methods in Kangsabati River: a barskimming, b pit excavation, c bar excavation, d sand and gravel traps. Source Authors . . . . . . . . . . . . . . . . . Floodplain sand mining methods in Kangsabati River: a pit excavation, b dry pit mining. Source Authors . . . . . . . . . Geomorphic threshold ranges of sand mining consequences from Aniket Dam to Debangai in Kapastikri segment: a resilience of threshold limit during 2003, b over the threshold limit during 2017. Source Authors are prepared from Google Earth Images 2003, 2017. . . . . . . . . . . . . . . . . . . . . . . . . . . . .  factor measured from gully channel. Source prepared by the authors, based on Renard (1997) . . . . . . . . . . . . . . . . . Field photography of soil loss in Lalgarh segment: a soil erosion across the rill and gully. b Mass failure across the bank margin. Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Field photography of SY: a delivery outlet across the Mohanpur; b SY across the Lalgarh. Source Authors . . . . . . . Mining induced consequences: a River bank erosion in Mohanpur, b huge sedimentation in Dherua. Source Authors Sediment grain size in different course: a Gravel at upper course, b coarser at upper course, c medium grain at middle course, d finer grain at lower course. Source Authors . . . . . . . Scouring and deposition process: a pit pool sites, b sandbar sites near Kapastikri divider point. Source Authors . . . . . . . . .

..

10

..

28

..

29

..

48

..

61

..

74

..

90

..

99

. . 113 . . 141

xxxiii

Chapter 1

River Sand Mining and its Management: A Global Challenge

1.1

River Sand Mining

Human disturbances in fluvial system in the form of inverse land use change, construction of dam or reservoir for hydroelectric power generation, flow diversion for flood mitigation and supply of irrigation water, gravel and sand mining are the causes of changing fluvial dynamics over the last decades throughout the world (Bhattacharyya 2011; Rascher et al. 2018). To ensure the augmentations of human requirements along with economic development, sand mining provided the multidimensional utilization ways like construction, land reclamations, etc.; however over exploitation of sand and gravel from instream and floodplain sites exaggerated the many adverse affects on river ecology and threaten on river biota specially in small alluvial river ( natural replenishment) in channel (Fig. 2.3a, b). But both geomorphic processes are tried to reach final equilibrium grade through temporal shifting from original equilibrium grade. Temporal grade shifting is occurred towards the upward

2.2 Geomorphic Threshold and Instream Sand Mining in Alluvial Channel

29

Plate 2.2 Floodplain sand mining methods in Kangsabati River: a pit excavation, b dry pit mining. Source Authors

of riparian and bank sites in sand deposited channel, while temporal grade shifted towards the downward of channel bed in mining prone channel. Moreover, huge deepening in channel bed carried out greater water volume than past flow, which is inundated across the floodplain sites. Consequently, floodplain sites are not longer support for providing the ecological and social roles to maintain the sustainable river ecosystem.

2.3

An Alluvial Quarried Reach in Kangsabati River

In view of changing riverine system caused by instream sand mining and its consequences on channel hydrology, morphology and river ecology; the present work has been undertaken to assess the environmental impact of sand mining in an alluvial reach of Kangsabati River. In terms of sand mining consequences, three different sites are observed in the entire river i.e. sandbar, mining sites, mining pits. Sandbar sites denotes that the channel sites where huge sediment replenishment occurs than sediment extraction, while mining sites demonstrated that the channel sites where sediment extraction much greater than sediment replenishment. Mining pits sites means in channel sites where huge removal of sediments creates pit formation along the river bed. Sediment formation along the channel not only depends on geomorphic processes but also guided by geological structures as well as geological formation. Rainfall intensity, physical and chemical properties of soil, elevation and bed slope etc. in this basin (Bhattacharya et al. 2020a). In contrary, rapid urbanization and preserve of others urban amenities at Khatra, Raipur, Lalgarh, Midnapore, Kharagpur, Ghatal and Panskura in this basin makes massive sand demand than natural replenishment. Moreover, Mukutmonipur dam and river bridge construction both are restricted the sediment supply towards the downstream, as well as abruptly break of slope along the middle and downstream, as a result, threshold limits are crossed through the river.

30

2 Geomorphic Threshold and Sand Mining: A Geo-environmental …

Fig. 2.3 Crossing the geomorphic threshold limits: a huge sedimentation (sand extraction < natural replenishment), b intensive sand mining (sand extraction > natural replenishment). Source Prepared by the authors based on the ideas of Lane (1955), Kondolf (1994, 1997)

2.3.1

Geo-environmental Setting of Kangsabati Catchment Area

Kangsabati River (also variously known as the Kasai or Cossye) is originated from the Chotonagpur plateau in the state of Jharkhand, India and passes through the districts of Purulia, Bankura and Paschim Medinipur in W.B. and draining in the Hooghly River. Kangsabati River has latitudinal extension from 21° 45′ N to 23°

2.3 An Alluvial Quarried Reach in Kangsabati River

31

30′ N and longitudinally 85° 45′ E to 88° 15′ E with covering an area of 9658 Km2. After rising near Jhalda in the Chotonagpur Plateau of Purulia district, Kangsabati River passes through Khatra and Ranibandh in the Bankura district and then enters in Paschim Medinipur at Binpur. In respect of drainage network, Kumari is the most important right hand tributary joining the trunk river at Ambikanagar, near Kangsabati-Kumari confluence point or Mukutmonipur dam (Fig. 2.4a). Another important tributary of Bhairabbanki and Taraphini River are joined in Kangsabati River at Sijua and then it split up into two branch at Kapastikri i.e. Rajnagar segment (Rupnarayan branch) and Panskura segment (Kheliaghai branch). Rajnagar segment flows through Daspur area and joined in Rupnarayan River at Palaspai, while Panskura segment flows in south–eastern direction and joined in Kheliaghai River. On the other hand, based on morphometric properties, source of extracted sediment in the entire catchment; Kangsabati basin is divided into twenty seven sub-basins (Bhattacharya et al. 2021). This alluvial river exhibited several drainage patterns like dendritic, rectangular, trellised and parallel including meandering, straight, and braided channel patterns. Dendritic and sub-dendritic patterns are mainly concentrated in the entire Khatra and Raipur sub-basins of Kangsabati basin where fringe plateau and intervening undulating plain surface are consisted with uniform resistant rock structure (Fig. 2.4b). Moreover, trellis drainage patterns are developed in Lalgarh sub-basin due to the presence of elevated undulating plain surface along with ridge and valley topography. This structure reveals that alternating bands of comparatively strong and weak rocks are predominant in the entire Lalgarh sub-basins. In contrary, rectangular pattern is developed in the right angle bands of Kumari and Taraphini River due to presence of strong bed rock joint in thin soil layer. While pinnate pattern is observed along the lower course of this basin where sand, silt and clay contents are formed extensive alluvial flood plain. On the other hand, radial pattern has been initiated at Panskura and Rajnagar segment along the lower course. Therefore, channel patterns play a crucial role to determine the geomorphic aspects of the basin and its geometrical forms of the surface through the interaction of structure and processes, respectively.

2.3.1.1

Geological Set up

Complexity of geological structure is one of the characteristic features in the Kangsabati basin. Basin lithological setup is arranged into two ways i.e. geological structure and geological formation sites (GSI 1991; Bhattacharya et al. 2020b). This is a vital factor to determine the multi cyclic landscape pattern and landscape pattern along this basin. Geologically this study area is constituted by various stratigraphic units which range from oldest Achaeans (Pre-Cambrian) to younger Tertiary-Quaternary formations (Mukhopadhyay 1992). There are fifteen geological groups as well as sixteen formations (Table 2.1). Most of the predominant structure is the mica schist occasionally garnitiferous and oxidized sand, silt and clay with insitu caliche groups. More than 85% river bed falls under this two major structure,

2 Geomorphic Threshold and Sand Mining: A Geo-environmental …

32

a

b

Fig. 2.4 a Kangsabati basin area. Source Authors. b Entire sub-basins in Kangsabati basin. Source Authors

where Arambag formation (garnitiferous structure) is predominant geological formation along the channel bed. On the other hand, sand, silt and gravel covers in the entire upper site including Mukutmonipur dam. Mahadeva formation of red sandstone and red clay is concentrated in upper catchment, but floodplain along the upper and middle stream are situated in Sijua formation (oxidized insitu caliche groups). While Panskura formation (oxidized sand, silt and clay) is observed in flood plain sites of downstream (Fig. 2.5).

2.3 An Alluvial Quarried Reach in Kangsabati River

33

Table 2.1 Geological succession beds in the Kangsabati basin Geological age

Geological formation

Consisting of structure

Geomorphic surface

Archaean to Paleo Proterozoic Archaean to Proterozoic

Chotonagpur formation

Hornblende schist, amphibolite, metaultrabasite

Chotonagpur Plateau

Singhbhum formation, Dolma Volcanics Mahadeva formation; Rajmahal Trap Basalt Dhalhum formation Dahitikri formation, Lalgarh formation Sijua formation

Chotonagpur granite gneiss complex; Granite gneiss including pegmatite and quartz veins Granite gneiss; Basalt

Plateau rim

Plateau fringe

Laterite

Plateau fringe

Laterite soil

Undulating surface

Oxidised Sand, Silt, Clay with Insitu Caliche Oxidised Sand, Silt, Clay

Undulating surface Plain surface

Semi-consolidated sediments composed of Sand, Silt, Gravel particles Sand, Silt, Clay

Floodplain surface

Mesozoic

Cainozoic Pleistocene

Pleistocene to Holocene Early Holocene Holocene

Kansai formation, Panskura formation Arambag formation

Late Present day Floodplain Holocene to deposits surface present day Source Prepared by authors from Geological Survey of India information (GSI)

2.3.1.2

Geomorphic Set up

Kangsabati basin morphology denotes mosaics of meso and micro environments which signified the diverse landforms that are evolved due to several morphological conditions at different altitude (Mukhopadhyay 1992). In terms of geomorphic setup, this basin is consisted mainly six different units i.e. pediment with laterite capping, buried pediments with laterite capping, deep buried pediments, valley fill deposits, and floodplain deposits (Fig. 2.6) (GSI 1991; Bhattacharya et al. 2020b). Rocky outcrop and buried pediments with laterite capping both are covered with steep ground slope in the entire upper basin, while valley fill deposits and floodplain deposits are covered in lower basin. Moreover, pediment with laterite capping and deep buried pediments are predominant geomorphic setup in the undulating intermediate parts of basin. On the other hand, basin elevation zonation includes seven categories i.e. Plateau proper (above 334 m), plateau rim (250–300 m), plateau fringe (200–250 m), elevated topography (150–200 m), undulating plain (100–

34

2 Geomorphic Threshold and Sand Mining: A Geo-environmental …

Fig. 2.5 Geological set up. Source Authors are prepared from Geological Survey of India map sheet 73I, J and N (http://www.portal.gsi.gov.in)

Fig. 2.6 Geomorphic set up of the Kangsabati basin. Source Authors are prepared from morphological map West Bengal

2.3 An Alluvial Quarried Reach in Kangsabati River

35

150 m), erosion plain (50–100 m) and flood plain (04–50 m). Highest altitude range seen in Khatra sub-basin situated entire Chotonagpur plateau fringe whereas lower altitude situated in floodplain area of Rajnagar and Panskura site. Owing to the natural terrain of the study area, it is observed that a huge amount of weathered materials drains into the river by several rills and gullies across the bank. Thus bedload transport plays a vital role to form flat sandy valley fill deposits during and after rainy season.

2.3.1.3

Climate

Modified tropical monsoon type of climate is characterized in upper basin, but this climate type is strongly concentrated in lower basin (below 15 m. mean sea level) of the Kangsabati River (Bagchi 1977). Transitional pattern of weather parameters are associated with many microclimatic characteristics where seasonal temperature, monsoonal rainfall along with severe local storms, and other associated features play significant role on river dynamics. At the sub-basin level, mean annual rainfall in undulating hummocky plain and plateau proper of Khatra and Raipur sub-basins are of 1080 mm. These sub-basins may be termed as low rainfall zone. In contrary, very high rainfall intensity is concentrated in lower basin like Kapastikri where mean annual rainfall are more than 1500 mm. Furthermore, mean annual rainfall varies from 1500 mm to 1000 mm in undulating intermediate parts of Dherua and Lalgarh sub-basins. Therefore, maximum rainfall receives from south west monsoon during June to September.

2.3.1.4

Soil

Soil is the end product of rock weathering by the action of climate and organisms upon the geological formations in Kangsabati basin. Several factors like topography, slope, drainage and biological process leads to formation of sixteen classes in the entire basin. Sixteen soil classes can be categorized under seven classes’ i.e. excellent, very good, good, moderate, moderate poor, poor and very poor in terms of fertility. Fine aeric ochraqualfs is most dominant soil category entire the segment. In upper basin, fine loamy and type haplustafs both are situated at Khatra and Lalgarh basin. In intermediate parts of basin, brown-loamy-sand covers are associated with undulating plain surface at Dherua and Mohanpur basin. In lower basin, fertile loamy soil covers concentrated at Kapastikri and Mohanpur basin. Pale-brown and light grey, sandy-clay loam soil groups are concentrated mainly in the lower part of Rajnagar segment. Panskura segment is covered by yellowish-brown-clay-loamy soil due to presence of high organic content under hydromorphic conditions.

2 Geomorphic Threshold and Sand Mining: A Geo-environmental …

36

2.3.2

Selection of the Channel Segments Along the Kangsabati River

In term of geo-environmental study, Mukutmonipur dam to Rajnagar (193.76 km) in Kangsabati River is divided into eight segments i.e. Khatra, Raipur, Lalgarh, Dherua, Mohanpur, Kapastikri, Panskura and Rajnagar based on short term and long term sand mining consequences, presence of river bridge across the channel and damming effect on sediment transport and channel planform change, respectively (Table 2.2; Fig. 2.7). Panskura is not a continuous segment under the study area. It is bifurcated from Kapastikri segment at Kapastikri and drained into Kheliaghai River. Total length from Mukutmonipur dam to Panskura extended up to 190.44 km. Channel planform in different segments of Kangsabati River is measured by three different indexes i.e. Sinuosity parameter (P), Braiding index (BI), Braid channel ratio (B) using of Friend and Sinha’s method (1993). Sinuosity Index (P) Meandering parameter is determined by Sinuosity Index (P) computing from Eq. 2.2 p¼

Lcmax LR

ð2:2Þ

Table 2.2 Characteristics of eight different channel segments Channel segment Khatra

Segment extension

Segment length (Km)

Segment area (Km2)

Dam to NH4 6.80 13.2 bridge Raipur NH4 bridge to 24.62 15.53 Raipur bridge Lalgarh Raipur bridge to 34.065 11.34 Lalgarh bridge Dherua Lalgarh to Dherua 18.54 49.6 bridge Mohanpur Dherua bridge to 39.969 25.43 Mohanpur bridge Kapastikri Mohanpur bridge 45.84 6.08 to Kapastikri Panskura Kapastikri to 24.62 3.47 Panskura Rajnagar Kapastikri to 21.24 1.67 Rajnagar P Sinuosity parameter, BI Braiding index, B Braid channel Source Calculated by authors

P

BI

B

Remark

1.21

0.81

0.67

1.23

2.48

2.07

1.37

2.26

1.73

1.46

1.39

0.95

1.34

2.27

1.72

1.77

1.13

0.65

1.66

1.23

0.73

1.42

1.41

1

Sinuous channel Braided channel Braided channel Meandering channel Braided channel Meandering channel Meandering channel Sinuous channel

ratio

2.3 An Alluvial Quarried Reach in Kangsabati River

37

Fig. 2.7 Eight different channel segments in Kangsabati River. Source Authors

where, LR means straight distance of mid channel length in measuring segment following the thalweg line (see footnote 6); Lcmax means overall curvature distance following the thalweg line7 or mid channel length from a single or multi-channel or widest channel. Braiding Index (Bi) Channel braiding index is measured channel braiding change into single channel flow as well as channel widening following Eq. 2.3 Bi ¼

Lctot Lcmax

ð2:3Þ

Lctot means determination of thalweg line length or mid-channel length along the main channel of entire eight segments; Lcmax means curvature length along the mid-channel of a widest channel in a specific reach.

7

According to hydrological perspective, thalweg line is a line connecting all the deepest points along the channel bed, indicated the water course direction.

38

2 Geomorphic Threshold and Sand Mining: A Geo-environmental …

Braid-Channel Ratio (B) Braid-channel ratio is used to estimate the channel incision rate and bank erosion susceptibility. This index is measured using coupling Eqs. 2.2 and 2.3 as follow B¼

2.3.2.1

Lctot p cot ¼ p p  LR

ð2:4Þ

Khatra Segment

This is the first segment in this study reach extending from Mukutmonipur dam to NH4 Bridge near Khatra bus stand. In this segment, average channel width is 110.24 m and depth average is 1.128 m (Fig. 2.8). Channel geometric characters are revealed that sinuous channel pattern; low braiding index and low braid-channel ratio are predominated in this segment (Table 2.2). Three cross profiles (AB, CD, and EF) revealed that average width depth (W/D) ratio gradually increases, but maximum channel depth continuously decreases towards the down reach (Table 2.3). On the other hand, long profile (GH) represents that channel bed slope is more irregular with steeper pattern. Average W/D ratio and maximum water depth both are demonstrated that low amount of bedload sediments mainly coarser

Fig. 2.8 Geometric characteristics and land cover along with different cross sectional position in Khatra segment. Source Authors

2.3 An Alluvial Quarried Reach in Kangsabati River

39

gain size i.e. gravel, boulder, and cobble are found here. Therefore, deposition of bedload sediments occurred on the channel bed than river bank. In addition in this segment, Mukutmonipur dam play signified role to control the channel dynamics like flow pattern, river bed slope, sediment discharge etc.

2.3.2.2

Raipur Segment

Raipur segment is selected from Khatra segment to Raipur Bridge where average channel width is 151.36 m, and depth average is 0.84 m (Fig. 2.9a). Several channel geometry likes sinuosity index, braiding index, channel braiding ratio are gradually increases in the entire segment (Table 2.2). Thus, braided channel pattern is to be generated. Three cross profile in different sites (AB, CD, and EF) denoted that average W/D ratio is increased than Khatra segment, but trend of maximum depths is more variable (Table 2.3). Long profile (GH) depicted that gradient along the river bed become steeper, but occasionally breaks down. In contrary, increase of average W/D ratio is the causes of bedload movement (cobble to coarser sand). As a result, channel deposition process and point bar formation both are occurred at the break point sites of river bank (right bank of CD, left bank of EF). Therefore, stream flow becomes interrupted, as well as channel width is increased towards the downstream.

2.3.2.3

Lalgarh Segment

Lalgarh segment is third segment in this study reach, which extended from Raipur Bridge to Lalgarh Bridge. Highest average channel width (403 m) is observed in this segment where depth average as 0.924 m (Fig. 2.9b). Channel geometric

Table 2.3 Average width-depth ratio and maximum depth distribution in different segments Cross section

Khatra

Raipur

Lalgarh

Dherua

Mohanpur

Kapastikri

Panskura

Rajnagar

Average width-depth ratio (m) AB

60.50

131.9

828.24

472.79

1208.01

82.92

184.07

61.56

CD

117.32

176.3

356.83

507.08

642.07

35.61

262.15

94.117

EF

128.63

267.37

748.14

489.73

545.32

434.29

603.32

GH

319.48

704.62

IJ

461.89

162.16

505.1 372.45

Maximum depth (m) AB

2.133

1.371

1.955

1.41

0.76

1.36

0.6096

1.48

CD

1.828

1.219

2.11

1.71

1.12

1.24

0.30

1.36

EF

1.219

1.57

1.57

1.11

0.48

1.626

0.27

0.914

GH

1.545

1.134

IJ

1.655

Source Calculated by authors, based on field survey (2015-2016)

2.611 2.64

40

2 Geomorphic Threshold and Sand Mining: A Geo-environmental …

Fig. 2.9 Geometric characteristics and land cover along with different cross sectional position: a Raipur segment, b Lalgarh segment. Source Authors

features depicted that Lalgarh segment is fall under braided channel category (Table 2.2). Curvature length of this segment is progressively grown up towards the down reach. In term of cross sectional position, maximum average W/D ratio is observed at AB section whereas minimum ratio is found at GH section. On the other hand, maximum depth is concentrated at CD section and minimum depth is observed at GH section (Table 2.3). This trend demonstrated that variation of average W/D ratio and maximum depth have not linear manner. Long profile (MN) indicates that steepness of channel bed is very irregular along with maximum break points throughout the segment. Therefore, bedload transport is continuously interrupted and numerous point bars are generated. On the other hand, two large tributaries of Taraphini and Bhairabbanki are transported huge sediments near Sijua in this segment. Consequently large amount of sediment deposited across at the break points of the slope across the bank.

2.3.2.4

Dherua Segment

This segment is started from Lalgarh Bridge to Dherua Bridge, where channel width average is 354.215 m and average depth is 0.631 m (Fig. 2.10). Channel sinuosity is increased but braiding index and braid channel ratio both are drastically reduces in Dherua segment. Thus, nature of channel geometry is fall under near meandering

2.3 An Alluvial Quarried Reach in Kangsabati River

41

Fig. 2.10 Geometric characteristics and land cover along with different cross sectional position in Dherua segment. Source Authors

pattern (Table 2.2). Cross sectional position revealed that maximum average W/D ratio is observed at GH section, and minimum ratio is concentrated at AB section. While highest maximum depth is found at CD section and lowest maximum depth presence at EF section (Table 2.3). This result demonstrated that both average W/D ratio and maximum depth have inversely progressed towards the down reach. On the other hand, long profile (PQ) indicated that steepness of channel bed is interrupted by break points throughout the segment. It can be said that increases of average W/D ratio allows huge bedload transport, but break points of bed slope

42

2 Geomorphic Threshold and Sand Mining: A Geo-environmental …

hamper the long way movement. As a result, huge sediment deposition makes point bar formation at the break points across the river bank; thus riparian sites gradually expanded.

2.3.2.5

Mohanpur Segment

Mohanpur segment is the second largest segment in this study reach of Kangsabati River, which is selected from the aforesaid road across the river near Dherua Bridge to Mohanpur Bus Bridge. Only one Rail Bridge is situated near Rangamati, Midnapore in this segment. Moreover, most of the Bridges are situated across this segment out of eight segments. Maximum channel width average is observed as 573.79 m, but depth average is gradually decreases from 0.58 to 0.54 m (Fig. 2.11a). Channel sinuosity index become decreases, but braiding index and channel braid ratio both are increases. Consequently, prominent braided channel pattern is developed throughout the segment (Table 2.2). According to cross sectional position, highest average width-depth ratio is observed at AB section in the entire segment. This is the highest ratio in the Kangsabati River. Lowest ratio is found at CD section in Mohanpur segment. In contrary, highest maximum depth is concentrated at CD section and lowest maximum depth situated at EF section (Table 2.3). This result denoted that relation between width-depth ratio and maximum depth is not inversely linear in the entire segment. On the other hand, long profile (GH) indicated that channel bed slope become drastically reduces with the presence of many structural knick points. Interruption of channel bed and irregular maximum depth with width-depth ratio both leads to vast deposition of bedload sediments across the riparian site to create point bar deposition throughout the Mohanpur segment.

2.3.2.6

Kapastikri Segment

Kapastikri segment is the last and largest segment in this study reach of Kangsabati River, which is selected from Mohanpur Bus Bridge to bifurcated point of main channel at Kapastikri. Channel width average is reduces (392.216 m) from Mohanpur segment, but depth average is increases of 1.21 m in the entire segment. Maximum meandering is observed along with low braid index and channel braid ratio in this segment (Table 2.2). Meandering axis is irregularly shifted that is the causes of pool-riffle alteration throughout the segment. Based on cross sectional position, five cross sections were taken from two different sites i.e. AB, CD taken from divider point near Kapastikri and IJ, GH, EF were taken from starting point near Midnapore Dam (Fig. 2.11b). Maximum width-depth ratio is observed at GH section whereas minimum ratio is concentrated at CD section. In contrary, maximum depth is measured at IJ section and minimum depth observed at CD section (Table 2.3). This result depicted that channel average W/D ratio and maximum depth have linear inverse relationship from IJ to CD cross section. On the other

2.3 An Alluvial Quarried Reach in Kangsabati River

43

Fig. 2.11 Geometric characteristics and land cover along with different cross sectional position: a Mohanpur segment, b Kapastikri segment. Source Authors

44

2 Geomorphic Threshold and Sand Mining: A Geo-environmental …

hand, long profile (KL) denoted that bed slope faces many break points and irregular gradation to create numerous point bar formation across the bank. Maximum channel meandering, successive change of width-depth ratio and maximum depth are interrupted bedload sediment transport, as a result, riparian area are increases both bank site in the entire segment.

2.3.2.7

Panskura Segment

Panskura segment is taken from Kheliaghai branch, which is selected from divider point to Panskura Bus Bridge. Channel width average is drastically reduces of 68.32 m as well as depth variation suddenly fall down of 0.25 m in this segment (Fig. 2.12a). Maximum channel meandering reduces the braid index and channel braid ratio in Panskura Segment (Table 2.2). Thus, single channel sinuosity hampers the channel braiding ratio throughout the segment. According to cross sectional position, highest average width-depth ratio and minimum depth both are measured at EF section and lowest ratio and maximum depth are found at AB section (Table 2.3). Meanwhile, width-depth ratio gradually increases, but depth variation regularly decreases in Panskura segment. On the other hand, long profile (GH) depicted that entire channel bed slope gradation restricted with the presence of numerous irregular break points caused by maximum channel meandering. Therefore, wetted perimeter become reduces but huge sedimentation occurred, which are extended the riparian area in the entire Panskura segment.

2.3.2.8

Rajnagar Segment

Rajnagar segment is taken from Rupnarayan branch, which is selected from divider point to Tremouni Ghat where channel is divided into two different parts i.e. Silabati confluence channel and Rupnarayan confluence channel (Fig. 2.12b). Channel width average at the divider point become 88.21 m, but depth average is increased of 0.88 m than Panskura segment. Channel sinuosity index reaches near meandering pattern, but braid index or braid channel ratio increases than Panskura and Kapastikri segment (Table 2.2). According to cross sectional position, highest average W/D ratio and lowest maximum depth are observed at EF section whereas lowest average W/D ratio and highest maximum depth observed at AB section (Table 2.3). This result is the same line like Kapastikri and Panskura segment. On the other hand, bed slope in Rajnagar segment is steeper with low amount of break points than Kapastikri and Panskura due to reduces of channel meandering. Therefore, wetted perimeter in channel become increases and riparian area gradually decreased that are the causes of flood susceptibility in the entire segment. Moreover, restriction of sedimentation process reduces the sandbar formation either point bar or mid channel bar.

2.3 An Alluvial Quarried Reach in Kangsabati River

45

Fig. 2.12 Geometric characteristics and land cover along with different cross sectional position: a Panskura segment, b Rajnagar segment. Source Authors

46

2.4

2 Geomorphic Threshold and Sand Mining: A Geo-environmental …

Sand Mining Crossed the Threshold Limit in Middle and Lower Reach of Kangsabati River

Several consequences such as interruption of channel hydraulic variables, reverse change of channel planform, decline of river ecology and degradation of habitat ecology are occurred along the middle and downstream of Kangsabati River (Bhattacharya et al. 2019b). In respect of segment distribution, maximum sands are extracted from Mohanpur and Kapastikri segments where most of the mining and pit sites were recorded (Table 2.4). Mining and pits area both are equally crossed the threshold limit than sandbar area of Mohanpur segment, thus wetted perimeter become increased along the channel (Fig. 2.13). As a result, several mining consequences like river bank erosion, habitat destruction, inversely geomorphic responses8 are gigantically expanded than others segment. Whereas Kapastikri segment has huge gap between mining area and sandbar area, therefore pits number leads to river bed disruption with the made of maximum depth variation along the bed (Plate 2.3a, b). On the other hand, mining data sheets are not available in Raipur and Khatra segment, but mining sites and pit numbers both are found in these two segments from field survey and Google Earth database during 2012–2016. Khatra is only segment where gravel mining sites are observed due to transported of boulder, gravel and cobble. Thus, sandbar area has become very low with smallest riparian sites for the presence of rocky out crop across the both bank side. On the other hand, mining sites and pit number both are increases in Raipur segment caused by coarser sediment transport and point sandbar formation (Fig. 2.13). Consequently, wetted perimeter in channel area becomes increases, but riparian area gradually decreases in this segment. In Lalgarh segment, extraction rate and sand mining site both are increased than Raipur segment, but Taraphini and Bhairabbanki tributary supplied huge sediment that extended the threshold limit of natural replenishment (Fig. 2.13). Therefore, mining pit formation is restricted as well as sandbar covering area increases. During 2002 to 2016, excessive sand extraction leads to river bank erosion that helps to cross the sandbar area and riparian extent. On the other hand, in Dherua segment, low rate of sand extraction, mining sites and pit number leads to resilience of threshold, meanwhile spatial extent of sandbar area more extended than mining and pit sites. Therefore, wetted perimeter become reduces. In Panskura segment, higher extraction rate and mining sites hampered the bed slope as well as helps to river bank erosion in the entire segment. In contrary, massive sedimentation and lowest pit number increases the riparian area in this segment. On the other hand, in Rajnagar segment, high wetted perimeter, low average W/D ratio, maximum depth variations are not support huge sand extraction but bed slope break points are allowed point bar formation that favour to develop of mining sites. However, 8

Geomorphic responses indicate the responses of geomorphic variables to certain non equilibrium condition under riverine process response system.

