Recent Trends in River Corridor Management: Select Proceedings of RCRM 2021 (Lecture Notes in Civil Engineering, 229) 9811699321, 9789811699320

Citation preview

Lecture Notes in Civil Engineering

Vinay Chembolu Subashisa Dutta   Editors

Recent Trends in River Corridor Management Select Proceedings of RCRM 2021

Lecture Notes in Civil Engineering Volume 229

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

Lecture Notes in Civil Engineering (LNCE) publishes the latest developments in Civil Engineering - quickly, informally and in top quality. Though original research reported in proceedings and post-proceedings represents the core of LNCE, edited volumes of exceptionally high quality and interest may also be considered for publication. Volumes published in LNCE embrace all aspects and subfields of, as well as new challenges in, Civil Engineering. Topics in the series include: • • • • • • • • • • • • • • •

Construction and Structural Mechanics Building Materials Concrete, Steel and Timber Structures Geotechnical Engineering Earthquake Engineering Coastal Engineering Ocean and Offshore Engineering; Ships and Floating Structures Hydraulics, Hydrology and Water Resources Engineering Environmental Engineering and Sustainability Structural Health and Monitoring Surveying and Geographical Information Systems Indoor Environments Transportation and Traffic Risk Analysis Safety and Security

To submit a proposal or request further information, please contact the appropriate Springer Editor: – Pierpaolo Riva at [email protected] (Europe and Americas); – Swati Meherishi at [email protected] (Asia - except China, and Australia, New Zealand); – Wayne Hu at [email protected] (China). All books in the series now indexed by Scopus and EI Compendex database!

More information about this series at https://link.springer.com/bookseries/15087

Vinay Chembolu · Subashisa Dutta Editors

Recent Trends in River Corridor Management Select Proceedings of RCRM 2021

Editors Vinay Chembolu Department of Civil Engineering Indian Institute of Technology Jammu Jagti, India

Subashisa Dutta Department of Civil Engineering Indian Institute of Technology Guwahati Guwahati, India

ISSN 2366-2557 ISSN 2366-2565 (electronic) Lecture Notes in Civil Engineering ISBN 978-981-16-9932-0 ISBN 978-981-16-9933-7 (eBook) https://doi.org/10.1007/978-981-16-9933-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

In the recent times, there is a growing necessity for protection and management of river corridors due to increased anthropogenic activities, river regulation and fluvial disturbances. These changes alter the fluvial hydro-ecological functioning of river systems. The river corridor activities including inland navigation, hydropower generation, river interventions and tourism are also important to develop with minimal environmental impact and a thorough understanding is required for preparation of river corridor development guidelines. With this aim, an international conference on river corridor management was organised during 25–27 February 2021 to highlight the issues, challenges and way forward for management of rivers. This book titled “Recent Trends in River Corridor Management” represents selected research articles of 1st International Conference on River Corridor Research and Management (RCRM 2021) on fluvio-hydro-ecological processes of river systems. The contents of the book can be a valuable reference for academicians and practitioners working in the areas of river science. There are 17 chapters in this book which are organised into four sections. Section 1 is built on theme, River Corridor Issues and Challenges, that addresses chapters on morphological controls in sand bed and gravel bed rivers, environmental planning of river corridors pertaining to dam construction or dam removal. Section 2 addresses a theme on river ecology. In this section, impacts of river interventions such as dams, barrages on aquatic habitat behaviour, their migration and reproductive biology are discussed. In Sect. 3, hydrodynamic modelling of river corridor vegetation and river training works, flood modelling and importance of buildings in urban flood simulations are discussed. Section 4 deals with upland catchment management. Specifically, this section addresses on glacial lakes vulnerability, catchment hydrology and sediment yield, and ground water development. We hope that this book will be a helpful resource to academicians and practitioners and supplement to existing literature on river processes. We acknowledge the administrative support, cooperation and help of IIT Jammu and IIT Guwahati in various ways. Jagti, India Guwahati, India

Vinay Chembolu Subashisa Dutta v

Contents

River Corridor Issues and Challenges Geomorphic Controls on Sediment Mobility and Channel Stability of a Riffle-Pool Gravel Bed Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marwan A. Hassan, Conor McDowell, Matteo Saletti, David A. Reid, Joshua Caulkins, and Jiamei Wang

3

Environmental Planning of River Corridors Considering Climate Change: A Brief Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shawn M. Chartrand

27

Assessment of Fluvial Controls and Cross-Sectional Recovery Indicators in a Large Regulated River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Pradhan, S. K. Padhee, S. Dutta, and Rishikesh Bharti

39

Holistic Environmental Flow Assessment by Building Block Method in Inaccessible Himalayan River Basins . . . . . . . . . . . . . . . . . . . . . . S. K. Padhee, V. Chembolu, A. Akkimi, K. K. Nandi, S. Dutta, Dibyendu Adhikari, Raghuvar Tiwary, Bikram Singh, and Saroj K. Barik

49

River Ecology: Mapping and Modelling Significance of Dam Altered Vis-À-Vis Free Flowing Stretches of a Himalayan River, Teesta with Special Reference to Icthyofaunal Diversity: Importance Towards Riverine Fisheries Sustainability . . . . . . Amiya Kumar Sahoo, Dharmendra Kumar Meena, Thangjam Nirupada Chanu, K. Lohith Kumar, Sourav Kumar Nandy, Debalina Sadhukhan, Srikanta Samanta, and Basanta Kumar Das Impact of Barricades on Habitat and Fish Migration in River Cauvery, India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. K. Manna, K. Lohith Kumar, S. Sibina Mol, C. M. Roshith, S. K. Sharma, M. E. Vijay Kumar, R. C. Mandi, S. Samanta, V. R. Suresh, and B. K. Das

71

85

vii

viii

Contents

Impacts on Reproductive Biology of Golden Mahseer Caused by Climate and Land Use Change in Western Himalaya . . . . . . . . . . . . . . . 111 Priyanka Rana, Soukhin Tarafdar, and Prakash Nautiyal Critical Mixing Depth Models for Eutrophicated Inland Water Bodies to Prevent Harmful Cyano-Bacterial Blooms . . . . . . . . . . . . . . . . . . 125 Jayatu Kanta Bhuyan, Eiichi Furusato, and Subashisa Dutta River Modelling and Management Significance of Representing Buildings in Urban Flood Simulations . . . . 141 R. Reshma and Soumendra Nath Kuiry Three-dimensional hydrodynamic modeling of permeable and impermeable river training works using CCHE 3D model and laboratory experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Riddick Kakati, Vinay Chembolu, and Subashisa Dutta Numerical and Experimental Investigation of Effect of Green River Corridor on Main Channel Hydraulics . . . . . . . . . . . . . . . . . . . . . . . . 165 S. Modalavalasa, V. Chembolu, V. Kulkarni, and S. Dutta Flood Inundation Modeling Using Coupled 1D–2D HEC-RAS Model in Lower Kosi River Basin, India with Limited Data . . . . . . . . . . . . 177 Ray Singh Meena and Ramakar Jha Upland Catchment Management A Study of the Impact of Some Land Use Land Cover Changes on Watershed Hydrology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Indulekha Kavila and Bhava V. Hari Modeling of Sediment Yield of Tawi Catchment to Identify the Critical Sources Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 Soban Singh Rawat, Bhaskar Ramchandra Nikam, Pradeep Kumar, and Prasun Kumar Gupta Use of Landsat and Sentinel-1 Data for Implementation of Bank Protection Work in Brahmaputra River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Ranjit Deka and Arup K. Sarma Potentially Dangerous Glacial Lake Risk Mapping and Assessment in Satluj River Basin, Himachal Pradesh Using Remote Sensing and GIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Gopinadh Rongali, K. C. Tiwari, and Poonam Vishwas

Contents

ix

A Geospatial Approach for Mapping and Delineation of Palaeochannels of Ghaggar Basin, North-West India, for Groundwater Development to Meet Sustainable Development Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 Ritambhara K. Upadhyay, Naval Kishore, and Mukta Sharma

About the Editors

Dr. Vinay Chembolu is currently working as Assistant Professor in Civil Engineering Department at Indian Institute of Technology Jammu. He obtained his B. Tech. (Civil) from UCEK-JNTU Kakinada, and M. Tech. and PhD in Water Resources Engineering from Indian Institute of Technology Guwahati. His major research interests include River Engineering, River Dynamics and Experimental and Field Hydraulics. He has published his research work in reputed international journals. He also got varied experience of working with research and consultancy projects for river training, development of waterways, dam break analysis, flood inundation modelling and environmental flows, all those pertaining to Indian Rivers. He has been actively involved in conducting hydrographic surveys in challenging rivers like the Brahmaputra, Kosi, Brahmani and Himalayan Rivers using advanced river surveying equipments. He is a member of British Society of Geomorphology, International Association of Sedimentologists and Association of Sciences of Liminology and Ocenography. Prof. Subashisa Dutta is working in the Department of Civil Engineering, Indian Institute of Technology Guwahati. He is currently working as a Professor of Civil Engineering and Professor-In charge of Space Technology Cell, IIT Guwahati. He was a visiting fellow in Technical University of Munich, Germany; Kyoto University in Japan and visited several other countries for research and academic collaborations. His research interests include fluvial hydraulics, hydroinformatics and advanced remote sensing for water resources applications, hill slope hydrology, urban hydrology and climatic change. He has been working for various sponsored and consultancy research projects in distributed hydrological modeling, sediment budgeting, flood inundation modeling, river basin management and bank protection. He was a member in water resources thematic group in Ganga River Basin Environment Management Plan, the Brahmaputra Board and several other academic and research organizations. He has published more than 100 research articles in reputed water resources journals and conferences. He has been the Associate Editor of Journal of Hydrology, Hydrological Sciences Journal and active reviewer for many reputed journals. He was awarded prestigious S.N. Gupta memorial award, xi

xii

About the Editors

R. J. Garde research award, DAAD fellowship and many others for his wide contribution in the Water Resources Engineering in India. He has supervised 10 PhD research thesis (and other in progress) in different areas of hydrological sciences and in advanced Satellite Remote Sensing.

River Corridor Issues and Challenges

Geomorphic Controls on Sediment Mobility and Channel Stability of a Riffle-Pool Gravel Bed Channel Marwan A. Hassan, Conor McDowell, Matteo Saletti, David A. Reid, Joshua Caulkins, and Jiamei Wang

Abstract Riffle-pool morphologies are common in gravel bed streams yet the conditions and feedbacks that maintain them remain poorly understood. In this paper, we examine temporal and spatial patterns of channel adjustment, sediment mobility and geomorphic stability of a riffle-pool reach to changes in flow and sediment supply regimes. To achieve our goals, we use a longitudinal monitoring campaign of 12 years of field measurements and flume experiments. The channel adjusts to flow and sediment supply regimes both vertically (erosion/deposition) and laterally, resulting in sediment movement through the reach while the riffle-pool morphology persists. This persistence suggests that the riffle-pool morphology is dynamically stable and is maintained through adjustment of internal processes. To better understand the evolution of stream channels with riffle-pool morphologies responding to changes in sediment supply and flow regime, we conducted a flume experiment with four runs. Our field and experimental results show that in a natural riffle-pool morphology changes in flow rates and sediment input can drive local changes in bed topography and cause dynamic trends in sediment transport volume and texture. However, rifflepool sequences possess clear evidence of geomorphic stability as the morphological sequence and character persists under a range of conditions. These sequences are also critical for determining particle mobility and transport in a wide range of flows and sediment supply regimes. Keywords Riffle-pool · Gravel-bed stream · Sediment transport · Geomorphic stability · Sediment supply · Flow regime

M. A. Hassan (B) · C. McDowell · M. Saletti · D. A. Reid · J. Caulkins · J. Wang Department of Geography, University of British Columbia, Vancouver, BC V6T 1Z2, Canada e-mail: [email protected] J. Caulkins Embry-Riddle Aeronautical University, Prescott, AZ, USA J. Wang State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 V. Chembolu and S. Dutta (eds.), Recent Trends in River Corridor Management, Lecture Notes in Civil Engineering 229, https://doi.org/10.1007/978-981-16-9933-7_1

3

4

M. A. Hassan et al.

1 Introduction Stream channels are composed of a sequence of morphological units which can create alternately converging and diverging flow. These features are related to the presence of mobile sediment in the system, but the precise mechanisms of their formation and stability are poorly understood. The dominant unit type varies according to the channel gradient, relative roughness, and grain size. In very steep channels (i.e., with a slope >3–5%), the dominant features are either cascade or step-pool units. Cascade units are a morphology characterized by a seemingly random organization of boulders in the channel. In step-pool morphologies, boulders are often arranged in transverse steps, held by keystones. In less steep channels (i.e., with a slope of ~1–4%), riffle-pool units dominate the morphology. Unlike step-pools, no single clast controls the form and location of riffles. In wide channels, riffles and pools often become associated with the approximately regular sinuosity of meander forms. Units of channel morphology have been identified as a particularly important scale of interest for understanding channel dynamics, namely, relating stream morphology to channel processes and habitat characteristics [42]. The typical gradient, grain size, and relative roughness associated with each morphology results in differing flow characteristics, sediment transport mechanisms, and hence channel stability. In fact, there remains uncertainty surrounding the mechanisms driving sediment mobility in riffle-pool channels and the frequency of sediment mobilization. In this paper, we focus on the riffle-pool morphology common to gravel bed streams in a wide range of environments [5, 17, 21, 28–30, 42, 48, 54, 60], although, they are most commonly observed in streams with a channel slope of Qc

# events > Qbf

8

8448

68

1.1

2006

3

10

74,879

173

4.7

2007

4

18,030

91

2

2008

2

24,895

90

1.6

2010

6

7079

45

1.3

2011

8

4015

48

0.9

2012

2

12

51,467

170

2.9

2013

10

4290

47

1.7

2014

2

13

10,340

65

2.5

2015

1

8

8763

58

2.5

2016

4

10

Qmax = maximum peak flow recorded for the survey year; T* = total flow time above the critical value for sediment entrainment; ΩT = total excess flow energy expended over the season in J/m (for details see [46]), Q = discharge; Qc = critical discharge for sediment entrainment; Qbf = bankfull discharge. No competence flows were recorded for 2009

2005

Survey year

Table 1 Summary information on sediment mobilizing events recorded between 2005 and 2016

6 M. A. Hassan et al.

Geomorphic Controls on Sediment Mobility and Channel …

7

sediment mobility using tracer data. Finally, we expand on the range of responses due to changing sediment supply and flow regimes with flume experiments.

2 East Creek Study Reach East Creek is a small-forested gravel stream in the Fraser Valley foothills of the Coast Mountains near Vancouver, British Columbia (Fig. 1). At the study site, East Creek drains an area of 1 km2 and an estimated bankfull discharge (Qbf ) of ~2 m3 /s. The watershed has a maritime climate with the majority of precipitation falling as rainfall in the fall and the winter. The 117 m long study reach is relatively straight, has a bankfull channel width of 2.5 m, and a reach-averaged slope of 0.018 m/m. The median grain size of the bed surface (D50s ) is 49 mm , and the subsurface (D50sub ) is 20 mm (Fig. 1c). Therefore, the surface is armored with an armour ratio (D50s /D50sub ) of the bed of 2.45. A culvert located about 100 m upstream of the study reach reduces the sediment supply from upstream. Consequently, most of the sediment input comes from bank erosion [58]. Overall, the sediment transport is low and, in most years, individual particles move relatively short distances within the same morphological unit (e.g., [46]). The morphology of the reach is characterized by alternating rifflepool sequences interspersed by runs, and distinct side bars (Fig. 1b). For more details on this field site see [7, 11, 12, 35, 46].

Fig. 1 a Location map of East Creek riffle-pool study reach (RP1) showing channel morphology, b photograph of the study reach, and c Grain size distribution of the surface, subsurface, and tracers

8

M. A. Hassan et al.

3 Flow Record in East Creek Continuous 15-min interval stage data has been collected at the reach since 2004 using a pressure transducer and has been converted to discharge estimates using a rating curve. Table 1 provides a summary of the yearly flow characteristics in the reach. To link the flow with the sediment transport regime and channel adjustment, we calculated several hydraulic variables. Based on field observations and sediment transport measurements, sediment movement begins at a critical flow (Qc ) of about 0.5 m3 /s and the bankfull discharge (Qbf ) is equal to 2 m3 /s (see [11, 43]). Other variables associated with sediment transport presented in this paper include (see Table 1): peak flow of the largest event recorded for the survey year (Qmax ), the total flow time above the critical value for sediment entrainment (T *), total excess flow energy expended over the season for flows exceeding the critical discharge (T ) (for details see [46]), and the number of recorded events larger than the bankfull discharge. Bankfull discharge is used as a relative scale for flow conditions in East Creek. However, effective discharge and other flow attributes in East Creek have been evaluated by [20], who found a high correlation between the effective and bankfull discharges. Between 2004 and 2016 we recorded 86 events capable of mobilizing sediment in the reach. The largest event recorded in this study period occurred in the 2006–2007 flow year and had a peak flow of 4.74 m3 /s (~2.4Qbf ). A total of ten events had a peak flow that exceeded the bankfull discharge and the duration of flows able to transport sediment ranged from 45 to 173 h. Table 1 suggests that East Creek experiences a few mobilizing events every year, some about bankfull discharge or larger. Such flows are important in terms of sediment transport and channel dynamics, and the maintenance of the riffle-pool morphology in East Creek. On average, about 90 h of duration of flows above the threshold of movement are available for sediment transport in the creek. Taken altogether, the flow data suggest that East Creek was a dynamic system for the period of our observations. For most years, both the number of events above bankfull and their duration suggest that sediment transport occurred frequently.

4 Channel Topography and Morphology Channel morphology was monitored using detailed topographic bed surveys, sediment texture measurements, and aerial photographs taken ~10 m above the bed surface. Data (spanning the period 2004–2018) were collected annually during summer low flow periods. Channel topography and morphology were surveyed using a total station. The mean point spacing of the topographic surveys was about 0.3 m (~10 per m2 ), which provides a dense enough grid of points to capture the main elements of local bedforms [23] and the channel reach morphology [42]. We created digital elevation models (DEMs) from our yearly total station survey data with a cell

Geomorphic Controls on Sediment Mobility and Channel …

9

size of 0.25 m2 [11, 12]. We then used DEM differencing to calculate the change in elevation of bedforms on a year-to-year basis (for details and DEM uncertainty see [11]). We calculated annual longitudinal profiles from DEMs in order to examine changes in channel morphology at the reach scale (Fig. 1). To demonstrate the impact of these transporting events across the study period, we begin by detecting changes in the longitudinal profiles along the thalweg of the riffle-pool reach. We highlight four longitudinal profiles, one from the start of the monitoring period (2004), one from after the largest flood recorded in the creek since 1971 (2007), one after several years of moderate flooding (2011), and one after several years of low to moderate discharges (2016) (Fig. 2). These four longitudinal profiles represent the full range of measured elevations during the study period. We estimate that the floods recorded during the 2007, 2011, and 2016 years have discharges approximating 2.3Qbf (return period of ~50 years), 0.6Qbf (90% of particles mobile) was recorded for all morphologies during the highest recorded T. Overall, partial mobility ( 90% of the tracers moved), Partial mobility (10–90% moved), and no mobility (< 10% moved). During relatively low flows (all individual floods in a given year were less than Qbf ), the partial and no mobility cells dominated the spatial mobility pattern (Fig. 6). The 2010–2011 panel demonstrates that neighboring cells within the same morphology have different levels of mobility. The proportion of cells displaying full mobility increases significantly during relatively high flows (Fig. 6). During this event, full mobility cells concentrated along the center part of the channel near the upper part of the pool and the side bar through the center and tail of the pool. There were no noticeable differences in mobility between the side bar and the riffle during any flow event. The pool during these events had a relatively low number of cells with tracers (Fig. 6). To link the observed spatial patterns of sediment mobility with the bed shear stress, we calculated the bed shear stress associated with the bankfull discharge using the 2D model FaSTMECH [34, 44]. The simulation and the calibration were conducted using the topography from 2012–2013 (for more details see [11, 19]). Since we are interested in sediment mobility, the model output was used to analyze the hydraulic forces represented by the bed shear stress effective in mobilizing sediment. In Fig. 7, we present three maps: the sediment mobility at bankfull flow, net change in bed elevation, and the spatial pattern of bed shear stress. The highest values of shear stresses during bankfull flow are concentrated in the center part of the channel, starting at the upstream end of the riffle into the pool center. This coincides with the locations of observed full mobility cells. This highlights how channel morphology strongly impacts spatial variation of shear stress at the local scale, causing the observed differences in sediment mobility. Such differences set the observed temporal and spatial patterns of bed adjustment and stability, even over small distances.

14

M. A. Hassan et al.

Fig. 6 Bed mobility maps for seasons with high and low flows for the RP1 subsection immediately downstream of the seeding riffle

Fig. 7 A section of riffle-pool in the 2012–2013 season showing mobility map (left), modeled bankfull bed shear stress binned as multiples of the critical shear stress (τcr ) associated with particle entrainment: 2τcr , change in bed elevation between 2012 and 2013 and bed elevation as measured in 2012

Geomorphic Controls on Sediment Mobility and Channel …

15

5.2 Burial Depth The depth of the active layer, and hence the vertical adjustment of a given section of the channel, depends on the flow characteristics, bed morphology, and sediment supply regime. In this reach, we approximated the depth of the active layer using the burial depths of tracer particles. While tracers are likely to underestimate the active layer depth, they provide a first-order approximation while also allowing for estimation of other tracer dispersion characteristics. Large flow events likely have a large active layer, dispersing the tracers deep within the bed. Small events, on the other hand, are expected to have a shallow active layer, with little tracer burial. The burial depth influences particle mobility as deeply buried particles are likely to remain immobile during floods. Therefore, the waiting time between movements for these particles is likely to be long [16, 24]. During the study period at East Creek, we observed a size dependence on tracer burial behavior. Larger tracers were much less likely to become buried than smaller tracers (Fig. 8a). For most years, over 60% of the small particles were found buried, while only 20% of the largest grains were. The only event in which this size-dependent pattern was not observed was during the large 2006–2007 event, where all particles regardless of size were found buried. Figure 8b shows the mean burial depth of each size fraction. Two patterns can be observed. First, larger events had larger mean burial depths. Second, smaller particles were buried deeper than large particles. For example, the mean burial depth of the smallest particle class (11–16 mm) was about twice that of the largest class (90–128 mm) (Fig. 8b).

5.3 Tracer Displacement Distances The mean travel distances of mobile particles generally increased with flow energy (Fig. 9). For all but the largest events, mobile particles remained within the same morphological unit. With the exception of the 2007 flood, most particles moved less than the distance required to leave a riffle-pool pair. This supports the idea that small floods reorganize sediment within or between neighboring morphologies, whereas large floods redistribute sediment across riffle-pool pairs. In order to test this idea, we used tracer displacements to calculate along-channel trapping probabilities, a measure of the depositional influence of channel segments [35, 36]. A high trapping probability for a channel segment indicates that particles that transport into that segment are more likely to deposit, whereas segments with a lower trapping probability are more likely to transport the particle downstream to the next bin. Figure 10 shows the calculated trapping probabilities for RP1 during three events during the study period (2007, 2010, and 2013). The largest flood, 2007, had lower trapping probabilities overall, with well-defined peaks where particles were more likely to deposit. These peaks were typically co-located either within, just before, or just after pools and generally coincide with locations of aggradation. During smaller

16

M. A. Hassan et al.

Fig. 8 a Fraction of buried grains and b mean burial depth versus particle size for selected events in the riffle-pool reach of East Creek. All particles were found buried after the large event of 2007 (the fraction of grain burial is 1). For comparison, we indicate the flow magnitude (relative to the bankfull discharge) of some of the seasons

Geomorphic Controls on Sediment Mobility and Channel …

17

Fig. 9 Mean travel distance plotted against the cumulative excess flow energy (T ) for the rifflepool reach in East Creek

floods, the peaks in trapping probability were much less defined, suggesting a more random distribution of motions along the channel. This suggests that during large events, segments of erosion and deposition occur that become “smoothed” as smaller events redistribute sediment along the channel. The field data allowed us to show the response of a riffle-pool channel to moderate changes in flow regime in terms of channel topography and particle mobility. To study the behavior of such morphologies as a function of different flow and sediment supply regimes, we present results from flume experiments in which flow rate and sediment supply were changed systematically, while changes in particle mobility and bed topography were recorded at high resolution.

6 Flume Experiments: Changes in Flow and Sediment Supply Regime To test the response of a riffle-pool to (a) large flows and (b) different sediment supply regimes, we conducted a series of experiments in the Mountain Channel Hydraulic Experimental Laboratory at the University of British Columbia. The experiments were conducted in an 18 m long flume with a width and depth of 1 m. We modified the flume width with plywood sidewalls to create a variable-width channel that scaled 1:5 with the RP1 reach of East Creek (details see [7, 8, 22] (Fig. 11a)). The flume channel width ranged from 0.37 to 0.78 m with a scaled grain size that ranged from 0.5 to 32 mm and a median size of 8.4 mm.

18

M. A. Hassan et al. 0.3

(a) 2007 (High flow)

Pool Riffle

0.2

0.1

Trapping probability

0 0.8

(b) 2010 (Low flow)

0.6 0.4 0.2 0 0.6

(c) 2013 (Medium flow) 0.4

0.2

0 0

20

40

60

80

100

120

Downstream distance (m)

Fig. 10 Estimated downstream trapping probabilities for three years in the riffle-pool reach of East Creek. The orange shaded area represents the 95% credible interval for the prediction (for Details see [36])

We conducted an experiment with step-wise discharge increments, ranging from 42 to 90 L/s (Fig. 11b), where 42 L/s is scaled to the bankfull flow in RP1. We summarize the flow hydraulics, sediment feed, and transport rates of the experiment in Table 2 and Fig. 11b. The initial bed consisted of well-mixed and flattened layer of sediment with a depth of 0.21 m. Material identical to the sediment mixture of the bed was fed at the upstream end of the flume at capacity rate (i.e., calculated following [56, 61]). The first run, 42 L/s, was used to develop the channel morphology and reached an equilibrium condition between sediment input and output. After reaching the equilibrium condition, the flow rate was held constant for four hours. After the four hours, the flow was increased to 50 L/s and held for another four hours. The same procedure was followed with 70 and 90 L/s (Fig. 11 and Table 2). Data collected throughout the experiment included DEMs, centerline water and bed surface profiles, centerline near-bed velocity, composite photographs of the drained bed surface (for details see Table 2 in [22]). At the start of the 42 L/s step, the bed surface was flat without a developed channel morphology and armor layer. Within the first hour, large amounts of sediment left the

Geomorphic Controls on Sediment Mobility and Channel …

19

Fig. 11 a A view of the flume with its widths and measurement locations, and b flow discharge, sediment supply rate, and sediment transport rate during the experiment. Sediment was fed near the upper part of the flume and the sediment transport was measured at the flume outlet using a sediment trap that was designed to collect all sediment Table 2 Summary data of flow and sediment transport. All flow data are flume averaged as measured at the end of each flow. Sediment transport was measured at the flume outlet at the end of the flow Run

Flow (L/s)

Sediment supply rate (Kg/min)

Measured sediment transport rate (Kg/min)

Mean velocity (m/s)

1

42

0.50

1.37

1.04

2

50

0.65

0.34

1.02

3

70

0.90

1.44

4

90

1.10

1.04

Mean water depth (cm)

Water surface slope

Bed surface slope

7.95

0.0163

0.0165

9.06

0.0151

0.0158

1.10

14.51

0.0120

0.0121

1.17

14.40

0.0122

0.0120

20

M. A. Hassan et al.

flume and the sediment transport rate was slightly higher than the feed rate (Fig. 11b). After some time, the sediment transport rate declined below the sediment feed rate before increasing again towards the end of the 42 L/s run. After we increased the flow to 50 L/s, the sediment transport rate increased again at the beginning of the run before decreasing to closely the feed rate. This same fluctuation in the transport rate occurred for each of the steps in discharge. Despite the increase in flow magnitude and feed rate for each step, the maximum transport rate for each step was similar. Observation showed that the median size of the transported material was finer than the mixture until the development of the bed topography [22]. The sediment flux coarsened after this point, as the bed profile and sediment flux were reaching equilibrium. The formation of the riffle-pool morphology and topographic adjustment during the 42 L/s (Run 1) are presented in Fig. 12. The experiments began from a flat, well-

Fig. 12 a Development of riffle-pool morphology under constant discharge (Run 1; 42 L/s). b Bed profiles at the end of each Run 1–4 for a range of flows. The elevation has been detrended by subtracting the flume slope (1.5%) from the profile. Modified from [22]

Geomorphic Controls on Sediment Mobility and Channel …

21

mixed bed, and the central pool and riffle formed shortly after the start of the experiment. Topographic changes were rapid and within the first 0.3 h of flow a sequence of riffles and pool developed around the major width contraction and expansion. The monitored pool and riffle, located at approximately the longitudinal midpoint of the flume (Fig. 12), had lower and higher elevations than the initial profile, respectively. Within the first two hours more minor topographic undulations also formed within the upstream most 4 m of the flume, with a wavelength of approximately 1 m and amplitude of approximately 2 cm. The riffle-pool sequences persisted in their locations for the entire duration of the experiment (see riffle-pool unit 7.8 and 9.8 m along the flume). After the establishment of the ruffle-pool morphology, adjustment in bed topography was small, sediment transport rate declined, and the channel longitudinal profile reached a steady state. The equilibrium profile that was conditioned under a discharge of 42 L/s responded to increased discharges with spatially variable degradation (Fig. 12). Degradation was generally lowest near the flume outlet, as this was a fixed elevation point. The longitudinal profile that developed during Run 1 (42 L/s) persisted through the higher flow discharges of Runs 2–4 (Fig. 12). However, although the longitudinal profile and the location of the riffle and pools did not change substantially, channel degradation was observed locally. The average bed elevation upstream of station 4 m decreased with each stepped increase of the flow (Fig. 12). Channel bed lowering was predominantly observed at the upstream end of the flume, and through the central riffle for the 50 L/s flow (Run 2). During the Run 3 (70 L/s), degradation was focused in the upstream most 6 m of the flume, with lower amounts occurring from approximately 4 to 10 m. Degradation primarily occurred in the central riffle, and the upstream most 2 m of the flume during the Run 4 (90 L/s; Fig. 12). In our experiment, the high bed elevation of the monitored riffle imposed a boundary condition for the upstream part of the flume (Fig. 12). During the development of the channel morphology in Run 1 (Fig. 12a), the riffle created an upstream backwater zone that resulted in sediment deposition and overall increase in the bed elevation for the whole upper part of the flume. Field observation (e.g., [47]) and modeling simulations (e.g., [15]) show that riffle crest elevations influence upstream water surface slopes until the water depth is roughly deeper than the elevation difference between downstream sequential riffle crests. In spite of the increase of flow discharge and sediment supply in Run 2 did not alter substantially the bed elevation developed during Run 1 (Fig. 12). Noticeable changes in the bed elevation were recorded in response to the increase in flow and sediment supply during Run 3 and Run 4. The flatting of the bed profile during the highest two flows probably because of the sediment supply rate and flow relaxation in terms of velocity and momentum adjustments downstream from the monitored pool at station ∼15 m [22]. Morgan and Nelson [43] conducted flume experiments using sediment supply rates that exceed capacity supply rates indicating an overall reduction of riffle-pool amplitude and an absence of emphasis on pool elevation adjustments. Consequently, sediment supply is an important factor in determining how riffle-pools respond to upstream supply changes (e.g., [33]).

22

M. A. Hassan et al.

We used a flume prototype to explore how a riffle-pool unit adjusts to increase in discharge when sediment supply is near capacity. Based on our experiment, a naturally established riffle-pool morphology in presence of channel width variations was rather stable, despite major changes in flow rates and sediment supply. Changes in sediment transport volumes and texture were fast but did not last long when the water discharge was increased, and sediment was fed into the flume. Pools and riffles that formed within the first hour persisted throughout the experiment and, despite localized channel degradation due to changes in boundary conditions, they kept the channel stable and helped the system to achieve an equilibrium by reducing topographic changes and balance the sediment yield at the outlet with the input at the inlet. This behavior is not unique to riffle-pool morphology. [50] observed a similar pattern in their step-pool experiments, in which a channel subject to changes in both flow rate and sediment input (over one order of magnitude) evolved in such a way that allowed the sediment yield to closely match the sediment supply.

7 Summary and Conclusions The mechanisms behind the maintenance of riffle-pool morphology and the stability of this morphology in mountain streams remain topics of ongoing research. In this paper, we have presented results from field measurements in East Creek (a small rifflepool channel), and flume experiments conducted at the UBC fluvial lab, focused on the formation and evolution of a riffle-pool morphology in mountain streams. Our data show that, despite frequent flows able to mobilize sediment, the overall riffle-pool morphology in East Creek was stable through time. Net changes in bed elevation were more common in pools, while riffles showed almost no elevation change during the 12-year record we examined. Overall bed erosion and deposition tended to balance each other out, yielding a stable channel. Results from a multi-year tracer study in the East Creek showed how particle movements are strongly dependent on local conditions. The spatial patterns of particle mobility and shear stress (obtained with 2-D numerical modeling) highlighted the interdependence between channel morphology and particle entrainment and transport, since full mobility was observed only in few locations, and such locations were the same for different flows. Since East Creek has not been subjected to changes in sediment input (which often drive morphological changes), we performed flume experiment in a flume whose geometry and particle grain size scaled the ones in East Creek. We subjected the bed to increasing flows, while also feeding sediment. A riffle-pool morphology quickly developed in the channel and persisted for the entire experiment, despite changes in the boundary flow and sediment feed conditions. Increases in sediment transport and fining of the bed surface following sediment input were rapid but did not persist. The channel adjusted without major changes in the riffle-pool morphology and the longitudinal profile of the channel reached an overall steady state, although local degradation was observed due to increase in flow rates. Our field and experimental

Geomorphic Controls on Sediment Mobility and Channel …

23

results show that in a natural riffle-pool morphology changes in flow rate and sediment input can drive local changes in bed topography and cause dynamic trends in sediment transport volume and texture. However, riffle-pool sequences possess a high degree of morphological stability and are key in determining particle mobility and transport across a wide range of flows and sediment supply regimes. The persistence of riffle-pool morphological units under a wide range of flow and sediment supply regimes in a field and experimental setting is supported by observations from other researchers’ studies. The simulated 90 L/s (Run 4) was about twice the bankfull measured at East Creek study site, yet the riffle-pool morphology persisted. The same was observed during the largest flood recorded in East Creek (2007). The riffles flattened and pools deepened during this flow event (2007 profile in Fig. 2 and Run 4 profile in Fig. 12). Low and moderate flows in East Creek resulted in pool filling and aggradation in riffles (Fig. 2). This highlights the local nature of sediment transport in East Creek. For most seasons the sediment moved within the same morphological unit and only in two seasons (2006–2007 and 2012–2013) were sediment tracer travel distances was larger than riffle-pool spacing. [15, 46] asserted that feedbacks between local sediment transport and bed surface grain size adjustments contribute to riffle-pool persistence. Collectively, our findings illustrate the surprisingly persistent and stable nature of riffle-pool morphologies, even when processes typically responsible for channel morphology are variable through time and across space. Acknowledgements East Creek study was designed by M.A.H., and data were collected by a large number of undergraduate and graduate students from UBC. The experiment was conducted in the Mountain Channel Hydraulic Experimental Laboratory at the Department of Geography, The University of British Columbia. Emma Buckrell conducted the experiment and Rawan Hassan assisted with data collection. Rick Kettler and Ryan Buchanan provided technical support. The research was funded by NSERC Discovery (to M.A.H.) and Canada Foundation for Innovation (to M.A.H.). This paper benefits from discussions with Michael Church, Elli Papangelakis, and Nate Bradley. Eric Leinberger prepared the figures.

References 1. Buckrell E (2017) The formation and adjustment of a pool-riffle sequence in a gravel bed flume. Master thesis Department of Geography, The University of British Columbia, Vancouver, British Columbia, https://doi.org/10.14288/1.0352000 2. Carling PA, Orr HG (2000) Morphology of riffle–pool sequences in the River Severn, England. Earth Surf Process Land 25:369–384 3. Carling PA, Wood N (1994) Simulation of flow over pool-riffle topography: a consideration of the velocity reversal hypothesis. Earth Surf Proc Land 19:319–332 4. Carling PA (1991) An appraisal of the velocity-reversal hypothesis for stable riffle-pool sequences in the River Severn, England. Earth Surf Process Land 16:19-31 5. Chartrand SM, Whiting PJ (2000) Alluvial architecture in headwater streams with special emphasis on step-pool topography. Earth Surf Proc Land 25:583–600 6. Chartrand SM, Hassan MA, Radi´c V (2015) Pool-riffle sedimentation and surface texture trends in a gravel bed stream. Water Resour Res 51(11):8704–8728

24

M. A. Hassan et al.

7. Chartrand SM, Jellinek AM, Hassan MA, Ferrer-Boix C (2018) Morphodynamics of a widthvariable gravel bed stream: new insights on pool-riffle formation from physical experiments. J Geophys Res Earth Surf 123:2735–2766 8. Chartrand SM, Jellinek AM, Hassan MA, Ferrer-Boix C (2019) What controls the disequilibrium state of gravel-bed rivers? Earth Surf Proc Land 44(15):3020–3041. https://doi.org/10. 1002/esp.4695 9. Church M, Hassan MA (2002) Mobility of bed material in Harris Creek. Water Resour Res 38:237. https://doi.org/10.1029/2001WR000753 10. Church M, Jones D (1982) Channel bars in gravel bed rivers. In: Hey RD, Bathurst JC, Thorne CR (eds) Gravel bed rivers. Wiley, Chichester, U.K., pp 291–338 11. Cienciala P, Hassan MA (2013) Linking spatial patterns of bed surface texture, bed mobility, and channel hydraulics in a mountain stream to potential spawning substrate for small resident trout. Geomorphology 197:96–107 12. Cienciala P, Hassan MA (2016) Sampling variability in estimates of flow characteristics in coarse-bed channels: effects of sample size. Water Resour Res 52:1899–1922 13. Clifford NJ (1993) Differential bed sedimentology and the maintenance of a riffle–pool sequence. CATENA 20:447–468 14. Clifford NJ (1993) Formation of riffle–pool sequences: field evidence for an autogenetic process. Sed Geol 85:39–51 15. de Almeida GAM, Rodríguez JF (2011) Understanding pool-riffle dynamics through continuous morphological simulations. Water Resour Res 47:W01502 16. Einstein HA (1937) Bed load transport as a probability problem. Ph.D. dissertation, Eidgenössische Technische Hochschule (ETH), Zurich, Switzerland, 105 pp 17. Grant GE, Swanson FJ, Wolman MG (1990) Pattern and origin of stepped-bed morphology in high gradient streams, western Cascades, Oregon. GeolSoc Am Bull 102:340–352 18. Haschenburger JK, Wilcock PR (2003) Partial transport in a natural gravel bed channel. Water Resour Res 39. https://doi.org/10.1029/2002WR001532 19. Hassan MA, Bradley DN (2017) Geomorphic controls on tracer particle dispersion in gravel bed rivers. In: Tsutsumi D, Laronne JB (eds) Gravel-bed rivers and disasters. Wiley, Chichester, UK, pp 159–184 20. Hassan MA, Brayshaw D, Alila Y, Andrews E (2014) Effective discharge in small formerly glaciated mountain streams of British Columbia: limitations and implications. Water Resour Res 50:4440–4458. https://doi.org/10.1002/2013WR014529 21. Hassan MA, Church M, Lisle TE, Brardinoni F, Benda L, Grant GE (2005) Sediment transport and channel morphology of small, forested streams. J Am Water Resour Assoc 41:853–876 22. Hassan MA, Radi´c V, Buckrell E, Chartrand SM, McDowell C (2021), Pool-riffle adjustment due to changes in flow and sediment supply. Water Resour Res 57:2020WR028048. https:// doi.org/10.1029/2020WR028048 23. Hassan MA, Smith BJ, Hogan DL, Luzi DS, Zimmermann AE, Eaton BC (2008) Sediment storage and transport in coarse bed streams: scale considerations. In: Habersack H, Piegay H, Rinaldi M (eds) Gravel-bed rivers VI: from process understanding to river restoration. Elsevier, Amsterdam, the Netherlands, pp 473–496 24. Hassan MA, Voepel H, Schumer R, Parker G, Fraccarollo L (2013) Displacement characteristics of coarse fluvial bed sediment. J Geophys Res Earth Surf 118. https://doi.org/10.1029/2012JF 002374 25. Hassan MA, Woodsmith RD (2004) Bed load transport in an obstruction-formed pool in a forest, gravelbed stream. Geomorphology 58:203–221 26. Jackson WL, Beschta RL (1982) A model of two-phase bedload transport in an Oregon coast range stream. Earth Surf Proc Land 7:517–527 27. Keller EA (1971) Areal sorting of bed-load material: the hypothesis of velocity reversal. Geol Soc Am Bull 82:753–756 28. Keller EA, Melhorn WN (1978) Rhythmic spacing and origin of pools and riffles. Geol Soc Am Bull 89:723–730

Geomorphic Controls on Sediment Mobility and Channel …

25

29. Leopold LB, Wolman MG, Miller JP (1964) Fluvial processes in geomorphology. W.H. Freeman, San Francisco, CA 30. Lisle TE (1979) A sorting mechanism for a riffle-pool sequence. Geol Soc Am Bull 90:1142– 1157 31. Lisle TE (1982) Effects of aggradation and degradation on riffle–pool morphology in natural gravel channels, Northwestern California. Water Resour Res 18:1643–1651 32. Lisle TE, Hilton S (1992) The volume of fine sediment in pools: an index of sediment supply in gravel-bed streams. Water Resour Bull 28:371–383 33. Lisle TE, Hilton S (1999) Fine bed material in pools of natural gravel bed channels. Water Resour Res 35:1291–1304 34. McDonald RR, Nelson JM, Bennett JP (2005) Multidimensional surface water modeling system user’s guide. US Geological Survey Techniques and Methods, in Book 6, US Geological Survey, Reston, VA 35. McDowell C, Gaeuman D, Hassan MA (2021) Linkages between bedload displacements and topographic change. Earth Surf Process Land 46. https://doi.org/10.1002/esp.5221 36. McDowell C, Hassan MA (2020) The influence of channel morphology on bedload path lengths: insights from a survival process model. Earth Surf Process Land 45:2982–2997. https://doi. org/10.1002/esp.4946 37. MacVicar B, Best J (2013) A flume experiment on the effect of channel width on the perturbation and recovery of flow in straight pools and riffles with smooth boundaries. J Geophys Res Earth Surf 118:1850–1863. https://doi.org/10.1002/jgrf.20133 38. MacVicar B, Chapuis M, Buckrell E, Roy R (2015) Assessing the performance of in-stream restoration projects using radio frequency identification (RFID) transponders. Water 7:5566– 5591. https://doi.org/10.3390/w7105566 39. Milan DJ (2013) Virtual velocity of tracers in a gravel-bed river using size-based competence duration. Geomorphology 198:107–114 40. Milan DJ (2013) Sediment routing hypothesis for pool-riffle maintenance. Earth Surf Proc Land 38:1623–1641 41. Milan DJ, Heritage GL, Large ARG (2002) Tracer pebble entrainment and deposition loci: influence of flow character and implications for riffle-pool maintenance. Geol Soc London Spec Publ 191:133–148. https://doi.org/10.1144/GSL.SP.2002.191.01.09 42. Montgomery DR, Buffington JM (1997) Channel-reach morphology in mountain drainage basins. Geol Soc Am Bull 109:596–611 43. Morgan JA, Nelson PA (2021) Experimental investigation of the morphodynamic response of riffles and pools to unsteady flow and increased sediment supply. Earth Surf Process Land 46:869–886 44. Nelson JM, Bennett JP, Wiele SM (2003) Flow and sediment-transport modeling. In: Piégay H, Kondolf GM (eds) Tools in fluvial geomorphology. Wiley, Chichester, UK 45. Nelson PA, Brew AK, Morgan JA (2015) Morphodynamic response of a variable-width channel to changes in sediment supply. Water Resour Res 51:5717–5734. https://doi.org/10.1002/201 4WR016806 46. Papangelakis E, Hassan MA (2016) The role of channel morphology on the mobility and dispersion of bed sediment in a small gravel-bed stream. Earth Surf Proc Land 41:2191–2206 47. Pasternack GB, Bounrisavong MK, Parikh KK (2008) Backwater control on riffle–pool hydraulics, fish habitat quality, and sediment transport regime in gravel-bed rivers. J Hydrol 357:125–139. https://doi.org/10.1016/j.jhydrol.2008.05.014 48. Richards KS (1976) Channel width and the riffle-pool sequence. Geol Soc Am Bull 87:883–890 49. Robert A (1997) Characteristics of velocity profiles along riffle-pool sequences and estimates of bed shear stress. Geomorphology 19:89–98 50. Saletti M, Hassan MA (2020) Experimental study of sediment supply control on step formation, evolution, and stability. Earth Surf Dynam 8:855–868. https://doi.org/10.5194/esurf-8-8552020 51. Sear DA (1996) Sediment transport processes in pool-riffle sequences. Earth Surf Proc Land 21:241–262

26

M. A. Hassan et al.

52. Thompson DM (2001) Random controls on semi-rhythmic spacing of pools and riffles in constriction-dominated rivers. Earth Surf Proc Land 26:1195–1212 53. Thompson DM (2013) Pool-riffle. In: Wohl E (ed) Fluvial geomorphology, vol 9. Academic Press, San Diego, CA, pp 364–378 54. Thompson DM (2018) Pool-riffle sequences. Earth Syst Environ Sci. https://doi.org/10.1016/ B978-0-12-409548-9.11029-2 55. Thompson DM, Wohl EE (2009) The linkage between velocity patterns and sediment entrainment in a pool–riffle unit. Earth Surf Proc Land 34:177–192 56. Wilcock PR, Crowe JC (2003) Surface-based transport model for mixed-size sediment. J Hydraul Eng 129:120–128 57. Wilcock PR, McArdell BW (1997) Partial transport of a sand/gravel sediment. Water Resour Res 33:235–245. https://doi.org/10.1029/96WR02672 58. Wlodarczyk K (2021) Channel adjustment in a small mountain stream over a long flood series: insights from the morphological method. Master thesis. Department of Geography, The University of British Columbia, Vancouver, British Columbia. http://hdl.handle.net/2429/ 77840 59. Wohl EE (2007) Channel-unit hydraulics on a pool-riffle channel. Phys Geogr 28:233–248 60. Wohl EE, Vincent KR, Merritts DJ (1993) Pool and riffle characteristics in relation to channel gradient. Geomorphology 6:99–110 61. Wong M, Parker G (2006) Reanalysis and correction of bed-load relation of Meyer-Peter and Muller using their own database. J Hydraul Eng 132:1159–1168 62. Yalin M (1971) On the formation of dunes and meanders. In: Proceedings of the 14Th congress of the international association for hydraulic research, pp C101–C108

Environmental Planning of River Corridors Considering Climate Change: A Brief Perspective Shawn M. Chartrand

Abstract Water reservoirs interrupt the supply of sediment to river reaches and floodplains downstream of dams. Consequently, reservoirs are sediment sinks, with accumulation rates that reflect the combined effects of local and regional hydroclimatology, prevailing landscape-scale erosion rates, and land-use practices. Sediment supply interruption has profound implications for the quality of downstream aquatic habitat, river bed architecture, and infrastructure safety. Resource managers attempt to address downstream impacts through a broad set of direct actions, which includes downstream habitat enhancement, sediment bypass or reintroduction downstream of dams, and complete dam removal. When confronted with climate change and uncertainty regarding precipitation projections, the environmental planning process used to review direct actions involving dams and reservoirs becomes more challenging. Specifically, if several different precipitation projections provide unique future hydroclimates, how do scientists, engineers and environmental planners decide which projection(s) to use within supporting technical analyses? Here, we offer a brief perspective on the need for the broader community to develop clear criteria on how to incorporate climate change information into the environmental planning process of rivers corridors. We frame our perspective around an approach used in evaluating the impacts associated with granular sediment release from a reservoir located on the central California coast, USA. We close with a review of limitations associated with our example approach and suggested directions for future research. Keywords Climate change · River science · Geomorphology · Dams · Reservoirs · Environmental planning

S. M. Chartrand (B) School of Environmental Science, Simon Fraser University, Burnaby, B.C., Canada e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 V. Chembolu and S. Dutta (eds.), Recent Trends in River Corridor Management, Lecture Notes in Civil Engineering 229, https://doi.org/10.1007/978-981-16-9933-7_2

27

28

S. M. Chartrand

1 Introduction Rivers and surrounding landscapes are complex systems influenced by stochastic events. The chance occurrence of a variety of natural phenomena—landslides, debris flows, forest fires, floods—as well human-directed events like land-use decisions often times triggers river change and adjustment, at the local and watershed scale. A well-known and extreme example of coupling between natural event and river change is the 1980 eruption of Mount St. Helens, WA, USA. The volcanic event triggered a catastrophic landslide, which dramatically reset the North Fork Toutle River corridor, as well as other regional lands and waters [see 1–3, andreferencestherein]. Eruption effects related to the release and delivery of sediment and large wood continue to increase flood risks and other associated hazards for communities downstream of the upper North Fork Toutle River basin [3]. Consequently, a key societal and scientific challenge for impacted communities is the difficulty of predicting (or projecting) when sediment delivery events may occur in the future. We offer a perspective on this issue here through the lens of climate change and efforts that seek to revitalize human-managed river systems through direct actions like dam removal and reservoir management [4–7] (Fig. 1). Our perspective focuses on the use of a numerical modelling framework applied to identify the range of plausible river corridor conditions associated with sediment release from a reservoir that is subject to downstream distribution by a large number of future possible hydroclimates.

Fig. 1 Photograph of reservoir deposit excavation and river construction in preparation for the removal of San Clemente Dam, Carmel River, CA, USA. Photograph by author

Environmental Planning of River Corridors Considering Climate Change …

29

Fig. 2 Overview of links between landscape-scale processes and rivers. The tectonic or landscape setting scales basin contributions of water and sediment, which together determines channel morphology as one moves down a river basin. Channel morphology, in turn, reflects local bed shape, and scale bed sediment texture and hydrodynamics. Local bed shape, bed surface sediment texture and hydrodynamics evolve in feedbacks, which tend to reinforce local responses in the absence of significant disturbances. Climate change influences basin contributions of water and sediment supply through changes to hydroclimate; these influences cascade down to the local scale. Landscape-scale vegetative communities are, in turn, influenced by climate change, introducing a secondary path of feedbacks to water and sediment supply, down to the local scale. Approximate temporal and spatial scales of these attributes are provided at the left. Figure motivated by [8–11] and modified from [12]

2 Environmental Planning, Climate Change and Rivers Rivers and watersheds are coupled to the Earth’s climate through the occurrence of wet and dry seasons, floods and droughts, and regional and global average air temperatures via feedbacks with the water cycle [11, 13]. Wet seasons bring rainfall driven mass movements and runoff floods of varying magnitude and frequency [14] and snowpacks of differing depth, water content and temperatures. Dry seasons and drought bring low flows and dry riverbeds, seasonally low groundwater and soil moisture storage, which when combined raise the chance of fire due to lightning strikes

30

S. M. Chartrand

and other causes. An outcome of these environmental circumstances is the production, delivery and eventual transport of granular sediment through watersheds [e.g. 15] (Fig. 2). In human-altered and managed watersheds, the nature of the sediment cascade—influenced by fire, significant runoff floods or land-use activities—brings risk and hazard to those who live along river corridors, be it whether one lives right next to the river’s bank, or within floodplain areas. There are two clear types of exposure to risk and hazard within river corridors. The first type involves direct impacts by floodwater damaging property, dwellings and critical infrastructure. The second type includes indirect impacts associated with adjustments of river bed elevation as well as particle sizes present on the riverbed, each of which influences flood inundation depths and patterns, as well as overbank deposition of granular and organic materials. In a strange twist of unintended consequences, engineering efforts aimed at lowering flood-related risk and hazard along river corridors can work in the opposite direction and amplify potential flood-related impacts. For example, modifications to the lower Mississippi River channel for flood protection purposes have contributed to an increase in the magnitude of the 100-year flood [16]. In the context of climate change, amplification of hazard-related impacts along river corridors raises a critical question: how should watershed planning studies reflect climate change in technical analysis of future plausible conditions? The importance of this question was alluded to by Macklin et al. [22, p. 2144], who stated: We would contend that there is a once-in-a-generation opportunity for fluvial geoscientists to play a leading role in global water resource and flood risk management through, for example, flood-series extension and validating climate and hydrological models that are used worldwide for the protection of life, property and infrastructure.

The stakes are high for managed watersheds and river corridors within the context of plausible future climate change trajectories. A recently published review of projections associated with the IPCC’s Sixth Assessment Report indicates that global populations should prepare for more intense heat waves, storm events and droughts [17] (see Sect. B2); separate work suggests that climate whiplash from one extreme to the other over annual time scales will also become more common in specific geographic locations [11, 18]. Given this set of environmental circumstances, potential planning and ethical issues at the intersection of climate change and human-directed watershed management actions like dam removal or reservoir sediment flushing come into a clearer focus. Namely, it is incumbent on the scientific and engineering communities to incorporate climate change information into watershed planning studies. More specifically, it is necessary to incorporate projection information in a manner which reflects uncertainties associated with precipitation—monthly and annual trends, magnitudes, and importantly, expectations of how extremes may change, etc. [11, 19]. This is critically important because the future sequence and magnitude of runoff flood events for any particular watershed shapes the plausible risks associated with fluvial hazards due to direct management actions [cf. 3]. Furthermore, future hydroclimates are the key link between watershed sediment production and associated river adjustment as a result of sediment distribution throughout river networks [20–22] (Fig. 2).

Environmental Planning of River Corridors Considering Climate Change …

31

Fig. 3 Two different projections of normalized total annual precipitation for four metropolitan areas [model grid cell containing each metropolitan area] over the time period 2020–2100 reported for the CCSM4 and CESM1-CAM5 global climate models. Precipitation has been normalized by the average of each timeseries. In each panel, the blue curve detail projections associated with the CCSM4 model, and the grey curve for the CESM1-CAM5 model. The projections were developed as a part of the Coupled Model Intercomparison Project 5 experiment, using the Representative Concentration Pathway 6.0 emissions trajectory, and the r1i1p1 ensemble run. Data downloaded from The Copernicus Climate Change Services Climate Data Store, for the CMIP5 monthly data on single levels data source

Despite a clear need to effectively incorporate climate change information into watershed planning studies—although it remains commonplace to effectively ignore the issue [e.g. 23], e.g.—clear guidance as to how this should be done is lacking. Much of the complication arises due to the fact that climate projections of precipitation associated with global climate models are different [cf. 22]. Precipitation projections examined within the vicinity of four different metropolitan areas on Earth, and for two different global climate models are in detail dissimilar (Fig. 3). For example, there is qualitatively not much in common between the two projections shown for the New Delhi (IND) region. The largest annual relative precipitation totals differ by 20% or more, the frequency or count of the lowest annual totals differs by 100%, and the sequence of annual precipitation totals differs over periods of time as long as 10–12 years. Similar albeit perhaps less pronounced differences can be identified for New York City (USA), Lagos (NG) and Asuncion (PARA), and overall the issue becomes more challenging as additional precipitation projections are added to the comparison. One possible way to deal with these issues is through the use of numerical simulations to evaluate how river corridors may adjust in response to different precipitation or streamflow runoff regimes, as well as specific watershed management actions [27– 29]. Consequently, in completing environmental planning studies for river corridors how does one choose which projection of precipitation to use, or which outcomes

32

S. M. Chartrand

should serve as the basis for planning-related decisions if multiple projections are used? These questions are particularly relevant for environmental planning studies of river corridors because project budgets available to complete model simulations are generally and relatively constrained, forcing practitioners, scientists and engineers to focus on one, or just a few different scenarios. Such a restricted basis for making planning decisions could have serious implications for public safety, and erode trust in science application to real-world watershed management problems if it turns out that the corridor adjustment projections differ markedly from on-the-ground future conditions. In order to elaborate on the ideas presented above, we focus our remaining discussion on the use of streamflow runoff within numerical simulations of river corridor adjustment. Re-focusing our discussion on streamflow is reasonable due to the intrinsic link between precipitation, runoff and streamflow. In this context, there are several possible approaches which address the question of how to incorporate climate change information, or expectations, into the environmental planning process. One option involves the assumption that the precise future sequence of flood events, droughts and wet periods over an extended period of time—e.g. >50 years—cannot be predicted with any degree of confidence. In fact, it is an impossibility due to the chaotic nature of climate [30]. To be clear, this does not cast doubt on the scientific foundation of climate change projections—far from it. This approach instead suggests that for environmental planning purposes of river corridors, we should look toward a statistical or probabilistic characterization of future river corridor adjustments due to (1) plausible hydroclimate forcing scenarios (i.e. a future sequence of annual hydrographs) and (2) watershed management actions like sediment release from reservoirs or dam removal (Fig. 1). A direct way to address this objective is to randomly generate a timeseries of annual hydrographs using locally available streamflow gaging records, assuming the statistics captured by the available records is a reasonable basis of expectation for future plausible hydroclimatic conditions [27, 29]. Done repeatedly in a Monte Carlo fashion this approach generates a larger number of realizations (ensemble) for streamflow runoff that can be usefully classified as relatively wet, average or dry, based on historical conditions or the generated timeseries. Applying an ensemble of streamflow hydrographs to a morphodynamic model of river profile and bed surface sediment texture adjustment provides results to understand how and if river corridor adjustments vary based on wetness or dryness, and whether plausible future fluvial conditions are similar regardless of hydroclimatic conditions, at points in time 50 or 100 years from the initial simulation condition. To our knowledge, the latter step in this process is uncommon. Rather, it is more common to use one or a few timeseries of annual hydrographs, or a statistical combination of numerous hydrographs, and develop associated plausible projections of river corridor adjustment [27, 29]. In highly managed or populated basins, this potentially constrains a critical aspect of watershed management decision-making to just one or a small number of future downstream conditions—not an ideal situation given that rivers commonly do not have room to be rivers [3].

Environmental Planning of River Corridors Considering Climate Change …

33

Whereas a randomly generated timeseries of annual hydrographs does, in principle, reflect our objective of incorporating climate change information into watershed and river corridor planning studies, it does lack a more comprehensive probabilistic outlook. Building from prior research, recent work has taken a step toward addressing this issue [25, 26]. The numerical modelling approach developed an ensemble [31, 32] of plausible river profile states that were used to inform regulatory and watershed planning-related decisions concerning the future possible release of granular sediment from the Los Padres Reservoir, Carmel River, CA, USA. This work represents an approach that more directly embraces uncertainty concerning future hydroclimates, and does so in two ways. First, a novel approach to generating an ensemble of hydrograph timeseries was produced, an algorithm referred to as the hydrograph simulator (see Fig. 3 and associated description of [25]). The hydrograph simulator is similar in spirit to the rainfall simulator STORM [19] and was designed to mine information from streamflow gaging records to develop statistically based realizations of annual hydrograph timeseries for the Carmel River, or any river with daily streamflow gaging records. Second, the 1D morphodynamic profile and bed surface sediment texture model BESMo [see 24, 25, for more details] was configured to run on a computing cluster to generate an ensemble of river corridor adjustment projections due to hydroclimatic forcing and differing reservoir sediment release scenarios. BESMo is a decoupled model, which uses simulated hydraulic and sediment transport conditions associated with upstream supplies of water and granular sediment in order to evaluate changes to cross-sectionally averaged riverbed elevations at discrete points along a river profile. For the Los Padres Reservoir analysis, BESMo approximates flow resistance for nonuniform conditions using the standard step method, sediment transport is estimated using the Wilcock-Crowe function [33] and the riverbed substrate was represented in 99 different layers beginning at the surface, each of which could change in grain size composition according to sediment transport and bed elevation adjustment conditions [34]. In total, 4,000 different 60-year morphodynamic simulations were completed to inform watershed management considerations and decisions regarding reservoir management, downstream risks and hazards, as well as potential ecologic and geomorphic impacts (see Fig. 4 for example output of one sediment release scenario). Results from the Los Padres numerical analysis provide better appreciation for the range of plausible river corridor adjustment conditions over the 60-year simulation time frame (Fig. 4). For example, the ensemble of corridor projections details the range of possible timing and magnitude of deposition or erosion of sediment near to the locations of critical infrastructure, or in the vicinity of heavily populated areas. As a result, the Los Padres Reservoir and Carmel River modelling framework can, in the best case, project the range of river profile adjustment possibilities along the Carmel River downstream of the dam, and their probabilities of occurrence. Consequently, this translates to a more informed and transparent planning process [35], providing a stronger rationale for decision pathways as well as setting stakeholder expectations. However, the Los Padres effort is only the start, and the opportunities for new and exciting work are plentiful.

34

S. M. Chartrand

Fig. 4 Example ensemble projections of river profile adjustment at simulation year 60 due to the pulsed release of granular sediment from Los Padres Reservoir, Carmel River, CA, USA for dry and wet hydroclimates. Each subplot illustrates 100 simulation results from the 333 available for each hydroclimate, and the 10th, 50th and 90th percentile results are shown in the green, blue and red curves, respectively. Wet and dry hydroclimate results are sampled from the ensemble of 1,000 simulations for the pulsed supply scenario. Hydroclimates are classified according to the cumulative streamflow condition at year 10 in all simulations. Wet hydroclimates account for the highest 333 cumulative streamflow totals whereas dry hydroclimates account for the lowest 333 totals. In all simulations, granular sediment is introduced at the Los Padres Dam node, where it then is transported downstream. See [24–26] for more details

3 What’s missing and future directions The Los Padres approach is not the panacea of river corridor environmental planning considering climate change and human-directed actions at dams and reservoirs. It does provide a useful direction for further development, but it admittedly is missing important considerations. For example, the hydrology simulator used historical streamflow records as the basis for the ensemble of simulated hydrographs. Despite the broad range of hydroclimates analyzed, it is not clear that the historical records capture or reflect the statistics of future plausible hydroclimates [18]. Nonetheless, the approach used in the Los Padres context as well as other approaches mentioned [27, 29] may offer a reasonable basis for environmental planning. A key aspect to this requires that a primary difference between historical and future projected hydroclimates relates to the duration of droughts or wet periods, as opposed to a significant shift in, for example, the magnitude of precipitation events during both wet and dry periods. If there is a clear need to consider shifts of hydroclimate, a more careful consideration of precipitation projections in relation to possible streamflow runoff

Environmental Planning of River Corridors Considering Climate Change …

35

responses is necessary. This could be addressed through the completion of focused hydrologic modelling. An additional limitation to the Los Padres approach is the implicit assumption that landscape-scale vegetative communities will not transition to new community structures over the simulation period of 60 years. Again, this may be a reasonable basis for environmental planning. However, an assumption of vegetative community stationarity out to 60 years or more into the future may be misguided when combined with considerations of climate change. For example, geographic locations that are expected to shift toward a drier climate will also experience changes to the annual water budget due to less precipitation and associated shifts in evapotranspirative losses [36–38]. As a result, in these locations, it is likely important to consider how climate change influenced vegetation transitions may impact hydrologic conditions at the storm and annual scale—equally important may be related impacts to watershed scale sediment production rates. As suggested above, hydrologic considerations could be addressed by using a hydrologic modelling framework in an experimental manner, testing outcomes for differing vegetative community transition scenarios. Last, the Los Padres approach discussed here considered only the physical aspects of potential river corridor adjustments, primarily due to concern for downstream hazards of flooding and possible loss of life. It is well understood that physical changes along river corridors are coupled with ecological responses [39]. Clearly, the potential character of ecological response for any specific management action should be considered in the planning and decision process—notably, this is presently in process for Los Padres. Furthermore, a beneficial area of new research and applied work could involve trying to couple models like BESMo, directly or indirectly, to ecological model frameworks, which have also been developed to consider a large number of realizations of future plausible conditions [40]. This effort will not be trivial but is critically needed [7]. It would require careful collaboration across disciplines to have confidence that model frameworks were applied in a defensible manner, and that results could realistically inform environmental planning of river corridors. If such directions are feasible, the benefits could be tremendous, not just for basic and applied science, but for species conservation, as well as providing motivation for the development of new methods that could help shape new paradigms in human-managed landscapes. In closing, we emphasize the critical need for the research community to reach out and to collaborate with applied scientists and engineers to develop clear criteria and repeatable methodologies for incorporating climate change projections, or expectations, into the environmental planning process of river corridors. Our own experience suggests that direct actions such as dam removal and reservoir sediment release should complete modelling efforts similar in scope to that developed for Los Padres in the planning process, or at a minimum the approach used for Marmot Dam [27]. However, even relatively basic at-a-cross-section approaches can provide useful information for planning if ensemble-like modelling applications are used. The importance of a climate-change focused perspective increases with reservoir capacity. Reservoirs that store years or decades or more worth of the annual total supply of granular sediment likely hold larger risks to downstream river corridors. Yet, it is

36

S. M. Chartrand

often dangerous to rely on rules of thumb in a broad sense as the catastrophic release of granular sediment from smaller capacity reservoirs may introduce unacceptable downstream risks, suggesting that each situation and set of circumstances are best handled by carefully considering climate change. The key is to be mindful of the fact that precise future precipitation regimes and watershed management decisions are impossible to predict in the present. It is therefore imperative to embrace and adopt a data-rich, climate change informed approach to river corridor environmental planning. Acknowledgements SMC developed most of this work while funded by a Natural Sciences and Engineering Research Council of Canada Postdoctoral Fellowship, and reports no financial conflicts of interest in completing this work or preparing this manuscript. However, SMC acknowledges that he acquired funding to hire Tobias Müller to collaborate on the Carmel River numerical modelling work discussed in this piece, which notably, the BESMo model code can be downloaded here: https://github.com/tobiasmllr/BESMo.git. SMC acknowledges contributions by Kealie Pretzlav in completing the Los Padres work, and critical review by a panel of technical experts assembled on behalf of the regulatory authorities and the Monterey Peninsula Water Management Agency in order to develop a robust modelling framework for the Carmel River corridor. SMC acknowledges the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling group NCAR (USA) for producing and making available their model output reported herein. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led the development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. Last, SMC thanks Amy East, David Jon Furbish and anonymous reviewer for critical reads of a draft of this manuscript.