2.4 Sand Mining Crossed the Threshold Limit …

47

Table 2.4 Land cover patterns in different segments of Kangsabati River Channel segment

Rate of mining (m ton)

Mining sites

Mining pits

Sandbar area (%)

Riparian area (%)

Khatra NA 5 2 0.78 1.199 Raipur NA 13 17 15.68 4.92 Lalgarh 75,058 22 5 25.39 9.75 Dherua 13,956 12 4 13.19 6.93 Mohanpur 313,617 50 26 34.77 31.9 Kapastikri 161,308 59 27 7.64 32.26 Panskura 19,541 20 2 1.92 9.215 Rajnagar 4673 10 4 0.58 3.789 Source District Land Revenue office of Paschim Midnapore and Bankura (2016), (2012–2016); NA Not Available

Channel area (%) 2.048 14.24 19.70 6.6 32.3 17.34 3.02 4.71 field survey

Fig. 2.13 Threshold limit of sand mining consequences in eight segments. Source Authors

absence of point bar helps to cross the threshold limit of sand replenishment. Despite of intensive sand mining, gigantic sources of sediment quantity helped to retrain the resilience capacity of all responsible hydraulic variables in upstream segments whereas over extraction than sources amount in midstream segments intended to inverse change or over the threshold limit of responsible variables. Contrastingly, in spite of low amount of sand extraction, lack of sediment yield and

2 Geomorphic Threshold and Sand Mining: A Geo-environmental …

48

Plate 2.3 Geomorphic threshold ranges of sand mining consequences from Aniket Dam to Debangai in Kapastikri segment: a resilience of threshold limit during 2003, b over the threshold limit during 2017. Source Authors are prepared from Google Earth Images 2003, 2017

least sediment inflow lead to inverse changes of responsible variables in downstream segments caused by abruptly slope breaking, turbulence flow affected pit lowering, respectively. It is noticed that sand mining is not only triggering to over the threshold limit but other variables including landscape dynamic changes it throughout the river.

2.5

Conclusion

Geomorphic threshold is more effective measure to determine the resilience state of sand mining in riverine process response system. Several geo-environmental descriptions like geological structure with formation, geomorphic set up, drainage structure, climatic characteristics, and soil textural patterns are help to estimate threshold limit of river sand mining in the entire Kangsabati River. Based on impact of sand mining consequences, number of bridge crossing and damming influence on geo-environmental set up as well as channel geometry; Kangsabati River is

2.5 Conclusion

49

divided into several segments to understand the threshold limit of riverine system. Mining induced geomorphic threshold in this river is two types i.e. resilience of threshold limit and crossing the threshold limit. Resilience of threshold limit is observed in the upper segments due to receiving of huge sediment from confluence points of tributaries as well as presence of maximum depth variation with low meandering channel. In contrary, crossing the threshold limit caused by mining is observed in the middle and lower segments due to increasing of average width-depth ratio, channel meandering and low sediment supply from tributaries. Therefore, maximum sand mining consequences are observed in the over threshold limit segments along the middle and downstream. Furthermore, estimation of sediment budget is effective measure to proper determine the threshold limit of mining consequences through the quantifying of natural replenishment of sediment source and sediment removing or sink in the entire catchment.

References Anthony E (2016) Impacts of sand mining on beaches in Suriname. Report to WWF, s.l Bagchi K (1977) The Damodar Valley development and its impact on the region. In: Indian urbanization and planning: vehicles of modernization, pp 232–241 Barman B, Kumar B, Sarma AK (2019) Impact of sand mining on alluvial channel flow characteristics. Ecol Eng 135:36–44 Bhattacharya RK, Chatterjee ND, Das K (2019a) Geomorphic response to riverine land cover dynamics in a quarried alluvial river Kangsabati, South Bengal, India. Environ Earth Sci 78 (22):633. https://doi.org/10.1007/s12665-019-8652-y Bhattacharya R, Dolui G, Chatterjee ND (2019b) Effect of instream sand mining on hydraulic variables of bedload transport and channel planform: an alluvial stream in South Bengal basin, India. Environ Earth Sci 78(10):30. https://doi.org/10.1007/s12665-019-8267-3 Bhattacharya RK, Chatterjee ND, Das K (2020a) Sub-basin prioritization for assessment of soil erosion susceptibility in Kangsabati, a plateau basin: a comparison between MCDM and SWAT models. Sci Total Environ 139474. https://doi.org/10.1016/j.scitotenv.2020.139474 Bhattacharya RK, Chatterjee ND, Das K (2020b) An integrated GIS approach to analyze the impact of land use change and land cover alteration on ground water potential level: a study in Kangsabati Basin, India. Groundwater Sustain Dev 100399 Bhattacharya RK, Chatterjee ND, Acharya P, Das K (2021) Morphometric analysis to characterize the soil erosion susceptibility in the western part of lower Gangetic River basin, India. Arab J Geosci 14(6):1–22 Biedenharn DS, Thorne CR, Watson CC (2006) Wash load/bed material load concept in regional sediment management. In: Reno, Proceedings of the eighth federal interagency sedimentation conference (8th FISC) Bull WB (1978) Geomorphic tectonic activity classes of the south front of the San Gabriel Mountains, California. U.S. geological survey contract report 14-08-001-G-394, Office of Earthquakes, Volcanoes and Engineering, Menlo Park, California, 59p Calle M, Alho P, Benito G (2017) Channel dynamics and geomorphic resilience in an ephemeral Mediterranean river affected by gravel mining. Geomorphology 285:333–346 Church M (2002) Geomorphic thresholds in riverine landscapes. Freshw Biol 47(4):541–557 Downs PW, Dusterhoff SR, Sears WA (2013) Reach-scale channel sensitivity to multiple human activities and natural events: Lower Santa Clara River, California, USA. Geomorphology 189:121–134

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Dufour S, Rinaldi M, Piégay H, Michalon A (2015) How do river dynamics and human influences affect the landscape pattern of fluvial corridors? Lessons from the Magra River, Central-Northern Italy. Landscape Urban Plann 134:107–118 Friend PF, Sinha R (1993) Braiding and meandering parameters, vol 75, No 1. Geological Society, London, pp 105–111 (Special Publications) Ghosh PK, Bandyopadhyay S, Jana NC, Mukhopadhyay R (2016) Sand quarrying activities in an alluvial reach of Damodar River, Eastern India: towards a geomorphic assessment. Int J River Basin Manage 14(4):477–489 Grabowski RC, Gurnell AM (2016) Hydrogeomorphology—Ecology interactions in river systems. River Res Appl 32(2):139–141 GSI (1991) Geological survey of India: district resource map Paschim Mednipur. Geological Survey of India, Kolkata, Govt. of India, West Bengal. https://www.gsi.gov.in/ Koehnken L, Rintoul M (2018) Impacts of sand mining on ecosystem structure, process and biodiversity in rivers. World Wildlife Fund International Kondolf GM (1994) Geomorphic and environmental effects of instream gravel mining. Landscape Urban Plann 28(2–3):225–243 Kondolf GM (1997) PROFILE: hungry water: effects of dams and gravel mining on river channels. Environ Manage 21(4):533–551 Lane EW (1955) Importance of fluvial morphology in hydraulic engineering. In: Proceedings (American society of civil engineers), vol 81, paper no. 745 Marchamalo M, Hooke JM, Sandercock PJ (2016) Flow and sediment connectivity in semi-arid landscapes in SE Spain: patterns and controls. Land Degrad Dev 27(4):1032–1044 Mukhopadhyay S (1992) Terrain analysis of river basin. Vora Publication, New Delhi Nair KM, Padmalal D (2006) Sand deposit and extraction in the rivers of Kerala: an assessment based on river conservation. XVI Swadeshi Science Congress Owens PN, Batalla RJ, Collins AJ, Gomez B, Hicks DM, Horowitz AJ, … Petticrew EL (2005) Fine-grained sediment in river systems: environmental significance and management issues. River Res Appl 21(7):693–717 Padmalal D, Maya K (2014) Sand mining: environmental impacts and selected case studies. Springer, Berlin Padmalal D, Maya K, Sreebha S, Sreeja R (2008) Environmental effects of river sand mining: a case from the river catchments of Vembanad Lake Southwest coast of India. Environ Geol 54 (4):879–889 Rinaldi M, Wyżga B, Surian N (2005) Sediment mining in alluvial channels: physical effects and management perspectives. River Res Appl 21(7):805–828 Rovira A, Batalla RJ, Sala M (2005) Response of a river sediment budget after historical gravel mining (the lower Tordera, NE Spain). River Res Appl 21(7):829–847 Sinha R, Jain V, Gaurav K (2019) Geomorphic changes and sediment dynamics in rivers: causes and consequences. IISc Press Sreebha S (2008) Environmental impact of sand mining: a case study in the river catchments of Vembanad Lake, Southwest India Stanley EH, Sponseller RA, Heffernan JB (2016) Landscape and regional stream ecology. In: Stream ecosystems in a changing environment. Academic Press, pp 389–415 Williams M (2012) River sediments. Philos Trans R Soc 370:2093–2122

Chapter 3

Fluvial Sediment Budget and Mining Impact Assessment: Use of RUSLE, SDR and Hydraulic Models

3.1

Introduction

Several anthropogenic activities like channelization, dam construction to generate hydroelectric power, instream and floodplain sand mining are greatly influences on channel geometry since long past (Rascher et al. 2018; Bhattacharya et al. 2019a). Recently many researchers proposed that sand mining intensively responses on channel dynamics1 with substantial changes on flow and sediment regime in respect to huge extraction than natural replenishment (Rinaldi et al. 2005; Rovira et al. 2005; Ghosh et al. 2016; Calle et al. 2017; Bhattacharya et al. 2019a, b). In alluvial channel, removal of sand from bed over the year, sediment budget2 in the entire catchment has been faces an imbalance situation in between sediment supply and sediment transport (Rascher et al. 2018). In this context, this chapter analysis the effects of instream sand mining on sediment budget in an alluvial stream. Sediment budget is a scientific quantitative measure to compute sediment source and sink (Creech et al. 2015). Source of sediment in a channel segment depends on sediment yield (SY) of segment contributing area and suspended sediment transport from upper segment, whereas sink of sediment are associated with removal of sand, sediment concentration and bedload3 sediment transport towards the lower segment (Fig. 3.1). Sediment budget detects the stability condition of each segment through the assigning of replacement and removing of sediment in a river. On the other hand, budget analysis involved with sediment generation and its movement in the entire basin. It also includes measuring of erosion and deposition zones. Another

Channel dynamics denotes natural autogenic occurrences in fluvial process resulting from human modifications, climatic factors. 2 Sediment budget demonstrated the assessment of source and sink of sediment in a geographic system with the considering of input, transport, storage and output of sediment. 3 Bedload transport explained the moving particles that are transported towards the longitudinal channel bed by rolling, sliding and saltating. 1

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. K. Bhattacharya and N. Das Chatterjee, River Sand Mining Modelling and Sustainable Practice, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-72296-8_3

51

52

3 Fluvial Sediment Budget and Mining Impact …

Fig. 3.1 Schematic diagrams of sediment budget with the following of source to sink system of riverine sediment. Source Prepared by the authors, based on Rhine sediment budget (Frings et al. 2014; Grimaud et al. 2018)

important dimension of sediment budget is assessed the rate of sediment deposition in respect of sediment delivery outlets, travel distance from sub-basin tributaries to outlets nearest channel. General objective of this chapter is to understand the stability status of each segment in respect to volume of SY and sediment deposition in source and sink at sub basin level, respectively.

3.2

Estimation of Sediment Source

Sediment source usually depend on SY, suspended bedload transport along the river bed in a segment of channel. Basin morphometric properties help to determine the SY at sub-basin level (Rajbanshi and Bhattacharya 2020; Aneseyee et al. 2020). SY is computed as multiply of soil loss and SDR (SDR) in a drainage basin (Bhattacharya et al. 2020a). Mean annual soil erosion (MASE) is calculated by the Revised Universal Soil Loss Equation (RUSLE) initially developed by Renard (1997). This equation predicts how several factors such as rainfall (rainfall erosovity factor-R), soil (soil erodivility factor-K), relief (slope factor-L, S) and land use land cover patterns (cover management factor-C, support practice factor-P) are controlled the rill and inter rill soil erosion in the upland topography (Toubal et al. 2018; Bhattacharya et al. 2020a). In respect of water action, soil erosion caused by terminal velocity of raindrop and surface run off movement (Shit et al. 2015). Whereas sediment delivery distributed model (SEDD) is applied to determine the

3.2 Estimation of Sediment Source

53

SDR in each morphological cells at sub-basin level (Ferro and Porto 2000; Rajbanshi and Bhattacharya 2020). SDR can be estimated through stream order, drainage density, soil type and spatial extent of watershed (Corbitt 1990; Bhattacharya et al. 2020a).

3.3

Soil Loss Assessment Using of RUSLE

In tropical alluvial river basin, sediment source at sub-basin level entirely dependent on soil particles detachment and its movement from uppermost layer, which is effectively controlled by dominant rill and gully network throughout the elevated basin (Toubal et al. 2018; Bhattacharya et al. 2020b). Soil erosion potentiality depends on distinct factors in each watershed. These are varies from one watershed to another based on impact of magnitude of rill and inter rill erosion that is determined by Universal Soil Loss Equation (USLE) (Wischmeir and Smith 1978). After forty years, USLE is enlarged and revised in form of Revised Universal Soil Loss Equation (RUSLE) (Renard 1997). Remote sensing (RS) and Geographical Information System (GIS) is used to calculate the potential spatial distribution and intensity of soil loss using RUSLE (Djoukbala et al. 2019; Bhattacharya et al. 2020b). GIS technique predicts useful and accurate values of magnitude of soil erosion in a large area (Wang et al. 2004; Vijith et al. 2018). GIS and RUSLE both have been used to estimate the RUSLE parameters, as well as to determine their role on soil erosion rate (Ganasri and Ramesh 2016; Djoukbala et al. 2019).

3.4

RUSLE Model Set Up

Sediment budget analysis at sub basin level is a huge challenge in every native catchment area where availability of sediments and runoff discharge database are very much insufficient. In particularly, rain gauge station is not available in some sub basin sites, which is crucial requirement to estimate the rainfall intensity at smallest interval (>30′). On the other hand, intensive model like USPED,4 WEPP,5 and soil erosion model like SWAT (soil and water assessment tool) could not be properly predicted as well validated of mean annual soil erosion due to unavailable database; however RUSLE (revised soil loss equation) model is effectively applied to estimate the mean soil erosion in selected native catchment area. All parameters of RUSLE model are assessed from rainfall database for preparing of rainfall 4

USPED means unit stream power based erosion deposition, which model is used to predict the soil erosion and deposition distribution at large scale in a steady state overland flow catchment area. 5 WEPP means water erosion prediction project model comprising the physical based erosion and deposition across the United State and World.

3 Fluvial Sediment Budget and Mining Impact …

54

TOPOSHEET

SATELLITE IMAGE

DEM

STUDY AREA EXTRACTION

STUDY AREA EXTRACTION WATERSHEDES EXTRACTION

LU/LC

SLOPE

FILL & FLOW DIRECTION

β FACTOR FLOW ACCUMULATION

m FACTOR

L FACTOR RAINFALL DATA

R FACTOR

S FACTOR

SOIL MAP

K FACTOR

L FACTOR

S FACTOR

C FACTOR

LAND MANAGEMENT

P FACTOR

OVERLAY A=R×K×L×S×C×P

SOIL EROSION MAP

Fig. 3.2 RUSLE methodological flow chart. Source Modified by authors, based on Bhattacharya et al. (2020b)

intensity, DEM for executing of slope steepness and length, soil sheet for detecting of soil types and its code, and satellite images for preparing of LULC patterns in the entire basin. Then, all parameters are accordingly integrated to ensure the successful run up of RUSLE model using Arc GIS software (version 10.2). All of the methodological procedure in this study can be represented through the following suitable flow chart (Fig. 3.2). In addition, soil loss is estimated by RUSLE model from each square cells size in a grid of every LULC patterns

3.4 RUSLE Model Set Up

55

throughout basin. MASE is estimated from modified USLE parameters using RUSLE equation follows (Renard 1997): A¼RKLSCP

ð3:1Þ

where, A means assessed the spatial distribution of potential mean annual soil loss and probable soil loss zone per unit area. Selection of K is expressed every units whereas R is selected to detect the requiring period in each units. All units are generally considered based on practice patterns. So, ‘A’ represents in tons per hectare per year (t ha−1/yr) while other. R means rainfall-runoff erosivity factor: runoff signifies with adding a factor in rainfall erosion index coming from snowmelt water (100 ft * tons/acre/y). K means soil erodibility factor: soil erosion index unit is measured as a standardized plot of erosion rate on a specific soil. Standard plot is computed of 72.6 ft (22.1-m) where 9% uniform slope length is continuously converted from clean to tilled fallow land. L means slope length factor: soil loss depends with the ratio of soil loss that is collected from field slope length as a 72.6-ft length under identical conditions. S means slope steepness factor: soil loss depends with the ratio of soil loss from the field slope where 9% slope fall under identical conditions. C means cover management factor: ratio of soil loss depends on specific cover and management type given in area where tilled are continuously converted into fallow land. P means support practice factor: several types of support practice such as contouring, strip cropping, terracing leads to determine rate of soil erosion along the straight-row farming up and down the slope. L and S both factors are considered for getting the dimensionless parameters of slope length and slope steepness whereas C and P factors as dimensionless parameters represent the impact of cropping and management systems on soil conservation practice. All those considering parameters are converted into normalized weight in respect to unit plot position.

3.5

Case Study: Estimation of Mean Annual Soil Erosion at Sub Basin Level of Kangsabati Basin Using RUSLE —A Case Study

Rill and gully dominated sedimentation in tropical river basin has been seen in Kangsabati basin where drainage outlets carried out massive sediments from upper catchment to main channel (Bhattacharya et al. 2020a). Deposition occurs in the concerned stretch as the gradient drastically reduces due to construction of Mukutmonipur dam in 1958 (During second five year Plan). RUSLE model is used here to predict the spatial distribution of soil erosion rates. This section is trying to assess and predict the spatial distribution, rate and volume of mean annual soil erosion (MASE) at twenty seven sub-basins of Kangsabati River.

56

3.5.1

3 Fluvial Sediment Budget and Mining Impact …

Estimation of RULE Factors

RUSLE is done using parameter like precipitation (R factor), soil characteristics (K factor), elevation (LS factor), and LULC patterns (Cropping management–C Factor) to predict the rate of MASE, assess of erosion susceptibility and deals with several development and conservation strategies in accordance to protect soil erosion based on LULC patterns (Boggs et al. 2001; Sahaar 2013; Nasir and Selvakumar 2018).

3.5.1.1

Rainfall Erosivity Factor (R)

Erosive capacity of rainfall is determined by rainfall erosivity factor (R). This factor does not only detected of soil detachment, but also helps to access the amount of transported particles from upper catchment to channel segments during storm and heavy rainfall events (Jain et al. 2001; Dabral et al. 2008; Vijith et al. 2018). In Kangsabati basin, R factor in RUSLE model is calculated by total number of days from total annual rainfall using Eqs. 3.2 and 3.3. This equation is followed by Renard and Freimund (1994) formula. Then they prepared annual rainfall erosivity index or Isoerodent map based on isohyet map of mean annual rainfall in sixteen rain gauge stations namely Lalgarh, Khariduar, Mohanpur, Simulia, Tusuma, Midnapore, Purihansa, Fulberia, Dherua, Mukutmonipur, Rajnagar, Bankura, Jhargram, Panskura, Barisha and Baka of Kangsabati basin during last thirty six years rainfall data (1980–2016). R ¼ 0:04830 p1:610

p\850 mm

R ¼ 587:7  1:219p þ 0:004

p\850 mm

ð3:2Þ ð3:3Þ

where R means the annual rainfall erosivity represent in MJ mm ha−1 year−1 P means the annual precipitation (mm). R in isoerodent map in this study showed that maximum value is concentrated in Baka (16,344.39 MJ mm ha−1 Year−1) and minimum value is found in Lalgarh station (2844.95 MJ mm ha−1 Year−1) between the year of 1980 and 2002 (Fig. 3.3a, b). On the other hand, maximum R factor is observed in Baka (11,749.9 MJ mm ha−1 Year−1) and minimum R value is found in Mohanpur station (4041.93 MJ mm ha−1 Year−1) between the year of 2002 and 2016. It can be said that most of the R value means rainfall intensity concentrated in lower basin than upper basin.

3.5 Case Study: Estimation of Mean Annual …

57

Fig. 3.3 Isohyets map and rainfall erosivity factor in the study area: a 2002, b 2016. Source Modified by authors, based on Bhattacharya et al. (2020a, b)

3 Fluvial Sediment Budget and Mining Impact …

58

3.5.1.2

Soil Erodibility Factor (K)

Soil erodibility factor (K) has an intensive integration with the magnitude of soil erosion (Nasir and Selvakumar 2018; Bhattachary et al. 2020b). It is influenced by rainfall intensity and run off on soil loss in a given soil (Haan et al.1994). K factor is computed based on soil textures (% silt plus very fine sand, % sand, % organic matter, soil structure and permeability) and then values are plotting on the following nomograph6 (Wischmeier and Smith 1978). K factor computed through the following Eq. 3.4.
K¼

 2:1  104 ð12  0M ÞM 1:14 þ 3:25ðS  2Þ þ 2:5ðP  3Þ 100

ð3:4Þ

where M = Product of primary particle size fractions (% of modified silt or the 0.002–0.1 mm size fraction); K = tons acre per erosion index (t ha h ha−1 MJ−1 mm−1) in SI units; S = Soil class for structure; p = Rate of permeability each soil structure. This factor can be experimentally expressed based on erosion rate or loss per rainfall erosion index unit given in a specific soil as measured in a unit plot (nomograph) that is numerically represented as 72.6 ft (22.1 m) long within a width of 6 ft (1.83 m) and 9% slope (Wischmeier and Smith 1978). This situation acts on continuously clean-tilled fallow condition where tillage can be performed up and down the slope. Then soil map is reclassified through raster format for measuring of K value (Fig. 3.4a, b). In particular, maximum K value found in fine loamy typic ustochreptas-66 (0.44) and minimum value is found in Loamy, lethic Haplustaifs-96 (0.09) out of sixteen soil types in Kangsabati basin (Table 3.1), meanwhile most of the soil types has lowest soil erosion rate. Following the nomograph prepared by Wischmeier and Smith (1978), high K value demonstrated that soil has high permeability and lower organic content, but lower amount of K value denoted that soil having low permeability and high organic content. Therefore, average status of soil erodibility in this basin has sandy loam to loamy sand soil texture, moderate permeability and enriches organic content.

3.5.1.3

Slope Length and Slope Steepness Factor (Ls)

Erosion process is entirely depended on topographical factors through the comprising of slope length (L) and slope steepness (S) intended to greatly influence on soil erosion intensity. A horizontal line as selected from starting point of overland flow to runoff concentrated point in a defined channel where slope steepness become sharply fall down increases the huge deposition, which is considered for

6

Nomograph is rapidly assessment graph for determining the soil erodibility factor (K) of RUSLE.

3.5 Case Study: Estimation of Mean Annual …

59

Fig. 3.4 Soil parameter: a spatial distribution map, b K factor. Source Modified by authors, based on Bhattacharya et al. (2020a, b)

3 Fluvial Sediment Budget and Mining Impact …

60

Table 3.1 Estimation of soil erodibility factor or K factor using soil taxonomy Soil sub group Id 38 47 65 66 67 68 69 70 95 96 97 99 108 109 110 111 112 Data

Dominated sub-group

Soil texture

Organic content

Very fine, vertic Silty clay to haplaquepts clay loam Very fine, aeric Silty clay haplaquepts Fine aeric Clay loam ochraqualfs Fine loamy typic Silt loam ustochreptas Coarse loamy Silt loam to typic haplustalfs loam Fine loamy ulti Silt loam paleustalfs Fine loamy aeric Loam ochraqualfs Fine loamy typic Clay loam paleustalfs Loamy, lethic Sandy loam to Haplustaifs loam Loamy, lethic Loamy sand Haplustaifs to sand Fine, typic Sandy clay haplustalfs Loamy, lethic Sandy loam to Haplustaifs loamy sand Loamy-skeletal, Silty clay lethic ustorthents loam Loamy, lethic Silty clay ustochrepts loam Fine loamy, type Silt loam to haplustalfs loam Fine loamy, type Silt loam haplustafs Fine loamy, type Clay loam, haplustalfs loam source West Bengal soil sheet, NBSSLUP,

Structure code

Permeability

K factor

0.5

3

2

0.25

0.5

2

5

0.21

>0.5

3

3

0.27

0.5

3

2

0.31

>0.5

3

2

0.43

>0.6

3

2

0.28

>0.5

3

3

0.27

>0.5

4

2

0.17

>0.5

4

1

0.09

0.5

4

4

0.11

0.5

3

3

0.09

0.5

3

2

0.32

>0.5

3

2

0.23

Govt. of West Bengal

determining the slope length (Plate 3.1). Flow accumulation and slope percentage both are considered as input parameter to measure the L factor using Wischmeier and Smith method (1978). Equation 3.5.1 is used to compute the L factor follow as:  L¼

 k m 22:13

ð3:5:1Þ

3.5 Case Study: Estimation of Mean Annual …

61

Plate 3.1  factor measured from gully channel. Source prepared by the authors, based on Renard (1997)

where L = slope length; k = slope length unit in meter; m value depends on variation of slope i.e. 0.5 taking in slopes steeper than 5%; 0.4 for slopes between 3 and 4%; 0.3 for slopes between 1 and 3% and 0.2 for slopes less than 1%. In contrary, slope steepness based algorithm is applied to determine the S factor (Renard 1997). Rate of soil erosion is faster within 9% slope direction than steeper intensity (McCool et al. 1987). S ¼ 10:8  sinH þ 0:03;

where slope\0:09

ð3:5:2Þ

S ¼ 16:8  sinH  0:5;

where slope  0:09

ð3:5:3Þ

where S = Slope steepness factor; H represent gradient of slope as in degrees unit. In term of proper estimation of L and S factor, sub-division of slope steepness has been divided into number of segments, sub sequentially terrain division in unit of contributing area has been made under two-dimensional plane (Bhattacharya et al. 2020a). This approach is estimated using of Desmet and Govers (1996) equation. Lij ¼

þ1 ðAi:jin þ D2 Þijm þ 1  Am i;jin m m þ 2 m D  Xi:j  22:13

ð3:5:4Þ

where, L_(ij) = Inlet wise slope length in grid cell I and j; AAi, j−in = contributing area at the inlet of grid cell (i, j) measured in m2; D = grid cell size unit in meters; Xi, j = sin aaij + cos aai, j; aai, j = aspect of direction of the grid cell (i, j) L and S parameters are considered to justify following Eqs. 3.5.5 and 3.5.6 for getting the m and b. Although m depends on the ratio of b of the rill to inter rill erosion then explain these two parameters (Fig. 3.5a–e).

62

3 Fluvial Sediment Budget and Mining Impact …

Fig. 3.5 Estimation slope parameter in Kangsabati basin: a LS factor, b flow accumulation, c flow direction, d m factor and e ß factor. Source Authors

3.5 Case Study: Estimation of Mean Annual …

Fig. 3.5 (continued)

63

3 Fluvial Sediment Budget and Mining Impact …

64

Fig. 3.5 (continued)

m¼ ¼

 þ1 sin  0:0896

ð0:56 þ 3  ðsin Þ0:8 Þ

ð3:5:5Þ ð3:5:6Þ

where ø = slope angle in degrees; m value ranges 0–1 where 0 indicates ratio of rill to inter rill achieves to erosion. It can be pointed that LS factor play a decisive role to assess the rate of soil erosion using of landscape scale of soil erosion models in specially RUSLE model (Desmet and Govers 1996). Slope map was prepared from SRTM DEM; it showed that sub-basin wise slope variation has been found along the Kangsabati River. In Kangsabati basin, LS values ranges from 0.04 to 17, this result represents that maximum sub-basins have moderate to steep slope category. Flow accumulation is positively correlated with slope that means higher slope steepness value increased the flow accumulation rate (Fig. 3.5b–e).

3.5 Case Study: Estimation of Mean Annual …

3.5.1.4

65

Cover Management Factor (C)

Cover management factor play vital role to determine the contribution of existing LULC with vegetation strength on soil erosion in respect to storm and heavy erosive rainfalls (Vijith et al. 2018; Bhattacharya et al. 2020b). In study area, cropping and management practices on soil erosion rate in agricultural lands detected by C factor, which is prepared from six different land use classes name as cropping pattern, fallow land, forest, settlement, barren land and surface water bodies (Fig. 3.6a–d). All those classes were taken for preparation of individual thematic layers using GIS techniques. LULC maps are prepared from Landsat images in the year of 2002 and 2016 where overall accuracy assessment values are 86% (2002) and 94% (2016) using Kappa statistics. Pixel cell size of six major LULC classes is calculated to obtain the cumulative area in each class using supervised classification method. Following several researchers, C factors are generated from total associated area in every LULC class value for 2002 and 2016. Reclassification of LULC maps have been made based on C factors of every class in raster format where value of C factor ranges from 0.01 to 0.5 during 2002 and 2016 (Fig. 3.6c, e). C factor value is not same in every LULC class as follow: value of 0.2 is considered for settlement class following of strategic environmental assessment (SEA) given by UN-FAO7 (2001), value of 0.5 is taken for barren land with laterite class following Bakker et al. (2008), 0.05 is accepted for degraded forest following of Bakker et al. (2008) and Jordan et al. of agriculture under ecosystems and environment (2005), 0.01 is considered for dense forest following of UN-FAO (2001), Bakker et al. (2008) and Jordan et al. (2005), 0 is taken for water body following of Cox et al. (1998). Moreover, values of 0.2 and 0.31 are regarded for single crop and multiple cropping systems with the following of Wischmeir, in Soil Science Society Proceeding (1960).

3.5.1.5

Support Practice Factor (P)

Support practice factor (P) is signified the soil erosion acceleration with the incorporating of straight-row farming process along the up and down slope where support practices provided the positive impacts on erosion susceptibility (Mahala 2018). Run off erosion potentiality is entirely depended on influences of drainage patterns, runoff concentration, runoff velocity and hydraulic forces that are exerted by runoff on soil cover. Thus, all those support practice types are P factor. In generally, P factor value is varies from 0 to 1, 0 value always denoted the good conservation practices but 1 value revealed the poor conservation practices, respectively. In Kangsabati basin, P factor is not properly seen in the entire basin area (Bhattacharya et al. 2020b). Therefore, value of 1 is considered for the support practice factor (P) during the progression of RUSLE model. Units represent by t acre−1/yr).

7

UN-FAO (2001) means Food and Agriculture Organization of the United Nation.

66

3 Fluvial Sediment Budget and Mining Impact …

Fig. 3.6 LULC pattern and estimation of c factor: a LULC during 2002, b C factor during 2002, c LULC during 2016 and d C factor during 2016. Source Modified by authors, based on Bhattacharya et al. (2020b)

3.5 Case Study: Estimation of Mean Annual …

Fig. 3.6 (continued)

67

68

3.5.2

3 Fluvial Sediment Budget and Mining Impact …

Delineation of Potential MASE

Spatial distribution of MASE in the entire Kangsabati basin is prepared by RUSLE using pixel-by-pixel based (30 m cell size) GIS analysis. In this basin, total amount of soil loss ranges from 305.59 ton/ha/y (2001–2002) to 299.96 ton/ha/y (2015– 2016), which is estimated by RUSLE model. The spatial pattern and distribution of soil loss potentiality has been classified into seven class’s i.e. very high (301–350 ton/ha/yr), high (251–300 ton/ha/yr), medium to high (201–250 ton/ha/yr), medium (151–200 ton/ha/yr), low to medium (101–150 ton/ha/yr), low (51–100 ton/ha/yr), and very low (0–50 ton/ha/yr), respectively. Figure 3.6a, b showed that twenty five sub-basins have low erosion potentiality out of twenty seven sub-basins in the entire basin during 2002 and 2016 (Fig. 3.7). 3.5.2.1

Potential Annual Soil Loss Estimation at Sub-basin Level

Mean potential soil loss at sub-basin level is reflected by five dominant RUSLE parameters in response to LULC practice and basin area (Ganasri and Ramesh 2016; Bhattacharya et al. 2020a). According to spatial distribution of potential soil loss in twenty five sub-basins, Khatra 1 SB has high erosion susceptibility (92.87 ton/ha/ yr), but its maximum sediments drained into Mukutmonipur dam. Several factors including basin area (70,865 ha), large amount of stream order and extensive barren land helps to huge acceleration of MASE in this sub-basin (Tables 3.2 and 3.3). But, amount of soil loss in this sub-basin does not drained towards the downstream due to huge trapping of sediment by Mukutmonipur dam (Bhattacharya et al. 2020a). On the other hand, Lalgarh-4 SB (65.58 ton/ha/yr) is another higher rate of potential MASE in Kangsabati basin caused by vast spatial extent of basin area (69,369 ha) and existing LULC patterns, where Taraphini and Bhairabbanki River and its adjoining tributaries carried out massive sediment and accumulated into Kangsabati main channel (Plate 3.2a). In contrary, due to the present of intense dense forest cover, low stream frequency and small basin coverage, Dherua 2, Mohanpur 6 and Raipur 3 SB have low amount of MASE rate. Furthermore, due to present of lower stream order, moderate slope and single crop practice, rest of twenty sub-basins has moderate MASE rate varies from 10.98 ton/ha/yr to 2.24 ton/ha/yr, respectively. 3.5.2.2

MASE Probability Zones at Sub-basin Level

RUSLE model predicted the probability zone of MASE to obtain the future prospect of soil erosion trends throughout the basin, which is prepared through the integrated of input parameters i.e. rainfall (R), soil (K), slope (LS) and LULC (C). All of those parameters must be required as a spatial mapping using weighted index overlay method. Based on future trends, several types of conservative practices are adopted to check the soil erosion rate with the following of probable zone of MASE. Probable MASE zones have been delineated into four class i.e. low,

3.5 Case Study: Estimation of Mean Annual …

69

Fig. 3.7 Potentiality of MASE distribution in Kangsabati basin: a MASE in 2002 and b MASE in 2016. Source Authors

Basin area (ha)

70,865.3 11,394.4 9662.89 9080.97 7546.67 3289.37 6395.76 2928.88 4888.29 7139.12 7186.16 69,369.5 5160.92 4301.29 6950.7 3183.48 3255.03 6017.51 4198.77 3446.16 3560.29 7707.74

Sub-basin

Khatra 1 Khatra 2 Khatra 3 Raipur 1 Raipur 2 Raipur 3 Raipur 4 Raipur 5 Lalgarh 1 Lalgarh 2 Lalgarh 3 Lalgarh 4 Lalgarh 5 Lalgarh 6 Dherua 1 Dherua 2 Dherua 3 Dherua 4 Dherua 5 Mohanpur 1 Mohanpur 2 Mohanpur 3

0.28 0.1 0.28 0.1 0.32 0.15 0.33 0.3 0.32 0.38 0.34 0.33 0.33 0.32 0.28 0.33 0.29 0.37 0.36 0.35 0.36 0.34

2002–2016

2016 4955 4795 4941 5183 5283 5772 5691 6068 6037 6318 6426 5923 6345 6103 6645 6256 7239 8075 6836 7748 7022 7140

2002

4625 5177 5737 5700 5536 5823 5616 5773 5589 5692 5276 2531 4528 3489 4818 3760 5988 7588 5124 6947 5606 6224

K

R

1.49 0.69 1.2 0.89 1.01 0.72 1.02 0.6 0.62 0.52 0.56 1.1 0.66 0.64 0.47 0.54 0.52 0.62 0.57 0.71 0.63 0.6

2002–2016

LS

Table 3.2 RUSLE parameter and soil loss in twenty seven sub basin during 2002 and 2016

0.23 0.2 0.26 0.31 0.27 0.3 0.25 0.26 0.26 0.23 0.27 0.26 0.26 0.23 0.29 0.24 0.29 0.26 0.25 0.28 0.21 0.23

2002

C

0.25 0.27 0.23 0.29 0.25 0.3 0.24 0.26 0.28 0.23 0.24 0.21 0.23 0.21 0.24 0.23 0.27 0.27 0.21 0.24 0.2 0.25

2016 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

2002–2016

P

86.01 3.213 11.02 4.835 9.609 2.291 7.81 2.651 4.918 6.297 6.767 83.98 4.519 2.488 4.235 1.707 3.148 9.056 3.848 5.79 3.303 7.102

92.87 3.36 8.737 3.816 9.726 2.105 8.388 2.796 5.55 7.008 6.941 65.58 5.079 3.7 5.199 2.763 3.477 10.98 4.062 4.838 3.523 9.123 (continued)

Soil loss (t/ha−1/ y−1) 2002 2016

70 3 Fluvial Sediment Budget and Mining Impact …

Basin area (ha)

Mohanpur 4 5836.09 Mohanpur 5 6726.76 Mohanpur 6 2386.21 Mohanpur 7 7328.8 Kapastikri 1 15,975.4 Data source Calculated by the authors

Sub-basin

Table 3.2 (continued)

0.37 0.35 30 0.3 0.33

2002–2016

2016 6569 6144 5425 5019 5218

2002

5638 5617 5136 4883 4989

K

R

0.59 0.53 0.69 0.5 0.47

2002–2016

LS

0.23 0.3 0.23 0.27 0.27

2002

C 2016 0.22 0.29 0.24 0.26 0.23

1 1 1 1 1

2002–2016

P

5.565 7.162 1.945 5.347 10.99

5.978 7.444 2.246 4.992 9.677

Soil loss (t/ha−1/ y−1) 2002 2016

3.5 Case Study: Estimation of Mean Annual … 71

Khatra 1 Khatra 2 Khatra 3 Raipur 1 Raipur 2 Raipur 3 Raipur 4 Raipur 5 Lalgarh 1 Lalgarh 2 Lalgarh 3 Lalgarh 4 Lalgarh 5 Lalgarh 6 Dherua 1 Dherua 2 Dherua 3 Dherua 4 Dherua 5 Mohanpur 1 Mohanpur 2 Mohanpur 3

Sub-basin

0.173 0.188 0.235 0.236 0.187 0.264 0.225 0.258 0.216 0.17 0.15 0.131 0.227 0.26 0.214 0.244 0.192 0.206 0.271 0.195 0.267 0.176

2002

2002–2016

2 0.48 0.88 0.54 0.89 0.29 0.75 0.6 0.5 1.08 1.15 3.8 0.98 0.93 0.75 1.11 0.81 0.84 1.07 0.82 0.76 1.03

SDR

Travel time (m)

0.11 0.21 0.26 0.24 0.24 0.239 0.27 0.218 0.22 0.156 0.13 0.11 0.19 0.23 0.2 0.21 0.17 0.18 0.25 0.16 0.23 0.17

2016 14.91 0.603 2.585 1.139 1.799 0.604 1.758 0.685 1.065 1.073 1.017 11.02 1.026 0.646 0.906 0.417 0.606 1.867 1.043 1.13 0.881 1.252

10.22 0.706 2.272 0.916 2.334 0.503 2.265 0.61 1.221 1.093 0.902 7.214 0.965 0.851 1.04 0.58 0.591 1.977 1.016 0.774 0.81 1.551

Mean SY (t/ha−1/ y−1) 2002 2016

Table 3.3 SDR and SY in twenty seven sub basin during 2002 and 2016

27.9 1.13 4.84 2.14 3.37 1.13 3.29 1.28 1.99 2.01 1.91 20.7 1.92 1.21 1.7 0.78 1.14 3.5 1.96 2.12 1.65 2.35

2002

SY (%)

22.55 1.558 5.015 2.022 5.153 1.111 5 1.346 2.696 2.413 1.992 15.93 2.13 1.879 2.296 1.281 1.305 4.364 2.242 1.709 1.789 3.424

2016 1,056,686.3 6876.1672 24,978.646 10,346.625 13,579.122 1987.7187 11,242.907 2006.7743 5204.0074 7658.8883 7308.3605 764,758.7 5297.2769 2780.7069 6298.6524 1326.6296 1971.9704 11,232.583 4380.83 3894.7376 3136.7177 9651.6244

Total yields (t/ha−1/y−1) 2002 723,963 8040.88 21,949.5 8315.81 17,615.8 1655.1 14,484.6 1785.41 5969.11 7804.29 6484.03 500,428 4980.38 3660.48 7227.33 1847.19 1924.24 11,896.4 4263.87 2667.58 2884.92 11,953.9 (continued)

2016

72 3 Fluvial Sediment Budget and Mining Impact …

2002–2016

Travel time (m)

Mohanpur 4 0.55 Mohanpur 5 0.53 Mohanpur 6 0.61 Mohanpur 7 2.07 Kapastikri 1 0.93 Data source Calculated by the authors

Sub-basin

Table 3.3 (continued)

0.184 0.171 0.298 0.194 0.133

2002

SDR

0.17 0.15 0.29 0.17 0.13

2016 1.022 1.226 0.58 1.037 1.46

1.016 1.117 0.651 0.849 1.258

Mean SY (t/ha−1/ y−1) 2002 2016 1.91 2.3 1.09 1.94 2.74

2002

SY (%)

2.243 2.465 1.438 1.873 2.777

2016 5961.6446 8250.305 1384.5979 7600.2331 23,319.192

Total yields (t/ha−1/y−1) 2002 5930.49 7510.63 1554.55 6219.46 20,096.2

2016

3.5 Case Study: Estimation of Mean Annual … 73

74

3 Fluvial Sediment Budget and Mining Impact …

Plate 3.2 Field photography of soil loss in Lalgarh segment: a soil erosion across the rill and gully, b Mass failure across the bank margin. Source Authors

moderate, high and very high erosion probability zone based on soil erosion intensity. In terms of sub basin level, most of the probable MASE rates occurred mainly in two sub-basins of Khatra-1 and Lalgarh-4 SB where total amount soil loss is 169.98 ton/ha/yr (56%), while rest of sub-basins are produces as 135 ton/ha/yr (44%) of soil loss in the year of 2002 (Table 3.2). In contrary, twenty five sub-basins are produces least erosion rate as 48% (141 ton/ha/y) whereas Khatra-1 and Lalgarh-4 both are produced the higher erosion rate as 52% (158 ton/ha/yr) in the year of 2016 (Table 3.3).

3.5.3

Relation Between Soil Erosion with Land Use/Land Covers (LULC) and Basin Area

Due to lack of previous records data and unavailability of sediment discharge station, RUSLE model is justified by two ways i.e. land use wise MASE and basin area wise soil loss estimate.

3.5.3.1

Land Use Based MASE at Sub-basin Level

Soil loss in existing LULC represents fluctuation scenario of loss in every sub-basin (Bhattacharya et al. 2020b). Lower erosion is seen in dense forest areas (0.95%) and water body (0.006%) during 2016 (Fig. 3.8b). Overall maximum erosion is shown in second crop practice (36.75%), in contrary water body restricted to erosion or no erosion take place during 2002. Maximum MASE is found in double cropped area (28.82%) and lower loss is associated with water body (3.73%). Barren land with single crop areas are more dominant in Khatra-1 and Lalgarh-4 SB (42%; 56%), but water body totally resist the MASE (0%) in these two sub-basin during 2002 (Fig. 3.8a). As a result, two sub-basins (Khatra-1 and Lalgarh-4) have maximum

3.5 Case Study: Estimation of Mean Annual …

a

75

27 25 23

Dense Forest

Sub Basins

21

Degraded Forest

19 17 15

Single Crop

13 11

Double Crop Baren land

9 7 5

selement

3 1

waterbody

0

20

40

60

80

100

% of Landuse & Landcover

Sub Basins

b

27 25 23 21 19 17 15 13 11 9 7 5 3 1

Dense Forest Degraded Forest Single Crop Double Crop Baren land selement waterbody

0

20

40

60

80

100

% of Landuse & Landcover

Fig. 3.8 Spatial distribution of soil loss in different LULC at sub-basin level: a during 2002, b during 2016. Source Authors

MASE. Trend line of LULC associated with soil loss varies from double crop practice (2002) to barren land (2016) in Khatra-1 and Largarh-4 SB. On the other hand, degraded forest land has been extensively grown up from 12.20% (2002) to 15.84% (2016) due to expanded of settlement area (2.37%) and single crop area (1.23%) in the rests sub-basin during last twelve years. Therefore, it may be pointed that degradation of forest plays a significant role to initiate the higher rate of MASE with the increases of barren land and double crop practice area.

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3.5.3.2

Estimated of Basin Area Based Soil Erosion at Sub-basin Level

Another important parameter of basin area is used for justification of RUSLE model, which has established a positive linear relationship (r = 0.983 in 2002, r = 0.948 in 2016) with basin area (Fig. 3.8a, b). Largest sub-basin area among twenty seven sub-basins are occupied in Khatra 1 SB (708.65 km2) and Lalgarh 4 SB (693.69 km2). Substantial result showed that MASE is estimated very high in Khatra 1 SB (92.87 ton/ha/yr) and Lalgarh 4 SB (65.58 ton/ha/yr). On the other hand, least amount of MASE in Mohanpur 6 SB (23.86 km2) and Raipur 5 SB (29.28 km2) are seen of 2.24 and 2.79 ton/ha/yr (Fig. 3.9a, b). Therefore, most of the area in Kangsabati basin has not achieved in any conservation practice (P factor-1) to protect the MASE especially Khatra, Lalgarh, Raipur sub-basin.

3.6

Sediment Delivery Ratio (SDR) and Sediment Yield (SY)

SDR is other important parameters for the estimation of SY, which is measured the amount of sediment transport in all tributaries under a drainage basin using Sediment Delivery Distribution model (SEDD) (Rajbanshi and Bhattacharya 2020). Delivery ratio is measured by computation of SY from the taken of cross section in a stream to gross erosion from delivery point at the upper catchment in a watershed (Julien 2010). Generally, delivery outlets in alluvial tropical drainage basin are carried out the eroded sediment from upper catchment and then accumulated into main channel; meanwhile SY gradually increases near the outlets (Bhattacharya et al. 2020a; Rajbanshi and Bhattacharya 2020). In contrary, magnitude of SY accumulation is lower order than MASE rate from hill slope in terrain surface (Edwards 1993). Maximum sediment delivery occurs in shortest travel distance

100

a

90

90

70

80

60

70

SOIL LOSS(ton/ha/yr)

SOIL LOSS(ton/ha/yr)

100

y = 0.001x - 2.115 R² = 0.983

80

50 40 30 20

Area*mean A(ton/ha/yr) Linear (Area*mean A(ton/ha/yr)

60 50 40 30 y = 0.001x - 1.314 R² = 0.948

20 10

10

0 0

10000 20000 30000 40000 50000 60000 70000 80000

BASIN AREA(ha)

b

0 0

10000 20000 30000 40000 50000 60000 70000 80000

BASIN AREA(ha)

Fig. 3.9 Relationship between basin area and soil loss: a 2002, b 2016. Source Authors

3.6 Sediment Delivery Ratio (SDR) and Sediment Yield (SY)

77

along the small stream bed whereas long travel distance along the stream bed does not carried out huge sediment delivery in a drainage basin (Wu et al. 2013; Fernández-Raga et al. 2017; Thomas et al. 2018). Stream length reflects the intensity of sediment delivery in response of sediment supply; furthermore, SDR has direct or indirect relation with soil erosion. In spite of soil erosion, several factors like existing LULC patterns, soil properties and its complex response on land surface including hydrological parameter of rainfall intensity and its duration are influenced on SDR (Magesh and Chandrasekar 2016; Rajbanshi and Bhattacharya 2020). In contrary, areal coverage of catchment area is a major controlling factor in the supplies of sediment from different delivery outlets. Following the contribution of several hydraulics and landscape parameters; catchment area, slope steepness and its length, and existing LULC are taken for estimation of SDRn throughout the basin (Wu et al. 2013). SDR is determined by estimating ß coefficient and travel time (ti) following Ferro and Minacapilli (1995), they are proposed the function on travel time of overland flow within a grid cell in each sub-basin. SDRi ¼ expð tiÞ

ð3:6Þ

where ti = travel time (hr) for cell I and ß = basin-specific parameter. Methodological flow chart represents the several steps to obtain the SDR and SY at sub-basin level (Fig. 3.10).

3.7

Case Study: Assessing of Sediment Delivery Ratio (SDR) and Sediment Yield (SY) at Sub Basin Level of Kangsabati Basin—A Case Study

Source of sediment along the river bed is based on sediment delivery rate than sediment transport in each segments due to gradation of sediment particles diameter does not similar along the Kangsabati River bed, while number of mining sites are set up across the delivery point on each segment. Moreover, after the construction of Mukutmonipur dam, sediment transport is restricted from Kansai and Kumari to downward of Kangsabati River. However, maximum bed sedimentation has been identified near the mouth of delivery outlets. In this context, SEDD is employed to estimate the SDR from every delivery outlets of twenty seven sub basins, subsequently SEDD and RUSLE both are applied to determine the SY in every sub-basin near the channel segment using annual average of delivery rate and soil loss rate in the year of 2002 and 2016.

3 Fluvial Sediment Budget and Mining Impact …

78

DEM TOPOSHEET

FLOW ACCUMULATION

SATELLITE IMAGE

FILL & FLOW DIRECTION

^ 0.5 FLOW

a COEFFICIENT

LENGTH SLOPE

(li )

WATERSHEDES EXTRACTION FLOW VELOCITY

(vi )

/3600 β COEFFICIENT

TRAVEL TIME (ti )

SDR= exp (-β × ti)

Soil Erosion (Ai)

SEDIMENT YEILD = SDRi × Ai

Fig. 3.10 Flow chart of SDR model. Source Modified by authors, based on Bhattacharya et al. (2020b)

3.7.1

Estimation of SDR Factors

3.7.1.1

ß Coefficient and Travel Time (ti)

Flow velocity including its travel distance is the prime factor for the determination of runoff travel time from a grid cell to another grid cell under a drainage basin (USDA-SCS,8 1975). Flow path direction from one cell to another cell is measured by grid based GIS analysis, which is directly assess with the taking of eight direction pour-point algorithm from delivery outlets in each segments of a nearest

8

USDA-SCS (1975) stated the United States Department of agriculture, soil conservation service.

3.7 Case Study: Assessing of Sediment Delivery Ratio (SDR) …

79

Fig. 3.11 Travel time in Kangsabati basin. Source Modified by authors, based on Bhattacharya et al. (2020a)

stream channel (Fernandez et al. 2003; Bhattacharya et al. 2020a). Travel time of a channel is measured by following equation (Jain and Kothyari 2000). ti ¼

np   X li i¼1

vi

ð3:6:1Þ

where li = segment length denotes I under the flow path of m which is equivalent to the side length or oblique distance of a cell; vi = cell wise flow velocity (m/s). In Kangsabati basin, travel time is calculated through the using a specific flow path where channel flow needed time to reach form I cell to neighbour channel traverses under Np cell (Fig. 3.11). According to US Soil Conservation Service, flows velocity entirely depended on land slope direction and existing LULC patterns (SCS 1975; Fernandez et al. 2003).

3.7.1.2

LULC (à Coefficient)

Different coefficient values (à) are estimated from various LULC patterns, which are helps to determine the value of overland flow and shallow concentrated flow (Fernandez et al. 2003). According to Haan et al. (1994), coefficient values (à) of overland flow in different LULC are: forest (0.76 m/s), contour (1.56 m/s), strip

3 Fluvial Sediment Budget and Mining Impact …

80

cropping (1.56 m/s), short grass (2.13 m/s), straight row cultivation (2.62 m/s) and paved surface (6.19 m/s), whereas coefficient values (à) of shallow concentrated flow in different LULC are: alluvial fans (3.08 m/s), grassed waterways (4.91 m/s) and small upland gullies (6.19 m/s), (Fig. 3.12a, b), respectively.

3.7.1.3

Slope Factor (si)

Travel time of a channel flow to reach the nearest channel reflected by flow length along a stream bed that is directly controlled by slope direction (Fernandez et al. 2003; Bhattacharya et al. 2020a). In contrary, slope factor play crucial role to determine the several coefficient values of existing LULC patterns (Rajbanshi and Bhattacharya 2020). Minimum grid cell slope including a small value helps to estimate the slope factor in response to travel time of a channel flow (Maidment et al. 1996). Slope value in Kangsabati basin has been considered only 0.3% (Bhattacharya et al. 2020a).

3.7.1.4

Flow Velocity (vi)

In term of relationship amongst the existing LULC patterns, flow velocity has been classified into two different types of overland flow and shallow channel flow (Haan et al. 1994). This value is calculated through the following information as given by USDA-SCS-TR-559 (1975). Vi ¼ disi1=2

ð3:6:2Þ

where si = slope of cell i (m/m); di = a coefficient for cell i dependent on surface roughness characteristics (m/s). di is also correlated with à coefficient based on land use and land cover wise overland flow and shallow concentrated flow (Fig. 3.13a, b).

3.7.1.5

Length of Segments (li)

Based on flow direction in the cell, length of segment (li) is measured by the taking of segment length (i) along the flow path (m) under a drainage network, which has equal length in respect to side length or diagonal length of a cell (Fernandez et al. 2003; Bhattacharya et al. 2020a) (Fig. 3.14).

9

USDA-SCS-TR-55 (1975) means Technical Release 55 Urban Hydrology for measuring the storm runoff volume, discharge peak rate etc. in a small watershed (1975).

3.7 Case Study: Assessing of Sediment Delivery Ratio (SDR) …

81

Fig. 3.12 Surface roughness in Kangsabati basin: a 2002; b 2016. Source Modified by authors, based on Bhattacharya et al. (2020a)

82

3 Fluvial Sediment Budget and Mining Impact …

Fig. 3.13 Flow velocity in the entire basin: a 2002 and b 2016. Source Modified by authors, based on Bhattacharya et al. (2020a)

3.7 Case Study: Assessing of Sediment Delivery Ratio (SDR) …

83

Fig. 3.14 Flow length of all sub tributaries. Source Modified by authors, based on Bhattacharya et al. (2020a)

3.7.1.6

Basin Specific Parameter (ß)

Inverse modelling approach is applied to derive the basin specific parameters of ß values from morphometric database of a watershed (Ferro and Minacapilli 1995). In this context, SDR (SDRi) is determined from basin specific parameters as ß value in a watershed. Weightage mean value of SDRi is calculated from following equation (Fernandez et al. 2003). SDRw ¼

PN i¼l

. exp½tili0:5 s2i ai R N

i¼l

li0:5 si2 di

ð3:6:3Þ

where N = total number of cells over the watershed; li = length of cell i along the flow path; si = slope of the cell and ai = area of the cell. Field database helps to estimation of SDRw value in each sub-basin in respect to basin morphometric properties like stream order, drainage density, soil type and catchment coverage (Corbitt 1990). In contrary, negative relation has been established when drainage area gradually increases due to reduces of SDR (Fernandez et al. 2003). In term of validation, Vanoni model is applied to determine the relationship between estimated SDRi values and field database.

84

3 Fluvial Sediment Budget and Mining Impact …

SDRw ¼ k ðawÞc

ð3:6:4Þ

where k and c represents dimensionless empirical coefficient values; aw = watershed area (m2). After the getting SDRw values, ß coefficient can be predicted following Eq. 3.6.4. The annual specific SY in each grid cell is estimated by multiplying of SDR and soil loss erosion, which helps to identify sediment source region in a watershed area (Rajbanshi and Bhattacharya 2020; Bhattacharya et al. 2020a). YS ¼ SDRi  Ai  ai

ð3:6:5Þ

where YS = sediment yield; SDRi = SDR; Ai = mean annual soil loss; ai = area of the cell. Spatial distribution of SY demonstrated that sediment accumulation rate influences the sediment source at sub-basin level, where each sub-basin separately contributed with the two different processes of detachment of soil particles and its delivery along a stream flow path from upper catchment to nearest channel segment at outlet points.

3.7.2

Delineation of Sediment Delivery Ratio (SDR)

Spatial distribution of SDR in this basin ranges from 0 to 0.999 during 2002 and 0.00014 to 1 during 2016 (Fig. 3.15a, b). Average value of SDR in the year of 2002 and 2016 are found 0.21 and 0.20 in all grid cell, in particular, settlement is supplied maximum delivery of 0.34 whereas dense forest restricted this delivery meanwhile amount become reduces of 0.049 in the entire basin. Mean annual SDR is estimated at outlet points in every sub-basins, which are well fitted with global simulated value (R = 0.094) following Vanoni equation (1975). Delivery ratio results demonstrated that surface roughness and dense forest covers both are reduces the flow velocity as well as extend the travel distance, in contrary, plain surface including settlement patch has comparatively higher rate of delivery ratio caused by present of open land surface and impervious rock strata (Kidane et al. 2019; Aneseyee et al. 2020). Therefore, delivery ratio has strong potential integration with storing and transportation of eroded soil particles in every sub-basin (Dai and Tan 1996).

3.7.3

Potential Annual SDR at Sub-basin Level

Delivery ratio becomes more fluctuated at sub-basin level in tropical plateau basin (Bhattacharya et al. 2020a). Tables 3.2 and 3.3 both are showed that maximum

3.7 Case Study: Assessing of Sediment Delivery Ratio (SDR) …

85

Fig. 3.15 Sediment delivery ratio (SDR): a 2002, b 2016. Source Modified by authors, based on Bhattacharya et al. (2020a)

3 Fluvial Sediment Budget and Mining Impact …

86

ratio is observed in Mohanpur-6 SB (0.29) and minimum ratio is observed in Khatra-1 and Lalgarh-4 SB (0.11) between the year 2002 and 2016. On the other hand, mean soil loss is inversely related with SDR in the twenty seven sub-basins. Maximum number of stream order in Khatra-1 (1409) and Lalgarh-4 (1649) restricted the delivery ratio, while least number of stream order supplies huge SDR in Mohanpur-6 SB (11). In order of stream number, most of the delivery outlets in Mohanpur and Kapastikri sub-basins are adjoining across the main channel that mean huge sediment deposition occurs in the channel bed. In spite of higher drainage frequency, absence of adjoining drainage outlet points across the channel restricted sedimentation or delivery ratio in channel bed of Khatra and Lalgarh sub-basins, respectively (Plate 3.2b). This result is validated by Fernandez et al. (2003) and Bhattacharya et al. (2020a). Moreover, this result demonstrated that trend of SDRi entirely dependent on morphometric properties of drainage basin. In spite of basin morphometric influence, LULC patterns play crucial role in the spatial distribution of SDR at sub-basin level in response to particle detachment and its movement towards the outlet points near the channel (Kidane et al. 2019; Aneseyee et al. 2020). Figure 3.16a, b demonstrated that maximum delivery concentrated in water body, settlement, double crop and barren land sites whereas least amount of delivery ratio is observed in dense forest and degraded forest sites. Average delivery ratio drastically reduces in barren land and water body, but these values are increases in degraded forest, double crop and settlement during the period between 2002 and 2016. In this context, sediment delivery gradually increases in the entire Dherua, Mohanpur and Kapastikri sub-basins due to expanding of degraded forest, double crop and settlement area, but delivery ratio gradually decreases in Khatra-1 and Lalgarh-4 SB due to low areal coverage of settlement and double crop. On the other hand, dense forest covers and single crop both are helps to restricted the delivery ratio through the lengthening of travel distance to reach the nearest channel. Therefore, two dominant parameters of basin morphometric setup and existing LULC patterns are influenced on the sediment delivery ratio in a catchment area.