References 1. Lipman P, Mullineaux D (1981) The 1980 eruptions of Mount St. Helens, Washington. US Geol Surv Prof Pap 1250:844 2. Newhall CG (2000) Mount St. Helens. Master teacher. Science (American Society for the Advancement of Science) 288:1181–1183 3. Major JJ (2020) Mount St. Helens at 40. Science (American Association for the Advancement of Science) 368:704–705 4. Oliver M, Grant G (2017) Liberated rivers: lessons learned from 40 years of dam removal. Sci Find (U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station) 193:5 5. Magilligan F et al (2016) River restoration by dam removal: enhancing connectivity at watershed scales. Elementa: Sci Anthropocene 4 6. Sneddon C, Barraud R, Germaine M-A (2017) Dam removals and river restoration in international perspective. Water Altern 10:648–654 7. Ryan Bellmore J et al (2017) Status and trends of dam removal research in the united states. WIREs Water 4:e1164 8. Church M, Jones D (1982) Channel bars in gravel-bed rivers. In: Hey RD, Bathurst JC, Thorne CR. Wiley, New York, p 338 (Gravel-bed rivers) 9. Church M (2006) Bed material transport and the morphology of alluvial river channels. Ann Rev Earth Planet Sci 34:325–354 10. Hassan MA et al (2008) Sediment storage and transport in coarse bed streams: scale considerations. In Habersack H, Piegay H, Rinaldi M (eds) Gravel-bed rivers VI: from process understanding to river restoration. Elsevier B.V, pp 473–496

Environmental Planning of River Corridors Considering Climate Change …

37

11. East AE, Sankey JB (2020) Geomorphic and sedimentary effects of modern climate change: current and anticipated future conditions in the Western United States. Rev Geophys (Wiley) 58:e2019RG000692 12. Chartrand SM (2017) Pool-riffle dynamics in mountain streams: implications for maintenance, formation and equilibrium. Ph.D. thesis. The University of British Columbia 13. Leopold LB, Wolman MG, Miller JP (1964) Fluvial processes in geomorphology. WH Freeman, San Francisco, p 522 14. Wolman MG, Miller JP (1960) Magnitude and frequency of forces in geomorphic processes. J Geol 68:54–74 15. Burt T, Allison R (2010) Sediment cascades: an integrated approach. Wiley-Blackwell, Chichester, West Sussex, Hoboken, NJ 16. Munoz SE et al (2018) Climatic control of Mississippi River flood hazard amplified by river engineering. Nature (London, Nature Publishing Group) 556:95–98 17. IPCC (2021) Summary for Policymakers. In: Masson-Delmotte V et al (eds) Climate change 2021: the physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press 18. Swain DL, Langenbrunner B, Neelin JD, Hall A (2018) Increasing precipitation volatility in twenty-first-century California. Nat Climate Change 8:427–433 19. Singer MB, Michaelides K (2017) Deciphering the expression of climate change within the Lower Colorado River basin by stochastic simulation of convective rainfall. Environ Res Lett (IOP Publishing) 12:104011 20. Parker G (2008) Transport of gravel and sediment mixtures. In: Garcia M (ed) Sedimentation engineering: theory, measurements, modeling and practice (ASCE Manuals and Reports on Engineering Practice No. 110). ASCE, Reston, VA, pp 165–251 21. Coulthard TJ, Van De Wiel MJ (2012) Modelling river history and evolution. Philos Trans R Soc A: Math Phys Eng Sci (Royal Society) 370:2123–2142 22. Macklin MG, Lewin J, Woodward JC (2012) The fluvial record of climate change. Philos Trans R Soc A: Math Phys Eng Sci (Royal Society) 370:2143–2172 23. Brice EM et al (2020) Impacts of climate change on multiple use management of Bureau of land management land in the intermountain West, USA. Ecosphere (Wiley) 11:e03286 24. Müller T, Hassan MA (2018) Fluvial response to changes in the magnitude and frequency of sediment supply in a 1-D model. Earth Surf Dyn 6:1041–1057 25. Müller JT (2019) Modelling fluvial responses to episodic sediment supply regimes in mountain stream. Ph.D. thesis. The University of British Columbia 26. Chartrand SM et al (2020) Evaluation of river profile adjustment potential due to sediment management at Los Padres Dam, Carmel River, CA. In: AGU Fall Meeting Abstracts, vol 2020, EP043-06 27. Cui Y, Wilcox A (2008) Development and application of numerical models of sediment transport associated with dam removal. In: Garcia M (ed) Sedimentation engineering: theory, measurements, modeling and practice (ASCE Manuals and Reports on Engineering Practice No. 110). ASCE, Reston, VA, pp 995–1020 28. Cui Y, Parker G, Braudrick C, Dietrich WE, Cluer B (2006) Dam removal express assessment models (DREAM). J Hydra Res (Taylor & Francis) 44:291–307 29. De Rego K, Lauer JW, Eaton B, Hassan MA (2020) decadal-scale numerical model for wandering, cobble-bedded rivers subject to disturbance. Earth Surf Process Land (Wiley) 45:912–927 30. Lorenz EN (1993) The essence of chaos. In: Jessie and John Danz lectures. University of Washington Press, Seattle, p 240 31. Furbish DJ, Haff PK, Roseberry JC, Schmeeckle MW (2012) A probabilistic description of the bed load sediment flux: 1. Theory. J Geophys Res: Earth Surf 117:F03031–F03031 32. Furbish DJ, Doane TH (2021) Rarefied particle motions on hillslopes-Part 4: philosophy. Earth Surf Dyn 9:629–664 33. Wilcock PR, Crowe JC (2003) Surface-based transport model for mixed-size sediment. J Hydra Eng (American Society of Civil Engineers) 129:120–128

38

S. M. Chartrand

34. Viparelli E, Sequeiros O, Cantelli A, Wilcock P, Parker G (2010) River morphodynamics with creation/consumption of grain size stratigraphy 2: numerical model. J Hydra Res 48:727–741 35. Foley MM et al (2017) Dam removal: listening in. Water Resour Res (Wiley) 53:5229–5246 36. Kelly AE, Goulden ML (2008) Rapid shifts in plant distribution with recent climate change. Proc Natl Acad Sci 105:11823–11826 37. Liu H et al (2018) Shifting plant species composition in response to climate change stabilizes grassland primary production. Proc Natl Acad Sci 115:4051–4056 38. Holsinger L et al (2019) Climate change likely to reshape vegetation in north america’s largest protected areas. Conserv Sci Pract 1:e50 39. Hauer FR et al (2016) Gravel-bed river floodplains are the ecological nexus of glaciated mountain landscapes. Sci Adv 2:e1600026–e1600026 40. Buisson L, Thuiller W, Casajus N, Lek S, Grenouillet G (2010) Uncertainty in ensemble forecasting of species distribution. Glob Change Biol 16:1145–1157

Assessment of Fluvial Controls and Cross-Sectional Recovery Indicators in a Large Regulated River C. Pradhan, S. K. Padhee, S. Dutta, and Rishikesh Bharti

Abstract In the twenty-first century, fluvial geomorphologists will play a key role in improving the process-based knowledge pertinent to the anthropogenically disturbed rivers. In particular, alluvial-regulated rivers in India are relatively understudied, and therefore demand thorough investigations on channel adjustments and underlying concepts. The present study is focused on understanding the geologic and human influence on morphological adjustments in the Mahanadi River basin. The study area encompasses a 120 km reach of the regulated Mahanadi River, which is subjected to varying antecedent and flux fluvial controls. The study reach has two similar geomorphic fragments connected through a nodal section having 12 km channel length. Long-term hydrological (flow and channel geometry) and geo-spatial (digital elevation model and Landsat) datasets along with Google Earth Engine (GEE) cloud computing platform are used to evaluate fluvial controls and cross-sectional recovery indicators in the study reach. Furthermore, the planform indices (sinuosity and braiding) are evaluated to comprehend the temporal variation of the system state. The results show that Mahanadi offers a dominant fixed control in terms of nodal valley confinement and macrochannel banks. The temporal variability of at-a-station hydraulic geometries reveals that Mahanadi has signs of undergoing geomorphic river recovery via bench growth and stabilization of low-flow channels. Finally, the historical assessment of planform indices shows three distinct system states have developed at an interval of 10–20 years in the Mahanadi River. Keywords Nodal · Fluvial control · River recovery · Regulated river

C. Pradhan (B) · S. K. Padhee · S. Dutta · R. Bharti Department of Civil Engineering, IIT Guwahati, Guwahati 781039, India e-mail: [email protected] S. K. Padhee International Water Management Institute, Delhi 110012, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 V. Chembolu and S. Dutta (eds.), Recent Trends in River Corridor Management, Lecture Notes in Civil Engineering 229, https://doi.org/10.1007/978-981-16-9933-7_3

39

40

C. Pradhan et al.

1 Introduction Fluvial systems encompass complex non-linear geomorphic variables. In recent years, pertinent concepts such as geomorphic effectiveness [6, 19, 23], recovery [9, 13, 32], sensitivity [12, 20, 22], threshold [5, 38, 39] and connectivity [26, 44, 45] have been developed to improve the understandings of process-form relationships. Furthermore, additional stresses in terms of riverine structures, channel regulation, and conservation measures have altered the existing natural regime and produced heterogeneous responses in the fluvial systems [7, 15, 21, 28]. Assessment of river recovery potential for such anthropogenically disturbed river systems is central to river management practices and is determined by estimating the connectivity of reaches throughout the catchment and evaluating the limiting factors to recovery (alteration in the flow-sediment regime, environmental practices, climate) [13]. Evaluation of resilience and fluvial controls are also crucial parameters to determine the potential of inherent resisting force to absorb disturbances specific to climate, tectonic and anthropogenic [41]. The vast majority of Indian rivers (Himalayan and peninsular) are now regulated through large dams, weirs, barrages, and river training structures [14, 17, 24, 34]. The controlled flow-sediment regime has initiated hydrologic [1, 16, 25, 27, 35], geomorphic [3, 30, 31, 37, 42], and ecologic [2, 18, 36, 40] impacts at different spatio-temporal scales. This makes it important to study the potential impact of regulated flow and physiographic settings on resilience (fluvial controls) and recovery trajectory in Indian river basins. The Mahanadi is one of the major peninsular rivers and is strongly governed by dams and nodal sections. Therefore, the objectives of the present study are to (i) identify the dominating role of fluvial controls in a regulatednodal section impacted reach and (ii) assess the temporal variability of cross-sectional indicators to comprehend the past-to-present status and future recovery trajectory.

2 Study Area The Mahanadi is the second largest basin in peninsular India, draining an area close to 1,41,589 km2 (Fig. 1) (India WRIS). This major inter-states flowing river has a total channel length of 851 km and annual average rainfall close to 1463 mm during the southwest monsoon (June to September). In addition, the Mahanadi drains along red and yellow soils, mixed red and black soils, laterite soils, and deltaic soils and has a drainage network consisting of several tributaries (the Seonath, the Jonk, the Hasdeo, the Mand, the Ib, the Ong, and the Tel) and distributaries (the Kathjodi, the Daya, the Devi, and the Kushabhadra). The present study area encompasses the lower sub-basin of the Mahanadi, which receives regulated flow from the Hirakud Dam and the Ong tributary and unregulated flow from the Tel River (Fig. 1). The lower Mahanadi is further sub-divided into the upper stretch (US), nodal stretch (NS), and lower stretch (LS). The length of the nodal

Assessment of Fluvial Controls and Cross-Sectional …

41

Fig. 1 The Mahanadi River basin showing the Hirakud dam and Tikarapara gauging station. The lower Mahanadi River is further sub-divided into the upper stretch (US), nodal stretch (NS), and lower stretch (LS)

section is 12 km, where valley confinement abuts the river banks. The Tikarapara gauging station is present 5 km upstream of the nodal stretch and facilitates an ideal physiographic setting to assess the potential impact of the nodal section in the lower Mahanadi River. The channel form is described as a macrochannel, where a smaller low-flow channel is inset in a larger channel. The width varies from 0.5 km (in NS) to 3.0 km (in LS), and the river is subjected to a high daily discharge variability of 30– 30,000 m3 /s at the Tikarapara gauging station. The planform state of the Mahanadi corresponds to a low sinuous-weakly braided channel with the presence of occasional chutes, secondary flow pathways, and vegetated landforms close to the thalweg. In addition, linear benches are the dominant instream geomorphic units which trap substantial sediment and facilitate an exposed surface for vegetation colonization.

3 Data and Methodology 3.1 Evaluation of Antecedent and Flux Fluvial Controls River systems are governed by sets of variables, majorly sub-divided as antecedent (fixed) and flux controls [4, 13, 29]. The antecedent controls adjust over a hundred to thousand years timeframe and consider the impacts of climate, substrate, drainage

42

C. Pradhan et al.

pattern, tectonics, and valley morphology. On the contrary, flux variables like flow, sediment, and vegetation alter within days to years. Hydrological and geo-spatial datasets are used to study the dominating role of flow regulation, nodal confinement, and physiographic settings in the Mahanadi River (Fig. 2). Long-term daily discharge data (at Tikarapara) is collected from India WRIS and normalized with maximum and minimum values to assess the inter-annual and intra-seasonal variability. Geo-spatial data as digital elevation model (SRTM) is processed in Google Earth Engine (GEE) cloud computing platforms to differentiate instream geomorphic units (low-flow channel, mid-channel/bank-attached bar, and bench) in the macrochannel. Following the recommendation proposed by [43], the low-flow channel is considered based on the orientation, shape, and position within the larger channel form. The midchannel and bank-attached bars are referred to as the geomorphic units present in the center of channel and close to the bank, respectively. Bench is adopted as the bank-attached geomorphic units subjected to intense aggradation and sediment trapping. Furthermore, the bank points are identified to distinguish the low-flow (inset) channel discharge from the bankfull discharge (Qb ), which is adopted as the flow that completely fills the macrochannel.

Fig. 2 Methodology for evaluation of fluvial controls and cross-sectional recovery indicators in the Mahanadi River

Assessment of Fluvial Controls and Cross-Sectional …

43

3.2 Assessment of Transport Effectiveness and Recovery Trajectory Long term at-a-station hydraulic geometries data (1982–2012) is used in this study to quantify the maximum breaks in slope between low-flow channel, mid-channel bars, benches, and macrochannel bank. The cross-sectional data of the post-monsoon seasons are projected with a single datum to evaluate the temporal variation in incision, erosion, and aggradation in macrochannel form. Furthermore, optical images (Landsat) are processed in the geo-spatial environment to extract the total length of reach, low-flow channel, and secondary flow pathways (chutes and flood channels). Finally, the sinuosity index is evaluated as the ratio of low-flow channel length to reach length and braiding index as the ratio of total channel length to low-flow channel length [8].

4 Results and Discussion 4.1 Role of Antecedent and Flux Controls in the Mahanadi River In the lower Mahanadi River, antecedent controls in terms of valley confinement are dominant for a stretch of 12 km near the Tikarapara gauging station (Fig. 3a). This nodal section (NS) joins the upper stretch (average river width of 1.2 km) with the lower stretch (average river width of 2.1 km), which further facilitates a dynamic reach for the high energy expenditure. The river system is entirely macrochannel and thereby offers additional fixed control on the river system. In addition, instream geomorphic units such as low-flow channel, bench, chutes, and bars have been developed at different hierarchical scales, and benches are at an elevation of 6–10 m higher than the base level. Such channel-in-channel physiography and stabilization of high macrochannel banks by bench development highlight the dominant role of antecedent controls in the Mahanadi. In several fluvial systems across the globe, bed slope, and distribution of confining features (terraces) are often considered as the first-order (antecedent) control on the river behavior [10, 11, 29]. In the Mahanadi, the bed slope varies between 1:4000 and 1:3333 in the US and LS reaches. However, in the NS, an elevation drop of 4 m is observed within a stretch of 12 km (average bed slope of 1:2500). In addition, the macrochannel width increases from 0.5 to 2.0 km and enables the fluvial system to dissipate the energy (unit stream power) at a higher rate (Fig. 3b). Such fixed controls often restrict the fluvial system to instigate major geomorphic adjustments during low-moderate floods. In a macrochannel river system like the Mahanadi, it is established that a portion of fluvial energy above the meanderingweakly braided threshold gets dissipated by forming chute channels close to the

44

C. Pradhan et al.

Fig. 3 Antecedent and flux fluvial controls in the Mahanadi River basin. a Significant variability in at-a-station hydraulic geometries between US, NS, and LS (obtained from Google earth engine), b alteration in bed elevation and probable energy expenditure zone, c temporal fluctuations of normalized daily discharge at Tikarapara

thalweg [33]. However, the Mahanadi River has observed significant variability in seasonal flux controls (Fig. 3c). The low-flow discharge of less than 100 m3 /s can be easily accommodated within the thalweg, and high macrochannel banks can carry up to discharge of 35,000 m3 /s during catastrophic floodings. Such extreme events are enriched with high total stream power, exceed the geomorphic thresholds, form chutes and flood channels, sculpt the depositional units and instigate in-stream geomorphic adjustments. To summarize, the lower Mahanadi offers a dominant fixed control in terms of nodal valley confinement and macrochannel banks. Such antecedent controls have further invoked a resilient geomorphic system that may override the influence of short-magnitude disturbing events.

4.2 Recovery Trajectory and Transport Effectiveness: Implication for a Regulated Macrochannel River System Differing forms of recovery in macrochannel systems include the formation of bankattached benches, distinct well-defined low-flow channels, and development of wellvegetated stable bars [13]. The Mahanadi has clear breaks in slope for the low-flow channel, bar, bench, and macrochannel bank. The temporal assessment of at-a-station

Assessment of Fluvial Controls and Cross-Sectional …

45

hydraulic geometries reveals that Mahanadi has signs of undergoing geomorphic recovery via bench growth and gradual stabilization of the low-flow channel (Fig. 4a). The low-flow channel has a width of 200–350 m and is located at an elevation of 15–20 m below the stage of bankfull discharge (Qb ). The number of low-flow channels at the 80–90% exceedance flow seldom changes, and occasionally, patterns of chutes and pools are created. In sand bed macrochannel systems, alteration of width to depth ratio can be adopted as an indicator for the recovery [13]. In the Mahanadi, sediment reworking, reorganization, and concentrated flows have created distinct low-flow channels. Furthermore, this macrochannel system has also shown a signature of increasing W/Dmax (W: width and Dmax : maximum depth) for the lowflow channel and initiated the cross-sectional recovery. Another form of geomorphic recovery is in terms of instream vegetation growth (up to 1–2% of total fluvial corridor area in the last 35 years) and sediment retention and trapping by bankattached bars and benches. The inter-comparison of decadal hydraulic geometries reveals a periodic bench formation and destruction in the 1980 and early 90 s. The catastrophic floods of 1994 have significantly eroded the bench and introduced a relative symmetrical channel in the Mahanadi. However, after 1995, sustained growth in bench height is observed at the Tikarapara reach, and aggradation of 5 m has contracted the macrochannel area, developed a compound form and initiated the cross-sectional recovery. Interlining transport effectiveness of flows with the overall river recovery process is crucial to understand the present form status and its future trajectory. In a longer time, moderate and extreme floods are capable of producing geomorphic works in the peninsular rivers [33]. The temporal assessment of planform indices (SI and BR) shows that three distinct system states have developed at an interval of 10–20 years in the Mahanadi (Fig. 4b). In between 1973 and 1991, the river has an average SI and BR of 1.24 and 1.96, respectively. This planform state can be referred to as a stable equilibrium where the thalweg is low sinuous in nature and occasionally gets straightened after the passage of the catastrophic floods (SI of 1.1). Such extreme

Fig. 4 a Recovery indicators in terms of stabilization of low-flow channel and bench growth in the Mahanadi, b temporal variability of sinuosity index (SI) and braiding index (BR) and development of stable and dynamic system states

46

C. Pradhan et al.

events were frequent in the 1990s and early 2000s and have disturbed the overall recovery trajectory via bench degradation, sculpting of bars, and channel incision. In this period, the temporal average of SI and BR corresponds to 1.20 and 2.1, respectively. Finally, in recent years (after 2004), the Mahanadi is morphologically less dynamic through increased stabilization of thalweg and secondary flow pathways, which is further attributed to the growth of bench and instream vegetated landforms.

5 Conclusion The present study has evaluated the dominating role of antecedent and flux fluvial controls in the lower Mahanadi River. In addition, the temporal variability of crosssectional indicators is assessed to understand the present status and future trajectory. The major conclusions of this study are as follows: (i)

(ii)

(iii)

The lower Mahanadi offers a dominant fixed control in terms of nodal valley confinement and macrochannel banks. Such antecedent controls have further invoked a resilient geomorphic system that overrides the influence of shortmagnitude disturbing events. The temporal assessment of at-a-station hydraulic geometries reveals the Mahanadi has signs of undergoing geomorphic river recovery via bench growth and gradual stabilization of low-flow channels. Finally, the temporal assessment of planform indices (SI and BR) shows three distinct system states have developed at an interval of 10–20 years in the Mahanadi.

Indian river systems are increasingly subjected to hydro-geomorphological and ecological alterations via natural and anthropogenic stresses. The present study has highlighted the role of the inherent resilience of a macrochannel system to absorb low-moderate magnitude floods and the emergence of cross-sectional recovery indicators. The study can be further extended to other regulated rivers in different geomorphic settings to better understand the process-form relationships for effective river-corridor management.

References 1. Bastia F, Equeenuddin SM (2016) Spatio-temporal variation of water flow and sediment discharge in the Mahanadi River, India. Glob Planet Change 144:51–66 2. Bhaumik U, Mukhopadhyay MK, Shrivastava NP, Sharma AP, Singh SN (2017) A case study of the Narmada River system in India with particular reference to the impact of dams on its ecology and fisheries. Aquat Ecosyst Health Manag 20(1–2):151–159 3. Biswas SS, Pani P (2021) Changes in the hydrological regime and channel morphology as the effects of dams and bridges in the Barakar River, India. Environ Earth Sci 80(5):1–20 4. Brierly GJ, Fryirs K (2005) Geomorphology and river management. Appl River Styles Framew

Assessment of Fluvial Controls and Cross-Sectional …

47

5. Church M (2002) Geomorphic thresholds in riverine landscapes. Freshw Biol 47(4):541–557 6. Dean DJ, Schmidt JC (2013) The geomorphic effectiveness of a large flood on the Rio Grande in the big bend region: insights on geomorphic controls and post-flood geomorphic response. Geomorphology 201:183–198 7. Erskine WD (1985) Downstream geomorphic impacts of large dams: the case of Glenbawn Dam, NSW. ApplGeogr 5(3):195–210 8. Friend PF, Sinha R (1993) Braiding and meandering parameters. Geol Soc London Spec Publ 75(1):105–111 9. Fryirs K, Brierley GJ (2001) Variability in sediment delivery and storage along river courses in Bega catchment, NSW, Australia: implications for geomorphic river recovery. Geomorphology 38(3–4):237–265 10. Fryirs K (2002) Antecedent landscape controls on river character, behaviour and evolution at the base of the escarpment in Bega catchment, South Coast, New South Wales, Australia. Zeitschrift für Geomorphologie 475–504 11. Fryirs K, Spink A, Brierley G (2009) Post-European settlement response gradients of river sensitivity and recovery across the upper Hunter catchment, Australia. Earth Surf Process Land 34(7):897–918 12. Fryirs KA (2017) River sensitivity: a lost foundation concept in fluvial geomorphology. Earth Surf Proc Land 42(1):55–70 13. Fryirs KA, Brierley GJ, Hancock F, Cohen TJ, Brooks AP, Reinfelds I et al (2018) Tracking geomorphic recovery in process-based river management. Land Degrad Dev 29(9):3221–3244 14. Gain AK, Giupponi C (2014) Impact of the Farakka Dam on thresholds of the hydrologic flow regime in the Lower Ganges River Basin (Bangladesh). Water 6(8):2501–2518 15. Graf WL (2006) Downstream hydrologic and geomorphic effects of large dams on American rivers. Geomorphology 79(3–4):336–360 16. Gupta H, Chakrapani GJ (2007) Temporal and spatial variations in water flow and sediment load in the Narmada river. Curr Sci 679–684 17. Gupta H, Kao SJ, Dai M (2012) The role of mega dams in reducing sediment fluxes: a case study of large Asian rivers. J Hydrol 464:447–458 18. Jumani S, Rao S, Kelkar N, Machado S, Krishnaswamy J, Vaidyanathan S (2018) Fish community responses to stream flow alterations and habitat modifications by small hydropower projects in the Western Ghats biodiversity hotspot, India. Aquat Conserv Mar Freshwat Ecosyst 28(4):979–993 19. Kale VS (2007) Geomorphic effectiveness of extraordinary floods on three large rivers of the Indian Peninsula. Geomorphology 85(3–4):306–316 20. Khan S, Fryirs K (2020) An approach for assessing geomorphic river sensitivity across a catchment based on analysis of historical capacity for adjustment. Geomorphology 359:107135 21. Li D, Lu XX, Chen L, Wasson RJ (2019) Downstream geomorphic impact of the three Gorges Dam: with special reference to the channel bars in the middle Yangtze River. Earth Surf Proc Land 44(13):2660–2670 22. Lisenby PE, Fryirs KA, Thompson CJ (2020) River sensitivity and sediment connectivity as tools for assessing future geomorphic channel behavior. Int J River Basin Manag 18(3):279–293 23. Miller AJ (1990) Flood hydrology and geomorphic effectiveness in the central Appalachians. Earth Surf Proc Land 15(2):119–134 24. Mittal N, Mishra A, Singh R, Bhave AG, van der Valk M (2014) Flow regime alteration due to anthropogenic and climatic changes in the Kangsabati River, India. EcohydrolHydrobiol 14(3):182–191 25. Pal S (2016) Impact of Massanjore dam on hydro-geomorphological modification of Mayurakshi river, Eastern India. Environ Dev Sustain 18(3):921–944 26. Poole GC, Stanford JA, Frissell CA, Running SW (2002) Three-dimensional mapping of geomorphic controls on flood-plain hydrology and connectivity from aerial photos. Geomorphology 48(4):329–347 27. Panda DK, Kumar A, Mohanty S (2011) Recent trends in sediment load of the tropical (Peninsular) river basins of India. Glob Planet Change 75(3–4):108–118

48

C. Pradhan et al.

28. Petts GE, Gurnell AM (2005) Dams and geomorphology: research progress and future directions. Geomorphology 71(1–2):27–47 29. Phillips JD (2010) Relative importance of intrinsic, extrinsic, and anthropic factors in the geomorphic zonation of the Trinity River, texas 1. JAWRA J Am Water Resour Assoc 46(4):807–823 30. Pradhan C, Bharti R, Dutta S (2017) Assessment of post-impoundment geomorphic variations along Brahmani River using remote sensing. In: 2017 IEEE international geoscience and remote sensing symposium (IGARSS). IEEE, pp 5598–5601 31. Pradhan C, Chembolu V, Dutta S (2019) Impact of river interventions on alluvial channel morphology. ISH J Hydraul Eng 25(1):87–93 32. Pradhan C, Modalavalasa S, Dutta S, Bharti R (2020) A geomorphic approach to evaluate river recovery potential for regulated river basin. In: River flow 2020. CRC Press, pp 1805–1809 33. Pradhan C, Chembolu V, Dutta S, Bharti R (2021a) Role of effective discharge on morphological changes for a regulated macrochannel river system. Geomorphology 385:107718 34. Pradhan C, Chembolu V, Bharti R, Dutta S (2021b) Regulated rivers in India: research progress and future directions. ISH J Hydraul Eng. https://doi.org/10.1080/09715010.2021.1975319 35. Rao KN, Subraelu P, Kumar KCVN, Demudu G, Malini BH, Rajawat AS (2010) Impacts of sediment retention by dams on delta shoreline recession: evidences from the Krishna and Godavari deltas, India. Earth Surf Process Land J Br Geomorphol Res Group 35(7):817–827 36. Sandhya KM, Lianthuamluaia L, Karnatak G, Sarkar UK, Kumari S, Mishal P et al (2019) Fish assemblage structure and spatial gradients of diversity in a large tropical reservoir, Panchet in the Ganges basin, India. Environ Sci Pollut Res 26(18):18804–18813 37. Sanyal J, Lauer JW, Kanae S (2021) Examining the downstream geomorphic impact of a large dam under climate change. CATENA 196:104850 38. Schumm SA (1973) Geomorphic thresholds and complex response of drainage systems. Fluvial Geomorphol 6:69–85 39. Schumm SA (1979) Geomorphic thresholds: the concept and its applications. Trans Inst Br Geogr 485–515 40. Talukdar S, Pal S (2017) Impact of dam on inundation regime of flood plain wetland of punarbhaba river basin of barind tract of Indo-Bangladesh. Int Soil Water Conserv Res 5(2):109–121 41. Thomas MF (2001) Landscape sensitivity in time and space—An introduction. CATENA 42(2– 4):83–98 42. Vercruysse K, Grabowski RC (2021) Human impact on river planform within the context of multi-timescale river channel dynamics in a Himalayan river system. Geomorphology 381:107659 43. Wheaton JM, Fryirs KA, Brierley G, Bangen SG, Bouwes N, O’Brien G (2015) Geomorphic mapping and taxonomy of fluvial landforms. Geomorphology 248:273–295 44. Wohl E (2017) Connectivity in rivers. Prog Phys Geogr 41(3):345–362 45. Wohl E, Brierley G, Cadol D, Coulthard TJ, Covino T, Fryirs KA et al (2019) Connectivity as an emergent property of geomorphic systems. Earth Surf Process Land 44(1):4–26

Web Reference 46. India WRIS. https://indiawris.gov.in/wiki/doku.php?id=mahanadi

Holistic Environmental Flow Assessment by Building Block Method in Inaccessible Himalayan River Basins S. K. Padhee, V. Chembolu, A. Akkimi, K. K. Nandi, S. Dutta, Dibyendu Adhikari, Raghuvar Tiwary, Bikram Singh, and Saroj K. Barik

Abstract Estimating the environmental flow (E-flow) of a river stretch prior to the construction of a hydroelectric project (HEP) is essential to conserve riverine ecosystems. The Building Block Method (BBM) is a widely used holistic technique for E-flow assessment (EFA) that uses hydrological flow information as fundamental to understand flow–ecology relationships. However, the application of methods like BBM is restricted in inaccessible river basins of the Himalayas due to data limitations such as flow, cross-section, and slope. This work presents a hybrid approach by supporting BBM with 90% dependable year analysis and hydrodynamic modeling to encounter the lack of data in EFA. Three case studies are demonstrated at the upcoming HEP on Tawang River, located in the Eastern Himalayan region. Ecosystem components like river biodiversity, river hydraulics, cultural requirement, livelihood requirement, and ecosystem function are considered as blocks of BBM. The input flow for hydrodynamic routing is fed by seasonal water availability, which is found by examining daily flow data with 90% dependable year analysis. Hydrodynamic simulation for multi-release scenarios is used to determine appropriate E-flow requirements for blocks of BBM at a critical stretch. A final comparison of seasonal release policy by existing ministerial recommendations with estimated E-flow requirement shows over-release in monsoon and under-release in remaining seasons in the case studies. Consequently, the new release policy is framed based on estimations of E-flow requirements. Keywords Environmental flow · Building block methodology · Hydrodynamic model · Ecosystem S. K. Padhee · A. Akkimi · K. K. Nandi · S. Dutta Department of Civil Engineering, IIT Guwahati, Guwahati, India V. Chembolu (B) Department of Civil Engineering, IIT Jammu, Jammu 181221, India e-mail: [email protected] D. Adhikari · R. Tiwary · B. Singh · S. K. Barik Plant Ecology and Climate Change Science Division, CSIR-National Botanical Research Institute, Rana Pratap Margaret, Lucknow, Lucknow, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 V. Chembolu and S. Dutta (eds.), Recent Trends in River Corridor Management, Lecture Notes in Civil Engineering 229, https://doi.org/10.1007/978-981-16-9933-7_4

49

50

S. K. Padhee et al.

1 Introduction The Himalayan river basins consist of an extensive network of high mountains, intermontane valleys, and plateaus and facilitate one of the largest sustainable freshwater supplies. These kinds of mountain ranges are also known as the “water towers of earth” in other terms. Himalayan mountains stretched throughout ten states having a length and width of about 2,500 km and 300 km, respectively, within the Indian boundary [1]. Besides its physical significance, the Himalayas have major social, cultural, and economic implications for the people of India. This hilly landscape’s glacier melts, and many springs feed the majority of northern India’s perennial river systems, such as the Indus, the Ganga, and the Brahmaputra. Over 50 million people live in this region, depending on these resources for their living and livelihood. It should be noted that water is the source of life and the foundation of ecological equilibrium, and the link between all environmental issues. So, maintaining river health is essential not only for the people who depend on it but also for the long-term structure, function, and services of the riverine ecosystem. Any change in the hydrological system always induced changes to the ecological system and vice-versa. To address this response mechanism of interaction between the hydrology and ecology of the system gives rise to the new topic of interest, i.e., Hydro-ecological security. Environmental flow (E-flow) is the necessary flow maintained in a river that ensures the sustainability of the riverine ecosystem’s structure, function, and services round the year. International Union for Conservation of Nature (IUCN) defines it as the water regime provided within a river, wetland, or coastal zone to maintain ecosystems and their benefits in the presence of competing water use and where flows are regulated [19]. The importance of maintaining environmental flow by river utility-based projects has been recognized in policies and legal frameworks in many countries [26]. Therefore, assessment of the impact of changed flow regime on river ecology and provision of E-flow has become necessary in the development of hydroelectric projects (HEP) [8, 21, 37, 42, 45]. The last decade has proven productive with the increasing support of scientists and engineers in defining these flows around the world [3]. Some popular methods currently followed for E-flow assessment (EFA) are (a) Hydrological methods, (b) Hydraulic rating, (c) Habitat simulation, and (d) Holistic methodologies [4, 17, 46]. The definition of the recommended E-flow varies with the methods selected by the policymakers. The hydrology-based approach defines E-flow as a proportion of annual/seasonal/monthly flow [11]. The hydraulic rating method is based on the relationship between flow and hydraulic conditions such as depth, velocity, and wetted perimeter [28]. Dunbar et al. [18] have shown that habitat simulation methods recommend E-flow based on the estimation of the amount of suitable habitat available during different flows and its requirement in a future scenario. Holistic approaches are multidisciplinary in nature, connecting multiple uses of rivers and their ecology and social importance where recommendations of Eflow are made with an integrated framework. In the last 40 years, recommendations

Holistic Environmental Flow Assessment by Building Block Method …

51

based on holistic ecosystem approaches have evolved as the main scope of worldwide advancements [34]. Despite global advancements, most of the policies in India have been established purely based on hydrological approaches [24]. The National Water Policy in 2002 by the Ministry of Water Resources (MoWR) recognized water as a part of the ecological system [30]. CWC [12] recommended different release schemes based on mountainous and plain reaches. Recommendations by MoWR for Himalayan rivers in mountainous reaches were based on water availability from snowmelt with a low annual dependence rate of 75%. The National Water Policy added due consideration of optimum utilization, environmental sustainability, and holistic benefit in 2012 [31]. Currently, there is no policy for flexibility of minimum E-flow releases in India. The Expert Appraisal Committee (EAC) for River Valley and Hydroelectric Projects of the Ministry of Environment & Forests (MoEF) in India recommends minimum Eflow during the low-flow season as 20% of the average discharge in four leanest months in 90% dependable year. Similarly, it recommends 30% and 20–30% release of 90% dependable year for monsoon and remaining seasons, respectively [13, 14]. River researchers define the E-flow of a river as a factor of quantity, quality, and timing of water flow to support the sustenance of aquatic and terrestrial ecosystems. The necessity of using scientific ways to estimate E-flow in Indian Rivers is seen to draw the interest of researchers and planners to include holistic approaches in few recent works [33, 44].

2 Study Area and Data Description 2.1 Study Area Himalaya is the origin of numerous freshwater river systems. It is viewed by the engineers as the next big venture for investment for hydropower generation [6, 7]. The Tawang River basin (study area, Fig. 1), an important region in Eastern Himalaya, is expected to be capitalized for the development of several HEP in the next decade. A diagram of a typical hydroelectric project and its elements is presented in Fig. 2. Tawang River (locally named as Tawang-chu) is the main river in the study area, which has two perennial tributaries; Nyukcharang Chu and Mago Chu forming two sub-basins named after it. It is a culturally and ecologically sensitive region having diversified ecosystem components, due to which EFA should include the scientific rationale for release policy definition.

52

S. K. Padhee et al.

Fig. 1 Geographical extent of study area, Tawang River basin Barrage

Available water Cross-section 1 Cross-section 2 Diverted water

E-flow

Cross-section n

Tail race Turbine

Critical stretch

Fig. 2 Diagram of a typical hydroelectric project and its elements

Holistic Environmental Flow Assessment by Building Block Method …

53

2.2 Data Description This work provided an opportunity to collect baseline data at the basin level through a rigorous field survey. However, the main problem faced in such studies around this region is data unavailability [24, 41]. Therefore, a serious effort has been made to collect the data in order to develop a more accurate hydrodynamic model. Channel cross-section data is a necessary input for setting up conditions for in hydrodynamic model to simulate natural flow. It is collected at intervals of approximately 50–200 m on a critical stretch of HEP sites. Elevation above mean sea level at each cross-section was collected using a GPS device. Other important details for the hydrodynamic model setup like riverbed materials, bankside land use, average flow depth, stream width, and surface water velocity were also collected in the survey. Daily flow at proposed HEP sites is collected from Central Water Commission under the Government of India (CWC, India). It is available from the year 1999 to 2012 for the study area. The hydrological year for India is counted from 1st June of a year to 31st May of next year, based on which seasonal release policies in India are operated. The entire analysis and estimations in this study are based on hydrological year due to which the terms ‘year’ and ‘annual’ reflects in the context of hydrological year in this paper. The components of the riverine ecosystem, such as biodiversity, cultural and livelihood requirements, and ecosystem structure and functions, are essential elements in the holistic approach. The preparation of baseline components for EFA is based on the presence and dependency of the components on the river [32]. Each of these components had various parameters that were selected based on their overall importance in the river basin (Tables 2, 3, and 4). River biodiversity included threatened fish species (Schizothorax richardsonii and Schizothorax progastus) [40], endemic species of periphyton, and zooplankton, and threatened bird, i.e., the black-necked crane [38]. Livelihood requirements include water use, river resources, and edible algae. Recognition of the multi-dimensional relationships, including cultural aspects that ethnic peoples have with rivers, is important [22, 23]. So, consideration of body disposal after death was also included as the cultural requirement, which is a ritual practice in local tribal communities. The complexity of biotic and abiotic structure, functions, and processes are the products of intricate system-inherent dynamics in critical ecosystems of the river [25]. Therefore, maintaining the structural and functional integrity of the river ecosystem is crucial for sustainable ecology. Values on the water requirements for each of the parameter were inferred through field-level observations, direct measurements, and experts’ opinions.

54

S. K. Padhee et al.

3 Methods The methodology used here couples BBM with hydrodynamic model routing to estimate E-flow and develop a release policy based on the estimations. The overall schematic diagram of the methodology followed is presented in Fig. 3. The building block method or BBM is one of the widely used holistic approaches around the world for cumulative environmental impact assessment. It is based on simple but effective tactics, in which experts set objectives for the environmental condition suitable for various ecosystem components of the river and assess modified flow that satisfies those objectives under certain assumptions [47]. The assumptions followed for BBM are: • The biota dependent on a river can cope with those low-flow conditions that naturally occurs in it often and maybe reliant on higher flow conditions that naturally occur in it at certain times. • Identification of what are felt to be the most important components of the natural flow regime and their incorporation as part of the modified flow regime will facilitate maintenance of the natural biota and natural functioning of the river.

Fig. 3 Method of E-flow estimation for proposed hydroelectric projects on Tawang River

Holistic Environmental Flow Assessment by Building Block Method …

55

• Certain types of flow have a greater influence on channel geomorphology than others, and identifying such flows and incorporating them into the modified flow regime would aid in the preservation of natural channel structure and the diversity of physical biotopes. E-flow released from available water is a dependable condition for the sustenance of the ecosystem components. This dependability is found statistically from the distribution of observed flow data. Generally, small samples involve large uncertainties [2]. One way to encounter the uncertainties is to assume high chances of flow occurrence [35] with distribution charted by Weibull analysis [43]. Daily flow data from the year 1999–2000 to 2011–2012 in Tawang River is used to find the annual flow volume (Q y ) as Qy =

365 

Qi ,

(1)

i=1

where Q is the daily observed flow volume, y is the hydrologic year, and i is the day of the corresponding year (i = 1, 2,…365 starting from June 1st). The calculated Q y for an available year are treated as statistical population and ranked in ascending order of magnitude, which is used to identify Y90 using the Weibull formula: P = m/(N + 1).

(2)

Here, P is the probability of occurrence of Q y , m is the rank of observed Q y in ascending order of magnitude, and N is the number of years. Annual flow volume with P closest to 90% is referred as Q 90 . Three hydrological seasons (low-flow, monsoon, and non-monsoon) are found in the region. These are outlined as low-flow (January–March), monsoon (May– September), and non-monsoon (April and October–December). The average seasonal flow in Y90 (F S90 ) at the critical stretch is set as the seasonal water availability (Fig. 2) in the hydrodynamic model. The F S90 is calculated as F S90 =

 n 

 Fi /n,

(3)

i=1

where F S90 is the seasonal water available in S hydrological season of Y90 (S is lowflow, monsoon, or non-monsoon seasons), F is observed daily flow in the hydrological season, i represents the day of the corresponding hydrologic season, and n is the number of days in the hydrological season. The Hydrologic Engineering Centre’s River Analysis Software (HEC-RAS) developed in Institute for Water Resources (IWR) is a widely used hydrodynamic model [15, 36]. The basic equation used in HEC-RAS for computation of water surface profile is the energy equation [16] with an iterative procedure which is called

56

S. K. Padhee et al.

as standard step method. It is used in this study for simulating multi-release scenario from available seasonal water (F S90 ). The inputs essential for the model are upstream boundary condition, stream bed conditions, and cross-section at the critical stretch. Stream bed conditions include Manning’s roughness coefficient and slope of the critical stretch. The average of the slopes calculated from elevations at the cross sections is set as the slope of the critical stretch. The range of Manning’s roughness coefficient is interpreted from the observations of stream bed materials used for modeling as suggested by [16]. Other hydraulic parameters which are used at upstream as boundary conditions includes depth and width of flow. Important ecosystem components are considered as blocks as per the second assumption of BBM. These are listed as (i) River biodiversity, (ii) River hydraulics, (iii) Cultural requirement, (iv) Livelihood requirement, and (v) Ecosystem structure and function. Parameters of ecosystem components are presented in detail in Tables 2, 3, and 4 of the appendix. Each parameter requires certain essential conditions (depth and width of flow) at a critical stretch for sustainability. The values of these essential conditions are determined based on direct field observations as well as experts’ opinions. The seasonal release policy is framed by considering it equivalent to seasonal E-flow. Equation 4 frames the release policy formulation where f S represents the percentage of water released out of available water in a hydrological season and expressed in percentage. Release from 0 to100% scenario imitates no release to total release of water for E-flow. Insertion of ecosystem components in BBM and hydrodynamic simulation to frame seasonal release policy justifies its scientific rationale, and it is compared with existing ministerial recommendations in the study region f S = (E − flow/FS90 ) × 100.

(4)

4 Results Three case studies for EFA and release policy establishment are demonstrated and discussed in this paper. The case studies include HEP at Mago, Nyukcharang, and Jang sites in the study area (Fig. 4). Nyukcharang and Mago sites are located just before the point of confluence of the tributaries of Tawang River, i.e., Nyukcharang Chu and Mago Chu, respectively, in the temperate climate zone. The daily flow data availability at Mago, Nyicharang, and Jang sites is available during the years 2002–03 to 2011–12, 1999–00 to 2011–12, and 2001–02 to 2011– 12, respectively. The total annual flow volume against percentage dependability is 10,448 MCM (million cubic meter) for Mago with 90.9% dependability in the year 2011–12, 12,858 MCM for Nyukcharang with 91.2% dependability in the year 2006– 07, and 30,494 MCM for Jang with 91.7% dependability in the year 2009–10 (Fig. 5). These years are selected as Y90 with seasons as reference hydrological conditions in the respective critical stretches.

Holistic Environmental Flow Assessment by Building Block Method …

57

Fig. 4 Mago, Nyicharang, and Jang sites in Tawang River basin

Annual and seasonal flows are analyzed to examine the variability in water availability (Fig. 6). It shows that the percentage of water volume in low-flow, monsoon, and non-monsoon seasons are 12, 63, and 25% of Q y at Mago; 12, 62, and 26% of Q y at Nyukcharang; and 13, 62, and 25% of Q y at Jang. The comparison shows that a similar proportion of seasonal hydrological conditions are faced over the years at all sites. However, Y90 shows the proportion of low-flow, monsoon, and non-monsoon is 10, 66, and 24% of Q 90 at Mago; 14, 51, and 35% of Q 90 at Nyukcharang; and 12, 61, and 27% of Q 90 at Jang. It is seen that the dependable water availability throughout the year varies drastically in Y90 in contrast to what is generally observed in overall years. The hydrodynamic model setup is shown in Fig. 7, and various inputs used for simulating outputs are presented in Table 1. The estimated E-flow is a resultant of dynamics of a considered parameter as blocks in BBM and seasonal water availability. A comparison of release policy by used method with that of existing ministerial recommendation by EAC MoEF is done (Fig. 8). In the figure, the blue region represents flow from 100% release out of F S90 , the dotted red line represents flow from release as per existing ministerial recommendations, and the green region represents E-flow which is equivalent to flow from release policy framed in the present study. The difference between blue and green regions is the flow and is restricted at the critical stretch to be diverted for power generation. Our analysis shows that flow released from barrage by existing recommendations is not certain to meet the E-flow. These recommendations deliver surplus flow in the monsoon season. However, it is unable to meet the E-flow in transient nonmonsoon and low-flow seasons. On the other hand, the approach in the present work uses estimated E-flow as the release policy due to which sustainability of ecosystem components could be maintained. The case studies conclude with recommended percent release ( f S ) in the low-flow, monsoon, and non-monsoon seasons as 70, 20,

2011-12

2010-11

2009-10

2008-09

2007-08

2006-07

2005-06

2004-05

80

3000 60

2000 40

1000 20

0

4000 80

3000 60

2000 40

1000 20

0

Jang

4000 80

3000 60

2000 40

1000 20

0

Dependability (%)

4000

Dependability (%)

2011-12

2010-11

2009-10

2008-09

2007-08

2006-07

2005-06

2004-05

2003-04

2002-03

2001-02

2000-01

1999-00

Nyicharang

Dependability (%)

2011-12

2010-11

2009-10

2008-09

2007-08

2006-07

2005-06

2004-05

2003-04

5000

2003-04

2002-03

Annual flow volume (MCM) 5000

2002-03

2001-02

Annual flow volume (MCM)

Water year

5000

Annual flow volume (MCM)

58 S. K. Padhee et al. 100

0

Water year

Mago 100

0

100

0

Water year

Fig. 5 Identification of flow for 90% dependable year at critical stretches of Mago, Nyicharang, and Jang sites

Holistic Environmental Flow Assessment by Building Block Method …

59

4500 Observed years

90% dependable year

Flow volume (MCM)

4000 3500

Annual

3000

Monsoon

2500

Non-monsoon

2000

Low-flow

1500

1000 500 0 Nyicharang

Mago

Jang

Nyicharang

Mago

Jang

Fig. 6 General flow and dependable flow variability in Tawang River basin

Fig. 7 Hydrodynamic model setup on critical stretches of Tawang River

and 53% to maintain E-flow of 5, 10, and 8 cumec at Mago. Similarly, 30, 30, and 27% to maintain E-flow of 6, 13, and 10 cumec at Nyukcharang and 27, 18, and 20% to maintain E-flow of 7.6, 20, and 10 cumec at Jang.

60

S. K. Padhee et al.

Table 1 Inputs and outputs of hydrodynamic model simulation Parameter

Mago

Nyicharang

Jang

Critical stretch (km)

2.9

1.5

16.5

Slope Manning’s roughness coefficient Flow depth (m)

Flow width (m)

FS90 (cumec)

E-flow (cumec)

Release policy (f S %)

0.0581

0.0807

0.0409

River bank

0.04

0.07

0.06

Stream bed

0.06

0.04

0.05

Low-flow

0.56

0.55

0.63

Monsoon

0.73

0.74

0.92

Non-monsoon

0.66

0.68

0.7

Low-flow

11.85

10.97

16.8

Monsoon

14.65

13.14

21

Non-monsoon

13.6

12.4

18.14

Low-flow

7

16

18

Monsoon

25

16

27

Non-monsoon

9

18

25

Low-flow

5

6

7.6

Monsoon

10

13

20

Non-monsoon

8

10

10

Low-flow

70

30

27

Monsoon

20

30

18

Non-monsoon

53

27

20

5 Discussion Developing countries like India are novice nations in including E-flow in the release policies. Jain and Kumar [24] have reasoned that, practically, complexities due to scientific rationale in EFA conflict with water management. Numerous studies have identified hybrid approaches as an efficient strategy to deal with EFA subjecting complexities [29, 48]. The present study acclaims a release policy framing strategy in the mountainous river of Eastern Himalaya with a hybrid approach which couples BBM and hydrodynamic modeling. BBM used in this study includes objectives of various river ecosystem components in a holistic manner, and the E-flow is decided by optimizing the simulations of the hydrodynamic model which meets these objectives (Table 1). The strategy to simulate released flow by hydrodynamic modeling is appropriate for deciding E-flow [5, 39]. The new release policy is established based on the estimated E-flow covering the flow requirement of the ecosystem, which is better than existing hydrology-based ministerial recommendations (Fig. 7). The scientific rationale includes ecosystem components, which are likely to involve highly plastic traits due to climate change, especially migratory birds [20]. It might include a shift in the migration period at present and future times. Species of mountainous fishes are also becoming vulnerable due to climate change. Bhatt et al. [9] have stated that

Holistic Environmental Flow Assessment by Building Block Method … 90

(a) Mago stretch

Discharge during 100% release scenario

80

Environmental flow

70

Recommended MOEF

60

Average flow observed in 10 years

Non-monsoon

Monsoon

40

Low-flow

30

20

Monsoon

50

Non-monsoon

Discharge (Cumec)

61

10

70

7-May

6-Apr

6-Mar

4-Feb

4-Jan

4-Dec

3-Nov

3-Oct

2-Sep

(b) Nyicharang stretch

80

Discharge during 100% release scenario Days of Water YearEnvironmental flow

Recommended MOEF

60

Average flow observed in 13 years

Non-monsoon

Monsoon

Low-flow

40

30 20

10

Monsoon

50

Non-monsoon

Discharge (Cumec)

2-Aug

2-Jul

90

1-Jun

0

Days of Water Year

200

26-May

26-Apr

27-Mar

26-Feb

27-Jan

28-Dec

28-Nov

29-Oct

Discharge during 100% release scenario Environmental flow

Recommended MOEF Average flow observed in 11 years

Low-flow

100 50

Monsoon

Non-monsoon

Monsoon

150

Non-monsoon

Discharge (Cumec)

29-Sep

(c) Jang stretch

250

30-Aug

31-Jul

1-Jul

1-Jun

0 300

May

Apr

Mar

Feb

Jan

Dec

Nov

Oct

Sep

Aug

Jul

Jun

0

Time in Water Year

Fig. 8 Estimated E-flow at critical stretches of proposed hydroelectric project at a Mago, b Nyicharang, and c Jang sites in Tawang River basin

discharge is the key component in Himalayan regions to regulate the diversity and distribution of fish. Hence, the discharge from operational release policy must remain within the confidence level of both scientific researchers and policymakers. Flora and fauna of Himalayan regions constitute significant biodiversity hotspots that require research attention and protection due to observed climatic trends and alarming future projections [10, 27]. Therefore, a reasonable negotiation in release policy at coarse period (e.g., seasonal scale) can justify the foundations of ecological engineering in inaccessible regions.

62

S. K. Padhee et al.

6 Conclusion The release policy should include environmental flow (E-flow) according to the requirement of ecological, social, and cultural components. This paper estimates E-flow as the minimum amount of flow to be maintained in the river to meet flow requirements by river biodiversities like aquatic and terrestrial habitat, cultural and religious activities, livelihood dependence on the river, and ecosystem functions. Building block methodology (BBM) coupled with hydrodynamic modeling can be used to consider the scientific rationale in the release policy for inaccessible regions, which might be neglected in simple hydrology-based recommendations. Three case studies of upcoming HEP sites on Tawang River in Eastern Himalaya reveal that ministerial recommendations cannot meet the site- and season-specific flow requirement of ecosystem components. It is found that the hydrological seasons other than the monsoon season can face flow deficit by operating a release policy based on ministerial recommendations. Therefore, a new release policy is established equivalent to the estimated holistic E-flow. This strategy of framing release policy can be helpful for many developing countries with complexity in water management in inaccessible regions. Acknowledgements The present work is a part of the project for cumulative environmental impact assessment in the Tawang River basin funded by the Government of Arunachal Pradesh. The support from the developers of hydroelectric projects (NHPC, SEW, and EDCL) during the fieldwork is appreciated. Long-term flow data from the Tawang river basin provided by the Central Water Commission, India, is gratefully acknowledged.

Appendixes See Tables 2, 3 and 4.

Table 2 Essential flow conditions for sustenance of blocks of BBM at critical stretch of Mago Block

River biodiversity

Indicator

Low flow season

Monsoon season

Non–monsoon season

Depth (m)

Width (m)

Depth (m)

Width (m)

Depth (m)

Width (m)

Schizothorax richardsonii

0.5–0.6

10–15

0.7–0.9

10–15

0.5–0.6

10–15

Schizothorax progastus

0.4–0.5

10–15

0.7–0.9

10–15

0.4–0.5

10–15

Threatened fish

(continued)

Holistic Environmental Flow Assessment by Building Block Method …

63

Table 2 (continued) Block

Indicator

Low flow season

Monsoon season

Non–monsoon season

Depth (m)

Width (m)

Depth (m)

Width (m)

Depth (m)

Width (m)

Endemic periphyton

0.2–0.4

10–15

0.7–0.9

14–18

0.2–0.4

11–13

Endemic zooplankton

0.3–0.4

10–15

0.7–0.8

14–18

0.3–0.4

11–13

River hydraulics

Bed composition

0.4–0.5

10–12

0.6–0.8

14–16

0.4–0.5

10–12

Cultural requirement

Dead body disposal

0.5–0.6

10–15

0.7–0.9

14–18

0.5–0.6

10–12

Livelihood requirement

Water use

0.4–0.5

10–12

0.7–0.8

12–15

0.4–0.5

10–12

River resources

0.4–0.6

10–12

0.7–0.8

12–15

0.6–0.7

10–12

Edible algae

0.4–0.5

10–11

0.7–0.9

14–15

0.4–0.5

10–11

0.4–0.5

7–8

0.7–0.8

14–16

0.4–0.5

7–8 10–11

Endemic species

Ecosystem Periphyton structure and density function Water quality

0.4–0.6

10–11

0.7–0.9

10–15

0.5–0.6

NPP

0.3–0.5

10–11

0.7–0.9

14–16

0.4–0.5

10–11

Invasibility

0.3–0.5

10–12

0.7–0.9

13–16

0.3–0.5

11–12

0.56

11.85

0.73

14.65

0.66

13.6

Environmental flow conditions

Table 3 Essential flow conditions for sustenance of blocks of BBM at critical stretch of Nyicharang Block

River biodiversity

Indicator

Low flow season

Monsoon season

Non–monsoon season

Depth (m)

Width (m)

Depth (m)

Width (m)

Depth (m)

Width (m)

Schizothorax richardsonii

NA

NA

NA

NA

NA

NA

Schizothorax progastus

NA

NA

NA

NA

NA

NA

Endemic periphyton

0.2–0.4

10–15

0.7–0.8

12–15

0.2–0.4

12–15

Endemic zooplankton

0.3–0.4

10–15

0.7–0.8

12–15

0.3–0.4

12–15

Threatened fish

Endemic species

(continued)

64

S. K. Padhee et al.

Table 3 (continued) Block

Indicator

Low flow season

Monsoon season

Non–monsoon season

Depth (m)

Width (m)

Depth (m)

Width (m)

Depth (m)

Width (m)

River hydraulics

Bed composition

0.5–0.6

10–12

0.7–0.9

10–15

0.5–0.6

10–12

Cultural requirement

Dead body disposal

0.5–0.6

10–15

0.7–0.9

10–15

0.5–0.6

10–15

Livelihood requirement

Water use

0.3–0.4

10–12

0.6–0.9

10–14

0.3–0.4

10–12

River resources

0.5–0.6

10–15

0.6–0.9

12–14

0.5–0.6

10–15

Edible algae

0.2–0.3

10–15

0.7–0.8

12–14

0.2–0.3

10–15

0.2–0.3

10–15

0.6–0.7

12–14

0.2–0.3

10–15

Ecosystem Periphyton structure and density function Water quality

0.2–0.3

10–15

0.7–0.9

12–14

0.2–0.3

10–15

NPP

0.2–0.3

10–15

0.7–0.9

12–14

0.2–0.3

10–15

Invasibility

0.6–0.7

10–15

0.7–0.9

12–14

0.6–0.7

10–15

0.55

10.97

0.74

13.14

0.68

12.40

Environmental flow conditions

Table 4 Essential flow conditions for sustenance of blocks of BBM at critical stretch of Jang Block

Low flow season

Monsoon season

Non–monsoon season

Depth (m)

Width (m)

Depth (m)

Width (m)

Depth (m)

Width (m)

Schizothorax richardsonii

0.5–0.6

10–15

0.7–0.9

10–15

0.5–0.6

10–15

Schizothorax progastus

0.4–0.5

10–15

0.7–0.9

10–15

0.4–0.5

10–15

Endemic periphyton

0.2–0.4

10–15

0.5–0.6

15–20

0.2–0.4

10–15

Endemic zooplankton

0.3–0.4

11–15

0.4–0.5

15–20

0.3–0.4

11–15

River hydraulics

Bed composition

0.4–0.6

15–20

0.7–0.9

20–25

0.6–0.7

15–20

Cultural requirement

Dead body disposal

0.5–0.6

10–15

0.8–0.9

25–25

0.5–0.6

10–15

River biodiversity

Indicator

Threatened fish

Endemic species

(continued)

Holistic Environmental Flow Assessment by Building Block Method …

65

Table 4 (continued) Block

Livelihood requirement

Indicator

Low flow season

Monsoon season

Non–monsoon season

Depth (m)

Width (m)

Depth (m)

Width (m)

Depth (m)

Width (m)

Water use

0.3–0.4

5–10

0.4–0.6

15–20

0.3–0.4

5–10

River resources

0.5–0.6

15–20

0.8–0.9

20–25

0.5–0.6

17–20

Edible algae

0.2–0.3

15–20

0.5–0.6

20–25

0.2–0.3

15–20

0.2–0.3

10–15

0.4–0.5

20–25

0.2–0.3

10–15

0.2–0.3

10–15

0.5–0.6

20–25

0.2–0.3

10–15

Ecosystem Periphyton structure and density function Water quality NPP

0.2–0.3

10–15

0.5–0.6

20–25

0.2–0.3

10–15

Invasibility

0.4–0.5

15–20

0.8–0.9

20–25

0.3–0.5

15–20

0.63

16.80

0.92

21.0

0.70

18.14

Environmental flow conditions

References 1. Aayog NITI (2017) Inventory and revival of springs in the Himalayas for water security. Department of Science and Technology, Government of India, New Delhi 2. Abernethy RB (2004) An overview of Weibull analysis. In: The New Weibull handbook: reliability and statistical analysis for predicting life, safety, supportability, risk, cost and warranty claims, 5th edn. North Palm Beach, Florida, USA, 1–11 p 3. Acreman M, Aldrick J, Binnie C, Black A, Cowx I, Dawson H, Dunbar M, Extence C, Hannaford J, Harby A, Holmes N (2009) Environmental flows from dams: the water framework directive. In: Proceedings of the institution of civil engineers-engineering sustainability. Thomas Telford Ltd., 162 13–22 p 4. Alcázar J, Palau A (2010) Establishing environmental flow regimes in a Mediterranean watershed based on a regional classification. J Hydrol 388:41–51 5. Babel MS, Dinh CN, Mullick MRA, Nanduri UV (2012) Operation of a hydropower system considering environmental flow requirements: a case study in La Nga river basin Vietnam. J Hydro-Environ Res 6:63–73 6. Baillie IC, Norbu C (2004) Climate and other factors in the development of river and interfluve profiles in Bhutan, Eastern Himalaya. J Asian Earth Sci 22:539–553 7. Bandyopadhyay J, Gyawali D (1994) Himalayan water resources: ecological and political aspects of management. Mt Res Dev 14:1–24 8. Barker I, Kirmond A 1998 Managing surface water abstraction. In: Hydrology in a changing environment; British Hydrological Society, London, UK, 1 249–258 p 9. Bhatt JP, Manish K, Pandit MK (2013) Elevational gradients in fish diversity in the Himalaya: water discharge is the key driver of distribution patterns. PLoS ONE 8. https://doi.org/10.1371/ annotation/c24aa49a-7eb5-4612-bde3-8285b21921a0 10. Bhattacharjee A, Anadón JD, Lohman DJ, Doleck T, Lakhankar T, Shrestha BB, Thapa P, Devkota D, Tiwari S, Jha A, Siwakoti M, Devkota NR, Jha PK, Krakauer NY (2017) The impact of climate change on biodiversity in Nepal: current knowledge, lacunae, and opportunities. Climate 5:80 11. Cavendish MG, Duncan MI (1986) Use of the instream flow incremental methodology: a tool for negotiation. Environ Impact Assess Rev 6:347–363 12. Central Water Commission (2007) Report of Working Group to advise WQAA on the minimum flows in the rivers; Ministry of Water Resources, Government of India, New Delhi

66

S. K. Padhee et al.

13. Central Water Commission (2013) Environmental flows. In: Cumulative impact and carrying capacity study of Siang sub-basin including downstream impacts. New Delhi, India, 1 9.1 p 14. Central Water Commission (2014) Cumulative impact and carrying capacity study of Subansiri sub basin including downstream impacts, vol 1. Ministry of Water Resources, Government of India, New Delhi 15. Chembolu V, Dutta S (2018) An entropy based morphological variability assessment of a large braided river. Earth Surf Proc Land 43(14):2889–2896 16. Chow VT (1959) Open channel hydraulics. McGraw-Hill Book Company Inc, New York 17. Davis R, Hirji R (2003) Environmental flows: concepts and methods. In: Water resources and environment technical note C.1. The World Bank, Washington, DC, USA 18. Dunbar MJ, Gustard A, Acreman MC, Elliot CRN (1998) Review of overseas approaches to setting river flow objectives. Environ Agency R&D Tech Rep Inst Hydrol (Wallingford, UK) W6B(96):4 19. Dyson M, Bergkamp G, Scanlon J (2008) Flow-the essentials of environmental flows. IUCN, Gland, Switzerland, p 6p 20. Fitzpatrick MJ, Edelsparre AH (2018) The genomics of climate change. Science 359:29–30 21. Hughes DA, Hannart P (2003) A desktop model used to provide an initial estimate of the ecological instream flow requirements of rivers in South Africa. J Hydrol 270:167–181 22. Jackson S, Pollino C, Maclean K, Bark R, Moggridge B (2015) Meeting Indigenous peoples’ objectives in environmental flow assessments: case studies from an Australian multi-jurisdictional water sharing initiative. J Hydrol 522:141–151 23. Jain SK (2012) Assessment of environmental flow requirements. Hydrol Process 26:3472–3476 24. Jain SK, Kumar P (2014) Environmental flows in India: towards sustainable water management. Hydrol Sci J 59:751–769 25. Jungwirth M, Muhar S, Schmutz S (2002) Re-establishing and assessing ecological integrity in riverine landscapes. Freshw Biol 47:867–887 26. Kiwango H, Njau KN, Wolanski E (2015) The need to enforce minimum environmental flow requirements in Tanzania to preserve estuaries: case study of mangrove-fringed Wami River estuary. Ecohydrol Hydrobiol 15:171–181 27. Kumar V, Chopra AK (2009) Impact of climate change on biodiversity of India with special reference to Himalayan region—An overview. J Appl Nat Sci 1:117–122 28. Loar JM, Sale MJ, Cada GF (1986) Instream flow needs to protect fishery resources; In: Water Forum’86: world water issues in evolution; proceedings of ASCE conference, Long Beach, California 29. Mazvimavi D, Madamombe E, Makurira H (2007) Assessment of environmental flow requirements for river basin planning in Zimbabwe. Phys Chem Earth Parts A/B/C 32:995–1006 30. Ministry of Water Resources (2002) National water policy; ministry of water resources. Government of India, New Delhi. http://jalshakti-dowr.gov.in/sites/default/files/National%20Water% 20Policy%201987%20English.pdf 31. Ministry of Water Resources (2012) National water policy; ministry of water resources. Government of India, New Delhi. http://jalshakti-dowr.gov.in/sites/default/files/nwp200256 17515534_1.pdf 32. North Eastern Hill University (2015) Perspective plan for development of Tawang River basin: cumulative impact assessment of proposed hydel power projects, determination of basin carrying capacity, and landscape biodiversity management plan; NEHU, Shillong, India. http:// www.moef.nic.in/sites/default/files/TAWANG-REPORT-JUNE-2015.pdf 33. O’Keeffe J, Kaushal N, Bharati L, Smakhtin V (2012) Assessment of environmental flows for the upper Ganga Basin; WWF-India, New Delhi, India. http://awsassets.wwfindia.org/downlo ads/wwf_e_flows_report.pdf 34. Poff NL, Tharme RE, Arthington AH (2017) Evolution of environmental flows assessment science, principles, and methodologies. In: Water for the environment. Academic Press, 203– 236 p 35. Pradhan C, Chembolu V, Dutta S (2018) Impact of river interventions on alluvial channel morphology. ISH J Hydraul Eng 25:1–7