3.7.4

Validation of SDR

3.7.4.1

Validation Using Drainage Area

During 1950, most of researcher established that SDR is highly correlated with basin area following the basin morphometric properties along with existing LULC patterns. This relationship denoted that large drainage area in a watershed have lowest delivery ratio, and small drainage area has highest delivery ratio. According to Julien (2002), chances of sediment trapping are high in large drainage area; as a result, delivery ratio gradually decreases across the channel; whereas small drainage area has low chance to trapped sediment. On the other hand, according to Boyce (1975), analyses of SY accumulation at sub-basin level is entirely dependent on

3.7 Case Study: Assessing of Sediment Delivery Ratio (SDR) …

87

SUB BASIN

a 27

25 23 21 19 17 15 13 11 9 7 5 3 1

Baren land Double crop Single crop Degraded Forest Dense Forest selement waterbody

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

LULC%

SUB BASIN

b

27 25 23 21 19 17 15 13 11 9 7 5 3 1

Baren land Crop 2 Crop1 Degraded Forest Dense Forest selement waterbody

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

LULC%

Fig. 3.16 Spatial distribution of SDR in different LULC at sub-basin level: a during 2002, b during 2016. Source Authors

SDR and drainage area. These dominant parameters of SY has a strong relationship that is measured by power function following equation as SDR ¼ 0:41Atð0:3Þ

ð3:7Þ

SDR result is validated by using of three different model predicted results of Vanoni (1975), exposed model of USDA (1972) and Boyce (1975), respectively. According to Vanoni (1975), correlation values of that relation are 0.093 and 0.094 using SDR power function (SDR = 1.7935–0.14191 log A). On the other hand, correlation value of exposed model of USDA (1972) is 0.094. In order of respective correlation values, our study gives the acceptable correlation value of 0.040 (Fig. 3.17a, b). Therefore, it can be said that our model result is well fitted with validated model results given by Boyce, Del Vanoni and Exposed model proposed by USDA.

3 Fluvial Sediment Budget and Mining Impact …

88

1.00

a

10

100

1000

1

b

1

10

100

1000

SDR

SDR

1

0.1 0.10

DRAINAGEAREA

DRAINAGEAREA

Fig. 3.17 Validation of SDR following model results: a SDR validated in 2002, b SDR validated in 2016. Source Authors

3.7.4.2

Validation Using Topographical Factors

According to Williams and Berndt (1972), topographic characteristic including slope variability along channel bed play crucial role to determine the SDR using following equation: LogðSDRÞ ¼ 0:627SLP0:403

ð3:8Þ

Value of slope factor (Sl) in this basin is justified by positive (R = 0.001) linear relationship with the SDR.

3.7.5

Delineation of SY

Based on MASE and SDR quantity at sub basin level, the average annual SY of all pixel cells in studied basin is accounted as 1.98 ton/ha/yr. Total quantity of SY decreases from 53.36 ton/ha/y (2002) to 45.30 ton/ha/y (2016) (Fig. 3.18a, b). Yield potentiality in Kangsabati basin has been categorised into five classes i.e. very high (16–20 ton/ha/y), high (11–15 ton/ha/y), medium (5–10 ton/ha/y), low (1–5 ton/ha/y) and very low (0–1 ton/ha/y), respectively. Spatial distribution of SY demonstrated that 14 SB out of 27 SB falls under very low delivery ratio (0–1), but high ratio only concerned in Khatra-1 and Lalgarh-4 SB. All those factors of

3.7 Case Study: Assessing of Sediment Delivery Ratio (SDR) …

89

Fig. 3.18 SY distribution in study area: a SY during 2002, b SY during 2016. Source Modified by authors, based on Bhattacharya et al. (2020a)

90

3 Fluvial Sediment Budget and Mining Impact …

Plate 3.3 Field photography of SY: a delivery outlet across the Mohanpur; b SY across the Lalgarh. Source Authors

RUSLE and SDR have relatively uniform values across the all sub-basins, which have similar impacts in the entire basin. Although LS (0.7) and C (0.24) are considered as more dominant factors on soil loss among 27 sub-basins, and these factors have positive relation (LS, r = 0.517) and negative relation (C, r = 0.017) with soil erosion.

3.7.6

Potential Annual SY at Sub-basin Level

Potential mean annual SY is more fluctuated at sub-basin level in Kangsabati basin (Bhattacharya et al. 2020a). Spatial distributions of Potential mean annual SY showed that Lalgarh-4 (500,427.66 ton/ha/yr), Khatra-1 (723,962.98ton/ha/yr) are highest SY deposited sub-basins, whereas Dherua-2 (1847.19 ton/ha/yr) and Mohanpur-5 (1554.55 ton/ha/y) are lowest SY deposited sub-basins in the entire basin (Tables 3.2 and 3.3). In spite of maximum sediment delivery from Khatra-1, due to construction of Mukutmonipur dam (1958), supplied sediments are trapped by dam, as a result, sediment transport are cut off from upper segments to down segments of Raipur, Lalgarh and Dherua etc. On the other hand, large supply from Lalgarh basin and cumulative holding of sediment from other sub-basins both are helps to huge sedimentation towards the downstream of Kangsabati River (188,722 ton/ha/y) (Plate 3.3a, b).

3.8

Sink of Sediment Budget

Sediment sinks are included mainly sediment concentration on reservoirs, channel bed with floodplain, sediment transport towards the downstream and ocean, and sediment removal in defined river. In recent past, several human activities like dam

3.8 Sink of Sediment Budget

91

construction, water lifting through irrigation and extraction of sediments from channel bed are directly or indirectly changes of sediment sinks as well as budget including hydrodynamics regime through the initiating of inverse channel morphological responses caused by flow shifting and sediment transport (Nabegu 2014; Creech et al. 2015). Sand and gravel mining is one of them that generated critical to sub critical or turbulence/hungry water flow10 to interrupt the bedload transport and flow characters, as a result hydraulic stability continuously deteriorated over the river. Huge extraction of sediment from instream and floodplain than natural replenishment created new structural knick points to initiate the reversal toposequence process in terms of pool-riffle alteration under the guidance of hungry water flow. Moreover, sand mining induced hungry water flow drastically accelerated the river bank erosion as well as other consequences due to lack of sediment transport. Many researchers successfully analysed the impact of sand mining and its consequences on stream hydraulics, sediment characterization, channel morphology, river ecology and ground water regime (Leopold and Miller 1956; Kondolf 1994a, 1994b, 1997; Rovira et al. 2005; Rinaldi et al. 2005; Nabegu 2014; Ghosh et al. 2016).

3.9

Case Study: Assessing of Sediment Sink and Sediment Budget in Kangsabati River

After the construction of Mukutmonipur dam (1958) at the confluence point of Kansai and Kumari, huge sediments are deposited on the bed caused by drastically fall down of channel gradient along the downstream in one hand, and sediment connectivity between upstream and downstream of dam is cut off along with huge siltation occurred in other hand. Simultaneously, massive sand extraction is started from channel bed and floodplain sites of longitudinal course in the Kangsabati River (Bhattacharya et al. 2019a, b). Intensive and quarrying activities have been identified along the Lalgarh, Mohanpur, Panskura and Kapastikri segments where removal of sediment beyond the carrying capacity of supplying sand. Based on long-term and short-term mining database, major mining spots are situated in Raipur, Sarenga, Bikrampur, Kankabati, Manidhaha, Barkola, Debangai, Charipal, Ballavpur and Jinsahor etc. including seventy four sites are situated along the upper, middle and lower course, respectively. This section is tried to estimate the amount of mining under the sink category, and sediment budget analysed to quantify the extract rate from instream and floodplain sites in respect of sediment replacement of different segments.

Hungry water flow is one kind of sub-critical flow generated from sediment starvation channel flow along the extracted channel bed.

10

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92

3.9.1

River Sand Mining in Kangsabati River

In Kangsabati River, sediment transport and its connectivity relatively interrupted across the break of slope in the entire channel; thus maximum sedimentation has been occurring on channel bed and floodplain sites (Mittal et al. 2016; Bhattacharya et al. 2020c). At that time, sand mining activities are started from two different sites i.e. instream and floodplain sand (Plate 3.2a, b). Instream sand mining practices stated the quarrying of sediment from instream channel bed whereas floodplain mining denotes extraction of sediment from floodplain riparian site and bank margin (Ghosh et al. 2016). Floodplain mining is more dominant along the middle course where Peak flow of water leads to huge sedimentation across the floodplain site during flood season.

3.9.1.1

Instream Sand Mining

An annual average quantity of sand as 2,091,587.728 ton is extracted from seven segments of Kangsabati River during 2002–2016. Based on District Land Revenue office (DL and LRO) of Paschim Midnapore and Bankura registered records, a major share of 549,984.61 ton/y sand come from quarrying sites as Lalgarh, Mohanpur and Kapastikri segments whereas rest segments are shared only 12,167,039 ton/y sand quarrying from Raipur, Dherua, Panskura and Rajnagar segments, respectively (Fig. 3.19a–e). Sediment budget flowchart presented the instream sand mining status in every segment, where Khatra segment has only one registered mining sites due to presence of rocky out crop but Mohanpur segment individually contributed maximum amount of sand as 313,617.71 ton (48%) (Fig. 3.20a, b). Moreover, maximum number of illegal mining sites and unregistered short term mining are gradually increases throughout the channel, which is a cause of major hindrance to accurately assess of sediment budget.

3.9.1.2

Floodplain Sand Mining

Based on registered long term mining lease records (DL and LRO, Bankura and Paschim Mednipur), only Mohanpur segment contributed as 1,503,432.12 ton of sand from floodplain sites during 2008–2014 (Fig. 3.20a). Most of the floodplain sand mining practice has been identified in Mohanpur segment. Abruptly changes of bed slope and huge sediments supply from entire sub-basins of Dherua and Mohanpur basin, both are allowed to build up the floodplain mining. Moreover, enough requirement of construction sector involving real estate business in fast developing nerve centres of Midnapore and Kharagpur town, bringing out the more economic viability of sand. To ensure the huge consumption in this segment, technical development of extraction process effectively supported using of JCP, mechanical diesel pump.

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93

a

c

b

d

e

Fig. 3.19 Sand mining sites in different segments along the Kangsabati river: a Raipur, b Lalgarh, c Dherua, d Rajnagar and e Panskura segment. Source Prepared by authors, based on DL & DLR, 2010–2015

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3 Fluvial Sediment Budget and Mining Impact …

Fig. 3.20 Sand mining sites along the Kangsabati river: a Mohanpur, b Kapastikri. Source Prepared by authors, based on DL & DLR, 2010–2015

3.9.1.3

Shifting of Sand Mining Sites

Sand mining sites are generally developed based on sediment grain size, rate of sediment concentration and sediment transport along the channel bed. In contrary, sediment accumulation in every segment entirely depends on SDR, sediment

3.9 Case Study: Assessing of Sediment Sink …

95

Fig. 3.21 Shifted of registered long term sand mining sites (2002–2016). Source Prepared by authors, based on DL & DLR, 2010–2015

concentration and bed load quantity. Entire stretch of the Kangsabati River is 191 km, where have approximately 74 registered mining sites and 96 mining pits (DL and LRO, Bankura and Paschim Mednipur, 2010–2015). Most of the mining sites are densely associated in Mohanpur segment (31), but other sites are randomly developed in Kapastikri (17), Panskura (9), Rajnagar segments (6), Raipur (6), Lalgarh (3), Dherua (1), Khatra (1), and respectively (Fig. 3.21). In terms of site number, numerous mining sites are developed in Debangai (123,018.87 ton), Barkola (127,317.39 ton) and Manguchak (175,253.52 ton) sites of Mohanpur and Kapastikri segments where annual extraction rate exceeds the natural replenishment over the year. Massive extraction of sediments are made the abandoned of all existing sites due to continuously decreasing of replenishment and cut off the sediment delivery at outlets. Contrastingly, new concentration sites including active delivery outlets are attracted to new set up mining sites, subsequently mining sites are shifted towards there.

3.9.2

Estimation of Sediment Transport (QT)

Sediment transport (QT) in each segment towards the next segment of channel is considered as important sink parameter of sediment budget (Bhattacharya et al.

96

3 Fluvial Sediment Budget and Mining Impact …

2019b). It is fact that QT is not recorded in every river; however field investigation must be required to compute the transport rate. Therefore, in this case study, twenty seven cross sections are selected to collect the all requirement datasets from segments during pre monsoon, monsoon and post monsoon season. Particle size, sediment concentration and water discharge are used to calculate the QT in different segment following Eqs. 5.13 and 5.15 (Ackers-White method 1973). QT is highly observed in Raipur and Lalgarh segment than Dherua, Mohanpur and Kapastikri segments (see 5.3.2.5), but transport rate drastically reduces in Rajnagar and Panskura segment (Figs. 3.22 and 3.23). Therefore, it is said that maximum sediments are transported from upper segment (0.6424 kg/m2/s) than middle (0.05324 kg/m2/s) and lower segment (0.002824 kg/m2/s), but intensive sand mining of middle course breaks up the bed load connectivity between upper and lower course through the huge sediment trapping into pits along the channel bed.

3.9.3

Estimation of Sediment Concentration (X)

Another sink of sediment budget parameter is sediment concentration (X) deriving from specific weight ratio of sediment under dry condition in a flux of sediment mixed water that collected from flowing stream water or stagnant water body (Blanchard et al. 2011). X plays crucial role to estimate the sink of the sediment budget in the entire segment (Creech et al. 2015). Sediment grain size, mean flow velocity and flow discharge are considered for estimating of X using Eq. 5.14 (Ackers and White 1973). In this case study, huge concentrations are found in Raipur, Lalgarh and Dherua segments than Khatra, Mohanpur and Kapastikri segments (Figs. 3.22 and 3.23) (see 5.3.2.6). Despite the location of upper segment, X is very low in Khatra caused by maximum trapping of sediment into Mukutmonipur dam. It is noticed that maximum sediment concentrations are occurred along the all upper segments but sharply drops down along the entire middle and lower segments due to reducing of delivery ratio from sub basin outlets (Bhattacharya et al. 2019b). But large quantity of sand quarrying and numerous mining sites are recorded in the entire middle and lower segments. Therefore, it can be point that over extraction sand mining does not only affected on sediment concentration but also interrupted in the entire sediment budget of defined channel.

3.9.4

Estimation of Sediment Budget in Eight Segments of Kangsabati River

In this case specific study, sediment budget consisted with two main category i.e. sediment source and sink, where source of sediment derived from SY and upstream sediment discharge while sink is associated with sediment transport, concentration

3.9 Case Study: Assessing of Sediment Sink … Source

Transport (ton)

Sink

Extraction (ton)

Khatra

723963.0

732003.86

711323

20096.24

8040.88 21949.50 8315.81

Raiputr

97

17615.80 1655.10

86486.96

37002

14484.59 49484.97

1785.407 14484.59

5969.113 6484.027 500427.7

575150.45

3660.482

1847.19 1924.243

175613.97

2667.581

5930.492 7510.633

191944.63

187771

313618

24007.6

161309

1554.552

4174

Mohanpur

13956.6

Pervious Storage

11953.92

6219.457

20096.24

24270.24

Pervious Storage

262.62

Kapastikri

164222

4263.874

2884.92

Panskura

75058

11896.4

11392

Dherua

7227.334

Rajnagar

426696

4980.385 148454.93

Lalgarh

7804.292

4673.04 230

Pervious Storage 19541.4

32.62

Pervious Storage

Fig. 3.22 Entire sediment budget analysis in Kangsabati basin. Source Authors

98

3 Fluvial Sediment Budget and Mining Impact …

Fig. 3.23 Sediment budget status in the eight segments of Kangsabati River. Source Authors

of sediment and removal of sediment (Figs. 3.22 and 3.23). With the following of segment level, sources of sediment in Khatra segment is only depended on SY deriving from Khatra-2, 3 SB. Quantity of sediment sources is estimated as 732,003 ton/y but sink amount is bifurcated into three categories name as sediment transport (20,680 ton/y), sediment concentration (40.40977453 ton/y) and instream gravel mining, respectively. Despite the least rate of gravel mining, there has no registered mining data; however rest amount of sources are deposited on the bed (558,065 ton/ y). Sediment budget status in this segment indicates as positive sediment bearer sites,11 as a result maximum amount of sediments are delivered to Raipur segment (Fig. 3.22). Source of sediment in Raipur segment come from SY of all Raipur (65,806 ton/y) and Khatra sub-basins (49,484 ton/y) while sink is associated with transport of sediment (49,484 ton/y) through the outgoing of sediment discharge. In Raipur segment, quantity of removal sand is predominated of 15,753,560 ton/y but amount of sediment concentration on river bed (25,483 ton/y) is abruptly fall down. Outcome of sediment sources encouraged to form of enough sandbars (188,777 ton/ y) across the bed to bank side. Budget status presented that Raipur segment is low positive sediment bearer site as quite delivered sediments to Lalgarh segment (Fig. 3.23). All the sub-basins in

11

Positive sediment bearer sites indicates that amount of sediment sources is greater than sediment sink quantity.

3.9 Case Study: Assessing of Sediment Sink …

99

Plate 3.4 Mining induced consequences: a River bank erosion in Mohanpur, b huge sedimentation in Dherua. Source Authors

Lalgarh segment are considered as highest sediment sources with the supplying of huge sediments as 529,325 ton/y. Sources of upstream sediment discharge comes from Raipur segment as 49,484 ton/y while sink quantity is divided into three out ways i.e. downstream sediment transport as 49,484 ton/y, sediment accumulation on channel bed as 25,483 ton/y and removal of sand as 75,058 ton/y. Rest of sediment is deposited across the both bank side (636,946 ton/y) intended to form of sandbar. Budget status predicts that Lalgarh is the high positive sediment bearing segment as well as delivered of enough sediment to Dherua segment (Fig. 3.23). In Dherua segment, sources of sediments depended on inflow sediment discharge and sediment yield at outlets of all sub basins as estimated of 76,644 ton/y while sink is also associated with outflow sediment discharge (1391 ton/y), sediment concentration (25,483 ton/y) and removal of sediment (13,956 ton/y), respectively. Remaining sediments creates massive sandbar either mid channel bar or point bar across the meandering bend (467,291 ton/y) (Plate 3.4a). Budget status of Dherua segment depicted that this is the highest positive sediment bearer segment as well as sources of huge sediment inflow to Mohanpur segment (Fig. 3.23). Sources of sediment is derived from entire Mohanpur sub basins to be almost 38,721 ton/y and upstream sediment discharge of 1391 ton/y, while sink is associated with three out ways i.e. sediment outflow of 1391 ton/y, bed sediment concentration of 8209 ton/y, and instream sediment extraction including floodplain of 313,617 ton/y (highest amount out of eight segments), respectively. Meagre amount of remaining sediment created such dissected point sandbar (123,520 ton/y) across the river bank. Budget assessment revealed that Mohanpur is the highest negative sediment bearer segment12 out of eight segments. There is low remained sediment (1391 ton/y) also leads to arising of several mining induce consequences at large scale (Fig. 3.23). River bank erosion is abruptly accelerated in the entire segment (Plate 3.4b). 12

negative sediment bearer segment indicates that amount of sediment sources is less than sediment sink quantity.

3 Fluvial Sediment Budget and Mining Impact …

100

Kapastikri segment is considered as second highest negative sediment bearer segment of lower course. Sources of sediment in this segment is estimated from SY of 20,096 ton/y and delivered outlets of 1391 ton/y, but Sink of sediment budget is bifurcated into three ways i.e. outflow sediment discharge of 1391 ton/y, bed sediment concentration of 8209 ton/y and removal of sediment as 161,308 ton/y, respectively. Rest of massive finer sediment forms an alluvial fan (454,093 ton/y) at the bifurcation point between Rajnagar segment (Rupnarayan branch) and Panskura segment (Kheliaghai branch) (Fig. 3.23). Moreover, least number of delivery outlets causes of low sediment yield likes Mohanpur segment. Sources of sediment in Panskura segment is completely depended on upstream inflow sediment discharge to be almost 76.904 ton/y, but there has no delivery outlets like previous segments due to absence of sub-basin. In addition, sediment concentration is very low due to lack of water flow except flood season (Fig. 3.23). Contrastingly, excessive sediment extraction (19,541 ton/yr) has been identified on the river bed using dry pit quarrying method except flood season. Therefore, budget assessment predicted that Panskura segment is negative sediment bearing segment facing various hydro ecological consequences such as channel shifting, bed disrupting, lowering etc. In Rajnagar segment of lower course, sediment source is only delivered from upstream sediment inflow. Amount of sediment source is very low due to absence of sub-basin delivery outlets as well as trapping of huge sediment into mining pit of the upper segments. On the other hand, maximum budgetary sink amount is involved mainly instream mining that take place only in riffle section (4673 ton/y) using bar skimming (monsoon season) and wet pit excavation method (dry season) (Fig. 3.23). Based on budget analysis, Rajnagar segment is negative sediment bearing segment which is suffering from mining induced consequences (Bhattacharya et al. 2019a).

3.10

Conclusions

Sediment budget analysis is very much needed to detect the stability status in every channel segment in respect to natural replenishment and sediment extraction along with find out the threshold state either under resilience state or crossed the threshold. If the channel crossed the natural autogenic, then mining induced consequences are cumulatively progressed over the courses. RUSLE and SDR models are employed to estimate the sediment yield for assessing of sediment source under sediment budget while many hydraulic techniques of sediment transport and sediment concentration are used to measure the inflow and outflow of sediment discharge for assessing of sediment sink. Moreover, previous mining records are considered for detecting the sediment removal quantity under sediment sink out ways. RUSLE and SEDD model predicted that sediment transport in each segment does not only rill and inter rill erosion, but also depend on sediment delivery outlets. Results of soil loss, delivery ratio and sediment yield proves that soil loss is positively related (r = 0.975) with sediment yield whereas delivery ratio has

3.10

Conclusions

101

established negative linear relation (r = −0.197) with yield in the entire sub-basins. In order of sediment delivery, maximum sediment supplied from upper catchment sub basins, but upstream dam plays vital role to cut off the sediment delivery from the upper segment; thus bed sediment load continues to drop down with the corresponding of sediment supply from upper sub-basins, respectively. In addition, spatial distribution of annual soil loss denotes that sediment deposition in every segment depends on existing LULC patterns. In this case specific studies along the Kangsabati and its tributaries, it is found that double crop practice, barren land and settlement area etc. are exhibited much greater erosion rates and SY than dense forest. Mean while degraded forest and double cropped farming areas leads to higher rate of mean annual soil loss whereas dense forest and water body resisted the soil erosion. In terms of sediment budget, maximum sediment supply comes from sediment yield to ensure the maximum mining in every segment except lower course segments. Shifting of mining sites denoted that existing mining sites always depends on the sediment delivery ratio. If the delivery ratio is reduced then the existing mining site converted into abandoned sites. Contrastingly, sedimentation on channel bed does not related to inter-course hydraulic status except flood season, but also depends on the delivery ratio as well as sediment yield. Therefore, it can be concluded that maximum mining induced negative consequences are tremendously increases along the negative bearer segments, while all responsible variables of positive sediment bearer segments are stayed under autogenic resilience category over the courses.

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Bhattacharya RK, Chatterjee ND, Das K (2020b) Land use and land cover change and its resultant erosion susceptible level: an appraisal using RUSLE and logistic regression in a tropical plateau basin of West Bengal, India. Environ Dev Sustain 1–36 Bhattacharya RK, Chatterjee ND, Das K (2020c) Sub-basin prioritization for assessment of soil erosion susceptibility in Kangsabati, a plateau basin: a comparison between MCDM and SWAT models. Sci Total Environ 139474. https://doi.org/10.1016/j.scitotenv.2020.139474 Blanchard RA, Ellison CA, Galloway JM, Evans DA (2011) Sediment concentrations, loads, and particle-size distributions in the Red River of the North and selected tributaries near Fargo, North Dakota, during the 2010 spring high-flow event. US Geological Survey Boggs G, Devonport C, Evans K, Puig P (2001) GIS-based rapid assessment of erosion risk in a small catchment in the wet/dry tropics of Australia. Land Degrad Dev 12(5):417–434 Boyce RC (1975) Sediment routing with sediment delivery ratios. Present and prospective technology for predicting sediment yields and sources, pp 61–65 Calle M, Alho P, Benito G (2017) Channel dynamics and geomorphic resilience in an ephemeral Mediterranean river affected by gravel mining. Geomorphology 285:333–346 Corbitt RA (1990) Standard handbook of environmental engineering Cox C, Madramootoo C (1998) Application of geographic information Ysstems in watershed management planning in St. Lucia. Comput Electron Agric 20(3):229–250 Creech CT, Brito Siqueira R, Selegean JP, Miller CJ (2015) Anthropogenic impacts to the sediment budget of São Francisco River navigation channel using SWAT Dabral PP, Baithuri N, Pandey A (2008) Soil erosion assessment in a hilly catchment of North Eastern India using USLE, GIS and remote sensing. Water Resour Manage 22(12):1783–1798 Dai D, Tan Y (1996) Soil erosion and sediment yield in the Upper Yangtze River Basin, pp 191– 203. 111: Proceedings of the exeter symposium erosion and sediment yield global and regional perspectives, International Association of Hydrological Sciences Publication 236 Desmet PJJ, Govers G (1996) A GIS procedure for automatically calculating the USLE LS factor on topographically complex landscape units. J Soil Water Conserv 51(5):427–433 Djoukbala O, Hasbaia M, Benselama O, Mazour M (2019) Comparison of the erosion prediction models from USLE, MUSLE and RUSLE in a Mediterranean watershed, case of Wadi Gazouana (NW of Algeria). Model Earth Syst Environ 5(2):725–743 Edwards K (1993) Soil erosion and conservation in Australia. In: World soil erosion and conservation, pp 147–170 Fernandez C, Wu JQ, McCool DK, Stöckle CO (2003) Estimating water erosion and sediment yield with GIS, RUSLE, and SEDD. J Soil Water Conserv 58(3):128–136 Fernández-Raga M, Palencia C, Keesstra S, Jordán A, Fraile R, Angulo-Martínez M, Cerdà A (2017) Splash erosion: a review with unanswered questions. Earth Sci Rev 171:463–477 Ferro V, Minacapilli M (1995) Sediment delivery processes at basin scale. Hydrol Sci J 40(6):703– 717 Ferro V, Porto P (2000) Sediment delivery distributed (SEDD) model. J Hydrol Eng 5(4):411–422 Frings RM, Gehres N, Promny M, Middelkoop H, Schüttrumpf H, Vollmer S (2014) Today’s sediment budget of the Rhine River channel, focusing on the Upper Rhine Graben and Rhenish Massif. Geomorphology. https://doi.org/10.1016/j.geomorph.2013.08.035 Ganasri BP, Ramesh H (2016) Assessment of soil erosion by RUSLE model using remote sensing and GIS—a case study of Nethravathi Basin. Geosci Front 7(6):953–961 Ghosh PK, Bandyopadhyay S, Jana NC, Mukhopadhyay R (2016) Sand quarrying activities in an alluvial reach of Damodar River, Eastern India: towards a geomorphic assessment. Int J River Basin Manage 14(4):477–489 Grimaud JL, Rouby D, Chardon D, Beauvais A (2018) Cenozoic sediment budget of West Africa and the Niger delta. Basin Res 30(2):169–186 Haan CT, Barfield BJ, Hayes JC (1994) Design hydrology and sedimentology for small catchments. Elsevier Jain MK, Kothyari UC (2000) Estimation of soil erosion and sediment yield using GIS. Hydrol Sci J 45(5):771–786

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Jain SK, Kumar S, Varghese J (2001) Estimation of soil erosion for a Himalayan watershed using GIS technique. Water Resour Manage 15(1):41–54 Jordan G, Van Rompaey A, Szilassi P, Csillag G, Mannaerts C, Woldai T (2005) Historical land use changes and their impact on sediment fluxes in the Balaton basin (Hungary). Agr Ecosyst Environ 108(2):119–13 Julien PY (2002) River mechanics. Cambridge University Press, Cambridge Julien PY (2010) Erosion and sedimentation. Cambridge University Press Kidane M, Bezie A, Kesete N, Tolessa T (2019) The impact of land use and land cover (LULC) dynamics on soil erosion and sediment yield in Ethiopia. Heliyon 5(12):e02981 Kondolf GM (1994a) Geomorphic and environmental effects of instream gravel mining. Landscape Urban Plan 28(2–3):225–243 Kondolf GM (1994b) Environmental planning in regulation and management of instream gravel mining in California. Landscape Urban Plan 29(2–3):185–199 Kondolf GM (1997) PROFILE: hungry water: effects of dams and gravel mining on river channels. Environ Manage 21(4):533–551 Leopold LB, Miller JP (1956) Ephemeral streams-hydraulic factors and their relation to the drainage net (No. 282-A). US Government Printing Office Magesh NS, Chandrasekar N (2016) Assessment of soil erosion and sediment yield in the Tamiraparani sub-basin, South India, using an automated RUSLE-SY model. Environ Earth Sci 75(16):1208 Mahala A (2018) Soil erosion estimation using RUSLE and GIS techniques—a study of a plateau fringe region of tropical environment. Arab J Geosci 11(13):335 Maidment DR, Olivera F, Calver A, Eatherall A, Fraczek W (1996) Unit hydrograph derived from a spatially distributed velocity field. Hydrol Process 10(6):831–844 McCool DK, Brown LC, Foster GR, Mutchler CK, Meyer LD (1987) Revised slope steepness factor for the universal soil loss equation. Trans ASAE 30(5):1387–1396 Mittal N, Bhave AG, Mishra A, Singh R (2016) Impact of human intervention and climate change on natural flow regime. Water Resour Manage 30(2):685–699 Nabegu AB (2014) Morphologic response of a stream channel to extensive sand mining. Res J Environ Earth Sci 6(2):96–101 Nasir N, Selvakumar R (2018) Influence of land use changes on spatial erosion pattern, a time series analysis using RUSLE and GIS: the cases of Ambuliyar sub-basin, India. Acta Geophysica 66(5):1121–1130 Rajbanshi J, Bhattacharya S (2020) Assessment of soil erosion, sediment yield and basin specific controlling factors using RUSLE-SDR and PLSR approach in Konar river basin, India. J Hydrol 124935 Rascher E, Rindler R, Habersack H, Sass O (2018) Impacts of gravel mining and renaturation measures on the sediment flux and budget in an alpine catchment (Johnsbach Valley, Austria). Geomorphology 318:404–420 Renard KG (1997) Predicting soil erosion by water: a guide to conservation planning with the revised universal soil loss equation (RUSLE) Renard KG, Freimund JR (1994) Using monthly precipitation data to estimate the R-factor in the revised USLE. J Hydrol 157(1–4):287–306 Rinaldi M, Wyżga B, Surian N (2005) Sediment mining in alluvial channels: physical effects and management perspectives. River Res Appl 21(7):805–828 Rovira A, Batalla RJ, Sala M (2005) Response of a river sediment budget after historical gravel mining (the lower Tordera, NE Spain). River Res Appl 21(7):829–847 Sahaar AS (2013) Erosion mapping and sediment yield of the Kabul river basin, Afghanistan. Doctoral dissertation, Colorado State University Shit PK, Nandi AS, Bhunia GS (2015) Soil erosion risk mapping using RUSLE model on Jhargram sub-division at West Bengal in India. Model Earth Syst Environ 1(3):28 Thomas J, Joseph S, Thrivikramji KP (2018) Assessment of soil erosion in a monsoon-dominated mountain river basin in India using RUSLE-SDR and AHP. Hydrol Sci J 63(4):542–560

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Toubal AK, Achite M, Ouillon S, Dehni A (2018) Soil erodibility mapping using the RUSLE model to prioritize erosion control in the Wadi Sahouat basin, North-West of Algeria. Environ Monit Assess 190(4):210 Vanoni VA (1975) Sedimentation engineering, ASCE manuals and reports on engineering practice —No. 54. American Society of Civil Engineers, New York, NY Vijith H, Hurmain A, Dodge-Wan D (2018) Impacts of land use changes and land cover alteration on soil erosion rates and vulnerability of tropical mountain ranges in Borneo. Remote Sens Appl Soc Environ 12:57–69 Wang J, Rich PM, Price KP, Kettle WD (2004) Relations between NDVI and tree productivity in the central Great Plains. Int J Remote Sens 25(16):3127–3138 Williams JR, Berndt HD (1972) Sediment yield computed with universal equation. J Hydraul Div 98:12 Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses—A guide to conservation planning. U.S. Department of Agriculture-Agriculture Handbook 537. U.S. Government Printing Office, Washington, DC Wu L, Long T, Liu X, Ma XY (2013) Modeling impacts of sediment delivery ratio and land management on adsorbed non-point source nitrogen and phosphorus load in a mountainous basin of the Three Gorges reservoir area, China. Environ Earth Sci 70(3):1405–1422

Chapter 4

Sediment Grain Size Analysis and Mining Intensity: Estimation by GRADISTAT, G-STAT and LDF Techniques

4.1

Introduction

Characterization of dynamic nature in sediment regime i.e. sediment provenance1, entrainment process, textural pattern and sediment movements have well analyses and easily presented using of grain size distribution in many literature, thus gain size parameters are considered as basic routine in sedimentlogy study (McLaren et al. 2007; Fan et al. 2015; Bhattacharya et al. 2016; Nugroho and Putra 2017; Oyedotun 2020). Textural properties of grain size parameters including mean size, sorting, kurtosis, skewness are taken to determine the statistical measurement of sediment regime in accordance to sedimentary facies with their depositional settings (Francke et al. 2013; Friedman 1961; Oyedotun 2020), and to pathways of infer-sedimentary movement (Folk and Ward 1957; Anthony and Hequette 2007; Oyedotun et al. 2012, Oyedotun et al. 2013; Oyedotun 2016). Scatter graph of sorting, skewness and Kurtosis distribution plotting against mean size (phi value) of particles are helped to discriminate the textural arrangement in different sands i.e. fluvial sands, ocean beach sands, lake sand, dune sands, and to accurately explain the interior properties of sediment regime and modes of deposition process (Folk and Ward 1957; Friedman 1961, 1979; Moiola and Weiser 1968; Bhattacharya et al. 2016; Azidane et al. 2020). Riverine sediments of suspended and bedload along with their ratio are key components for maintaining the river health of the drainage basin (Adhami and Sadeghi 2016; Sadeghi and Kheirfam 2015; Sadeghi et al. 2018). In

1

Sediment provenance revealed the study of characteristics of a sediment source area with the analysis of composition and textural attributes of sediment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. K. Bhattacharya and N. Das Chatterjee, River Sand Mining Modelling and Sustainable Practice, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-72296-8_4

105

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4 Sediment Grain Size Analysis and Mining Intensity …

particularly, suspended sediment2 has crucial role to maintain the ratio of riverine sediments, which has been floating caused by turbulence flow and transported by flow water over the long distance before the deposition at the base level of lower course (Kheirfam and Vafakhah 2015) where landforms dynamic and geomorphic processes are continuously affected (French et al. 2016). Textural arrangement of sediments on river bed has been setup as graded manner i.e. coarse grain of gravel, coarse sand in upper course, medium sand in middle course, and fine grain of finer sand, silt and clay in lower course, respectively. Moreover, finer sediments including silt and clay particles are always waxes by numerous fluvial inlets and dominant sea shore current in fresh water and salt water near estuarine sites along the lower course (Nair and Ramachandran 2002; Bhattacharya et al. 2016). In contrary, stream hydraulics properties of sediment regime such as sediment entrainment, transport, aggregation, deposition and gravitational circulation, and flow regime are guided in accordance to grain size distribution over the course (Wai et al. 2004; Ghoshal et al. 2011; Bhattacharya et al. 2019a, b, c). Moreover, sediment entrainment process is entirely dependent on shear stress and critical shear stress that are driven by grain size distribution; meanwhile sediment motion is initiated during threshold state when shear stress is crossed over the critical shear stress (Montgomery and Buffington 1997; Ritter et al. 2002; Church 2006; Charlton 2007). In term of energy requirement, high energy environment must be required for entraining the coarser sediment but, fine textural sediment entrainment occurred in low energy environment (Mason and Folk 1958; Folk 1966; Friedman 1961, 1967; Moiola and Weiser 1968; Mueller et al. 2005). SGD is intimately related with sorting behaviour during sediment entrainment that means homogenous resistance force generated from well sorted sediment bed, and heterogeneous resistance force initiated from poorly sorted sediment bed (Fig. 4.1). On the other hand, finer particles provided the homogenous resistance force, but coarser particles initiated the heterogeneous resistance force over the river course. Therefore, finer particle size distribution always helps to determine the spatial distribution of homogenous resistance force generating from poorly sorted sediment, while coarser particles distribution identified the well sorted sediment induced heterogeneous resistance force across the channel bed to bank.

4.2

Sand Mining Response on SGD

Human activities like river sand mining has secured to structural displacement of bed sediment, subsequently influences on suspended sediment behaviour throughout the channel (Kondolf et al. 2002; Bhattacharya et al. 2016; Li et al.

Suspended sediment means moving of detachment particles in a flowing fluid by suspension transport mode.

2

4.2 Sand Mining Response on SGD

107

Fig. 4.1 Sediment grain size related with erosion/deposition. Source Authors are digitised from Hjulström-Sundborg diagram (1935)

2016; Wyss et al. 2016; Sadeghi et al. 2018). Thus, sand mining affected sediment transport rates, particles size distribution and other related responses under sediment regime can be noteworthy influences on water quality (Baer et al. 2016; Bhattacharya et al. 2019b), aquatic habitat (Abarca et al. 2017; Bhattacharya et al. 2019b), urban and deltas landforms (Schwartz and Smith 2016), and channel morphology in mined river (Sadeghi and Zakeri 2015; Bhattacharya et al. 2019c). Sediment extraction from channel bed changes the availability of shear stress in mining sites where water flow propagated towards downstream along the bed slope with the expanding of channel steepness and flow depth (Mueller et al. 2005; Church 2006; Charlton 2007). In mined channel, sediment entrainment is initiated with the increasing of critical shear stress caused by alteration of flow resistance due to more fluctuation of sorting parameters like sheltering3, imbrications4, armouring5 including other variation (Charlton 2007; Clayton 2010; Bhattacharya et al. 2016). 3

Sheltering means sediments are accumulated along the channel bed when critical shear stress is much greater than shear stress during sediment transport. 4 Imbrications is primary depositional fabric consisted mainly clastic oriented rock with the following of consistent overlapping manner. 5 Armouring is generated if bed surface of gravel dominated river bed is coarsened relative to the sub surface. This is measured as a ratio between d50 particle size surface and d50 particle size surface area.

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4 Sediment Grain Size Analysis and Mining Intensity …

DuBoys equation is applied to measure the shear stress and critical shear stress value that are estimated the sediment entrainment threshold during pre mining and post mining over the channel (Bhattacharya et al. 2016, 2019a). In addition, sediment entrainment6 in excavated channel bed is goes down in accordance to friction angle of particles guided by grain size distribution and sheltered particles (Wiberg and Smith 1987; Charlton 2007; Clayton 2010).

4.3

Sediment Grain Size Analysis

According to sedimentlogical view point, particle size analysis carried out three main aspects i.e. (1) adaptation of methods for measuring of sediment grain size and then representing these in terms of grade scale, (2) application of various techniques for quantifying the entire database regarding grain size attributes in one hand and to represent them in both form of graphical and statistical in other hand, (3) finally prepare the genetic significance of these database (Boggs 1995). However, above view point signified mainly two methods of graphical and mathematical for representing the grain size attributes, in graphical parameters like bivariate diagrams including log-probability plots, and in statistical parameters like mean for verbal mean size, standard deviation for verbal sorting, skewness, kurtosis and mode have been widely applied to grain size analysis in sedimentlogy studies (Baiyegunhi et al. 2017). Bivariate means two component diagrams plotting one statistical parameter against another parameter such as mean grain size versus standard deviation or sorting, mean grain size versus skewness and mean grain size versus Kurtosis, which is used to easily discriminate the sand from various environments i.e. separation between beach and river sand based on statistical parameters (Friedman 1961, 1979). In contrastingly, log-probability plots are used to measure the environmental significance through the preparing of cumulative curves based on shapes parameter of grain size distribution (Visher 1969; Sagoe and Visher 1977). Probability curves constituted by two or three segments as a substitute of single straight line predicting the normal distribution of sediment population along with denoted the different transportation mechanism of sub populations like traction, saltation and suspension. Consequently, shape differences in curves and its segment truncation points both are clearly allocated the discrimination of sediment regime from various environment (Baiyegunhi et al. 2017). In this context, Folk and Ward (1957) were used of graphical methods for calculating the mean, standard deviation, skewness and kurtosis as follow: Graphic mean ðMZ Þ ¼

6

;16 þ ;50 þ ;84 3

ð4:1Þ

Sediment entrainment is a threshold condition or critical condition when surface sediments are incorporated into fluid flow to intend the opening of erosion process.

4.3 Sediment Grain Size Analysis

109

Inclusive of graphic standard deviation ð@I Þ ¼ Inclusive of graphic skewness ðSKi Þ ¼

;84  ;16 ;95  ;5 þ 4 6:6

ð4:2Þ

ð;84 þ ;16  2;50 Þ ð;95 þ ;5  2;50 Þ þ 2ð;84  ;16 Þ 2ð;95 þ ;5 Þ ð4:3Þ

Graphic Kurtosis ðKG Þ ¼

ð;95  ;5 Þ 2:44ð;75  ;25 Þ

ð4:4Þ

where ø5, ø16, ø25, ø50, ø75, ø84 and ø95 denotes percentiles of 5th, 16th, 25th, 50th, 75th, 84th and 95th in cumulative curve

4.3.1

Application of GRADISTAT Software for Measuring the SGD

Graphical and geometric methods (Folk and Ward 1957; Blott and Pye 2001) are employed to arrange and extract the different textural arrangement or granulometric components such as graphic mean (Mz), inclusive graphic standard deviation or sorting (ó1), inclusive graphic skewness (SK1) and inclusive graphic kurtosis (KG) from prepared sediment samples using GRADISTAT 8.0 software package (Sadeghi and Zakeri 2015; Sadeghi et al. 2018). This is more popular grain size analysis software to measure the textural variability incorporate with sand and gravel mining in different seasons, physiographic region as well as to predict the nature and forms of suspended and bedload sediment movements in the entire courses (Bhattacharya et al. 2016; Sadeghi et al. 2018).

4.4

Case Study: Accessing the Relationship Between Stream Energy and Sediment Grain Size Distribution in Kangsabati River Using GRAD Stat

Stream energy including hydraulic variables are interconnected with SGD that relationship controlled the erosion and deposition sequences throughout the river course; however several anthropogenic activities like sand mining play leading role to interrupt the hydraulic resilience (Bhattacharya et al. 2016). In alluvial channel, hydraulic variables of sediment entrainment, transport, deposition and erosion are depended on SGD to vary by different environmental energy conditions from pre-monsoon to monsoon season. Similar set up has been identified in Kangsabati River to investigate the available shear stress and critical shear stress under controlled energy environment of sediment erosion and deposition process in respect to

110

4 Sediment Grain Size Analysis and Mining Intensity …

SGD of sandbar, mining and pit sites. Moreover, incorporation between stream energy and SGD has been changes during pre-monsoon and post monsoon season, thus three course sites i.e. upper, middle and lower are taken to analysis the above fact in the entire Kangsabati River.

4.4.1

Preparation of Sampling Process

With this view, ninety sediment samples were collected from three categorical sites of sandbar, mining and pits during pre monsoon and monsoon season along the upper, middle and lower course. During field survey, every category is included thirty samples and also every season is picked up forty five samples throughout the Kangsabati River (Fig. 4.2). Every sample has filled with 30–80 gm sands and then all samples were kept on a dry piece paper that are used to prepare the successive coning and quartering on the upper most sieves of stacked sieves setup order by order arrangement. Contrast sieve arrangement sets up from coarser top sieve to finest bottom sieve, where pan is always presence at the bottom to trap the passing sediment from lowest and finest sieve. After the collection of samples, these were repetitively processing through washing, drying and mixing up, respectively. Samples were sieving through contrast sieve layers within 10–15 min by shaking machine as prepared with standard ASTM Erode colt sieve at half phi intervals. Finally, all residual samples were fall down on every sieve layers and pan, and then residuals were collected and weighted using a balance with an accuracy of 0.001 gm .

4.4.2

Textural Characterization

Textural attributes of sediment such as mean (Mz), standard deviation (ó1), skewness (SK1) and kurtosis (KG) are intimately incorporated with sediment entrainment process and guided the depositional mechanism of sediments that are carefully studied in different sedimentary environments (Mason and Folk 1958; Friedman 1961, 1967; Visher 1969; Wolanski et al. 1997; Angusamy and Rajamanickam 2006; Rajganapathi et al. 2013; Bhattacharya et al. 2016; Azidane et al. 2020). In contrary, textural arrangement of sediment in depositional environment is entirely depended on flow length and sediment transport energy, redox conditions, adjacent land configuration and climate condition, respectively (Bhatia and Crook 1986; Fralick and Kronberg 1997; Bhattacharya et al. 2016).

4.4 Case Study: Accessing the Relationship …

111

Fig. 4.2 Sediment sample sites in Kangsabati River. Source Prepared by the authors

4.4.2.1

Descriptions of Graphic Mean (Mz)

Graphic mean size in SGD reveals the significant role of kinetic velocity of moving particles on leading agents of sediment deposition process (Sahu 1964), in contrary mean size also dependent on requiring energy condition of sediment deposition and its processes, transport agent and sediment delivery ratio (Visher 1969, Thomas et al. 2007). In terms of SGD, mean size is varies from coarser to finer grain in the entire course, however fine grain including clay-silt composed sediments are dominated in the lower course of river (Plate 4.1a, b, c, d) (Muraleedharan and Ramachandran 2002). Tables 4.1 and 4.2 denotes descriptive statistical analysis of SGD in upper, middle and lower course of Kangsabati River during pre monsoon and monsoon season. In sandbar sites, maximum average Mz is observed in upper course (1.92 /) but average size is gradually reduces in lower course (1.13 /) during pre-monsoon season while average Mz is increased in upper (1.03 /) and middle course (1.07 /) but drop down in lower course (0.87 /) during monsoon season, respectively (Fig. 4.3a, b, c). Contrastingly, SGD gradation is interrupted in mining and pit sites over the course that means highest average Mz is dominated in mining and pit sites of lower and middle course than upper course during pre-monsoon and monsoon season. On the other hand, coarser and medium sub-categories of Mz have been dominated in sandbar sites but very coarser Mz has been trapping in pit sites during

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4 Sediment Grain Size Analysis and Mining Intensity …

pre-monsoon season whereas coarser sub-category has predominantly deposited in mining and pit sites during monsoon season (Tables 4.3, 4.4), respectively. These results revealed that maximum coarser grain size sedimentation occurs during monsoon than pre monsoon due to present of moderate to high energy condition; however course wise SGD gradation has reversed order in mining and pit sites caused by huge sediment extraction and trapping the coarse and medium textural sand in middle and lower course than upper course. Finer sediment always deposited towards their depositional pathway due to reduces of energy condition in the transporting medium of sandbar sites. Therefore, hydraulics of sediment and fluvial regime greatly affected in mining and pit sites due to abruptly increases of stream energy.

4.4.2.2

Descriptions of Graphic Sorting (ó1)

Inclusive standard deviation is used to measure the sorting or uniformity of SGD that denoted the nature and trend of fluctuations in the hydrodynamic kinetic energy conditions of various depositional environments (Sahu 1964; Ramanathan et al. 2009). Subsequently, sorting distribution is depended on water turbulence capacity and variability in water velocity of deposit agents like current (Irudhayanathan et al. 2011). Sorting is gradually decreases from upper to lower course in sandbar sites, but sorting trend is interrupted in mining and pit sites of Kangsabati River during pre monsoon and monsoon season (Tables 4.1, 4.2). On the other hand, moderately well sorted and moderately sorted textural sub-categories are dominated over the channel during pre monsoon and monsoon season (Tables 4.3, 4.4). In respect of sediment extraction, maximum well sorted sediment has been trapping in mining and pit sites during pre monsoon season while well sorted sediment are highly received in sandbar sites during monsoon season. Moreover, poorly sorted sediment is only concentrated in sandbar sites during pre monsoon season. Contrastingly, maximum well and moderately well sorted sediments are found along the upper course, but moderately sorted sediments are highly dominated along the middle and lower course (Fig. 4.1). Therefore, coarse sediment always increases the maximum textural sorting category as well as increases the sediment passing tendency, but finer sediment increases the minimum textural category along with increased the sediment concentration capacity in the entire channel. It is point that massive sediment extraction changes the sorting behaviour including passing tendency and concentration capacity in mined river.

4.4.2.3

Descriptions of Graphic Skewness (SK1)

Nature of frequency distribution in SGD is measured by sensible indicator of skewness that can easily detected the mixing of subpopulation and prevailing energy conditions in depositional environment using symmetrical or asymmetrical graph (Duane 1964; Azidane et al. 2020). In this case study, maximum negative

4.4 Case Study: Accessing the Relationship …

113

Plate 4.1 Sediment grain size in different course: a Gravel at upper course, b coarser at upper course, c medium grain at middle course, d finer grain at lower course. Source Authors

skewness is dominated along the upper course in the presence of coarser and symmetrical skewness whereas positive skewness is gradually increases along the middle and lower course in the presence of fine and very fine skewness (Tables 4.3, 4.4). Asymmetrical skewness distribution of sediment sample demonstrated negative progression occurrences in especially upper and middle course sites during monsoon season while symmetrical distribution informed positive progression occurrences in lower course sites during pre monsoon season. On the other hand, skewness distribution is more variable in mining and pit sites than sandbar sites in the entire river where skewness gradation has inverse order caused by huge sediment extraction than replenishment. Trend of skewness distribution in Kangsabati River indicates that negative progression induced winnowing action always removed the finer particles under high energy condition in coarser grain sediments whereas positive progression induced waxing action led huge sedimentation of finer particles under low energy condition in finer grain sediments. In addition, unidirectional path way of flow regime is initiated in positive skewness distribution sites of lower course caused by massive sheltering of finest particles under low energy environment throughout the year except monsoon season. Above views are similar in the line of Friedman (1961) and Brambati (1969).

Sandbar Min

Max

Upper Mean 0.40 1.92 Sorting 0.64 1.05 Skewness −0.35 0.39 Kurtosis 0.74 1.21 Middle Mean 0.54 1.52 Sorting 0.66 1.02 Skewness 0.03 0.43 Kurtosis 0.88 1.25 Lower Mean 0.66 1.13 Sorting 0.69 0.91 Skewness 0.04 0.46 Kurtosis 0.79 1.08 Source Calculated by authors

Grain 0.68 0.19 0.33 0.22 0.45 0.14 0.16 0.14 0.17 0.09 0.20 0.11

1.04 0.84 0.21 1.02

0.95 0.81 0.26 0.93

Std

1.26 0.78 0.08 0.99

Average

0.80 0.76 −0.13 0.79

0.46 0.76 −0.04 0.82

0.02 0.46 −0.26 1.07

Mining Min

1.89 0.96 0.50 1.01

1.53 1.07 0.55 1.43

2.05 0.83 0.68 1.43

Max

Table 4.1 Descriptive statistical analysis for the SGD during pre monsoon season

1.17 0.83 0.21 0.89

0.97 0.87 0.30 1.15

0.56 0.60 0.30 1.18

Average

0.42 0.08 0.26 0.10

0.41 0.12 0.23 0.23

0.84 0.14 0.35 0.15

Std

1.31 0.94 0.41 1.14

1.22 0.97 0.41 1.47

−0.13 0.30 0.06 0.82 0.33 0.44 0.02 0.86

0.87 1.00 0.68 1.39

Max

−0.01 0.39 0.07 0.75

Pit Min

0.81 0.78 0.23 0.99

0.57 0.73 0.29 1.00

0.44 0.76 0.36 0.97

Average

0.36 0.21 0.16 0.12

0.49 0.27 0.15 0.28

0.38 0.27 0.22 0.27

Std

114 4 Sediment Grain Size Analysis and Mining Intensity …

Sandbar Min

Max

Upper Mean 0.39 2.19 Sorting 0.37 0.92 Skewness −0.25 0.37 Kurtosis 0.74 1.88 Middle Mean 0.36 1.45 Sorting 0.43 0.75 Skewness −0.16 0.43 Kurtosis 0.70 1.10 Lower Mean 0.42 1.31 Sorting 0.36 0.87 Skewness 0.08 0.51 Kurtosis 0.90 1.89 Source Calculated by authors

Grain 0.81 0.26 0.27 0.46 0.45 0.13 0.21 0.17 0.38 0.21 0.17 0.39

1.07 0.64 0.12 0.94

0.87 0.62 0.28 1.27

Std

1.03 0.63 0.12 1.22

Average

0.80 0.75 0.07 0.78

0.21 0.54 −0.09 0.86

0.32 0.54 0.07 0.73

Mining Min

1.42 0.87 0.43 1.03

2.20 0.89 0.68 1.34

1.38 0.96 0.45 1.23

Max

Table 4.2 Descriptive statistical analysis for the SGD during monsoon season

1.07 0.79 0.25 0.93

1.14 0.76 0.17 1.10

0.91 0.71 0.26 0.90

Average

0.30 0.05 0.17 0.09

0.71 0.15 0.30 0.23

0.39 0.18 0.18 0.19

Std

0.53 0.33 0.01 0.75

0.36 0.57 −0.08 0.71

0.13 0.57 −0.06 0.74

Pit Min

1.25 0.84 0.31 1.29

0.70 0.73 0.28 1.16

1.76 1.00 0.57 1.01

Max

0.96 0.61 0.16 0.96

0.44 0.63 0.16 0.98

0.88 0.69 0.32 0.86

Average

0.31 0.20 0.14 0.20

0.15 0.07 0.15 0.19

0.79 0.18 0.27 0.14

Std

4.4 Case Study: Accessing the Relationship … 115

116

4 Sediment Grain Size Analysis and Mining Intensity …

Fig. 4.3 SGD in every class weight during pre monsoon and monsoon: a upper course, b middle course, c lower course. Source Prepared by the authors

4.4.2.4

Descriptions of Graphic Kurtosis (KG)

Graphic Kurtosis is well estimated the sharpness or peakedness of SGD, which can measured the nature of sediment deposition in various environments based on sorting intensity under the different energy condition (Duane 1964; Friedman 1961; Baiyegunhi et al. 2017). Three sub textural category of kurtosis i.e. leptokurtic, mesokurtic and playkurtic are discriminated different types of sediment where leptokurtic sub textural category indicated coarser grain size deposition, mesokurtic

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117

Table 4.3 Summary of grain size statistical parameters (in percentage of the total number at each location) and sediment type during pre monsoon season Copurse Attributes

Upper Sandbar

Mining

Pit

Middle Sandbar

Mining

Pit

Lower Sandbar

Mining

Pit

Mean VCS 0 0 20 0 0 20 0 0 0 CS 40 80 80 40 60 60 60 40 80 MS 60 0 0 60 40 20 40 60 20 FS 0 20 0 0 0 0 0 0 0 Sorting WS 0 20 20 0 0 0 0 0 20 VWS 0 0 0 0 0 20 0 0 0 MWS 80 60 20 20 0 20 20 0 20 MS 0 20 60 60 80 60 80 100 60 PS 20 0 0 20 20 0 0 0 0 Skewness VFS 40 60 60 40 60 60 60 40 40 FS 20 20 20 20 20 20 0 20 40 S 20 0 20 40 20 20 40 20 20 CS 20 20 0 0 0 0 0 20 0 Kurtosis VPK 0 0 0 0 0 0 0 0 0 PK 40 0 60 20 20 60 40 60 40 MK 40 40 20 60 20 20 60 40 40 LK 20 60 20 20 60 20 0 0 20 VLK 0 0 0 0 0 0 0 0 0 M-Monsoon, P- pre-monsoon monsoon, PM post-monsoon, CS coarse sand, WS medium sand, WS fine sand, WS well sorted, MWS moderately well sorted, MS moderately sorted, VFS very fine skew, FS fine skewness, S symmetry CS course skewness, VPK very platykurtic, PK platykurtic, MK Mesokurtic, LK leptokurtic, VLK very-leptokurtic Source Calculated by authors

category pointed out medium grain size deposition, and playkurtic category denoted finer grain size deposition. Three categorical depictions are revealed the dynamicity and acting role of hydraulic variables under flow regime in various depositional environments (Seralathan and Padmalal 1994; Baiyegunhi et al. 2017; Sadeghi et al. 2018). Kurtosis distribution in Kangsabati River showed that maximum playkurtic and mesokurtic categorical sediments deposition are occurred in the sample sites of lower course, and leptokurtic categorical sediments are enormously deposited in upper and middle course sites during pre monsoon and monsoon season (Tables 4.3, 4.4). In recent past, instream and floodplain sand mining changes the natural kurtosis distribution throughout the Kangsabati River, in particularly mining and pit sites constantly faces reverse changes caused by irregular trapping of coarse particles and removing of finer particles during maximum peak flow of monsoon

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118

Table 4.4 Summary of grain size statistical parameters (in percentage of the total number at each location) and sediment type during monsoon season Course Attributes

Upper Sandbar

Mining

Mean VCS 0 0 CS 60 60 MS 20 40 FS 20 0 Sorting WS 40 0 VWS 0 0 MWS 20 60 MS 40 40 PS 0 0 Skewness VFS 40 40 FS 20 20 S 20 40 CS 20 0 Kurtosis VPK 0 0 PK 40 60 MK 0 20 LK 40 20 VLK 20 0 Source: Calculated by authors

Pit

Middle Sandbar

Mining

Pit

Lower Sandbar

0 60 40 0

0 40 60 0

0 20 60 20

0 100 0 0

0 60 40 0

0 60 40 0

0 60 40 0

0 0 80 20 0

20 0 20 60 0

0 0 20 80 0

0 0 80 20 0

40 0 20 40 0

0 0 0 100 0

20 20 20 40 0

60 20 20 0

20 20 40 20

20 20 60 0

0 80 20 0

40 40 20 0

40 20 40 0

20 40 40 0

0 60 40 0 0

0 40 60 0 0

0 40 20 40 0

0 20 40 40 0

0 20 20 40 20

0 20 80 0 0

0 40 40 20 0

Mining

Pit

season. Consequently, in sandbar sites, finer particles are irregularly added in one hand, and removed the coarser particles during monsoon season in other hand. Therefore, maximum leptokurtic categorical sediments are gradually increases in mined sites, concurrently mesokurtic category is regularly increased in sandbar sites along the quarried channel.