Holistic Environmental Flow Assessment by Building Block Method …

67

36. Pradhan C, Chembolu V, Dutta S, Bharti R (2021a) Role of effective discharge on morphological changes for a regulated macrochannel river system. Geomorphology 385:107718 37. Pradhan C, Chembolu V, Bharti R, Dutta S (2021b) Regulated rivers in India: research progress and future directions. ISH J Hydraul (Accepted). https://doi.org/10.1080/09715010.2021.197 5319 38. Qian F, Wu H, Gao L, Zhang H, Li F, Zhong X, Yang X, Zheng G (2009) Migration routes and stopover sites of black-necked Cranes determined by satellite tracking. J Field Ornithol 80:19–26 39. Renöfält B, Jansson R, Nilsson C (2010) Effects of hydropower generation and opportunities for environmental flow management in Swedish riverine ecosystems. Freshw Biol 55:49–67 40. Sehgal KL (1999) Coldwater fish and fisheries in the Indian Himalaya: rivers and streams. In: Fish and fisheries at higher altitudes: Asia, p 385. Food and Agriculture Organization of the United Nations Technical Paper, pp 41–63 41. Sharma E, Chettri N, Tse-Ring K, Shrestha A B, Jing F, Mool P, Eriksson M (2009) Climate change impacts and vulnerability in the Eastern Himalaya. ICIMOD, Kathmandu, Nepal 42. Souchon Y, Keith P (2001) Freshwater fish habitat: science, management, and conservation in France. Aquat Ecosyst Health Manag 4:401–412 43. Stein WE, Dattero R (1984) A new discrete Weibull distribution. IEEE Trans Reliab 33:196–197 44. Tare V, Gurjar SK, Mohanta H, Kapoor V, Modi A, Mathur RP, Sinha R (2017) Ecogeomorphological approach for environmental flows assessment in monsoon-driven highland rivers: a case study of Upper Ganga, India. J Hydrol: Reg Stud 13:110–121 45. Tennant DL (1976) Instream flow regimens for fish, wildlife, recreation and related environmental resources. Fisheries 1:6–10 46. Tharme RE (2003) A global perspective on environmental flow assessment: emerging trends in the development and application of environmental flow methodologies for rivers. River Res Appl 19:397–441 47. Tharme RE, King JM (1998) Development of the building block methodology for instream flow assessments, and supporting research on the effects of different magnitude flows on riverine ecosystems. Water Research Commission Report, No. 576/1/98, 452 p 48. Thoms MC, Sheldon F (2002) An ecosystem approach for determining environmental water allocations in Australian dryland river systems: the role of geomorphology. Geomorphology 47:153–168

River Ecology: Mapping and Modelling

Significance of Dam Altered Vis-À-Vis Free Flowing Stretches of a Himalayan River, Teesta with Special Reference to Icthyofaunal Diversity: Importance Towards Riverine Fisheries Sustainability Amiya Kumar Sahoo, Dharmendra Kumar Meena, Thangjam Nirupada Chanu, K. Lohith Kumar, Sourav Kumar Nandy, Debalina Sadhukhan, Srikanta Samanta, and Basanta Kumar Das Abstract River Teesta rises from the Eastern Himalayas and known as a transboundary river between India and Bangladesh. In India, the river crosses through two states viz. Sikkim and West Bengal before entering into Bangladesh. The study was undertaken in the 47.6 km stretch, from Triveni to Teesta barrage of the Teesta River. The selected study stretch has two major dams, Teesta Low Dam Project (TLDP)-III and TLDP IV and a barrage Teesta barrage, with a combined reservoir length of 17 and 9 km of combined water deprived downstream of dam/barrage representing altered river stretches. Therefore, allowing 21.6 km free flowing river stretches. The study aimed at assessing the fish diversity, distribution, and abundance in the selected study stretch of the river representing reservoir, water deprived downstream, and free flowing stretches of the river Teesta. Fish samples were collected seasonally from five sites representing free flowing (1 sites), reservoir (2 site), and just downstream of dam/barrage (2 sites) using different gears between May 2018 and October 2019. Total 41 fish species under 6 orders and 9 families were recorded. Cyprinidae represented the maximum (26) number among all the families. Critically endangered (Schistura spiloptera), vulnerable (Cyprinion semiplotum, Schizothorax richardsonii, and Garra arupi), and near threatened (Botia lohachata and Neolissochilus hexagonolepis) fishes were recorded. Migratory fish species, viz. Schizothorax richardsonii (Snow trout) and Neolissochilus hexagonolepis (Chocolate Mahseer) were dominated in the free flowing stretches of the river. The Simpson’s diversity index (D) ranged from 0.3 to 0.8, the Shannon index (H) varied from 0.5 to 2.61, and the evenness index varied from 0.5 to 0.9 across the stations and seasons. Cluster analysis indicated formation of two major groups with 90% dissimilarity and 3 sub-groups with similarity varying from 20 to 50%. All the diversity indices showed that the lowest icthyofaunal diversity was in the water deprived downstream

A. K. Sahoo (B) · D. K. Meena · T. N. Chanu · K. L. Kumar · S. K. Nandy · D. Sadhukhan · S. Samanta · B. K. Das (B) ICAR-Central Inland Fisheries Research Institute, Kolkata, West Bengal 700120, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 V. Chembolu and S. Dutta (eds.), Recent Trends in River Corridor Management, Lecture Notes in Civil Engineering 229, https://doi.org/10.1007/978-981-16-9933-7_5

71

72

A. K. Sahoo et al.

of dam stretch as compared to free flowing and reservoir stretch. The study documented for the first time the icthyofaunal diversity in the different river ecosystems representing reservoir, free flowing, and dam obstructed water deprived downstream stretch of river Teesta and recorded the lowest fish diversity in the water deprived downstream of dam stretch. The study highlights for future studies on ecological flows release from the dams for sustainable fisheries particularly in the downstream of the dam stretch, through proper scientific fish based habitat suitability study. Keywords Teesta River · Dam alteration · Fish diversity · Migratory fish

1 Introduction India is blessed with a vast area of freshwater water resources, which include a combined length of 0.388 million km of rivers and canals, 3.52 million ha reservoirs, 2.47 million ha of ponds and tanks, 1.16 million ha of brackish water bodies, and 1 million ha of flood-plain lakes and derelict water bodies [1] supporting excellent biodiversity placing India in 9th position in terms of richness in biodiversity [2]. Among the aquatic resources, rivers are arguably the most essential, supporting the economies and life of human populations. In India, the Ganga–Brahmaputra basin is the largest river basin with a catchment area of 1.1 million km2 (43% of the catchment area of all the major river basin in the country). The Ganga and the Brahmaputra are the two main rivers with numerous large and small tributaries including the river Teesta [3]. The Teesta River, also considered as the Sikkim’s lifeline, is one of the East India’s most scenic and the largest river of North Bengal. An important tributary to the river Brahmaputra, the river Teesta rises in the Eastern Himalayas at an elevation of 5280 m in the Indian state of Sikkim and flows through the state of West Bengal before draining into the Brahmaputra near Fulchhari in Bangladesh. On the river Teesta, about 40 major/large hydroelectric projects are being planned, of which some are commissioned and others are in various stages of development [4]. Fish faunal diversity in the river Teesta was studied by various researchers. Anonymous [5] reported 26 fish species from the Teesta River of which 9 were commercially important and 5 were migratory [5]. Das and Mukherjee [6] studied the fish fauna in the Teesta River and reported a strong decline in the fish diversity. About 19 threatened fish species which were recorded in other parts of India were also recorded from the Teesta River of which 15 species were rare and 4 species were endangered [7]. Acharjee and Barat [8] surveyed 28.4 km stretch in the Teesta River and recorded 65 fish species belonging to 39 genera and 10 families [8]. The developmental projects on the river and in the basin are known to impact adversely in terms of social, environmental, and ecological senses including fish and fisheries [9–11] necessitating studies at regular intervals to appraise the status of ecology, fish, and fisheries in the rivers. The present study was undertaken to assess the fish species diversity, distribution, and abundance in river Teesta between Triveni

Significance of Dam Altered Vis-À-Vis Free Flowing Stretches …

73

and Teesta barrage, a stretch of 47.6 km with two large dams, Teesta Low Dam Project (TLDP)-III and TLDP IV operated by National Hydropower Corporation Ltd (NHPC) and one barrage, i.e., Teesta Barrage. These dams and barrage have already modified the river into reservoir area and water deprived downstream area, showing adverse impact on the fish diversity particularly migratory fish species and their population. The study is of its first kind to assess the fish diversity and analyse their abundance in the study stretches representing different river ecosystems viz. free flowing stretch, reservoir area, and water deprived downstream of dam stretch in the river Teesta.

2 Materials and Methods 2.1 Study Area Five sampling sites representing the free flowing, reservoir, and water deprived downstream of dam stretch [Triveni free flowing (Site I), TLDP III reservoir (Site II), TLDP III dam downstream (Site III), TLDP IV reservoir (Site IV), and Teesta Barrage downstream (Site V)]. Site I represents free flowing stretch, Site II and IV represent upstream (reservoir) stretch, and Site III and V represent water deprived dam/barrage downstream stretch (Fig. 1 and Table 1), situated in between (27.079244°N; 88.432346°E and 26.751160°N; 88.586401°E), a stretch of 47.6 km of river Teesta.

2.2 Sample Collection Fish samples were collected from these representative five sites seasonally; lean (February), pre-monsoon (April), monsoon (May), and post-monsoon (October) between May 2018 and April 2019. Multi mesh size gill nets, hook and line, cast net, and local traps were used to collect the fish samples. To the possible extent, fish species were identified in the field, and unidentified specimens were preserved in 10% formaldehyde for laboratory identification consulting the standard taxonomic literature [12–14]. Fish species observed, count, length, and weight data were recorded. The scientific nomenclature of each fish species was ascertained as per the Eschemeyer catalogue of fishes [15].

74

A. K. Sahoo et al.

Fig. 1 Study area of Teesta River

Table 1 List of sampling sites in the Teesta River and their geographic coordinates

Sl.no

Name of the station

Latitude

Longitude

1

Triveni

27.079244 N

88.432346 E

2

TLD-III reservoir

27.001901 N

88.442283 E

3

TLD-III downstream

26.994722 N

88.433548 E

4

TLD-IV

26.926974 N

88.455037 E

5

Teesta Barrage

26.75116 N

88.586401 E

2.3 Statistical Analysis Diversity indices such as Simpson’s index (D), Shannon index (H) and Evenness index were calculated to assess the fish diversity and distribution. Cluster analysis using Bray–Curtis abundance matrix was performed to understand the similarity in

Significance of Dam Altered Vis-À-Vis Free Flowing Stretches …

75

spatial distribution of fish species and followed by SIMPER analysis to understand which species are contributing to the dissimilarity using PAST software.

3 Results and Discussion A total of 41 fish species under 6 orders and 9 families were recorded in the selected study stretch of the Teesta River. Family Cyprinidae was represented by maximum (26) number of species followed by Sisoridae (5), Nemachelidae (3), Bagridae (2), and one species each under family Cobitidae, Notopteridae, Ambassidae, Belonidae, and Synbranchidae (Table 2). The number of fish species recorded in the present study was higher than [5, 16] but less number than reported by Acharjee and Barat [8],which may be due to difference in approach of sampling and sampling locations [8]. Barilius barila, Barilius bendelisis, Barilius vagra, Labeo calbasu, Neolissochilus hexagonolepis, Puntius sophore, Notopterus notopterus, Chanda nama, and Mystus bleekeri were commercially important fish species recorded in the entire study stretch. Of the 41 fish species recorded, 01 was critically endangered (Schistura spiloptera), 02 species were vulnerable (Cyprinion semiplotum and Garra arupi), and 01 species was near threatened (Botia lohachata) (Table 2). Mukherjee et al. [17] reported 22 species of endangered/vulnerable/ threatened fishes from Darjeeling and 8 species from Siliguri [17]. The fish abundance analysis showed variation across the seasons and sampling stations (Fig. 2). Cyprinidae was the most dominant family across all the seasons. In terms of weight, Cyprinid fishes contributed 87%, 92%, 85%, and 84% to the total fish caught during pre-monsoon, monsoon, the post-monsoon, and lean season, respectively. While fish family Ambassidae contributed 7 and 5% in pre-monsoon and monsoon, respectively, fish species of Bagridae and Belonidae dominated in the total fish catch after the cyprinids in the post-monsoon season. Family Nemachelidae contributed to the tune of 16% during lean season across the stations. Barilius sp. was the most dominant species across the seasons. Simpson’s Index of Diversity (Dominance index) is a measure of the proportion of population density occupied by the most frequent species. Simpson’s diversity index is one of the most used which considers species richness and evenness of distribution. In the present study, the Simpson’s diversity index (D) was higher (0.7–0.88) in site IV and site V across the seasons which may be attributed to the diverse habitat and stream width in site IV and V. Site IV is a larger reservoir of a length of 6 km and situated at lesser elevation. In site I and II the Simpsons diversity was higher (0.8) in post-monsoon season. The lowest diversity was recorded at site III during monsoon and lean season with a value of 0.3 and 0.5 respectively (Fig. 3). Shannon index (H) is another popular method of measuring species diversity within a population considering both abundance and evenness of species distribution. Shanon index is sensitive to outliers, even the rare species with a single individual is accommodated. So for surveying an area with rare species, Shannon index might be a good choice. The index generally ranges between 1.5 and 3.5 in the real population. In the present

76

A. K. Sahoo et al.

Table 2 Recorded fish species of Teesta River, depicting IUCN, and utilization status Sl. no

Order

Species

IUCN status#

Utilization*

Notopterus notopterus (Pallas 1769)

LC

FF

Acanthocobitis botia (Hamilton 1822)

LC

OR

Schistura devdevi (Hora 1935)

NT

OR

Order: Osteoglossiformes 1

Family: Notopteridae

Order:Cypriniformes 2

Family: Nemachelidae

3 4

Family: Botiidae

Botia lohachata (Chaudhuri 1912)

NE

OR

5

Family: Cyprinidae

Bangana dero (Hamilton 1822)

LC

FF/ OR

6

Barilius barila (Hamilton 1822)

LC

FF

7

Barilius bendelisis (Hamilton 1807)

LC

FF

8

Barilius vagra (Hamilton 1822)

LC

FF/ OR

9

Cabdio morar (Hamilton 1822)

LC

FF

10

Chagunius chagunio (Hamilton 1822)

LC

FF

11

Crossochielus latius (Hamilton 1822)

LC

OR

12

Cyprinion semiplotum (McClelland 1839)

VU

OR

13

Danio dangila (Hamilton 1822)

LC

OR

14

Devario aequipinnatus (McClelland 1839)

DD

OR

15

Garra anandalei (Hora 1921)

LC

OR

16

Garra arupi (Nebeshwar, Vishwanath & Das 2009)

NE

OR

17

Garra gotyla (Gray 1830)

LC

OR

18

Garra mullya (Sykes 1839)

LC

OR

19

Garra quadratirostris (Nebeshwar & Vishwanath 2013)

NE

OR

20

Labeo calbasu (Hamilton 1822)

LC

FF (continued)

Significance of Dam Altered Vis-À-Vis Free Flowing Stretches …

77

Table 2 (continued) Sl. no

Species

IUCN status#

Utilization*

21

Order

Labeo dyochielus (McClelland 1839)

LC

FF

22

Neolissochilus hexagonolepis (McClelland 1839)

NT

FF

23

Opsarius barna (Hamilton 1822)

LC

OR

24

Pethia gugunio (Hamilton 1822)

LC

OR

25

Puntius sophore (Hamilton 1822)

LC

FF/ OR

26

Puntius terio (Hamilton 1822)

LC

FF/ OR

27

Raiamas sp. (Hamilton 1822)

LC

FF

28

Rasbora sp

LC

OR

29

Salmostoma bacaila (Hamilton 1822)

LC

FF

30

Schistura spiloptera (Valenciennes 1846)

CR

OR

31

Schizothorax richardsonii (Gray 1832)

VU

FF

Gagata cenia (Hamilton 1822)

LC

FF

33

Glyptothorax cavia (Hamilton, 1822)

LC

OR

34

Pseudecheneis sulcata (McClelland, 1842)

LC

OR

35

Glyptothorax sp.

36

Glyptothorax telchitta (Hamilton 1822)

LC

OR

Mystus bleekeri (Day 1877)

LC

FF

Mystus cavasius (Hamilton 1822)

LC

FF

Xenentodon cancila (Hamilton 1822)

LC

OR

Monopterus cuchia (Hamilton 1822)

LC

FF

Order: Siluriformes 32

37

Family: Sisoridae

Family:Bagridae

38

OR

Order: Beloniformes 39

Family: Belonidae

Order: Synbranchiformes 40

Family: Synbranchidae

Order: Perciformes (continued)

78

A. K. Sahoo et al.

Table 2 (continued) Sl. no

Order

Species

IUCN status#

Utilization*

41

Family: Ambassidae

Chanda nama (Hamilton 1822)

LC

FF/ OR

*FF: Food fish, OR: Ornamental fish; # LC: Least Concern, NT: Near Threatened, NE: Near Extinct, CR: Critical endangered, VU: Vulnerable, DD: Data deficient

Fig. 2 Abundance (%) fish families in all four sampling season

Fig. 3 Dominance index at different sampling stations

study, the values of H varied from 0.5 at site III in monsoon to 2.61 at site V in post-monsoon indicating low to moderate diversity in the sampled stations (Fig. 4). The values of evenness index varied from 0.5 to 0.9 across stations and seasons indicating a fairly even distribution of fish species (Fig. 5). Site III was found be the least diverse as compared to all other sites. Site III is a representation of downstream of dam stretch, which is totally impaired due to reduced water quantity. The lack of water availability could be the major factor for the least diverse fish species. In the recent years environmental flows/ecological flows are being considered as the partial

Significance of Dam Altered Vis-À-Vis Free Flowing Stretches …

79

Fig. 4 Shannon index at different sampling stations

Fig. 5 Evenness index at different sampling stations

solution to the fisheries improvement in the impacted zones made by dams/barrages in the river. Therefore, there is a need for the estimation of water requirement as ecological flows in the downstream of the dam TLDPIII and TLDPIV impaired stretch for sustainable fisheries. Similarity in the spatial distribution in terms of species diversity was analyzed by cluster analysis using Bray–Curtis abundance matrix for different sampling sites by pooling the data for all the four seasons (Fig. 6). The results indicated formation of two groups, i.e., group A and B. Group A includes site V forming one distinct group while group B include sites I–IV with three sub-groups within it. The distribution of fish species at site II and site IV was most similar (more than 50%). Both sites II and IV being upstream of dams have similar type of habitats which explains the similarity in species distribution. Species distribution at Site II and IV was 40% similar with site I. Though, the site I is a free flowing river, it more or less resembles the upstream

80

A. K. Sahoo et al.

Fig. 6 Cluster dendrogram showing similarities among spatial fish assemblages

of dam. Unlike in the peninsular reservoirs, the upstream of the reservoirs in the hill areas does not have ubiquitous lentic environment. The sites I, II, and IV were related to site III with a meager 20% similarity. As the site III represents the downstream of the dam and all other 3 sites represent the upstream of the dam, the dissimilarity of about 80% was evident. Site V was highly distinct from all other four sites in terms of habitat and environment; hence, the dissimilarity percentage of species distribution was also very high (90%). The river width in sites I–IV was about 150–200 m, whereas at site V it was more than 400 m. The river Teesta enters the plains after the site IV and is joined by tributaries on the left bank resulting in the increase of width. Also, habitat in site I to IV have riffles, rapids, pools, and the dominant substrate consists of bedrocks, cobbles, boulders, and pebbles, whereas site V, the habitat is run and pool and dominate substrates are sand, sediment with marginal as well as submerged macrophytes. Simper analysis carried out to understand the distinct grouping between site I–IV and site V revealed that 9 fish species contributed to 80% of the dissimilarity (Table 3). Barilius barila contributed most to the dissimilarity (23%) as the species was highly abundant at this site. This might be attributed to the presence and persistence of preferred physical meso-habitat in the form of sandy bottom, clear shallow water, which is the preferred habitat of B. barila [18]. Further, the occurrence of Crossochielus latius has been associated with the presence of sandy bottom with

Significance of Dam Altered Vis-À-Vis Free Flowing Stretches … Table 3 Spatial heterogeneity in fish species assemblage among group A and B

81

Sl No

Taxon

Contribution %

Cumulative %

1

Barilius barila

23.16

23.16

2

Crossochielus latius

11.94

35.1

3

Barilius bendelisis

11.21

46.31

4

Puntius sophore

7.747

54.06

5

Salmophasia bacaila

7.101

61.16

6

Chanda nama

6.294

67.45

7

Cabdio morar

5.003

72.46

8

Puntius terio

3.873

76.33

9

Barilius vagra

3.46

79.79

pebbles and it is generally found in the middle stretch of the northern Himalayan Rivers [12]. Similarly, other species such as Barilius bendelisis, Puntius sophore, Salmophasia bacaila, Chanda nama, Cabdio morar, Puntius terio, and Barilius vagra which contributed to the dissimilarity are commonly found in transparent lentic waters and slow flowing habitats with sufficient macrophyte cover in foot hills of the Himalayan rivers. Fragmentation of rivers is because of water obstruction for generating hydropower, irrigation, etc. Running water is perhaps the region’s most influential ecosystem as it is widely used for water supply, water irrigation, electricity production, disposal of waste, rampant fishing, and others [9]. The region of high biodiversity and endemism is directly at risk of extinction of species and loss of habitat, primarily because of immense pressures caused by demotechnic development and natural changes [19]. The Himalayan region is witnessing numerous hydroelectric projects [9] that have largely impacted the native flora and fauna adversely. Similarly, studies related to the assessment of fish diversity are also being undertaken by various researchers. Sehgal (1999) studied the biodiversity of Eastern and Western Himalayas, and Barat et al. [16] documented the fish diversity of the Indian Himalayan region [16, 20]. Similarly, Chakraborty (2008) investigated ichthyofaunal resources of South Dinajpur District and Patra et al. [22] reported ichthyofauna diversity of Karala River in Jalpaiguri, a total of 55 species belonging to 8 orders and 20 families were identified [21, 22]. Regular surveys and assessment of fish diversity in the Himalayan region and specifically in the major waterways harboring rich fish diversity is important to appraise the status and have reference values for comparative studies in the future.

82

A. K. Sahoo et al.

4 Conclusions The present study reported 41 fish species with conservational status in the different river ecosystems stretches representing free flowing, reservoirs, and water deprived dam downstream stretch in 47.6 km of the river Teesta. Different diversity indices showed that downstream of the dam (site III) had least diversity compared to the free flowing stretch and reservoir area. Therefore, our study suggests for a deeper study on the water requirement in terms of ecological flows in the downstream of the dam stretch for sustaining the riverine fisheries in the critically zones particularly below dam/barrages. Furthermore, the study results highlight the list of fish species of conservational importance viz. critically endangered (Schistura spiloptera), vulnerable (Cyprinion semiplotum, Schizothorax richardsonii and Garra arupi), and near threatened (Botia lohachata and Neolissochilus hexagonolepis), those are currently available needs to be protected for future challenges including climate change and reduced river discharges due to upcoming dams and barrages in the river Teesta. Acknowledgements The authors are thankful to NHPC (National Hydroelectric Power Corporation Ltd.) for financial support and ICAR (Indian Council of Agricultural Research)-CIFRI for laboratory and other infrastructural support during the investigation. Conflict of Interest The authors declare that there is no conflict of interest.

References 1. Fisheries Statistics Division (2018) Handbook on fisheries statistics. Department of Fisheries, Ministry of Fisheries, Animasl Husbandry & Dairying, Government of India, New Delhi 2. Mittermeier RA, Mittermeier CG (1997) Megadiversity: earth’s biologically wealthiest nations. Cemex 3. India-WRIS (2012) River Basin Atlas of India. RRSC-West, NRSC, ISRO, Hyderabad, India 4. Stokes D (2013) Political economy analysis of the Teesta River Basin. Asia Foundation 5. Anonymous (2002) Environment impact assessment, teesta low dam project, stage-III, vol I. National Hydroelectric Power Corporation, Faridabad, Haryana, India, West Bengal 6. Das RC, Mukherjee AB (2005) Important fish fauna of river Teesta, their biodiversity and the need of their conservation. In: Mahanta PC, Singh AK (eds) Proceeedings of the national symposium on reassessment of fish genetic resources in India and needs to evolve sustainable methodology for conservation. Asian Fisheries Society, Indian Branch and NBPGR, Lucknow, pp 39–40 7. Menon AGK, Singh HR, Kumar N (2000) Present eco-status of cold water fish and fisheries. Coldwater fish and fisheries, pp 1–36 8. Acharjee ML, Barat S (2013) Ichthyofaunal diversity of Teesta River in Darjeeling Himalaya of West Bengal, India. Asian J Exp Biol Sci 4(1):112–122 9. Bhatt JP, Tiwari S, Pandit MK (2017) Environmental impact assessment of river valley projects in upper Teesta basin of Eastern Himalaya with special reference to fish conservation: a review. Impact Assess Project Apprais 35(4):340–350 10. Meetei LI, Pattanayak SK, Bhaskar A, Pandit MK, Tandon SK (2007) Climatic imprints in quaternary valley fill deposits of the middle Teesta valley, Sikkim Himalaya. Q Int 159(1):32–46

Significance of Dam Altered Vis-À-Vis Free Flowing Stretches …

83

11. Mullick MRA, Babel MS, Perret SR (2011) Discharge-based economic valuation of irrigation water: evidence from the Teesta River, Bangladesh. Irrig Drain 60(4):481–492 12. Talwar PK, Jhingran AG (1991) Inland fishes of India and adjacent countries, vol 2. CRC Press 13. Jayaram KC (1999) The freshwater fishes of the Indian region. Narendra Publishing House, New Delhi, p 571 14. Vishwanath W, Lakra WS, Sarkar UK (2007) Fishes of North East India. National Bureau of Fish Genetic Resources, Indian Council of Agricultural Research 15. Fricke R, Eschmeyer WN, Van der Laan R (eds) (2021) Eschmeyer’s catalog of fishes: Genera, Species, References 16. Barat S, Jha P, Lepcha RF (2005) Bionomics and cultural prospects of Katli, Neolissocheilushexagonolepis (McClelland) in Darjeeling district of West Bengal. In: Coldwater fisheries research and development in North-east Region of India. NRCCWF, Bhimtal. Vikrant Computers, Haldwani, pp 66–69 17. Mukherjee M, Lepcha RF, Chakraborty C (2013) Fish and fisheries of Himalayan and Tarai Region of West Bengal with Ornamental Touch. Daya Publishing House 18. Menon AGK (1999) Check list—Fresh water fishes of India. Rec Zool Surv India Misc Publ Occas Pap. No. 175, 366 p 19. Johal MS, Rawal YK (2005) Key to the management of the Western Himalayan Hillstreams in relation to. Hydrobiologia 532(1):225–232 20. Sehgal KL (1999) Coldwater fish and fisheries in the Indian Himalayas: rivers and streams. Fish and fisheries at higher altitudes: Asia. Food Agricult Organ United Nations Tech Pap 385:41–63 21. Chakraborty T, Bhattacharjee S (2008) The ichthyofaunal diversity in the freshwater rivers of south Dinajpur district of West Bengal, India. J Bombay Nat Hist Soc 105(3):292–298 22. Patra AK, Sengupta S, Datta T (2011) Physico-chemical properties and ichthyofauna diversity in Karala River. In: A Tributary of Teesta River at Jalpaiguri District of West Bengal, India

Impact of Barricades on Habitat and Fish Migration in River Cauvery, India R. K. Manna, K. Lohith Kumar, S. Sibina Mol, C. M. Roshith, S. K. Sharma, M. E. Vijay Kumar, R. C. Mandi, S. Samanta, V. R. Suresh, and B. K. Das

Abstract River Cauvery, the major peninsular river in India has been impounded by a number of dams and barrages which has totally modified the habitat of the river impacting the migration of fishes. A recent survey revealed that the entire river stretch has reduced depth and regulated flow especially during lean season allowing the submerged macrophytes to proliferate. Different habitat parameters of the river were described in the light of impoundments. The changed behavioural pattern of the migratory fishes against the river barricades at Mayanoor, Grand Anicut, and Lower Anicut has been described. Cornered fishes below and above the barrage are indiscriminately targeted using modified fishing gears specially designed for manipulating the migrating fish behaviour. The necessity of fish species-specific migration-friendly structures below the barrage was identified for sustainable fishery of the river. Keywords Barrage · Growth overfishing · Sustainable fishery

1 Introduction Progress of human civilization is intimately associated with increased water demand to meet their requirement of different kinds—power generation, agriculture and industrial use, daily consumption, etc. River or lake water is considered as one of the easily accessible sources of water; being present at the surface. Water is abstracted from rivers and used by several means, sometimes treated based on the requirement. R. K. Manna (B) · K. L. Kumar · C. M. Roshith · R. C. Mandi · S. Samanta · V. R. Suresh · B. K. Das ICAR-Central Inland Fisheries Research Institute, Barrackpore, Kolkata, West Bengal 700120, India S. S. Mol · M. E. V. Kumar Bangalore Centre of ICAR-CIFRI, Hessarghata Lake Post, Bangalore, Karnataka 560089, India S. K. Sharma Regional Centre, ICAR-CIFRI, Allahabad, Uttar Pradesh 211002, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 V. Chembolu and S. Dutta (eds.), Recent Trends in River Corridor Management, Lecture Notes in Civil Engineering 229, https://doi.org/10.1007/978-981-16-9933-7_6

85

86

R. K. Manna et al.

However, there is often lacking required volume and flow in most of the rivers to maintain a steady supply of water, especially during dry seasons. Hence, rivers are being barricaded by different kinds of obstructions like dams and barrages where water is being stored in the form of reservoirs for future use as per necessity. In this process of meeting human needs, the other ecological service of river water is often ignored. Decreased volume of river water with significantly reduced flow regime affects aquatic organisms. Riverine fishes used to perform various kinds of migration to fulfil their different types of biological needs like breeding, feeding, nursing, etc. Artificially created river obstructions deny migratory fishes their natural movement and many riverine fishes become regionally extinct being unable to negotiate the barrage [14]. It is a matter of great concern from a biodiversity point of view as often highlighted by the researchers. The impact of impoundment on fish diversity has been reported from all over the world. In river Mekong, dams were identified for creating total hindrance in riverine fish migration, and loss of ecosystem services in the river basin leading to loss of precious biodiversity [11, 32]. The worst affected are the diadromous fishes which migrate from rivers to sea (catadromous) or from sea to rivers (anadromous) as many barrages have come in lower or even estuarine zone [31]. About 120 species were identified as diadromous out of about 20,000 fish species that reside in open water [5]. Besides, there is the possible impact of dams on local or potamodromous migration as many of the riverine fishes perform the long-distance journey in rivers for various biological activities [6]. Asian river fishes were the worst affected as flows of most of the Asian major rivers are highly regulated [10]. In India, river Cauvery, the third-largest peninsular river was totally engineered as the habitat of the entire stretch of the river was thoroughly modified with several obstructions in the form of dams, barrages, anicuts, etc. River Cauvery originates in Brahmagiri hills of the Western Ghats in Kodagu district, Karnataka, India and traverses about 800 km through the states of Karnataka (41% area of approximately 89,600 km2 river basin) and Tamil Nadu (54% river basin) to finally emptying into the Bay of Bengal (BoB) at Poompuhar and Pazhayar in Tamil Nadu. Based on the physiographical features of the draining area, nature of the bottom, gradient, etc., the river course can be broadly divided into three zones— mountainous (303 km, from the origin up to Sivanasamudram), plateau (88 km, from Sivanasamudram up to Hogenakkal), and plains course (410 km, from Hogenakkal to its confluence in the sea). Each of the zones was characterized by unique geological features having a visible influence on the distribution of fish fauna in the river [18]. However, several flow obstructions in the form of dams and barrages have been made along the entire basin of river Cauvery, both on the main river channel as well as on the tributaries/distributaries to meet the ever-increasing water requirement in the catchment area for the development of different social sectors from time to time. At least 19 major obstructions are there on the main river channel, including Mettur dam, Grand Anicut, Lower Anicut, etc. Accordingly, the flow and depth of the river were totally modified and were now dependent on the controlled water discharge from those obstructions except during monsoon with heavy rain. Controlled water release from those obstructions not only influenced the overall water quality of the river but also created a barrier for fish migration. Unable to migrate, fishes were

Impact of Barricades on Habitat and Fish Migration …

87

observed to accumulate below barrages during water release and continuously trying to reach upstream. Taking the advantage of the fishes cornered by the obstructions, fishers use various kinds of fishing gear suitably modified in relation to changed fish behaviour in the area below barrages. The development of sustainable riverine fisheries necessitates habitat-specific management practices and policies. For this purpose, an extensive survey was performed during 2017–2020 to understand fish species distribution in the modified riverine habitat created by man-made barricades as well as different kinds of fishing methods presently being operated in river Cauvery. The present paper describes the experiences of changed fish behaviour above and below barrage and different modified fishing strategies which are being employed below and above barrage in river Cauvery. Indiscriminate killing of fishes especially juveniles and also migratory fishes who were totally cornered by obstructions made in the river by different fishing gears as recorded in Grand Anicut and other barrages have been recorded; hence calls for immediate management intervention to sustain precious riverine fish diversity.

2 Materials and Methods Seasonal surveys were conducted at 13 selected centres (Fig. 1 and Table 3) in river Cauvery from May 2017 to January 2020 for understanding the fish behaviour in relation to the present habitat condition of the multi-barricaded river and prevailing fishing practices targeting the migratory fishes. Out of 13 stations, Grand Anicut

Fig. 1 Sampling sites (number in white colour with blue circular shade) and barricades (shown as *) of river Cauvery

88

R. K. Manna et al.

and Lower Anicut areas observed severe water scarcity and remained near-dried conditions both above and below barrage during non-monsoon months and hence no environmental data was collected from those two stations. All ecological data related to the habitat, viz., physico-chemical parameters of water and sediment, macrophyte infestation, etc. were collected through on-field measurement as well as laboratory analysis with triplicate observations following standard methods [1]. In most of the cases, primary information on fishing methods was collected by observing the operation of various fishing gears in the river above and below barrage as well as measuring the dimensions of the gear, mesh size, recording of catch composition, etc. Secondary information on a particular fishing method was collected through interaction with the respective fishers. During monsoon and post-monsoon, when Grand Anicut and Lower Anicut received some water from upstream, seasonal fishery was observed which were mostly destructive in nature and described herewith in perspective of their role in sustainable fishery. The classification of the recorded fishing methods has been done following [12, 13, 30].

3 Results and Discussion 3.1 Description of the Sampling Stations River Cauvery is one of the water-scarce rivers of India providing less than 1000 Cu m water per capita in its catchment area. To meet the ever-increasing water requirement, several obstructions have been made along the entire river basin, both on the main river channel as well as on all the tributaries/distributaries. Mukkombu dam at Trichy divides the river into two distributaries, Kollidam river and the Cauvery river. Kallanai dam on river Cauvery at Grand Anicut again divided the river into three major distributaries. The left-wing channel meets the Kollidam (or Koleroon) river, the middle channel retained the original name river Cauvery, and the right channel proceeds as the Vennar river. Sampling for habitat parameters was performed at 11 stations (excluding Grand and Lower Anicut as hardly any water left during nonmonsoon months), and data on the present state of fishing practices were collected at selected 13 stations as given in Fig. 1. Distance from the origin, location, and brief description of the sampling stations were given in Table 3 (provided as Appendix).

3.2 Obstructions in River Cauvery At least 19 major obstructions have been made on the main river channel which were marked with Asterix (*) in Fig. 1, a list of which was given in Table 1. Karnataka stretch of the river observed less (6) number of obstructions, whereas Tamil Nadu

Impact of Barricades on Habitat and Fish Migration …

89

Table 1 Dams, barrages, and anicuts in the main channel of river Cauvery S. No. Name

Latitude

Longitude

1

Kattepura Dam

12°36 25.0 N

76°02 44.0 E Karnataka

2

Chamarajendra Anicut

12°31 15.2 N

76°14 24.6 E

Chunchunkatte Dam

12°30 11.7

4

Krishnarajasagar Dam

12°25 31.8 N

5

Madhavmantri Mini Hydel Station

12°12 33.0 N 77°01 24.1 E

6

Satthegela Mini Hydel Power Station

12°15 10.5 N 77°09 22.4 E

7

Stanley Reservoir (Mettur Dam)

11°54 16.2 N 77°49 53.0 E Tamil Nadu

8

Chekkanur Dam

11°43 39.9 N

77°46 47.9 E

Poolampatti Hydro Powerhouse

11°38 35.3 N

77°45 28.7 E

10

Kuthiraikalmedu Hydro Electric Power Station

11°34 31.0 N

77°44 30.7 E

11

Uratchikottai Barrage

11°29 4.88 N 77°42 12.2 E

12

Bhavani Kattalai Barrage

11°23 9.94 N 77°42 53.0 E

13

Vendipalayam Barrage

11°19 56.1 N 77°45 32.0 E

14

Pasur Dam

11°14 33.9 N 77°51 52.1 E

15

Jedarpalayam Dam

11°09 33.8 N 77°52 54.1 E

16

Mayanoor Barrage

10°57 51.6 N 78°14 39.8 E

17

Mukkombu Dam (Upper Anicut)

10°53 11.1 N 78°34 46.4 E

18

Grand Anicut

10°49 47.0 N 78°49 6.15 E

19

Lower Anicut

11°08 34.9 N 79°27 2.44 E

3

9

State

N 76°17 33.5 E 76°34 20.8 E

stretch observed more (13) obstructions, including Grand Anicut, one of the oldest river barricades in the world.

3.3 Habitat of River Cauvery Important habitat parameters like bottom profile, water depth, flow, transparency, dissolved oxygen, macrophyte infestation, etc. which can influence fish behaviour were given in Table 2.

3.4 Bottom Profile As the river was running through the Deccan plateau, a significant portion of the river bottom consisted of rocks, higher in Valnoor (55%) and Kudige (45%) upstream and at Hogenakkal to Mayanoor (30–40%) (Table 2). Low water discharge often creates

Dissolved oxygen (mg/l)

BV (>100)

140

65

10–25 (18)

BV (>250)

5–10 (8)

BV (>350)

10–20 (13)

220

40–70 (53)

BV (>125)

10–15 (12)

20

105

Nil-10 (7)

20

0 0

0

0

0 10

5

ND

ND

120

Depends Depends Tidal on water on water stretch, release release 10–15 (12)

0

0

0

170

Tidal stretch, 25–30 (28)

0

0

(continued)

BV

Tidal stretch, Nil-40 (27)

0

0

0

0

100

Transparency (cm)

40–60 (54)

45

10

0

5

95

10–50 (33)

30

0 20

5

Flow (cm/s)

15

25

20

5

80

0

0

100

50–150 250–560 110–350 140–250 275–450 870–2320 80–250 270–400 Dried up Dried up 140–290 330–410 40–80 (105) (410) (227) (215) (322) (1410) (135) (310) (211) (377) (58)

15

20

25

5

95

0

0

100

0

PMR

Depth (cm)

20

10

15

10

95

10

0

65

0

PZR

15

25

20

10

60

0

0

5

35

KLD

Boulder (%)

30

25

5

50

0

0

5

95

LAN

10

10

5

30

0

10

95

GAN

25

15

15

40

0

50

MYN 90

Cobble (%)

25

45

35

30

35

BVN 30

Pebble (%)

20

15

15

25–30

35–40

HGL 10

20

10

0

30–40

SSM 15–20

30

45

15

40

TNP 60–70

Gravel (%)

5

KDG

15

Sand (%)

55

In-stream rock 20 cover

50–70

5–10

50–95

Run (%)

WLN

10–25

Rapids/Riffles 0 (%)

0–30

Pool (%)

BGM

Table 2 Important habitat parameters (range and average) in river Cauvery

90 R. K. Manna et al.

5

BGM

20

WLN

10

KDG 65

TNP 45

SSM 10

HGL 30

BVN 5

MYN 5

GAN 5

LAN 15

KLD 0

PZR

Note Full names of the sampling stations are mentioned in Table 3 in Appendix like BGM: Bhagamandala; BV: Bottom visible; ND: Not done

Aquatic macrophyte (% area covered)

Table 2 (continued) 0

PMR

Impact of Barricades on Habitat and Fish Migration … 91

92

R. K. Manna et al.

pool formation within the rocky riverbed during non-monsoon months and supports the growth of macrophytes/filamentous algae. Besides rocks, rest of the area was mostly covered with a sandy bottom, having higher organic carbon in the macrophyteinfested area contributed by their detrital loading as observed in T. Narasipura (0.53 ± 0.29; up to 1.2% organic C) and Sivanasamudram (0.49 ± 0.50; up to 1.7% organic C). On the other hand, higher soil organic carbon (0.42 ± 0.27%; up to 0.78%) at Bhavani was an indication of anthropogenic sewage loading in absence of regular flushing besides some contribution from littoral macrophyte cover. The upper stretch of the river (Bhagamandala to Kudige) represents typical hill stream habitats with narrow channel (9.5–55 m width), high canopy cover on stream (>60%), medium depth, high riparian vegetation (>80%), and higher flow velocity. The river channel in the middle stretch (Bhavani and Mayanoor) has an average width of 315 m. The lower estuarine stretch of Cauvery at Kollidam, Poompuhar, and Pazhayar represented typical run habitats.

3.5 Water Depth Water depth was less than 3.0 m at most part of the river during non-monsoon months except at Valnoor (deep pool; av. depth 3.5 m; highest depth 5.6 m), below Hogenakkal falls (rocky gorge; av. depth 9.3 m; highest depth 24.4 m during postmonsoon), and Mayanoor and Sivanasamudram (barrage) (Table 2 and Fig. 2). Water release from the dams and barrages controlled the habitat of the Cauvery river in a significant way. During most of the year, water depth was too low at all the stations except during water discharge in monsoon. As a result, most part of the river became almost dry in case of low water release during non-monsoon months creating small pools of water. Figures 3 and 4 depict a stretch of the river at Bhavani in pre-monsoon 1400

Water depth (cm)

1200 1000 800 600 400 200 0

Fig. 2 Average water depth (cm) in river Cauvery

Pre-monsoon Monsoon Post-Monsoon

Impact of Barricades on Habitat and Fish Migration …

93

Fig. 3 Exposed rocky riverbed of river Cauvery at Bhavani revealing lack of discharge from the upstream dam (May 2018)

Fig. 4 High water discharge from the Mettur dam submerged Bhavani stretch during monsoon (August 2018)

with a water depth of 80–120 cm which was totally flooded by water release from upstream Mettur dam in the monsoon of 2018. The 190 km stretch of Mayanoor barrage to Kollidam remains mostly dry as water released from the Mettur dam gets totally blocked at Mayanoor barrage and diverted for other purposes.

94

R. K. Manna et al.

The water depth of the river was found to be too low most of the year, except during water discharge in monsoon at some stations (Fig. 2). Water depth seldom exceeded 3.0–4.0 m. Higher water depth was recorded at Hogenakkal, where a deep pool (highest depth recorded 24.4 m during post-monsoon) was there below the falls. Habitat of Cauvery river was now entirely modified and longitudinal connectivity was poor or lost as observed in 190 km stretch of Mayanoor barrage to Kollidam in Tamil Nadu which remains mostly dry as water released from Mettur dam gets totally blocked at Mayanoor barrage (commissioned in 2014) and diverted through Mayanoor barrage right canal depriving the main channel of the river, especially during non-monsoon months. The dried and exposed riverbed of this stretch was entirely sandy in nature with some pools here and there. Pre-barrage study [28] mentioned some fishery in this stretch of the river when the Mayanoor barrage was not there. Obstructions at Sivasamudram and Mayanoor helped in creating a near-lentic habitat with a relatively higher depth as compared to nearby sampling stations.

3.6 Water Flow River impoundment has its impact in the form of transformation of the lotic nature of rivers to lentic habitats of reservoirs. In river Cauvery, water flow was significantly low (0.07–0.33 m/s) in the entire river stretch up to Mayanoor during non-monsoon months except at Hogenakkal falls and Valnoor with a higher average velocity of 0.5 m/s. However, most parts of the river were dominated by pools, less run, and very less rapids with a fast flow (Table 2). Controlled water discharge from dams or barrages supports the growth of macrophytes/filamentous algae. Very less water flow (350 cm) during pre-monsoon (Fig. 6). Slightly low transparency was observed above barrage due to plankton turbidity in near lentic condition as observed in Mayanoor. In the upper stretch, slightly higher turbidity was observed at Kudige.

3.8 Dissolved Oxygen Sufficient dissolved oxygen (mg/l) was recorded on most of the occasions except in some areas where local pollution was observed (Fig. 7). Hogenakkal area having steady flow, waterfalls, and deep pool observed sufficient dissolved oxygen throughout the year with less seasonal variation. Higher dissolved oxygen at T.

96

R. K. Manna et al. 9

Dissolved oxygen (mg/l)

8.5 8 7.5 7 6.5 6 5.5 5

Pre-monsoon

Monsoon

Post-Monsoon

4.5

Fig. 7 Dissolved oxygen (mg/l) in river Cauvery

Narasipura during pre-monsoon was due to thick infestation of submerged macrophytes in shallow river water. On the other hand, higher dissolved oxygen at Mayanoor may be attributed to the partial lentic character created by Mayanoor barrage with higher plankton abundance.

3.9 Macrophyte Infestation Sluggish flow and low water depth aided in the proliferation of different kinds of aquatic macrophytes in river Cauvery. At T. Narasipura and Sivanasamudram, 45– 65% of the river spread was densely choked with submerged aquatic macrophytes. Controlled water release from dams and barrages supports the growth of macrophytes, at least in the littoral zone as observed in Bhavani (30%) during non-monsoon months (Table 2). Submerged macrophytes and sluggish flow often contributed to high water transparency especially at T. Narasipura and Sivanasamudram during non-monsoon months (Fig. 7). In many cases, bottom visibility was recorded during those periods due to less river depth as well as suspended silt deposition by submerged macrophytes.

3.9.1

Barrage Vis-à-Vis Fish Migration

From river Cauvery, a total of 109 fish species have been reported [21]. During our survey, a total of 142 fish species have been recorded from river Cauvery including six exotic fishes. Modified river habitat with significantly reduced flow regime along with pollution has its visible impact with the dominance of hardy exotic fishes. Cauvery

Impact of Barricades on Habitat and Fish Migration …

97

river reported the existence of two known migratory fishes—anadromous Tenualosa ilisha, catadromous Anguila bengalensis, and catadromous giant river prawn Macrobrachium malcolmsonii. However, Tenualosa ilisha has become extinct in river Cauvery as reported long back by [15, 29] as an impact of the Mettur dam. During our survey, we could record a significant number of catadromous eel Anguila bengalensis and giant river prawn Macrobrachium malcolmsonii. This may be attributed to heavy flood during the monsoon of 2018 when all the gates of barrages were open in the lower stretch of river Cauvery. This might have facilitated the migration of the eel and giant river prawn as eel and prawns are reported as a better climber in barrages [20]. Other than those known migratory fishes, a high number of potamodromous migratory fishes was recorded below barrages. As soon as some water started trickling through barrages, those fishes (especially minor carps) started jumping to reach the barrage upstream. Other than those fishes, many other fishes were also observed to accumulate below barrage as observed.

3.9.2

Observations on Fish Migration at Grand Anicut, Tamil Nadu, India

The Grand old barrage (locally called Kallanai dam, originally constructed during the reign of Chola king Karikalan in c. 100 BC–c. 100 AD) is still functional but observes severe water scarcity now during non-monsoon months due to water diversion at Mayanoor barrage exposing the sandy riverbed.

Structure of the Grand Anicut Barrage Downstream The Grand Anicut barrage has bounded the river by an extended ‘U’-shaped structure to generate three distributaries—Kollidam river in left, Cauvery river in middle, and Vennar river on the right, besides a canal on the right. A number of lock gates are there on all the sides which are operated whenever required. Water released through the middle portion of the barrage rolls for a few meters through a concrete floor before falling in deep rectangular chambers (ski-jump spillways) and then overflows to the Cauvery river. The height of the floor is more than a meter from the water surface of the rectangular chambers (Fig. 8a). During monsoon, when water is released from the barrage, it falls directly in the gorge of deep rectangular chambers and after flow reduction water flows to the river (Fig. 8b). Some portion of the left wing has a gentle concrete staircase with steps of about 1 ft height and width of 2–3 ft (Fig. 8c). Those steps offer a gentle slope which can be comfortably negotiated by some riverine fishes during water release in the monsoon and post-monsoon periods (Fig. 8d). The remaining portion has a gentle slope through which released water can roll down to the river once released through the gates.

98

R. K. Manna et al.

Fig. 8 a Barrage downstream with deep ski-jump spillways at the middle portion in Grand Anicut during pre-monsoon. b Released water directly falls in the deep spillways at barrage downstream at the middle portion in Grand Anicut during monsoon. c Gentle steps in left wing below barrage (rear view). d Gentle steps in left wing below barrage (front view)

Observation on Fish Migration Behaviour During Monsoon Unique fish behaviour at the barrage downstream in the middle portion of Grand Anicut was observed when water is released during monsoon (September 2019). Fishes are observed jumping hard to negotiate about 1 m height to reach and cross the barrage. In this case they are mostly taking help of the corner side to jump (Fig. 9a) and once reached the concrete basement they are struggling to negotiate the heavy flow to cross the barrage (Fig. 9b) and reach upstream. As there is no flow at the base of the pillars, they are observed to accumulate in huge numbers there (Fig. 9c).

Observation on Fish Migration Behaviour During Winter at Grand Anicut During winter sampling, it was observed that water is very less released through the central portion of Cauvery river. Instead, most of the water is released through left wing to Kollidam river. Fishes were observed to climb the staircases or gentle slopes to reach upstream (Fig. 10a). Even very less water depth or absence of stairs at barrage downstream was unable to stop their upstream migration (Fig. 10b).

Impact of Barricades on Habitat and Fish Migration …

99

Fig. 9 a Fishes jumping through the corner to reach the platform below barrage at the middle portion. b Huge number of fishes accumulated in less flow zone below barrage. c Fishes accumulate at the base of the pillar to avoid washing down by the heavy flow of released water

Fig. 10 a Fishes were observed climbing to reach barrage upstream even very less water coming out below the locked gates. b Fishes were observed climbing to reach barrage upstream avoiding high flow regime

3.9.3

Experiences in Other Barrages (Lower Anicut and Mayanoor Barrage)

Fishes were observed to accumulate in little accumulated water below barrage and started jumping or climbing to reach the upstream through barrage gate from which water is coming out. Jumping or climbing behaviour of the accumulated fishes was utilized by the fishers to catch them out of water by using very unique uncommon fishing gears suitably modified as per fish behaviour.

100

R. K. Manna et al.

3.10 Below Barrage Behaviour of Fishes Vis-à-Vis Fishery 3.10.1

Observation at Grand Anicut

As migratory fishes (both diadromous and potamodromous) accumulate in huge numbers at barrage downstream and unable to negotiate the barrage, fishers adopt some highly destructive fishery, especially at below barrages during water release in monsoon and post-monsoon period. At Grand Anicut left-wing fishers use a drive-in-fishing gear consisting of a bag net with stakes, locally known as Kaccha valai to harvest the juvenile fishes climbing the staircases. The length × breadth × height of Kaccha valai was 50 × 10 × 5 feet (Fig. 11a). The webbing of 5 mm mesh size was attached to head rope and foot rope and the stakes of bamboo or wood of 5 ft height were placed at an interval of about 4–5 ft vertically between the head rope and foot rope to keep the mouth of the net in an open position. The climbing behaviour of fish is exploited to harvest the fishes using this gear. The flow of the released water is set high enough to deter the fishes from climbing, and after whole-day water release, a large number of fishes was observed to be accumulated on the cascades trying hard to reach barrage upstream. During operation, the gear was placed at the lowermost steps and the water flow rate is suddenly reduced by putting the gates down (Fig. 11b). About 30 fishermen are involved in operating this gear. Each stake of the gear is held by individual fishermen while keeping the footrope grounded by placing their foot on the foot ropes on the last step of the cascading outlet. Two fishermen hold the head and foot ropes on either side of the gear. Once the gear is in place, many fishermen disturb the accumulated fishes by walking roughly and rubbing their feet on the steps. Then the disturbed fishes are manually driven into the gear. These harvested fishes are later transferred to hapa, that is, kept tied in the river until marketed. The whole operation was completed in about one hour and about 300–400 kg of juvenile fish was observed to be harvested in a single operation (Fig. 11c). The harvested fishes were mostly juveniles of Labeo bata, Labeo boggut, and Cirrhinus reba. The length range of the juvenile fishes ranged from 6.7 to 12.8 mm with a corresponding weight of 2.3–15.7 gm, respectively. The gear was operated from July to February subject to

Fig. 11 a Line diagram of kaccha valai, a form of bag net with stakes. b Setting of gear Kaccha valai; c Harvested fishes being transferred to hapa

Impact of Barricades on Habitat and Fish Migration …

101

water availability in upstream. These fishes are freshly consumed and priced between Rupees 100–120 per kg.

3.10.2

Targeted Fishery at Mayanoor Barrage

At Mayanoor barrage, a unique cylindrical trap ‘Pari’ was specifically used to catch catadromous eel, Anguilla sp. below Mayanoor barrage during their upstream migration (Fig. 12a). Pari was a cylindrical trap (Fig. 12a) specifically used for catching catadromous Anguilla bengalensis (Velangu) below Mayanoor barrage. The trap was made of bamboo splits and was fabricated in different lengths ranging from 60 to 90 cm with a diameter of approx. 16 cm. The trap entrance was a unidirectional conical valve made of stitched midribs of coconut leaves that were flexible enough to allow entry of eels and the rear or exit was covered using a PVC-made circular plate with small holes, fixed to the trap with twines. Fishers, from their understanding of the behaviour of the species, set the trap at the river bottom near boulders with shallow water. Small boulders were kept over the traps to prevent their displacement by river flow or by other means. Rathakrishnan et al. [25] described another trap from Nagapattinam district with a similar name. Falling gear ‘Ootha’ was not common in other major rivers, but in Cauvery, obstructed fish movement below the Mayanoor barrage was utilized by fishers to use this gear (Fig. 12b). ‘Ootha’ was a falling gear made of bamboo sticks tied together with coir ropes. There were two openings, one at the bottom around 70 cm in diameter and one on top with around 12 cm diameter, to insert a hand to collect the trapped fish. It was efficiently operated below Mayanoor barrage with transparent knee-deep water where a lot of fish accumulates trying to cross the barrage to reach upstream. Hornell [16], Muthukumar et al. [22], and Rathakrishnan et al. [25] have earlier reported the gear from lentic waters of Tamil Nadu. Similar cover pots have also been reported from other parts of India like ‘Ottal’ from Thrisur, Kerala [27], ‘Odhe’ in Andhra Pradesh [24], and ‘Polo/Juluki/Jolpi’ from northeastern states of India [3].

Fig. 12 a ‘Pari’, the trap targets catadromous Anguilla sp. below Mayanoor barrage. b ‘Ootha’, the falling gear was used in shallow water to catch accumulated fishes. c ‘Visuruvalai’, the modified cast net targets anadromous giant river prawn Macrobrachium malcolmsonii

102

R. K. Manna et al.

Fig. 13 a ‘Paachvalai’, the areal trap at Lower Anicut. b Cast net ‘Manivalai’ used below barrage at Lower Anicut. c Simple cloth is used as scoop net below barrage at Grand Anicut

The cast net ‘Kalluvalai’ has been suitably modified to make ‘Veechuvalai or Visuruvalai’ for catching migratory giant river prawn Macrobrachium malcolmsonii in lentic water above Mayanoor barrage (Fig. 12c). The height of the ‘Visuruvalai’ was 1.0 m. Small sinkers were tightly knitted along the periphery, and the mesh size of the net was 30 mm. ‘Kalluvalai’, the cast net which is generally operated in flowing water and the catch was collected by diving underwater, was observed to be used for catching large-sized fishes accumulated below Mayanoor barrage taking the advantage of lower water depth and higher water transparency.

3.10.3

Observation at Lower Anicut

At Lower Anicut, fishers use the areal trap ‘Paachvalai’ taking the advantage of fishes getting obstructed below the barrage and their subsequent jumping behaviour to reach above barrage (Fig. 13a). ‘Paachvalai’ was a rectangular zero-mesh bag net supported with two bamboo poles, of about 2 m length at opposite sides operated in Lower Anicut. When water was released from the barrage fish got accumulated below the gate and started jumping to reach upstream. The cradle-type aerial trap was then lowered to keep it just above the water level so that jumping fishes fall over it and got trapped. Earlier studies [28] reported extensive use of ‘Thonguvalai’, a similar aerial trap with gunny bag in place of the net at many barrages on river Cauvery. Manna et al. [19] reported a similar ‘cradle trap’ operated at Prakasam barrage at Vijayawada in river Krishna, to catch prized juveniles of giant river prawn, Macrobrachium malcolmsonii during their upstream migration. ‘Manivalai’, the cast net with a pocket was also observed in a similar situation to catch the accumulated fishes below barrage where the approach is difficult (Fig. 13b). Even simple cloth is used as a scoop net at some places which can be considered as an excellent example of catching the cornered fishes intending to migrate (Fig. 13c). The present study documented the present status of targeted fishing presently being operated below and above the barrages in river Cauvery. It was observed that most of the fishing gears which were common in lentic waters were also effectively used

Impact of Barricades on Habitat and Fish Migration …

103

here as the river becomes near lentic during the non-monsoon period. Fishers cleverly followed the behavioural pattern of the fish in the altered habitat and accordingly improvised many traditional fishing gears. There were many destructive fishing gears like ‘Kachhavalai’, ‘Paachvalai’ which were operational in the Cauvery river and thus is a matter of great concern from a sustainability point of view. This type of fishing gear causes growth overfishing, i.e., harvesting fish at a size smaller than the size that would produce maximum yield per recruit. Such gears should be banned or controlled as those gears were indiscriminately used to catch the fish juveniles below the barrages. Again, at most of the places below the barrage, the construction was not friendly for upstream fish migration due to the huge difference of height between riverbed and barrage gate opening and absence of gentle slope therein. Fishes were observed to be continuously jumping to negotiate the height as observed barrage downstream in the middle portion of Grand Anicut and caught easily by fishing gear like ‘Manivalai’ (cast net with pocket) at Lower Anicut or ‘Scoop net’ at Grand Anicut. Injury caused to the fishes as a result of repeated, fruitless attempts to reach barrage upstream must not be overlooked. Again, free fall to the ski-jump spillways below the middle portion of the Grand Anicut barrage may cause injury and even mortality to the fishes migrating downstream. Such inconvenient structures may be identified and suitably modified to facilitate both upstream and downstream migration across barrages, especially during water release. Left and right wing of the Grand Anicut barrage, though have a steady decline suitable for fish migration, released water flow pattern set by the fishers prevented fish migration. As described earlier, the flow regime of released water is manipulated by the fishers to catch the fishes accumulated on the steps there below barrage. We have observed that though the river stretch at Grand and Lower Anicut recorded many fish species, only three species were able to successfully climb the staircases to reach the gates. Other species were observed to be accumulated below the last steps of the cascades. Hence, site and fish speciesspecific suitable barrage downstream structure is needed to facilitate fish migration for the cause of sustainable riverine fishery and precious aquatic biodiversity [23]. Increased predation by predatory fishes over the accumulated fishes below barrage also cannot be ignored as increased availability of highly carnivorous exotic Clarius gariepinus has been recorded below Mayanoor barrage. The negative impact of impoundments on fish migration in all major rivers was understood long back in India [9, 15] but fewer attempts were made to address the issue as compared to other nations [4]. Different types of fish pass facilities were provided in some weirs, dams and barrages in India to facilitate fish migration [7, 8]. However, a survey revealed that most of the fish passes became ineffective due to different management problems like completely dried lower stretch, choking by garbage due to lack of regular cleaning, etc. An effective site and species-specific fish pass is the need of the hour as India is adding more and more river barricades to address various water requirements, especially hydropower [2, 26]. The present document thus will be helpful for researchers and fishery managers to identify the detrimental fishing methods and unfriendly riverine habitat and below barrage structures for fishes, recommending strategies and drafting policies for the

104

R. K. Manna et al.

development of sustainable fisheries of not only river Cauvery but most of the large rivers as the habitat of almost all the rivers are more or less modified to corner the fishes and made riverine fisheries highly unsustainable.

4 Conclusion Dams and barrages in large rivers created a great barrier for fish migration and wiped out many indigenous fishes. Observation at Grand Anicut revealed that fishes are unable to negotiate the barrage from downstream to upstream due to inappropriate structure as well as flow regime that existed below barrage. Released water flow pattern is also often manipulated by the fishers to catch the fishes accumulated on the steps there below barrage. Strict vigilance is warranted to avoid such huge destruction of fish juveniles below barrage as it is highly detrimental to sustainable riverine fishery. Regulation of fishing methods plays an essential role in combating fish species depletion and decline in fish catch from rivers because of over-exploitation and other anthropogenic activities. A collaborative effort of engineers and fish biologists is the need of the hour as fish species-specific suitable barrage downstream structure as well as fish pass is needed to facilitate fish migration for the cause of sustainable riverine fishery. Acknowledgements The authors were grateful to the Indian Council of Agricultural Research, New Delhi for funding the study. They were indebted to the fishers of river Cauvery for their whole-hearted cooperation during the study. Support from Officials of Dept. of Fisheries, Govt. of Karnataka, and Govt. of Tamil Nadu was highly appreciated. Support from Technical Officers and Support Staffs Mr. A. Roychoudhury, Mrs. A. Sengupta, Mr. R. Pal, Mr. D. Das, Mr. M. Penappa and Mr. A. Prasad during the field survey of river Cauvery was thankfully acknowledged.

Appendix See Table 3.

Impact of Barricades on Habitat and Fish Migration …

105

Table 3 Brief description of sampling centres along with morphometry S. No.

Sampling centre

1

Bhagamandala (BGM) (12°23 6.99 N, 75°29 28.62 E), 0 km

2

Picture of the river stretch

Description

Fig. 1a Bhagamandala

Hilly terrain. The area was a religious place with a regular inflow of tourists. Riverbed was full of stone, otherwise sandy in nature. To ensure sufficient water for religious rituals, an obstruction was created to increase water depth. Bottom visible during non-monsoon months with periphytic algae on stones. Narrow river width was almost covered with canopies

Valnoor (VLN) (12°20 28.50 N, 75°53 29.87 E), 78 km

Fig. 1b Valnoor

3

Kudige (KDG) (12°30 35.16 N, 75°57 52.15 E), 119 km

Fig. 1c Kudige 4

T. Narasipura (TNP) (12°13 9.04 N, 76°54 37.58 E), 255 km

Fig. 1d T. Narsipura

A hilly station with mild flow over pool and high flow at runs. The area was a protected one with sport fishing targeting Mahseer and large-sized carps like Labeo sp. The run habitat was largely used by those fishes as a breeding ground as reported by local fishers The area was hilly but near plain. The sampling centre was just above the Harangi river confluence. Water flow was higher due to the presence of large-sized boulder in the riverbed and also higher slope of the riverbed Water regulation by upstream Krishnarajasagar dam made the stretch water deficient with thick infestation of submerged aquatic macrophytes. Discharge of untreated city waste aggravated the situation with deteriorated water quality (continued)

106

R. K. Manna et al.

Table 3 (continued) S. No.