4.4.3

Bivariate Scatter Graphs of Grain Parameters

Scatter graphs between grain size parameters are employed to discriminate the sedimentation process between different depositional environments, which is also revealed the requiring energy conditions, medium of transporting agents and mode of deposition in sedimentary environments (Folk 1980; Rajganapathi et al. 2013; Nugroho and Putra 2017; Azidane et al. 2020). Six bivariate scatter plots such as

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119

mean versus sorting, mean versus skewness, mean versus kurtosis during pre monsoon (3) and monsoon season (3) are constructed to demonstrate the interrelation between statistical parameters of SGD while these plots are clearly translated about the nature of depositional environments under certain energy conditions (Azidane et al. 2020).

4.4.3.1

Graphic Mean Size Versus Sorting

Two bivariate scatter plots of graphic mean against sorting are prepared from collected samples of Kangsabati River during pre monsoon and monsoon season, which is established the different sedimentation process in sandbar, mining and pit sites of upper, middle and lower course (Fig. 4.4a). Scatter plots indicates that maximum cluster pattern concentration around coarser to medium size within moderately well sorted to moderately sorted during pre monsoon. But this clustering is constantly fragmented or scattered due to extension of sorting from well sorted to moderately sorted against very coarse to finer mean size during monsoon season (Fig. 4.4b). According to Griffiths (1967), stream hydraulic variables are driven factors to control the mean grain size and sorting nature in sediments. It can be said that best sorted sediments are concentrated or moved in finest grain size sands that means more sorted sediments such as well sorted and moderately well sorted are associated with coarser to finer sands under high energy conditions during peak monsoonal flow. On the other hand, mining sites are concentrated in these high energy sedimentary environments where maximum sediment supply is occurred. In terms of course distribution, maximum best sorted sediments observed in upper and middle course sample sites than lower course sample sites due to present of extensive mean sizes range from very coarser to finer under high stream energy condition.

4.4.3.2

Graphic Mean Size Versus Skewness

Bivariate plot of graphic mean versus skewness denoted that maximum sediments are clustered around coarse skewed to very fine skewed against very coarser to coarser mean size whereas symmetrical to fine skewed sediments are concentrated against medium to finer grain size during pre monsoon season (Fig. 4.5a). In contrary, maximum symmetrical skewed and very fine skewed sediments are rounded against very coarser and coarser mean size whereas most of the symmetrical and fine skewed sediments are clustered against medium and finer grain size during monsoon season (Fig. 4.5b). This result demonstrated that maximum fluctuation in skewness distribution is occurred during monsoon season but skewness distribution in sediments is concentrated as cluster manner during pre monsoon season, respectively. Moreover, coarser sediments are belongs with positive skewness distribution whereas medium and finer sediments are more negatively skewed in both seasons over the Kangsabati River. It is point that

120

4 Sediment Grain Size Analysis and Mining Intensity …

Fig. 4.4 Grain size versus sorting: a Pre monsoon, b monsoon season. Source Prepared by the authors

maximum fluctuation and negative skewness values in skewness distribution is occurred during monsoon season caused by high energy conditions while maximum clustering and positive skewness values in skewness distribution are generated during pre monsoon season under relatively low energy conditions (Brambati 1969; Karuna Karuda et al. 2013). Recently, sand mining in clustering zone creates the inverse change of skewness values against grain size that means some coarser

4.4 Case Study: Accessing the Relationship …

121

Fig. 4.5 Grain size versus skewness: a Pre monsoon, b monsoon season. Source Prepared by the authors

sediments are altered to more positively skewness as well as some finer and medium sediments are altered to more negatively skewness in mining and pit sites throughout the course.

4 Sediment Grain Size Analysis and Mining Intensity …

122

4.4.3.3

Graphic Mean Size Versus Kurtosis

Bivariate plot of graphic mean versus kurtosis indicates a complex and theoretical relationship in grain size distribution under various energy conditions (Folk and Ward 1957; Baiyegunhi et al. 2017; Azidane et al. 2020). In this study, very leptokurtic to playtikurtic sediments are rounded against very coarser to coarser grain size, and very playtikurtic to leptokurtic sediments are concentrated around medium to finer grain size during pre monsoon season (Fig. 4.6a). On the other hand, maximum sediments are sheltered around playtikurtic to leptokurtic nature against very coarser to coarser size while maximum sediments are clustered around playtikurtic to mesokurtic nature against medium to finer grain size during monsoon season (Fig. 4.6b). Plots result shows the maximum clustering occurrence during pre monsoon season than monsoon season that means sediments are generated as homogeneous nature under stable energy condition but heterogeneous nature sediment generation processes are occurred under fluctuated energy condition. Sand mining induced kurtosis distribution against the sediment grain size demonstrated that huge extraction of coarser and medium sand increased the leptokurtic kurtosis in mining and pit sites of middle and lower course from pre monsoon to monsoon season over the years. Subsequently, mesokurtic to platykurtic kurtosis sediments are gradually increased pre monsoon to monsoon season along the upper course. It is notice that sand mining can changes the sedimentation process through the interruption of energy conditions in the entire dense mined river.

4.5

Case Study: Estimation the Transporting Mechanism and Depositional Environment in Kangsabati River Using G-STAT (Grainsize Statistics) Software

G-STAT (grainsize statistics) is important software package to analyses the sand-silt-clay ratio, granulometric properties,7 mode of sediment transport and detection of sediment sources in different sedimentary environments. Cumulative weight percentage diagrams for textural distribution, triangular diagrams (Folk and Ward 1957; Blott and Pye 2001) for granulometric analysis, CM (coarsest median) diagrams and tractive current deposit for sediment transport mode, sediment trained diagrams for sediment transport direction are prepared using of G-STAT software

7

Granulometric properties are used for sediment trend analysis with the considering of mean grain size, sorting, skewness and kurtosis in a sedimentary environment.

4.5 Case Study: Estimation the Transporting Mechanism and Depositional …

123

Fig. 4.6 Grain size versus kurtosis: a Pre monsoon, b monsoon season. Source Prepared by the authors

that was designed and developed by Dinesh (2009). Coarsest one percentile of grain size distribution (C) and median of grain size (M) both are considered to construct CM diagrams based on Passage’s hypothesis (1964, 1957). Moreover, in this case study, G-STAT software is employed to determine the sediment attributes, transporting mechanism and depositional environment in sandbar, mining and pit sites of upper, middle and lower courses during pre monsoon and monsoon season.

124

4.5.1

4 Sediment Grain Size Analysis and Mining Intensity …

Cumulative Weight Percentage Diagrams of Sediment Textural Ratio

Sediment textural distribution likes sand, silt, clay are graphically presented by the construction of cumulative weight percentage diagram from collected sediment samples, which stated the variable energy conditions and local morphological configuration in respect to grain size distribution (Pradhan et al. 2020). In Kangsabati River, two cumulative weight percentage diagrams are prepared against mean grain size in every course to understand the occurrence of different textural ratio during pre monsoon and monsoon season. In upper course, cumulative weight of sediments varies 1–65% during pre monsoon around with mean grain size as −1/–+3/ scale whereas it expanded up to 55% during monsoon with mean size as −1/–+3/ (Fig. 4.7a, b). In middle course, cumulative weight of sediments varies from 1 to 70% during pre monsoon plotting against mean size of −1/–+2/ scale while it ranges from 1 to 60% during monsoon plotting against same mean size (Fig. 4.8a, b). In lower course, cumulative weight percentage ranges 1 to 60% during pre monsoon plotting against mean size of 4/– 9/ but it varies 1–55% during monsoon with same mean size (Fig. 4.9a, b). Below 1% cumulative sediment weight during monsoon around mean grain size of −2/– +1/ revealed that amount of coarser sediments are available in upper and middle course compared to pre monsoon. In addition, less percentage weight of sediment during monsoon depicted that coarser and medium sediments are more concentrated in lower course compared to pre monsoon. Despite the lower energy condition in middle and lower course, over extraction of coarser sediments increases the wide range of mean grain size up to 80% cumulative percentage weight of sediments. In spite of high energy condition in upper course, finer sediment concentration in sandbar sites decreases the mean size range within cumulative percentage weight of sediments due to huge trapping of coarse sediments in mining and pit sites during monsoon. Therefore, it is point that sand mining can greatly influenced on mean size distribution against cumulative percentage weight and sediment transport process in the entire channel.

4.5.2

Analysis of Granulometric Properties Using Triangular Diagram

Sediment textural database is generally large and more complicated; therefore actual numerical information is very difficult to easily understand and interpret it. Recently, geologists are commonly used more graphical presentations to reduce complexities of sub-textural attribute, and recognize the trends and distribution of sediment samples dataset through the developing of several hypotheses. With the graphical presentation, equilateral triangular diagrams are used to plot the three dominant textural groups either gravel, sand, silt or sand, silt and clay in percentage

4.5 Case Study: Estimation the Transporting Mechanism and Depositional …

125

Fig. 4.7 Sediment distributions (phi) in upper course sample sites of Kangsabati River: a pre monsoon, b monsoon. Source Prepared by the authors

Fig. 4.8 Sediment distributions (phi) in middle course sample sites of Kangsabati River: a pre monsoon, b monsoon. Source Prepared by the authors

126

4 Sediment Grain Size Analysis and Mining Intensity …

Fig. 4.9 Sediment distributions (phi) in lower course sample sites of Kangsabati River: a pre monsoon, b monsoon. Source Prepared by the authors

under G-STAT platform. Folk (1980) was first scholar to prepare the sediment classification in triangular diagram through plotting the percentage of sand, silt and clay. In recent past, sediment textural classification has been rapidly done using Blott and Pye method (2012). In this case study, maximum sediment samples nearly 85% have reaches in moderate textural class while rest of samples are belongs in coarser class along the downstream (Fig. 4.10a, b, c, d). In addition enormous coarser sediment textural grain size concentrated along the upper and middle course of Kangsabati River. In terms of energy condition, huge coarser sands are transported than silt and clay under extreme energy condition in the entire course during peak flow or monsoon season while medium and finer grain size sands are transported during pre monsoon season.

4.5.3

Analysis of Transport Mechanism and Mode of Deposition Using CM Diagram

Sediment genesis process, transport mechanism, depositional environment in respect of sediment size, range, energy condition during transport including its determination processes are well demonstrated using of CM diagram (Passega 1964; Visher 1969). This diagram is constructed with the considering of one percentile grain size (C) plotting against median grain size (M) of sediment textural

4.5 Case Study: Estimation the Transporting Mechanism and Depositional …

127

Fig. 4.10 Triangular diagrams at course level: a upper, b middle, c lower, d trend of textural distribution. Source Prepared by the authors

attribute on a double log paper following of Passega’s method (1957, 1964) (Pradhan et al. 2020). Furthermore, Passega’s method helps to details exploration about the evolution of transport mode and various sources of sediment genesis in different environment. Six types of sediments are demarcated in every CM diagrams i.e. tills, pelagic, river-terrace gravel, tractive current, beach and beach gravel under various transport mechanism like rolling or creeping, saltation, suspension processes on channel bed. Bottom turbulence is guided the sorting process to effectively influence on C and M relationship over the channel bed. In Kangsabati River, six CM diagrams are prepared for three courses of upper, middle and lower during pre monsoon and monsoon seasons, which is presented the mode of sediment deposition incorporated with tractive current deposits. Maximum tractive current deposits and beach sedimentation are occurred along the upper course during pre monsoon and monsoon season (Fig. 4.11a, b). In middle course, more beach sedimentation is concentrated during pre monsoon season but such

128

4 Sediment Grain Size Analysis and Mining Intensity …

tractive current deposits are arrested during monsoon season (Fig. 4.12a, b). In lower course, wide range of pelagic sediments are deposited in the entire lower course where scatter type pelagic sedimentation occurred during pre monsoon, but dense pelagic sedimentation is happened during monsoon season due to maximum sediments trapping in mining pit sites of middle course (Fig. 4.13a, b). On the other hand, tills, river-terrace gravel and beach gravel sediments are totally absence in Kangsabati River. It is said that mode of sediment deposition is guided by tractive current resulting for energy dissipation from one course to another course.

4.5.4

Estimation of Tractive Current Deposits at Course Level

Tractive current deposits are entirely depended on mode of transport mechanism agent facing frequently interruption caused by break of slope along the channel. Subsequently, sediments are drop down on the channel bed. In particular, tractive current leads sediment deposition caused by rolling and suspension processes. Generally tractive current deposits are classified into five type’s i.e. NO-rolling,8 OPQ-bottom suspension and rolling,9 QR-graded suspension no rolling,10 RS-uniform suspension11 and S-pelagic suspension.12 In this case study, in upper course, nature of tractive current deposits are clustered around the rolling to graded suspension no rolling category during monsoon season while deposition clusters are fragmented with wide ranges as graded suspension to rolling category under high energy condition during monsoon season (Fig. 4.14a, b). In middle course, rolling process is more dominant as cluster manner to deposit the tractive current sediment and beach sediment during pre monsoon season whereas dominance of rolling process is scattered up to bottom suspension and rolling category leading tractive current deposition under high energy condition during monsoon season, respectively (Fig. 4.15a, b). In lower course, maximum pelagic suspension process leads to dense pelagic sediments deposition during monsoon season while cluster of pelagic sediment deposition is scattered by medium mode of pelagic suspension process under low energy condition during pre monsoon (Fig. 4.16a, b). Therefore, seasonal energy conditions in different course might have relatively changes the tractive current deposits.

8

Rolling is a longitudinal bedload sediment transport mode along the channel bed. Bottom suspension and rolling sediment transport mode denotes the sediments are transported through rolling traction with suspended mode in the water flow near channel bed. 10 Graded suspension no rolling indicates a stage when movable sediments are reached to grade without rolling. 11 Uniform suspension is another suspension mode where homogeneous particles are uniformly suspended in the water flow. 12 pelagic suspension means fine-grained sediments are transported by suspension transport mode. 9

4.6 Linear Discriminate Function (LDF)

129

Fig. 4.11 CM diagrams predict mode of sediment transport in upper course: a pre monsoon, b monsoon. Source Authors

Fig. 4.12 CM diagrams predict mode of sediment transport in middle course: a pre monsoon, b monsoon. Source Authors

4.6

Linear Discriminate Function (LDF)

Effective statistical analysis provided an open platform to give the detail interpretation on variations in energy and fluidity factors during sediment deposition or priority to sedimentation that have always establishing a good correlation with the different processes in any depositional environments (Sahu 1964). Linear discriminate function (LDF) is most effective statistical tool composed by following axis as Y1 (shallow agitated water and beach sediment), Y2 (beach and shallow agitated water), Y3 (shallow marine and fluvial) and Y4 (turbidity and fluvial sediment deposition), which is used to derive and discriminate between

130

4 Sediment Grain Size Analysis and Mining Intensity …

Fig. 4.13 CM diagrams predict mode of sediment transport in lower course: a pre monsoon, b monsoon. Source Prepared by the authors

Fig. 4.14 Tractive current deposits in upper course: a pre monsoon, b monsoon. Source Prepared by the authors

sedimentation process and its depositional environments (Baiyegunhi et al. 2017). All axes are computed as follows: Y1 is used to discriminate between beach water (BW) and shallow agitated water (SW) as follow Eq. 4.5 Y1ðBW :SW Þ ¼ 3:5688M þ 3:7016r 2  2:0766SK þ 3:1135KG

ð4:5Þ

where if Y1 value is  −2.7411 that means depositional environment is “beach” and if Y1 value is  −2.7411, indicated environment as “shallow agitated water”. Y2 is used to discriminate between beach water (BW) and shallow marine deposition (SM) as follow Eq. 4.6

4.6 Linear Discriminate Function (LDF)

Fig. 4.15 Tractive current deposits in middle course: a pre monsoon, b monsoon. Prepared by the authors

131

Source

Fig. 4.16 Tractive current deposits in lower course: a pre monsoon, b monsoon. Source Prepared by the authors

Y2ðBW:SM Þ ¼ 15:6534M þ 65:7091r 2 þ 18:1071SK þ 18:5043KG

ð4:6Þ

where if Y2 value is  −63.3650 that means depositional environment is “beach” and if Y2 value is  −63.3650, indicated environment as “shallow marine” deposition Shallow marine (SM) and fluvial deposition (FD) is successful discriminated using Y3 as follow Eq. 4.7

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4 Sediment Grain Size Analysis and Mining Intensity …

Y3ðSM:F Þ ¼ 0:2852M  8:7604r 2  4:8932SK þ 0:0482KG

ð4:7Þ

If Y3 value of >−7.4190 denotes to “Shallow marine” deposition but if the value of Y 10000 denotes to “turbidity” deposition but if the value of Y4 < 10000, the deposition is “Fluvial sedimentary”. Whereas M, r, SK, and KG stated mean grain size, standard deviation or sorting, skewness and kurtosis, respectively.

4.6.1

Case Study: Derivation of Sediment Depositional Environment in Kangsabati River Using LDF

After the computation of LDF for the Kangsabati River, Y1, Y2, Y3 and Y4 values are ranges from −3.23 to 7.42, 33.84 to 119.88, −12.36 to −0.81, and 5.03 to 12.60 during pre monsoon season but these values are ranges from −2.81 to 6.92, 44.58 to 101.058, −10.41 to −0.62, and 3.68 to 14.06 during monsoon season (Figs. 4.17, 4.18, 4.19), respectively. With the course distribution of Y1 values, entire samples are fall under beach depositional environment along the lower (100%) and middle course (100%) followed by upper course (93%) during pre monsoon while all samples are belongs in beach depositional environment along the lower course (100%) followed by middle (93%) and upper course (87%) during monsoon season, respectively (Table 4.5). In case of Y2 values, maximum samples are belongs under agitated water deposition category along the lower (87%) and middle course (87%) followed by upper course (67%) during pre monsoon whereas all samples live in agitated water deposition sites along the lower course but beach sedimentary depositional environment is more dominant along the middle course sample sites (53%) than upper course (40%) during monsoon season. In case of Y3 values, total samples fall in the fluvial beach depositional environment during pre monsoon and monsoon season. With the reference of Y4 values, maximum from upper, middle and lower course sites fall in shallow fluvial sedimentary (FS) environment, while few samples fall in turbidity sedimentation during pre monsoon and monsoon season, respectively.

4.6 Linear Discriminate Function (LDF)

133

Fig. 4.17 Relationship between discriminate functions of Y1 and Y2: a pre monsoon, b monsoon. Source Prepared by the authors

4.6.2

Bivariate Graph of Sediment Depositional Environment During Pre Monsoon and Monsoon

LDF bivariate graph between Y1 and Y2 is prepared through the plotting of beach water (BW) and shallow agitated water (SW) against the beach water (BW) and

134

4 Sediment Grain Size Analysis and Mining Intensity …

Fig. 4.18 Relationship between discriminate functions of Y2 and Y3: a pre monsoon, b monsoon. Source Prepared by the authors

shallow marine deposition (SM), which denotes maximum samples falling in both beach/littoral and beach shallow agitated water depositional environment during pre monsoon and monsoon season in Kangsabati River (Fig. 4.17a, b). Bivariate graph between Y2 and Y3 is plotted of beach water (BW) and shallow marine deposition (SM) against shallow marine (SM) and fluvial deposition (FD), which reveals maximum samples depositing towards fluvial agitated environment

4.6 Linear Discriminate Function (LDF)

135

Fig. 4.19 Relationship between discriminate functions of Y3 and Y4: a pre monsoon, b monsoon. Source Prepared by the authors

during pre monsoon (Fig. 4.18a), but samples are expanding towards the fluvial beach depositional environment during monsoon (Fig. 4.18b). On the other hand, bivariate plot of Y3 and Y4 is constructed of shallow marine (SM) and fluvial deposition (FD) around with turbidity (T) and fluvial sedimentary (FS) deposition, which denotes maximum samples fall under fluvial deposition as form cluster shape

Course

Upper Middle Lower Monsoon Upper Middle Lower Source Calculated by authors

Pre- monsoon

Season

7 0 0 13 7 0

Y1 (%) Aeoline 93 100 100 87 93 100

Beach 33 13 13 40 53 0

Y2 (%) Beach 67 87 87 60 47 100

Agitated

Table 4.5 Summary of estimated environments using discriminate functions

0 0 0 0 0 0

Y3 (%) Fluvial 100 100 100 100 100 100

FLU_BEACH

13 20 0 7 7 7

Y4 (%) Turbidity

87 80 100 93 93 93

Shallow

136 4 Sediment Grain Size Analysis and Mining Intensity …

4.6 Linear Discriminate Function (LDF)

137

during pre monsoon (Fig. 4.19a) but mode of deposition shifted towards the turbidity sedimentation during monsoon (Fig. 4.19b), respectively. In addition, Y4 is more dominant fraction than others in Kangsabati River due to mode of sedimentation as FS and T both occurring at same season over the courses. In terms of mining intensity, depositional environment is much disturbed in mining sites than sandbar sites mean while cluster sample position in scatter plot become fragmented from pre monsoon to monsoon in the entire channel.

4.7

Grain Size Related to Bed Shear Stress (s0) and Critical Shear Stress (U*)

SGD is important indicators of cohesive and non-cohesive sediments to determine the erosion resistance capacity based on critical shear stress values and erosion rate (Ahmad et al. 2011). Generally sediment particles are moved towards the downward along the loose sedimentary river bed when critical shear stress is under stated than shear stress. Particle movement is intimately correlated with flow velocity, grain size and critical shear stress that means incipient motion of particle is initiated if flow induced bed shear stress exceeds certain critical shear stress values. If shear stress value is crossed the threshold range then critical shear stress is initiated, consequently, sediment entrainment and channel bed or bank degradation both are changes over the channel (Montgomery and Buffington 1997; Ritter et al. 2002; Church 2006; Charlton 2007). Spatial distribution of shear stress is not equally distributed along the bed surface in every season. Sediment moved towards a particular reach when entire incoming sediment supply is equal to present sediments that are being entrained, as a result entrainment sediments are replaced by reachable sediment (Lane 1955; Charlton 2007; Clayton and Pitlick 2008). But this entrainment process is interrupted if sediment discharge is drastically increases from upstream sites and then huge aggradations are occurred towards the downstream river (Lane 1955). Particle size induced every shear stress also able to carry out same size grain; however sand generated shear stress is unable to move any large size grain like gravel. Therefore, it is point that particles transportation is fully depended on flow condition, fluid density, grain size, sediment density including channel configuration. In terms of sediment loads, three type loads of contract, saltation and suspended are occurred during sediment transport where relatively low shear stress are associated with contract loads through the sliding or rolling of sediment particles to continuous contract on river bed. In case of saltation load, particles have loose contract within the river bed for some time again particles are bounces point by point towards the flow direction, but suspended load is generated when/where transported particles are in suspended nature caused by cumulatively increases of shear stress. In this context, measurement of shear stress and critical

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4 Sediment Grain Size Analysis and Mining Intensity …

shear stress is very essential to interlink of erosion and deposition process with grain size. DuBoys formula (1879) is applied to compute the shear stress as follow:
Shear stress I ¼ ogds

ð4:9Þ

where, ó stated water density (1.00 gm/cm3), g indicate acceleration rate of gravitational force required by hydraulic radius (m) and slope (s). Critical shear stress means amount of force needed to transport the sediment of a particular size, which is computed following Shield formula (1936) as:
Shield critical shear stress Icr ¼ Kg ð os   oÞD

ð4:10Þ

where, K denotes constant value (0.045), g means gravitational acceleration, ós denotes sediment density (2.65 gm\cm3, Knighton’s constant value 1998), ó denotes water density, D refers median grain size (/).

4.7.1

Case Study: Erosion and Deposition Process in Relation to Mining Intensity During Pre Monsoon and Monsoon in Kangsabati River

Available shear stress and critical shear stress both have been calculated in respect to sediment grain size during pre monsoon and monsoon season in Kangsabati River (Tables 4.6, 4.7). During pre monsoon season, maximum sediments deposition are occurred along the middle and lower course sample sites caused by available shear stress crossing the critical shear stress values, but maximum erosion is concentrated along the upper course sample sites where critical shear stress go over the shear stress (Fig. 4.20a). Moreover, minimum deposition only observed along the upper course sites while minimum erosion is fully absent in upper and middle course and meagre erosion found in lower course sites. On the other hand, during monsoon season, maximum deposition is occurred in upper and middle sites (available shear stress  critical shear stress) but maximum erosion is found in middle and lower course sites (available shear stress  critical shear stress) throughout the Kangsabati River (Fig. 4.20b). In addition, minimum sediment deposition is identified in middle and lower course sites whereas minimum erosion occurred in upper course sites. In terms of mining intensity, minimum deposition has found in mining and pit sites of upper course and maximum deposition occurred in mining and pit sites of middle and lower course during pre monsoon. In case of monsoon season, maximum deposition observed in mining and pit sites of upper course but minimum deposition is identified in middle and lower course mining and pit sites, respectively. Furthermore, maximum erosion process is more dominated in sandbar sites of upper and middle course during pre monsoon season while erosion process is

4.7 Grain Size Related to Bed Shear Stress (s0) and Critical Shear Stress (U*)

139

Table 4.6 Descriptive statistic of shear stress and critical shear stress during pre-monsoon season Course

Parameter

Range

Upper

Median 0.0009 Stress 0.2313 Critical 0.6206 Middle Median 0.0008 Stress 0.3375 Critical 0.5741 Lower Median 0.0006 Stress 0.0884 Critical 0.4645 Source Calculated by authors

Minimum

Maximum

Mean

Std. error

Std. deviation

Variance

0.0002 0.1735 0.1675 0.0003 0.0330 0.2523 0.0003 0.0348 0.1851

0.0011 0.4047 0.7881 0.0011 0.3705 0.8264 0.0009 0.1232 0.6496

0.0007 0.2531 0.5238 0.0006 0.1517 0.4672 0.0006 0.0872 0.4125

0.0001 0.0138 0.0588 0.0001 0.0282 0.0419 0.0000 0.0072 0.0289

0.0003 0.0535 0.2279 0.0002 0.1093 0.1622 0.0002 0.0277 0.1118

0.0000 0.0029 0.0519 0.0000 0.0120 0.0263 0.0000 0.0008 0.0125

Table 4.7 Descriptive statistic of shear stress and critical shear stress during monsoon season Course Upper

Parameter

Range

Median 0.0008 Stress 0.2791 Critical 0.6043 Middle Median 0.0009 Stress 2.6941 Critical 0.6200 Lower Median 0.0009 Stress 2.6941 Critical 0.6200 Source Calculated by authors

Minimum

Maximum

Mean

Std. error

Std. deviation

Variance

0.0002 0.3765 0.1579 0.0002 0.1451 0.1582 0.0002 0.1451 0.1582

0.0010 0.6556 0.7623 0.0011 2.8392 0.7781 0.0011 2.8392 0.7781

0.0006 0.4823 0.4475 0.0006 0.9219 0.4465 0.0006 0.9219 0.4465

0.0001 0.0230 0.0510 0.0001 0.1751 0.0451 0.0001 0.1751 0.0451

0.0003 0.0890 0.1974 0.0002 0.6781 0.1748 0.0002 0.6781 0.1748

0.0000 0.0079 0.0390 0.0000 0.4598 0.0306 0.0000 0.4598 0.0306

highly progressed as scouring13 form in sandbar sites of middle course during monsoon season. Although pool-riffle alteration in mining and pit sites of lower course proceeds higher erosion as scouring form cause behind strong shifting of shear stress during monsoon (Plate 4.2a) but huge sedimentation concentration in sandbar sites (Plate 4.2b). It is stated that shear stress values is exceeds the critical shear stress in a particular grain size causes to easy downward sediment movement along the mining sites of middle and lower course during monsoon, but critical shear stress crossed the shear stress during pre monsoon season, as a result, absence of hindrance for clearing of sediments leads huge sedimentation in the mining sites of upper course. Sediment deposition along the middle and lower course where critical shear stress is

Scouring is initiated either channel bed or bank margin if channel flow has more capability of sediment transport but sediments in flow is very small.

13

140

4 Sediment Grain Size Analysis and Mining Intensity …

Fig. 4.20 Available and critical shear stress at course level: a pre monsoon, b monsoon. Source Prepared by the authors

4.7 Grain Size Related to Bed Shear Stress (s0) and Critical Shear Stress (U*)

141

Plate 4.2 Scouring and deposition process: a pit pool sites, b sandbar sites near Kapastikri divider point. Source Authors

increased by several factors like imbrications, packing of grains, sheltering, grain fabric effects,14 adhesion forces,15 and organic mats16 (Charlton 2007; Clayton 2010). After the crossing of threshold range in critical shear stress, sediment particle is initiated to entrain downward in available requiring shear stress. It is fact that entrainment process started if threshold state of shear stress is generated through decreasing of particle size (0.2 mm). While increasing of particle size leads to smaller threshold value for submerging into laminar sub-layer. In contrastingly, shear stress is decreased during turbulence flow due to present of resistance force initiating from poorly sorted sediment where particle size range is very large (Komar and Cui 1984). Resistance force variation is entirely dependent on textural size of sediments that means coarser grain size induced resistance force is different in one region to another region along the channel bed. Thus, homogeneous resistance force can be generated from well sorted sediment and heterogeneous resistance force initiated from poorly sorted sediment deposits across the channel bed (Mueller et al. 2005). In addition, friction angle is significant parameter for sediment deposition correlating with grain size of the inter locking organized sediment, which angle helps to increase the shear stress as well as initiate the particle entrainment process. Therefore, it is point that particles deposition and movement both processes are depended on critical shear stress highly correlating with frictional angle (Wiberg and Smith 1987).

14

Grain fabric of sediment play significant role to control the rock porosity, permeability and ability to hold or transmit the water content. 15 Adhesion forces mean water attraction force to other substances, which is determined the surface moisture capacity. 16 Organic mats means several organic substances created a cover as like mat, which interlocked cover signified the bed shear stress.

4 Sediment Grain Size Analysis and Mining Intensity …

142

4.7.2

Erosion and Deposition Process in Relation to SGD

Descriptive statistic of shear stress and critical shear stress demonstrated that mean value of median grain size and shear stress are gradually reduces towards the lower course during pre monsoon, but minimum critical shear stress value is increases from upper to lower course (Table 4.6), respectively. During monsoon season, descriptive statistic revealed that median grain size and critical shear stress are continuously decreases towards the lower course but shear stress is abruptly increases towards the lower course (Table 4.7), respectively. This denotes maximum sediment deposition occurring during pre monsoon season (available shear stress  critical shear stress) and maximum erosion accelerating during monsoon season (available shear stress  critical shear stress) in Kangsabati River. Based on textural attributes, grain fabric effect play crucial role to interlock arrangement of sediment grain structure along the coarser river bed whereas finer texture sediment leads to increase the adhesion force adjoining into mutual electrochemical attraction of surface tension effects17. Adhesion force is must be needed in silted deposit channel bed constituting as clay, mud and silt, but this force has not significant contribution to channel forms. While organic mats created from silt or finest textural channel bed to initiate the force for entraining the movement of independent particle. Therefore, surface algae mat is generally developed along mud and sand dominated bed when river flow become continuance low over the course.

4.8

Conclusion

Grain size distribution and stream hydraulic parameters are actively involved to mode of sediment transport, erosion and deposition along the river bed under different energy condition over the season. Statistical measurement of grain size parameters demonstrated that coarser grain oriented well sorting sediments fall under negative asymmetrical skewness distribution and leptokurtic to very leptokurtic kurtosis distribution along the upper course, but finer and medium grain oriented moderate well sorting sediments fall under positive asymmetrical and platykurtic to mesokurtic distribution along the lower course, respectively. Medium and coarser grain oriented moderately sorted sediments fall in fine to very fine skewness and mesokurtic to leptokurtic distribution along the middle course. Massive sedimentation is occurred in mining sites of lower and middle course with the presence of finer grain under low stream energy but coarser sediments are continuously released along the upper course sandbar sites during pre monsoonal flow with the presence of leptokurtic kurtosis distribution under high energy condition. On the other hand, sedimentation processes are 17

Surface tension is cohesive force generating from attraction amongst the several molecules in the water.

4.8 Conclusion

143

generated on the channel bed of upper and middle course mining sites where stream energy gradually declined than critical transported required energy during monsoon peak flow. It is concluded that sources of sediment deposition in the entire channel depended on sandbar intensity of upper course where well sorted coarser grain size increased the entrainment of sediment loads towards the downstream during pre monsoon season, but monsoonal peak flow leads to release sediment from sandbar sites of middle course and made more erosion along the lower course with the presence of pool-riffle alteration. CM diagrams and tractive current deposits reveal the different role of rolling, bottom suspension & rolling, graded suspension no rolling on sediment deposition process, however deposition process faces more fluctuated in mining and pit sites due to having interruption of inter connection between grain size and hydraulic variables of sediment transport in mined Kangsabati River. In spite the spatial distribution of available shear stress and critical shear stress at course level, mining intensity play significant role to interrupt the erosion and deposition sequences along the middle and lower course.

Supplementary Table

Suppl. Table 1 Shear stress and critical shear stress during pre monsoon season Sample

Meter

Depth

Slope

t = pgds

Kg (ps-p)D

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

4.21 3.29 4.01 3.91 3.98 2.99 3.33 3.17 2.95 3.93 3.54 3.85 2.89 3.01 2.98 4.15 2.12 2.95 3.20 1.01 1.95

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.40 0.23 0.27 0.22 0.26 0.26 0.26 0.23 0.22 0.17 0.20 0.26 0.23 0.27 0.31 0.37 0.16 0.27 0.30 0.06 0.09

0.25 0.51 0.61 0.18 0.22 0.49 0.79 0.75 0.65 0.40 0.17 0.68 0.78 0.76 0.63 0.55 0.31 0.27 0.30 0.57 0.57

Erosion/deposition E D D E E D D D D D E D D D D D D E E D D (continued)

4 Sediment Grain Size Analysis and Mining Intensity …

144 Suppl. Table 1 (continued) Sample

Meter

Depth

Slope

t = pgds

22 0.00 2.03 0.00 0.13 23 0.00 3.01 0.00 0.29 24 0.00 3.35 0.00 0.04 25 0.00 2.80 0.00 0.03 26 0.00 2.95 0.00 0.04 27 0.00 3.23 0.00 0.11 28 0.00 3.54 0.00 0.16 29 0.00 3.01 0.00 0.16 30 0.00 2.84 0.00 0.06 31 0.00 7.35 0.00 0.08 32 0.00 7.09 0.00 0.10 33 0.00 6.92 0.00 0.12 34 0.00 5.98 0.00 0.12 35 0.00 6.02 0.00 0.06 36 0.00 7.49 0.00 0.11 37 0.00 8.11 0.00 0.10 38 0.00 7.44 0.00 0.09 39 0.00 7.62 0.00 0.11 40 0.00 7.11 0.00 0.09 41 0.00 7.15 0.00 0.08 42 0.00 6.95 0.00 0.12 43 0.00 6.87 0.00 0.05 44 0.00 7.20 0.00 0.05 45 0.00 7.10 0.00 0.03 Source Calculated by authors from field database

Kg (ps-p)D 0.54 0.25 0.33 0.50 0.83 0.32 0.54 0.55 0.58 0.45 0.52 0.36 0.43 0.33 0.37 0.44 0.65 0.52 0.29 0.49 0.36 0.44 0.34 0.19

Erosion/deposition D E D D D D D D D D D D D D D D D D D D D D D D

Suppl. Table 2 Shear stress and critical shear stress during monsoon season Sample

Meter

1 2 3 4 5 6 7 8 9 10 11 12 13

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Depth

Slope

t = pgds

Kg (ps-p)D

6.54 5.29 5.12 4.02 3.98 3.83 4.95 4.62 4.13 3.83 3.28 3.92 3.65

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.59 0.45 0.45 0.40 0.44 0.53 0.66 0.65 0.53 0.46 0.38 0.44 0.40

0.55 0.54 0.56 0.22 0.16 0.42 0.40 0.40 0.68 0.29 0.24 0.53 0.75

Erosion/deposition E D D E E E E E D E E D D (continued)

Supplementary Table

145

Suppl. Table 2 (continued) Sample

Meter

Depth

Slope

t = pgds

14 0.00 4.25 0.00 0.45 15 0.00 4.42 0.00 0.43 16 0.00 6.94 0.00 0.65 17 0.00 7.85 0.00 0.92 18 0.00 8.25 0.00 1.01 19 0.00 8.94 0.00 0.88 20 0.00 8.35 0.00 1.80 21 0.00 8.12 0.00 0.96 22 0.00 6.38 0.00 0.76 23 0.00 6.95 0.00 0.91 24 0.00 7.12 0.00 1.08 25 0.00 8.12 0.00 0.19 26 0.00 7.01 0.00 0.16 27 0.00 9.89 0.00 0.56 28 0.00 9.92 0.00 0.95 29 0.00 9.86 0.00 0.15 30 0.00 9.98 0.00 2.84 31 0.00 10.01 0.00 1.67 32 0.00 9.99 0.00 1.54 33 0.00 8.82 0.00 0.10 34 0.00 11.12 0.00 0.18 35 0.00 11.56 0.00 1.95 36 0.00 12.13 0.00 2.01 37 0.00 11.95 0.00 2.05 38 0.00 11.50 0.00 1.96 39 0.00 11.83 0.00 1.94 40 0.00 10.51 0.00 1.39 41 0.00 10.99 0.00 1.57 42 0.00 11.25 0.00 1.87 43 0.00 11.02 0.00 1.92 44 0.00 11.58 0.00 1.66 45 0.00 11.36 0.00 1.86 Source Calculated by authors from field database

Kg (ps-p)D 0.21 0.76 0.38 0.66 0.28 0.30 0.30 0.61 0.61 0.57 0.57 0.43 0.33 0.78 0.16 0.36 0.37 0.55 0.52 0.32 0.48 0.29 0.45 0.54 0.42 0.31 0.31 0.44 0.47 0.29 0.48 0.28

Erosion/deposition E E E E E E E E E E E D D D E D E E E D D E E E E E E E E E E E

References Abarca M, Guerra P, Arce G, Montecinos M, Escauriaza C, Coquery M, Pastén P (2017) Response of suspended sediment particle size distributions to changes in water chemistry at an Andean mountain stream confluence receiving arsenic rich acid drainage. Hydrol Process 31(2):296–307 Adhami M, Sadeghi SHR (2016) Sub-watershed prioritization based on sediment yield using game theory. J Hydrol 541:977–987 Ahmad MF, Dong P, Mamat M, Wan Nik WB, Mohd MH (2011) The critical shear stresses for sand and mud mixture. Appl Math Sci 5(2):53–71

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Angusamy NG, Rajamanickam V (2006) Depositional environment of sediments along the southern coast of Tamil Nadu, India. Oceanologia 48(1):87–102 Anthony EJ, Hequette A (2007) The grain-size characterisation of coastal sand from the Somme estuary to Belgium: sediment sorting processes and mixing in a tide- and storm-dominated setting. Sed Geol 202(3):369–382. https://doi.org/10.1016/j.sedgeo.2007.03.022 Azidane H, Michel B, Bouhaddioui ME, Haddout S, Magrane B, Benmohammadi A (2020) Grain size analysis and characterization of sedimentary environment along the Atlantic Coast, Kenitra (Morocco). Mar Georesources Geotechnol, 1–8 Baer T, Barbour SL, Gibson JJ (2016) The stable isotopes of site wide waters at an oil sands mine in northern Alberta, Canada. J Hydrol 541:1155–1164 Baiyegunhi C, Liu K, Gwavava O (2017) Grain size statistics and depositional pattern of the Ecca Group sandstones, Karoo Supergroup in the Eastern Cape Province, South Africa. Open Geosci 9(1):554–576 Bhatia MR, Crook KA (1986) Trace element characteristics of graywackes and tectonic setting discrimination of sedimentary basins. Contrib Miner Petrol 92(2):181–193 Bhattacharya RK, Chatterjee ND, Dolui G (2016) Grain size characterization of instream sand deposition in controlled environment in river Kangsabati, West Bengal. Model Earth Syst Environ 2(3):118 Bhattacharya R, Dolui G, Chatterjee ND (2019a) Effect of instream sand mining on hydraulic variables of bedload transport and channel planform: an alluvial stream in South Bengal basin, India. Environ Earth Sci 78(10):303 Bhattacharya RK, Chatterjee ND, Dolui G (2019b) Consequences of sand mining on water quality and instream biota in alluvial stream: a case-specific study in South Bengal River, India. Sustain Water Resour Manag 5(4):1815–1832 Bhattacharya RK, Chatterjee ND, Das K (2019c) Geomorphic response to riverine land cover dynamics in a quarried alluvial river Kangsabati, South Bengal, India. Environ Earth Sci 78 (22):633 Blott SJ, Pye K (2001) GRADISTAT: a grain size distribution and statistics package for the analysis of unconsolidated sediments. Earth Surf Proc Land 26(11):1237–1248 Blott SJ, Pye K (2012) Particle size scales and classification of sediment types based on particle size distributions: review and recommended procedures. Sedimentology 59(7):2071–2096 Boggs S (1995) Principles of sedimentology and stratigraphy. Prentice-Hall, Englewood Cliffs, NJ, pp 79–93 Brambati (1969) Stratigraphy and sedimentation of Siwaliks of North Eastern India. Proc Int Sem Int Basins Geol Res Chiang Mai Thail 427–439 Charlton R (2007) Fundamentals of fluvial geomorphology. Routledge, pp 234 Church M (2006) Bed material transport and the morphology of alluvial river channels. Annu Rev Earth Planet Sci 34:325–354 Clayton J (2010) Local sorting, bend curvature and particle mobility in meandering gravel be rivers. Water Resour Res 46:W02601. https://doi.org/10.1029/2008WR007669 Clayton J, Pitlick J (2008) Persistence of the surface texture of gravel—Bed River during a large flood. Earth Surf Process Landf 33(5):661–673. https://doi.org/10.1002/esp.1567 Dinesh AC (2009) G-Stat-A software in VB6 for grain size statistical analyses, CM diagrams, trend diagrams, etc. designed and developed by A.C. Dinesh, Geologist, Marine wing, GSI, Mangalore Du Boys P (1879) Let Rhone et les Rivieres a Lit Affouillable. Ann Ponts Chaussees 5(18):141–195 Duane DB (1964) Significance of skewness in recent sediments, western Pamlico Sound, North Carolina. J Sediment Res 34(4) E Fan D, Shang S, Cai G, Tu J (2015) Distinction and grain-size characteristics of intertidal heterolithic deposits in the middle Qiantang Estuary (East ChinaSea). Geo-Mar Lett 35(3):161– 174. https://doi.org/10.1007/s00367-015-0398-2 Folk RL (1966) A review of grain-size parameters. Sedimentology 6(2):73–93

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Folk RL (1980) Petrology of sedimentary rocks. Walter geology library. Hemphill Publishing Company, Austin, Tex Folk RL, Ward WC (1957) Brazos River bar: a study in the significance of grain size parameters. J Sediment Petrol 27(1):3–26. https://doi.org/10.1306/74D70646-2B21-11D7-8648000102C 1865D Fralick PW, Kronberg BI (1997) Geochemical discrimination of clastic sedimentary rock sources. Sed Geol 113(1–2):111–124 Francke A, Wennrich V, Sauerbrey M, Juschus O, Melles M, Brigham-Grette J (2013) Multivariate statistic and time series analyses of grain-size data in quaternary sediments of Lake El’gygytgyn, NE Russia. Clim Past 9(6):2459–2470. https://doi.org/10.5194/cp-9-2459-2013 French J, Payo A, Murray B, Orford J, Eliot M, Cowell P (2016) Appropriate complexity for the prediction of coastal and estuarine geomorphic behaviour at decadal to centennial scales. Geomorphology 256:3–16 Friedman GM (1961) Distinction between dune, beach, and river sands from their textural characteristics. J Sediment Petrol 31(4):514–529. https://doi.org/10.1306/74d70bcd-2b2111d7-8648000102c1865d Friedman GM (1967) Dynamic processes and statistical parameters compared for size frequency distribution of beach and river sands. J Sediment Res 37(2) Friedman GM (1979) Differences in size distributions of populations of particles among sands of various origins: addendum to IAS Presidential Address. Sedimentology 26(6):859–862 Ghoshal K, Purkait B, Mazumder BS (2011) Size distributions in suspension over sand–pebble mixture: an experimental approach. Sed Geol 241(1–4):3–12 Griffiths JC (1967) Scientific method in analysis of sediments. McGraw-Hill, New York Irudhayanathan A, Thirunavukkarasu R, Senapathi V (2011) Grain size characteristics of the Coleroon estuary sediments, Tamilnadu, East coast of India. Carpath J Earth Environ Sci 6 (2):151–157 Karuna Karuda T, Jagannadha RM, Ganesh B, Avatharam P (2013) Studies on textural characteristics of Erra Kalva River, West Godavari District, Andhra Pradesh, East Coast of India. Int J Geomat Geosci 4(2):289–292 Kheirfam H, Vafakhah M (2015) Assessment of some homogeneous methods for the regional analysis of suspended sediment yield in the south and southeast of the Caspian Sea. J Earth Syst Sci 124(6):1247–1263 Knighton D (1998) Fluvial forms and processes: a new perspective. Arnold Publishers, London, p 383 Komar PD, Cui B (1984) The analysis of grain-size measurements by sieving and settling-tube techniques. J Sediment Res 54(2) Kondolf GM, Piégay H, Landon N (2002) Channel response to increased and decreased bedload supply from land use change: contrasts between two catchments. Geomorphology 45(1–2):35–51 Lane EW (1955) Design of stable alluvial channels, Transactions. Am Soc Civ Eng 120 (2776):1234–1260 Li Z, Xu X, Yu B, Xu C, Liu M, Wang K (2016) Quantifying the impacts of climate and human activities on water and sediment discharge in a karst region of southwest China. J Hydrol 542:836–849 Mason C, Folk RL (1958) Differentiation of beach, dune and aeolian flat environments by size analysis, Mustang Island, Texas. J Sediment Res 28:211–226 McLaren P, Hill SH, Bowles D (2007) Deriving transport pathways in a sediment trend analysis (STA). Sediment Geol 202(3):489–498. https://doi.org/10.1016/j.sedgeo.2007.03.011 Moiola RJ, Weiser D (1968) Textural parameters; an evaluation. J Sediment Res 38(1):45–53 Montgomery DR, Buffington JM (1997) Channel-reach morphology in mountain drainage basins. Geol Soc Am Bull 109(5):596–611 Mueller ER, Pitlick J, Nelson JM (2005) Variation in the reference Shields stress for bed load transport in gravel‐bed streams and rivers. Water Resour Res 41(4)

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Muraleedharan NMN, Ramachandran KK (2002) Textural and trace metal distribution in sediments of Beypore estuary. Indian J Mari Sci 31:295–304 Nair MNM, Ramachandran KK (2002) Textural and trace elemental distribution in sediments of the Beypore estuary (SW coast of India) and adjoining innershelf. Indian J Mar Sci 31:295–304 Nugroho SH, Putra PS (2017) Spatial distribution of grain size and depositional process in tidal area along Waikelo Beach, Sumba. Marine Georesour Geotechnol 36(3):299–307. https://doi. org/10.1080/1064119x.2017.1312649 Oyedotun TDT (2016) Sediment characterisation in Estuarine—coastal systems. J Coast Zone Manag 19(3):433. https://doi.org/10.4172/2473-3350.1000433 Oyedotun TDT (2020) Compositional and multivariate statistical analyses for grain-size characterisation of intertidal sedimentary facies in an estuarine environment. Geol Ecol Landsc 1–7 Oyedotun TDT, Burningham H, French JR (2012) Characterisation of estuary and adjacent beach sediments in the Gannel Estuary, south-west England. Geosci South-west Engl 13(1):70–76 Oyedotun TDT, Burningham H, French JR (2013) Sediment sorting and mixing in the Camel Estuary, UK. In: Conley DC, Masserlink G, Russell PE, O’Hare TJ (eds) Proceedings 12th international coastal symposium (held at Plymouth, England, UK), journal of coastal research, [Special Issue No 65] (pp 1563–1568). https://doi.org/10.2112/S165-264.1 Passega R (1957) Texture as characteristics of clastic deposition. Bull AAPG 41(9):1952–1984 Passega R (1964) Grain size representation by CM pattern as a geological tool. J Sediment Petrol 34(4):830–847 Pradhan UK, Sahoo RK, Pradhan S, Mohany PK, Mishra P (2020) Textural analysis of coastal sediments along East Coast of India. J Geol Soc India 95(1):67–74 Rajganapathi VC, Jitheshkumar N, Sundararajan M, Bhat KH, Velusamy S (2013) Grain size analysis and characterization of sedimentary environment along Thiruchendur coast, Tamilnadu, India. Arab J Geosci 6(12):4717–4728 Ramanathan AL, Rajkumar K, Majumdar J, Singh G, Behera PN, Santra SC, Chidambaram S (2009) Textural characteristics of the surface sediments of a tropical mangrove Sundarban ecosystem India Ritter DF, Kochel RC, Miller JR (2002) Process geomorphology. McGraw-Hill, Boston Sadeghi SHR, Kheirfam H (2015) Temporal variation of bed load tosus-pended load ratio in Kojour river, Iran. CLEAN–Soil, Air, Water 43(10):1366–1374 Sadeghi SH, Zakeri MA (2015) Partitioning and analyzing temporal variability of wash and bed material loads in a forest watershed in Iran. J Earth Syst Sci 124(7):1503–1515 Sadeghi SH, Gharemahmudli S, Kheirfam H, Darvishan AK, Harchegani MK, Saeidi P, …, Vafakhah M (2018) Effects of type, level and time of sand and gravel mining on particle size distributions of suspended sediment. Int Soil Water Conserv Res 6(2):184–193 Sagoe KM, Visher GS (1977) Population breaks in grain-size distributions of sand; a theoretical model. J Sediment Res 47(1):285–310 Sahu BK (1964) Depositional mechanisms from the size analysis of clastic sediments. J Sediment Res 34(1) Schwartz SS, Smith B (2016) Restoring hydrologic function in urban landscapes with suburban subsoiling. J Hydrol 543:770–781 Seralathan P, Padmalal D (1994) Textural studies of the surficial sediments of Muvattupuzha river and central Vembanad Estuary, Kerala. Geol Soc India 43(2):179–190 Thomas MC, Parsons ME, Foster JM (2007) The use of multivariate statistics to elucidate patterns of floodplain sedimentation at different spatial scales. Earth Surf Proc Land 32:672–686. https://doi.org/10.1002/esp.1440 Visher GS (1969) Grain size distributions and depositional processes. J Sediment Res 39(3):1074– 1106 Wai OW, Wang CH, Li YS, Li XD (2004) The formation mechanisms of turbidity maximum in the Pearl River estuary, China. Mar Pollut Bull 48(5):441–448 Wiberg PL, Smith JD (1987) Calculations of the critical shear stress for motion of uniform and heterogeneous sediments. Water Resour Res 23(8):1471–1480

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Chapter 5

Mining Response on Alluvial Channel Flow and Sediment Transport: Application of Hydro-Morphological Techniques and Principal Component Analysis (PCA)

5.1

Introduction

River bed stability or instability dynamic likes aggradations, degradations are governed by sediment transport and water discharge in the entire course (Dey 2014). Based on sediment transport mode, sediment transport is classified into three type i.e. bedload, suspended load and wash load with the following of three dominant dimensionless parameters as grain size, mobility and transport rate (Ackers and White 1973; Bhattacharya et al. 2019a). Sediment grain size, textural arrangement in sediment and stream hydraulics variables in sand or gravel bed are controlled the bedload capacity for generating the sediment transport in an alluvial channel when resistance force is very much lower than tractive force1 of bed material to initiate in the particular flow condition (Church 2006; Barman et al. 2017; Sulaiman et al. 2017). In terms of river bed material extraction, bedload sediment transport is very essential to require huge demand and determined the sand and gravel mining sites (Chalov et al. 2018; Bhattacharya et al. 2019a). Sediment transport reaches dynamic equilibrium state where no sediment deficit or surplus are occurred due to steady flow condition but excessive sediment mining from channel bed are causes the sediment deficit to create interruption on dynamic equilibrium state in river system. Consequently, several physical changes such as river bed lowering, bank erosion, degradation of river bed material that generated many adverse affects as classified into hydrological, morphological, ecological and environmental consequences (Rinaldi et al. 2005; Barman et al. 2017, 2018a). Sand mining induced hydraulic changes are altered into morphological configuration of

1

Tractive force is one kind of sediment motion generating forces, which is initiated between particles and tangential surface resulting from dry friction and shear force of the bed surface. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. K. Bhattacharya and N. Das Chatterjee, River Sand Mining Modelling and Sustainable Practice, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-72296-8_5

151

152

5 Mining Response on Alluvial Channel Flow and Sediment …

channel, which is extended in ecology and environment of the river system (Barman et al. 2018b; Bhattacharya et al. 2019a, b, c, 2020). In over-excavated alluvial channel, turbulence flow is most effective ways to initiate the bedload sediment transport through the complex interaction between flow and mobile channel bed (Erskine 1990; Kim 2005). Sediment transport gradually increases towards the channel bed in respect to progressive shear stress mean while water and sand both are fully saturated during sand feeding stage. In contrary, sediment transport is interrupted on sand and gravel bed through the continuously mixing both of them in mined channel where hungry water is generated due to decreasing of shear stress towards the lower convex slope. Subsequently, transport capacity gradually increased in extraction sites but there have insufficient movable bed materials to being transported sediments (Curran 2007). This irregular sediment delivery leads the fluctuation of hydraulic responses involving in movable bed materials (Sulaiman et al. 2017; Bhattacharya et al. 2019a). On the other hand, boundary shear stress2 near the river bank controlled the sediment transport during bankfull discharge or peak flow (Kondolf 1994a, b, 1997). Recent past several human activities like sand mining, dam, bridge and road construction are greatly hampered the boundary shear stress throughout the channel (Migiros et al. 2011; Costea 2018). In particularly, massive sand mining creates numerous wet and dry pits on channel bed, which is disturbed the bed slope due to positive changes of bed roughness. Mining pits are playing significant role to change the boundary shear stress when any interruption will occurred between critical shear stress and movable sediments along the thalweg line then down cutting erosion initiated on over the channel bed with the decreasing of shear stress (Parker et al. 1982; Meyer-Peter and Müller 1948). Simultaneously, mining pit generated knick points are shifted into structural knick points caused by absence of sediment discharge, thus bank erosion immediately occurred at wide rate across the cliff side of convex slope (Kondolf 1994a, b; Neyshabouri et al. 2002; Martin-Vide et al. 2010; Bhattacharya et al. 2019a, b). Hence, flood vulnerability across the left and right bank mining sites drastically increases through the generating of hungry water in one hand and huge coarser sand extraction leads to massive finer sedimentation at irregular rate in other hand. Furthermore, shear stress faces more disruption between channel flow and sediment regime in the entire mining affected channel (Sreebha 2008; Tamang 2013). It is point that sand mining has greatly affect on hydraulic variables of water flow and sediment entrainment over the bedload and suspended sediment transport to create interruption on channel planform and hydromorphic setup over the courses.

2

Boundary shear stress indicates the tangential component generating from hydrodynamic forces, which is acting along the channel bed, and also controlled the flow regime in open channel flow.

5.2 Mining Genesis Turbulence Flow …

5.2

153

Mining Genesis Turbulence Flow and Its Affected Hydraulic Variables of Sediment Transport

Based on Reynolds experiment (1883), channel flows are broadly categorized into two class i.e. laminar flow and turbulence flow (Dey 2014). Laminar flow is generated at low flow velocity channel sites where fluid layers are smoothly slide over each and other as well as absent of fluid mass exchange between layers. While turbulence flow is occurred if flow velocity crossed the certain limit of threshold value that means stable laminar flow converted into unstable eddy formation in the entire fluid medium. Therefore, turbulence flow means irregular and fluctuating flow in between fluid layers including random fluid mass exchange in open channel. In case of sand mining pits, turbulence flow is generated if bed materials acts like the mobile boundary over flow, which has greatly influenced on sediment transport mechanism and its associated morphdynamical change (Barman et al. 2018b). Acceleration of bed material motion is started when turbulence flow transferred its momentum to the entrained bed materials, at the same time channel bed resistance force tried to restrict the sediment motion and its entrained process with the occurring of fluid energy dissipation. Contrastingly, high speed movable bed materials increased the sediment transport towards the downstream from pit sites, which significantly responses on turbulence parameters to lead the change of flow characters near mining sites or downstream of mining sites. Consequently, upstream edge of pits are migrated along with sediment deposition particles transported from upstream pit sites caused by drastically reduces of flow velocity, but bed elevation is continuously lowering towards the downstream when transfer momentum between flow and bed sediment is increases in pit sites with the increasing of erosion towards downstream from pits (Barman et al. 2017, 2018a, 2020).

5.2.1

Measure of Hydraulic Variables of the Flow Regime

Sand mining affected hydraulic variables in flow regime along with turbulence parameters of flow are investigated in between non mining sites or sandbar and mining pit sites using of Reynolds number, Froude number, Manning coefficient, Chezy coefficient and roughness coefficient. Reynolds and Froud numbers are employed to measure the channel flow characteristic while Manning coefficient, Chezy coefficient, roughness coefficient are applied to calculate the flow resistance force in fluid medium.