Sampling centre

5

Shivanasamudra (SSM) (12°15 0.98 N, 77°9 10.56 E), 303 km

Picture of the river stretch

Fig. 1e Sivanasamudra

6

Hogenakkal (HGL) (12°6 56.29 N, 77°46 36.86 E), 391 km

Fig. 1f Hogenakkal water falls in river Cauvery

7

Bhavani (BVN) (11°25 14.50 N, 77°40 54.99 E), 493 km

Fig. 1g River Cauvery at Bhavani during pre-monsoon

8

Mayanoor (MYN) (10°57 29.32 N, 78°13 48.94 E), 591 km

Fig. 1h Mayanoor at barrage upstream

Description Water retention by obstruction made for hydroelectric power generation created higher depth with lentic condition. The area also observed visit by lot of tourists. Near plain with big boulders in patches. High density of submerged macrophytes created very high bottom visibility (>3.0 m) Hilly terrain famous for waterfalls. Deep pool (>20 m depth) below the falls. Run above the falls. Sandy bottom with large boulders. Visit of a large number of tourists observed throughout the year except monsoon due to very high flow regime. Intense hook and line fishing was observed in the deep pool below the falls Water depth and flow were mostly controlled by discharge from the upstream Mettur dam. Bottom with huge boulders used to be exposed during non-monsoon months with less and regulated water release. The stretch was also facing severe anthropogenic pollution from cities and industrial effluents The barrage constructed in 2014 diverts most of the river water through right bank canal resulting in severe water scarcity in rest of the river stretch with almost dry condition exposing sandy riverbed. Released water from upstream Mettur dam was retained here and hence the area observes sufficient water more than nine months in a year (continued)

Impact of Barricades on Habitat and Fish Migration …

107

Table 3 (continued) S. No.

Sampling centre

9

Grand Anicut (GAN) (10° 49 48.6 N, 78°49 07.62 E), 659 km

Picture of the river stretch

Fig. 1i Grand Anicut during pre-monsoon

10

Lower Anicut (LAN) (11° 08 20 N, 79°27.00 06 E), 741 km

Fig. 1j Lower Anicut on River Kollidam during pre-monsoon

11

Kollidam (KLD) (11° 0 44.59 N, 79°18 15.66 E), 784 km

Description The grand old barrage (locally called Kallanai dam, originally constructed during the reign of Chola king Karikalan in c. 100 BC–c. 100 AD) was still functional but observes severe water scarcity now during non-monsoon months due to water diversion at Mayanoor barrage exposing the sandy riverbed. Highly destructive fishery targeting migratory fishes was recorded below barrage during water release in monsoon and post-monsoon period Condition similar or worse with respect to Grand Anicut. Whatever water received from upstream was diverted through left and right canal. Pool formation here and there in sandy riverbed during non-monsoon months was observed. Similar destructive juvenile fishery was recorded during monsoon and post-monsoon water release

Plain land. Estuarine character. Water flow and depth depend upon tidal inflow. Mostly saline water due to lack of freshwater discharge from upstream especially during Fig. 1k River Kollidam pre-monsoon season. (Coleroon) (distributary of Condition improved during River Cauvery) at Kollidam monsoon and post-monsoon seasons (continued)

108

R. K. Manna et al.

Table 3 (continued) S. No.

Sampling centre

12

Pazhayar (PZR) (11°21 30.11 N, 79°49 39.25 E), 801 km

Picture of the river stretch

Fig. 1l River Kollidam at Pazhayar

13

Poompuhar (PMR) (11°14 5.19 N, 79°50 35.95 E), 793 km

Fig. 1m River Cauvery at Poompuhar

Description It was the river mouth of the Kollidam river, the major distributary of river Cauvery. Lack of freshwater discharge from upstream made it as backwater instead of estuary, especially during pre-monsoon months. Mangroves were there in a very small area and in patches, maybe due to less freshwater inflow from the river Situated at the mouth of the minor distributary of river Cauvery (retaining the original river name). Severe siltation on river mouth making the manoeuvring of the fishing vessel difficult during low tide. Water scarce condition observed even during monsoon

References 1. APHA (1998) Standard methods for the examination of water and Wastewater. 20th edn. American Public Health Association, Washington DC 2. Baumgartner LJ, Wibowo A (2018) Addressing fish-passage issues at hydropower and irrigation infrastructure projects in Indonesia. Mar Freshw Res 69:1805–1813 3. Bhattacharjya BK, Manna RK, Choudhury M (2004) Fishing crafts and gear of North East India. Bulletin (CIFRI, Barrackpore) (142):67 4. Brink K, Gough P, Royte J, Schollema PP, Wan-ningen H (2018) From Sea to Source 2.0. Protection and restoration of fish migration in rivers worldwide. © World Fish Migration Foundation 5. Cohen DM (1970) How many recent fish are there? In: Proceedings of the California academy of science, vol 38, pp 341–345 6. Daget J (1988) Conservation. In: Levêque C, Bruton MN, Ssentongo GW (eds) Biologie et écologie des poissons d’eau douce africains. Editions de l’ORSTOM, Paris 7. Das BK, Roshith CM, Sahoo AK, Koushlesh SK, Meena DK, Chanu TN, Swain HS, Gogoi P, Raman RK (2017) Review of research on fish pass facilities in India. Bulletin No. 199, ICAR-Central Inland Fisheries Research Institute, Barrackpore, 57p 8. Das MK, Hassan MA (2008) Status of fish migration and passes with special reference to India. Bulletin No. 156, ICAR-Central Inland Fisheries Research Institute, Barrackpore, 38p 9. Day F (1873)Report on the fresh water fish and fisheries of lndia and Burma. Calcutta 10. Dudgeon D (2011) Asian river fishes in the anthropocene: threats and conservation challenges in an era of rapid environmental change. J Fish Biol 79:1487–1524

Impact of Barricades on Habitat and Fish Migration …

109

11. Dugan PJ, Barlow C, Agostinho AA, Baran E, Cada GF, Chen D, Cowx IG, Ferguson JW, Jutagate T, Mallen-Cooper M et al (2010) Fish migration, dams, and loss of ecosystem services in the Mekong Basin. Ambio 39:344–348 12. FAO (1968) Modern fishing gear of the world. Fishing News Books Ltd, London, p 607 13. FAO (1987) FAO catalogue of small-scale fishing gear, IInd. Fishing News Books Ltd., London, p 224 14. Gujja B, Hunziker DO (2000) The impact of Dams on life in Rivers. WWF Research Report. WWF International. Gland, Switzerland, 33 pp 15. Hora SL (1940) Dams and the problems of migratory fishes. Curr Sci 9:406–407 16. Hornell J (1938) Fishing methods of Madras presidency, part 2: the Malabar coast. Madras Fish Bull 271:69 17. Hubbs C, Pigg J (1976) The effects of impoundments on threatened fishes of Oklahoma. Annal Oklahoma Acad Sci 5:133–177 18. Jayaram KC, Venkateswarlu T, Ragunathan MB (1982) A survey of the Cauvery river system with a major account of its fish fauna. Records of the Zoological Survey of India. Miscellaneous publication occasional paper no. 36, 115 p 19. Manna RK, Das AK, Rao DSK, Karthikeyan M, Singh DN (2011) Fishing crafts and gear in river Krishna. Indian J Tradit Knowl 10(3):491–497 20. Mitchell C (1995) Fish passage problems in New Zealand. In: Proceedings of the international symposium on fishways ’95, Gifu, Japan 21. Mogalekar HS, Jawahar P, Jhonson C (2016) Fish diversity of rivers of Karnataka. J Inland Fish. Soc. India 48(1):56–83 22. Muthukumar S, Sundaramoorthy B, Ravikumar T, Neethiselvan N (2016) Ootha: a traditional fishing pot of inland waters of Tamil Nadu. J Exp Zool India 19(1):615–618 23. Puijenbroek PJTMV, Buijse AD, Kraak MHS, Verdonschot PFM (2019) Species and river specific effects of river fragmentation on European anadromous fish species. River Res Appl 35:68–77 24. Raju CS, Rao JCS, Rao KG, Simhachalam G (2016) Fishing methods, use of indigenous knowledge and traditional practices in fisheries management of Lake Kolleru. J Entomol Zool Stud 4(5):37–44 25. Rathakrishnan T, Ramasubramanian M, Anandaraja N, Suganthi N, Anitha S (2009) Traditional fishing practices followed by fisher folks of Tamil Nadu. Indian J Tradit Knowl 8(4):543–547 26. Sanz-Ronda FJ, Fuentes-Pérez JF, García-Vega A, Bravo-Córdoba FJ (2021) Fishways as downstream routes in small hydropower plants: experiences with a potamodromous Cyprinid. Water 13:1041. https://doi.org/10.3390/w13081041 27. Shaji CP, Laladhas KP (2013) Monsoon flood plain fishery and traditional fishing methods in Thrissur district Kerala. Indian J Tradit Knowl 12(1):102–108 28. Singh DN, Murugesan VK, Das AK, Krishna Rao DS, Palaniswamy R, Manoharan S (2003) River Cauvery—Environment and fishery. Bulletin No. 119. ICAR-Central Inland Fisheries Research Institute, Barrackpore, Kolkata 700120, 28 p 29. Sundar Raj B (1942) Dams and fisheries; Mettur and its lessons for India. Proc Indian Acad Sci 14B:341–358 30. Von Brandt A (1984) Fish catching methods of the world. Fishing News Books Ltd, London, 418 p 31. Yoon JD, Kim JH, Park SH, Kim E, Jang MH (2017) Impact of estuary barrage construction on fish assemblages in the lower part of a river and the role of fishways as a passage. Ocean Sci J 52(1):147–164 32. Ziv G, Baran E, Nam S, Rodríguez-Iturbe I, Levin SA (2012) Trading-off fish biodiversity, food security, and hydropower in the Mekong River Basin. Proc Natl Acad Sci USA 109:5609–5614

Impacts on Reproductive Biology of Golden Mahseer Caused by Climate and Land Use Change in Western Himalaya Priyanka Rana, Soukhin Tarafdar, and Prakash Nautiyal

Abstract Climate as well as land use land cover (LULC) changes over past few decades have influenced the air temperature, precipitation, snow cover, soil moisture, groundwater recharge, and thereby ecology of snow and rain-dominated springfed Himalayan rivers. The Golden Mahseer, Tor putitora, is one of the megafauna Himalayan cold-water endangered fishes which can tolerate only narrow thermal range. The species inhabits glacier-fed river Ganga and is known to migrate into suitable tributaries, specifically the Nayar River for breeding. The species requires specific tributaries with optimum water temperature, hydrology, and bottom substrate for spawning along with appropriate growth and development of early life stages. The breeding migration toward these tributaries is governed by onset of monsoon and snow melt which results in increased thermal regime in abode. Alteration in water temperature could have detrimental impact in directing the migratory pathway of this truly stenothermal cold-water fish. Climatic changes as well as LULC of river basin alter thermal regime disturb food web and degrade breeding cum nursery grounds by altering water flow, sediment transport, and increased exposure to predators. Shift in optimum habitat and climatic conditions could affect the breeding and recruitment process which could push this endangered species toward extinction. Thus, it is an utmost need to evaluate the integrated impact of climate and LULC changes on the thermal regime and river ecology in relation to reproductive biology of Tor putitora for its conservation and management process. Keywords Tor putitora · Thermal regime · Breeding migration · Breeding/nursery grounds

P. Rana (B) · P. Nautiyal Aquatic Biodiversity Unit, Department of Zoology, HNB Garhwal University, Srinagar, Uttarakhand, India S. Tarafdar G.B. Pant, National Institute of Himalayan Environment & Sustainable Development, Garhwal Unit, Srinagar-Garhwal, Uttarakhand, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 V. Chembolu and S. Dutta (eds.), Recent Trends in River Corridor Management, Lecture Notes in Civil Engineering 229, https://doi.org/10.1007/978-981-16-9933-7_7

111

112

P. Rana et al.

1 Introduction Barring higher vertebrates, most aquatic organisms are poikilotherms and hence depend on water temperature for all metabolic functions. The water thermal regime influences the growth, development, food availability, migration and reproduction of fishes, and other elements of food web. Thus, alteration in thermal regime would have pessimistic impacts on hydrology, and thereby physiological processes and behavioral activities [57] of aquatic organisms. Recently, on June 17, 2020, the Ministry of Earth Sciences (MoES) have reported the human-induced climate change during the twenty-first century and beyond and stated that the average temperature of India has risen by around 0.7 °C during 1901–2018 [20]. Impact of changing climate is more evident in high-altitude region which have resulted in melting of glaciers at exceptional rates in recent decades [6]. Snow cover melting rate influences river flow, mainly of snow-fed rivers which would have significant impact on river water temperature [52]. However, snow cover alone does not contribute to river water; some of the water flowing in rivers comes from seepage of groundwater into the streambed which may alter the stream temperature, thereby altering the maximum stream temperature [19]. Additionally, a strong association between mean air and water temperatures has also been seen [14] which can act as a determining factor of the thermal regime of freshwater ecosystems. However, Subehi et al. [50] reported that the change in stream water temperature is influenced more by the change in water discharge due to rainfall than by the change in air temperature. Thus, there seems to be some conflicts in relation of water temperature with air temperature and river discharge due to rainfall. However, the glacier-fed rivers are more sensitive to climatic variations as snow cover responds rapidly to slightest shift in temperature and rainfall [53]. Therefore, shift in temperature and rainfall pattern could alter the hydrological cycle which may lead to frequent/extreme climatic events such as cloud bursts and avalanche/glacier retreat. All these events cause extreme flooding in river systems which leads to deposition of sediments in deep pool regions along the riverbank [39] which serve as spawning grounds for fishes. The complete loss or destruction of breeding/nursery grounds due to increased suspended load could jeopardize the recruitment process and its rate. In addition to climatic influences, the aquatic systems are also influenced by river morphology, river bed, and several anthropogenic activities such as land use land cover, construction, and operation of hydropower projects in the river basin or catchment area. The increasing urbanization and land use leads to reduced forest cover which influences the seasonal rainfall pattern. All these factors collectively alter river morphology, run-off, and drainage area. Further, the hydropower projects alter substrate characteristics, sediment transport, and sudden flooding of stream flow which in some cases serve as migratory cue for fishes. Additionally, the altered temperature pattern downstream of the dam could alter timing of migration, spawning, and egg hatching for the fish [9]. Thus, alteration in LULC aggravates and exerts a long-term impact on the ecological integrity of numerous headwater streams and interlinked downstream large rivers. The modified LULC, in turn, influences the

Impacts on Reproductive Biology of Golden Mahseer Caused …

113

Fig. 1 Map showing possible spawning tributaries of Mahseer across the Himalayan Region, prepared by WWF expert Tom in personal communication with Prof. Prakash Nautiyal (Unpublished Data)1

local climatic conditions at varying magnitude. Therefore, climate and LULC mutually contribute toward thermal regime and hydrological characteristics of freshwater ecosystem which plays a major role in life cycle of aquatic organisms. Further, these alterations could have a detrimental impact in directing the migratory pathway of several lotic species and the truly stenothermal cold-water fishes as they can tolerate only a narrow range of temperature. Himalayan or Golden Mahseer, Tor putitora (Hamilton), is one of the coldwater, stenothermal, rheophilic fish which is endemic to Himalayan Region. The Golden Mahseer is popular for its sporty behavior around the world. In India, the catch and release angling of this highly prized sport fish has been authorized only in small stretch of Ramganga River flowing outside the Corbett National Park of Uttarakhand along with Jia Bharali River flowing within the Nameri Tiger Reserves on Assam-Arunachal border for the Mahseer protection and management purpose [17]. However, indiscriminate and unauthorized fishing of adults and brooders has pushed its population almost toward the edge of extinction. Naturally, the Golden Mahseer has been found distributed throughout the rivers of the South Himalayan drainage (namely, the Indus, Ganges, and Brahmaputra) and the Eastern Brahmaputra catchments [42]. In Himalayan river systems, Golden Mahseer requires specific tributaries for breeding and nursery grounds (Fig. 1) which are very few in numbers, namely, Alikahd, Seerkhad, and Gamberkhad (tributaries of Sutlej River) in 1

Under project entitled “Discussion on identifying ecological criteria for No-Go areas for Hydro. Development, River Kali case study” (November 24, 2009).

114

P. Rana et al.

Himachal Pradesh, Tadi and Khahare, Khola (tributaries of Trishuli River) in Nepal, streams of Garo hills in Meghalaya and Korang River near Islamabad, Pakistan [8]. However, the juveniles of T. putitora have also been reported by Bhatt and Pandit [8] from Rimbi-Riyang River (tributaries of Teesta River) in Sikkim and Darjeeling. In Uttarakhand (Garhwal region), T. putitora inhabits the foothill stretch of glacier-fed river Ganga, usually between Haridwar to Rishikesh and seems to migrate in the headwater tributaries of Ganga such as Alaknanda, Bhagirathi, Nayar, and Saung either for utilization of food resources and breeding [32], possibly to achieve differential distribution. All these tributaries show significant variations in their water temperature and hydrology as compared to the abode river, Ganga [30]. Besides, Saung and Nayar, T. putitora also utilizes the Mandal, Khoh, and Kolhu Rivers (tributaries of Ramganga) as spawning grounds in Uttarakhand [5]. These tributaries of Ramganga shows similar thermal regime but significant differences in substratum condition [5]. In Kumaon Region of Uttarakhand, glacier-fed river, Sharda, and its tributaries (Goriganga, Sarju) also provide feeding and breeding grounds to T. putitora similar to Ganga and its tributaries in lesser Himalayan region.

2 Migratory Pattern in Relation to Water Temperature T. putitora majorly follows thermal changes as migratory instinct rather than altitudinal regime of the inhabited tributaries [25]. However, along with water temperature, certain abiotic and biotic factors such as current velocity, turbidity, and conductivity also stimulate migration of T. putitora [35]. The migratory movement of juveniles, adolescents, and brooders of T. putitora with respect to water temperature is shown in Table 1. The given table clearly shows movement of T. putitora toward colder stretch of tributaries with the exception of spawning migration toward warmer tributaries which may be attributed to conducive habitat for eggs, fry, fingerlings, and juveniles. The first ascending phase of migration is accompanied by snow melting (mid-February) which results in increased water temperature and sediment load in the abode region, thereby stimulating migration of adolescents and prospective brooders toward colder stretch of Alaknanda and Bhagirathi followed by descending migration by the end of June/July as the temperature of the abode homogenizes due to more humid conditions during monsoon. The second ascending phase (spawning migration) is performed during the onset of monsoon/flood by ripe brooders toward warmer tributaries as the optimum temperature for breeding is 21–25 °C [26]. The third phase of migration is descending phase which involves movement of spent brooders and yearlings back to cold water of abode. Therefore, only early stages inhabit warmer tributaries till they attain optimum size for encountering the diverse conditions of the Ganga, which later become its abode while adult ones show seasonal migration facilitated by migratory cues for different purposes such as maintenance of food supply, population density, and physiological needs other than reproduction. Thus, adults of T. putitora migrate in different tributaries to disperse its population in tributaries having required range

13

17

11

Nov

Dec.

12

16

16

Oct

17 17

18 17

19

16

Aug Sept

July

11

12

23

28 23

27

12

13

14

29 20

25

-

(Descending of brooders and 1 yr old juveniles) Ganga◄Saung/ Nayar Stock in Ganga

(Ascending of brooders in Mid July) Ganga ► Saung/ Nayar (spawning migration) Migratory Stock in Saung/ Nayar

-

-

23°/14° to 16°

18◦ to 28°/29° 17◦ to 23°/20°

19◦ to 25°/27°

-

-

7◦ ▼

10◦/11◦▲ 6◦ /3◦▲

3◦▲

G

G

G

G G

G

G

G

G

G G

G

G

G

G

S/N S/N

S/N

Table 1 Migration of T. putitora in different tributaries of Ganga with respect to water temperature [30] (J = Juveniles, Ad = Adolescents, B = Brooders, G = Ganga, S = Saung, N = Nayar, A = Alaknanda) Water Fish Temperature Temperature G A N S Migration Movement Shift J Ad B Water(◦C) From Jan 11 10 18 18 G G G Feb (Ascending of 1 yr old 11 10 18 18 juveniles, Adolescents & 1◦▼ A A A 11◦ to 10◦ Brooders in Mid-Feb) Ganga ►Alaknanda Mar 14.5 15 23 16 Migratory 0.5◦▲ A A A 14.5◦ to 15◦ Stock in Alaknanda Apr 16 14 18 24 16◦ to 14◦ 2◦▼ A A A May 17 18 24 24 17◦ to 18◦ 1◦▲ A A A (Descending of juveniles, Adolescents and brooders, June 18 18 26 24 in Mid June) G G G Ganga ◄ Alaknanda

Impacts on Reproductive Biology of Golden Mahseer Caused … 115

116

P. Rana et al.

of water temperature. However, the migration of T. putitora is also seen in spring fed river, Ramganga which witnesses minimal snow melt (due to snow-fed tributaries in upper stretches of Dudhatoli range) as compared to glacier-fed river Ganga, therefore, encounters minimal thermal and sediment changes in abode region of Mahseer. This ongoing migration could also be attributed to increased water current and discharge that possibly provides ascending cues [30] besides thermal stimulation.

2.1 Vulnerability of Himalayan/Golden Mahseer The Himalayan Mahseer, T. putitora, is truly a stenothermal cold-water fish, for which reason, as soon as the water temperature of its abode (Ganga) starts rising, it ascends toward colder tributaries from February onward till the end of June-July (in case of non-brooders). It copes with temperature increase in respective months but the difference in water temperature in migrating areas from that of its abode (foothill section) is barely 1 °C (see Table 1). Therefore, any possible temperature rise in future could cause upward shift in the present range (max. 1800 m elevation in India, [10]) from abode and no or limited access to migrating tributaries. This anticipated upward shift would lead to conflict among Himalayan fishes and non-native fishes living at altitudes higher than 1800 m. Therefore, along with increasing human and climate-induced impacts in the habitat region, the T. putitora has to compete with these non-native species, with higher growth rate, larger body size, and longer life span, for similar food resources and habitat [16]. The stock of Mahseer is already getting replaced by silver carps in Gobind Sagar Reservoir (1500 ft), Himachal Pradesh, which was popular for Mahseer in past [51]. Earlier, the population of Himalayan Mahseer could be seen in upper stretch of the Ganga in plains (near Aligarh) but now they are restricted only in foothill section of Ganga [38]. Also in Himachal Pradesh, the migration of Golden Mahseer has been seen in the Beas up to Sultanpur, Kullu Valley in earlier period which is now limited over lower stretches [47] along with inefficiency to migrate in Kangra Valley in Western Himalayas due to dam at Pandoh [36]. The demarcation of T. putitora in lower stretches may be attributed possibly to its stenothermal behavior or population decline due to overfishing or habitat fragmentation due to several small and large-scale barrages and hydroelectric dams which prevent access to migratory channels [28]. Since different life stages of T. putitora require specific thermal range, alteration in the preferable thermal regime and limited access to migratory channels may lead to inadequate metabolic and physiological functioning. Moreover, the snow melting and monsoon flood which act as migratory cues for T. putitora are directly influenced by climatic conditions. Therefore, changes in climate could influence the migratory pattern and timing in T. putitora which can have crucial impact leading to breeding failure. Besides providing migratory cues, monsoon season is also needed for flooding the breeding grounds of spring-fed tributaries which provides cover to these large-sized fishes and proper cover from humans and other predators. Along with this, significant variations in water temperature and hydrology of inhabited tributaries provide

Impacts on Reproductive Biology of Golden Mahseer Caused …

117

conducive habitat for the optimum growth and development of early and adult life stages. Moreover, Mahseer is a slow breeder having low fecundity as compared to other indigenous carps and also requires longer hatching period under optimum environmental and thermal conditions [28]. Therefore, influence of climate as well as LULC on morphology, hydrology, and thermal regime of respective tributaries could lead to delayed growth and development, thereby alteration in reproductive functioning of T. putitora. Although the status of T. putitora among endangered species has extremely endorsed their captive breeding programs for stock enhancement in natural habitat conditions. But this method relies on wild caught female brooders as females exhibit reproductive dysfunctioning in captivity [1]. However, repeated induced breeding in captivity could result in loss of genetic variability in captive stocks [43]. Along with captivity-induced genetic variations, long-term climatic and land use changes in native catchment area could further lead to changes in its genetic structure [56], as it is very sensitive to changes in water temperature and hydrology of habitat rivers.

3 Major Research Requirements Immense literature has been cited about T. putitora which largely pertains reproductive biology, growth [26, 27, 29], ecological conditions [8, 35], population structure [7, 8], and stock assessment [38]. Several studies have also been done in the field of induced breeding or captive breeding for stock replenishment in natural habitats such as Ogale [40], Akhtar et al. [1–4]. However, the impact of climate and LULC on the food resources, reproductive biology, migratory pattern, and breeding/nursery grounds of T. putitora remain uninvestigated.

3.1 Disturbances in Food Web The food web in the foothill stretch of Himalayan river Ganga is much diverse as compared to upper and middle stretch of Ganga [34]. The feeding habits of T. putitora also show diverse diet from early life stages till maturing into adults. On one hand, the fry utilizes diatoms and algae as chief food source while fingerlings and juveniles consume plant and insect matter. However, the mature adults are omnivorous mainly feeding upon insect diet [28]. The diversity in the diet of the T. putitora also varies with increasing size as well changing season [8]. During monsoon season, the glacier-fed rivers usually have low phytobenthos and macroinvertebrate density as compared to spring-fed river [37]. The heavy monsoon flood increases sediment load in river basin resulting in increased turbidity, reduced photic zone, and thus reduced primary productivity while snow melt in glacier-fed rivers also leads to decreased density as compared to the spring-fed rivers. However, the glacier-fed rivers are rich in diversity which may be attributed to variable water current in large areas

118

P. Rana et al.

as compared to smaller spring-fed streams [37]. Therefore, for maximum utilization of food resources, the Golden Mahseer shows differential distribution, thereby classifying different tributaries as feeding grounds for early and adult life stages. The influence of climatic and other anthropogenic factors on aquatic ecosystem may interfere with primary production, bio-geochemical cycling [45], and energy transfer through the food web. The stream water temperature also influences the growth of phytoplanktonic and zooplanktonic community along with some potential changes in species and size composition of algal community [21]. Some aquatic insects, fundamental components of the food web, are especially sensitive to stream temperature and cannot survive without the cooling effects of glacial melt water (USGS). A distinct temperature limit is also required for the development of different life stages of aquatic insects which is more apparent for egg development. Li et al. [22] have reported that temperature increase in stream water would result in loss of coldwater species such as Plecoptera, Ephemeroptera, and Trichoptera in peninsular environment. Further, a reduction of about 25% in stream macroinvertebrates has been predicted for every 1 °C rise in atmospheric temperature along with reduction in diversity and abundance [12]. The combined effect of climatic drivers may result in declined weight and survival rate of insects while other complexity in responsive behavior may increase in future [46]. Other than climatic impact, the loss of certain aquatic insects such as stone flies, may flies, and caddis flies have been observed due to high suspended loads due to continuous changing land use patterns [13].They had also observed decline in macroinvertebrate community with increased construction in riparian zones. These climatic as well as anthropogenic influences on the community of producers and primary consumers would interfere with the food supply and consumption in the upper trophic levels of fishes. Scarcity of quality food in early stages may delay the rate of sexual maturation which is observed in terms of decreased size of the fish [28].Thus, damage to the food web is more detrimental for annual fish recruitment rate as the nature of consumed food is responsible for attainment of sexual maturity and fecundity in fishes [24]. Therefore, there is an immense need to monitor all these long-term changes in the life history of lower trophic levels of aquatic food web under climatic and anthropogenic influences.

3.2 Alteration in Breeding Cum Nursery Grounds It is reported that the groundwater has substantial contribution in stream flow which may be limited to the headwater region or may be extended throughout the length of the stream [54]. The climatic influence on rising temperature has also affected the replenishment of groundwater level. Additionally, the continuous irrigation and land use changes have indirectly affected the groundwater resource [55]. Climatic impact on hydrology of spring/rain-fed rivers which mainly includes groundwater seepage may lead to drying up of these freshwater systems along with thermal and hydrological alterations. The low phased flooded breeding/nursery grounds are necessary to provide optimum habitat conditions for harboring the growth of eggs, fry, fingerlings,

Impacts on Reproductive Biology of Golden Mahseer Caused …

119

and juveniles. Drying up of groundwater resources which forms major contribution in freshwater system could have direct impact on spawning, maturation period, and thereby on recruitment processes. Several other Gangetic fishes are already showing reproductive failure as the shifted rainfall pattern has led to drying up of their suitable breeding grounds [11]. On the other hand, the brooders usually lay eggs on the bank of the stream so as to prevent the eggs from the shooting water current during monsoon [31]. Therefore, sudden/extreme flooding due to cloud burst or other climatic events could lead to mass mortality or relocation of early fragile stages to a long distance in unfavorable and diverse conditions. Further, the climatic as well as anthropogenic influences boost degradation of spawning habitat, primarily changes in river substratum which may affect the stock density [18]. The alteration in nature and size of these breeding/nursery grounds influences biota which further influences growth rate and size at first maturity [28]. Therefore, there is a need to explore the phenomenon of either drying up or sudden inundation of breeding grounds and habitat degradation under the climatic and various other anthropogenic factors.

3.3 Shift in Reproductive Potential The occurrence of low monsoon flood, changes in thermal regime, heavy rainfall, and other climatic factors induce the act of spawning migration. The seasonal cycle of migration under migratory cues stimulates gonadal maturation, and thereby facilitates reproductive functioning. All these biotic and abiotic factors stimulate the ascending migration throughout the year. However, this migratory pathway is getting hampered by the construction of hydroelectric power plants in the catchment area of Ganga, Yamuna, Nayar, Ramganga, Kosi, Kaliganga, Saryu, etc. which serve as a feeding and breeding ground for T. putitora. There are about 600 hydropower dams (either active or under construction, [58]) along the catchment area of Himalayan river system. Out of 600 hydropower dams, 292 are proposed in Indian Himalayas [15] among which 24 are active on Ganga itself [59]. The network of these hydropower dams along the migratory channels has blocked the breeding migration of Mahseer from lower abode region to upper suitable breeding tributaries. In Himachal Pradesh, a sharp decline in Mahseer catch has been observed from Satluj and their tributaries whose probable cause has been considered as construction of barrages/dams in Pandoh, Chamera, Pong, Bhakra, and Giribata. The alternate fish passage/ladders provided at dam sites are acting more as a trap rather than alternate passage to upper stretches [44]. However, construction of hydropower dams in upper catchment area could also slow down the sediment transport and water current in abode stretch, thereby affecting the natural stimulatory cue of migration as water current, increased sedimentation, and thereby increased turbidity in turn act as ascending cue for Golden Mahseer during monsoon season in adjacent tributaries. The Mahseer inhabiting the dam area of Bhagirathi and Bhilangana Rivers has already shown ineffective spawning behavior, and an annual decline of about 73% has been estimated in Mahseer productivity

120

P. Rana et al.

in respective catchment area [48]. However, the reservoirs of various hydropower dams are getting utilized as hatchery for Mahseer management purposes, but the stagnant behavior of reservoir is inappropriate for Mahseer to breed which usually requires lotic and preferred thermal range for maturation and breeding purposes [49]. Therefore, T. putitora migrate toward adjacent tributaries for spawning as soon as the reservoirs get swollen with flooded water and return back to inhabited reservoirs such as Gobindsagar Reservoir in Himachal Pradesh and Kalagarh Reservoir in Uttarakhand when spawning is done. Further, the fluctuations in preferable thermal range under the climatic influences have resulted in reproductive dysfunction in some of the Indian major carps [11], which also suggest that fluctuation in optimum thermal range may alter reproductive potential in T. putitora too as it is one of the stenothermal cold-water fishes. In a study, Akhtar et al. [3] have found that temperature exerts a profound effect on gonadal development of female T. putitora in captivity. The elevated temperature found to induce maturity in females of T. putitora in captivity which were earlier showing reproductive dysfunctioning in captivity. However, the elevated temperature also resulted in poor health and immunity in captive stocks [4]. As these conditions have been found in captivity, there is a need to evaluate these phenomena in their natural habitat. In addition, the Golden Himalayan Mahseer which is known for its large gigantic size has also shown decrease in their average size in last decade. The largest size recorded in 1882 (Hamilton) was 275 cm with a weight of about 54 kg. Thereafter, the maximum sized T. putitora recorded from the Garhwal Region of Uttarakhand was 133.7 cm with a weight of about 22 kg in the year 1980–81 [38]. However, the fishes >30 cm in size and >5 kg in weight are rarely seen in recent times [23] whose harvestable size was once computed to be 65 cm [33]. Moreover, a continuous shift in spawning migration period has been observed which was earlier seen from June to September [41] and now has been reduced from July to September [18]. The shift in rainfall pattern either early or delayed monsoon season in future could lead to further shift in spawning period. Therefore, there is a need to monitor migratory behavior, shifts in length and weight relationship, fecundity, relative condition factor, Gonadosomatic index (GSI), dietary habits, and other reproductive phenomenon by comparing it with earlier literatures. Thus, mutual impact of changing climate and LULC on river hydrology and morphology, thereby on breeding/nursery grounds and reproductive potential of T. putitora, should be the area of concern in future studies.

4 Concluding Remarks The Himalayan Mahseer, T. putitora, is considered as the flag ship species of Himalayan rivers and popularly known as tiger of water due to its sporty size and traits. However, its ecological status among endangered species should be major area of concern in future research works. Migration under thermal stimulations is an important phenomenon in Mahseer’s life cycle; therefore, under current climatic

Impacts on Reproductive Biology of Golden Mahseer Caused …

121

impact on thermal regime, Mahseer could undergo probable upward shift from the current abode stretch but the possibility of finding suitable breeding/feeding grounds in upper stretches is still a question. Further, its sensitive behavior toward thermal fluctuations and blockage in the migratory stimuli and path due to several hydropower projects in the migratory channels has restricted its movement in glacier-fed and spring-fed tributaries of Himalayan rivers. Therefore, either absence/reduction of migratory cue or migratory route may jeopardize the recruitment success. Besides hampering the migratory behavior, the hydroelectric power plants have also degraded their natural habitat by inundation of spawning grounds, unusual changes in river hydrology, river bed, and sediment load. Along with direct impact on Mahseer population, the climatic as well as man-driven land use impact on aquatic food web has indirectly affected the Mahseer population by threatening the food resources. Thus, dynamic climate changes as well as increased urbanization and land use in river basin is a matter of great concern as all these factors mutually exert short- or longterm impact on river systems. Meanwhile, there is a need to assess the reproductive biology of Himalayan Mahseer with respect to climate and land use derived shift in river morphology and hydrology. The integrated evaluation of all these factors is essential to monitor these river systems which not only serve as feeding ground but also provide suitable breeding cum nursery grounds to T. putitora. This will help in providing better measures for long-term conservation and management program of this endangered Himalayan species. Acknowledgements The authors sincerely acknowledge the financial assistance (NET-JRF) provided by CSIR (UGC). We thank Head, Department of Zoology, H.N.B. Garhwal University, Srinagar, Uttarakhand for providing laboratory facilities.

References 1. Akhtar MS, Ciji A, Sarma D, Rajesh M, Kamalam BS, Sharma P, Singh AK (2017) Reproductive dysfunction in females of endangered golden Mahseer (Tor putitora) in captivity. Anim Reprod Sci 182:95–103 2. Akhtar MS, Pal AK, Sahu NP, Ciji A, Mahanta PC (2013) Thermal tolerance, oxygen consumption and haemato-biochemical variables of Tor putitora juveniles acclimated to five temperatures. Fish Physiol Biochem 39(6):1387–1398 3. Akhtar MS, Rajesh M, Ciji A, Sharma P, Kamalam BS, Patiyal RS, Singh AK, Sarma D (2018) Photo-thermal manipulations induce captive maturation and spawning in endangered Golden Mahseer (Tor putitora): a silver-lining in the strangled conservation efforts of decades. Aquaculture 497:336–347 4. Akhtar MS, Rajesh M, Kamalam BS, Ciji A (2020) Effect of photoperiod and temperature on indicators of immunity and wellbeing of endangered golden mahseer (Tor putitora) broodstock. J Thermal Biol 93:102694 5. Atkore VM (2005) Conservation status of fishes in the tributaries of Ramganga with special reference to golden Mahseer (Tor putitora) Hamilton (Doctoral dissertation, Saurashtra University, Rajkot)

122

P. Rana et al.

6. Bajracharya SR, Mool PK, Shrestha BR (2007) Impact of climate change on Himalayan glaciers and glacial lakes: case studies on GLOF and associated hazards in Nepal and Bhutan. Int Centre Integr Mn Dev (ICIMOD) 7. Bhatt J, Nautiyal P, Singh HR (2000) Population structure of Himalayan mahseer, a large cyprinid fish in the regulated foothill section of the river Ganga. Fish Res 44:267–271 8. Bhatt JP, Pandit MK (2016) Endangered Golden mahseer Tor putitora (Hamilton): a review of natural history. Rev Fish Biol Fish 26(1):25–38 9. Caissie D et al (2006) The thermal regime of rivers: a review—CAISSIE—2006. In: Freshwater biology. Wiley Online Library 10. Cordington KDEB (1946) Notes on Indian Mahseer. J Bombay Nat History Soc 46:334–336 11. Das MK, Sharma AP, Sahu SK, Srivastava PK, Rej A (2013) Impacts and vulnerability of inland fisheries to climate change in the Ganga River system in India. Aquat Ecosyst Health Manag 16(4):415–424 12. Durance I, Ormerod SJ (2007) Climate change effects on upland stream macroinvertebrates over a 25 year period. Glob Change Biol 13(5):942–957 13. Gage MS, Spivak A, Paradise CJ (2004) Effects of land use and disturbance on benthic insects in headwater streams draining small watersheds North of Charlotte, NC. Southeast Natural 3(2):345–358 14. Gray BR, Robertson DM, Rogala JT (2018) Effects of air temperature and discharge on upper Mississippi River summer water temperatures: upper Mississippi River summer water temperatures. River Res Appl 34(6):506–515 15. Grumbine RE, Pandit MK (2013) Threats from India’s Himalaya dams. Science 339(6115):36– 37 16. Gupta N, Everard M, Nautiyal P, Kochhar I, Sivakumar K, Johnson JA, Borgohain A (2020) Potential impacts of non-native fish on the threatened mahseer (Tor) species of the Indian Himalayan biodiversity hot spot. Aquat Conserv Mar Freshwat Ecosyst 30(2):394–401 17. Gupta N, Nautiyal P, Borgohain A, Sivakumar K, Mathur VB, Chadwick MA (2016) Catchand-release angling as a management tool for freshwater fish conservation in India. Oryx 50(2):250–256 18. Joshi VD (1978) Fish fauna of Khoh Basin, District Pauri Garhwal, U.P. Prani, No. 1. 12–19 pp 19. Kaandorp VP, Doornenbal PJ, Kooi H, Peter Broers H, de Louw PGB (2019) Temperature buffering by groundwater in ecologically valuable lowland streams under current and future climate conditions. J Hydrol X(3):100031 20. Krishnan R, Sanjay J, Gnanaseelan C, Mujumdar M, Kulkarni A, Chakraborty S (2020) Assessment of climate change over the Indian region: a report of the ministry of earth sciences (MOES), government of India. Springer Nature, p 226 21. Lefébure R, Degerman R, Andersson A, Larsson S, Eriksson L-O, Båmstedt U, Byström P (2013) Impacts of elevated terrestrial nutrient loads and temperature on pelagic food-web efficiency and fish production. Glob Change Biol 19(5):1358–1372 22. Li F, Chung N, Bae M-J, Kwon Y-S, Kwon T-S, Park Y-S (2013) Temperature change and macroinvertebrate biodiversity: assessments of organism vulnerability and potential distributions. Climatic Change 119 23. Menon AGK (1999) Check list—Fresh water fishes of India. Rec Zool Surv India Misc Publ Occas Pap. No. 175, 366 p 24. Nikolsky GV, Birkett L (1963) The ecology of fishes, vol 352. Academic press, London 25. Nath R, Baruah D, Das P, Sarma D (2016) Present status, threats and conservation Measures of Mahseer resources in the Central Rivers of Meghalaya. SKUAST J Res 18:32–40 26. Nautiyal P (1984) Natural history of the Garhwal himalayan mahseer Tor putitora (Hamilton) II. Breeding biology. Indian Acad Sci (Anim Sci) 93:97–106 27. Nautiyal P (1985) Observations on the length-weight relationship and relative condition factor of Garhwal Himalayan Mahseer with reference to its fishery. Indian J Anim Sci 55:65–70 28. Nautiyal P (1989) Mahseer conservation: problems and prospects. J Bombay Nat Hist Soc JBOMAA 86(1)

Impacts on Reproductive Biology of Golden Mahseer Caused …

123

29. Nautiyal P (1990) Natural history of Garhwal Himalayan mahseer: growth rate and age composition in relation to fishery, feeding and breeding ecology. In: Hirano R, Hanyu I (eds) Proceedings of 2nd Asian fisheries forum, Tokyo, pp 769–772 30. Nautiyal P (1997) Migratory phenomenon of the endangered Himalayan Mahseer Tor putitora (Ham.) in relation to ecology of the Ganga river. Technical report submitted to the Ministry Of Environmental and Forests Government of India, New Delhi 31. Nautiyal P (2001) Spawning ecology and threats to Mahseer. In: Singh HR, Lakra WS, pp 291–306 32. Nautiyal P (2002) The Himalayan Mahseer: migratory pattern in relation to ecological characteristics of the Ganga river system in Garhwal Himalaya. In: Vass KK, Raina HS (eds) Highland fisheries and aquatic resource management. National Research Centre on Coldwater Fisheries (ICAR), Bhimtal, pp 172–195 33. Nautiyal P (2006) Assessment of ecological health of fish populations of the upper reaches of Ganga (Garhwal Himalaya). In: Dutta Munshi JS, Singh HR (eds) Advances in fish research. Narendra Publishing House, Delhi, pp 211–238 34. Nautiyal P (2010) Food chains of Ganga River ecosystems in the Himalayas. Aquat Ecosyst Health Manag 13(4):362–373 35. Nautiyal P, Bahuguna SN, Thapliyal RP (2001) The role of ecological factors in governing the direction, time and purpose of migration in Himalayan Mahseer Tor putitora (Ham.). Appl Fish Aquacult 1:133–138 36. Nautiyal P, Nautiyal R, Singh HR (1991) Proposed Tehri Dam and reservoir fisheries: an ecological perspective. Ecol Mt Waters 365 37. Nautiyal P, Nautiyal R, Semwal VP, Mishra AS, Verma J, Uniyal DP, Uniyal M, Singh KR (2013) Ecosystem health indicators in the Ganga Basin (Uttarakhand, India): biodiversity, spatial patterns in structure and distribution of benthic diatoms, macro-invertebrates and ichthyofauna. Aquat Ecosyst Health Manag 16(4):362–373 38. Nautiyal P, Rizvi AF, Dhasmanaa P (2008) Life-history Traits and Decadal Trends in the Growth Parameters of Golden Mahseer Tor putitora (Hamilton, 1822) from the Himalayan Stretch of the Ganga River System. Turk J Fish Aquat Sci 8(1):125–132 39. Nones M (2019) Dealing with sediment transport in flood risk management. Acta Geophys 67(2):677–685 40. Ogale SN (2002) Mahseer breeding and conservation and possibilities of commercial culture. In: The Indian experience. FAO Fisheries Technical Paper, pp 193–212 41. Pathani SS (1978) Note on secondary sexual characters in Kumaun Mahseer Tor tor (Hamilton) and Tor putitora (Hamilton) in Uttar Pradesh. Indian J Anim Sci 48(10):773–775 42. Pinder AC, Britton JR, Harrison AJ, Nautiyal P, Bower SD, Cooke SJ, Lockett S, Everard M, Katwate U, Ranjeet K, Walton S, Danylchuk AJ, Dahanukar N, Raghavan R (2019) Mahseer (Tor spp.) fishes of the world: status, challenges and opportunities for conservation. Rev Fish Biol Fish 29(2):417–452 43. Pradhan N, Wagle SK, Gurung TB (2011) Genetic variation of Mahseer (Tor putitora) populations from hatchery, lake and rivers of Nepal. In: Proceedings of the 8th national workshop on livestock and fisheries research. Nepal Agricultural Research Council (NARC), Nepal, pp 35–43 44. Raut S, Gupta N, Nautiyal P, Everard M, Kathmandu N (2019) Re-establishment of fish passage for conservation of threatened migratory species of Western Indian Himalayan river systems. River Res Appl 104 45. Richardson TL, Jackson GA (2007) Small phytoplankton and carbon export from the surface ocean. Science 315(5813):838–840 46. Scherber C, Gladbach DJ, Stevnbak K, Karsten RJ, Schmidt IK, Michelsen A, Albert KR, Larsen KS, Mikkelsen TN, Beier C, Christensen S (2013) Multi-factor climate change effects on insect herbivore performance. Ecol Evol 3(6):1449–1460 47. Sehgal KL (1999) Coldwater fish and fisheries in the Indian Himalayas: rivers and streams. In: Fish and fisheries at higher altitudes: Asia, vol 385. Food and agriculture organization of the United Nations Technical Paper, pp 41–63

124

P. Rana et al.

48. Sharma R. C., & Phil, D. (2003). Protection of an endangered fish Tor tor and Tor putitora population impacted by transportation network in the area of Tehri dam project, Garhwal Himalaya, India. Making Connections 9 49. Shrestha TK (1990) Behaviour of the golden mahseer Tor putitora (Ham.) in nature and captivity. J Freshw Biol 2(3):209–219 50. Subehi L, Fukushima T, Onda Y, Mizugaki S, Gomi T, Kosugi K, Hiramatsu S, Kitahara H, Kuraji K, Terajima T (2010) Analysis of stream water temperature changes during rainfall events in forested watersheds. Limnology 11(2):115–124 51. Tandon KK, Johal MS (1982) Impact of the introduction of common carp on the fisheries of Gobind Sagar. In: Proceedings of the 1st All India workshop on F.F.O.A., Kamal 52. Van vliet MTH, Franssen WHP, Yearsley JR, Ludwig F, Haddeland I, Lettenmaier DP, Kabat P (2013) Global river discharge and water temperature under climate change. Glob Environ Change 23(2):450–464 53. Wang X, Luo Y, Sun L, Zhang Y (2015) Assessing the effects of precipitation and temperature changes on hydrological processes in a glacier-dominated catchment. Hydrol Process 29(23):4830–4845 54. Winter TC (2007) The role of ground water in generating streamflow in headwater areas and in maintaining base flow1. JAWRA J Am Water Resour Assoc 43(1):15–25 55. Wu W-Y, Lo M-H, Wada Y, Famiglietti JS, Reager JT, Yeh PJ-F, Ducharne A, Yang Z-L (2020) Divergent effects of climate change on future groundwater availability in key mid-latitude aquifers. Nat Commun 11(1):3710 56. Yadav P, Kumar A, Hussain SA, Gupta SK (2020) Evaluation of the effect of longitudinal connectivity in population genetic structure of endangered Golden Mahseer, Tor putitora (Cyprinidae), in Himalayan rivers: implications for its conservation. PLOS ONE 15(6):e0234377

Referencing Sites 57. Brett and Groves (1979) Fish physiology—Google books. https://books.google.co.in/books? hl=en&lr=&id=CB1qu2VbKwQC&oi=fnd&pg=PA279&dq=(Brett+and+Groves+1979)+& ots=y52leIK3HP&sig=a4FZGKO5TkljmFqydeRcTj3-YDE&redir_esc=y#v=onepage&q= (Brett%20and%20Groves%201979)&f=false 58. Down to Earth. https://www.downtoearth.org.in/news/environment/about-70-hydropower-pro jects-in-himalayas-at-risk-of-quake-triggered-landslides-61766 59. Government of India, Ministry of Water Resources (2016). https://pib.gov.in/newsite/PrintR elease.aspx?relid=148503. Accessed 08 Aug 2016 60. USGS. https://www.usgs.gov/faqs/what-are-impacts-glacier-loss-other-losing-aesthetic-lan dscape-feature?qt-news_science_products=0#qt-news_science_products

Critical Mixing Depth Models for Eutrophicated Inland Water Bodies to Prevent Harmful Cyano-Bacterial Blooms Jayatu Kanta Bhuyan, Eiichi Furusato, and Subashisa Dutta

Abstract Critical depth hypothesis (CDH) is a predictive criterion for primary production of phytoplanktons by photosynthesis, not only in the ocean but also in inland water bodies like lakes and reservoirs. In this study, a review of general models for estimating the critical depth (zcr ) is studied with the selection of saturated-type Photosynthesis–Irradiance (P-I) curve equations. A comparison of the various zcr equations has revealed a remarkable quantitative difference between the zcr values. From the point of view of engineering applications, a bilinear-type P-I curve equation has been given more emphasis considering its accuracy. Finally, implications of the importance of zcr in determining the air diffuser depth in eutrophication countermeasure are discussed, such as artificial circulation methods in lakes and reservoirs. Application of Critical depth hypothesis (CDH) shall provide a framework to interpret blooms of harmful cyanobacteria which will lead to further design and management of bubble circulation countermeasures. Keywords Critical depth · P-I equations · Artificial circulation · Cyanobacteria

J. K. Bhuyan · S. Dutta Department of Civil Engineering, Indian Institute of Technology, Guwahati, India e-mail: [email protected]; [email protected] S. Dutta e-mail: [email protected] J. K. Bhuyan Biozatra Science and Technology Pvt. Ltd, Guwahati, India E. Furusato (B) Institute for Regional Co-Creation in Southern Kyusyu and the Nansei Islands, University of Kagoshima, Kagoshima, Japan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 V. Chembolu and S. Dutta (eds.), Recent Trends in River Corridor Management, Lecture Notes in Civil Engineering 229, https://doi.org/10.1007/978-981-16-9933-7_8

125

126

J. K. Bhuyan et al.

1 Introduction Dams are important hydraulic structures which are built across rivers for water management for municipal and industrial water supply, hydropower production, etc. However, dam reservoirs have a tendency to cause water quality problems. The deterioration of water quality is due to eutrophication by cyanobacterial bloom. As one of the effective methods, artificial circulation has been applied in various countries to countermeasure eutrophication problem [7, 28]. Although much scientific and technological research related to artificial circulation has been conducted, empirical applications are still being used in technical guidelines. In particular, the depth at which the diffuser should be implemented for effective measure has to be revised. Artificial circulation breaks the stratification and changes the thermocline position in reservoirs by generating circulatory flow using bubble plume. This is done by regulating the mixing depth which is the central method for preserving water quality through a decrease in the abundance of phytoplankton, including cyanobacteria [11, 28]. The relationship between mixing depth and primary production by photosynthesis is one of the important habitat conditions for phytoplankton. Prior to designing of diffuser depth to control algal bloom it is important to quantify the depth at which it has to be implemented. The evaluation procedure is based on a mathematical model for both nutrient depletion and light limitation. For discussing the light-limiting processes, a classical concept known as critical depth (zcr ) plays an important role in phytoplankton dynamics. Basically, zcr is an oceanographic concept which was put forward by [39] defining as the maximum mixing depth for positive net primary production of phytoplankton in the surface mixed layer. A schematic diagram of aquatic ecosystem for oceans and lentic water bodies is depicted in Fig. 1. Figure 1 shows us the vertical profile of photosynthesis and respiratory rate of phytoplankton. In the upper layer, there is net positive growth of the species due to its exposure to light. As it moves down production is decreased as light is attenuated and there will be a depth where the amount of oxygen produced will just be equal to the oxygen consumed. This depth is known as compensation depth. The production will eventually decrease leading to a negative growth rate where total integral net photosynthesis is balanced by respiratory losses, termed as zcr . Thus, zcr is the deepest depth to which a phytoplankton species can be mixed so that total production equals total respiration. Sverdrup critical depth model [39] has been applied, adapted, and tested across many aquatic systems. In particular, the concept of zcr has been explored extensively for oceanography and remote sensing applications [33]. Although not many studies have been conducted for inland water bodies, its application has been used in Environmental Protection Agency (EPA) Lorenzen model to assess mixing depths that produce light-limiting conditions [23]. This concept plays an important role in reservoir water quality management, as it provides a basis for setting air discharge depths for bubble circulation measures [12] by altering the mixing depth (zm ).

Critical Mixing Depth Models for Eutrophicated Inland …

127

Fig. 1 An illustration of aquatic ecosystem in oceans and lentic water bodies

In spite of the importance and significance which serves as a basis for understanding bloom dynamics, Sverdrup’s explicit assumptions [39] contain a very fundamental and extremely significant problem being linear relationship between photosynthesis and light intensity (P-I). In formulizing an analytical form of zcr equations by other researchers as well this issue was mentioned. Platt et al. [30] proposed a new zcr equation based on exponential model [29]. Furusato et al. [10] and Huisman [14] also proposed another equation based on Monod type due to mathematical simplicity. Recently, a new developed model has been formulated from the point of view of engineering application with a bilinear-type P-I equation [12]. This paper reviews several zcr equations and a comparison is shown to provide a scientific basis for the design of a bubble circulation system as an engineering method to prevent harmful cyanobacteria growth. More emphasis to select a saturated P-I curve is made as the basis for finding the zcr . This is due to the fact that harmful cyanobacteria such as Microcystis and Anabaena, which can cause algal bloom, have buoyancy control function by the gas vesicles [44]. As they are prone to high light conditions they tend to propagate in the reservoirs where current is stagnant in the surface part under stable density stratification with a shallow zm .

128

J. K. Bhuyan et al.

2 Photosynthesis–Irradiance (P-I) Equations 2.1 P-I Parameters and Shape of the P-I Curve Photosynthetic rate and light intensity (P-I) models are widely used to determine the algal productivity. There are several P-I curve equations representing the relationship between photosynthetic rate and light intensity. The first model was proposed by Blackman in 1905 followed by other researchers depending on different mathematical forms. Table 1 shows the P-I equations based on both saturation and photoinhibition models. Table 1 Different formulations of photosynthesis irradiance equations with both saturation and photoinhibition models Abb*

Ba35

Equations  I α : I < IS P= Pm : I ≥ I S   I P = Pm I +I S

Sm36

P = Pm

Sv53

P = Iα

Bk05



St62

  I I 2 +I S2

⎡

P = Pm ⎣ I Iopt e ⎡

 1−

I

⎤

Iopt ⎦

Wb74

⎤ −I/ I S ⎦ P = Pm ⎣1 − exp

JP76

P = Pm tanh

P = Pm ⎣



I IS

Blackman [4]

Rectangular hyperbola (Monod equation)

Baly [1]

Modified rectangular hyperbola

Smith [35]

Linear

Sverdrup [39]

Exponential

Steele [38]

Exponential

Webb et al. [46]

Hyperbolic tangent

Jassby and Platt [16]

⎤ I

1

⎦

P= ⎡ Pm

Bilinear



(I b +I Sb ) /b

Pe80

References



⎡ Bn79

Form

  ⎤ −β I/ − I/I P S m ⎣1 − e ⎦×e

Abb*: Abbreviation of each equation

Generalized Bannister [2] rectangular hyperbola

Exponential

Platt et al. [29]

Critical Mixing Depth Models for Eutrophicated Inland …

129

Table 2 Symbols and units Symbols

Descriptions

Units

I

Irradiance (photosynthetically available radiation: PAR)

µE m−2 s−1

I0

Irradiance at surface

µE m−2 s−1

Is

Irradiance indicating onset of light saturation of photosynthesis

µE m−2 s−1

I*

Normalized irradiance (I/I s )

Dimensionless

P

Photosynthesis rate

d−1

Pm

Light-saturated maximum photosynthesis rate

d−1

P*

Normalized photosynthetic rate (P/Pm )

Dimensionless

α

Initial slope of P-I curve, Pm /I s

m2 s µE−1 d−1

β

Photoinhibition parameter

m2 s µE−1 d−1

z

Depth

m

zm

Mixed depth

m

zeu

Euphotic depth

m

zIs

Depth of underwater irradiance corresponding to I s

m

zcr

Critical depth

m

ε

Attenuation coefficient

m−1

R

Total losses

d−1

DINP

Depth integral net photosynthetic rate

m d−1

The notation and the overview of the equations used to generate these curves are given in Table 2. The initial slope is same for the P-I curves at low light intensity (α). The slope is represented as α (=Pm /I s ), I s [17] is the saturation onset parameter point where the initial slope coincides (I opt for Steele’s model) with Pm (the maximum photosynthesis rate in the absence of photoinhibition). The initial slope is also α, when photoinhibition is present, which means that the [29, 38] equations can be written in terms of Pm and α, along with a single photoinhibition parameter (β) for the later. An assumption has been made for these P-I equations, and their parameters (Pm , α, I s , and β) do not vary with depth or throughout the day [25] because these are biological and physiological parameters. Another way to formulate the models was proposed by [40] with the parameter I s . I s being the light saturation constant is independent of biomass and is frequently used for comparison of photoaccimilation of algae [13]. However, it is a sensitive parameter because the change in I s changes the nature of the curve. This paper intends to give an overview of the effects of saturated P-I equations on critical depth. Figure 1 shows the normalized P-I relationship curve according to the saturated-type P-I equations. From Fig. 2, it can be observed, the difference in the characteristics of P-I curves under low light intensity conditions being subsurface layer around the euphotic depths. In any individual experiment, differences in the error variance between some of the curves may not be significant. But among all the models the most widely used in all phytoplankton ecology is Baly’s equation [1]. Because of the frequent

130

J. K. Bhuyan et al.

Fig. 2 Normalized general form of the P-I equations from Table 1. There are six saturated models shown here which comprises seven models. Bannister [2] equation reduces to the equation of [1] for b = 1, to the equation of [35] for b = 2

application of this function, in analogy with Michaelis-Menten enzyme kinetics, to describe nutrient uptake by phytoplankton, it became more convenient for its use to describe other phenomena, including photosynthesis [16].

3 Critical Depth Critical depth as defined by [39] is a surface mixing depth at which phytoplankton community growth is precisely matched by losses of phytoplankton biomass within this depth interval. There are numerous studies and theories that have been carried out during the past decades to find the critical depth [33]. As stated by [39], the depth of a mixing surface layer must be less than the critical depth for spring bloom to occur. When the mixing depth exceeds critical depth, then the stability of the water body will break and mixing of the water will bring the phytoplankton population below the compensation depth where photosynthesis is impossible, and it will be unfavorable for the overall population to increase in biomass. However, when mixing depth is shallower than critical depth, enough of the phytoplankton remains above the eutrophic or the compensation depth to give the community a positive net growth rate and causes spring blooms and growth of cyanobacteria. Many researchers and scholars also explained the critical depth in their own way and have their own point of view.

3.1 DINP and zcr for 1D Analysis Models of daily phytoplankton production and growth are based on either idealized relationship between net photosynthesis and irradiance or measurements of net primary production. The photosynthesis—irradiance equations from the various P-I models can be used to estimate net primary production by calculating photosynthetic

Critical Mixing Depth Models for Eutrophicated Inland …

131

rates corresponding to changes in solar irradiance, integrating these photosynthetic rates and then subtracting the daily respiratory loss associated with cell maintenance and growth. Thus, in general, if the rate of production of biomass of phytoplankton at different depths is integrated then we can get the amount of growth rate in units of time (per day). To clarify the effects of differences among P-I equations, an index, the depth integral net photosynthesis (DINP), is used which defines the integration or addition of all the productivity in the mixing layer depth at different light levels that are experienced by the phytoplankton (Fig. 3). DINP changes with zm so that at a certain depth it reaches zero. The depth corresponding to a DINP of zero is called the critical depth (zcr ). Thus, by using the DINP equation, zcr can be obtained based on the relationship that is shown in Fig. 3. When zm is shallower than zeu , being positive, net photosynthesis, DINP, increases with increasing zm . On the other hand, when zm is deeper than zeu , DINP decreases with increasing zm . Thus, zcr is the depth of zm when DINP equals zero. DINP is balanced by respiratory losses then the depth is called the zcr when mixed depth (zm ) is same as zcr . So, in this study, firstly DINP has been calculated from several P-I equations and is equated to 0 which gives the zcr equations.

Fig. 3 Diagrammatic figures of a the relationships between photosynthetic rates and light intensity, b vertical profiles of underwater light intensity, photosynthetic rates, and respiration (loss) rate with light saturation layer (LS) above zIs and the light limitation layer (LL) below. c Variation in DINP (depth integral net photosynthetic rate) with (zm ) (mixing depth) (from Fig. 1 of [12])

132

J. K. Bhuyan et al.

3.2 DINP and zcr Equations For the comparison of numerous DINP equations reviewed in this paper, the following depth-integrated equations obtained from saturation types, such as bilinear [4], rectangular hyperbola [1], linear [39], exponential [46], and tangential hyperbolic [16] P-I equations are used (Table 1). We have listed them in the proposed chronological order (Table 3 and Fig. 4), along with the P-I equations used. Note that the value used here was selected with consideration for the characteristics of the water body such as eutrophication. Thus, we consider mainly the saturated-type P-I curves for the comparison. Figure 4 shows DINP profiles of the primary production index for the saturated P-I equations (Table 1) based on the selected parameters (Table 4). Even for the Table 3 DINP equations based on saturated P-I equations P-I types

Wb74

DINP       Pm I0 I0 −εz m e ln + 1 − − R · z m ** ε Is IS    I S +I0 Pm − R · zm ε ln I S +I0 e−εzm  α I0  −εz m − R · z m ε 1−e ⎡  ⎤ −εz

− I0 e  zm I s ⎦dz − R · z m Pm 0 ⎣1 − e

JP76

Pm

Bk05 Ba35 Sv53

 zm 0

 −εz  tanh I0 e Is dz − R · z m

References Furusato et al. [12] Furusato et al. [10] Bhuyan [3] Bhuyan [3]

Bhuyan [3]

Note **This DINP equation for Bk05 P-I curve can be applied only for the condition zm > zIs . If zm is shallower deeper than zIs , DINP = (Pm − R)zm

Fig. 4 DINP Vertical profiles of primary production indexes for selected saturated P-I curves with respect to mixing depths

Critical Mixing Depth Models for Eutrophicated Inland … Table 4 Analytical conditions [12]

Symbol

133

Value

Unit

References

I0

1000

µEm−2 s−1

Lalli and Parsons [18]

Is

250

µEm−2 s−1

Rey [31]

Pm

1.0

d−1

Rey [31]

α

0.004

m2 s µE−1 d−1

Pm /I s

ε

0.5

m−1

Kirk [17]

0. 1

d−1

10% of Pm

R

same underwater condition DINP profiles of the P-I equations differ. Especially the difference among the Ba35, Bk05, and Sv53 models is remarkable (Fig. 4). Due to the high photosynthesis rate in the surface layer, Sv53 zcr is deeper than other P-I curves (Bk05, Ba35, Wb74, JP76), which are the light saturation types. Moreover, the discrepancy between Sv53 and Ba35 is larger than two times the original value even for the same parameter values used in this study. Depth integral solution based on P-I equations such as Wb74 and Pe80 has no closed form causing problems in parameter estimation. For instance, numerical expressions are available for Pe80 equation with a series solution [30] which takes a lot of time for computation even on a fast computer as it is an iterative process [32]. On the other hand, JP76 equation although has high accuracy among the saturated P-I equations, an analytical solution cannot be obtained due to the restriction for the range of I 0 [16]. Thus, we are obliged to use numerical integration for Wb74 and JP76 P-I equations (Fig. 4). To test the quantitative aspects for the depth integration, P-I equation proposed by Bk05 is more appropriate as the analytical form can be easily estimated [12]. Table 5 gives the critical depth equations from the DINP form. The zcr equation for Bk05 includes a term similar to Sv53 because the same linear P-I equation is used for Bk05 types when zm is deeper than zIs . Sv53 is based on linear-type curve equation and is not a light saturation type, so there is no concept of maximum photosynthesis rate (Pm ). This is one of the differences between the original zcr equation and the other equations. Moreover, researchers such as Nelson and Smith [26] reformulated [39] equation by using recent optical and physiological information which is applied to data from the Southern Ocean. Another simplified form of [39] equation that has been used by various researchers is presented in [18]. However, from Fig. 4 we can observe that the Sv53 model overestimates the critical depth by two to three times, Table 5 zcr equation of selected P-I equations

P-I types

zcr equations

Bk05

z cr Is /I0 [ln(I0 /Is )+1]−e−εz cr

Ba35 Sv53



I0 IS 

ln



Pm εR z cr

Is +I0 Is +I0 e−εz cr

z cr 1−e−εz cr

=

 I0 α εR

References

=

Pm εR

=

Furusato et al. [12]

Furusato et al. [10] Sverdrup [39]

134

J. K. Bhuyan et al.

and this difference should not be neglected. Thus, in future application of zcr model with a linear P-I equation should be avoided based on its inaccuracy for estimating the zcr [3, 12].

4 Discussions 4.1 Limitation of the Study This study used the same assumptions as Sverdrup’s zcr model with the exception of the P-I equation. The general theory, Beer–Lambert’s law (I(z) = I 0 e –εz ), is considered for underwater light intensity profile. Also, this manuscript gave a review focusing on P-I equation types among the seven assumptions of Sverdrup zcr model [39], the simplest assumption for light attenuation underwater is used and is assumed to depend on only “ε”. In this study for estimating zcr integration of the P-I curves was done. However, the integration should be calculated not only over depth but also over time due to temporal changes in surface irradiance over a day, noon, and at night [30, 40, 45]. Since this study focuses on the difference in zcr due to different P-I curves from a relative point of view, integration is done only over depth. Also, zcr is the depth within the mixed depth, since zcr can be defined as the maximum mixing depth of a phytoplankton species. In recent years, there are intensive research conducted on the assumptions of mixed depth [8, 9, 15, 19]. For discussing Sverdrup’s zcr model, estimation of mixed depth by using vertical profiles of water temperature and/or density is not fully appropriate [9, 43]. Franks [9] explained a difference between “mixed” and “mixing” depth and finally concluded that the usefulness of Sverdrup’s zcr model must be studied with enough attention to the assumptions considered. Moreover, hyperbolic tangent equation given by [16] has the highest accuracy in almost all the species followed by [4, 12]. But the limitation is the integration of JP76 equation which is very complex from analytical point of view. While comparing all the P-I equations with Bk05 equation it is found that JP76 equation almost resembles similar characteristics. The only difference is in the low light condition around saturation irradiance (I s ). But one of the concerns of [4] P-I equation is that it shows sharp break at the I s [37]. For mathematical simplicity [4], equation has been implied in [12] for estimating zcr .

4.2 Importance of zcr Concept in Practical Engineering Generally, the concept of zcr has been studied in oceanography in various aspects such as with initiation of spring bloom [5, 6, 24, 34, 36, 39, 40] and remote sensing [5, 6, 27, 34, 36]. Its application and importance have been increasing from the past decades [33]. But, only a few studies have been conducted on stagnant water bodies such as

Critical Mixing Depth Models for Eutrophicated Inland …

135

lakes [40, 42, 43]. One important characteristic of lakes and reservoirs compared to ocean-size water bodies is the possibility of regulating the mixing depth artificially by using bubble plumes [7, 23]. Therefore, in the future not only for scientific interest, but also from a practical engineering point of view, it is necessary to focus on the critical depth concept as applied to limnology or water environmental engineering. Theoretically, zcr differs depending on the physiological traits of each phytoplankton [20–22]. Generally, harmful bloom-forming cyanobacteria (such as Microcystis) adapt to high light conditions because they can remain in the surface shallow layer depth due to regulation of cell buoyancy by gas vesicles [44]. Therefore, it is possible that the zcr for harmful cyanobacteria is shallower than that of other nonbuoyant phytoplanktons. Phytoplankton with a large I S value is a result of adaptation to surface layer light conditions and its utilization of red light as a relatively long wavelength light, the attenuation coefficient for red light is generally larger than other wavelengths [11, 17]. These above properties also result in a shallower zcr depth. The phytoplanktons such as bloom-forming cyanobacteria that inhibits these certain characteristics will play important roles in the development of design and management methods for artificial circulation by estimating its zcr [12]. Finally, for estimating zcr for actual water body, various measurements should be discussed in detail such as temperature profile, production rate of species, mixing depth condition, and attenuation coefficient. Thus, in the future, an efficient engineering system based on the accurate estimation of zcr for harmful cyanobacteria can be constructed especially for inland water bodies.

5 Conclusions Although several P-I equations and DINP values are calculated, each describes the curvature in a different way, according to its nature. The basic assumption of a linear P-I equation for Sverdrup’s zcr model was trivial. But in reality, this assumption overestimates the DINP compared to other P-I models. By comparing with other P-I models, we can estimate the range of difference among the P-I curves. Even though the P-I curve equation depends on the physiology of phytoplankton species, it plays an important role in estimating zcr . Thus, the model based on [4] P-I model should be useful for artificial circulation application as the estimation method for habitat condition of phytoplankton including harmful cyanobacteria. Moreover, we have not overlooked the potential of integration over time and a more accurate analytical form based on hyperbolic form which are the themes of work in progress.

136

J. K. Bhuyan et al.

References 1. Baly ECC (1935) The kinetics of photosynthesis. Proc R Soc London B: Biol Sci 117(804):218– 239 2. Bannister TT (1979) Quantitative description of steady state, nutrient-saturated algal growth, including adaptation. Limnol Oceanogr 24:76–96 3. Bhuyan JK (2017) Development of new methods for estimating Habitat conditions in Eutrophicated water body to prevent Harmful Cyano-Bacterial Blooms (Maser Thesis). Brandenburg Technological University (BTU), Cottbus, Germany 4. Blackman FF (1905) Optima and limiting factors. Ann Bot 19(2):281–296 5. Chiswell SM (2011) Annual cycles and spring blooms in phytoplankton: don’t abandon Sverdrup completely. Mar Ecol Prog Ser 443:39–50 6. Chiswell SM, Bradford GJ, Hadfield MG, Kennan SC (2013) Climatology of surface chlorophyll a, autumn–winter and spring blooms in the southwest Pacific Ocean. J Geophys Res 118(2):1003–1018 7. Cooke GD, Welch EB, Peterson S, Nichols SA (2005) Restoration and management of lakes and reservoirs, 3rd edn. CRC Press, Taylor & Francis Group, Boca Raton, USA, p 616p 8. Enriquez RM, Taylor JR (2015) Numerical simulations of the competition between wind-driven mixing and surface heating in triggering spring phytoplankton blooms, International Council for the Exploration of the Sea. J Mar Sci 72(6):1926–1941 9. Franks PJS (2015) Has Sverdrup’s critical depth hypothesis been tested? Mixed layers versus turbulent layers. International council for the exploration of the sea. J Marine Sci 72(6):1897– 1907 10. Furusato E, Asaeda T, Sudo R (2003) Theoretical analysis of optical and hydraulic conditions of cyanobacteria growth based on the characteristics of antenna pigments absorption-underwater light regime and mixed depth. J Jpn Soc Water Environ 26:277–284 (In Japanese with English Abstract) 11. Furusato E, Asaeda T (2004) The relation between the type of antenna pigments of dominant cyanobacteria and the ambient stratification condition in reservoirs. International symposium on river and lake environments. Rep Res Edu Ctr Inland Water Environ 2:97–103 12. Furusato E, Bhuyan JK, Marteinssen M, Nixdorf B, Imamoto H (2019) Critical depth model (Modified Sverdrup equation)—Design method for bubble circulation. J JSCE 7:185–192 13. Henley WJ (1993) Measurement and Interpretation of photosynthetic light—Reponse curves in algae in the context of photoinhibition and diel changes. J Phycol 29:729–739 14. Huisman J (1999) Population dynamics of light-limited phytoplankton: microcosm experiments. Ecology 80(1):202–210 15. Huisman J, Oosteen PV, Weissing FJ (1999) Critical depth and critical turbulence: two different mechanisms for the development of phytoplankton blooms. Limnol Oceanogr 44(7):1781–1787 16. Jassby AD, Platt T (1976) Mathematical formulation of the relationship between photosynthesis and light for phytoplankton. Limnol Oceanogr 21(4):540–547 17. Kirk JTO (2011) Light and photosynthesis in aquatic ecosystems, 3rd edn. Cambridge University Press, Cambridge 18. Lalli CM, Parsons TR (1997) Biological oceanography: an introduction, 2nd edn. Elsevier Butterworth Heinemann, Oxford, England 19. Levy M (2015) Exploration of the critical depth hypothesis with a simple NPZ model. International council for the exploration of the sea. J Mar Sci 72(6):1916–1925 20. Lewandowska AM, Striebel M, Feudel U, Hillebrand H, Sommer U (2015) The importance of phytoplankton trait variability in spring bloom formation. International council for the exploration of the sea. J Mar Sci 72(6):1908–1915 21. Lindemann C, Backhaus JO, St John MA (2015) Physiological constraints on Sverdrup’s Critical Depth Hypothesis: The influences of dark respiration and sinking. International Council for the Exploration of the Sea. J Mar Sci 72(6):1942–1951 22. Lindemann C, St. John M (2014) A seasonal diary of phytoplankton in the North Atlantic. Front Mar Sci1(37):1–6

Critical Mixing Depth Models for Eutrophicated Inland …

137

23. Lorenzen M, Mitchell R (1973) Theoretical effects of artificial destratification on algal production in impoundments. Environ Sci Technol 7(10):939–944 24. Lucas LV, Cloern JE, Kosef JR, Monismith SG, Thompson JK (1998) Does the Sverdrup critical depth model explain bloom dynamics in estuaries? J Mar Res 56(2):375–415 25. McBride GB (1992) Simple calculation of daily photosynthesis by means of five photosynthesis-light equations. Limnol Oceanogr 37(8):1796–1808 26. Nelson DM Jr, Smith WO (1991) The role of light and major nutrients. Limnol Oceanogr 36(8):1650–1661 27. Obata A, Ishizaka J, Endoh M (1996) Global verification of critical depth theory for phytoplankton bloom with climatological in situ temperature and satellite ocean color data. J Geophys Res 101(C9):20657–20667 28. Pastorok RA, Lorenzen MW, Ginn TC (1982) Environmental aspects of artificial aeration and oxygenation of reservoirs: a review of theory, techniques and experiences, Technical report E-82-3. Environmental Protection Agency (EPA), United States 29. Platt T, Gallegos CL, Harrison WG (1980) Photoinhibition of photosynthesis in natural assemblages of marine phytoplankton. J Mar Res 38:687–701 30. Platt T, Bird DF, Sathyendranath S (1991) Critical depth and marine primary production. Proc R Soc Lond B: Biol Sci 246(1317):205–217 31. Rey F (1991) Photosynthesis—Irradiance relationships in natural phytoplankton of the barents sea. Polar Res 10(1):105–116 32. Richter O, Sondegrath D (1990) Parameter estimation in ecology: the link between data and models. VCH, Wenheim, Germany 33. Sathyendranath S, Ji R, Browman HI (2015) Revisiting Sverdrup’s critical depth hypothesis. International council for the exploration of the sea. J Mar Sci 72(6):1892–1896 34. Siegel DA, Doney SC, Yoder JA (2002) The North Atlantic spring phytoplankton bloom and sverdrup’s critical depth hypothesis. Science 296:731–733 35. Smith EL (1936) Photosynthesis in relation to light and carbon dioxide. Proc Natl Acad Sci 22(8):504–511 36. Smith WO Jr, Jones RM (2015) Vertical mixing, critical depths, and phytoplankton growth in the Ross Sea. International council for the exploration of the sea. J Mar Sci 72(6):1952–1960 37. Singh BN, Lal KN (1935) Limitations of Blackman Law of Limiting Factors and Harder’s Concept of Relative Minimum as applied to Photosynthesis. J Plant Physiol 10(2):245–268 38. Steele JH (1962) Environmental control of photosynthesis in the sea. Limnol Oceanogr 7(2):137–150 39. Sverdrup HU (1953) On conditions for the vernal blooming of phytoplankton. J du Conseil/Conseil Perm Int pour l’Exploration de la Mer 18(3):287–295 40. Talling JF (1957) The phytoplankton population as a compound photosynthetic system. New Phytol 56:133–149 41. Taylor JR, Ferrari R (2011) Shutdown of turbulent convection as a new criterion for the onset of spring phytoplankton blooms. Limnol Oceanogr 56(6):2293–2307 42. Tilzer MM (1987) Light-dependence of photosynthesis and growth in cyanobacteria: implications for their dominance in eutrophic lakes. NZ J Mar Freshwat Res 21(3):401–412 43. Townsend DW, Keller MD, Sieracki ME, Ackleson SG (1992) Spring phytoplankton blooms in the absence of vertical water column stratification. Lett Nat 360(6399):59–62 44. Walsby AE (1994) Gas vesicles. Microbiol Rev 58(1):94–144 45. Walsby AE, Yacobi YZ, Zohary T (2003) Annual changes in the mixed depth and critical depth for photosynthesis by Aphanizomenon ovalisporum that allow growth of the cyanobacterium in Lake Kinneret Israel. J Plankton Res 25(6):603–619 46. Webb WL, Newton M, Starr D (1974) Carbon dioxide exchange of Alnus rubra. Oecologia 17(4):281–291

River Modelling and Management

Significance of Representing Buildings in Urban Flood Simulations R. Reshma and Soumendra Nath Kuiry

Abstract The population explosion and industrialization around the world have escalated the rate of urbanization and settlements. This has altered the natural terrain, physiography, and balance of the environmental systems leading to large variations in the occurrence, frequency, and impact of natural disasters such as floods, droughts, and landslides. The damages and losses incurred by these disasters have led to finer and integrated research worldwide. Among these, urban floods are the most dangerous ones. Various urban features such as buildings, their alignment, road, and drainage networks influence the flow pattern within an urban area. Experimental and numerical model studies on urban floods are the primary resources for better understanding, managing, and damage mitigation of the event. The representation of the various urban features in the model studies is a necessary factor to improve the accuracy of the simulations. The paper analyses the significance of the representation of buildings in urban flood simulations. The Chennai (India) flood of 2015 is considered as the case study for the purpose. The currently available datasets for the built-up area are also briefed herein. The inclusion of buildings incorporates the noflow regions in the field and thus confines the flood flow through the available flow path precisely as in the real-time scenario. The obstructions and deviations induced by built-up area can hence be reproduced, thereby giving a pragmatic view of the real-world urban flood events. Keywords Urban flood simulation · Building representation · Flood flow pattern · Chennai flood

1 Introduction Urban floods have become one of the most frequent and hazardous natural disasters of time. The losses incurred in terms of life, property, and money are quite huge and significant [1]. Urban flood damage mitigation, management, and prevention are R. Reshma · S. N. Kuiry (B) Department of Civil Engineering, Indian Institute of Technology Madras, Chennai 600036, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 V. Chembolu and S. Dutta (eds.), Recent Trends in River Corridor Management, Lecture Notes in Civil Engineering 229, https://doi.org/10.1007/978-981-16-9933-7_9

141

142

R. Reshma and S. N. Kuiry

now a primary concern for disaster management authorities worldwide. Flood hazard zoning, implementation of land use guidelines, and generation of flood inundation maps are the preliminary steps adopted for flood damage mitigation and management [6]. Numerical simulations are looked up as the primary source for the purpose. Commercial software such as MIKEFLOOD and FLO-2D and free software such as TELEMAC and HEC-RAS are usually adopted for these simulations, HEC-RAS being the most widely used one [3]. HEC-RAS stands for Hydrologic Engineering Center River Analysis System. Though the package was initially developed by the Hydrologic Engineering Center (HEC) of the Institute for Water Resources (IWR), United States (US), for US Army Corps of Engineers back in 1995, it is freely accessible for public users including the research community. Recently, the latest version 6.1 was released. HEC-RAS comprises provision for one dimensional (1D), two dimensional (2D), and coupled 1D-2D steady and unsteady river hydraulic simulations, inundation mapping, water quality modeling, and sediment transport simulations [13]. The software is quite user friendly, easy to execute and has a good graphical user interface. It has a built-in geospatial module—RAS mapper, to generate, visualize and modify the geometrical details of the hydraulic model including terrain data. Numerical model studies have been proved to be a sturdy technique for the simulation of flood events [1]. However, the process is not as simple as it sounds due to the complexities involved with respect to urban terrain and the associated micro-features [1, 4]. Besides, the accuracy and precision of the developed maps are a function of the extent, level, and complexities of the incorporated data. The inclusion of microfeatures such as buildings, roads, drainage networks, and other urban features is highly recommended to improve the accuracy of the results [4, 7]. Though the use of bare earth digital elevation models (DEM) in numerical simulations also provide flood inundation extents, the results would be approximate as the overlying urban features are not incorporated. The existence of the buildings creates additional noflow paths in the urban areas, which will not be replicated in the simulations when a bare-earth DEM is used. In a real-time flood event, floodwater flows around the urban structures rather than through the space [2]. Besides, the presence of buildings and other urban features reduces the available flow width, increasing the inundation depth. Hence, in order to capture and reproduce the evolution of real-time flood flow patterns, one needs to incorporate the various micro-features with the minute details. There are many open-source and commercial sources for the building footprint data on a global scale (e.g., Open Street Map (OSM Buildings, Open Buildings and Building Footprints (Bing Maps)). The building footprints of the area considered can be easily obtained from any of these sources. With the advent of low-cost photogrammetry and image processing techniques, the availability of these data to common public has become much easier. Researchers have identified different ways to represent the building data in the hydraulic or hydrologic studies. Building hole (BH), building block (BB), building porosity (BP), and building resistance (BR) methods are the most commonly adopted ones [5, 11]. BH method adopts a deletion approach wherein the mesh cells are generated such that the cells corresponding to the building footprints are not built,

Significance of Representing Buildings in Urban Flood Simulations

143

i.e., in BH method holes represent the building areas with no slip condition being imposed along the building boundaries [10]. In the case of BB method, terrain data in the building footprints are raised to roof height and hence the buildings appear as blocks [10]. For BR method, a higher Manning’s roughness coefficient is assigned to the built-up areas, whereas in BP method, instead of incorporating the actual building geometries, porosity or drag coefficient is employed to introduce the building impact [5]. The objective of this study is to assess the significance of the building data in urban flood simulations. BB method is investigated herein to incorporate the building data in the urban flood simulations.

2 Study Area The study considers the capital city of Tamil Nadu (India)—Chennai. It is a lowlying coastal city on the North-Eastern side of the state. The city is highly urbanized and densely populated, with many religious, educational, cultural, and administrative institutions and infrastructure. The Coromandel Coast of the Bay of Bengal forms the eastern boundary for the city. Besides, it lies downstream of the three major rivers of Tamil Nadu—Adyar, Cooum, and Kosathalayar. The topography of the area is nearly flat, the majority being built-up land. The city has a hot-humid climate with tropical wet-dry seasons. The monsoon season falls from September to December, with an average annual reception of 1400 mm rainfall. The city had lost many of its waterbodies and green land owing to urbanization, industrialization, and the associated environmental pollution and degradation. The current study focuses on a smaller urban area on the downstream floodplains of the Adyar River as shown in Fig. 1. The red colored shaded region represents the urban area downstream of the Adyar basin, considered for the current study.

3 Methodology Urban flood simulations are the primary source for the generation of probable flood inundation maps and flood hazard zoning. The accuracy and precision of the developed maps are a function of the extent, level, and complexities of the incorporated data. An attempt is made to analyze the significance of building representation and the available sources of the building data. Chennai city of Tamil Nadu, India, is considered for the present study. Chennai is a highly urbanized coastal city. It is subjected to frequent disastrous flooding due to a combination or individual scenarios of river overflow, coastal flooding, and pluvial flooding (Gupta and Nair 2010; Padmanabhan et al. 2017). The research is carried out in three parts—(i) simulation of flooding pattern using bare earth DEM, (ii) simulation of flooding pattern using the DEM with the building data punched in, and (iii) analysis of influence of building data in flood inundation simulations.

144

R. Reshma and S. N. Kuiry

Fig. 1 Study area map

3.1 Urban Flood Simulations Hydraulic simulation software HEC-RAS is used in the current study for urban flood simulations. Flood hydrograph pertaining to Chennai flood of 2015 as reported by Nithila et al. [3] is considered for the purpose. The simulation is carried out using different terrain data. Initially, a bare-earth DEM is used for the simulation, wherein a 2 m resolution LiDAR DEM [8] of the city is adopted. Further, the DEM is overlaid with the building data, as obtained from the open-source platform—OSM Buildings, using geospatial tools. The hydraulic simulation is carried out for the second time for the same flood event using this modified DEM in which buildings are raised by 5 m [14] following the BB method. For both the scenarios, a uniform Manning’s roughness value 0.03 m−1/3 s was used for the area considered [3, 12]. Tidal data and flow hydrograph pertaining to the 2015 flood event as reported and used by Nithila et al. [3, 8] is provided as the downstream boundary and upstream boundary conditions, respectively. A grid size of 30 m is chosen for the simulations, adopting the sub-grid approach in HEC-RAS, since a grid size equivalent to the resolution of base DEM selected (i.e., 2 m) leads to computationally cumbersome process with a very high execution time [8, 13]. Simulations are run from November 01 to December 13 midnight, 2015, with a variable time step auto-controlled by Courant number option to ensure stability of the model. A range of 0.5–1 is given for the adoptable Courant number [13]. The flood inundation extent and depth are analyzed closely

Significance of Representing Buildings in Urban Flood Simulations

145

for the smaller urban area considered to assess the significance of building data. The representation of building data in the urban flood simulations plays a significant role in capturing the flood flow pattern.

3.2 Building Data Repositories Advancements in remote sensing and artificial intelligence have enabled the availability of global scale building footprint data at our fingertips these days. Both opensource and commercial datasets are available currently—OSM Buildings (https://osm buildings.org/data/), Open Buildings (https://sites.research.google/open-buildings/), 3D Buildings (https://3dbuildings.com/data/#11.4/13.067/80.1813), and Building Footprints (Bing Maps) are to name a few. OSM Buildings is an open-source repository of building footprints made available by Microsoft. Building footprint data is available on a global scale in OSM Buildings. Open Buildings, on the other hand, comprises the data for African continent alone, and is a commercial repository. Bing Maps and 3D Buildings, though comprise data for world, are commercial data hubs.

4 Results and Discussion Chennai flood of 2015 is simulated using the HEC-RAS model. Two different terrains are used for the study, as mentioned in the methodology—the bare-earth DEM and the one with building data punched-in as shown in Figs. 2 and 3, respectively. The calibration and validation of the model was performed with reference to the simulation

Fig. 2 Bare-earth DEM

146

R. Reshma and S. N. Kuiry

Fig. 3 DEM with building data punched-in

results reported in [3, 8] owing to lack of actual flood flow hydrograph corresponding to 2015 flood event. The simulation results are assessed over a smaller urban area (red shaded region in Fig. 1) to depict the significance of building data in urban flood simulations. Figures 4 and 5 show the flood inundation maps for the urban area (yellow circle in Figs. 4 and 5) obtained from the hydraulic simulations using bare-earth DEM and DEM with building data punched-in, respectively. To have a precise understanding, the DEM with building data is kept as the base map for both

Fig. 4 Flood inundation map using DEM without building data

Significance of Representing Buildings in Urban Flood Simulations

147

Fig. 5 Flood inundation map using DEM with building data

the figures. It is evident from Fig. 4 that when a bare-earth DEM is used, the floodwater moves according to the terrain characteristics alone, without considering the actual built-up area that would be present in an urban settlement. The water is even seen to flow over the buildings though the flow depth is in the range of 0.1–1 m. The minimum height of a single-storey building itself comes to around 3 m. Thus, the results present an unrealistic picture of the urban flood. i.e., the inundation maps obtained from such a simulation is approximation of real-world scenarios. On the other hand, when a building data punched-in DEM is used as the base terrain map, the flow evolves considering both the topographical characteristics and the building data. As seen in Fig. 5, the flow, instead of moving through the buildings, propagates around the buildings and extends further to a larger area as in a real-time flood event. Since the flow volume remains constant, the inundation depth increases to accommodate the flow in the smaller available space. Thus, the incorporation of building data improves the precision of the developed inundation maps and hence provides a pragmatic view of the real-time flood event. Velocity of floodwaters is another important factor that defines the intensity of damages that the event can cause. Evolution of flood flow velocity pattern is also highly influenced by the urban features as shown in Figs. 6 and 7. Figure 6 shows the velocity vector map generated using a bare earth DEM while Fig. 7 represents the map with building data incorporated. Inclusion of building data enables the model to capture the actual available flow path and width, thereby generating a velocity vector map that better resembles the real-time scenario. Also, with the decrease in flow area, corresponding increase in velocity is found so as to accommodate the same flow. The variation in flow depth and velocity observed with the incorporation of building data is quantitatively presented in Figs. 8 and 9, for two randomly selected points within the floodplains. One point is selected in between buildings and the other

148

R. Reshma and S. N. Kuiry

Fig. 6 Flood flow velocity vector map using bare earth DEM

Fig. 7 Flood flow velocity vector map using DEM with building data

on one of the street locations to depict the influence of urban data on the results. The presence of buildings influences the flow evolution and hence the velocity as shown in the graphs. Flow velocity amid the buildings raises by ~0.1 m/s owing to the narrower flow path available. Also, the depth of inundation raises by ~0.15 m in the streets as shown in the graphs. Besides, the flood water consumes more time to recede from the streets in the case of a real-time urban flood event. This is clearly captured by the inclusion of urban data as shown in Fig. 9.

Significance of Representing Buildings in Urban Flood Simulations

149

Fig. 8 Flood flow velocity graph

5 Conclusions The significance of building data inclusion in urban flood simulations is analyzed considering the Chennai flood of 2015 as a case study. The study attempts to present the variation of the flood flow pattern evolution under the influence of built-up areas. The following conclusions were obtained from the work: (i) (ii) (iii)

The evolution of flood flow pattern in an urban area is primarily attributed to the characteristics of terrain and urban structures. There is an increase in flood depth which may be attributed to the reduction of available flow path. Inclusion of building data aids to capture and simulate the real-time flood flow evolution to a great extent.

150

R. Reshma and S. N. Kuiry

Fig. 9 Inundation depth graph

Acknowledgements The authors acknowledge the effort of OSM Buildings team for making the building footprint data freely available for the public. The authors owe their gratitude to Nithila Devi for providing the hydro-meteorological data required for the current work. This work is supported by SERB, Govt. of India through the Grant No. EMR/2017/000642.

References 1. Bulti DT, Abebe BG (2020) A review of flood modeling methods for urban pluvial flood application. Model Earth Syst Environ 6(3):1293–1302 2. Chen AS, Evans B, Djordjevi´c S, Savi´c DA (2012) A coarse-grid approach to representing building blockage effects in 2D urban flood modelling. J Hydrol 426:1–16 3. Devi NN, Sridharan B, Kuiry SN (2019) Impact of urban sprawl on future flooding in Chennai city, India. J Hydrol 574:486–496 4. Glenis V, Kutija V, Kilsby CG (2018) A fully hydrodynamic urban flood modelling system representing buildings, green space and interventions. Environ Model Softw 109:272–292 5. Li Z, Liu J, Mei C, Shao W, Wang H, Yan D (2019) Comparative analysis of building representations in TELEMAC-2D for flood inundation in idealized Urban Districts. Water 11(9):1840 6. National Disaster Management Authority (NDMA). Urban floods. https://ndma.gov.in/NaturalHazards/Urban-Floods. Accessed 01 July 2021

Significance of Representing Buildings in Urban Flood Simulations

151

7. National Remote Sensing Centre (NRSC) (2015) Hydrological simulation study of flood disaster in Adyar and Cooum Rivers, Tamil Nadu. Indian Space Research Organization (ISRO) 8. Nithila Devi N, Sridharan B, Bindhu VM, Narasimhan B, Bhallamudi SM, Bhatt CM et al (2020) Investigation of role of retention storage in tanks (Small Water Bodies) on future urban flooding: a case study of Chennai City, India. Water 12(10):2875 9. Padmanaban R, Bhowmik AK, Cabral P, Zamyatin A, Almegdadi O, Wang S (2017) Modeling urban sprawl using remotely sensed data: a case study of Chennai city, Tamil Nadu. Entropy 19(4):163 10. Schubert JE, Sanders BF, Smith MJ, Wright NG (2008) Unstructured mesh generation and landcover-based resistance for hydrodynamic modeling of urban flooding. Adv Water Resour 31(12):1603–1621 11. Schubert JE, Sanders BF (2012) Building treatments for urban flood inundation models and implications for predictive skill and modeling efficiency. Adv Water Resour 41:49–64 12. Suriya S, Mudgal BV, Nelliyat P (2012) Flood damage assessment of an urban area in Chennai, India, part I: methodology. Nat Hazards 62(2):149–167 13. U.S. Army Corps of Engineers. Hydrologic Engineering Centre (US ACE) (2021) River analysis system—Technical reference manual. Version 6.0. USACE, USA 14. Wang Y, Chen AS, Fu G, Djordjevi´c S, Zhang C, Savi´c DA (2018) An integrated framework for high-resolution urban flood modelling considering multiple information sources and urban features. Environ Model Softw 107:85–95

Three-dimensional hydrodynamic modeling of permeable and impermeable river training works using CCHE 3D model and laboratory experiments Riddick Kakati, Vinay Chembolu, and Subashisa Dutta

Abstract Protection of river banks is an inseparable part of river training works. Permeable and impermeable structures are most commonly used for riverbank protection. Porcupines impose a mild impact on the river by implementing its effect gradually. However, during high flow conditions, these structures are ineffective and often get washed away. On the other hand, impermeable spurs impose a sudden impact on the river system and drastically reduce the velocity in its zone of influence. Due to this, turbulence is generated near the nose of the structure leading to the formation of scour hole, which results in structural instability. Therefore, an attempt has been made to study the effectiveness of the interventions mentioned above in stabilizing and protecting the rivers. Due to several limitations of the physical models, such as scale effect, steady-state flow, and high cost, which make it difficult to carry out in the case of a braided river system, a three-dimensional hydrodynamic model was used. In this study, the performance of the 3D hydrodynamic model CCHE 3D is evaluated in terms of velocity reduction potential by comparing it with experimental results. It was observed that initially, the velocity was in the range of 0.1 m/s under emergent condition, which reduced by more than 50% in the downstream of single porcupine screen, more than 75% in the downstream of two porcupine screens, and more than 94.36% in case of geobag layout. Flow deflection was also observed, but it was not significant. Keywords Alluvial river · Riverbank erosion · River training works · Numerical modeling

R. Kakati (B) · S. Dutta Indian Institute of Technology Guwahati, Guwahati 781028, India e-mail: [email protected] V. Chembolu Indian Institute of Technology Jammu, Jammu and Kashmir 181221, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 V. Chembolu and S. Dutta (eds.), Recent Trends in River Corridor Management, Lecture Notes in Civil Engineering 229, https://doi.org/10.1007/978-981-16-9933-7_10

153

154

R. Kakati et al.

1 Introduction From history, it can be seen that most of the civilizations began on river banks. Considering the dependency of mankind on rivers, the protection of river banks plays an important role. Rivers in India are a great source of income and also a boost to the economy. In the past, rivers in India were mainly responsible for the inland transportation of agricultural goods. Also, during times of war, they served an essential role in transporting the machinery and soldiers. Today, India is a majorly agricultural-dependent nation and depends more on our rivers for irrigation purposes. Thus, rivers have become an inseparable part of human lives. Rivers, in general, are meandering in nature. Hence it is scarce for a river to follow a straight path. Excessive meandering usually causes erosion in the outer bank and deposition in the inner bank. Bed erosion also occurs due to a shift in Thalweg. Due to this, some rivers tend to shift their course while others follow an entirely new path, resulting in energy dissipation and often leading to braiding [1, 8, 10, 11]. This dynamic nature of rivers causes a lot of damage to life and property within a short period [1, 4–6, 8, 9]. Riverbank erosion induces several negative impacts, such as loss of lives and properties [12]. Bank erosion in Majuli Island of Assam is a living example. It damages several residential and agricultural lands every year, resulting in a loss of household income sources. It also damages several historically and ecologically significant areas. Also, high sediment load during floods causes problems of navigation during low flow time. High sediment load also causes silting of reservoirs, reducing its capacity, and hence causing flooding. For example, the Yellow River in China carries enormous amounts of sediments with a long-term average of 1.6 billion tons of sediment per year from 1919 to 1960. Riverbank erosion also causes social instability and the migration of people. For example, in the Brahmaputra river, annual loss from floods is estimated to be Rs. 200 crores. Erosion has also reached a critical stage in Majuli Island, threatening the very existence of the river island [7]. Bank erosion also leads to the meandering of rivers to leave their original course and force them to flow along a new path, thus devastating vast land and valuable structures nearby. Thus, bank erosion leads to an overall increase in the instability of the river regime, which leads to various morphological changes. In a country like India, economic and efficient riverbank protection measures must be practiced on a large scale. Modeling river training works before implementation will help achieve these goals by assisting in designing adequately and thus reducing chances of failure. Various river training methods are practiced nowadays to maintain the course of a river along a well-defined path, such as guide vanes, groynes, bank pitching, etc. Stone pitching, gabions, rock riprap, retaining walls, in addition to flow deflectors like spurs, bend weir are the most widely used riverbank protection methods in large alluvial rivers [3]. These river training works can be categorized into permeable and impermeable river training works. Permeable works allow flow to pass through them partially, whereas impermeable works do not allow the same. The provision of a particular type of river training work

Three-dimensional hydrodynamic modeling of permeable …

155

depends on the importance of the area to be protected and the river response to the intervention [13]. Bank erosion is a severe problem, and hence there is a need for effective bank protection measures, which do not alter river response and ecology. Due to the high cost of these river training works, countries like India have been adopting economic river training measures. Board fencing, bandalling, jack-jetty systems, and tetrahedral frames are some of the cost-effective solutions. Porcupine systems have also been deployed in large rivers like the Brahmaputra and the Ganga with reasonably good results. From previous research in the area of riverbank erosion and protection, it can be seen that flow deflectors like dikes (spurs) and bendway weirs were considered as the most versatile riverbank protection techniques in large rivers, where a lot of morphological and hydrodynamic changes are observed [14]. Various bank protection measures were installed to confine the flow along the main path. To study hydrodynamics and morphological changes in a river, physical modeling and mathematical modeling can be used. Physical modeling is insufficient to expose the detailed velocity field, depth variations, and turbulent flow features. So numerical hydraulic modeling techniques are used nowadays as they are time-saving and also scale distortion can be avoided. Mathematical modeling of rivers is carried out using advanced hydraulic models such as MIKE, Delft 3D, or CCHE. In this study, the CCHE hydrodynamic model was used to evaluate the effects of bank protection structures on river morphology. CCHE has an advantage over MIKE and Delft 3D in handling mesh and applying boundary conditions, which leads to instability. Flow resistance of porcupine and geobag layouts was quantified in terms of velocity reduction potential. This study aims to protect the riverbank from erosion (through artificial interventions) while maintaining the environmental integrity of the natural stream.

2 Methodology 2.1 Mathematical Modeling This study uses a three-dimensional finite element method-based hydrodynamic CCHE3D model, an integrated package developed at the National Centre for Computational Hydroscience and Engineering, the University of Mississippi. It is a numerical model for three-dimensional simulation and analysis of free surface flows as water flows in rivers, lakes, reservoirs, and estuaries. Using this model, the water flowdominated processes of sediment transport, morphological change, water quality, etc. can also be studied. These processes are solved with full three-dimensional Reynold’s equations and mass conservation equations after discretization using an efficient element numerical method. In addition, turbulence closure scheme, parabolic, mixing length eddy viscosity models, wind-driven flow eddy viscosity, linear and nonlinear k- models

156

R. Kakati et al.

are available in this model. The CCHE3D model is developed on the same platform as the CCHE2D model, a two-dimensional hydrodynamic model. The results from the CCHE2D are often used as the initial condition for the 3D simulations. This model uses 3D mesh with the horizontal mesh same as CCHE2D. CCHE mesh generation tool is standard for both CCHE2D and CCHE3D. The 3D mesh is developed based on the 2D mesh by stacking multiple levels. The graphic user interface (GUI) helps to drive and manage simulation cases, visualize, analyze, and animate the results from simulation. Starting with the mesh, GUI allows to setup parameters, boundary conditions to run the simulation and visualize the results. The reader may refer to the CCHE user manual for details of the model (https://www.ncche.olemiss.edu).

2.1.1

Governing Equations

For general application purposes, full three-dimensional Reynolds’ transport equations are solved. The incompressible continuity equation is solved for mass conservation. The momentum equations and continuity equation are defined in the Cartesian coordinate system: u i,t + u i u i, j +

 pi  + −u i u j j + f i = 0 ρ

(1)

u i,i = 0

(2)

To simulate surface water flows, the free surface kinematic equation is applied: ηt + u η η,x + vη η,y − ωη = 0

(3)

Here, u i are velocity components in x i directions, t is the time, η is the water surface elevation, ρ is the density of water, and f i are the forcing terms. For clear water simulations, f i will be non-zero in the vertical direction: g, the gravitational acceleration. When aquatic vegetation is involved, f i will also be nonzero in horizontal directions to account for the drag force if the studied flow has vertical density stratification or thermal flow (kg/m3 ). For turbulent flows, the turbulence Reynolds stresses in Eq. 1 is approximated according to Boussinesq’s assumption that they are related to the rate of the strains of the flow field with a coefficient of eddy viscosity.

2.1.2

3D Solution with Hydrostatic Pressure Assumption

When the applications of the model are focused on general free surface flows, the vertical accelerations of the flow are generally negligible and assumed to be zero. In this case, the third equation becomes the hydrostatic pressure expression. Equations for the two horizontal directions read:

Three-dimensional hydrodynamic modeling of permeable …

157

∂τx y ∂u ∂u ∂u ∂u ∂η ∂τx x ∂τx z +u· +v· +w· = −g + + + ∂t ∂x ∂y ∂z ∂x ∂x ∂y ∂z

(4)

∂τ yy ∂τ yz ∂v ∂v ∂v ∂η ∂τ yx ∂v +u· +v· +w· = −g · + + + ∂t ∂x ∂y ∂z ∂y ∂x ∂y ∂z

(5)

∂vh ∂η ∂uh + + =0 ∂t ∂x ∂y

(6)

Equation 6 is used for solving the water surface elevation. Here, h is the water depth, u and v are the depth-integrated horizontal velocities. Hydrostatic pressure is applied to the flow field via the water surface gradient terms.

2.1.3

Turbulence Closure Schemes

Free-surface flows are often highly turbulent when unaffected by irregular channel topography, bed roughness, engineering structures, etc. For handling applications of the CCHE3D model in these conditions, several turbulence closure schemes have been developed. Because CCHE3D solves the Reynolds averaged equation, turbulence stresses are modeled based on Boussinesq’s assumption: 2 τi j = −u i u j = − · ρκδi j + 2 · ρvt Di j 3

(7)

Turbulence stress is a linear function of the rate of strain: Di j =

 1 u i, j + u ji 2

(8)

and vt is the eddy viscosity coefficient. There are many turbulence closure models in the literature. CCHE3D has selected those most applicable to free surface flows and sediment transport for effective applications. In this study, mixing length eddy viscosity model was used.

2.1.4

Mixing Length Eddy Viscosity

The mixing length closure model is a little more complicated than the parabolic scheme. It is more applicable to realistic flow conditions because the method includes the three-dimensional mixing effect of the flow.  vt = l

2

∂u j ∂u i + ∂x j ∂ xi



∂u i ∂x j

(9)

158

R. Kakati et al.

 z l = κ.z 1 − h

(10)

where l is the mixing length; the scheme will convert into the parabolic scheme under the uniform flow condition.

2.1.5

Model Setup

A three-dimensional mathematical model is set up for a 20 m long and 1 m wide laboratory channel with a different number of porcupines placed in a row, and the slope of the bed is kept as mild (1.5 × 10–3 ). The height of the model (porcupine) is 12 cm when placed in a channel. The computational mesh for an area with a length of 20 m and a width of 1 m is generated using a mesh generator. At the porcupine zones, a finer grid is generated than other parts of the mesh. The channel is interpolated to get the bed elevation at all mesh nodes, and the geometry file is created. Once the geometry file is created, GUI is used for further analysis like setting flow initial and boundary conditions and running the simulations. A uniform roughness height (k s ) of 0.0032 m is used throughout the domain. Considering the computational and modeling difficulties associated with the inclined members in CCHE3D, vertical members are used in this study. Discharge is given as an upstream boundary condition, and the corresponding water level is provided as a downstream boundary condition. Initially, 2D simulation is carried out, and the results of the 2D simulation are further used as the initial condition for the 3D simulation. The flow simulations are carried out for different layouts (Fig. 1) of porcupines placed halfway along the channel length. Table 1 shows the different designs of porcupines, water depths, and discharges used for simulations.

2.2 Physical Modeling Experiments were conducted in a 20 m long, 1 m wide, and 0.72 m deep tilting flume. A tank that is 2.8 m long, 1.5 m wide, and 1.5 m deep was provided upstream of the flume to straighten the flow before its introduction into the flume. The bed slope was fixed at 0.15%, which was kept fixed for all experiments. The flow in the flume was driven by three 10 HP centrifugal pumps. Flow in the test section is affected by the entry and exit conditions. If the water is pumped into the flume by the pipes directly, it will cause intense circulations, so the water was first collected into the upstream collection tank of the flume. The water level in this tank rises gradually before entering the flume channel. Baffles were installed in the upstream collection tank located just upstream of the channel entrance to facilitate the smooth entry of water into the channel. Also, the free overfall of the water of the tailgate region

Three-dimensional hydrodynamic modeling of permeable …

159

Fig. 1 Porcupine screens placed in a channel in physical and mathematical model setup

Table 1 Discharge, water depth, and layout of porcupines used for modeling

Water depth (m)

Discharge (m3 /s)

Layout of porcupines

Flow condition

0.08

0.01128

Single screen

Emergent

0.12

0.02165

Single screen

Transition

0.16

0.03416

Single screen

Submerged

0.08

0.01128

Dual screen

Emergent

0.12

0.02165

Dual screen

Transition

0.16

0.03416

Dual screen

Submerged

causes acceleration of the flow near the tailgate region. To minimize these effects, a test section was selected for the study. Porcupines have been fabricated using 5 mm iron rods. Each member of a single porcupine has a length of 4 cm, and the effective height of the porcupine from the bed is 4 cm. The test section of this experiment is taken as 5 m in the middle of the flume, the depth of flow is 12 cm, and the bed slope is 0.001. Before placing porcupine screens, the sediment bed of flume is leveled with the help of a leveling scrapper. The measured quantity of discharge (Q = 10 L/s) is allowed to flow into the flume, and the water level inside the flume is maintained by controlling the tailgate at the downstream end of the flume. Uniform flow without sediment motion

160

R. Kakati et al.

Fig. 2 Top view of the experimental setup with cross-sections

corresponding to a selected discharge is established with the help of a tailgate. Flow depth is measured with a point gauge having a least count of 0.1 mm. These clear water runs continued for half an hour. Then the motor was shut down, and water was gradually discharged with the tailgate operation, such as not disturbing the sand bed. The values that control the flow regulation into the flume are not altered to maintain the same discharge into the flume for the flowing sediment run. After the water is drained, a group of five porcupines is placed in the flume, and the sediment bed of the flume is again leveled around the porcupine field. Then the flow is introduced very slowly by closing the tailgate so that no scouring occurs around the porcupine. The details of the placement of the porcupine field in the flume are shown in Fig. 1. After 2 h, velocity readings were recorded using ADV at the upstream section, flow deflected region, and downstream section. Figure 2 shows the top view of the experimental setup and cross-sections taken (A1, A2, A3, A4, A5, A6, B1, and B2) at 12 and 30 cm distance across the section of 1 m width flume. The measured velocity time series data using Acoustic Doppler Velocimeter (ADV, model named Nortek Vectrino Plus) was later extracted to find out instantaneous velocities using software called Vectrino+. These measured velocity components included spikes because of interference between transmitted and received signals. Despiking of these data was done using MATLAB code based on kernel algorithm [2]. These despiked data were used to analyze and predict the behavior of porcupine screens when placed in the channel.

3 Results and Discussions The CCHE3D numerical model was simulated for the different layouts of porcupine screens with suitable discharge and water depth. In this section, the effect of porcupine screens on the flow velocity is discussed. The model study used the parabolic eddy viscosity model to carry out 3D flow simulations for a 3 h duration with a time step

Three-dimensional hydrodynamic modeling of permeable …

161

of 10 ms. The total depth was vertically divided into ten layers, and corresponding velocity distributions were plotted at different locations along the channel.

3.1 Single-Screen Porcupines

y = Flow Depth (m)

In this case, single-screen porcupines were placed at the center of the channel. Roughness was incorporated as uniform roughness height (ks) of 0.0032 throughout the channel. The model was set up by providing suitable initial and boundary conditions, as discussed in Table 1. Steady flow simulations were carried out for a 3 h period, and the simulated final velocity (m/s) is presented in Fig. 3. From Fig. 3, it can be observed that initially, the velocity was in the range of 0.1 m/s under emergent conditions, which reduced by more than 50% downstream of the porcupine screens. In the rest of the sections, there was no change in velocity profiles. Flow deflection was also observed, but it was minimal. In the case of transition and submerged conditions, it can be observed that the effect of porcupines gradually reduces as the depth of water increases. It was observed from the simulated results and the experimental results that the velocity distributions followed the same trend. Figs 3 and 4 show the velocity distribution along depth for simulated and experimental results, respectively. It was also observed that in lower layers, the reduction in velocity was higher than in upper layers. Also, as the experiments were conducted in submerged flow conditions, the porcupines’ effect in deflected regions is not significant. Far Upstream

Upstream

Downstream

Deflected

0.2

0.2

0.2

0.15

0.15

0.15

0.15

0.1

0.1

0.1

0.1

0.05

0.05

0.05

0.05

0.2

0

0 0

0.2

0.2

0.1

0

0 0

0

0.2

Far Downstream

0.2

0 0

0.05 0.1

0

0.1

x = Flow Velocity (m/s)

Fig. 3 Depth-wise velocity profile of single-screen porcupines obtained from simulation

0.2

R. Kakati et al.

Flow Depth (cm)

162

8 7 6 5 4 3 2 1 0

Upstream Downstream Deflected 0

0.1

0.2 Flow Velocity (m/s)

0.3

0.4

Fig. 4 Depth-wise velocity profile for single-screen porcupines obtained from experiments under submerged condition

3.2 Dual-Screen Porcupines

y = Flow Depth (m)

In this case, the model was set up by providing similar conditions as discussed in the case of single-screen porcupines. Steady flow simulations were carried out for 3 h, and the simulated final velocity (m/s) is presented in Fig. 5. From Fig. 5, it can be observed that initially, the velocity was in the range of 0.1 m/s under emergent conditions, which reduced by more than 75% downstream of the porcupine screens. In the rest of the sections, there was no change in velocity profiles. Flow deflection was also observed, but it was minimal. In the case of transition and submerged conditions, it can be observed that the effect of porcupines gradually reduces as the depth of water increases. A similar trend was observed as in the case of single-screen porcupines. Experimental and simulated results showed the same trend. Figs 5 and 6 show the velocity distribution along depth for simulated and experimental results, respectively. Further Far Upstream 0.2

Upstream

Downstream

Deflected

0.2

0.2

0.2

0.2

0.15

0.15

0.15

0.15

0.15

0.1

0.1

0.1

0.1

0.1

0.05

0.05

0.05

0.05

0.05

0 0.2

0 0 0.2 0 x = Flow Velocity (m/s)

0

0

0 0

0

0.2

Far Downstream

0.02 0.04

0

Fig. 5 Depth-wise velocity profile of dual-screen porcupines obtained from simulation

0.1

0.2

Flow Depth (cm)

Three-dimensional hydrodynamic modeling of permeable …

163

8 7 6 5 4 3 2 1 0

Upstream Downstream Deflected 0

0.1

0.2 Flow Velocity (m/s)

0.3

0.4

Fig. 6 Depth-wise velocity profile for dual-screen porcupines obtained from experiments under submerged condition

reduction in velocity was observed in the case of dual-screen porcupines. Dual-screen porcupines further reduced the velocity in its downstream by 75%. Flow diversion, in this case, is almost the same as in the case of single-screen porcupines.

3.3 Geobag Layout In this case, the model was set up by providing similar conditions as discussed in the previous cases. It was observed that the percentage decrease of velocity in the downstream in this case was 94.36%.

4 Conclusion Permeable structures are the cost-effective alternative method for training a braided river. The basic principle of permeable elements is to reduce the velocity by offering flow resistance and promoting sediment deposition. In the present study, the CCHE3D model was set up, and porcupine and geobag structures were incorporated in the models by increasing the local roughness of the channel at different locations by increasing the height of the bed at those locations. Based on experimental and simulation results, it can be concluded that the presence of porcupines can cause a considerable reduction in flow velocity, which further increases with an increase in the number of porcupine screens. It was observed that initially, the velocity was in the range of 0.1 m/s under emergent condition, which reduced by more than 50% in the downstream of single porcupine screen, more than 75% in the downstream of two porcupine screens, and more than 94.36% in case of geobag layout. Flow deflection was also observed, but it was minimal. However, in the case of high submergence,

164

R. Kakati et al.

the effect of porcupines in flow deflection is negligible. Hence porcupines are not suitable for high flow conditions. Thus, a 3D numerical model such as CCHE3D can be used to predict the behavior of river training work at a site before implementation. This can help in preventing the failure of these structures during high flow and thus help in economic design.

References 1. Chembolu V, Dutta S (2018) An entropy based morphological variability assessment of a large braided river. Earth Surf Proc Land 43(14):2889–2896. https://doi.org/10.1002/esp.4441 2. Islam R, David Z (2013) Kernel density-based algorithm for despiking ADV data. J Hydraul Eng 139:785–793. https://doi.org/10.1061/(ASCE)HY.1943-7900.0000734 3. Julien PY (2002) River mechanics. Cambridge University Press, Cambridge, U.K 4. Karmaker T, Dutta S (2011) Erodibility of fine soil from the composite river bank of Brahmaputra in India. Hydrol Process 25(1):104–111. https://doi.org/10.1002/hyp.7826 5. Karmaker T, Dutta S (2013) Modeling of seepage erosion and bank retreat in a composite river bank. J Hydrol 476:178–187. https://doi.org/10.1016/j.jhydrol.2012.10.032 6. Karmaker T, Dutta S (2015) Stochastic erosion of composite banks in alluvial river bends. Hydrol Process 29(6):1324–1339. https://doi.org/10.1002/hyp.10266 7. Kotoky P, Bezbaruah D, Baruah J (2003) Erosion activity on Majuli-the largest river island of the world. Erosion activity on Majuli- the largest river island of the world. Curr Sci 84: 929–932 8. Nandi KK, Pradhan C, Sultan J, Dutta S, Khatua KK (2020) Energy dissipation modeling in highly braided Brahmaputra River. In: Proceedings of hydro 2020 international conference. Paramount Publishing House, Rourkela, Odisha, India, pp 366–373 9. Pradhan C, Bharti R, Dutta S (2017) Assessment of post-impoundment geomorphic variations along Brahmani River using remote sensing. In: 2017 IEEE international geoscience and remote sensing symposium (IGARSS). IEEE, pp 5598–5601 10. Pradhan C, Chembolu V, Dutta S (2018) Impact of river interventions on alluvial channel morphology. ISH J Hydra Eng 25(1):87–93. https://doi.org/10.1080/09715010.2018.1453878 11. Pradhan C, Chembolu V, Dutta S, Bharti R (2021a) Role of effective discharge on morphological changes for a regulated macrochannel river system. Geomorphology 385:107718. https://doi. org/10.1016/j.geomorph.2021.107718 12. Pradhan C, Chembolu V, Bharti R, Dutta S (2021b) Regulated rivers in India: research progress and future directions. ISH J Hydra (Accepted). https://doi.org/10.1080/09715010.2021.197 5319 13. Sarker MH, Akter J, Ferdous MR (2011) River bank protection measures in the BrahmaputraJamuna River: Bangladesh experience. International Seminar on ’River, Society and Sustainable Development, Dibrugarh 14. Yoo KH (2003) Nature friendly River Training Structure using Groynes. Water Resources Research Department, Korean Institute of Construction Technology, Korea

Numerical and Experimental Investigation of Effect of Green River Corridor on Main Channel Hydraulics S. Modalavalasa, V. Chembolu, V. Kulkarni, and S. Dutta

Abstract The riparian zones of the river are generally covered with vegetation patches and this makes it important to study vegetation influence on river turbulence. An inter-comparison study is carried out between numerical model simulations and experimental results for evaluating the performance of the CFD model in predicting the hydrodynamics structure with the influence of floodplain vegetation in the meandering channel. The present study discussed the hydraulic characteristics such as primary velocity and turbulent kinetic energy behavior in the main channel captured by FLOW-3D solver using the RNG (Renormalization-Group) turbulence model. The averaged streamwise velocities in the no-vegetation condition at the apex section for the simulated data were approximately 1.2 times the experimental result. The results obtained from the study have shown the capability of the RNG model to capture the shear layer mechanism at the channel-floodplain transition. Keywords River turbulence · Vegetation cover · FLOW-3D CFD solver · RNG model

1 Introduction Flow structures in the meandering channel are more complex than the regular channels due to their complex three-dimensional nature. Vegetation in the riverine environment plays an important role by changing the velocity distribution, turbulence S. Modalavalasa (B) IIT Guwahati, Guwahati, Assam, India e-mail: [email protected] V. Chembolu IIT Jammu, J & K, India V. Kulkarni Department of Mechanical Engineering IIT Guwahati, Guwahati, Assam, India S. Dutta Department of Civil Engineering, IIT Guwahati, Guwahati, Assam, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 V. Chembolu and S. Dutta (eds.), Recent Trends in River Corridor Management, Lecture Notes in Civil Engineering 229, https://doi.org/10.1007/978-981-16-9933-7_11

165

166

S. Modalavalasa et al.

structures, incidence of periodic vortices, spreading deposition, increase in flow resistance, and decrease in conveyance efficiency. Several past studies have investigated the interaction between flow and flexible vegetation (such as [1–3, 13, 28, 36, 37]). The authors of [15, 18, 19] conducted the experiments to determine the velocity distributions and turbulence properties in the vegetated channels along with bridge pier interference. The experimental and numerical studies on flow through vegetation in open channels were investigated by [4, 6, 7, 21, 22, 29, 34, 35]. The turbulence structures in the main channel affected by floodplain vegetation have been examined through laboratory experiments and numerical studies. In addition, energy losses in meandering to the braided river are significantly related to instream vegetation growth and many Indian rivers are currently facing emerging challenges like floodplain encroachment and loss of riverine habitat and planform change [20, 25–27, 30]. For the detailed evaluation of the safety of river embankments, an experimental and numerical study was conducted by [9]. In [23] the authors stated that the main turbulent models for hydrodynamic applications can be divided into three categories: (1) no averaging direct numerical simulation (DNS) models, (2) spatial averaging of the Navier-Stokes equations of large Eddy simulation (LES) models; and (3) Reynolds averaged Navier-Stokes (RANS) models with temporal averaging of the Navier-Stokes equations. Their study mentioned that the DNS method is extremely expensive, particularly for complex geometries and heterogeneous cases at higher Reynolds numbers. Reference [24] discussed the hydro-morphological study along with numerical modeling using the sediment transport model in FLOW-3D model. Reference [5] used the LES approach to simulate flows influenced by aquatic vegetation. Reference [10] obtained the LES experimental results, which studied the effect of plant density on large-scale coherent structures inside the canopy sublayer. The k − E turbulence model has been used to simulate flows impacted by vegetation [4, 17]. These studies demonstrate the model capabilities of simulating the vegetation effects on the flow characteristics. However, due to configurable parameters and coefficients for flows with vegetation cover, the model still has some limitations. Considering the importance of vegetation for the flow phenomena of a river, this objective hydrodynamic change due to the presence of vegetation cover on the floodplain is studied using a three-dimensional computational fluid dynamic model, FLOW-3D. This study provides the assessment between the simulation and experimental data by conducting a range of comparisons. The present study assesses the simulation of experimental sinuous channel for analyzing the flow features like velocity distribution and turbulent structures in the apex, bend, and cross-over regions by the turbulence modeling such as Renormalization-Group (RNG) model in the FLOW-3D [FLOW-3D V.12, FLOW-3D HYDRO v1.0 u 1]. The CFD software FLOW-3D was used to predict the 3D flow patterns in the main channel due to floodplain vegetation. The turbulence modeling using FLOW-3D was satisfactorily tested against experimental data of turbulence in the main channel due to the effect of with and without floodplain vegetation. The effects of floodplain vegetation on turbulence flow were investigated by comparing different results obtained from the experiments with numerical models.

Numerical and Experimental Investigation of Effect of Green River …

167

2 Methodology 2.1 Experimental Conditions A series of experiments were conducted in the fluvial tray containing the dimensions of 18 m long and 1 m wide of a low sinuous channel. Median sand particle diameter used (D50 ) 0.37 mm for the bed in the fluvial hydro-ecological laboratory at IIT Guwahati. The three-dimensional velocity measurements were acquired from an acoustic Doppler velocimeter (ADV) (4 probes, 10 MHz Vectrino ADV manufactured by Nortek) with a sampling frequency of 100 Hz. The study area of 1.2 m length is selected while conducting the experiments. The data were collected for a duration of 2 min and the number of samples collected was 10,000. The signal-to-noise ratio of the measurements is maintained above 15 dB, to ensure the quality of the data collected. Figure 1 shows the representation of the vegetation in the model geometry as well as the replication of the experimental flume. Instantaneous three-dimensional velocity components were measured with ADV throughout the flow depth at an increment of 1 cm from the near bed. Thus, 14 measurement points at each vertical

Fig. 1 Simulated model geometry of floodplain vegetation condition

Table 1 Summary of the experiments with and without floodplain vegetation H (cm)

h (cm)

α

β

Q (cumecs)

U∗ (m/s)

U m (m/s)

Fr

Re

No-vegetation

14

8

1.88

0.43

0.01536

0.029

0.17

0.18

15,470

100 Plants/m2

14

8

1.88

0.43

0.01536

0.029

0.19

0.20

16,926

H is the total flow depth, h is the floodplain depth, α is relative width ratio, β is relative flow depth, Q is total discharge, U ∗ is shear velocity, U m is average velocity in the channel, F r is Froude number, and Re is the Reynolds number

168

S. Modalavalasa et al.

profile were obtained. The experimental parameters were discussed in Table 1 for different cases.

3 Computational Modeling 3.1 Flow 3D Modeling and Approaches The computational fluid dynamic (CFD) solver used in this study is the FLOW-3D model. The RNG turbulence model is used to compute the interaction between flow and vegetation in a laboratory channel. The selection of mesh sizes was chosen based on mesh size optimization. The model was simulated for two different cases: (a) no floodplain vegetation condition; (b) floodplain vegetation with the density of 100 plants/m2 . In all the cases the model simulations were carried out for flow depth 14 cm, and turbulence parameters were analyzed. The CFD model geometry is a simplified 3D rectangular cube, as shown in Fig. 1. The vegetation, which has been modeled as a rigid cylinder, has 3.6 m in length on both sides of the floodplain.

3.2 Governing Equations FLOW-3D solves the Reynolds averaged Navier-Stokes (RANS) equation with a finite volume approach, using the fractional area/volume obstacle representation (FAVOR) technique to define problem geometry and a free-gridding technique for mesh generation [8]. The below equations are continuity and RANS equations with FAVOR variables that are applied for incompressible flows. ∂ (u i Ai ) = 0 ∂X   1 ∂p 1 ∂ ui ∂ ∂ ui uj Aj = + + gi + f i (u i Ai ) = ∂ Xi ∂t VF ∂x j ρ ∂ xi where ‘ui ’ is the velocity in x i direction, ‘t’ is time, ‘A’ is the fractional area in respective directions, ‘V F ’ is the volume of fluid fraction, ‘p’ is hydrostatic pressure, ‘ρ’ is density, ‘g’ is the gravitational force, and ‘f i ’ is Reynold’s stresses.

Numerical and Experimental Investigation of Effect of Green River …

169

3.3 Turbulence Model The RNG model is considered to be a more extensively utilized approach than the standard k − ε model. The RNG model, in particular, is more accurate than the usual k − ε model in flows with strong shear zones, and it is defined to characterize low-intensity turbulent flows. The 3D governing equations could be expressed as follows: The general mass continuity equation is VF

   ∂  ∂ρ ∂  ∂  ρu Ax + ρu Ax + R ρv A y + ρw A Z + ξ = RDIF + RSOR ∂t ∂x ∂y ∂z x

where terms denoted as ‘RDIF ’ is turbulent diffusion term and ‘RSOR ’ is mass source. Ax , Ay , and Az are the fractional areas open to flow in the x, y, and z directions, respectively. The velocity components (u, v, w) are in the (x, y, z) or (r, θ, z) coordinate directions.

3.4 Boundary Conditions In the present study, a structured and identical block of mesh was used to simulate the main channel and floodplain. Boundary conditions included inflow discharge for the main channel and the lateral channel entrance, constant level outflow for the main channel outlet, wall for bed, right and left boundaries of the main channel, and atmospheric pressure for the top free surface. Solid components were used to create other essential wall conditions. As per the limitations in the available processing configuration, the study area mesh block is considered with finer mesh block to increase the accuracy at the study section and reduce the number of mesh cells in less important areas like total channel mesh block. A mesh size optimization process was carried out to establish a good balance between the consumed simulation time and the accuracy, taking into account the constraints in processing power (Xeon (R) CPU of 4.10 GHz). To simulate the fluvial channel with dimensions of 18 m length, 1 m width, and 0.14 m height and comprising 4,424,000 cells for the mesh domain, different cell sizes were chosen for the entire experimental setup and study area of the channel to reduce the simulation time. The front view of both the simulated cases in this study is shown in Fig. 2.

170

S. Modalavalasa et al.

Fig. 2 Front view of a no-vegetation b with floodplain vegetation of submerged simulated conditions

4 Results 4.1 Experimental and Model Results of With and Without Floodplain Vegetation Case 1: Without Vegetation Cover over the Floodplain Figures 3 and 4 represent the comparison between experimental (left panel) and model (right panel) data for without floodplain vegetation condition. Figure 3 compares the measured streamwise velocity profiles with the simulated ones of different cross-sections in the main channel such as apex, bend, and cross-over sections. The figure shows the experimental velocity values were almost close to the simulated data as the streamwise velocity is observed as 0.24 m/s at the apex section. The result indicates a reasonable agreement between simulated and experimental contour plots. From the results, the averaged streamwise velocities at the apex section in the simulated data (U sim = 0.17 m/s) were approximately 1.2 times the experimental result (U exp = 0.14 m/s). The mean velocity of streamwise velocity is around 5u∗ for the experimental condition, and for the simulated case it is 5.9u∗ . The I

II

a)

a) 0.75

z/h

5

5

00

00 0

1

2

3

4

5

0

1

2

3

4

5

0

1

2

3

4

5

0

1

2

3

4

b)

b)

1.125

1.125 0.75

z/h

0.75 15 0.37510

0.37515 10

5

5

00

00 0

1

2

3

4

5

c)

c)

0.1

1.125

z/h

0.75 0.37515 10

0.37515 10

z/h

0.18

1.125

1.125

z/h

1.125

0.75

z/h

0.75 0.37515

0.37515

10

10

5

5

00

00 0

Inner bank

1

2

3

Y/h

4

5

Outer bank

Inner bank

Y/h

5

Outer bank

Fig. 3 Streamwise velocity contour plots for (I) experimental and (II) simulated data of novegetation condition of a apex, b bend, and c cross-over sections

Numerical and Experimental Investigation of Effect of Green River …

171 II

I

a)

c)

0.009

1.125 0.75

z/h

1.125 0.75

z/h

0.37515

0.37515

10

10

5

5

00

00 0

1

2

3

4

5

c)

b)

1.125 0.75

z/h

1

2

3

4

5

0

1

2

3

4

5

0

1

2

3

4

0.75

z/h

0.37515

0.37515

10

10

5

00

5

00 0

c)

1

2

3

4

5

c)

0.0095

1.125

z/h

0

1.125

0.75

z/h

1.125 0.75 0.37515

0.37515

10

10

5

00

5

00 0

1

2

3

4

Y/h

Inner bank

5

Outer bank

5

Y/h

Outer bank

Inner bank

Fig. 4 Turbulent kinetic energy contour plots for (I) experimental and (II) simulated data of novegetation condition of a apex, b bend, and c cross-over sections

percentage increase in streamwise velocity of no-vegetated condition from experimental to simulated condition is estimated to be approximately 15%. Figure 4 shows the contour plots for the TKE of no-vegetation condition for both the experimental and simulated conditions. The maximum TKE is observed at near bed surface in the simulated run which is not following the experimental patterns. The percentage increase in turbulent kinetic energy for experimental is 0.11% more at apex, 0.13% more at bend, and 0.21% more at COR compared to simulation case. Case 2: Vegetation Patch Density of 100 Plants/m2 over the Floodplain Figures 5 and 6 compare the experimental (left panel) with the simulated (right panel) results in an open channel with a low sinuosity of vegetation density of 100 I

a) 1.125 z/h

II

a) 1.125

0.15

0.75

z/h

0.37510 5

5

00

00 0

b) z/h

1

2

3

4

5

0

1

2

3

4

5

0

1

2

3

4

5

0

1

2

3

4

b) 1.125

1.125 0.75

z/h

0.75 15

0.37515 10

0.37510 5

5

00

00 0

1

2

c) 1.125 z/h

0.75 15

15

0.37510

3

4

5

c)

0.13

1.125

0.75

z/h

0.75 15

0.37510

15

0.37510

5

5

00

00 0

Inner bank

1

2

3

4

5

Outer bank

Inner bank

5

Outer bank

Fig. 5 Contour plots of streamwise velocities for (I) experimental and (II) simulated results of floodplain vegetated density of 100 plants/m2 , a apex, b bend, and c cross-over sections

172

S. Modalavalasa et al. I

II

a)

a)

1.125

1.125

z/h

0.75

0.75

z/h

15

15

0.37510

0.37510

5

5

00

00 0

1

2

3

4

5

1.125

z/h

1

2

3

4

5

0

1

2

3

4

5

0

1

2

3

4

1.125

0.75

z/h

0.75 15

0.37515 10

0.37510 5

5

00

00 0

1

2

3

4

5

c)

c) 1.125

1.125

z/h

0

b)

b)

0.75

z/h

0.75 15

0.37510

15

0.37510

5

5

00

00 0

1

2

Inner bank

3

4

5

Outer bank

Inner bank

5

Outer bank

Fig. 6 Contour plots of turbulent kinetic energy for (I) experimental and (II) simulated results of floodplain vegetated density of 100 plants/m2 , a apex, b bend, and c cross-over sections

plants/m2 . Figure 5 compares the measured streamwise velocity profiles with the simulated ones of different cross-sections in the main channel. From the results, the averaged streamwise velocities of the simulated data (U sim = 0.32 m/s) were approximately 1.5 times the experimental result (U exp = 0.2 m/s). The mean velocity of streamwise velocity is around 7u∗ for the experimental condition, and for the simulated case it is 11u∗ . However, the apex and cross-over section in the main channel measured at locations show reasonable results in view of the velocity core region between simulated and experimental contour plots. At the bend section, the plot is relatively different, indicating simulated one was not following the experimental observations. The percentage increase of mean streamwise velocity for floodplain vegetated condition for the simulated condition is estimated to be approximately 47% at apex, bend is 49.8%, and COR is 44% compared to experimental condition. Figure 6 shows the contour plots for TKE of floodplain vegetated condition. The percentage increase in turbulent kinetic energy for experimental is 46% more at apex, 72% more at bend, and 67% more at COR as compared to simulation case. The flow structures produced by the CFD model agree with the data collected in our experimental channel. The results demonstrate some differences between the model and the actual data, especially when it comes to turbulent kinetic energy features. This deviation possibly will be due to the inability of the CFD model to reproduce the micro-turbulence structures that occur in the mainstream and the flow separation. Model Performance This study enumerates the numerical investigation of the green river corridor effect on the hydrodynamics of a low sinuous river. Laboratory experiments and numerical modeling are used to investigate flows with and without floodplain vegetation. Figure 7 shows the result comparison between experimental and model data for the no-vegetation case, and it represents a good correlation between the model and experimental data without floodplain vegetation condition with (coefficient of regression)

Numerical and Experimental Investigation of Effect of Green River …

173 P1- Inner bank P5- Mid channel P10- Outer bank

Simulated velocity (m/s)

0.21

0.16

P1

0.17

0.13

0.13

0.10

0.09

0.07

P5

0.030

P10

0.025

0.020

0.015 0.04

0.05

y = 0.6696x + 0.0157 R² = 0.8712 0.01 0.05

0.1

0.15

0.2

0.25

y = 0.4492x + 0.0233 R² = 0.8864

0.01 0.05

0.1

0.15

0.2

0.25

y = 0.0612x + 0.0152 R² = 0.67 0.010 0.05

0.1

0.15

0.2

0.25

Experimental velocity (m/s)

Fig. 7 Comparison of streamwise velocities between simulation and experimental data of novegetation condition at apex section

R2 values of 0.87, 0.89, and 0.67 at the apex section near inner bank (P1), mid-channel (P5), and outer bank (P10), respectively. Figure 8 represents the comparison between experimental and model data of vegetation density of 100 plants/m2 condition. The results showed that the correlation between the model and experimental data is R2 = 0.83 for leaving of surface values and it shows R2 = 0.7 for inclusion of surface-level values. In the experimental condition, the vegetation blades are flexible. whereas in modeling the vegetation is

Simulated velocity (m/s)

0.25

0.25

a)

0.2

0.2

0.15

0.15

b)

0.1

0.1

y = 1.2237x - 0.1142 R² = 0.8307

y = 1.2275x - 0.1184 R² = 0.7091

0.05

0.05

0 0.15

0.17

0.19

0.21

0.23

0.25

0.27

0 0.15

0.17

0.19

0.21

0.23

0.25

0.27

Experimental velocity (m/s)

Fig. 8 Comparison of streamwise velocities between simulation and experimental data of floodplain vegetation density, i.e., 100 plants/m2 , a at the crest of vegetation and b just below the surface level

174

S. Modalavalasa et al.

considered rigid. So, a difference could be observed for the values at the surface level. The prediction from correlation data at apex and COR is good, but for the bend section it is quite different. According to Fig. 8a, the streamwise velocity at the apex section near the vegetation patch attains a maximum value close to top of the vegetation, which is caused by the high-velocity gradient in this area. In Fig. 8a, b, the difference in correlation coefficient can be noticed. The result provides useful insights into the laboratory and numerical investigation of flow structures of a sinuous channel.