154

5.2.1.1

5 Mining Response on Alluvial Channel Flow and Sediment …

Reynolds Number (Re)

Sand mining affected flow characters like laminar, turbulence are easily determined during pre and post mining using of Reynolds number. This number is calculated as follow Eq. 5.1 (Julien 2002):
2 q VL qVL VL ¼ Re ¼ lV  ¼ l # L2

ð5:1Þ

Here, q denotes water density (1.00 gm/cm3), V2 stated average velocity, L means length dimension to take the hydraulic radius (R), and l means dynamic fluid viscosity (kg/(m-s)), # means kinetic viscosity (m2/s). Based on Re values, channel flow is classified into three types i.e. laminar flow (Re < 2000), turbulent flow (Re > 3500), transitional flow (Re 2000–3500), respectively.

5.2.1.2

Froud Number (Fr)

Froud number is another important technique to measure the flow characteristic in open channel, which is calculated as follow Eq. 5.2 (Julien 2002): V Fr ¼ p gD

ð5:2Þ

where, V denotes flow velocity in channel, g indicates gravitational acceleration (9.81 m/s2), D refers water depth. Based on Fr values, channel flow is categorized into three different flow i.e. subcritical flow or tranquil/slow flow (Fr < 1), critical flow (Fr = 1), and supercritical flow or rapid flow (Fr > 1), respectively. Critical flow reveals unstable water flow and regularly set up in standing fluid motion between subcritical and supercritical flow. Super critical flow3 is occurred when actual water depth reaches as below the critical depth under high energy condition while subcritical flow is occurred when actual depth reaches as above critical depth under low energy condition.

5.2.1.3

Chezy Coefficient (C) and Manning Coefficient (v)

Chezy, a well known French engineer (Chow 1959) gives the flow resistance force to establish the correlation amongst the hydraulic radius, average channel slope and boundary roughness coefficient. These relationships are intimately related with bed Super critical flow velocity is generated when critical depth is greater than flow depth of the channel, critical velocity is less than flow velocity and critical slope is less than channel slope.

3

5.2 Mining Genesis Turbulence Flow …

155

resistance and uniform open channel flow (Lindeburg 1992; Julien 2002; Bhattacharya et al. 2019a). So, computation of hydraulic radius (rh), flow velocity (v) and slope (S) are very essential to accurately measure the bed roughness as follow Chezy formula (C) (Lindeburg 1992): v rh  S

ð5:3Þ

pffiffiffiffi v ¼ C rh  S

ð5:3:1Þ

C¼p

rh ¼ S¼

1
v2 S c 1
v 2

rh C

ð5:3:2Þ ð5:3:3Þ

Then, Chezy gives an equation name as Chezy coefficient (C) denoted flow resistance function controlling by hydraulic radius and bed roughness (Lindeburg 1992). 1

1 2 C ðRSÞ1=2 ¼ nR Sð2Þ 3

ð5:4Þ

Chezy coefficient (Eq. 5.4) is modified as revised form to proper measure the bed roughness of the channel bed following Manning coefficient (n). 1 C ¼ ðrh Þ1=6 n

ð5:5Þ

On the other hand, Robert Manning, a popular Irish engineer (1891) modified the hydraulic radius of Chezy coefficient as revised form names Manning coefficient (n) as follow Eq. 5.6: n¼

1 Cðrh Þ1=6

rh ¼ ðC  N Þ6

ð5:6Þ ð5:6:1Þ

According to Julien (2002), C has negative correlation with n in respect to channel slope, hydraulic radius and flow resistance in fluid channel. In terms of over sediment extraction, inverse correlation of C and n with bed roughness are interrupted from non mining sandbar sites to mining pit sites (Bhattacharya et al. 2019a). Finally, Manning coefficient (n) and Chezy coefficient (C) are applied to determine the roughness coefficient on channel bed with making a new formula of roughness coefficient (Rc) as given by Te Chow (1959), Julien (2002).

156

5 Mining Response on Alluvial Channel Flow and Sediment …

Rc ¼

ðx  1ÞD1=6 6:78ðv þ 0:95Þ

ð5:7Þ

Here, v denotes flow velocity in channel, D denotes channel depth.

5.2.2

Measure of Hydraulic Variables of the Sediment Transport

Mining affected hydraulic variables of sediment transport is easily determined with the analysis of particles diameter, shear stress, critical shear stress, shear velocity, settling velocity, incipient motion, sediment concentration, bedload and total sediment transport rate using of applicable hydraulic formulations. Impact of turbulent flow structure has great variation on hydraulic variables of sediment transport from sandbar to pit sites during pre monsoon to post monsoon season along the mined river.

5.2.2.1

Shear Stress (so Þ

Shear stress is important indicator to estimate the threshold of sediment motion and bedload transport rate (Mueller et al. 2005). Sediment motion is initiated when flow induced bed shear stress (so Þ is crossed the threshold limit of bed shear stress (soe Þ; and then all particles near bed surface are arranged to motion towards down slope (Dey 2014). Bed shear stress (so Þ is calculated as follow Eq. 5.8 (Du Boys 1879) : so ¼ qgrh S

ð5:8Þ

Here, q means water density (1.00 gm/cm3), g denotes gravitational acceleration, rh indicates hydraulic radius (m), S means slope.

5.2.2.2

Critical Shear Stress (scr )

Critical shear stress (scr ) stated a condition when shear stress is more than bed shear stress then sediment motion to be started (Ahmad et al. 2011). Critical shear stress (scr ) is intimately related to the flow energy condition near bed and compactness of particle diameter. Du Boys (1879) provided the exponential formula of critical shear stress under weak sediment transport condition as follow: scr ¼ KgðqS  qÞd

ð5:9Þ

5.2 Mining Genesis Turbulence Flow …

157

K stated constant value (0.045), qS denotes sediment density (2.65 g/cm3), q indicate water density, d means particle diameter that taken as median grain size in meter (d50) (Knighton 1998).

5.2.2.3

Shear Velocity (u Þ

Shear velocity indicates the kinematic substitute for the dynamic change of bed shear stress in turbulent flow boundary layers. This velocity helps to determine the sediment transport from kinematic-velocity profiles in non-equilibrium open channel flow. Threshold state of particle velocity is calculated using Julian method (2002) follows: ð5:10Þ Here,

5.2.2.4

denotes vertical z direction Settling Velocity (x0 Þ

Settling velocity (x0 Þ or fall velocity reveals the threshold condition of particle motion in between moving water flow and stagnant water mass. Sediment transport is started towards downstream under a threshold condition when settling velocity is very much low than vertical layers of the fluid velocity (Julien 1995). Therefore, particle fall velocity is effectively measured as follow Eq. 5.11 8# x0 ¼ ds

( 1þ

ðG  1Þg 72#2

)

0:5 ds3

1

ð5:11Þ

where, ds means particle diameter, # means kinematic viscosity, G means specific gravity, g means gravitational acceleration

5.2.2.5

Incipient Motion (ym)

Initial motion of sediment particles are defined as incipient motion that means transitional condition from one stationary state to initial or incipient motion of the particles caused by increases of hydro dynamic forces directly acting on loose sediment dominated bed surface (Simões 2014). Incipient motion (ym) is determined a threshold condition to separate the sedimentation from erosion along with assess the significance role of hydraulic variables of the sediment transport (Julien 1995). Calculation of ym is follow Eq. 5.12

158

5 Mining Response on Alluvial Channel Flow and Sediment …

!

1 1000 8v ds

ym ¼

5.2.2.6

ð5:12Þ

Total Sediment Transport (QT Þ

Dynamic trend of stability or instability in the entire streambeds like aggradations, degradations are governed by water discharge and sediment transport (Dey 2014). Availability of transported material and energy of transport availability of the stream both are greatly influenced on sediment transport rate in every cross sectional site. In this study, Ackers–White (1973) is applied to find out the interruption of sediment transport caused by sand mining with the considerable of some coefficient values as follow Eq. 5.13  d ¼ d



1=3 g ys  1 #2 y

ð5:13Þ

where, ys means specific weight of sediment, y means specific weight of water. Sediment transport equation considered some coefficient value i.e. C1, C2, C3 and C4 that are estimated as follow: 1\d \60

ð5:13:1Þ

C1 ¼ 1:00  0:56 log d h i C2 ¼ e 2:86 log d  ðlog d Þ2 3:53

ð5:13:2Þ

C3 ¼

0:23

ð5:13:3Þ

þ 0:14

ð5:13:4Þ

9:66 þ 1:34 d

ð5:13:5Þ

1

d2 C4 ¼

Then particle mobility number is computed as follow  Fgr ¼ uC 1

2 31C1

1=2 ys v 6 7 1 g:d 4qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi5 y 32 log 10D d

where, D means mean depth. Finally function of sediment transport (Ggr) explained as Eq. 5.13.7

ð5:13:6Þ

5.2 Mining Genesis Turbulence Flow …

159

Ggr ¼ C2

5.2.2.7

Fgr 1 C3

C4 ð5:13:7Þ

Sediment Concentration (X)

Concentration of sediment (X) is defined as ratio between wet mixture weight and dry wet of the sediment in a water-sediment mixture, which obtained from a stream and other water body (Blanchard et al. 2011). With the prediction of hydraulic relationship between sandbar stability and mining instability, Ackers–White (1973) is employed to measure the sediment concentration (X) in parts per million units considering some fluid weight in an open fluid medium as follow Eq. 5.14 X ¼ Ggr



d y s v C1 D y u

ð5:14Þ

Therefore, total sediment transport load is measured as multiplying with sediment concentration (X) and water discharge (Q) expressing in Eq. 5.15 Q T ¼ ðQ  X Þ

5.2.2.8

ð5:15Þ

Bedload Estimation (Qb)

Bedload relatively composed with fragmented sand and gravel particles to be initiated motion in a sliding or rolling mode under small excess bed shear stress (so  scr ) (Hassanzadeh 2007; Dey 2014). Bedload transport in open channel is estimated through the assigning of critical threshold condition to affect the bed sediment transport using Meyer-Peter and Muller method (1948) as follow Eqs. 5.8, 5.9, 5.16 Qb ¼ 0:253ðso  scr Þ3=2

5.3

ð5:16Þ

Case Study: Sand Mining Affected Interruption of Hydraulic Variables in Flow Regime of Kangsabati River

After the construction of Mukutmonipur dam (1958), longitudinal slope along the Kangsabati River has been gradually breaks up, then huge sediment loads carrying from upper catchment are deposited at maximum rate in knick point sites of channel bed. Simultaneously sand quarrying activities are massively grown up from

160

5 Mining Response on Alluvial Channel Flow and Sediment …

Mukutmonipur dam to downstream sites of Kangsabati River (Bhattacharya et al. 2019a, b). In middle course, excessive sand mining than natural replenishment disrupted the bed slope and increases the bed roughness to create asymmetrical water surface across the both bank sites. Therefore, wide bank erosion abruptly expanded across the convex cliff-off sites. Maximum amount of coarser grain sand extraction helps to irregular finer sediment deposition in the entire middle course in one hand and disrupted the shear stress to lead hydraulic interruption in between flow regime and sediment discharge in other hand. Mining induced anastomosing flow4 forms the maximum channel braiding and braid channel ratio through the reducing the water slope along the middle course. Sediment discharge deficiency in lower course generated hungry water flow or turbulent flow leads to form of numerous structural knick points in mining pits to increase the flood vulnerability across the bank. Based on above fact, sand mining induced flow regime faces more interruption and generated more channel instability that made negative consequences on river system. Therefore, measurements of flow characteristics are essential to compare mining affect during pre mining and post mining.

5.3.1

Hydraulic Variables of Flow Regime and Mining Intensity

Hydraulic parameters of flow regime like bankfull discharge, mean velocity, bed roughness and flow characteristics are indicators of stability status of channel dynamics, but several human activities like sand mining directly or indirectly imposed the interruption or helped to cross the threshold limit of hydraulic resilience capacity (Barman et al. 2018a, b, 2019; Bhattacharya et al. 2019a, b). As a result, several negative consequences are abruptly rises on channel morphology, ecology and environment over the years in the entire mined channel (Kondolf 1994a, b, 1997; Ghosh et al. 2016). In this case study, every nine cross sections are considered for upper, middle and lower course in respect of sandbar, mining and pit sites during pre monsoon, monsoon and post monsoon out of 27 sites (see Figs. 2.11b, 2.13a, b). All the analyses are based on these field experimental databases along the Kangsabati River (2015–2016).

5.3.1.1

Effects on Bankfull Discharge (Q)

In open channel, flow discharge is key component to generate the particles motion or entrainment incorporating other hydraulic variables (Ghosh and Guchhait 2014; Hajbabaei et al. 2017). Discharge variation in different sites of river course caused by human induced resistance force or bed slope variation is effectively measured Anastomosing flow means variable direction of flow pattern in defined channel.

4

5.3 Case Study: Sand Mining Affected …

161

using of stage discharge curve (Bhattacharya et al. 2019a). In case specific study of Kangsabati River, maximum discharge is observed as 4723.742 Cumec in pit sites during monsoon season and minimum discharge falls as 25.35 Cumec in mining sites during post monsoon in upper course while peak discharge or bankfull flow in middle course reaches of 3475.04 Cumec in pit sites during monsoon but discharge sharply falls of 25.65 Cumec in mining sites during post monsoon (Tables 5.1, 5.2, 5.3), respectively. In lower course, maximum discharge increases up to 683 Cumec in pit sites during monsoon but water mass flow decreases very lower amount as 5.841 Cumec in mining sites during post monsoon, respectively. Discharge distribution demonstrated that peak discharge is observed in pit sites during monsoon while lean discharge concentrated mainly mining sites during post monsoon. It is point that monsoonal rainfall is prime source of Kangsabati channel flow and sand mining pits increased the channel depth to expand the water supplier capacity meanwhile both are the main causes for increasing the flow discharge.

5.3.1.2

Effects on Flow Velocity (v)

Longitudinal mean flow velocity play crucial role to determine the flow regime and sediment transport, but it is not same in every site due to variation of hydraulic radius and wetted perimeter (Ghosh and Guchhait 2014). However, various anthropogenic activities are changes the velocity distribution over the river courses (Bhattacharya et al. 2019a, b), in particular sand mining pit, velocity is dropped down caused by drastically increases the flow depth and hydraulic radius of channel (Ghosh et al. 2016; Barman et al. 2019). According to Barman et al (2018a), flow velocity is inversely grown-up at the bottom layer in center point of a symmetrical pit whereas velocity is lower than mean flow velocity of bed surface in recirculation zone. In addition, flow separation is occurred in the center part of longitudinal axis of the pit (Alfrink and Van Rajin 1983; Barman et al. 2018a, 2019). In Kangsabati River, maximum v (2.6 m/s) is observed in mining and pit sites of upper course during monsoon and pre monsoon season but least v is stabilized in sandbar sites during post monsoon, respectively (Tables 5.1, 5.2, 5.3). While v is progressively grown-up in mining and sandbar sites of middle course during monsoon and pre monsoon but v is abruptly drop down in pit sites during post monsoon, respectively. In lower course, maximum v is chiefly concentrated in sandbar sites during pre monsoon and monsoon but velocity is reduces in pit sites of post monsoon season, respectively. It is noticed that mean velocity always increases in mining and pits of upper course caused by huge trapped of sediment fills the pit depth (D < 0.38 m) during monsoonal peak flow. In contrary, over extraction of sediments than trapping are causes of velocity reduction in pit sites of middle and lower course due to continuously depth increases on channel bed (D > 4 m) throughout the season. Moreover, pits are always received low velocity than bed surface velocity for lacking of flow discharge during post monsoon.

Upper Sandbar

Mining

Pit

Middle Sandbar Mining

Pit

Lower Sandbar Mining

Pit

Flow regime Q (Cumec) 2093.8764 2254.727 2286.144 173.587 394.508 183.732 110.25 154.1475 67.8037 V(m/s) 1.87 1.95 1.92 0.46658 0.53529 0.5234 0.873 0.775 0.6125 n 0.05 0.0547 0.055 0.033 0.0529 0.0495 0.011 0.022 0.0384 C 22.16 20.726 21.069 26.556 19.1759 18.68 98.029 53.1134 27.371 Rc 0.6648 2.138 0.7343 0.762513 0.51315 0.9871 3.89 0.848 0.636 Re 3478200 4133500 4665600 4240000 39450800 1840000 1374000 77500 826875 Fr 0.437 0.4265 0.393 0.1561 0.1152 0.1492 0.22 0.1548 0.168 Sediment regime d50 (ø) 1.05 0.84 1.03 0.35 0.3 0.58 0.53 0.49 0.45 QT 528 337 101 25 26.3 178 5.27 2.45 0.54 X 2.53 1.5 2.15 1.45 0.67 0.51 0.48 0.25 2.37 s0 9.13 10.44 11.92 16.26 17.18 4.39 1.55 2.51 5.3 U* 0.91 1.04 1.8 1.62 1.4 0.67 0.15 0.26 0.53 W° 0.77 0.96 0.78 4.98 4.39 7.83 1.46 1.6 1.76 Ym 1.19 1.09 2.31 0.33 0.32 0.09 0.11 0.16 0.3 Qb 5.84 7.57 17.67 16.04 13.19 3.82 0.26 0.73 2.75 Q Discharge, V Mean Velocity, n Manning Coefficient, C Chezy Coefficient, Rc Roughness Coefficient, Re Reynolds Number, Fr Froud Number, d50 Median grain; QT Sediment transport; X Sediment concentration; s0 Shear stress; U* Shear velocity; W° Settling velocity; Ym Incipient motion; Qb Bed sediment transport. Data source Calculated by the authors

Hydraulic variables

Table 5.1 Hydraulic variables of flow and sediment regime during pre monsoon

162 5 Mining Response on Alluvial Channel Flow and Sediment …

Upper Sandbar

Flow regime Q (Cumec) 3829.201 V(m/s) 2.23 n 0.0674 C 17.539 Rc 0.6491 Re 6110200 Fr 0.43 Sediment regime d50 (ø) 1.03 QT 733 X 1.92 s0 13.44 U* 1.34 W° 7.75 Ym 0.17 Qb 11.1 Data source Calculated by the authors

Hydraulic variables

4723.742 2.6 0.082 15.13 0.6472 9516000 0.43 0.76 297 1.3 17.95 1.19 0.13 9.1 9.46

1.07 289 7 14.95 1.5 0.75 2 13.13

Pit

4134.231 2.5 0.0771 15.6 0.6089 7620000 0.457

Mining

0.46 8.7 0.1 24.7 2.5 6.52 0.38 0.75

831.567 1.57 0.00339 33.36481 16.842 2018300 0.2117

Middle Sandbar

Table 5.2 Hydraulic variables of flow and sediment regime during monsoon

0.64 230 0.74 5.41 0.54 8.3 0.06 2.66

3099.52 1.67 0.04578 26.514 0.62275 5344000 0.298

Mining

0.16 4.48 0.24 6.67 2.64 1.86 1.41 2.24

3475.04 1.45 0.0495 18.68 0.9871 656186 0.1492

Pit

0.44 1.27 0.07 9.81 0.98 1.79 0.55 2.21

590.48 1.342 0.028 46.697 4.6697 6710000 0.19

Lower Sandbar

0.4 0.0002 0.15 4.4 0.4 1.9 0.23 2.03

583.3696 1.456 0.0376 34.125 3.96 6537440 0.219

Mining

0.56 15.32 0.001 11.27 1.17 1.41 0.8 8.87

683.0753 1.573 0.04349 29.8763 1.0124 7589725 0.23

Pit

5.3 Case Study: Sand Mining Affected … 163

Upper Sandbar

Flow regime Q (Cumec) 48.024 V(m/s) 0.87 n 0.00926 C 94.93 Rc 0.643 Re 400200 Fr 0.405 Sediment regime d50(ø) 0.76 QT 13 X 2.73 s0 0.45 U* 0.05 W° 1.04 Ym 0.04 Qb 0.39 Data source Calculated by the authors

Hydraulic variables

73.2 0.8 0.0165 59.325 0.463 488000 0.327 0.76 14 1.98 0.6 0.06 1.05 0.06 0.01

0.75 9 3.48 0.29 0.03 1.07 0.03 0.07

Pit

25.35 0.65 0.0264 31.01 0.169 195000 0.3788

Mining

0.12 0.82 2.19 0.75 0.07 2.62 0.03 0.13

37.4136 0.393 0.0648 15.0213 0.1364 334000 0.1364

Middle Sandbar

Table 5.3 Hydraulic variables of flow and sediment regime during post monsoon

0.14 0.12 0.04 4.47 0.45 1.6 0.28 2.28

25.65 0.36 0.065 14.013 0.299 2052200 0.1522

Mining

0.15 6.07 1.96 0.47 0.05 0.21 0.22 0.05

30.954 0.33 0.0722 12.959 0.1287 221000 0.1287

Pit

0.39 0.42 0.25 5.98 0.6 2.4 0.3 3.35

16.11675 0.285 0.083 11.772 0.2905 247950 0.097

Lower Sandbar

0.25 1.38 0.002 2.95 0.3 2.83 0.1 1.13

5.841 0.247 0.08064 10.865 0.1885 106210 0.12

Mining

0.22 0.28 0.22 2.63 0.26 3.04 0.09 0.95

11.267 0.251 0.086 10.879 0.177 168170 0.097

Pit

164 5 Mining Response on Alluvial Channel Flow and Sediment …

5.3 Case Study: Sand Mining Affected …

5.3.1.3

165

Effects on Flow Characteristics

In respect of flow structure, sand mining induced morphological changes are expanded due to occurring of turbulent flow in mining pit sites on channel bed (Barman et al. 2018a). Nature of turbulent structure in channel flow is easily determined with analysis of Reynolds (Re) and Froud number (Fr) (Sulaiman et al. 2017; Bhattacharya et al. 2019a). Based on Eqs. 5.1 and 5.2, three different flow characteristics are identified in Kangsabati River i.e. sub-critical flow along the upper course (Fr 0.40, Re > 2000), subcritical-transitional flow along the middle course (Fr 0.17, Re 500–2000), and subcritical-turbulent flow along the lower course (Fr 0.15; Re 500–2000). On the other hand, huge sand extraction affected turbulent flow directly converted into hungry water flow or sediment deficiency flow in pit sites (Fr 0.15, Re < 2000) of middle and lower course (Fig. 5.1). While maximum Fr number is found in sandbar sites during pre monsoon but this value is increased in mining sites during monsoon season. This result demonstrated that subcritical flow helps to trap sediments through increase the rolling process along the upper course whereas subcritical transitional flow separated the channel flow and bifurcated from main channel to deepest thalweg line created by mining along the middle course; thus massive sedimentation along with finer sediment concentration are occurred. In lower course, subcritical turbulent flow enhanced the boundary shear stress to increase the critical velocity across the both bank.

5.3.1.4

Effects on Flow Resistance

Hydraulic relation amongst the bed slope, flow velocity and hydraulic radius signified the flow resistance near the surface bed of channel computing by Chezy coefficient (C), Manning Coefficient (n) and roughness coefficient (Rc) (Lindeburg 1992; Julien 2002). Flow resistance is interrupted if sediment grain resistance is altered caused by sediment removal from channel bed (Barman et al. 2018b; Bhattacharya et al. 2019a). In this present case study, applying of Eqs. 5.4 and 5.6, C value is inversely changes with n value from upper course to lower course throughout the season. Coarser sediment grain size decreases C value of 33.05 m1/6 but increases the n value of 0.048 in upper course sites, but finer sediment increases the C value of 35.85 m1/6 and decreases the n value of 0.046 in lower course sites. Maximum average n value is observed in pit (0.054) and mining sites (0.052) than sandbar sites (0.038) while C value is inversely increases in sandbar sites (40.67 m1/6) than pit (23.77 m1/6) and mining sites (25.1 m1/6), respectively. Contrastingly, maximum average n value (0.05) and minimum C value (27 m1/6) are found during monsoon but minimum n (0.048) and maximum C value (34.99 m1/6) also found during pre monsoon (Tables 5.1, 5.2, 5.3), respectively. This result reveals that coarser sediment trapping increased the n value in mining and pit sites during monsoonal peak flow but finer grain sedimentation increases the

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5 Mining Response on Alluvial Channel Flow and Sediment …

Fig. 5.1 Mining pit induced turbulent flow near Rangamati (middle course). Source Authors

C value in sandbar sites. Moreover, sediment grain size gradation always increasing the C value and decreasing the n value towards the downstream.

5.3.2

Hydraulic Variables of Sediment Transport and Mining Intensity

Parameters of sediment transport i.e. particle grain size diameter, bed shear stress, critical velocity of shear stress, bed shear velocity, settling velocity, sediment concentration and bedload are relatively changes either sedimentation or erosion caused by natural or human induced interruption of hydraulic variables (Rinaldi et al. 2005; Sulaiman et al. 2017). In recent past, many researchers are addresses the interruption of sediment transport due to construction of dam, reservoir, bridge, road, and sand mining (Wu et al. 2020). In particular, over sand extraction of sand than replenishment causes the scarcity of sediment as well as creates the channel instability (Padmalal and Maya 2014).

5.3.2.1

Effects on Particle Diameter (d50)

Particle grain size distribution significantly implies as sub-normal population on sediment transport in the form of rolling, saltation and suspension mode (Inman 1949). Contrastingly, several factors like climate, spatial composition of adjacent lands, sediment transport length and energy condition plays crucial role to

5.3 Case Study: Sand Mining Affected …

167

determine the textural attributes of particle deposition on bed surface (Fralick and Kronberg 1997). Moreover, bed shear stress and critical shear stress both are controlled the sediment entrainment threshold, erosion and sedimentation process in respect of particle grain size distribution and other hydraulic variables (Bhattacharya et al. 2016). In term of sand mining, huge extraction of sediment from instream and floodplain directly lead the structural displacement of particle size, as a result suspended and bed sediment behavior are inversely changes throughout the channel (Li et al. 2016; Wyss et al. 2016; Sadeghi et al. 2018). In Kangsabati River, maximum large particle median diameter (d50) is found in mining sites of upper course during monsoon season while small particles size is obtained from sandbar sites of middle course during post monsoon (Tables 5.1, 5.2, 5.3), respectively. Gradation of median size at course level exhibited the gradually decreases the energy condition towards the downstream. In respect of sand mining, average large sediment particles are concentrated in sandbar sites than mining and pit sites of upper course (Fig. 5.2a, b, c) while average grain size increases in mining sites and pit sites than sandbar of middle course (Fig. 5.3a, b, c). Maximum sediment deposition than extraction made huge coarser sand accumulation in sandbar sites of upper course but huge extraction lead the shifting of coarser sand from sandbar to mining sites, simultaneously maximum finer sediment deposition occurred in sandbar sites of middle and lower course (see 4.7.1).

5.3.2.2

Effects on Shield Parameters

Three dominant parameters of shear stress (s0), critical shear stress (scr) and shear velocity (u*) are considered as shield parameters (Mueller et al. 2005). Particles motion along the bed surface is initiated when bed shear stress exceeds the critical shear stress acting on per unit area of river bed. Particle diameter is leading hydraulic factor to oscillate the sediments but excess threshold value of bed shear stress is required force for entraining the sediment (Kundu 2017). With the linear relationship between shear velocity and shear stress, threshold condition of sediment entrainment is initiated if shear velocity being required the mass exchange capacity for sediment particles from top of the bed layer (Dey 2014). Hence, shield parameters are more responsible for potential degradation of river bed or bank (Montgomery and Bufferington 1997; Ritter et al. 2002; Church 2006), but huge sediment extraction changes the availability of shear stress in mined channel that increased the bed steepness and flow depth to interrupt the threshold condition of sediment entrainment (see 4.2). In alluvial mined channel as Kangsabati River, average s0 is constantly reduces from upper (8.79 N/m2) to middle (8.33 N/m2) and lower course (4.27 N/m2) whereas average bed s0 is increases during monsoon (12.06 N/m2) than post monsoon (1.47 N/m2) period at course level (Tables 5.1, 5.2, 5.3). On the other hand, average s0 is relatively grown up in pit sites of upper (10.21 N/m2) and lower course (6.4 N/m2) but it is more enriches in sandbar sites of middle course (13.9 N/ m2), respectively. In case of shear velocity, spatial distribution is same manner as

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5 Mining Response on Alluvial Channel Flow and Sediment …

Fig. 5.2 Nature of sediment transport along the upper course: a mode of sediment transport in sandbar site near Lalgarh (upper course), b huge bed extraction reduces sediment transport in mining sites near Sarenga, c trapping of sediment and nutrients in pits near Bikampur. Source Authors

5.3 Case Study: Sand Mining Affected …

169

Fig. 5.3 Hydrodynamic interruption along the middle course: a transitional flow based ripple mark near Mohanpur (middle course), b mining induced anarbrancing flow creates numerous braid channels near Kankabati, c sediment traps in recirculation zone of pit sites near Debangai (middle course). Source Authors

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5 Mining Response on Alluvial Channel Flow and Sediment …

like shear stress that means maximum u* is found in middle (1.1 N/m2) and upper course (0.87 N/m2) than lower course (0.51 N/m2), respectively. Maximum u* is received in pit sites of upper and lower course but u* is progressively grown up in sandbar sites of middle course. Flood or monsoonal peak flow induced shear stress and velocity to entrain and transport mainly suspended sediment (