5 Discussions This section focuses on interpreting the results of numerical analysis to understand the interaction between the main channel and floodplain. From Figs. 3, 4 to 5, the results of simulation and experimental plots of no-vegetation and with floodplain vegetation showed that the velocity isovel lines bulge along the bed of the channel. A similar trend is also noticed by Singh et al. (31). In the above figures, the characterization of shear layer (i.e., the width of the shear layer) due to the velocity gradients is affected by the main channel and different floodplain roughness, and this effect is significantly implicated on other turbulence parameters in the channel. Jahra et al. [12] also observed a similar kind of observations in their study. According to Fig. 8a, b, there is a slight discrepancy between the measured and the simulated values at the top of vegetation and just below the water surface of it. This may be due to the effect of flexibility of vegetation in experimental modeling and rigid vegetation elements in the numerical model. Although FLOW-3D model is not able to simulate the flexible vegetation, it helps to show the shear layer mechanism and the strong circulation in the main channel of all the cases with the rigid vegetation. In [33] the authors reported minor disagreement between the measured and simulated observations at the top of vegetation by using the porous zone model due to its inability to capture the flow separation and secondary circulation near the vegetation patch. Siniscalchi and Nikora [32] and Jahadi et al. [11] discussed in their study that the maximum velocities were observed at top of vegetation. According to [16] the predicted turbulent kinetic energy was 100% larger than their experimental observations. The present study noticed that the turbulent kinetic energy is high in the vicinity of the interface (i.e., channel floodplain transition) near the inner bank, and the contour lines near the junction bulge toward the free surface due to secondary currents. The same feature is observed by [14] in their turbulence study. However, the results are overpredicted in the simulated results than the observed results. From Figs. 7 to 8 the correlation trend between the experimental and model simulations is found satisfactory.

Numerical and Experimental Investigation of Effect of Green River …

175

6 Conclusions In this study, the numerical modeling is carried out for low sinuous channels with and without floodplain vegetation cases to evaluate the CFD model performance with the experimental results. The hydraulic parameters such as streamwise velocities and turbulent kinetic energy behavior in the main channel were analyzed by FLOW3D solver using RNG turbulence model. The following conclusions are the major observations in the study: (i)

(ii)

The percentage increase of mean streamwise velocity in floodplain vegetated condition for the simulated data is estimated to be approximately 47% at apex; bend is 49.8%; and COR is 44% compared to experimental condition. For floodplain vegetation density of 100 plants/m2 condition, the correlation between the model and experimental data is R2 = 0.75. This is due to the difference in vegetation representation in laboratory experiments (flexible) and numerical simulation (rigid).

References 1. Aberle J, Järvelä J (2013) Flow resistance of emergent rigid and flexible floodplain vegetation. J Hydraul Res 51(1):33–45 2. Carollo FG, Ferro V, Termini D (2005) Flow resistance law in channels with flexible submerged vegetation. J Hydraul Eng 131(7):554–564 3. Carollo FG, Ferro VITO, Termini D (2002) Flow velocity measurements in vegetated channels. J Hydraul Eng 128(7):664–673 4. Choi SU, Kang H (2004) Reynolds stress modeling of vegetated open-channel flows. J Hydraul Res 42(1):3–11 5. Cui J, Neary VS (2008) LES study of turbulent flows with submerged vegetation. J Hydraul Res 46(3):307–316 6. Defina A, Bixio AC (2005) Mean flow and turbulence in vegetated open channel flow. Water Resour Res 41(7) 7. Erduran KS, Kutija V (2003) Quasi-three-dimensional numerical model for flow through flexible, rigid, submerged and non-submerged vegetation. J Hydroinf 5(3):189–202 8. FLOW-3D® Version 12.0 Users Manual (2018) FLOW-3D [Computer software]. Santa Fe, NM: Flow Science, Inc. https://www.flow3d.com (or) Flow Science, Inc., Santa Fe, NM, USA 9. Gragnano CG (2017) Numerical and experimental investigation into the effect of retention properties of partially saturated soil for the stability analysis of River Embankments 10. Huang J, Cassiani M, Albertson JD (2009) The effects of vegetation density on coherent turbulent structures within the canopy sublayer: a large-eddy simulation study. Bound-Layer Meteorol 133(2):253–275 11. Jahadi M, Afzalimehr H, Ashrafizaadeh M, Kumar B (2020) A numerical study on hydraulic resistance in flow with vegetation patch. ISH J Hydraul Eng 1–8 12. Jahra F, Yamamoto H, Hasegawa F, Kawahara Y (2010) Turbulent flow structure in meandering vegetated open channel. River Flow 2010:153–162 13. Järvelä J (2002) Flow resistance of flexible and stiff vegetation: a flume study with natural plants. J Hydrol 269(1–2):44–54

176

S. Modalavalasa et al.

14. Kang H, Choi SU (2006) Turbulence modeling of compound open-channel flows with and without vegetation on the floodplain using the reynolds stress model. Adv Water Resour 29(11):1650–1664 15. Liu D, Diplas P, Fairbanks JD, Hodges CC (2008) An experimental study of flow through rigid vegetation. J Geophys Res: Earth Surf 113(F4) 16. Liu J, Piomelli U, Spalart PR (1996) Interaction between a spatially growing turbulent boundary layer and embedded streamwise vortices. J Fluid Mech 326:151–179 17. López F, García MH (2001) Mean flow and turbulence structure of open-channel flow through non-emergent vegetation. J Hydraul Eng 127(5):392–402 18. Modalavalasa S, Chembolu V, Nandi KK, Kulkarni V, Dutta S (2021) Effect of bridge pier induced turbulence on vegetated meander river morphology. In: EGU general assembly conference abstracts, pp EGU21–1465 19. Modalavalasa S, Pradhan C, Kulkarni V, Dutta S (2019a) Flow structure in meandering channel with vegetation. In: Conference paper: 16th annual meeting, AOGS 2019, Singapore 20. Modalavalasa S, Pradhan C, Siddharth A, Dutta S (2019b) National Symposium on Innovations in geospatial technology for sustainable development with special emphasis on NER (ISGISRS, 2019) 21. Nepf HM, Vivoni ER (2000) Flow structure in depth-limited, vegetated flow. J Geophys Res: Oceans 105(C12):28547–28557 22. Nepf HM (1999) Drag, turbulence, and diffusion in flow through emergent vegetation. Water Resour Res 35(2):479–489 23. Nikora V, McEwan I, McLean S, Coleman S, Pokrajac D, Walters R (2007) Double-averaging concept for rough-bed open-channel and overland flows: theoretical background. J Hydraul Eng 133(8):873–883 24. Pourshahbaz H, Abbasi S, Pandey M, Pu JH, Taghvaei P, Tofangdar N (2020) Morphology and hydrodynamics numerical simulation around groynes. ISH J Hydraul Eng 1–9 25. Pradhan C, Modalavalasa S, Dutta S, Bharti R (2020) A geomorphic approach to evaluate river recovery potential for regulated river basin. In: River flow 2020. CRC Press, pp 1805–1809 26. Pradhan C, Chembolu V, Dutta S, Bharti R (2021a) Role of effective discharge on morphological changes for a regulated macrochannel river system. Geomorphology 385:107718 27. Pradhan C, Chembolu V, Bharti R, Dutta S (2021b) Regulated rivers in India: research progress and future directions. ISH J Hydraul Eng 28. Righetti M (2008) Flow analysis in a channel with flexible vegetation using double-averaging method. Acta Geophys 56(3):801–823 29. Shimizu Y (1994) Numerical analysis of turbulent open-channel flow over a vegetation layer using a κ − ε turbulence model. J Hydrosci Hydraul Eng JSCE 11(2):57–67 30. Siddharth A, Pradhan C, Modalavalasa S, Dutta S (2019) Effect of in-stream variable on the lower Mahanadi River, India. In: Conference paper: 16th annual meeting, AOGS 2019, Singapore 31. Singh PK, Tang X, Rahimi H (2020) A Computational Study of Interaction of Main Channel and Floodplain: Open Channel Flows. J Appl Math Phys 8(11):2526–2539 32. Siniscalchi F, Nikora VI (2012) Flow-plant interactions in open-channel flows: a comparative analysis of five freshwater plant species. Water Resour Res 48(5) 33. Sonnenwald F, Stovin V, Guymer I (2016) Feasibility of the porous zone approach to modelling vegetation in CFD. In: Hydrodynamic and mass transport at freshwater aquatic interfaces. Springer, Cham, pp 63–75 34. Stephan U, Gutknecht D (2002) Hydraulic resistance of submerged flexible vegetation. J Hydrol 269(1–2):27–43 35. Stoesser T, Wilson CAME, Bates PD, Dittrich A (2003) Application of a 3D numerical model to a river with vegetated floodplains. J Hydroinf 5(2):99–112 36. Temple DM (1986) Velocity distribution coefficients for grass-lined channels. J Hydraul Eng 112(3):193–205 37. Wu FC, Shen HW, Chou YJ (1999) Variation of roughness coefficients for unsubmerged and submerged vegetation. J Hydraul Eng 125(9):934–942

Flood Inundation Modeling Using Coupled 1D–2D HEC-RAS Model in Lower Kosi River Basin, India with Limited Data Ray Singh Meena and Ramakar Jha

Abstract In the present work coupled 1D–2D hydraulic modeling has been done with the limited available data in the lower Kosi river basin, India, which is prone to flood inundation. The research was carried out using data from the Shuttle Radar Topography Mission-Digital Elevation Model (SRTM-DEM), land use/land cover (LULC), daily water level (WL), and precipitation. The river cross-sections were extracted using the SRTM-DEM 30 m, Google Earth, and measured at Baltara (upstream) and Kursela (downstream) during the field visit. The Manning’s roughness coefficients (n) for the channel and 2D flow area were assigned according to land use/land cover. The analysis has been performed during the monsoon season (July–October) for the years 2011–2015. The water level was defined as a boundary condition at upstream (u/s) and downstream (d/s). The HEC-RAS model was capable of estimating inundated areas in the main channel and low-lying surrounding areas. SRTM-DEM with 30 m spatial resolution was used to extract the river channel, riverbanks, flow paths, and river cross-sections. The flood inundation maps were developed from 2005 to 2016 using water level data. The maximum inundation depth and inundated area found in the year 2011 were 7.33 m and 177.79 km2 , respectively. The inundated areas were compared with the observed inundated area, which showed a good R2 = 0.811. The inundated area was compared with the observed inundated area obtained from the Flood Management Information System (FMIS) and National Remote Sensing Agency (NRSA). Further, the daily precipitation was used as a boundary condition for the two-dimensional flow area and the flood inundated area in the floodplain was estimated. The coupled hydraulic model was applied successfully in the main river and the surrounding areas. Integration of easily available data, i.e., DEM, land use/land cover, water level, and precipitation may be helpful to simulate inundation in the floodplain, in case of limited data. R. S. Meena (B) Department of Civil Engineering, National Institute of Technology Hamirpur, Hamirpur, Himachal Pradesh 177005, India e-mail: [email protected] R. Jha Department of Civil Engineering, National Institute of Technology, Patna, Bihar 80000, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 V. Chembolu and S. Dutta (eds.), Recent Trends in River Corridor Management, Lecture Notes in Civil Engineering 229, https://doi.org/10.1007/978-981-16-9933-7_12

177

178

R. S. Meena and R. Jha

Keywords Kosi river · Hydraulic · Flood inundation · Land use · SRTM-DEM · Water level

1 Introduction Floods are the most recurrent, catastrophic, broad, and regular natural disasters that impact many countries around the world, including India, year after year [4, 19, 29, 30, 32, 33]. Because of financial constraints, the severity of flooding is very visible in a country where there is insufficient structural affordability [6, 35]. River floods are currently a global issue, posing a serious problem for riverside residents [7]. Floods, as well as loss of life and property due to floods, are most common in the plains of north Bihar, India. For flood control and management in the past, both structural and non-structural solutions are used. The assessment of the rainfall/runoff process according to watershed hydraulic parameters of the soil, physical characteristics of the soil, changes in land use/land cover, slope, morphology, rainfall intensity, and runoff/discharges at the watershed outlet are among the non-structural measures. The non-structural methods are crucial in assessing and managing floods, as well as obtaining flood inundation areas of various magnitudes [6, 11, 18, 28]. The nonstructural approach is an alternative to laboratory experiments or field data. Also, to predict the quantum of flood and its possible inundation obtained using numerical models are useful for the management of floodplain systems [3]. Maps illustrating flood inundation are the fundamental framework for flood risk management and mapping. These maps provide information to the government, the general public, and city planners on flood-prone areas in a zone. One-dimensional (1D), two-dimensional (2D), and/or coupled (1D–2D) hydraulic–hydrodynamic models are used to create most of the flood inundation maps. Hydrologic assessments are used to determine discharge of maximum flow for specific return periods, hydraulic simulations are used to estimate water surface elevations, and terrain analysis is used to simulate the inundation area [1, 2, 5, 10, 22, 24, 25, 27, 31, 36] (Knebl et al. [20]). The Kosi river originates in Tibet and Nepal from the Great Himalayas. It is essential to understand the soil physical and hydraulic characteristics, morphology, land use/land cover, and rainfall/runoff process to assess the flood inundation phenomena in the lower Kosi basin, India. The assessment of stream flows and flood inundation is best handled by simulating the hydrological situation that will prevail in an area under the projected weather conditions, as well as its land use, terrain, soil characteristics, and soil moisture condition. Flood extends and inundation depth mapping studies using mathematical models in the Kosi river basin are in infancy. Such research would aid planners in preparing river flood warning maps, reducing human misery, crop and vegetation damage, and infrastructure devastation. A comprehensive study in the lower Kosi river basin, India, is performed with this in view. The primary goals of this study are as follows: i.

To simulate flood inundated area in lower Kosi flood plains using the coupled 1D–2D HEC-RAS model and

Flood Inundation Modeling Using Coupled 1D–2D HEC-RAS Model …

ii.

179

To evaluate the applicability of coupled 1D–2D HEC-RAS model with limited data in the study area.

2 Study Area The Kosi river, which flows through Tibet, Nepal, and India, is a transboundary river. It is one of the major tributaries of the Ganga river. The Kosi basin is one of the Ganga river’s sub-basins (Fig. 1). It is known as “Sapta Kosi” for its seven upper tributaries in Nepal and Tibet. It is also known as “Sorrow of Bihar”, as it is causing the most devastating floods in the northern region of Bihar, India. The upper catchment lies in Nepal and Tibet. The Kosi river enters the Bihar region of India near Bhimnagar after draining a wide area in Tibet and Nepal and joins the Ganga river near Kursela gauge station Bihar. The Kosi river basin is located between 85°22 19 and 88°55 44 East and 25°20 30 and 29°07 48 North. A number of tributaries, including the Trijunga, Bhutahi Balan, Kamla-Balan, Baghmati, and Adhwara groups, as well as the three-stream channels, Sun Kosi, Arun Kosi, and Tamur Kosi, which merge to form the Kosi river, join the river before it reaches the Ganga river near Kursela. The average annual rainfall in the Kosi basin is 1456 mm on an average (FMIS, [9]). The temperature varies from 8 to 38 °C in the study area. The lower Kosi basin is dominated by the cropland area (approximately 76% of the research area). The soils of the lower Kosi river catchment are considered as a big inland delta constructed by the river’s massive sandy deposits.

Fig. 1 The location map of the Kosi river basin situated in Tibet, Nepal, and India

180

R. S. Meena and R. Jha

3 Materials and Methods 3.1 Materials The SRTM-DEM with a spatial resolution of 1 arc-second (~30 m) is downloaded from https://earthexplorer.usgs.gov/. The land use/land cover data for the lower Kosi basin are collected from the NRSA, Hyderabad and transformed as input to the hydraulic model. The daily discharge and water level data for the period of 2005–2016 (only for monsoon season) are obtained from Central Water Commission (CWC) Patna and Water Resources Department Bihar. These data are obtained for the gauge sites at Baltara and Kursela. Because these obtained data are highly restricted and confidential to direct use, they are modified prior to analysis. Historical flood inundation maps are obtained from FMIS and NRSA, Hyderabad, for validation purposes. These flood inundation maps demonstrate the spatial extent of floodwater spread. These images are georeferenced using image to image geo-referencing technique and the inundated area is calculated using supervised classification in ArcGIS 10.2. The land use/land cover data for the lower Kosi basin are collected from the NRSA, Hyderabad and transformed as input to the hydraulic model.

3.2 Coupled 1D–2D Hydraulic Model The HEC-RAS software is a computer program for simulating river flow in open natural channels and calculating water surface profiles [8, 20]. Because of its capabilities and abilities to simulate unsteady flow and identify flood-prone areas where the surface ground level is lower than the computed water profile, HEC-RAS has been accepted and is being used for river simulation by hydraulic engineers and different researchers [25]. It also allows the researcher to visualize the flood extent along a river course [22, 33]. The HEC-RAS hydraulic model has been used for flood inundation modeling in the present study. This model can perform 1D steady, 1D and 2D unsteady flow simulation, water quality and temperature modeling, and sediment transport modeling [12, 13]. The model is programmed to perform 1D, 2D, or coupled 1D–2D hydraulic calculation. The theory and formulation of basic governing equations have been discussed in this section. The continuity equation’s two-dimensional form, like the 1D form, states that the net mass flux into the control volume equals the change in storage in the control volume [14]. The only difference is that the mass fluxes are now computed in two dimensions. The two-dimensional continuity equation is written as follows: ∂(hu) ∂(hv) ∂H + + +q =0 ∂t ∂x ∂y

(1)

Flood Inundation Modeling Using Coupled 1D–2D HEC-RAS Model …

181

where t denotes time, u and v denote velocity components in the x and y directions, respectively, and q denotes the source or sink flux term. The momentum balance is based on the same principle as in the 1D case: the sum of forces acting on an element equals the rate of change of momentum. The 2D momentum balance equations can be written as follows, taking into account gravity, eddy viscosity (momentum exchange), friction, and the Coriolis effect [15]. In the x-direction, momentum balance is written as  2  ∂ u ∂u ∂u ∂H ∂ 2u ∂u +u +v = −g + vt + 2 − cfu + f v ∂t ∂x ∂y ∂x ∂x2 ∂y

(2)

Momentum balance in the y-direction is written as  2  ∂ v ∂v ∂v ∂H ∂ 2v ∂v +u +v = −g + vt + − cfu + f v ∂t ∂x ∂y ∂y ∂x2 ∂ y2

(3)

where u and v represent Cartesian velocities, g represents gravity acceleration, vt represents horizontal eddy viscosity constant, cf represents bottom roughness constant, R represents hydraulic mean depth, and f represents Coriolis variable. The acceleration terms are represented on the left side of the equation, while the internal or external forces acting on the fluid are represented on the right side. The combined 1D–2D hydraulic modeling required 2D flow area; 2D computational mesh; and linking of the 2D flow areas to 1D elements of a model. The full Saint Venant/2D diffusion wave equation is used for the analysis. The 2D Saint Venant equations are applicable to a wider range of problems and get numerically stable and accurate modeling [16]. The Saint Venant equation (diffusion wave equation) is given as C=

V T ≤ 2.0(with a max C = 5.0) X

(4)

where C denotes the courant number, V denotes the velocity of the flood wave in m/s, T denotes the time for computation in seconds, and X denotes the mean cell size in meters.

4 Results and Discussions In this study, flood inundation modeling has been done using combined 1D–2D hydraulic models in HEC-RAS. The geometric data such as river centerline, riverbanks, flow path lines, and river cross-sections are generated to prepare the HEC-RAS Import file. The stream centerline is digitized from upstream to downstream, and river and reach names are assigned. To verify the river reach connectivity, stream centerline

182

R. S. Meena and R. Jha

attributes such as topology, lengths/stations, and elevation are determined after the river and reach have been labeled. The left and right bank lines of the Kosi river have been digitized. From upstream to downstream, the flow path lines are digitized and labeled as leftover bank, main channel, and rightover bank. The river cross-sections are retrieved from the DTM at 3600 m intervals and a width of 2500 m using the construct XS cut lines tool (Fig. 2). The land use/land cover Manning’s table is constructed and for each cut line, Manning’s (n) values are extracted. Attributes such as river/reach names, stationing, banks stations, downstream (d/s) reach lengths, and elevations are included in the river cross-sectional cut lines layer using the X-S Cut line attributes menu. The terrain model of the channel region is created from the HEC-RAS cross-sections. Terrain data is exported to an image to have a background image in HEC-RAS geometric data editor. The 2D flow area polygon is created to denote the boundary of the 2D area. After creating the 2D flow area, the points can be edited to obtain the area of interest. The computational mesh is generated by selecting the grid size of Dx = 30 m and Dy = 30 m spacing. The 2D flow area is connected to the 1D element using 2D area boundary condition lines at Baltara (upstream) and Kursela (downstream). Hydraulic property tables for the 2D flow area because terrain data is necessary for any mapping of calculated results, for

Fig. 2 Extraction of HEC-RAS import data in HEC-GeoRAS

Flood Inundation Modeling Using Coupled 1D–2D HEC-RAS Model …

183

Table 1 Inundation depth in the lower Kosi river basin Inundated area (km2 )

S. No

Year

Minimum depth (m)

Maximum depth (m)

1

2005

0.00109

5.93

70.36

2

2006

0.00150

5.82

73.47

3

2007

0.00153

5.67

65.71

4

2008

0.00359

5.05

58.03

5

2009

0.00152

6.08

77.10

6

2010

0.00940

5.05

52.13

7

2011

0.00131

7.33

177.79

8

2012

0.00595

5.28

64.17

9

2013

0.01127

5.77

53.76

10

2014

0.00210

4.61

56.74

11

2015

0.00118

6.11

103.37

12

2016

0.00109

6.47

122.39

both 1D and 2D areas. After completion of terrain data, 2D flow area and coupling of 1D and 2D the boundary conditions are defined for each year (2005–2016) at Baltara (upstream gauge station) and Kursela (downstream gauge station) using an unsteady flow data tool. Using an unsteady flow analysis tool, the plan file is created for each year and the geometric file which contains the combined 1D and 2D is connected. The full Saint Venant/2D diffusion wave equation is used for the analysis. The 2D Saint Venant equations can be used to model a wide range of problems and provide numerically stable and accurate outcomes [17]. The analysis was carried out to create flood inundation maps in the study area from July 2005 to October 2016 (monsoon season). After the successful run of the model, the stored results (depth, water surface elevation, velocity, inundation boundary, duration, etc.) can be viewed using RAS Mapper. Further, minimum/maximum inundation depth and inundated area are estimated for the years 2005–2016. Table 1 shows the inundation depth and area for different years. It is observed that the maximum inundation depth (7.33 m) and inundated area (177.79 km2 ) are for the year 2011. Figure 3 demonstrates the flood inundation depth in terms of water depth at gauge stations Baltara and Kursela for the year 2011. Inundation pattern is estimated in the river channel and surrounding low-lying areas near Kursela gauge station at downstream. Further, precipitation data of the year 2007 is also incorporated as a boundary condition for the 2D flow area to simulate and understand the inundation depth pattern in Kosi flood plains. The computed inundated area using the HECRAS model near Kursela at downstream is compared with the observed inundated area (Fig. 4). The HEC-RAS model predicts lower values than observed values. The HEC-RAS model predicts inundation based on the water depth and does not consider the initial condition of the soil. Figure 5 shows flood inundation resulting from water depth and precipitation as provided information. It is important to notice that the flood inundated area is increasing as the number of provided information increases.

184

R. S. Meena and R. Jha

Fig. 3 Flood inundation map for the year 2011

Fig. 4 Comparison of observed and computed inundated area

However, the land use/land cover, morphometry, and initial soil moisture situation does not change throughout the duration of the study. It is essential to evaluate the flood delineation area and depth, after generating the flood inundation map. Figure 6 presents a qualitative comparison of model and timeseries analysis of flood inundation maps for points 1–10. It is important to notice that the locations over points 2, 3, 6–10 are on the river Kosi’s concave side, with higher altitude along the riverbank but depression/low elevation in the encompassing regions. As a result, once the floodwaters recede from high elevation areas, the flood

Flood Inundation Modeling Using Coupled 1D–2D HEC-RAS Model …

185

Fig. 5 Simulated flood inundation map with the precipitation (2007)

inundated area transforms into a water-logged area. Again, the areas above points 1, 4, 5, and 9 are actual flood inundation, which decreases as the water level falls and the intensity of the rainfall decreases to zero.

5 Conclusions In the lower Kosi basin, it is necessary to understand the soil physical and hydraulic characteristics, morphology, land use/land cover, rainfall/runoff process to assess the flood inundation phenomena under current and future alterations. An attempt is made to develop flood inundation models in the present research work toward the potential use of coupled 1D–2D HEC-RAS model. The study would be helpful especially for poorly gauged, data-scarce, and large basins for flood inundation assessment. It is necessary to better understand flood inundation processes in floodplain wetlands, especially the two-directional model coupling. The following conclusions are derived from the present study: i.

ii.

The HEC-RAS layers are generated using DTM; river length is measured 80 km from Baltara to Kursela gauge stations. The river cross-sections are extracted from the DTM at an interval of approximately 3.6 km and a width of 2.4 km. The river cross-sections measured from Google Earth and field visits improved the river cross-sections. The coupled 1D–2D HEC-RAS model has been applied successfully in the lower Kosi river basin during the monsoon season of 2005–2015. The maximum inundation depth and area were observed for the year 2011 with 7.33 m and 177.79 km2 , respectively. The computed inundated area in downstream is compared with the observed inundated area and showed good agreement between observed and computed with R2 = 0.811.

186

Fig. 6 Time-series analysis of flood inundation at different locations

R. S. Meena and R. Jha

Flood Inundation Modeling Using Coupled 1D–2D HEC-RAS Model …

iii.

iv.

v.

187

The daily rainfall data are incorporated as a boundary condition for the 2D flow area to estimate flood inundated areas in the Kosi floodplain. The time-series analysis showed how the flood inundated area (point 1, 4, 5, and 9) transforms into water-logged area (point 2, 3, 6–10) (Fig. 5). Furthermore, flood inundation modeling can be conducted with a variety of data, including slope, groundwater level, water level, discharge, land use/land cover, rainfall, soil moisture, and so on. High-resolution DEMs and additional river cross-sectional data can be used to model flood inundation. It can be done more precisely if hourly flow data in the study area is taken into account.

Acknowledgments The authors would like to extend their gratitude to the NIT, Patna, and the NIT, Rourkela, for providing all of the necessary facilities and support for this study.

References 1. Alfieri L, Salamon P, Bianchi A, Neal J, Bates P, Feyen L (2014) Advances in pan-European flood hazard mapping. Hydrol Process 28(13):4067–4077 2. Aronica GT, Franza F, Bates PD, Neal JC (2012) Probabilistic evaluation of flood hazard in urban areas using Monte Carlo simulation. Hydrol Process 26(26):3962–3972 3. Asselman N, Bates P, Woodhead S, Fewtrell T, Soares-Frazao S, Zech Y, Lhomme J (2009) Flood inundation modelling-model choice and proper application. Floodsite Report T08-09-03 11, p 142 4. Bapalu GV, Sinha R (2005) GIS in flood hazard mapping: a case study of Kosi River Basin, India. GIS Dev Weekly 1(13):1–3, 43 5. Bradley AA, Cooper PJ, Potter KW, Price T (1996) Floodplain mapping using continuous hydrologic and hydraulic simulation models. J Hydrol Eng 1(2):63–68 6. Chen YW, Tsai JP, Chang LC, Ho CC, Chen YC (2014) The development of a real-time flooding operation model in the Tseng-Wen Reservoir. Hydrol Res 45(3):490–503 7. Cirella GT, Iyalomhe FO (2018) Flooding conceptual review: Sustainability-focalized best practices in Nigeria. Appl Sci 8(9):1558 8. Duvvuri S, Narasimhan B (2013) Flood inundation mapping of Thamiraparani River Basin using HEC-GeoRAS and SWAT. Int J Eng Res Technol 2(7):1408–1420 9. Flood Management Information System (FMIS) (2013) Water Resources Department Government of Bihar. Flood Report 48 10. Gangrade S, Kao SC, Dullo TT, Kalyanapu AJ, Preston BL (2019) Ensemble-based flood vulnerability assessment for probable maximum flood in a changing environment. J Hydrol 576:342–355 11. Gee DM, Anderson MG, Baird L (1990) Large-scale floodplain modelling. Earth Surf Proc Land 15(6):513–523 12. HEC-GeoRAS (2012) GIS Tools for Support of HEC-RAS using ArcGIS 10, user’s manual, Version 10, U.S. Army Corps of Engineers 13. HEC-RAS (2016a) Hydrologic engineering center-river analysis system (HEC-RAS), user’s manual. US Army Corps of Engineers 14. HEC-RAS (Hydrologic Engineering Center-River Analysis System) (2016b) 2D Modeling user’s manual, Version 5.0. U.S. Army Corps of Engineers 15. HEC-RAS (Hydrologic Engineering Center-River Analysis System) (2016a) Applications guide, Version 5.0. U.S. Army Corps of Engineers

188

R. S. Meena and R. Jha

16. HEC-RAS (Hydrologic Engineering Center-River Analysis System) (2016c) Hydraulic reference manual, Version 5.0. U.S. Army Corps of Engineers 17. HEC-RAS (Hydrologic Engineering Center-River Analysis System) (2016d) User’s manual, Version 5.0. U.S. Army Corps of Engineers 18. Iwasa Y, Inoue K (1982) Mathematical simulations of channel and overland flood flows in view of flood disaster engineering. Nat Disaster Sci 4(1):1–30 19. Jonkman SN (2005) Global perspectives on loss of human life caused by floods. Nat Hazards 34(2):151–175 20. Knebl MR, Yang ZL, Hutchison K, Maidment DR (2005) Regional scale flood modeling using NEXRAD rainfall, GIS, and HEC-HMS/RAS: a case study for the San Antonio River Basin Summer 2002 storm event. J Environ Manag 75(4):325–336 21. Lamichhane N, Sharma S (2018) Effect of input data in hydraulic modeling for flood warning systems. Hydrol Sci J 63(6):938–956 22. Macchione F, Costabile P, Costanzo C, De Lorenzo G (2019) Extracting quantitative data from non-conventional information for the hydraulic reconstruction of past urban flood events a case study. J Hydrol 576:443–465 23. Maidment DR (2017) Conceptual framework for the national flood interoperability experiment. JAWRA J Am Water Resour Assoc 53(2):245–257 24. Papaioannou G, Loukas A, Vasiliades L, Aronica GT (2016) Flood inundation mapping sensitivity to riverine spatial resolution and modelling approach. Nat Hazards 83(1):117–132 25. Papaioannou G, Varlas G, Terti G, Papadopoulos A, Loukas A, Panagopoulos Y, Dimitriou E (2019) Flood inundation mapping at ungauged basins using coupled hydrometeorologicalhydraulic modelling: the catastrophic case of the 2006 flash flood in Volos City. Greece Water 11(11):2328 26. Razi MAM, Marimin NA, Ahmad MA, Adnan MS, Rahmat SN (2018) HEC-RAS hydraulic model for floodplain area in Sembrong River. Int J Integr Eng 10(2):151–157 27. Samela C, Albano R, Sole A, Manfreda S (2018) A GIS tool for cost-effective delineation of flood-prone areas. Comput Environ Urban Syst 70:43–52 28. Samules PG (1985) Modeling of river and floodplain flow using the finite element method. Hydraulic Research, Technical Report No. SR61, Wallingford, U.K 29. Simonov Y (2017) Latest tools and methodologies for flood modeling 30. Smith K, Ward R (1998) Floods: physical processes and human impacts. Wiley 31. Sole A, Giosa L, Albano R, Cantisani A (2013) The laser scan data as a key element in the hydraulic flood modelling in urban areas. In: Proceedings of the international archives of the photogrammetry, remote sensing and spatial information sciences—ISPRS archive 32. Tamiru H, Dinka MO (2021) Application of ANN and HEC-RAS model for flood inundation mapping in lower Baro Akobo River Basin, Ethiopia. J Hydrol: Region Stud 36:100855 33. Teng J, Jakeman AJ, Vaze J, Croke BF, Dutta D, Kim S (2017) Flood inundation modelling: a review of methods, recent advances and uncertainty analysis. Environ Model Softw 90:201–216 34. Timbadiya PV, Patel PL, Porey PD (2011) Calibration of HEC-RAS model on prediction of flood for Lower Tapi River India. J Water Res Prot 03(11):805–811 35. Wakuma Abaya S, Mandere N, Ewald G (2009) Floods and health in Gambella region, Ethiopia: a qualitative assessment of the strengths and weaknesses of coping mechanisms. Glob Health Action 2(1):2019 36. Whiteaker TL, Robayo O, Maidment DR, Obenour D (2006) From a NEXRAD rainfall map to a flood inundation map. J Hydrol Eng 11(1):37–45

Upland Catchment Management

A Study of the Impact of Some Land Use Land Cover Changes on Watershed Hydrology Indulekha Kavila and Bhava V. Hari

Abstract The state of Kerala in the southwestern tip of India supports a large population density over its extent. The area has suffered extensive and meteoric land use land cover (LULC) changes from pristine forests to monoculture plantations and settlements. The region, which receives an annual rainfall ~3000 mm, is recording progressively worsening dry season water shortages with steady water table drawdown and increasing peak streamflow as well as number of zero flow days for rivers. A comparative study of the vertical profile of relevant soil physical characteristics of rubber plantations and forests/sacred groves was done using available data. Differences in soil organic matter content and water retention are noticed. The results are examined against changes noticed in the streamflow characteristics of the Vamanapuram river and LULC changes in the Chittar watershed. Chittar is the main tributary of the Vamanapuram river. The implications of such LULC changes for the initiation of flood drought cycles that are increasingly being noticed as getting established in various regions, including Kerala, Sri Lanka, etc. are also examined. Suggestions for mitigation of these negative impacts are also presented. Keywords Land use land cover change · Soil moisture · Forest soil · Rubber soil · Soil organic matter

1 Introduction Water resources are getting depleted worldwide. Under rising population pressure, and because of the economic viability of rubber plantations due to the large demand for latex, in recent periods, large tracts of pristine forests have been converted to rubber plantations all over the world. Here, a depth resolved comparative study, I. Kavila (B) School of Pure and Applied Physics, Mahatma Gandhi University, Kerala 686 560, India e-mail: [email protected] B. V. Hari Electronic City Phase 1, Bengaluru, Karnataka 560100, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 V. Chembolu and S. Dutta (eds.), Recent Trends in River Corridor Management, Lecture Notes in Civil Engineering 229, https://doi.org/10.1007/978-981-16-9933-7_13

191

192

I. Kavila and B. V. Hari

of landscape scale data on soil moisture and soil organic matter content and the correlation between them, between rubber growing soil and forest soil from the southwest coast of India is presented. The data pertain to a hundred rubber plantations and twenty-one contiguous forests/sacred groves. The data used here are extracts from the set of detailed data collected under the project “Soil Fertility Assessment and Soil Health Monitoring in Traditional Rubber-growing Areas of Kerala, Tamil Nadu, and Karnataka” by Anil Kumar et al. [2]. Increased runoff losses, compared to natural forests, is generally reported for monoculture plantations. The possibility of flood drought cycles consequent to large-scale land use change from natural forests is pointed out. Some strategies for mitigation are also outlined. LULC changes from natural forests to rubber plantations have been particularly extensive in densely populated South East Asia [22]. More than 2 Mha (Million hectare) of cultivation area has been established over the last decade itself [22, 39]. It has already been pointed out that anthropogenic land use changes at very large scales, due to their impact on the structure and function of terrestrial ecosystems, could be raising a serious risk that goes beyond global climate change [46]. Stationarity of the characteristics of streamflow is an assumption usually made in projecting future consumption of ecosystem resources and services, for use in planning and administration. It is recognized that climate change and other anthropogenic interventions like large-scale changes in LULC may impact streamflow characteristics. The analysis of possible non-stationarity of streamflow driven by the above two factors, independently/interactively, is the concern in many recent works. For example [48, 40] explore, respectively, worsening drought and flood situations. From a global, comprehensive assessment of long term stationarity of annual streamflow for 11 069 catchments [50] reported that, while 79% of catchments which have undergone only minimum human intervention evince long term stationarity, streamflow has remained stationary only for 38% of catchments which have undergone substantial human interventions. Report of a study of the Chittar watershed of the Vamanapuram river in the State of Kerala in South India presents the pattern of streamflow of the river from the years 2000 and 2015 [8]. A distinct change is noticed in the pattern of persistent flow, with the flow showing a sudden and marked dip with negligible flow thereafter. A steady and slow decrease was the earlier pattern. From being perennial earlier, the river became non-perennial, evincing no flow for more than four months a year. Many other rivers of Kerala are also drying up. The Vamanapuram river is special in that it has no irrigation or hydroelectric power projects implemented across it. Hence, the possibility of confusion between the impact of reservoirs and the possible effects of land use change in the watershed area is not there for this river. The annual report of the National Center for Earth Science Studies presents the changes in LULC of the Chittar watershed that has taken place over the preceding century [8]. The change is from ever-green forests to plantations of eucalyptus, rubber, and Acacia mangium and also homesteads. Chittar is one of the main tributaries of the Vamanapuram river. The areal extent of the Chittar watershed is about 42 km2 which is about 5.9% of the area of the Vamanapuram river basin which has an extent of 712 km2 [3]. In the Chittar watershed, during the period from 1967 to

A Study of the Impact of Some Land Use Land Cover Changes …

193

2015, the area under rubber increased from 4 to 16 km2 while the area covered by forests/natural vegetation decreased from 23 km2 to 13 km2 [3]. In the whole of the State of Kerala, area under rubber cultivation increased from 0.115 Mha in 1961 to 0.534 Mha by 2001 [6] (Economic Reviews for the year 2001). Kerala is hydrologically isolated and the catchment areas of all its rivers lie within the boundaries of the state. It is also a historically densely populated region, particularly in its low and midland portions. Hence, the increase in area under rubber has been, for a large part, by land use change from natural forests in the upper catchment areas of the rivers. The impact of land use change from natural forest to rubber plantations is extensively studied (for a recent review see [39]), particularly because of reports of downstream dry season water shortage and water table drawdown, subsequent to the change (see, for example [44]). [21] studying variability of streamflow under land use change point to differences in infiltrability of the soil, as the cause of differences in the impact of change in land use on streamflow, under two different modes of generating land cover, viz., afforestation by natural regeneration vs. by planting. Different vegetation types can also differently affect the infiltrability as well as water storing capacity of soils (see, for example [13] and for a recent study [12]). Soil moisture release curves also can differ from soil to soil. The shape of this curve can differ with soil texture, bulk density, the amount of organic matter, and the actual makeup of the pore structure. Some vegetation types can induce hydrophobicity in soils, Eucalypti being a well-known example among trees grown in plantations [47]. Recent investigations into the possible changes in the water release characteristics of artificially hydrophobic soils show that, while there is little change for gravitational and hygroscopic water, the release of capillary water is eased [23]. Instead of the flow slowly and steadily decreasing, an initial slow decrease was followed by a rapid decline in the flow with very little flow thereafter. Such a change in the flow pattern has significant implications for the ability of rivers to provide round-the-year service. The possibility that such a change in the pattern of flow is a consequence of large-scale LULC changes in the catchment areas to crops that can change the water retention characteristics of the soil merits exploration. Motivated by the above, here, we explore the dependence of soil water content on the amount as well as nature of soil organic matter (SOM) by a depth resolved comparative study of residual water content of field moist soil and SOM, in core samples from a hundred rubber plantations and twenty-one contiguous natural forests/sacred groves. The plantations and forests/sacred groves lie along a 0.5 and excess kurtosis >1.0 are marked in bold face Sample/soil Depth range Mean attribute (cm)

Median SD

CoV

Skewness Ex. Kurtosis n

Sample size 0–20

16.69

16

3.53

0.211

0.66

1.39

23

S (cm)

20–40

20.57

19

4.83

0.235

1.05

0.595

21

40–60

24.76

25

4.83

0.195

0.277

0.526

17

60–80

28.18

27

5.64

0.200 −0.152

0.014

17

80–100

27.45

28

6.90

0.251

100–164

31.13

32

7.79

0.25

0.446

1.005

11

−0.329

−0.375

32

Soil water

0–20

0.317

0.326

0.066 0.207 −0.016

0.947

23

W (gm/cc)

20–40

0.307

0.306

0.058 0.189 −0.378

0.653

21

40–60

0.295

0.292

0.059 0.199 −0.670

1.144

17

60–80

0.312

0.320

0.080 0.258 −0.449

−0.164

17

80–100

0.328

0.313

0.093 0.282

0.310

−0.115

11

100–164

0.339

0.335

0.069 0.20

0.166

−0.811

32

SOM

0–20

0.0526 0.0513

0.014 0.268

0.290

−0.624

23

(gm/cc)

20–40

0.0291 0.0322

0.009 0.320 −0.140

−1.075

21

40–60

0.0202 0.0204

0.006 0.318

0.401

0.287

17

60–80

0.0142 0.0128

0.005 0.348

0.815

0.567

14

80–100

0.0115 0.0110

0.004 0.317 −0.222

−0.821

11

100–164

0.0087 0.0083

0.004 0.408

−0.374

32

0.600

A Study of the Impact of Some Land Use Land Cover Changes …

197

Table 4 The mean, median, standard deviation, coefficient of variation, skewness and excess kurtosis, of sample size (in cm), moisture content per unit volume of field soil (in gm), organic matter content per unit volume of soil (in gm), and the number of samples from the particular depth range, for the rubber growing soil samples. Cases with absolute skewness >0.5 and excess kurtosis >1.0 are marked in bold face Sample/soil attribute

Depth range (cm)

Mean

Median

SD

CoV

Skewness

Ex. Kurtosis

n

Sample size

0–20

15.81

16

3.68

0.233

0.431

−0.098

110

S (cm)

20–40

22.27

21

4.800

0.216

1.054

2.088

100

40–60

24.90

24

5.148

0.207

−0.045

0.099

73

60–80

28.76

28.5

7.241

0.252

0.562

0.854

74

80–100

30.06

29

7.058

0.235

0.867

0.648

50

100–156

30.73

30

7.775

0.253

0.305

0.300

126

Soil water

0–20

0.234

0.224

0.071

0.303

0.455

−0.258

110

W (gm/cc)

20–40

0.238

0.230

0.064

0.268

0.399

−0.259

100

40–60

0.246

0.245

0.063

0.255

−0.318

0.643

73

60–80

0.270

0.269

0.066

0.245

0.076

−0.610

74

80–100

0.268

0.255

0.069

0.258

0.480

−0.485

50

100–156

0.297

0.296

0.071

0.240

0.257

−0.0370

126

SOM

0–20

0.0572

0.0544

0.0196

0.343

0.432

−0.098

110

(gm/cc)

20–40

0.0367

0.0344

0.0128

0.348

0.608

0.504

100

40–60

0.0256

0.0239

0.0113

0.441

1.172

1.606

73

60–80

0.0194

0.0178

0.0086

0.442

1.595

4.402

74

80–100

0.0150

0.0146

0.0070

0.462

1.858

7.672

50

100–156

0.0103

0.0106

0.0050

0.486

0.782

1.360

126

3.3 Statistical Test for the Significance of the Median Values of Sample Sizes Not Being Different Between Forest Soil and Rubber Growing Soil Samples and the Median Value for Soil Moisture Content/SOM Being, Respectively, Greater/Lesser for Forest Soil Compared To Rubber Growing Soil The non-parametric Mann-Whitney U test was run [30] to check for the abovementioned inequalities and the results are given in Table 5. The test checks the null hypothesis that for two random variables say X and Y, selecting random pairs of values, one from one set and the other from the other, the probability of X > Y is equal to the probability of Y > X. The Mann-Whitney test is used in situations where the two samples are independently and randomly drawn from the source populations, the scale of measurements used for both samples do not have the characteristics of an equal interval scale, and the two variable values are not necessarily normally

198

I. Kavila and B. V. Hari

Table 5 Results of the Mann-Whitney U test to check whether a the median of the sample sizes of forest and rubber growing soils are significantly different b the median moisture content of forest soil is significantly greater than that of rubber growing soil and c median SOM is significantly greater in rubber growing soil than in forest soil, in each of the depth ranges. Cases which show an opposing trend to the general behavior for the particular sample/soil attribute are marked in boldface Soil attribute

Depth range (cm)

Median (Forest)

Median (Rubber)

z-score

Confidence level (%)

Sample size

0–20

16

16

1.0

84.15

S (cm)

20–40

19

21

1.75

96.02

40–60

25

24

0.06

52.41

60–80

27

28.5

0.14

55.48

80–100

28

29

0.9

81.66

100–156

32

30

0.57

71.7

Soil water

0–20

0.326

0.224

4.59

100

W (gm/cc)

20–40

0.306

0.230

4.25

100

40–60

0.292

0.245

2.95

99.69

60–80

0.320

0.269

2.13

96.71

80–100

0.313

0.255

2.08

96.27

100–156

0.335

0.296

2.85

99.56

SOM

0–20

0.0513

0.0544

0.81

79.01

(gm/cc)

20–40

0.0322

0.0344

2.27

98.85

40–60

0.0204

0.0239

1.99

97.7

60–80

0.0128

0.0178

2.63

99.57

80–100

0.0110

0.0146

1.95

97.45

>100

0.0083

0.0106

1.67

95.25

distributed. If the variables X and Y are continuous, the Mann-Whitney test may be interpreted as a test for difference in medians. Here, the twenty-one forest/sacred groves from which samples were taken are adjacent to rubber plantations. However, since samples were taken from a total of a hundred rubber plantations—almost five times the number of forest-transects sampled—we may consider the two sets of data to be independent to a reasonable extent. The soil attributes are not from samples that are equally spaced depth wise. Also, particularly in the case of SOM, the distribution within the depth ranges is not approximately normal, with rubber soil samples showing more deviation than forest soil samples. The Mann-Whitney U statistic is calculated using the formula. U=

n1  n2    S Xi , Y j , i=1 j=1

A Study of the Impact of Some Land Use Land Cover Changes …

199

where S(X i , Y j ) = 1 if X i > Y j , = 1/2 if X i = Y j and = 0 if X i < Y j . If either n1 or n2 is greater than ~20, the U statistic may be considered to be approximately normally distributed. U is calculated by treating one and then the other as the first sample. Since the sum of the two U values obtained as above is equal to n 1 n 2 , the smaller of the two values may be easily ascertained and used for conducting the test. The standardized variable z = Uσ−U will be approximately a unit normal and p > U √ 1 +n 2 +1) ) (if there are tied ranks 0.05 if |z| > 1.645. Here, U = n 12n 2 and σU = ( n 1 n 2 (n12 a formula for σU that adjusts for the ties is available and that has to be used).

3.4 Linear Fit to the Soil Moisture Content Versus SOM Relation in Various Depth Ranges—Comparative Study of the Intercept and Slope for Forest Soil and Rubber Growing Soil For each depth range, the correlation between soil moisture content and SOM is explored next by linear regression analysis. The plots along with the fitted lines are given in Fig. 1. The details of the regression analysis are given in Table 6. For the indicated depth ranges, for SOM and soil water content data of rubber soil, forest soil and for the combined set of data for the two land uses, the intercept and slope of the linear fit are given. The sum of squared residuals (SSR) from the fit and Pearson’s r correlation coefficient are given next. Pearson’s r values obtained are not significant at the 95% level. The number of data pairs n for samples of rubber soils, forest soils and for the combined set of data pairs of rubber and forest soils is given after that. The Chow test statistic F, which follows an F-distribution with k and n1 + n2 − 2 k degrees of freedom, and the critical value for this statistic F* such that the p-value for the test is less than 0.05 if F > F* are given in the last two columns. The Chow test is a test of equality between sets of coefficients in two linear regressions. The Chow test statistic is evaluated using the formula F = {[SSR12 − (SSR1 + SSR2 )]/k}/{[SSR1 + SSR2 ]/(n 1 + n 2 − 2k)}, where SSR12 refers to the SSR of the regression for the two data sets pooled together and k is the number of parameters in the regression relation. For a linear relation k = 2.

200

I. Kavila and B. V. Hari

Fig. 1 Soil moisture content per unit bulk volume (in gm) versus soil organic matter content (SOM) per unit bulk volume (in gm) for the various depth ranges, along with the fitted lines and 95% confidence intervals. The range of depth to which each subplot pertains is given atop each of the subplots. The red crosses and lines are for forest soils and the blue crosses and lines are for rubber soils. The 95% confidence interval for each fit is delineated in the respective colour for the range of SOM within each range of depth

4 Results and Discussion 4.1 Distribution of Sample Size, Soil Moisture Content and SOM Within Each Depth Range The basic statistics of depth resolved data, on sample size, soil moisture content and SOM of the 121 samples of forest soil are given in Table 3. It is noted that the mean and median are close to each other and the coefficient of variation is low. The skewness is in general low and the distribution is fairly symmetric, other than in a very few of the cases for which the distribution is moderately skewed. The excess kurtosis is also generally low except for a few cases, where also it is not large. The number of samples n lying in the various depth ranges is not large; it ranges from 11 to 32. Altogether, the distribution of the various soil attributes may be considered to be approximately normal, across the samples within each depth range. Data on a total of 533 samples were available for rubber growing soils. In the case of rubber growing soils, from Table 4 it is noted that the mean and median are close

A Study of the Impact of Some Land Use Land Cover Changes …

201

Table 6 The intercept and slope of the fitted linear relation giving soil moisture content (in gm) as a function of SOM (in gm) for various depth ranges, the squared sum of residuals (SSR), Pearson’s correlation coefficient r and the number of data points n, for rubber growing soils, forest soils and for the combined data for rubber growing and forest soils. F is the Chow statistic detailed in the text and F* is such that if F > F* the p-value for the test is 100

Slope

SSR

0.0615

0.0842

r

n

F

F*

0.07

110

16.228

3.07

0.34

23

0.065

133 11.074

3.07

4.548

3.10

5.170

3.10

2.200

3.15

4.638

3.06

−0.17 0.30

0.049 −0.19

100 21

32 158

to each other and the coefficient of variation is low, in each of the layers, except for being comparatively high for SOM. As regards skewness, in the case of the sizes of the samples taken, for a few depth ranges, the distribution is moderately skewed; the excess kurtosis however is generally low, except for a few cases where also it is not large. Skewness as well as excess kurtosis are both low for soil moisture content and hence the distribution is in this case inferred to be fairly symmetric. As regards the skewness for the distribution of SOM, within each depth range the distribution is heavy to moderately positively skewed. The kurtosis is also in general large for SOM. Both increase steadily with depth down to a meter and decrease substantially beyond that. The number of samples n within the various depth ranges is not small; it ranges from 50 to 126. Altogether, within each depth range, the distribution of soil moisture content may be considered to be approximately normally distributed across the samples. SOM, however, is not normally distributed within the depth ranges. Within layers, some of the samples show comparatively large values for SOM, and

202

I. Kavila and B. V. Hari

this is particularly to be noticed for the deeper soil layers. In contrast, forest soil does not show such a variation in behavior with depth (Table 3).

4.2 Comparison Between Forest Soil and Rubber Growing Soil—Median Value of Sample Size, Soil Moisture Content, and SOM The results of the Mann-Whitney test (Table 5) show that (a) the median of the sample size is significantly different between forest soil and rubber growing soil in the 20–40 cm depth range, but is not particularly so in the other ranges (b) in every depth range median moisture content for forest soil is significantly larger compared to rubber growing soil and (c) in the 20–100 cm depth range, median SOM content in forest soil is significantly less than the median SOM content of rubber growing soil. The conclusion with respect to SOM is consistent with the results on organic matter content reported by Anil Kumar et al. [2].

4.3 Comparison Between Forest Soil and Rubber Growing Soil—Variation of Soil Moisture Content and SOM with Depth In the case of forest soil, as may be noticed from Table 3 as well as from Fig. 2, soil water is showing a decreasing trend at first which then reverses, and the water content increases, slightly decreases, and again increases with depth. In the case of rubber

Fig. 2 On the left—median value of the soil moisture content per unit volume of forest soil and for rubber growing soil (in gm) as a function of depth. On the right—median value of soil organic matter content (in gm) as a function of depth. Color code: Blue is used for rubber soil and red for forest soil. The error bars are 1 sigma

A Study of the Impact of Some Land Use Land Cover Changes …

203

growing soils (Table 4 and Fig. 2), soil water content is, on the average, showing an increasing trend with depth; the soil moisture content in the deeper three layers is higher than that in the upper three layers. SOM shows a steady decrease with depth for both types of soils. Altogether, in the case of soil water, between forest soil and rubber growing soil, the trends with depth are opposite in the upper half meter and thereafter the trends are similar though the actual magnitudes are different. In all the depth ranges, the median value of moisture content is greater in forest soil and in all the depth ranges the median value of SOM is greater in rubber growing soil.

4.4 Linear Regression Analysis—The Relation Between Soil Moisture Content and SOM Within the Various Depth Ranges and Its Variation with Depth The following may be noticed from Table 6 and Fig. 3. In forest soils, there is a weak positive correlation between soil moisture content and SOM in the upper layers. There is an initial slight weakening with depth, but the graph starts rising again around 50 cm and the correlation is moderately positive (0.4 < r < 0.59) by a depth of around 70 cm. Beyond this, r drops, goes to zero by around 90 cm of depth and stays a little below/above zero beyond this. Soil moisture content is very weakly positively correlated (0.0 < correlation coefficient r < 0.19) with SOM in the upper layers of rubber growing soils. The correlation very soon changes to a very weakly negative one (0.0 > r > −0.19) beyond a depth of around 10 cm. The anti-correlation continues to be very weak till it strengthens to become a weak anticorrelation (−0.2 > r > −0.39) beyond a depth of around 60 cm. Though the plots

Fig. 3 On the left—the correlation coefficient of soil moisture content with SOM for various depth ranges for forest soils and rubber growing soils and, on the right—the slope of a least square fit linear relation giving soil moisture content per unit volume (in gm) as a function of SOM per unit volume (in gm). Color code: Blue is used for rubber soil and red for forest soil

204

I. Kavila and B. V. Hari

are vertically separated and the changes in trend from decreasing to increasing/vice versa of r happens at different depths and are quantitatively different, the general behavior of the variation of the value of r with depth, for forest soils and rubber growing soils, is similar (Fig. 3—left). The slope of the linear relation between soil moisture content and SOM, for forest soil samples, is positive and increasing with depth in the upper layers. The slope reaches a maximum around a depth of 0.75 m and drops and decreases to zero by around 90 cm of depth and stays below/above zero beyond this. For rubber soil samples, the slope is negative at all depths except in the surface layer and it decreases with depth almost monotonically. The slope has a small positive value in the top 0–10 cm layer. It may be recalled here that the results of the Mann-Whitney test reported in Table 5 indicate that there is no statistically significant difference between forest soil and rubber soil in the matter of quantity of SOM per unit volume for the top 0–20 cm layer; and as seen here, the correlation between soil water content and SOM is qualitatively the same in the top layer. There is a quantitative difference in that, though positive in both cases in this zone, in the case of rubber growing soil the correlation is comparatively weaker. At all other depths, between forest soil and rubber growing soil, there is a quantitative difference in SOM content and there is qualitative difference in the way soil water content varies with SOM—the correlations run in opposite directions, except for the 80–100 cm range. It is noted that, in the 80–100 cm range, the difference in the slope and intercept between the linear fits for forest soil and rubber growing soil is not significant (Table 6). The correlation between SOM content and soil water content for forest and rubber growing soils, as reported and noted in Table 6, is significant at the 95% level for only a few cases. Also the data had some outliers in a few of the cases for either SOM or soil water content. Hence, Theil-Sen estimate of the slopes for linear fits was also made and the correlation was assessed with Kendall’s tau also. The sign of the slope for the various cases remained unaltered, except for forest soil at >1 m depth where the sign changed and was negative as per the new estimate. The absolute value of the slopes decreased. The intercept values did not change very much. The trend with depth remained the same. Theil-Sen estimate for the slope, which is the median of the slopes of all lines between pairs of points, is insensitive to outliers. Kendall’s tau, which is a rank correlation coefficient is less sensitive to outliers than Pearson’s r. The Chow test was done using the new estimates for the slope and intercept; the results were unaltered. That is, except in the 80–100 cm range, the slope and intercept of the linear fits were significantly different for forest and rubber growing soils, as was the case when these parameters were determined by least square fitting.

4.5 Discussion The median of the values for soil moisture content and SOM determined for core samples from depths up to slightly more than 1.5 m, collected from plantations and contiguous forest/sacred groves along about 700 km of the west coast of south India

A Study of the Impact of Some Land Use Land Cover Changes …

205

during summer, and for each sample assigned to the mid-depth of core samples which have sizes ranging from 9 to 55 cm in the case of rubber soil samples and from 10 to 45 cm in the case of forest samples, are compared in a depth resolved fashion. The influence of the parameters SOM, soil depth and the categorical variable land use type on soil moisture content is presented. At all depths explored, the median value for the soil moisture content is significantly larger for forest soils. At all depths, except in the 0–20 cm range, the median value for SOM is significantly larger for rubber growing soils. In the case of rubber growing soils, in the deeper layers the distribution of SOM is significantly positively skewed and the excess kurtosis is also large. This is not so for forest soil. For rubber growing soils, the correlation between moisture content and SOM is found to be very weakly positively correlated in the top 0–10 cm layer. Surprisingly, as we go deeper in rubber growing soils, the positive correlation changes to a very weakly negative one and then to a weakly negative one by a depth of around 60 cm and stays weakly negative beyond this depth. In the case of forest soils, from being weakly positive in the upper layers, the correlation strengthens to become moderately weak around a depth of 75 cm. Beyond this the correlation drops and becomes very weakly negative/positive beyond around 90 cm of depth. Soil Organic Carbon (SOC) is considered a significant factor, along with bulk density and clay content, for explaining available water content (AWC) of soils. It is generally considered to improve the water retention characteristics of soil. Fu et al. [12] in a study covering three land use types (irrigated pasture, dryland pasture and irrigated cropping), over soil of three different drainage types (well, moderately and poorly drained) have recently pointed out that, while these factors can explain about half the observed variability in AWC, inclusion of the categorical variables land use and soil type, and soil depth, can explain an additional 6–13% of this variance. In the case of SOC, the possibility that the specific nature of SOC might influence the movement of water by influencing the surface tension of the soil solution was recognized early [11]. Fu et al. [12] conclude that there is potential benefit in including the categorical variables land use (long term) and soil type, and soil depth and their interaction with continuous variables like bulk density, soil organic carbon and clay content in developing pedotransfer functions for available water content and saturated hydraulic conductivity of soils. Pedotransfer functions as developed, permit estimates of subsets of soil physical, mechanical, chemical, biological, and hydraulic properties that are more difficult to determine to be made with minimal inaccuracy, by logically linking them to soil parameters/attributes that may be measured more easily and with less uncertainty. The results presented here indicate the possibility that the categorical variable nature of land use–whether natural forest/rubber plantation—does influence carbon sequestration and soil moisture. While at all depths except in the top 10 cm, the content of SOM is statistically larger for rubber growing soil, rubber soil is statistically drier than forest soil. In particular, the overall anti-correlation between the SOM content and the moisture content of rubber growing soil may be considered commensurate with SOM in rubber growing soil working against water retention. Results from experiments with artificially hydrophobic soils indicate the possibility

206

I. Kavila and B. V. Hari

of a faster drain-out of capillary water from such soils. Such a behavior is consistent with interpreting the anti-correlation between soil moisture content and SOM content of rubber growing soil, as seen here for soil samples collected in the summer, as arising from a possible antipathy of the SOM of rubber growing soil for water. The lignin content of rubber litter is proposed as a possible reason for this antipathy since lignin is known to be water repellent: it might also be making rubber litter resistant to degradation thus accounting for the higher SOM content in the deeper layers of rubber soil. Deeper layers are aerated to a lesser extent. The comparative dryness of rubber growing soil is consistent with the rapid drain-down observed in the persistent flow of the Vamanapuram river. This drain-down may presumably be due to the large-scale change in LULC, from natural forests to plantations of rubber, acacia and eucalyptus (for the comparatively high lignin content of acacia litter see [35]/for soil water repellency induced by eucalyptus reforestation see [47]) that has taken place in the catchment area of this river, over some decades past. The possibility and the picture of rapid drainage of rubber growing soils, which this study points out, is consistent with the reports of dry season water shortage downstream of areas that have gone through such a land use change. It may be noted that it is difficult to attribute the change in the pattern of persistent flow, similar to what is noticed in the case of the Vamanapuram river, to global warming or climate change. Global warming, which increases evaporation losses, intensifies the concerns that the conclusions of this study raise. The results of this study calls also for caution in using below-ground carbon stock by itself as an indicator of ecosystem health [29, 38]. The relative importance of the other continuous variables, bulk density and clay content in controlling soil water content, and the joint effect of these factors and SOM content needs further investigation. Varying the analysis by incorporating assumed variations in the SOC to SOM conversion factor (which, for both soils, is here given the standard value in use) for different litter types at different depth ranges, within the limits proposed by [33], can check whether the results of this study are robust to variations in this factor. Also, a paired study of data on samples from adjoining forestrubber plantation pairs and a chrono-sequenced study with respect to the number of years since change of land use from forest to rubber will be informative.

4.5.1

The Possibility of Flood Drought Cycles Consequent to Large-Scale LULC Change from Natural Forests

Floods and Droughts Human settlements and agricultural and other economic activities can change the characteristics of soil and also the interactions of soil with other components of nature and hence affect soil ecosystem functions. There can be a variety of consequences for hydrological phenomena. In this context, the problem of repeated floods and droughts that is being noticed in the South Asian region (see, for example [15, 27]) is discussed next.

A Study of the Impact of Some Land Use Land Cover Changes …

207

Currently, Sri Lanka, which suffers from a serious deforestation problem, is facing annual droughts and floods (see, for example [19], which reports on concomitant problems). Rubber was first brought to Kerala from Sri Lanka (latitudes 5°55 and 9°51 N and longitudes 79°41 and 81°53 E), more than a century ago. The situation in Kerala, which in recent years has repeatedly seen floods (in 2018, 2019 and 2021), along with devastating landslides, is raising serious concern [26]. The National Center for Earth Science Studies, situated in Kerala, report increase in the number of low or no flow events–significant decrease in river discharge during non-monsoon months– in the rivers taken up for their study and, increasing loss of water to evapotranspiration and declining ground water levels over major portions of Kerala over the past few decades. The latter two of the changes noticed can arise from global warming as well as from LULC changes on the ground [9]. Increased runoff losses, soil erosion due to entrainment and loss of biodiversity are some of the negative aspects reported in the case of monoculture plantations. Various studies report increased runoff losses due to LULC change from natural forests to pasture, monoculture plantations including rubber plantations etc., as occurring due to reduction in infiltration rates (see for example [14, 41, 49]). Floods can occur when rainfall is intense and of a longer duration, infiltration is low and runoff is large. In the case of the Western Ghats, [34] report from an experimental study that, infiltration rates are extremely high–high enough to preclude overland flow even under heavy rainfall. Infiltration is rapid and vertical, there is very little/no lateral flow through the soil matrix. But once the water table rises and reaches near the surface, water is carried toward streams by the comparatively faster flow through a network of micro/macro pipes. The pipes, which can form, for example, via soil erosion upward from the exit points of the flows, can get damaged by afforestation/cultivation efforts, which can then result in overland flow from out of the opening, when the soil is already saturated. Since overland flow is much faster than pipe flow, this drains out water from the soil earlier and faster and can lead to dry season water shortages. Putty and Prasad [34] note that landslides can occur along slopes where pipes have been damaged by human intervention, causing release of stored subsurface water leading to immediate drawdown of ground water levels. It is also pointed out here that, farming activities can damage the pipe network and construction activities and farming activities with heavy machinery can damage pipes as well as cause soil compaction. Compact soil offers higher resistance to water flow through the soil matrix. The region of compaction can act like a subsurface dam in view of the already damaged pipes. This can lead to a build-up of subsurface water upslope of the region of pipe damage and compaction. Further heavy rainfall and infiltration in the upslope can increase the pressure due to the weight of the accumulated water on the region of compaction. When the region of compaction gives way under the weight of saturated soil, a landslide occurs. The original cause of the obstruction like houses sitting on the ground can be carried away with the flow of soil saturated with water. The water stored in the soil uphill can flow away through the breach. Hillslopes act like storage tanks from which water is released into streams and thence to rivers. Breaches reduce the stored volume of active water which could otherwise have contributed to dry season persistent flow, which normally follows an exponentially

208

I. Kavila and B. V. Hari

decaying pattern and continues until the active ground water is exhausted. A number of instances of landslides which carry away human settlements in sloping regions are being repeatedly reported from Kerala. Two such recent devastating landslides are the ones at Pettimudi (in the year 2018) and Koottickal (in the year 2021).

LULC Changes in Kerala—Natural Forests, Rubber Plantations, Urban Areas The South Western Ghats carries montane rain forests, occurring at elevations above a 1000 ft and with annual rainfall exceeding 2,800 mm. These forests are thick. Since in reporting forest cover for the state, plantations also get included in the category of forests, any reasonable estimate of the area under natural forest or the area that has undergone conversion from natural forest to plantation is difficult to make. As per India State of Forest Report (2019) [18] 54.42% of the state’s total geographical area is under forest cover, of which 4.9% is very dense forest (tree canopy density > 70%). These are based on the interpretation of satellite data for the period December 2017 to March 2018. The area under forest cover reported by the state in 1959 is 25.8% [6]. In 1983, the state reported area under forest cover as ~24% of which plantation forest (teak, eucalyptus and softwood) is 16%. This amounts to 3.84% of the total area. This implies that, presumably, less than 21% of the area was under natural forest cover in 1983, thus giving a measure of the loss of the area under natural forest as 5.64%, over the 24 years from 1959 to 1983. Assuming a similar rate of loss of natural forest for the next 36 years from reporting year 1983 to the reporting year 2019, a further loss of about 8.46% of the total area is estimated. This leaves natural forest cover of under 13% of the total area by 2019. This estimate is not incommensurate with the report of 4.9% as the area under very dense forest cover in the state by 2017–18. Currently, rubber plantations extend over slightly more than 14% of the geographical area of the state [7]; it was slightly less than 3% in 1961 [6]. Approximately half of the area of the state is in the category of highland with elevation >76 m and most of the rubber plantations are on sloping terrain. Steepness of terrain aggravates runoff losses. From data on urbanization in the state [43] the urban population of the state increased six-fold from about 2.64 million in 1961 to about 15.93 million by 2011. Increase in urban built-up area, for houses, roads, and buildings for work and trade and other institutional activities, generate a continuum of impermeable surfaces. This can also result in large runoff losses.

Role of earthworms Fragoso et al. [10] in their study of worm populations advocate that communities of tropical agro ecosystems are composed primarily of endogeic species. Chaudhuri et al. [5] in their study of earthworm populations in rubber plantations in Tripura, India point out that, mostly surface living endogeic worms are present and that the

A Study of the Impact of Some Land Use Land Cover Changes …

209

deep burrowing anecic worms are absent. They conjecture that this might be due to the comparatively higher lignin content of rubber litter because of which it is not easily biodegradable and thus is not very palatable to worm populations. At and near the surface, better aeration permits easier degradation. Anecic worms can burrow deep. Burrows go vertically down from the surface to more than 2 m, have diameters greater than a centimeter, can last long, and are also networked. Such worm burrows have been noted as conducive to significantly increasing the infiltrability of soils [37] and their usefulness for alleviating the negative effects of extreme rainfall events on soil and plants has been pointed out (see, for example [1]). Reduction in below the surface worm populations could be a very significant factor that contributes to the reduced infiltration rates and increased runoff losses of soils of other monoculture plantations like those of eucalyptus, Acacia mangium, teak, etc. also. Litter from these trees do not degrade easily (see, for example [4, 16, 31]). Presence of pesticides and fertilizers in the soil can kill earthworm populations.

4.5.2

Possibilities for Mitigation of Reduced Infiltration and Increased Runoff Loss

The reduction in infiltration is in many cases attributed to the reduction in understorey above ground (for example [32]) and reduction in the number and length of roots underground (for example [49]). Some of the mitigation strategies suggested include fodder and no-till cover systems [45], agroforestry [24], intercropping [49], onceweeding [25], less frequent weeding [32] and a double-row plantation system along with intercropping for rubber [17]. Singh et al. [39] making a comprehensive analysis suggest that including agroforestry or polyculture, integrated pest management, cover cropping, mulching, and composting can improve the multiply debilitated ecosystem functions of rubber plantations to some extent. It is noted that these mitigation measures can reduce profits as well as the ease and efficiency of management of plantations, while not completely alleviating all the problems. Soil hydrophobicity can occur in plantations of various kinds (see, for example [28]). It can reduce infiltration rates and also hasten water release. Various cultivation practices and other physical, biological, and chemical means are suggested for ameliorating hydrophobicity of surface soil (see, for example [36]). These can be expensive and some may not be practical for hydrophobicity in the deeper layers of soil and for plantations like those of trees. Application of surfactants, which improve wettability, can reduce hydrophobicity and thus improve infiltration and reduce runoff. However, repeated applications of surfactants can have other concomitant problems (see, for example [42]).

4.5.3

Overview and Suggestions for Recovery

Kerala is witnessing repeated incidences of flood and the state is recording increasing evapotranspiration rates and also decreasing ground water levels. Climate change,

210

I. Kavila and B. V. Hari

leading to changes in the intensity, frequency, duration, and phase (IFDP) of rainfall, with more instances of high intensity rainfall without much overall change in total precipitation, as well as increasing instances of other extreme weather events like heat waves, can further aggravate such problems. Comparing Kerala with other extensively densely populated regions of the world, Economic Reviews 1961 [6] had recorded the alarmingly high population density of the state (1091 per sq. mile with the next two highest reported standing at 754 and 655 per sq. mile for Belgium and Japan—current values for the three regions are, respectively, 2300, 991, and 899 per sq. mile) and concomitant problems like job insecurity and low per capita availability of land resources. In fact, the erstwhile princely State of Travancore, which currently constitutes a major portion of South Kerala, started importing food grains by the last quarter of the 19th century and export of food grains stopped by the end of that century. The urgent need for population control measures was also pointed out in Economic Reviews [6]. In 1952, India became the first country in the world to adopt a family planning policy “to stabilize the population at a level consistent with the requirement of national economy.” Kerala, despite its comparatively high literacy rate, registered a subsequent jump in population growth. While the decadal increase in population between the censuses of 1971 and 1961 was only 1.7 million, subsequent decades saw, respectively, increases by 4.1, 3.9, and 2.8 million, dropping to 1.5 million (i.e., below 1.7 million) only by 2001–2011. There was a 1.7-fold increase in population in the half-century between the censuses of 1961 and 2011. Kannan and Hari [20] point out that, by 2017–18 the total number of Kerala’s working emigrants stood close to the total number employed in Kerala’s organized sector, which is estimated to be around 2 million. The state did well economically. However, the ecological cost in supporting the large population was large, with large LULC changes, including deforestation, taking place. In the light of the study presented and analyzing the run up to the present situation in Kerala of repeated floods and dry season water shortages against the backdrop of high and increasing population density and ill effects from global warming and climate change, the following policy measures are proposed: a scaling down of human presence by adoption of one/two-child norm and rewilding of the Western Ghats. All the rivers of Kerala originate and run their full course within the state. Thus, hydrologically, Kerala is like an island or a continent. Hence, these considerations may, mutatis mutandis, be applied to other regions of the world too after adjusting for local annual rainfall/water availability.

5 Conclusion Across the rubber plantations and forests along the southwestern coast of India studied, and at all depths studied, the median value of water content of moist soil is seen to be larger for forest soil than for rubber growing soil. The median value of Soil Organic Matter content is seen to be lesser for forest soil than for rubber

A Study of the Impact of Some Land Use Land Cover Changes …

211

growing soil. Other than for a layer at the surface, water content is seen to be negatively correlated with SOM for all depths, in the case of rubber growing soil. This correlation is positive for forest soil at most depths; at depths greater than around 90 cm it varies between being very weakly negative and very weakly positive. The possibility that Kerala might be witnessing floods and dry season water shortages due to its high population density and consequent extensive changes to LULC is pointed out. Measures for amelioration are also suggested. Acknowledgements The data used in this study is from the project “Soil Fertility Assessment and Soil Health Monitoring in Traditional Rubber-growing Areas of Kerala, Tamil Nadu and Karnataka” a collaborative effort of the Rubber Research Institute of India (RRII) of the Rubber Board and the Indian Council of Agricultural Research—National Bureau of Soil Survey and Land Use Planning (ICAR-NBSS&LUP). IK acknowledges discussions with G Gopinathan and M K Nair.

References 1. Andriuzzi WS, Pulleman MM, Schmidt O et al (2015) Anecic earthworms (Lumbricus terrestris) alleviate negative effects of extreme rainfall events on soil and plants in field mesocosms. Plant Soil 397:103–113. https://doi.org/10.1007/s11104-015-2604-4 2. Anil Kumar KS, Nair MK, Ramesh Kumar SC, Jessy MD (2016) Soil quality monitoring sites (SQMS) for traditional rubber-growing areas of South India (NBSS Technical Report) Indian Council of Agricultural Research (ICAR)-National Bureau of Soil Survey and Land Use Planning Regional Centre—Bengaluru, Rubber Research Institute of India (Rubber Board, Govt. of India) 3. Arulbalaji P, Maya K (2019) Effects of land use dynamics on hydrological response of watershed: a case study of Chittar Watershed, Vamanapuram River Basin, Thiruvananthapuram District, Kerala, India Water Conserv Sci Eng 4:33–41 4. Bachega LR, Decomposition of Eucalyptus grandis and Acacia mangium leaves and fine roots in tropical conditions did not meet the home field advantage hypothesis. Forest Ecol Manag 359:33. https://doi.org/10.1016/j.foreco.2015.09.026 5. Chaudhuri PS, Nath S, Paliwal R (2008) Earthworm population of rubber plantations (Hevea brasiliensis) in Tripura, India. Trop Ecol 49(2):225 6. Economic Review (1959/1960/1961/2001) Economic Review Archive, Government of Kerala, State Planning Board. https://spb.kerala.gov.in/en 7. Economic Review (2020) Government of Kerala, State Planning Board, vol 2, p 70 https://spb. kerala.gov.in/sites/default/files/2021-01/English-Vol-2_0.pdf 8. ESSO-NCESS Annual Report 2017–18: National Centre for Earth Science Studies (NCESS), Earth System Sciences Organization (ESSO), Ministry of Earth Sciences, Govt. of India, p 49. https://www.ncess.gov.in/images/Annual_Reports/AR_2017-18_Final.pdf (ESSONCESS Annual Report 2017–18) 9. ESSO-NCESS Annual Report 2019–20: National Centre for Earth Science Studies (NCESS), Earth System Sciences Organization (ESSO), Ministry of Earth Sciences, Govt. of India, p 82. https://www.ncess.gov.in/images/NCESS_Annual_Report__2019-2020_E.pdf 10. Fragoso C, Lavelle P, Blanchart E, Senapati BK, Jimenez JJ, Martinez MA, Decaens T, Tondoh J (1999) Earthworm communities of tropical agro ecosystems: origin, structure and influence of management practices. In: Lavelle P, Brussaard L, Hendrix P (eds) Earthworm management in tropical agroecosystems. CAB International, Wallingford, UK, pp 27–55

212

I. Kavila and B. V. Hari

11. Free EE (1911) Studies in soil physics, II. In: The Movements of Soil Water. The plant world, vol 14, no 3 59–366. Wiley on behalf of the Ecological Society of America Stable. https:// www.jstor.org/stable/43476850 12. Fu Z, Hu W, Beare M, Thomas S, Carrick S, Dando J, Langer S, Müller K, Baird D, Lilburne L (2021) Land use effects on soil hydraulic properties and the contribution of soil organic carbon, J Hydrol 602:126741. ISSN 0022-1694. https://www.sciencedirect.com/science/article/pii/S00 22169421007915 13. Gajic B, Dugalic G, Sredojevic Z, Tomic Z (2008) Effect of different vegetation types on infiltration and soil water retention Cereal Research Communications. In: Supplement: proceedings of the VII. Alps-Adria scientific workshop, Stara Lesna, Slovakia, pp 991–994 14. Germer S, Neill C, Krische AV, Elsenbeer H (2010) Influence of land-use changes on nearsurface hydrological processes: undisturbed forest to pasture J Hydrol 380:473–480. https:// www.cabdirect.org/cabdirect/abstract/20103053074 15. Gupta J, Bhushal R (2021) Droughts and late monsoon floods batter South Asia. Eco-Business. https://www.eco-business.com/news/droughts-and-late-monsoon-floods-batter-south-asia/ 16. Gusmailina, Komarayati S, Wibisono HS (2020) Potential uses of teak leaf litter for liquid smoke and of other utilizations: a review. IOP Conf Ser: Mater Sci Eng 935:012015. https:// doi.org/10.1088/1757-899X/935/1/012015 17. Huang J, Pan J, Zhou L, Zheng D, Yuan S, Chen J, Li J, Gui Q, Lin W (2020) An improved double-row rubber (Hevea brasiliensis) plantation system increases land use efficiency by allowing intercropping with yam bean, common bean, soybean, peanut, and coffee: a 17-year case study on Hainan Island, China. J Clean Prod 263:121493. https://doi.org/10.1016/j.jcl epro.2020.121493 18. India State of Forest Report 2019: Forest Survey of India, Ministry of Environment Forest and Climate Change, Government of India p 24; https://fsi.nic.in/isfr19/vol1/chapter2.pdf. Accessed 27 Sep 21 19. International Labour Organization Report 2018 (2018). Assessment of the effects of annual drought and floods on child labour (hazardous and non-hazardous) and child welfare in Sri Lanka/International Labour Office, ILO Country Office for Sri Lanka and the Maldives, Fundamental Principles and Rights at Work Branch (FUNDAMENTALS). Geneva, ILO. ISBN: 978-92-2-031266-7 (Print). 978-92-2-031267-4 (Web pdf) https://www.ilo.org/wcmsp5/gro ups/public/---ed_norm/---ipec/documents/publication/wcms_651800.pdf 20. Kannan KP, Hari KS (2020) Revisiting Kerala’s gulf connection: half a century of emigration, remittances and their macroeconomic impact 1972–2020. Ind J Lab Econ 63:941–967. https:// doi.org/10.1007/s41027-020-00280-z 21. Lacombe G, Ribolzi O, de Rouw A, Pierret A, Latsachak K, Silvera N, Pham Dinh R, Orange D, Janeau J-L, Soulileuth B, Robain H, Taccoen A, Sengphaathith P, Mouche E, Sengtaheuanghoung O, Tran Duc T, Valentin C (2016) Contradictory hydrological impacts of afforestation in the humid tropics evidenced by long-term field monitoring and simulation modelling. Hydrol Earth Syst Sci 20:2691–2704. https://hess.copernicus.org/articles/20/2691/ 2016/ 22. Lang R et al (2019) Converting forests into rubber plantations weakened the soil CH4 sink in tropical uplands. Land Degrad Dev 30:2311–2322 23. Liu H, Ju Z, Bachmann J, Horton R, Ren T (2012) Moisture-dependent wettability of artificial hydrophobic soils and its relevance for soil water desorption curves. Soil Sci Soc Am J 76:342– 349. https://doi.org/10.2136/sssaj11.0081 24. Liu W, Luo Q, Lu H, Wu J, Duan W (2017) The effect of litter layer on controlling surface runoff and erosion in rubber plantations on tropical mountain slopes, SW China. CATENA 149:167–175. ISSN 0341-8162. https://doi.org/10.1016/j.catena.2016.09.013. https://www.sci encedirect.com/science/article/pii/S0341816216303824 25. Liu H, Yang X, Blagodatsky S, Marohn C, Liu F, Xu J, Cadisch G (2019) Modelling weed management strategies to control erosion in rubber plantations. CATENA 172:345–355. ISSN 0341-8162, https://doi.org/10.1016/j.catena.2018.08.041. https://www.sciencedirect.com/sci ence/article/pii/S0341816218303655

A Study of the Impact of Some Land Use Land Cover Changes …

213

26. Madhusoodhanan CG, Sreeja KG (2019) Can floods lead to drought? After the flood, severe drought looming over Kerala” in South Asia Network on Dams, Rivers and People. https://san drp.in/2019/02/13/after-the-flood-severe-drought-looming-over-kerala/ 27. Mahapatra S, Jha MK, Biswal S, Senapati D (2020) Assessing variability of infiltration characteristics and reliability of infiltration models in a tropical sub-humid Region of India. Sci Rep 1515:2045–2322. https://doi.org/10.1038/s41598-020-58333-8 28. Mandal D, Jayaprakash J (2009) Water repellency of soils in the lower Himalayan regions of India: impact of land use. Curr Sci 96(1):148–152. http://www.jstor.org/stable/24104743 29. Mandal UK, Sharma KL, Venkanna K, Pushpanjali Adake RV, Masane RN, Prasad JVNS, Venkatesh G, Rao CS (2017) Sustaining soil quality, resilience and critical carbon level under different cropping systems in semi-arid tropical Alfisol soils. Curr Sci 112(9):1882–1895. https://en.x-mol.com/paper/article/1421203969439965184 30. Mann-Whitney U test calculator. Soc Sci Stat, as used on 10 October 2021. https://www.soc scistatistics.com/tests/mannwhitney/ 31. Martins C, Natal-da-Luz T, Sousa JP, Gonçalves MJ, Salgueiro L, Canhoto C (2013) Effects of essential oils from Eucalyptus globulus leaves on soil organisms involved in leaf degradation. PLoS One 8(4):e61233. https://doi.org/10.1371/journal.pone.0061233. PMID: 23577212; PMCID: PMC3618273 32. Neyret M, Robain H, de Rouw A, Janeau J-L, Thibaut D, Kaewthip J, Trisophon K, Valentin C (2020) Higher runoff and soil detachment in rubber tree plantations compared to annual cultivation is mitigated by ground cover in steep mountainous Thailand. CATENA 189:104472. https://doi.org/10.1016/j.catena.2020.104472 33. Pribyl DW (2010) A critical review of the conventional SOC to SOM conversion factor. Geoderma 156(3–4):75–83, ISSN 0016-7061. https://doi.org/10.1016/j.geoderma.2010. 02.003. https://www.sciencedirect.com/science/article/pii/S0016706110000388 34. Putty MRY, Prasad R (2000) Runoff processes in headwater catchments—An experimental study in Western Ghats, South India. J Hydrol 235:63–71. http://wgbis.ces.iisc.ernet.in/biodiv ersity/sahyadri_enews/newsletter/issue53/bibliography/176-runoff-processes-in-headwatercatchments-an-experimental-study-in-wg-si.pdf 35. Rahman MM, Tsukamoto J, Rahman MM, Yoneyama A, Mostafa KM (2013) Lignin and its effects on litter decomposition in forest ecosystems. Chem Ecol 29(6):540–553. https://doi. org/10.1080/02757540.2013.790380 36. Roper MM, Davies SL, Blackwell PS, Hall PJM, Bakker DM, Jongepier R, Ward PR (2015) Management options for water-repellent soils in Australian dryland agriculture. Soil Res 53:786–806 https://doi.org/10.1071/SR14330 37. Shipitalo MJ, Butt KR (1999) Occupancy and geometrical properties of Lumbricus terrestris L. burrows affecting infiltration. Pedobiologia 43:782. http://www.urbanfischer.de/journals/pedo 38. Shukla MK, Lal R, Ebinger M (2006) Determining soil quality indicators by factor analysis. Soil Tillage Res 87(2):194–204. ISSN 0167-1987. https://doi.org/10.1016/j.still.2005.03.011. https://www.sciencedirect.com/science/article/pii/S0167198705001200 39. Singh AK, Liu W, Zakari S, Wu J, Yang B, Jiang XJ, Zhu X, Zou X, Zhang W, Chen C, Singh R, Nath AJ (2021) A global review of rubber plantations: impacts on ecosystem functions, mitigations, future directions, and policies for sustainable cultivation. Sci Total Environ. 796:148948. https://doi.org/10.1016/j.scitotenv.2021.148948. PMID: 34273842 40. Singh N, Chinnasamy P (2021) Non-stationary flood frequency analysis and attribution of streamflow series: a case study of Periyar River, India. Hydrol Sci J. https://doi.org/10.1080/ 02626667.2021.1968406 41. Song L, Boithias L, Sengtaheuanghoung O, Oeurng C, Valentin C, Souksavath B, Sounyafong P, de Rouw A, Soulileuth B, Silvera N, Lattanavongkot B, Pierret A, Ribolzi O (2020) Understory limits surface runoff and Soil Loss in Teak tree plantations of Northern Lao PDR. Water 12(9):2327. https://doi.org/10.3390/w12092327 42. Song E, Goyne KW, Kremer RJ, Anderson SH, Xiong X (2021) Certain soil surfactants could become a source of soil water repellency after repeated application. Nanomaterials 11:2577. https://doi.org/10.3390/nano11102577

214

I. Kavila and B. V. Hari

43. State Urbanization Report Kerala: 2012 A study on the scattered human settlement pattern of Kerala and its development issues. Department of town and country planning, Government of Kerala, p 28 https://townplanning.kerala.gov.in/town/wp-content/uploads/2018/12/SUR.pdf. Accessed 03 Oct 21 44. Tan Z-H, Zhang Y-P, Song Q-H, Liu W-J, Deng X-B, Tang J-W, Deng Y, Zhou W-J, Yang L-Y, Yu G-R, Sun X-M, Liang N-S (2011) Rubber plantations act as water pumps in tropical China. Geophys Res Lett 38:L24406. https://doi.org/10.1029/2011GL050006. https://en.x-mol.com/ paper/article/1247285563670597632 45. Valentin C, Boonsaner A, Janeau JL et al (2014) Lessons from long-term monitoring of soil erosion in three Southeast Asian agricultural catchments undergoing rapid land use changes. In: Hydrology in a changing world: environmental and human dimensions proceedings of FRIEND-water 2014, Hanoi, Italy 46. Vitousek PM, Mooney HA, Lubchenco J, Melillo JM (1997) Human domination of Earth’s ecosystems. Science 277:494–499. https://doi.org/10.1126/science.277.5325.494 47. Walden LLL, Harper RJ, Mendham DS, Henry DJ, Fontaine JB (2015) Eucalyptus reforestation induces soil water repellency. Soil Res 53:168–177. https://www.researchgate.net/publication/ 272822994_Eucalyptus_reforestation_induces_soil_water_repellency 48. Wang Y, Duan L, Liu T, Li J, Feng P (2020) A non-stationary standardized streamflow index for hydrological drought using climate and human-induced indices as covariates. Sci Tot Environ 699:134278. ISSN 0048-9697. https://doi.org/10.1016/j.scitotenv.2019.134278. https://www. sciencedirect.com/science/article/pii/S0048969719342615 49. Xing M, Lacombe G, Harrison R, Xu J, van Noordwijk M (2019) Expanding rubber plantations in Southern China: evidence for hydrological impacts. Water 11(4):651. https://doi.org/10. 3390/w11040651 50. Yang Y, Roderick ML, Yang D et al (2021) Streamflow stationarity in a changing world. Environ Res Lett 16:064096. https://iopscience.iop.org/article/https://doi.org/10.1088/17489326/ac08c1

Modeling of Sediment Yield of Tawi Catchment to Identify the Critical Sources Area Soban Singh Rawat, Bhaskar Ramchandra Nikam , Pradeep Kumar, and Prasun Kumar Gupta

Abstract Tawi, a major River in Jammu region, plays a vital role for sustaining Jammu and Udhampur, the most populous cities in the region. The catchment of this river faces a severe problem of soil erosion. High rainfall coupled with fragile terrain, and high relief conditions in the Tawi catchment are conducive to soil erosion. Additionally, the heavy population load causes the ecological degradation, which has accelerated the soil erosion from the catchment. Most part of this huge sediment load ultimately gets deposited in the surface of the river channel which significantly reduces the channel’s carrying capacity and results in flood during monsoon season. The study was dedicated toward the development of a GIS-based spatially distributed sediment yield model, which estimates spatially distributed sediment yield from the catchment by taking into account the transport capacity of each pixel. Available observed sediment yield data was used to calibrate and validate the model. Based on the model output the sediment contributing areas was been identified and grouped into six intensity classes from slight to very severe sediment yield. The classified/categorized soil erosion/sediment yield maps were used to identify the critical source areas in the catchments. The critical sources area maps generated in the study can be an important input for watershed prioritization and selection of suitable conservation measures (biological or engineering) in catchment. Keywords Himalayan catchment · Sediment yield · Soil erosion · Tawi river · Transport capacity

S. S. Rawat (B) · P. Kumar National Institute of Hydrology, Roorkee, Uttarakhand, India B. R. Nikam Water Resources Department, Indian Institute of Remote Sensing, Dehradun, Uttarakhand, India e-mail: [email protected] P. K. Gupta Geo-Informatics Department, Indian Institute of Remote Sensing, Dehradun, Uttarakhand, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 V. Chembolu and S. Dutta (eds.), Recent Trends in River Corridor Management, Lecture Notes in Civil Engineering 229, https://doi.org/10.1007/978-981-16-9933-7_14

215

216

S. S. Rawat et al.

1 Introduction River Tawi, the left bank tributary of Chenab river is endowed with vast water resources with irrigation, domestic water, and hydropower potential which are yet to be assessed in details. The increasing demand of the development of Tawi River for beneficial uses of the huge population of Jammu region (almost 20% population of the Jammu and Kashmir Union Territory of India) calls for the systematic hydrological studies for the river. These studies will be immediately beneficial for the development of several undergone recreational projects at Jammu such as construction of artificial lake and Tawi River-front development. However, the heavy population load in the region is caused the ecological degradation (Change in the land use pattern, deforestation and low growth rate of vegetation, construction of new roads and bridges) which has accelerated the severe erosion in the catchment. Very high sediment yield has been experienced by the field engineers during monsoon season in the river. This huge debris ultimately gets deposited in the surface of the river channel when enters in the plain area. Consequently, the channel capacity reduces significantly and river gets overflow. The devastating floods of September 2014 highlighted the need for adequate quantification of the sediment load in the river channel as well as to identify the sources of soil erosion and deposition in its catchment to prioritize the treatment measures, accordingly. The elements involved process of soil loss from a catchment, viz., generation sediment load, sediment transport, and sediment deposition are described in details in Refs. [11, 14, 44]. Wischmeier and Smith [51] proposed an equation popularly known as Universal Soil Loss Equation (USLE), On the basis of the experiments conducted on 10,000 plots in United States of America, to estimate the soil erosion from agriculture watersheds. Renard et al. [40] modified it as Revised Universal Soil Loss Equation (RUSLE). The USLE and RULSE are the most preferred models for estimation of rainfall-runoff induced soil erosion [9, 12, 19, 21, 27]. Notable that the entire load of soil eroded from an area does not reach at the outlet of the catchment. In reality, it is controlled by parameters governing detachment of soil particles, transport capacity of the flowing water that carries the sediment to the outlet of the catchment. However, the establishing quantitative relation between spatially distributed soil erosion from the entire catchment and the sediment yield observed at the outlet is often challenging due to the deficiency of detailed input data at a catchment scale [43]. To overcome this, several researchers [3, 8, 20, 41, 47, 49] used sediment delivery ratio (SDR) approach to estimate sediment yield from a catchment using the catchment soil erosion values. However, due to its empirical and lumped nature the SDR-based sediment yield approach ensures good results with the data of catchment belonging to inherent region [1, 3, 48, 49]. Nevertheless, the policymakers and researchers working the field on sediment control and management should not solely focus on the areas that are directly contributing the sediment to the river channels, however, more emphasis should be on the critical areas, which are the major contributor of the sediment [48]. In this context, the lumped SDR-based method is not helpful in prioritization of catchment area treatment measures.

Modeling of Sediment Yield of Tawi Catchment …

217

Though many physically based sediment transport models are available in the literature, the application of simple empirical model such as USLE and its derivatives for estimation of soil erosion and sediment yield from a watershed is prevalent among the soil conservation, watershed management officials globally. However, the popular soil erosion models USLE, MUSLE, RUSLE and its derivative are generally developed for plot size area and hence do not perform very well when applied to a large area or catchment. It has been observed that USLE over-predicts combined length-slope value (LS) at higher slope and longer slope-lengths. In reality, the term slope length (λ) used in the estimation of LS-factor in USLE is only applicable to 2-D non-converging and non-diverging hill slope. Therefore, the equation cannot be extended for real 3-D landscape. Furthermore, owing to spatial variation of rainfall and catchment properties the soil erosion and sediment yield exhibits large spatial variability in a catchment. The technique of Geographical Information System (GIS) is well suited for storing, representing, and analyzing the catchment heterogeneity by partitioning the catchment into small homogenous grids [7, 10, 17, 18, 23, 33, 36, 53]. Keeping all the above discussion in view, a simple distributed sediment yield model has been proposed here which is parsimonious in respect of data, time, and funds. The accuracy of the developed model has been verified by the historical sediment data of Tawi catchment. Furthermore, the spatial capability of the model has been cross-checked with the sediment yield data observed at Jammu, outlet of the catchment. Most of the model parameters are taken from the geospatial data which is most updated, easily available, and probably free of cost throughout the globe. The net soil erosion deposition maps were generated using the model output and the critical source area, the higher sediment contributing areas, of the catchment were mapped using these net soil erosion deposition maps. The critical sources area maps generated in the study can be an important input for watershed prioritization and selection of suitable conservation measures (biological or engineering) in catchment.

2 Study Area In the present study, the catchment of Tawi River, located in Jammu division of Jammu and Kashmir (J&K) Union Territory (UT) of India (Fig. 1) is selected as the study area. Tawi River originates from Kali Kundi glacier in Bhaderwah and it is drained into River Chenab in Pakistan. The length and drainage area of the river from its origin to Jammu River gauging station, which is considered as outlet in the present case, is about 150 km and 2163 km2 , respectively. The important role of river for sustaining the most populous cities in the region, Jammu and Udhampur has been considered while selecting the basin. The river has a very high social impact as it is the only source of water for drinking, agricultural and industrial needs and serving to the almost 20% population of the whole J&K UT. However, this heavy population load causes the ecological degradation (change in the land use pattern, deforestation

218

Fig. 1 Location map of Tawi River catchment of Jammu and Kashmir

S. S. Rawat et al.

Modeling of Sediment Yield of Tawi Catchment …

219

and low growth rate of vegetation, construction of new roads and bridges) which has accelerated the already severe problem of erosion in the catchment. The hydro-metrological monitoring in the Tawi catchment is very sparse and poor. The catchment has only one meteorological observatory of India Metrological Department (IMD) and stream gauging site of Central Water Commission (CWC) at Jammu City. Sixteen years (1977–1994) seasonal (June–September) sediment yield data of Tawi catchment at Jammu site were collected from CWC local office at Jammu. However, the rainfall data collected by Western Himalayan Regional Central (WHRC), Jammu, one of the regional centers of National Institute of Hydrology (NIH), Roorkee from different local agencies such as Irrigation & Flood Control (I&FC), etc., has been utilized in this study.

3 Assessment of Spatially Distributed Sediment Yield A model was developed for spatially distributed estimation of soil erosion and sediment yield. The developed model consists of three major constituents (1) estimation of spatially distributed gross soil erosion; (2) estimation of spatially distributed transport capacity; and (3) sediment routing from all the sources areas to the outlet. The spatial maps of sediment yield and sediment deposition were combined to analyze the net gain–loss of soil from each pixel and to identify the critical source area in the catchment.

3.1 Estimation of Gross Soil Erosion Revised Universal Soil Loss Equation (RUSLE) is well-proven method to estimate soil erosion from small geographical regions like micro, mini watersheds, and even watersheds as well [9, 16, 15, 19, 21, 22, 36, 38, 52]. Though RUSLE is a lumped method, however, it has been a part of many spatially distributed, conceptual models for the estimation of soil erosion. This is possible due to the portioning of heterogeneous catchment into small homogeneous unit using ArcGIS. In the present study, RUSLE is used to estimate gross soil erosion from each of the catchment cells. In RUSLE the gross soil erosion at a grid cell level is estimated as GSEi = Ri × K i × LSi × Ci × Pi , where GSEi is gross soil erosion in grid cell i (MT ha−1 year−1 ). R is rainfall erosivity factor (MJ mm ha−1 h−1 year−1 ). K i is soil erodibility factor i (MT ha h ha−1 MJ−1 mm−1 ).

(1)

220

S. S. Rawat et al.

LSi is slope steepness and length factor for the grid cell i (dimensionless). C i is cover management factor (dimensionless). Pi is supporting practice factor for grid cell i (dimensionless).

3.2 Estimation of Transport Capacity The eroded soil from each cell follows a distinct drainage path to reach the catchment outlet. The amount of sediment transported from each grid cell depends on the transport capacity of the runoff from the cell [25]. The sediment flowing out of a cell is equal to amount of eroded soil from the cell plus incoming sediment contribution from upstream cells if the transport capacity of the cell is equal or greater than the summation. If transport capacity of the cell is less than the aforementioned summation, also known as available sediment in the cell, then amount of sediment equal to transport capacity of the cell will be transported to the immediate downstream cell and reaming sediment gets deposited in the cell [52]. Desmet and Govers [4], Van Oost et al. [37], and Van Rompaey et al. [43] considered the various factors that influence the mean annual transport capacity and proposed the following equation for quantifying transport capacity of a grid cell in the watershed: TCi = K TCi RKi (LSi − aSIR ),

(2)

where TC is the transport capacity (kg m−2 year−1 ). K TC is the transport capacity coefficient. S IR is the inter-rill slope gradient factor. From Eq. 2, it is clear that transport capacity does rely on same topological variable as gross soil erosion depends (Eq. 1). Van Rompaey et al. [42] found poor performance (R = 0.25, for mountainous part) for the Italian catchments following the stratified calibration procedure whereby a distinction was made between mountainous and non-mountainous parts of the catchment. To overcome this problem an upslope contributing area term is incorporated by Verstraeten et al. [48] for the computation of sediment transport capacity for an Australian catchment named Murrumbidgee basin. The upslope contributing area factor represents the value of actual flow accumulation at any cell and the equation for transport capacity for ith cell can be written as TCi = K TCi RKi Si βASi γ

(3)

Modeling of Sediment Yield of Tawi Catchment …

221

where Asi is the upslope contributing area for cell i. The major advantage of this equation is to solve the problem of deposition of huge amount of sediment at the flow convergent point (normally at the toe of the slope) which frequently occurred in the hilly catchment. Using Eq. (3), transport capacity of such points will be sufficiently high, even having low slope gradient to carry the sediment coming from the steep slopes. The value of exponent of upslope area (γ ) and slope gradient (s) is taken as 1.4 for both exponent [39].

3.3 Sediment Routing Algorithm Sediment outflow from each grid cell follows a definite path which is defined by direction of surface flow and its amount is decided by TC of respective cells. If the TC of a cell is smaller than the total sediment available, then the sediment deposition is estimated as discussed in the previous section. The basic assumption of this approach is that the transport of sediment transport is not necessarily transporting a limited system. If the TC is higher than the sediment available in the cell, then sediment transport process will be supply limited. Thus, by introducing the transport capacity coefficient (K TC ) a more realistic representation of sediment transport is achieved. To implement this procedure a tool/program was developed using Interactive Data Language (IDL). The developed tool uses terrain derivatives viz. flow direction, flow accumulation, and surface erosion parameters, viz., gross soil erosion, transport capacity maps to estimate sediment yield and deposition at each grid cell. The procedure starts the estimation of sediment transport from ridge pixels (i.e., pixels with flow accumulation = 0). The tool compares the GSE and TC of the flow in that cell as shown in Eq. (4). The destination of this transported sediment for the cell is determined using flow direction map. The tool generates spatially distributed maps outputs of sediment yield and sediment deposition. For cell-based system transport limited accumulation can be computed as Touti = min(GSEi + Di = GSEi +





Tini ,TCi )

Tini − Touti ,

(4) (5)

where Tini and Touti are sediment inflow from upstream cells and sediment outflow from the cell i, respectively. Di is deposition in cell i. The steps involved in practical implementation of this model are depicted in Fig. 2.

222

Fig. 2 Flow chart of the proposed sediment yield model

S. S. Rawat et al.

Modeling of Sediment Yield of Tawi Catchment …

223

4 Formation of Input Database Sixteen years seasonal (June–September) rainfall and sediment yield data of Tawi catchment are used in the present study to model the sediment. Other input parameters have been extracted from different geospatial maps. It is worth emphasizing here that the result of a spatially distributed model greatly depends on the spatial and temporal quality of the input dataset. Therefore, proper care should be taken in preparation/selection of error free digital elevation model (DEM), appropriate land use and soil map, realistic schematization of drainage network of the watershed, and finally, more important is to provide adequate value of different input parameters for each cell. In the following text, formation of input database for developed model is being described in detail. In the present study Shuttle Radar Topography Mission (SRTM) derived DEM with a resolution of 3 arc-seconds (~90 m) (Fig. 1) downloaded from Global Land Cover Facility (GLCF) provided by The University of Maryland was used to generate different topographical maps, viz., slope, gradient, length-slope factor, stream network, and finally delineation of catchment boundary. The selection of channel initiation threshold is critical in the process of extraction of stream networks of the catchment from DEM. In the present study the stream network seen on the Survey of India (SOI) 1:50,000 scale Toposheet was considered as reference and the channel initiation threshold for DEM hydro-processing was selected to match this reference stream network as suggested by [16]. Finally, threshold value of 0.24 km2 is found to be appropriate to define channel cells. The extracted stream network of the catchment from SRTM data is depicted in Fig. 1.

4.1 Preparation of Geodatabase for Estimation of Gross Soil Erosion 4.1.1

Rainfall Erosivity (R)

Erosive power of the rainfall is represented by the R-factor. Many researchers have attempted estimation of R-factor using long time interval rainfall data [13, 26, 31, 35, 45]. For Indian conditions, Babu et al. [2] have developed linear relationships between average annual or seasonal rainfall and R-factor values for different zones. In this study, rainfall erosivity was estimated using the formula given by [2] as R = 71.9 + 0.361P → (for 293 ≤ P ≤ 3190), where P is the seasonal rainfall in mm.

(6)

224

4.1.2

S. S. Rawat et al.

Soil Erodibility (K)

The inherent susceptibility of the soil to be eroded is represented by the K-factor. The value of K-factor depends on the soil’s particle-size distribution, its organicmatter content, structure, and permeability. The soil map of the Tawi catchment was generated through digitizing the soil survey report prepared by National Bureau of Soil & Landuse Planning [32] using ArcGIS. Total 18 soil map units have been demarcated in the study area and shown in (Fig. 3a). Detail information like fraction of sand, silt, clay, organic matter, and other related parameters for various mapping units of the catchment are taken from NBSS&LUP [32]. Procedure defined by Haan et al. [14] for estimation of K-factor was followed in the present study to calculate K-factors for each of the mapping units of the study area.

4.1.3

Length-Slope Factor (LS)

It is well known that the combined slope length-slope steepness (LS) factor in the RUSLE is a measure of the sediment transport capacity of overland flow [5, 24, 27–29, 52] and can also be derived from the DEM of the study area. The best suited equation of LS-factor for integration with the GIS was proposed by Moore and Burch [28, 30] and Moore and Wilson [29] as  LSi =

Asi 22.13

n 

sinθi 0.0896

m (7)

where Asi

is the specific area at cell i defined as the upslope contributing area for overland grid (Aup ) per unit width normal to flow direction.

θi

is the slope gradient in degrees for cell i. value of n and m is taken as 0.6 and 1.3, respectively, for consistent results [29].

4.1.4

Crop Management Factor (C)

The C-factor is the factor that is most readily changed human activity. It represents the impact of land cover and management variables on the erosion from the area. A lower C value represents a cover type that is more effective at defending against soil erosion. C-factor map of the study area is prepared using Land Use and Land Cover (LULC) map. Hence LULC map is prepared first using the LANDSAT 5 Thematic Mapper (TM) data corresponding to November 1, 1992 (path 140–141 and Row 43–44) downloaded from GLCF site. The images are analyzed using the ERDAS Imagine™ [6], image processing software. The land surface covered by vegetative cover was

Fig. 3 Geo-spatial database of study area: a Soil map and b LULC map

Modeling of Sediment Yield of Tawi Catchment … 225

226

S. S. Rawat et al.

differentiated from other surface cover types using the Tassel Cap transformations (TCT), Vegetation Index (VI), Water Index (WI). Final land use/land cover (LULC) map of the catchment, with major classes as forest, agriculture, wasteland, grassland, built-up, and water (Fig. 3b) was generated using Maximum Likelihood supervised classification technique. Based on the LULC classes, values for the C-factor are referred from [14, 46, 52].

4.1.5

Management Practice Factor (P)

The P-factor by definition is the ratio of soil loss from any area with conservation support to that the soil loss form area with up and down slope tillage. It is used to evaluate the effects of soil conservation practices on the erosion. In the present study the value of P-factor was taken 0.9 for crop lands as mostly contour cultivation, 0.6 for cultivated land without contour, and 1.0 for other LULC classes and all these added in the attribute table of the map.

5 Model Application and Critical Area Selection 5.1 Generation of Erosion Potential Maps Assessment of GSE of Tawi catchment is done by implementing RUSLE in the “Raster Calculator” environment of ArcGIS. The layers of soil erodibility factor K, length-slope factor (LS), crop management factor C, and support practice factor P were overlaid. Then evaluated values of LS, K, C, and P maps are multiplied by values of R-factor for respective years, to estimate the gross soil erosion in tons per annum/season for all sixteen years (1977–1994). The spatial variation of GSE for the year 1977 is shown in Fig. 4a.

5.2 Transport Capacity Maps Transport capacity of overland flow is estimated for each season and each pixel from the relationship stated in Eq. (3). The parameter KTC appearing in Eq. (3) is taken as unity at the beginning and then its value is calibrated by minimizing error between observed and computed values of five years sediment data (1979–83) by varying K TC values. To find the optimum value of KTC for Tawi catchment Nash and Sutcliffe Model Efficiency [34] and Relative Root Mean Square Error (RRMSE) are used in the calibration stage. Model efficiency (ME) can be calculated as follows:

Fig. 4 Developed, a gross soil erosion map and b transport capacity maps of the study area

Modeling of Sediment Yield of Tawi Catchment … 227

228

S. S. Rawat et al.

 2 i=1 Yobs − Ypred , n 2 i=1 (Yobs − Ymean ) n

ME = 1 −

(8)

where Y obs observed sediment yield (tons). Y pred is predicted sediment yield (tons). Y mean is mean of the observed sediment yield (tons). n is the number of data points. Value of ME ranges from −∞ to 1, the value close to 1 indicated that model performed very well. Relative Root Mean Square Error is estimated by the following formula:   n 2 1 i=1 (Yobs − Ypred ) n RRMSE = . (9) 1 n i=1 Yobs n It is evident from Fig. 5 that at KTC value of 0.06, ME is the highest (0.97) and RRMSE is at the lowest (0.10). Changing the KTC value from 0.06, RRMSE and ME increased and decreased, respectively. TC maps are generated using calibrated KTC value for all the years (1977–94). TC map for year 1977 is presented in Fig. 4b as an illustration. It is evident from this figure that the transport capacity is high in channel areas.

Fig. 5 Calibration of KTC for Tawi watershed using five years (1979–83) seasonal rainfall-sediment yield data

Modeling of Sediment Yield of Tawi Catchment …

229

5.3 Computation of Transport Limited Sediment Accumulation and Outflow As discussed in the previous sections, all eroded soil within a grid cell does not necessarily get transported out of the cell. Therefore, to convert gross erosion into spatially distributed sediment yield, transport limited accumulation concept is applied. Using Eq. (4), the GSE from each grid cell is routed following the drainage path to generate map of cumulated sediment yield and deposition by considering the TC of each cell. This process was repeated for all sixteen years (1977–94) for which the observed data was available. These cumulated sediment yield and deposition maps provide the amount of sediment transported in and out of every grid cell, hence are useful for estimating sediment outflow from any location of the. Figure 6a depicts the sediment yield map for the year 1977 as an illustration. The spatially distributed sediment yield and deposition maps generated through this process are unique toward the information their content, traditional soil erosion and sediment yield models, especially the lumped ones lack this ability of providing sediment yield at any point in the catchment except at the pre-defined outlets. This unique feature of spatially distributed sediment yield and deposition maps, generated in this study, has potential to improve the further applicability of these dataset in real-world problems. The quantification of actual soil loss for an area is much more important in watershed planning, management, and conservations activities, in comparison to the potential soil loss or gross soil erosion given by most of the popularly available models. The pixel values of the sediment yield map denote the amount of sediment flowing out of the particular pixel to the next downstream pixel. Comparison of predicted sediment yield with the observed sediment yield for all years from 1979 to 1994 is shown in Fig. 7. As discussed earlier, the observed sediment yield data of years 1977–1981 was used in the study for calibration of the model and the rest of twelve years data (1982–94) for validation. It is evident from Fig. 7 that the average errors in validation period are 44% which is acceptable in sediment yield modeling, more sophisticated models are found to produce results with similar or larger amount of errors. In reality, the large errors may be attributed to probable uncertainties in observations and/or model formulation. It can be seen from Fig. 7 that the large errors are mostly negative (over-prediction of model), which is probably attributed to large temporal variability in sediment yield that influenced the observation of sediment data at outlet greatly. In the present study rainfall data was only available for once stations, hence spatial variability of rainfall in the catchment was not properly represented. It is a well-known fact that precipitation exhibits highly dynamic behavior both temporally and more dominantly spatially. This dynamic becomes more prominent in the hilly regions like Tawi catchment. The absence of spatially distributed meteorological stations in and around the catchment limits our ability of incorporating the spatio-temporal dynamics rainfall in the soil erosion and sediment yield model. Further, the dynamic nature of vegetation which greatly influences the transport capacity and crop management factor for overland regions may be another reason for large error observed in the model outputs.

Fig. 6 Developed, a sediment yield and b net erosion/deposition map maps of the study area

230 S. S. Rawat et al.

Modeling of Sediment Yield of Tawi Catchment …

231

Fig. 7 Year-wise comparison between observed and predicted sediment yield at the outlet

5.4 Identification of Critical Source Area Erosion Spatially distributed sediment deposition is generated using Eq. (5). Further, a net soil erosion/deposition maps are produced by overlay analysis of sediment deposition map and gross erosion map. Such maps are helpful in identifying areas vulnerable to soil loss, sediment deposition, and most importantly the critical source area in the catchment. Net soil erosion/deposition map for year 1987 is illustrated in Fig. 6b. It can be clearly seen from Fig. 6b that deposition of sediment is prominent along the banks of stream. The reduction in transport capacity of these grid cell, due gentle slope of these areas, appears to be the major governing factor in this deposition process. It is easy to identify the critical source areas, areas within the catchment which contributing most of the sediment to the main river. In present case the critical sources areas are not always coinciding with the areas having highest soil erosion rates. This further proves that most of the eroded soil deposited before reaching the main river and for most part of the hilly catchment the process of soil loss/sediment yield is transport capacity limited. The net erosion maps generated for the catchment are categorized into six classes as per the guidelines suggested by [46] for Indian conditions (Table 1). The area under “Severe” and “Very Severe” erosion category is marked as a critical source area of the Tawi catchment. Such a categorization of net soil erosion as illustrated in Fig. 6b can have a crucial role in planning and implementation of the suitable measures for catchment treatment. Since the rate of actual soil in these areas is more than 40 t ha−1 year−1 immediate interventions are needed to conserve these areas. The conservation majors, both biological or engineering must be implemented in these areas in tandem.

232 Table 1 Erosion classes and their priority ranking

S. S. Rawat et al. Priority ranking

Sediment yield range (t ha−1 year−1 )

Slight

0–5

Moderate

>5–10

High

>10–20

Very high

>20–40

Severe

>40–80

Very severe

>80

Source Singh et al. [46]

6 Summary A simple model comprising fundamental processes of soil erosion, sediment transport, and deposition is used to predict the sediment delivery from hill slopes to catchment outlet. Input thematic layers representing different factors of RUSLE are generated and overlaid to compute spatially distributed gross soil erosion maps for Tawi catchment using recorded rainfall of 16 years. A concept of transport limited accumulation (TLA) is formulated and used in geospatially environment to generate spatially distributed transport capacity maps. Gross soil erosion, estimated using RUSLE modified for hilly catchments, is routed along the hydrological drainage paths to the catchment outlet and in the process spatially distributed sediment yield maps are generated. Transport capacity coefficient is calibrated using five-year observed rainfall-sediment yield data, value of K TC (0.06) is found. Very low calibrated value of parameter K TC indicates good vegetation cover which reduces transport capacity. According to the recommended range of net soil erosion for Indian conditions, the entire watershed is categorized. The overlay analysis sediment deposition map with gross erosion map resulted in the identification of critical source and sink areas (i.e., areas vulnerable to soil erosion and deposition) in the catchment. The values of net soil erosion/deposition (up to a small unit, i.e., a pixel) are described through maps for their use in the field for implementation of suitable protection measures.

References 1. Atkinson E (1995) Methods for assessing sediment delivery in river systems. Hydrol Sci 40(2):273–280 2. Babu R, Dhyani BL, Kumar N (2004) Assessment of erodibility status and refined ISO-erodent map of India. Indian J Soil Conserv 32(2):171–177 3. Bazoffi P, Baldasarre G, Vasca S (1996) Validation of the PISA2 model for the automatic assessment of reservoir sedimentation. In: Albertson M (ed) Proceedings of the international conference on reservoir sedimentation. Colorado State University, Colorado, pp 519–528 4. Desmet PJ, Govers G (1995) GIS-based simulation of erosion and deposition patterns in agricultural landscape: a comparison of model results with soil map information. CATENA 25:389–401

Modeling of Sediment Yield of Tawi Catchment …

233

5. Desmet PJ, Govers G (1996) A GIS-procedure for the automated calculation of the USLE LS-factor on topographically complex landscape units. J Soil Water Conserv 51:427–433 6. Earth Resources Data Analysis System (ERDAS) (2005) ERDAS Imagine 8.6. ERDAS Inc., Atlanta, Georgia, USA 7. Eldho TI, Jha A, Singh AK (2006) Integrated watershed modelling using a finite element method and a GIS approach. Int J River Basin Manag, IAHR 4(1):1–9 8. Ferro V, Minacapilli M (1995) Sediment delivery processes at basin scale. Hydrol Sci J 40(5):703–717 9. Ferro V, Porto P, Tusa G (1998) Testing a distributed approach for modelling sediment delivery. Hydrol Sci J 43:425–442 10. Gosain AK, Sandhya R (2004) GIS-based technologies for watershed management. Curr Sci 87(7) 11. Govindaraju RS, Kavvas ML (1991) Modeling the erosion process over steep slopes: approximate analytical solutions. J Hydrol 127:279–305 12. Griffin ML, Beasley DB, Fletcher JJ, Foster GR (1988) Estimating soil loss on topographically non-uniform field and farm units. J Soil Water Conserv 43:326–331 13. Grim M, Jones RJA, Rusco E, Montanarella L (2003) Soil erosion risk in Italy: a revised USLE approach. Eur Soil Bur Res Report No. 11, EUR 19022 EN 14. Haan CT, Barfield BJ, Hayes JC (1994) Design hydrology and sedimentology for small catchments. Academic Press, New York 15. Jain MK, Das D (2009) Estimation of sediment yield and areas of soil erosion and deposition for watershed prioritization using GIS and remote sensing. Water Resour Manage. https://doi. org/10.1007/s11269-009-9540-0 16. Jain MK, Kothyari UC (2000) Estimation of soil erosion and sediment yield using GIS. Hydrol Sci J 45(5):771–786 17. Jain MK, Kothyari UC, Ranga Raju KG (2005) Geographic information system based distributed model for soil erosion and rate of sediment outflow from catchments. J Hydraul Eng ASCE 131:755–769 18. Jain MK, Kothyari UC, Ranga Raju KG (2004) A GIS based distributed rainfall runoff model. J Hydrol 299:107–135 19. Jain SK, Goel MK (2002) Assessing the vulnerability to soil erosion of the Ukai Dam catchments using remote sensing and GIS. Hydrol Sci 47(1):31–40 20. Klaghofer E, Summer W, Villeneuve JP (1992) Some remarks on the determination of the sediment delivery ratio. IAHS Publ 209:113–118 21. Kothyari UC, Jain MK, Ranga Raju KG (2002) Estimation of temporal variation of sediment yield using GIS. Hydrol Sci 47(5):693–705 22. Lee S (2004) Soil erosion assessment and its verification using the universal soil loss equation and geographic information system: a case study at Boun Korea. Environ Geol 45(3):457–465 23. Lilhare R, Garg V, Nikam BR (2014) Application of GIS coupled modified MMF model to estimate sediment yield on a watershed scale. J Hydrol Eng ASCE 20(6):C5014002-1:16. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001063 24. McCool DK, Foster GR, Mutchler CK, Meyer LD (1989) Revised slope length factor for the Universal soil loss equation. Trans ASAE 32(1571–1):576 25. Meyer LD, Wischmeier WH (1969) Mathematical simulation of the processes of soil erosion by water. Transac Am Soc Agric Eng 12:754–759 26. Millward A, Mersey JE (1999) Adapting the RUSLE to model soil erosion in a mountainous tropical watershed. CATENA 38:109–129 27. Mishra SK, Tyagi JV, Singh VP, Singh R (2006) SCS-CN-based modeling of sediment yield. J Hydrol 324:301–322 28. Moore I, Burch G (1986) Physical basis of the length-slope factor in the universal soil loss equation. Soil Sci Soc Am J 50:1294–1298 29. Moore I, Wilson JP (1992) Length slope factor for the revised universal soil loss equation: simplified method of solution. J Soil Water Conserv 47(5):423–428

234

S. S. Rawat et al.

30. Moore I, Burch G (1986b) Modeling erosion and deposition: topographic effects. Trans ASAE 29(6):1624–1630, 1640 31. Morgan RPC (1995) Soil erosion and conservation, 2nd edn. Harlow, Longman Group 32. NBSS&LUP (2004) Soils of Uttar Pradesh for optimizing land use: Executive Summary, National Bureau of Soil Survey and Land Use Planning, ICAR, Publication No 68, Nagpur, India 33. Naik GM, Rao EP, Eldho TI (2009) Finite element method and GIS based distributed model for soil Erosion and sediment yield in a watershed. J Water Resour Manag 23:553–579 34. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models. Part I a discussion of principles. J Hydrol 10:282–290 35. Natalia H (2005) Spatial modeling of soil erosion potential in a tropical watershed of the Colombian Andes. CATENA 63:85–108 36. Onyando JO, Kisoyan P, Chemelil MC (2005) Estimation of potential soil erosion for river perkerra catchment in Kenya. Water Resour Manag 19:133–143 37. Van Oost K, Govers G, Desmet PJJ (2000) Evaluating the effects of changes in landscape structure on soil erosion by water and tillage. Landsc Ecol 15(6):579–591 38. Pandey A, Chowdary VM, Mal BC (2007) Identification of critical erosion prone areas in the small agricultural watershed using USLE, GIS and remote sensing. Water Resour Manag Springer 21:729–746 39. Prosser IP, Rustomji P (2000) Sediment transport capacity relations for overland flow. Prog Phys Geogr 24(2):179–193 40. Renard KG, Foster GR, Weesies GA, Porter, JP, (1991) RUSLE: revised universal soil loss equation. J Soil Water Conserv 30–33 41. Roehl JW (1962) Sediment source areas, delivery ratios and influencing morphology factors. In: Symposium of Bari. IAHS Publ. no. 59, pp 202–213 42. Van Rompaey A, Bazzoffi P, Jones RJA, Montanarella L (2005) Modelling sediment yields in Italian catchments. Geomorphology 65:157–169 43. Van Rompaey A, Verstraeten G, Van Oost K, Govers G, Poesen J (2001) Modeling mean annual sediment yield using a distributed approach. Earth Surf Proc Land 26(11):1221–1236 44. Rose CW (1993) Erosion and sedimentation. In: Bonell M, Hufschmidt MN, Gladwell JS (eds) Hydrology and water management in the humid tropics, Chapter 14. Cambridge University Press, Cambridge, UK, pp 301–343 45. Shamshad A, Azhari MN, Isa MH, Wan Hussin WMA, Parida BP (2008) Development of an appropriate procedure for estimation of RUSLE EI30 index and preparation of erosivity maps for Pulau Penang in Peninsular Malaysia. CATENA 72:423–432 46. Singh G, Babu R, Narain P, Bhusan LS, Abrol IP (1992) Soil erosion rates in India. J Soil Water Conserv 47:97–99 47. Vanoni VA (ed) (1975) Sedimentation engineering. In: Manuals & reports on engineering practice, vol 54. ASCE, New York, USA 48. Verstraeten G, Prosser IP, Fogarty P (2007) Predicting the spatial patterns of hillslope sediment delivery to river channels in the Murrumbidgee catchment. Aus J Hydrol 334:440–454 49. Walling DE (1983) The sediment delivery problem. J Hydrol Data 65:209–237 50. Williams JR (1975) Sediment routing for agricultural watersheds. Wat Resour Bull 11:965–974 51. Wischmeier WH, Smith DD (1965) Predicting rainfall-erosion losses from cropland east of the Rocky Mountains. Agr Handbook No. 282. US Dept Agr, Washington, DC 52. Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses. Agricultural Handbook no.537, US Dept of Agriculture, Science and Education Administration 53. Wu S, Li J, Huang G (2005) An evaluation of grid size uncertainty in empirical soil loss modelling with digital elevation models. Environ Model Assess 10:33–42

Use of Landsat and Sentinel-1 Data for Implementation of Bank Protection Work in Brahmaputra River Ranjit Deka and Arup K. Sarma

Abstract In Brahmaputra Basin, bank erosion is a serious problem as it causes permanent loss of land and other properties to the valley’s agro-based people. Hence, the formulation of integrated water resource management program for erosion control requires the analysis of satellite imagery along with mathematical and physical model study. The objective of this study is to find the best location of dredging channel, which will be proposed for creation to reduce erosion at bank. For the present investigation, Landsat series images from 1988 to 2019 and Sentinel-1 SAR data from March 2021 to September 2021 are used. To prevent the bank erosion in Majuli, four deflecting spurs were constructed and this series of spurs are able to deflect the flow from the bank promoting silting in the neighbouring areas. During high flow, 30,000–50,000 cumec of water flows through the channel and moves to downstream towards Nimati Ghat, Jorhat, and this water flow causes the erosion at Nimati Ghat. A physical model study is to be carried out for a proposed dredging the channel to control river bank erosion. A temporal study with satellite data is conducted, and location of a channel to be dredged is selected. Keywords River bank erosion · Dredging · Landsat · Sentinel 1 · Brahmaputra

1 Introduction The high discharge together with the fragile nature Himalayan catchments burdened with heavy sediment load result in the Brahmaputra to flow listlessly through the valley attacking its banks and eating away large chunks of fertile land of the valley. All along its course within this alluvium region, the river is braided with innumerable R. Deka (B) NEHARI, Brahmaputra Board, Guwahati, India IIT Guwahati, Guwahati, India A. K. Sarma Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 V. Chembolu and S. Dutta (eds.), Recent Trends in River Corridor Management, Lecture Notes in Civil Engineering 229, https://doi.org/10.1007/978-981-16-9933-7_15

235

236

R. Deka and A. K. Sarma

large Sand Island. While in the dry seasons, it breaks numerous channels of various sizes, during monsoon it becomes a vast sheet of water submerging all Sand Island. The instability of the river and its easily erodible bank are the main causes of its large-scale bank erosion. Till 1950, the river was flowing in a more or less uniform channel without any major problem of bank erosion. The channel was deep enough to accommodate the flood discharge with minimum spilling of bank. During the great earthquake of 1950, the upper catchment of the river was affected greatly by land sliding etc. and all the floating derbies got deposited in the bed of tributaries of upper reach. Within a span of 2–3 years, these depositions were carried down to the bed of upper reach of river Brahmaputra. Due to such depositions, the upper reach of the river lost its dynamic equilibrium, resulting in severe bank erosion. The eroded bank materials increase the sediment load of the river, which got deposited in the river bed of downstream reach in the form of oblique channel and caused extensive bank erosion, resulting in increase of width of the river. Satellite data provide information on the temporal river channel configuration, revealing much-needed data on changes in river morphology and their impact on the land, stable and unstable reaches of river banks, changes in the Brahmaputra River’s main course, and so on. The combined use of the study’s data with other data is expected to contribute significantly to a more relevant approach to planning and implementing means and procedures to counteract repeated floods and erosion [1]. Gradual increase of width of the river over the years resulted in further reduction of velocity, which encouraged deposition of sediment load in the bed. Due to this phenomenon, the banks of river were seriously affected by bank erosion, increasing its width whereas depth of the Brahmaputra reduced considerably due to siltation in bed. The river that was almost uniform channel section till 1950 got braided in its entire length by the Seventies. Investigators have been using remote sensed data to study the channel changes of Brahmaputra River. The data collected during the study have been used to estimate the effect of the river to flood. SAC and Brahmaputra Board in 1996 carried out a study to check and delineate the locations in Majuli Island, which were affected by the erosion of the river. The study led to the preparation of a status report on the issue. It’s worth noting that, from 1963 to 2004, Majuli Island’s land mass has been shrinking year after year. In January 2004, the Brahmaputra Board took on the task of protecting Majuli Island from flooding and degradation. There has been no loss of land mass on Majuli Island since then. The following are the primary tasks completed (a) closing of 20 breaches, (b) repairing of embankment (c) casting and providing permeable RCC porcupine screens, spurs, and dampeners at various sites (d) building five boulder spurs using geobags (e) bank revetment with geo-bags and boulders, (f) raised Platform at five sites. Anti-erosion measures taken by the Brahmaputra Board at the Jengrai, ChelekPathali, and Natun Chapori areas along the Kherkutia-Subansiri bank were also very efficient in preventing erosion. Villagers who had previously fled their communities

Use of Landsat and Sentinel-1 Data for Implementation …

237

Fig. 1 Location of boulder spurs with geobags. Image credit: Google Earth

in Salmara and Kordoiguri have returned to their homes since the situation has improved (Fig. 1).

1.1 Bank Erosion Problem of Nimatighat Nimatighat is located at about 20 km from Jorhat Town towards north on the bank of river Brahmaputra in Jorhat District, Assam. The geographic coordinates which bound the area are latitude 26◦ 51 27.17 –26◦ 51 47.07 N to longitude 94◦ 15 10.20 – 94◦ 14 16.89 E. Nimatighat has been the busiest river port connecting Jorhat and Majuli by different river ghats such as Kamalabari, Dakhinpat, Aphalamukh and Upper reaches of Majuli.

1.2 Present River Configuration and Problem In the year 2017, Nimatighat area experienced heavy erosion, and a major chunk of the Nimatighat was eroded away forcing the District Administration to stop plying of heavy vehicles and the Kamalabari ghat had to be shifted towards upstream. Emergent flood fighting measures were adopted by dumping of RCC porcupines as well as sand filled Geo-bags in the affected reach of the ghat. In consideration of the urgency of the situation, the TAC, Water Resources Department, Government of Assam visited the erosion affected area and made detailed reconnaissance of the entire area on 24 June 2017. The important recommendations of TAC-BB to combat the severe erosion problems faced by the Nimatighat reach are. 1. 2.

Construction of adequate numbers of RCC porcupine screens in the reach. Physical model study should be taken up immediately to solve the problem.

The reach of Majuli Island (30 km) from 8 km upstream of spur constructed by Brahmaputra Board to 5 km downstream of Kamalabari Ghat at Majuli Reach of

238

R. Deka and A. K. Sarma

Brahmaputra is reproduced at North Eatern Hydraulic and Allied Research Institute (NEHARI), Assam. The reach covers Nimatighat. The entire existing spurs at left bank as well as at right bank are reproduced in the model so that the effect of existing structure could be visible. Hence, the area of interest is reproduced fully. By maintaining or increasing the cross-section or realigning watercourses, dredging is used to increase or maintain the discharge or flow capacity of rivers, channels or natural waterways. It is proposed to create a dredging channel so that flow thrust at Nimatighat is reduced. Therefore, a Satellite Imagery study of the area is done with imagery from 1982 to 2020 to select the location of the dredging channel.

1.3 Materials and Methods The morphological parameters of river Brahmaputra along the study reach have been analyzed and trends sought through both statistical analysis and qualitative interpretation. For this purpose, satellite images are collected from 1988 to 2020. Most of images are downloaded through earth explorer (Landsat series data). LISS III and LISS IV imagery of study area are collected from the projects “Construction of anti-erosion works for protection of Majuli Island from flood and erosion” and “Mathematical Model Study of river Brahmaputra with Emphasis on Climate Change” (Fig. 2). NDWI technique was used to get the river bank line. However, river bank line and main channel are finally digitized as Brahmaputra is a large braided river. The river bank line and main channel of the year 1988, 2005, 2012 and 2019 are shown (Fig. 3).

Fig. 2 Google earth image of the study area

Use of Landsat and Sentinel-1 Data for Implementation …

239

Fig. 3 River bank line and main channel of the year 1988, 2005, 2012 and 2019

1.4 Radar Data Analysis for Proposed Dredging Channel Optical sensors used in Landsat series are affected by the cloud cover. In Brahmaputra basin, average cloud is more than 50% for the month from April to November in a year, severely limiting the use of optical sensors in this region. The benefit of Synthetic Aperture Radar (SAR) that it is operated at wavelengths that are not hampered by cloud coverage or a lack of brightness of light, and can collect image over a location at any time of day or night, in all weather. The first flood detection methods and algorithms incorporating SAR were established in the early 1980s and have been refined since then [4]. Sentinel-1 SAR data from the C-band instrument are freely accessible. The radar sensor is carried by two satellites, Sentinel-1A and Sentinel-1B, which are both in the same orbit. Sentinel-1A and Sentinel-1B were launched by the European Space Agency in 2014 and 2016, respectively. Sentinel-1 uses C-band imaging in four different types, each with a different resolution (down to 5 m) and coverage (up to 400 km). (https://sentinel.esa.int/). Radarsat-2, TerraSAR-X and Cosmo-SkyMed are examples of SAR imagery that are not openly available. River channel maps are created using Sentinel-1 data from April to November in this study. The study was divided into three stages to achieve this goal: (i) processing of Sentinel 1 data, (ii) mapping process and (iii) creation of KML file and evaluation of the vector files.

240

R. Deka and A. K. Sarma

SAR data from Sentinel-1 were downloaded after registration at the Sentinels Scientific Data Hub. A search option appears in this Data Hub, which may be used to set data requirements such as region of interest, product type, sensor mode and sensing period, among other things. Sentinel-1 data from Level-1 Ground Range Detected (GRD) are used in this case, which has already undergone some simple preprocessing. Stripmap (SM), Interferometric Wide Area (IW), Extra Wide (EW) and Wave are the four modes available on the Sentinel-1 sensors, each with various resolutions, extents, polarizations, and incidence angles (WV). It was decided to employ the IW area mode. This mode, which has single and dual polarizations (VV: vertical transmitting-vertical receiving; and VH: vertical transmitting-horizontal receiving), is the primary acquisition mode over land [6]. In several prior investigations, VV polarized pictures were found to be more effective than VH for detecting floods and water bodies [5] ([10]; [7]). As a result, VV polarized pictures were used in this study (Fig. 4). The imageries were handled with the free package SNAP (Sentinel Application Platform) Tool (http://step.esa.int), created by ESA for the study of the data taken by Sentinel satellites. One Sentinel 1 imagery for every month from April 2020 to

SAR Images (Sentinel 1- Amplitude_VV) Subset for region of Interest Radiometric Calibration Speckle filtering Processing- Binarization- Histogram- Band Math

Post-processing - Geometric correction Range-Doppler Terrain Correction Export --> View as Google Earth KMZ Finalization of Proposed Dredging Channel Fig. 4 Flowchart of the methodology

Use of Landsat and Sentinel-1 Data for Implementation …

241

September 2020 was used to finalize the proposed dredging channel. After cropping area of study in these images, pre-processing like radiometric calibration, speckle filtering were done. The last step in image processing is to perform terrain correction. The distortions caused by variations in landscape and incidence angle with the earth from the nadir are effectively eliminated. Range Doppler terrain Correction and the Digital Elevation Model (DEM)—SRTM-3s were utilized for geometric calibration in this investigation. The UN-SPIDER Recommended the Practice for radar-based flood mapping (https://www.un-spider.org/). Finally, the KMZ and Vectors file are generated after analyzing with this practice.

2 Results and Discussion From satellite images of 1988 and 1990, it can be clearly observed that there is a very small channel from Salmara to Kamalabari, on the northern bank of river Brahmaputra. In the early stage, the Sumoimari channel was very insignificant, but later it carried substanital flood water with high velocity. In August, 1993, the channel breached the Majuli dyke. The breach opening initially was of 2 km length; later on it was increased to about 2.6 km. The channel has been shifting towards Tuni River and joined the Tuni River in August, 1994 at Nam Sonowal village. Flooding has occurred in the area surrounding Kamalabari Township and Kamalabari Satra as a result of the Breach opening. Severe bank erosion occurred near Kamalabari Police Station during flood receding phase. Erosion of river bank was also noticed near the embankment from Kamalabari to Burakalita. River bank erosion at upstream of Dhakhinpat and Bechamara had also been observed during 1994. On the other hand, river bank erosion at Jengraimukh by kherkatiasuti had also been noticed during the floor of 1993 and 1994. From satellite mages of 2000 onwards Sumoimari channel between Salmara to Kamalabari, on the northen bank of river Brahmaputra is clearly visible. The Brahmaputra Board’s anti-erosion efforts have so far generated favourable results. It is possible to declare that in 2007, the Sumoimari channel of the Brahmaputra river, which flows along the southern shore of Majuli Island for approximately 25 km from Dakhinpat to Kamalabari, transported a significant discharge of Brahmaputra water (more than 40% of total discharge of Brahmaputra). The usage of RCC porcupine screens in the Aphalamukh–Dakhinpat area, i.e. at the entrance of the Sumoimari channel, caused extensive siltation, resulting in a significant reduction of flow in the channel. Similarly, careful deployment of porcupine screens in the upper and downstream locations of Tekeliphuta favoured heavy siltation formation. As a result of the foregoing, there was no erosion in the upper Majuli reach of the Brahmaputra during the monsoons of 2009 and 2010. This demonstrates that the Brahmaputra Board’s anti-erosion efforts had far-reaching impacts and changes in river morphology, resulting in significant positive effects on river regime. River bankline and main river channel are drawn on satellite images 1988 onwards. It is observed that the main channel was on south bank on southern side in the study

242

R. Deka and A. K. Sarma

reach till 1995. However, the main channel is divided into two halves, creating a new channel on northern side. These two channels are observed till 2010. Due to the execution of work by Brahmaputra Board, the main channel is shifted to southern side.

2.1 River Sand Bar Analysis River Bar dynamics relate to the morphologic behaviour of braided river and especially to the erosion and deposition processes. In a braided river, planforms are defined by multiple transitory mid-channel bars and channels. Due to the relatively high energy and strong bed load transport condition in a sand bed, the planform changes swiftly and unpredictably, which causes instability, especially for mid-channel bars ([2]; [8]). Due to upstream and downstream erosion and sedimentation, these bars reveal diverse planforms that appear to be stage-dependent and finally develop a very wide range of alluvial planforms ([4]; [3]). To analyze the dynamics of the morphology such as large scale sand bars, a time series satellite image analysis has been conducted. Six geo-referenced satellite images of 2015–2020 have been superimposed to assess sand bar movements over the years (Fig. 5).

Fig. 5 Locations of sand bar in the study area from 2015 to 2020

Use of Landsat and Sentinel-1 Data for Implementation …

243

2.2 Finalization of Possible Dredging Channel After Sentinel 1 Data Analysis The resulting river channel maps in vector format of the study area are shown in figure below. The location of the proposed dredging channel was initially fixed based on non-monsoon satellite image of Landsat series. This location is checked in these river channel vector maps. It is found that the proposed location works as main branch channel in monsoon season. Therefore, the proposed location is finalized for dredging after analysis of sentinel 1 data (Fig. 6).

Fig. 6 River channel maps of the study area from April, 2020 to September, 2020

244

R. Deka and A. K. Sarma

3 Conclusions and Future Work The important sand bars in the study area are referred as sandbar-1, sandbar-2 and sandbar-3 as shown below. Changes of these sand bars in each year were observed. It is found that sandbar-1 is stable but erosion has been going on western side of the sand bar-2 and sandbar-3. As earlier discussed, the construction of spurs at Majuli was done in 2010–2014, it is now clearly visible the water thrust increased on western side of the sand bar-2 and sandbar-3 from 2015 onwards. This water thrust is finally creating erosion at Nimatighat. It is clearly visible that there is a thin channel just southern side of Sand Bar-1, which is found unstable. It is, therefore, proposed a dredging channel by re-sectioning existing thin natural channel as shown below to divert flow from present position. However, some RCC porcupine screens will be suggested just upstream of Nimatighat. Location of the dredging channel is finalized in Brahmaputra River for the implementation of bank protection work at NimatiGhat with this satellite imagery study.

References 1. Archana S, GRD, Nayan S (2012) RS-GIS based assessment of river dynamics of brahmaputra river in India. J Water Resour Prot 02 63–72. https://doi.org/10.4236/JWARP.2012.42008 2. Ashmore P (2013) Morphology and dynamics of braided rivers. 3. Ashworth PJ, Sambrook Smith GH, Best JL, Bridge JS, Lane SN, Lunt IA, ... & Thomas RE (2011) Evolution and sedimentology of a channel fill in the sandy braided South Saskatchewan River and its comparison to the deposits of an adjacent compound bar. Sedimentology 58(7) 1860–1883. 4. Bridge JS (1993). The interaction between channel geometry, water flow, sediment transport and deposition in braided rivers. Geological Society, London, Special Publications 75(1) 13–71. 5. Brisco B, Touzi R, Van der Sanden JJ, Charbonneau F, Pultz TJ, D’lorio M (2008) Water resource applications with RADARSAT-2 a preview. J Int J Digit Earth 1:130–147. https://doi. org/10.1080/17538940701782577 6. Cazals C, Rapinel S, Frison PL, Bonis A, Mercier G, Mallet C, Corgne S, Rudant JP (2016) Mapping and characterization of hydrological dynamics in a coastal marsh using high temporal resolution Sentinel-1A images. Remote Sens 8(7):570. https://doi.org/10.3390/rs8070570 7. Clement, M. A., Kilsby, C. G., & Moore, P. (2018). Multi-temporal synthetic aperture radar flood mapping using change detection. Journal of Flood Risk Management, 11(2), 152-168. 8. Egozi R, & Ashmore P (2009) Experimental analysis of braided channel pattern response to increased discharge. Journal of Geophysical Research: Earth Surface, 114(F2). 9. ESA Scientific Data Hub—Copernicus (2018). https://www.scihub.copernicus.eu/dhus/#/ home/ 10. Manjusree P, Prasanna Kumar L, Bhatt CM, Rao GS, & Bhanumurthy V (2012) Optimization of threshold ranges for rapid flood inundation mapping by evaluating backscatter profiles of high incidence angle SAR images. International Journal of Disaster Risk Science 3(2) 113–122. 11. Tavus B, Kocaman S, Gokceoglu C, & Nefeslioglu HA (2018) Considerations on the use of SENTINEL-1 data in flood mapping inurban areas: Ankara (Turkey) 2018 flood. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences.

Potentially Dangerous Glacial Lake Risk Mapping and Assessment in Satluj River Basin, Himachal Pradesh Using Remote Sensing and GIS Gopinadh Rongali, K. C. Tiwari, and Poonam Vishwas

Abstract The hazards linked to glaciers and glacial lakes in the mountainous environments, as well as their downstream implications, are highly alarming. Climate change and variability have exerted quite a substantial influence on the life span of glaciers in the Himalayan region in recent decades. As a consequence, several enormous glaciers melted, leading to the formation of numerous glacial lakes that have the potential to erupt and adversely affect the human and physical resources downstream. The present study seeks to identify the probable glacial lakes in the Satluj river basin that may be susceptible to a glacial lake outburst flood (GLOF) (GLOF). The study applies a three-step based semi-automatic methodology to locate the glacial lakes and further classify their outburst potentiality using Landsat timeseries data. A total of 15 glacial lakes have been found in the Sutlej basin. Between 1990 and 2018, the frequency and extent of glacial lakes in the Satluj basin rose by 65% and 71%, respectively. Two of the 15 lakes discovered have grown moderately by 64% and 39%, respectively; 2 have grown only by 23% and 16%, respectively; and 3 lakes have grown insignificantly between 2004 and 2014 (decadal). The glacial lake (Latitude 31°39 40.81 N and Longitude 78o 10 7.32 E) is one of the possible highest potentially dangerous lakes, whose maximum surface area is approximately 0.20 km2 acquired on 16 September 2018 of Landsat-8 satellite image data. Though further research is needed to anticipate GLOF, it is recommended that an early warning system be constructed for the study area, which includes the deployment of a real-time sensors network at vulnerable lakes, as well as GLOF simulation models. Keywords Glacial lake outburst floods (GLOF) · Satluj river basin · Landsat-8 · Western Himalaya

G. Rongali (B) · K. C. Tiwari · P. Vishwas Multidisciplinary Centre for Geoinformatics, Delhi Technological University, Delhi 110042, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 V. Chembolu and S. Dutta (eds.), Recent Trends in River Corridor Management, Lecture Notes in Civil Engineering 229, https://doi.org/10.1007/978-981-16-9933-7_16

245

246

G. Rongali et al.

1 Introduction Proofs have been accumulated for as long as archives have been preserved that glaciers and glacial lakes pose a risk to society and resources downstream. Within the last half-century, a huge number of glacial lakes have emerged at various mountain sites around the world as a result of glacier retreat and global warming [32]. Global glaciers have been influenced by climate change in general, but the glaciers of the Himalayan region have been particularly affected by the rate and pace of ice melting [17]. The Himalayas encompass over 15,000 glaciers as well as 9,000 glacial lakes, as per the inventory prepared by the International Centre for Integrated Mountain Development (ICIMOD) [16]. Isolated lakes situated far from glaciers in the mountains and valleys may not be glacial in nature. These secluded lakes, around 3,500 altitude, are more likely the remains of glacial lakes that vanished owing to glacier recession [3]. Retreating glaciers provide ample amount of water to formulate glacial lakes behind semi-permanently features like moraines, which can be breached given a combination of circumstances and can trigger glacial lake outburst floods causing mayhem in the downstream areas [16, 17, 30]. Hence, it is vital to monitor the health of glacial because they render the surrounding settlements and resources vulnerable. Traditional surveys find it difficult to monitor these lakes owing to their remote locations and high elevation. Remote sensing, thus, can be a valuable tool to map and monitor most of the glacial lake parameters [1]. Crucial aspects to be looked into while monitoring GLOF’s through remote sensing techniques are glacial lake location, lake volume and rate of formation, lake stability and movements, the possibility of the mass moment, response to climate change, width and height of moraine, and relative location of the settlements downstream [19, 20, 22]. Many studies have indicated that remote sensing can be used to monitor glacier lakes and prepare inventories from the Alps to Hindukush Himalayas to Indian Himalayas. Based upon the changes in the lake water, Watanabe et al. [30] have investigated the potential of GLOF’s in Khumbu Himalaya. Most commonly used approaches for mapping glacier and glacier lakes are supervised classification, unsupervised classification, and ratio methods of satellite images that have been employed by various studies [10, 11]. NDSI (Normalized Difference Snow Index) is mostly used for snow cover mapping utilizing satellite data. NDSI is mainly used for snow cover mapping using satellite data. In a study conducted by Bolch et al. [1] on Imja glacial lake, a normalized difference water index (NDWI)-based automatic detection approach has been employed. In Nepal Himalaya, the expansion rate of a glacial lake in Rolwaling using heat balance method has been calculated by Sakai et al. [23]. Microwave databased monitoring of the glacial lakes has also been done by Lichtenegger et al. [13] for Imja glacial lakes as well. DGPS (Differential Global Positioning System) survey-based techniques are another popular alternative that has been employed by studies like Thompson et al. [24] to measure the perimeter of the major glacial lakes in the Himalayas. A glacial lake inventory of Bhutan has been prepared by Ukita et al. [25] using ALOS

Potentially Dangerous Glacial Lake Risk Mapping and Assessment …

247

(Advanced Land Observing Satellite) data. Multicriteria decision analysis (MCDA)based technique of GLOF’s assessment has been applied by Kougkoulos et al. [9] in the glacier region of Bolivian Andes. All these methods used by various studies have their own shortcomings and advantages as well based upon the objective of the research and location of the glacial lakes. The present work is an attempt to apply remote sensing data and a geospatial approach to add to our understanding of glacial lakes and assess their geographical distribution as well as temporal evolution in the Satluj basin in the vicinity of the north-western part of the Indian Himalaya. Another objective is to establish and prioritize potentially dangerous glacial lakes (PDGL) for further research. A combination of remotely sensed satellite data and supplementing information about the glacier lake’s location, characteristics, development trend, and surroundings, a multi-temporal glacial lake inventory is generated for the research area. The data gathered will be used to build a foundation for the GLOF vulnerability assessment in the Satluj basin. In this study, GLOF mapping has been carried out in a remote sensing and GISbased environment. Landsat data have been used for glacial lake mapping, classification, and change detection. NDWI has been used for the identification of temporal variation of glacial lakes. A 30 m shuttle radar topography mission (SRTM) digital elevation model (DEM) data has been used for calculating parameters such as max, min, mean elevation, slope, aspects, etc. The volume and depth have been determined by applying Huggle’s empirical equation, while other characteristics such as area, slope, aspects, and drainage network have been generated using ArcGIS 10.8. The research results will help to assist in the development of risk management plans, spatial planning, and better preparedness for future potential hazards of GLOF’s.

2 Study Area The Satluj River is one of the key tributaries of the Indus river system. The river’s overall length is 1448 km, and the entire drainage area up to Bhakra reservoir is around 56,500 km2 . The basin area is located in the Himachal Pradesh and expands over the area of Lahaul and Spiti, Kinnaur, Shimla, Solan, Mandi, Kullu, and Bilaspur. However, in this study, the Indian part of the Satluj River basin (30°22 –32°42 N and 75°57 –78°51 E) up to Bhakra reservoir has been selected (Fig. 1). The Indian portion of the Satluj basin spans 22,305 km. The elevation spans from 500 to 7000 m, with just a little region over 6000 m. Furthermore, the basin encompasses the outer, middle, and larger Himalayan ranges, with the greater Himalayas accounting for most of its area. Because of the wide range of elevation and rainfall patterns, the basin has a diverse climate. Kinnaur is located in the northeast part of Himachal Pradesh, bordering Tibet to the east, and is about 235 km from the state capital, Shimla. Three high mountain ranges, the Zanskar and Himalayas, encircle the Sutlej, Spiti, and Baspa valleys, as well as their tributaries. Thick forests, orchards, pastures, and hamlets dominate the hills.

248

G. Rongali et al.

Fig. 1 Location of the potentially dangerous glacial lake in the Satluj basin

Shivling, a natural rock, is located at the summit of Kinnaur Kailash Mountain (Shiva lingam). Outsiders were allowed into the district in 1989. The old Hindustan-Tibet Road travels through the Kinnaur valley, following the Sutlej River’s bank, before entering Tibet at the Shipki La pass. Kinnaur, after Solan, is the second wealthiest district in terms of per capita income in Himachal Pradesh.

3 Dataset and Methodology 3.1 Database Generation In this study, Landsat data of October 21, 1990; October 11, 1998; October 22, 2008, and September 16, 2018 have been used for the identification of glacial lakes. For topographic data, ASTER GDEM (global DEM) has been applied. With acceptable

Potentially Dangerous Glacial Lake Risk Mapping and Assessment …

249

Table 1 Data used in the study Sensor/source

Product

Date

Resolution

LANDSAT, TM, ETM+ and L8 Satellite image 21 October 1990–16 September 23.5–30 m 2018 TERRA, ASTER

Satellite image –

Google Earth images

GDEM

30 m

–

errors, GDEM can be used to generate river centerlines and cross-sections for hydrodynamic modeling of GLOF in the Himalayan areas [27]. Manual inspection and analysis are also carried out using Google Earth photos. The information on the data that was used is provided in Table 1. The Landsat images used in the study for glacial lake delineation were all taken between the second week of September to the last week of October to obtain cloudfree data as the monsoon season is over around this time. During this time, there is little or no permanent snow cover, and lakes have a more extensive area due to monsoon precipitation-induced runoff from snow/glacier melt. As a result, it can be used to locate and depict glacial lakes and glaciers. The topographical characteristics such as gradient, elevation, catchment extraction, and defining shadowed zones utilizing hill shade in the Satluj basin are driven by GDEM version 2 of ASTER received from the United States geological survey (http://earthexplorer.usgs.gov). For the classification of Glacier Lake and visual interpretation of the lake’s environs, high-resolution Google Earth imageries are used as supplementary data.

3.2 Glacial Lake Mapping and Inventrorization The NDWI was utilized to delineate the glacial lakes in this analysis. As a result, inevitable shadow misclassification was observed, resulting in lake exaggeration. For typical lake surfaces, the NDWI value varies from −0.60 to −0.85 [7]. For the present study area, an index value of −0.65 to −0.90 has been determined to be suitable for glacial lake mapping. The final lakes were mapped after rigorous manual editing and delineation using Google Earth images. Because some lakes are ice-covered or not visible, they are not included in the list. The glacial lakes have been assigned numbers based on their size in relation to the area. An inventory of glacier lakes is required to examine the spatial distribution as well as temporal dynamics of glacial lakes, which is a prerequisite to locate potentially hazardous lakes. Automated image processing algorithms, along with visual image evaluation and Google Earth high-resolution photographs, were utilized to detect ice lakes and estimate lake boundaries from remote sensing images. In this investigation, we adopted a three-step automated procedure to construct the glacier lake inventory (Fig. 2).

250

G. Rongali et al.

Fig. 2 Methodology for glacial lake mapping

Li and Sheng [12] explain that the first stage of the approach is automatic mapping of glacial lake pixels by computing the NDWI and afterwards manually establishing appropriate thresholds for image segmentation and extraction of water-containing pixels. NDWI was estimated using TOA reflectance in green (Band 2) and near infrared band (Band 4) of Landsat images, predicated on the presumption of high reflection of water sources in the visible spectrum (highest in green wavelength) and considerable absorption in NIR wavelength [7]. NDWI =

BNIR − BBLUE BNIR + BBLUE

(1)

The DEM-based terrain analysis is the second stage of the analysis to eliminate mountain shadows that were misidentified as glacier lakes because of spectral similarities with water sources. To remove any spurious lakes indicated by mountain shadows, a shadow mask was generated utilizing sun azimuth angle and the elevation information from satellite data in ArcGIS. The boundaries of glacial lakes have been calculated using a slope threshold. A water pixel with a slope less than 5° has been considered to be a water pixel because water surface slope is theoretically much more petite than mountain shadow, and glacial lakes exist on 2°–6° surface gradients [21].

Potentially Dangerous Glacial Lake Risk Mapping and Assessment …

251

Third phase is a manual examination of the lakes identified by automated categorization using false-color composite and high-resolution Google Earth imageries and subsequent correction as well. For assessing, reviewing, and manual corrections in the delineation of lake boundaries, ArcGIS has synchronized with Google Earth. Synchronization is crucial because it ensures relatively accurate knowledge about the lake, distance, dam type, and lake drainage pattern, as well as the exclusion of glacier lakes that are more than 10 km from the glacier terminus. Image quality, pixel resolution, pre-processing/processing, cloud, and snow coverage of the image, and expert competence all increase the precision of the glacial lakes area and derived significant information [5]. Because there are no ground-based field measurements, it is impossible to investigate the ambiguity and discrepancies in glacial lake perimeter demarcation and area estimation employing remote sensing data. Spatial and spectral and data pre-processing inadequacies are systematic, have a minor influence, and are not crucial for spatio-temporal alterations in lakes at a regional scale [28]. Since the images were gathered in September and October, the inaccuracy due to seasonal fluctuations was limited.

3.3 Identification of Potentially Dangerous Lakes The following parameters were used to select potentially hazardous lakes. (1)

(2)

(3)

(4)

Lakes with an area smaller than 0.1 km2 are categorized as harmless owing to their less water holing capacity (size and volume are empirically associated in Huggel’s formulae). Lakes that are still linked to or somehow near to the parent glaciers are considered as more dangerous as they can expand. Isolated lakes are mostly saturated as they do not expand and, in most cases, pipe their water volume rather than bursting. Overlay analysis was used to look into the position of the lakes with regard to the glaciers for this purpose. Glacial lakes were examined for the existence of supraglacial lakes nearby or lakes that flow into their catchment region. These lakes are linked in the way that when one bursts, it influences the other to explode as well. Lakes have also been looked into for the steep slopes around them (which can cause influx into them). Visual observation and distance measurement based on high-resolution satellite data on Google Earth was used to determine the moraine’s thickness (thick or thin).

3.4 Identification of the Most Vulnerable Lake A geospatial approach was employed to find PDGLs and quantify the outburst likelihood using high-resolution Google Earth data. Leveraging accessible Google Earth high-resolution data, a remote sensing-based quantitative technique was applied to

252

G. Rongali et al.

find PDGLs and assess possible outbursts. The lakes were classified as potentially hazardous based on the following criteria: (i) size (>0.1 km2 ) and potential for future development (area expansion) [2], (ii) water source of the lake (glacier-fed lakes) [18], and (iii) distance from closets glacier terminus [29]. Outbursts are more likely in lakes with steep surrounding topography adjacent to or near a glacier with the potential for calving [8, 15, 26]. Other characteristics of moraine-dammed lakes that increase the likelihood of outburst include an unstable dam design, decreased freeboard, and a steep distal face slope [6, 27]. As a result, five key parameters were used to assess the identified lakes’ outburst potential: I lake and glacier characteristics, (ii) dam type, (iii) dam geometry, (iv) freeboard, and (v) the possibility for lake impacts. The five primary indications were chosen based on three criteria: first, they were used to estimate the vulnerability of glacial lakes to outbursts all over the world [2, 4, 6, 14, 31]. Second, utilizing readily available remote sensing satellite data, selected indicators may be assessed and evaluated. Third, the indicators can be assessed subjectively or semi-quantitatively to assess the possibility of an eruption because they are continuous and nominal. As a result, five key parameters were used to assess the identified lakes’ outburst potential: (i) lake and glacier attributes, (ii) dam type, (iii) dam geometry, (iv) freeboard, and (v) the possibility for lake effects. Three criteria guided the selection of the five major indicators: first, they were used to estimate the vulnerability of glacial lakes to outbursts all over the world [2, 9, 5, 14, 31]. The general decision procedure utilized for the final assessment of glacial lake outburst probability is shown in (Fig. 3). Finally, the most hazardous lake must be determined for the final GLOF modeling. Volume is usually the most important consideration for determining the most hazardous lake, and the largest lake is usually selected. When identifying the most hazardous lake in this study, four key variables were considered: (1) (2) (3) (4)

Lake area. Distance from the outlet of the basin; Expansion of the lake area since 2009 (change detection); Slope of the lake. Volume of Glacial lake L1 has been worked out by Huggel’s formula, V = 0.104 · A1.42

(2)

where V = volume of lake and A = surface area of the lake. The lake depth has been calculated by using the following empirical equation. D = 0.104 · A0.42 where D is the depth of the lake in m and A is the lake area in m2 .

(3)

Potentially Dangerous Glacial Lake Risk Mapping and Assessment …

253

Fig. 3 Methodology for the hazardous lake map

L15 has been identified in the Satluj basin as highly risk whose maximum surface area is approximately 0.2396 km2 , acquired on 16 September, 2018. The area, volume, and depth of the L15 have been calculated using the Huggel’s formulae. The volume and depth of the identified potentially critical glacial lake in Satluj basin (L15) at 31°39 40.81 N, 78°10 7.32 E are 4,527,981.19 m3 and 18.8980 m, respectively.

4 Results and Discussion 4.1 Glacial Lakes Inventory A total of 15 potentially significant glacial lakes have been located and cataloged in the Satluj basin using Landsat imagery. Glacial lakes have been classified in three ways; the first one is 1–5 ha (less vulnerable), second one is 5–10 ha (medium vulnerable), and third one is >10 ha (very vulnerable) (highly vulnerable). In total, 15 key glacial lakes have now been identified in the Satluj river basin (Fig. 4), and

254

G. Rongali et al.

Fig. 4 Glacial lake inventory map in the Satluj river basin using NDWI technique (left) and Google Earth (right)

Potentially Dangerous Glacial Lake Risk Mapping and Assessment …

255

Fig. 4 (continued)

their position, area, and elevation have been presented in Table 2. It has been noticed that large-sized lakes are fewer in number. As the contemporary glaciers are at higher elevations and lakes are forming and expanding in the forefields of glaciers that have Table 2 Potentially critical glacial lakes identified in Satluj basin Lake

Latitude

Longitude

Elevation (m)

Area (km2 )

L1

32°19 50.58 N

78°43 16.92 E

3867

1.121

L2

31°59 34.31 N

78°80 44.34 E

5780

0.210

L3

32°5 24.39 N

78°56 49.42 E

5297

0.001

L4

32°6 26.33 N

78°56 31.68 E

4893

0.161

L5

32°6 24.88 N

78°55 57.66 E

5624

0.133

L6

32°7 11.03 N

78°57 2.74 E

4438

0.002

L7

32°7 20.95 N

78°56 51.69 E

5394

0.001

L8

32°7 45.14 N

78°58 12.57 E

4595

0.005

L9

32°54 49.80 N

78°50 24.15 E

5583

0.184

L10

32°8 42.24 N

78°19 46.80 E

5721

0.081

L11

31°39 46.40 N

78°10 1.14 E

4193

0.096

L12

32°1 45.33 N

78°50 39.63 E

5628

0.161

L13

31°55 5.75 N

78°47 6.24 E

5376

0.143

L14

32°3 32.23 N

78°48 26.17 E

5572

0.122

L15

31°39 40.81 N

78°10 7.32 E

4261

0.239

256

G. Rongali et al.

evacuated owing to glacier retreat, these observations of lakes in relation to elevation and distance from glaciers reflect the basin’s continual glacier retreat.

4.2 Glacial Lake Changes 1990–2018 and Analysis for the Identification of Most Vulnerable Lake in the Basin From 1990 to 2018, the evolution and modifications of glacial lakes in the Satluj basin have been exceedingly complicated, with recently developing lakes, as well as the growth and disappearance of preexisting glacial lakes. Between 1990 and 2018, the density and extent of glacier lakes in the Satluj basin expanded considerably (Fig. 5). Newly emerging lakes (recently formed lakes), rising lakes (substantial increase in area), and steady lakes (no significant changes in the expansion area) are the three groups based on their evolution conditions and trends from 1990 to 2018. Between 1990 and 2018, certain ice-dammed lakes and bedrock-dammed lakes also developed. Growth and advancement of glacier lakes in Satluj basin have been highly complex, encompassing rapidly evolving lakes, enlargement, and elimination of preexisting glacial lakes from 1990 to 2018. The frequency and extent of glacier

Fig. 5 Potentially critical lake expansion from 1990 to 2018 in Satluj basin using Landsat data

Potentially Dangerous Glacial Lake Risk Mapping and Assessment …

257

lakes rose considerably from 1990 to 2018 in the Satluj basin (Fig. 5). Lakes are categorized into three groups based on their evolutionary conditions and trends from 1990 to 2018: emerging lakes (recently formed lakes), rising lakes (major growth in the size), and constant lakes (no significant changes in the area). Some ice-dammed lakes and bedrock-dammed lakes emerged from 1990 to 2018. Most of the emerging lakes close to the parent glacier are progressing along the course of the glacier in the vacuum generated by glacier retreat. Beyond 500 m from the glacier, bedrockdammed lakes and moraine-dammed lakes are constant lakes as their area does not change substantially. As a consequence, lakes with a closer hydrological link to glaciers expand faster than lakes with a distant or no hydrological relationship.

4.3 Potentially Dangerous Glacial Lake Based on a precursory evaluation, lake 15 in the Satluj basin, with a total size of 0.239 km2 , was categorized as PDGL. Table 2 offers data on the lake and its environs that can be utilized to construct a qualitative assessment of the possibility of an eruption. Between 2004 and 2014, two lakes grew by 64% and 39%, respectively; two lakes grew by just 23% and 16%, respectively, while three lakes grew insignificantly (decadal). Of all three lakes were in direct contact with the potential glacier as it emerged. Two lakes have a high outburst possibility, one has a medium outburst probability, and four have a low outburst probability, according to the eruption probability and priority scheme presented in Fig. 3. The temporal history of lakes with a high likelihood of outburst is depicted in Fig. 6.

5 Conclusions Monitoring glacial lakes is vital to mitigate and prevent glacial hazards. Digital satellite image processing coupled with a geospatial approach (GIS) can be an extremely efficient tool for investigating glacial lakes and glacial lake outburst floods (GLOFs). In the present study, a three-step semi-automatic technique has been opted to detect and further classify the outburst potentiality of glacier lakes in the Satluj basin. In total, 1–15 glacial lakes have been detected in 1990, 1998, 2008, and 2018, respectively, in the Satluj basin. These glacial lakes cover area of 1.121, 0.210, 0.001, 0.161, 0.133, 0.002, 0.001, 0.005, 0.184, 0.081, 0.096, 0.161, 0.143, 0.122 and 0.239 km2 , respectively. Identified lakes were further categorized into three groups and characterized using both qualitative and quantitative data. Between 1990 and 2018, both the number as well as the area of glacial lakes in the Satluj basin rose by 65% and 71%, respectively. Ice-dammed glacial lakes formed new lakes at a faster rate than bedrock-dammed lakes, with annual averages of 0.74 and 0.56 lakes, respectively. Moraine-dammed lakes grew at a quicker pace than other types of lakes (0.085 km2 annual growth rate). The glacial lakes’ formation and expansion correspond to the

258

G. Rongali et al.

Fig. 6 The highly susceptible potentially dangerous glacial lake in Satluj basin of Landsat-8 16 September 2018 (right) and Google earth (left)

basin’s glacier retreat caused by continual climate change (increased temperature and decreased precipitation). Glacial lake growth is proportional to their distance from the parent glacier, implying that the nearer the lake to the corresponding glacier, the faster it grows. As a consequence, the main supply of water for the region’s lake replenishment is glacial meltwater seeping into the lakes. As a response to the glacier retreat and indeed the continuing extension of lakes, GLOF is a rising concern in the Satluj basin. There are fifteen PDGLs that have been found, and their outburst likelihood measured statistically, with two being very prone to violent outbursts, one moderate, and four having a low risk of an outbreak. Lakes having a moderate to high risk of outburst should undergo a more thorough risk and vulnerability investigation. For additional hazard assessment, we recommend (i) constant observation and review of glaciers and glacial lakes to assess future hazards, (ii) glacial lake outburst flood modeling and possible hazard identification, and (iii)

Potentially Dangerous Glacial Lake Risk Mapping and Assessment …

259

extensive study of the emergence of local supraglacial lakes, bedrock-dammed lakes with regard to their proximity to the parent glaciers, and advancement of proglacial moraine-dammed lakes and their implications on glacier mass balance. Acknowledgements The authors are grateful to NASA for the readily available Landsat and ASTER GDEM datasets freely available through USGS web server. The authors express their gratitude towards Dr. Chembolu Vinay from IIT Jammu and Dr. Subashisa Dutta from IIT Guwahati for their valuable suggestions and guidance during the 1st International Conference on River Corridor Research and Management (RCRM) 2021. Financial support of the work is sourced by National Mission on Himalayan Studies (NMHS), MoES & CC, India, under the Himalayan Research Fellowship (HRF) program.

References 1. Bolch T, Buchroithner MF, Peters J, Baessler M, Bajracharya S (2008) Identification of glacier motion and potentially dangerous glacial lakes in the Mt. Everest region/Nepal using spaceborne imagery. Nat Hazard Earth Syst Sci 8(6):1329–1340 2. Bolch T, Peters J, Yegorov A, Pradhan B, Buchroithner M, Blagoveshchensky V (2011) Identification of potentially dangerous glacial lakes in the northern Tian Shan. In: Terrigenous mass movements. Springer, Berlin, Heidelberg, pp 369–398 3. Campbell JG (2005) Inventory of glaciers, glacial lakes and the identification of potential glacial lake outburst floods (GLOFs) affected by global warming in the mountains of India, Pakistan and China/Tibet Autonomous Region, Technical Report of APN Project 2004-CMY-Campbell 4. Emmer A, Vilímek V (2014) New method for assessing the susceptibility of glacial lakes to outburst floods in the Cordillera Blanca Peru. Hydrol Earth Sys Sci 18(9):3461–3479 5. Hall DK, Bayr KJ, Schöner W, Bindschadler RA, Chien JY (2003) Consideration of the errors inherent in mapping historical glacier positions in Austria from the ground and space (1893– 2001). Remote Sens Environ 86(4):566–577 6. Huggel C, Haeberli W, Kääb A, Bieri D, Richardson S (2004) An assessment procedure for glacial hazards in the Swiss Alps. Can Geotech J 41(6):1068–1083 7. Huggel C, Kääb A, Haeberli W, Teysseire P, Paul F (2002) Remote sensing based assessment of hazards from glacier lake outbursts: a case study in the Swiss Alps. Can Geotech J 39(2):316– 330 8. Iribarren Anacona P, Norton KP, Mackintosh A (2014) Moraine-dammed lake failures in Patagonia and assessment of outburst susceptibility in the Baker Basin. Nat Hazard 14(12):3243–3259 9. Kougkoulos I, Cook SJ, Edwards LA, Clarke LJ, Symeonakis E, Dortch JM, Nesbitt K (2018) Modelling glacial lake outburst flood impacts in the Bolivian Andes. Nat Hazards 94(3):1415– 1438 10. Kulkarni AV, Bahuguna IM (2009) Role of satellite images in snow and glacial investigations. Geol Surv India Spec Publ 53:233–240 11. Kulkarni AV, Philip G, Thakur VC, Sood RK, Randhawa SS, Chandra R (2001) Glacier inventory of the Satluj Basin using remote sensing technique. Himal Geol 20(2):45–52 12. Li J, Sheng Y (2012) An automated scheme for glacial lake dynamics mapping using Landsat imagery and digital elevation models: a case study in the Himalayas. Int J Remote Sens 33(16):5194–5213 13. Lichtenegger J, Gurung DR, Mool PK, Bigot J (2008) Near real-time monitoring of glacial lake Imja(Khumbu-Everest region). In: Proceeding of the ESA living planet symposium, Bergen, Norway

260

G. Rongali et al.

14. McKillop RJ, Clague JJ (2007) Statistical, remote sensing-based approach for estimating the probability of catastrophic drainage from moraine-dammed lakes in southwestern British Columbia. Glob Planet Change 56(1–2):153–171 15. Mergili M, Schneider JF (2011) Regional-scale analysis of lake outburst hazards in the southwestern Pamir, Tajikistan, based on remote sensing and GIS. Nat Hazard 11(5):1447–1462 16. Mool PK (1995) Glacier lake outburst floods in Nepal. J Nepal Geol Soc 11:273–280 17. Mool PK, Wangda D, Bajracharya SR, Kunzang K, Gurung DR, Joshi SP (2001) Inventory of glaciers, glacial lakes and glacial lake outburst floods. Monitoring and early warning systems in the Hindu Kush-Himalayan Region: Bhutan. Inventory of glaciers, glacial lakes and glacial lake outburst floods. Monitoring and early warning systems in the Hindu Kush-Himalayan Region: Bhutan. 18. O’Connor JE, Hardison JH, Costa JE (2001) Debris flows from failures of Neoglacialage moraine dams in the three sisters and mount Jefferson Wilderness areas, Oregon. US Department of the Interior, US Geological Survey 19. Paul F, Andreassen LM (2009) A new glacier inventory for the Svartisen region, Norway, from Landsat ETM+ data: challenges and change assessment. J Glaciol 55(192):607–618 20. Quincey DJ, Richardson SD, Luckman A, Lucas RM, Reynolds JM, Hambrey MJ, Glasser NF (2007) Early recognition of glacial lake hazards in the Himalaya using remote sensing datasets. Glob Planet Change 56(1–2):137–152 21. Reynolds JM (2000) On the formation of supraglacial lakes on debris-covered glacier. IAHS publication, pp 153–164 22. Richardson SD, Reynolds JM (2000) An overview of glacial hazards in the Himalayas. Q Int 65:31–47 23. Sakai A, Chikita K, Yamada T (2000) Expansion of a moraine-dammed glacial lake, Tsho Rolpa, in Rolwaling Himal Nepal Himalaya. Limnol Oceanogr 45(6):1401–1408 24. Thompson S, Benn D, Luckman A, Kulessa B (2010) The evolution of supraglacial lakes, Ngozumpa Glacier, Khumbu Himalaya, Nepal. Geophys Res Abstr 12 EGU2010-11396-1 25. Ukita J, Narama C, Tadono T, Yamanokuchi T, Tomiyama N, Kawamoto S, Abe C, Uda T, Yabuki H, Fujita K, Nishimura K (2011) Glacial lake inventory of Bhutan using ALOS data: part 1, methods and preliminary results. Ann Glaciol 52(58):2011 26. Wang B, Bao Q, Hoskins B, Wu G, Liu Y (2008) Tibetan Plateau warming and precipitation changes in East Asia. Geophys Res Lett 35(14):L14702 27. Wang X, Liu S, Ding Y, Guo W, Jiang Z, Lin J, Han Y (2012) An approach for estimating the breach probabilities of moraine-dammed lakes in the Chinese Himalayas using remote-sensing data. Nat Hazard 12(10):3109–3122 28. Wang X, Liu Q, Liu S, Wei J, Jiang Z (2016) Heterogeneity of glacial lake expansion and its contrasting signals with climate change in Tarim Basin Central Asia. Environ Earth Sci 75(8):1–11 29. Wang W, Yao T, Gao Y, Yang X, Kattel DB (2011) A first-order method to identify potentially dangerous glacial lakes in a region of the southeastern Tibetan Plateau. Mt Res Dev 31(2):122– 130 30. Watanabe T, Ives JD, Hammond JE (1994) Rapid growth of a glacial lake in Khumbu Himal, Himalaya: prospects for a catastrophic flood. Mt Res Develop 329–340 31. Worni R, Huggel C, Stoffel M (2013) Glacial lakes in the Indian Himalayas—From an areawide glacial lake inventory to on-site and modelling based risk assessment of critical glacial lakes. Sci Total Environ 468:S71–S84 32. Xu B, Cao J, Hansen J, Yao T, Joswia DJ, Wang N, Wu G, Wang M, Zhao H, Yang W, Liu X, He J (2009) Black soot and the survival of Tibetan glaciers. Proc Natl Acad Sci 106:22114–22118. https://doi.org/10.1073/pnas.0910444106

A Geospatial Approach for Mapping and Delineation of Palaeochannels of Ghaggar Basin, North-West India, for Groundwater Development to Meet Sustainable Development Goals Ritambhara K. Upadhyay, Naval Kishore, and Mukta Sharma Abstract The palaeochannels or palaorivers are the remains of the rivers or stream courses that carried water in the past, but in the present scenario, they are lost rivers and are left with riverine sediments deposited along their courses. There are various techniques to detect these palaeochannels. In the present study, remote sensing and GIS-based techniques are applied for the delineation of palaeochannels in the Ghaggar River basin, North-West, India. LandSat 8, ETM + PAN and STRM DEM satellite datasets have been used for the demarcation of palaeochannels in the study region. Image enhancement techniques such as Normalized Difference Vegetation Index (NDVI), Hill Shade and Pan-sharpening have been applied on these satellite datasets for obtaining better spectral signatures. Drainage analysis through morphometry of the study region clearly shows high drainage density overlaying on the palaeochannels. The present work strongly emphasizes the potential of geospatial approach in the identification of palaeochannels and the high potential of these palaeochannels in meeting the groundwater sustainability as the palaeochannels have been found to carry high groundwater potential. These palaeochannels can be best utilized for meeting the Sustainable Development Goals (SDGs) as these serve as suitable sites for storm water harvesting and artificial recharge of aquifers during rainy seasons. Such measures can adequately address the rapidly depleting groundwater levels in the region and cater to the water needs as it is devoid of any major river flowing through it. Keywords Palaeochannel delineation · Palaeochannel mapping · Geospatial approach · Morphometric analysis · Sustainable development goals

R. K. Upadhyay (B) · N. Kishore Centre of Advanced Studies in Geology, Panjab University, Chandigarh, India M. Sharma School of Built Environment, IKGPTU, Mohali Campus, Kharar, Punjab, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 V. Chembolu and S. Dutta (eds.), Recent Trends in River Corridor Management, Lecture Notes in Civil Engineering 229, https://doi.org/10.1007/978-981-16-9933-7_17

261

262

R. K. Upadhyay et al.

1 Introduction The civilizations are the proof of major river in the region as most of the civilizations across of the world have been known to exist along the banks of the rivers. The drainages keep changing their courses and this could be attributed to geo-tectonic, environmental or climatic variability. The drainage deflection or drying of rivers is believed to have greatly affected the survival of civilizations along these banks [4, 7, 12, 17, 25, 27]. The landscape too is continuously evolving under changing climatic conditions and geo-tectonic activities, which makes it difficult for the trace the path of the river channels that have been rendered defunct and exist as palaeochannels in the current scenario. The palaeochannels can be defined as the inactive river courses, and in the current scenario, they can be witnessed as younger sediments [14]. These are the testimony of previous of flood activities, and this aspect can be well utilized for predicting the consequences of flood events in the future so that proper flood mitigation strategies can be formulated. These are also known to influence the sub-surface and surficial hydrology in a significant manner. Emergency management and regional planning procedures in the densely populated floodplain-dominant regions can also be benefitted through this study. These carry high groundwater potential [8, 26]. These are also very helpful in mapping the groundwater potential zones especially in regions devoid of major active river. Remote sensing is advantageous in its ability to cover wider areas at fixed time intervals, and the satellite data obtained can be easily processed and analyzed. This saves a lot of time as field observations of large areas are very time consuming. Moreover, satellite imageries provide synoptic view, which further facilitates the demarcation of the boundaries of these palaeochannels. The property to detect the spatial features through the optical and thermal variations is a remarkable property of the sensors. This property can be well utilized for the delineation of palaeochannels [1–3]. As a result, satellite remote sensing is an extensively used technology for the acquisition of spatial data relating to surface geomorphology, soil type, soil moisture content, depositional processes and surface drainage regimes [4]. The data obtained by sensors onboard satellites are highly efficient in the identification and subsequently mapping of the palaeochannels over wide areas without the huge costs as in the case of the Ground Penetrating Radar or LIDAR or drone mapping or imaging activities [5, 6, 11, 12]. The satellite datasets such as LANDSAT series, LISS or Sentinel are available free of cost. Many researchers have made the use of geospatial techniques (remote sensing and GIS) for the delineation and mapping of palaeochannels and applied it for the mapping of groundwater resources, site suitability for groundwater water recharge and rain water harvesting structures [15, 19, 18, 23]. The terrain visualizations can be further enhanced by overlaying the optical satellite dataset over Digital Elevation Models (DEM). For better knowledge and authentication of these delineated palaeochannel, field-based studies such as bore hole drilling or core analysis of the strata obtained, Ground Penetrating Radar surveys, resistivity

A Geospatial Approach for Mapping and Delineation …

263

surveys, etc. [16, 17, 20]. Thus, studies have proved beyond doubt the efficacy and convenience attached with remotely sensed satellite imagery as an initial tool for aiding in the identification of palaeochannels.

2 Study Area The study area majorly comprises parts of two north-western states of India viz. Punjab and Haryana. It extends between geographic co-ordinates 27°39 N–32 º32 N, 73 º55 E–77°36 E (Fig. 1) and covers an area of approximately 20,000 km2 . Located in the fertile great North Indian Plain, the study area shares its borders with Pakistan on the west, on its northern frontier by the Jammu and Kashmir, Himachal Pradesh on the east while the south is bordered by Rajasthan. The major area falls in the belt of the great north Indian fertile land with an abundance of Alluvial Soil, which is irrigated by a wonderful system of rivers and manmade canals. To add to this, a string of mountains strengthen the land. On an average, the height of these is almost 300 m above sea level. On the southern part, the soil become semi-arid, this gives way to the Thar. The Shivaliks also run along the state and form as the foothills of the great Himalayas. According to the Geological Survey of India, the region is classified into Newer Alluvium, Older Alluvium and Shiwaliks. The Newer Alluvium is marked by loose grey sand, aeolian sand sheet, light bluish-grey micaceous sand, intercalations of purple red clay and kankar. The Older Alluvium is characterized by sticky clay, medium to coarse-grained micaceous sand, unsorted pebbles, cobbles, gravels, etc. The Upper Shivalik Group comprises of purple clay, grey-brown micaceous sandstone and conglomerates. Geomorphologically, the region is divided into six major physiographic units, namely, Siwalik Hills, piedmont plain, alluvial plain, sand dunes, flood plains and paleochannels. The Satluj is the main perennial river flowing through the region, whereas Yamuna flows along the eastern boundary. Ghaggar along with seasonal rivulets or choes are active during the monsoon season and they are characterized by continuous erosion and deposition (sediments are young and stratified without any significant alteration of sediments) owing to which there is no consolidation of sediments into pedogenic horizons. The Ghaggar River is structurally controlled by tectonic activities with multiple faults, folds and lineaments. The Yamuna Thrust and the Main Boundary Thrust or Fault have controlled its movement to a large extent. This has also significantly affected the amount of water that the river carried as incidence of river piracy has also been recorded. These developments have led to the formation of wide palaecochannels in the region. The paleochannels are present majorly in the flat-land topography in the states of Punjab and Haryana. These are the resultant of the continual changes in the courses of the major rivers (Ravi, Satluj, Beas and Ghaggar) and their tributaries, which are rendered defunct and silted over a period of time.

264

R. K. Upadhyay et al.

Fig. 1 Location map of the study area with delineated palaeochannels

3 Materials and Method In the floodplain region, fluvial channel migration is one of the significant geomorphological processes. The channel adjustment, usually lateral migration is shown with the geo-informatics, which helps vividly in representing the channel migration

A Geospatial Approach for Mapping and Delineation …

265

or deviation over space and time. The current study is aimed at the demarcation of the paleochannels, which are significant geomorphological features in the alluvial plains of Punjab and Haryana. Thus, satellite datasets viz. optical (Landsat) images and SRTM DEM have been used in the study for the visual interpretation of these defunct palaeochannels as they provide synoptic view. For palaeochannel mapping, coarse-resolution satellite datasets of LandSat ETM+, LandSat 8, LISS-IV, Sentinel and SRTM DEM have been used. ERDAS IMAGINE and ArcGIS software were used to carry out the analysis. The optical datasets were mosaicked and subjected to various image processing techniques such as Histogram Equalization, Band-rationing, Principal Component Analysis, Low and High-Pass Filters, Hill Shade, etc. for the elucidation of palaeochannels. The drainage density was calculated using SRTM DEM of 30 m resolution in ArcGIS software. The processing of the DEM data and hill shade analysis is helpful in revealing the scarps present in the alluvium and lineaments that control the movement of water along the channels. The ability to capture wider view and map-like appeal of the satellite remote sensing imagery make it ideal for regional data on landforms.

4 Results The spectral signatures and image processing techniques have facilitated the identification of the palaeochannels [13, 22–25, 27–24]. The Principal Component Analysis (PCA) and Normalized Difference Vegetation Index (NDVI) of Landsat 8 dataset clearly show the presence of braided palaeochannels as the vegetation signatures too show fluvial pattern. Here too, the width of the palaeochannels is clear indicative of a wide drainage network in the past. The hill shade analysis of the SRTM DEM, all indicates presence of meandering in the region, thereby confirming the presence of channels. Depressed and meandering patterns show path of river body. in the Ghaggar River basin, multiple patterns of this kind can be seen from which it can be inferred that there has been an extensive network of water bodies in the past, eventually referring to palaeochannels. The Normalized Difference Vegetation Index (NDVI) of Landsat 8 dataset (Fig. 2) clearly shows the presence of braided palaeochannels as the vegetation signatures too show fluvial pattern. Here too, the width of the palaeochannels is clear indicative of a wide drainage network in the past. The hill shade analysis of the SRTM DEM, all indicates presence of meandering in the region, thereby confirming the presence of channels. Depressed and meandering patterns show path of river body. in the Ghaggar River basin, multiple patterns of this kind can be seen from which it can be inferred that there has been an extensive network of water bodies in the past, eventually referring to palaeochannels. The elevation difference demarcated by variation in color (Fig. 3) has helped in identifying the broad palaeochannel in the region. The width of the palaechannel as seen through the hill shade analysis has confirmed beyond doubt that these channels are remnants of a huge river that flowed in the past.

266

R. K. Upadhyay et al.

Fig. 2 NDVI of landsat 8 showing braided palaeochannel signatures in the region

Fig. 3 Hill shade analysis shows the presence of palaeochannel

A Geospatial Approach for Mapping and Delineation …

267

Fig. 4 LandSat ETM + PAN showing the presence of palaeochannels in the region

The LandSat ETM+ PAN image of the study area shows braided pattern. From the figure (Fig. 4), it can be clearly inferred that the presence of signatures of vegetation in a meandering, braided pattern and such types of pattern are indicative of a fluvial pattern, most probably of palaeochannels. Figure 5 depicts the drainage density in the study region. The Ghaggar River is the major river flowing through the region. It is a small seasonal river that collects water during the rainy season (Ghaggar River Map). When we closely observe the width of the Ghaggar River in comparison with the drainage density obtained, we find that the area under drainage density is much wider than the width of the river. Also, majority of the area under study falls in the very high, high and moderate drainage density zone. This is a clear indication that the river might have been a major river in the past. The palaeochannels in the Ghaggar river basin are sinuous or serpentine shaped and characterized by high moisture content and thick vegetation cover. The paleochannels have very different hydrogeological characteristics, and they act as passage for high hydraulic conductivity. The integrated approach of using geospatial techniques, borehole data, OSL dating, groundwater level data, field studies and isotopic dating can be used for identification and elucidation of palaeochannels. The methodology can be applied for the identification of the redundant river channels.

268

R. K. Upadhyay et al.

Fig. 5 Drainage density map of the study area

The groundwater level bore well data provided by the Central Ground Water Board (CGWB) clearly show that in the majority of the region the available at a very shallow level of less than 8 m (Fig. 6). In fact, in some places, the water level is as low

Fig. 6 Groundwater level data for the study region (CGWB, 2020)

A Geospatial Approach for Mapping and Delineation …

269

as 1 m below the surface. These locations with shallow groundwater levels clearly coincide with the presence of palaeochannels in the region. This amply signifies the groundwater potential that the regions dominated by palaeochannels hold.

5 Discussion and Conclusion Various satellite datasets have clearly revealed innumerable major and minor palaeochannels in the Ghaggar river basin, in North-West, India. The DEM is not only useful in mapping the palaeochannels, but also shows elevation difference in the surrounding region. The palaeochannels show varying morphology and burial compaction. The newer palaeochannels are relatively flat in comparison with the older ones. The abandoned river channels get filled with sediments over a period of time and eventually serve as suitable sites of vegetation, which can be clearly seen in the optical satellite imageries. The contrast is one of the crucial elements in visual interpretation of the satellite data. The hill shade DEM too played a significant role in delineation of these palaeochannels. Nevertheless, additional investigations such as field surveys, validation with groundwater level data are an important part in characterizing these palaeochannels. The coinciding Spectral signatures on Satellite data and Drainage Density clearly reveal presence of major palaochannels in the region. Owing to the large area under palaeocahnnel floodplains, the palaeochannels are part of a major river, probably the Vedic Saraswati River. The palaeochannels provide geomorphological evidence of the hydrological activities in the past, the fluvial behavior, sedimentation or deposition under climatological or tectonic influence. The examination of these historical events can provide knowledge to help forecast and anticipate future events, such as drainage response to climate variations and potential flood events, especially within the floodplain regions. Moreover, due to the lack of authoritative research on palaeochannels in the region there is a robust incentive to commence such research studies as this can be immensely helpful in the formulation of flood mitigation strategies, better groundwater governance and land utilization. The palaeochannels in the study region i.e. parts of Punjab and Haryana hold huge significance as it has no major river flowing through it. Studies have shown that the sub-surface water levels along the palaecochannels hold immense potential in tapping these resources. It is the presence of these defunct channels that account for the high groundwater levels in the regions as can be validated from the ground water level data of bore wells provided by Central Ground Water Board. Also, owing to the depleting groundwater levels at a rapid pace, it is the need of the hour to take concrete measures for groundwater restoration through artificial recharge during rainy days through various rain water harvesting techniques such as creation of artificial ponds and promoting extensive roof-top rainwater harvesting in every home and industry so that the rainwater is tapped and doesn’t go wasted as surface run-off in the polluted drainages where effluents from industries are discharged.

270

R. K. Upadhyay et al.

Acknowledgements The authors acknowledge NASA and BHUVAN portals for providing freely available datasets.

References 1. Argialas DP, Mavrantza OD (2004) Comparison of edge detection and Hough Transform techniques for the extraction of geo-logic features. In: Proceedings of the XXth ISPRS congress of the international society of photogrammetry and remote sensing, Istanbul, Turkey, pp 1682–1750 2. Balkiwal PC, Grover AK (1988) Signatures and migration of Saraswati River in Thar Desert, Western India. Record Geol Survey Ind 116:77–86 3. Bhadra BK, Gupta AK, Sharma JR (2006) Subsurface water oozing at Kalayat village, Jind district, Haryana in December 2005: Possible connection with Saraswati palaeochannel. J Geol Soc India 68:946–948 4. Bisson M, Bini M (2012) A multidisciplinary approach to reveal palaeo-hydrographic features: the case study of Luna archaeological site surroundings. Int J Geogr Info Sci 26(2):327–343. https://doi.org/10.1080/13658816.2011.592647 5. Breeze PS, Drake NA, Groucutt HS, Parton A, Jennings RP, White TS, Clark-Balzan L, Shipton C, Scerri EM, Stimpson CM, Crassard R (2015) Remote sensing and GIS techniques for reconstructing Arabian palaeohydrology and identifying archaeological sites. Q Int 382:98– 119. https://doi.org/10.1016/j.quaint.2015.01.022 6. Evans RL, Law LK, Louis BS, Cheesman S (2000) Buried paleo-channels on the New Jersey continental margin: channel porosity structures fromelectromagnetic surveying. Mar Geol 170(3–4):381–394. https://doi.org/10.1016/S0025-3227(00)00081-5 7. Flexer A, Gilat A, Hirsch F, Honigstein A, Rosenfeld A, Rueffer T (1989) Late Cretaceous evolution of the Judean Mountains as indicated by ostracodes. Terra Nova 1(4):349–358 8. Gupta AK, Sreenivasan G, Sharma JR, Srivastava (2003) Confirming the presence of palaeochannels in western part of Jaisalmar District, Rajasthan through integration of space and ground methods. Proc Indian NatnSci Acad 69(2):231–239 9. Gupta RP, Kumar S, Samadder RK (2011) Palaeochannels as reservoirs for watershed management. In: Proceedings of the 4th international conference on research in watersheds, Fairbanks, U.K, pp 156–161 10. Hill SM, Eggleton RA, Taylor G (2003) Neotectonic disruption of silicified palaeovalley systems in an intraplate, cratonic landscape: regolith and landscape evolution of the Mulculca range-front, Broken Hill Domain, New South Wales. Aust J Earth Sci 50(5):691–707. https:// doi.org/10.1111/j.1440-0952.2003.01020.x 11. Hou B, Mauger AJ (2005) How well does remote sensing aid palaeochannel identification?— An example from the Harris Greenstone Belt. SA MESA J 38:46–52 12. Hou B, Frakes LA, Alley NF (2007) Palaeochannel evolution, northwestern Gawler Craton, South Australia. In: Anand RR, de Broekert P (eds) Regolith landscape evolution across Australia; a compilation of regolith landscape case studies with regolith landscape evolution models. Cooperative Research Centre for Landscape Environments and Mineral Exploration (CRC LEME), Bentley, WA, Australia, pp 116–229 13. Islam ZU, Iqbal J, Khan JA, Qazi WA (2016) Paleochannel delineation using Landsat 8 OLI and Envisat ASAR image fusion techniques in Cholistan desert, Pakistan. J Appl Remote Sens 10(4):046001. https://doi.org/10.1117/1.JRS.10.046001 14. Jonathan DAC (2009) Palaeovalley, palaeodrainage, and palaeochannel—What’s the difference and why does it matter? Transa R Soc South Australia Incorporated: incorporating the records of the South Australian Museum 133(1). https://doi.org/10.1080/03721426.2009.10887111

A Geospatial Approach for Mapping and Delineation …

271

15. Kar A, Shukla JP (2000) Remote sensing of ground water along the Saraswati palaeochannels in western part of Jaisalmer district, Thar Desert. MAEER’s MIT Pune J (Special issue) 1999– 2000:28–36 16. Karmanov V, Chernov N, Alexey V, Zaretskaya NE, Vasil’evich VA (2013) Palaeochannel studies in archaeology: the case of the vychegda River. Northeastern European Russia, Archaeology, Ethnology and Anthropology of Eurasia 41(2):83–93 17. Pati P, Parkash B, Awasthi AK, Acharya V (2011) Holocene tectono-geomorphic evolution of parts of the upper and middle gangetic plains, India. Geomorphology 128(3–4):148–170. https://doi.org/10.1016/j.geomorph.2011.01.001 18. Rao SSN, Bhallamudi SM, Thandaveswara BS, Sreenivasulu V (2005) Planning groundwater development in coastal deltas with paleo channels. Water Resour Manag 19(5):625–639. https:// doi.org/10.1007/s11269-005-5604-y 19. Rao L, Ali AK, Rehan S, Rahman Z, Siddiqui U, Ruby P, Harit SM, Islam ZU (2009) Hydrogeomorphological studies for groundwater prospects using IRS-1D, LISS III image, in parts of Agra district along the Yamuna River, U.P., India. J Environ Res Dev 3(4):1204–1210 20. Rossetti DF, Valeriano MM, Góes AM, Thallês M (2008) Palaeodrainage on Marajó Island, northern Brazil, in relation to Holocene relative sea-level dynamics. Holocene 18:923–934 21. Rossetti DF, Valeriano MM, Furini CH (2009) Evaluating remote sensing products to delineate paleodrainages in forested areas of southwestern Marajó Island. In: Simpósio Brasileiro de Sensoriamento Remoto, 14, Natal. Anais... São José dos Campos, INPE, p 3333–3339 22. Sharma JR, Bhadra BK (2012) Signatures of palaeorivers network in northwest India using satellite remote sensing. In: Bala S, Mishra K (eds) Historicity of Vedic and Ramayan Eras. Institute of Scientific Research on Vedas, New Delhi, pp 171–192 23. Shukla SM (2014) Spatial analysis for groundwater potential zones using GIS and remote sensing in the tons Basin of Allahabad District, Uttar Pradesh, (India). Proc Natl AcadSci India Sect A Phys Sci 84:587–593. https://doi.org/10.1007/s40010-014-0157-1 24. Steven MD, Malthus TJ, Baret F, Xu H, Chopping MJ (2003) Intercalibration of vegetation indices from different sensors. Remote Sensing of Environment. 88. 412–422. https://doi.org/ 10.1016/j.rse.2003.08.010 25. Tarolli P (2014) High-resolution topography for understanding Earth surface processes: opportunities and challenges. Geomorphology 216(1):295–312 26. Upadhyay R, Sharma N, Sharma M (2021a) Delineation and mapping of palaeochannels using remote sensing, geophysical, and sedimentological techniques: a comprehensive approach. Water Sci 35(1):100–108 27. Upadhyay RK, Tripathi G, Sharma NK, Sharma M (2021b) GI-science morphometric analysis for geo-hydrological studies of Ghaggar River Basin, North-West, India. Turkish Online J Qual Inq 12(9):2487–2499 28. Valdiya KS (2017) Prehistoric River Saraswati, Western India geological appraisal and social aspects. Society of. ed. Springer 29. Yashpal SB, Sood RK, Agrawal DP (1980) Remote sensing of the lost Saraswati river. Proc Indian Acad Sci (Earth & Planet Sci) 89:317–337