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

Foreword
Preface
Acknowledgements
Contents
List of Figures
List of Tables
List of Plates
1 River Sand Mining and its Management: A Global Challenge
	1.1 River Sand Mining
	1.2 Past Work on River Sand Mining
		1.2.1 Sand Mining and Channel Hydrology
		1.2.2 Sand Mining and Channel Morphological
		1.2.3 Sand Mining and Riverine Ecology
	1.3 Past Work on River Sand Mining in India
	1.4 Sand: Mineralogical Structure, Origin and Types
	1.5 Environmental Sensitivity of Sand
	1.6 Economic Significance of Sand
	1.7 Global Challenge for Sustainable Sand Mining During Twenty-First Century
	1.8 Scope of the Present Study
	1.9 Selection of the Study Area
	References
2 Geomorphic Threshold and Sand Mining: A Geo-environmental Study in Kangsabati River
	2.1 Introduction
	2.2 Geomorphic Threshold and Instream Sand Mining in Alluvial Channel
		2.2.1 Alluvial River Sand as Geomorphic Product
		2.2.2 Sand Mining Exceeding Threshold Limits
		2.2.3 Sand Mining Process and Consequences
			2.2.3.1 Methods of Instream Sand Mining Process
			2.2.3.2 Methods of Floodplain Sand Mining Process
	2.3 An Alluvial Quarried Reach in Kangsabati River
		2.3.1 Geo-environmental Setting of Kangsabati Catchment Area
			2.3.1.1 Geological Set up
			2.3.1.2 Geomorphic Set up
			2.3.1.3 Climate
			2.3.1.4 Soil
		2.3.2 Selection of the Channel Segments Along the Kangsabati River
			2.3.2.1 Khatra Segment
			2.3.2.2 Raipur Segment
			2.3.2.3 Lalgarh Segment
			2.3.2.4 Dherua Segment
			2.3.2.5 Mohanpur Segment
			2.3.2.6 Kapastikri Segment
			2.3.2.7 Panskura Segment
			2.3.2.8 Rajnagar Segment
	2.4 Sand Mining Crossed the Threshold Limit in Middle and Lower Reach of Kangsabati River
	2.5 Conclusion
	References
3 Fluvial Sediment Budget and Mining Impact Assessment: Use of RUSLE, SDR and Hydraulic Models
	3.1 Introduction
	3.2 Estimation of Sediment Source
	3.3 Soil Loss Assessment Using of RUSLE
	3.4 RUSLE Model Set Up
	3.5 Case Study: Estimation of Mean Annual Soil Erosion at Sub Basin Level of Kangsabati Basin Using RUSLE—A Case Study
		3.5.1 Estimation of RULE Factors
			3.5.1.1 Rainfall Erosivity Factor (R)
			3.5.1.2 Soil Erodibility Factor (K)
			3.5.1.3 Slope Length and Slope Steepness Factor (Ls)
			3.5.1.4 Cover Management Factor (C)
			3.5.1.5 Support Practice Factor (P)
		3.5.2 Delineation of Potential MASE
			3.5.2.1 Potential Annual Soil Loss Estimation at Sub-basin Level
			3.5.2.2 MASE Probability Zones at Sub-basin Level
		3.5.3 Relation Between Soil Erosion with Land Use/Land Covers (LULC) and Basin Area
			3.5.3.1 Land Use Based MASE at Sub-basin Level
			3.5.3.2 Estimated of Basin Area Based Soil Erosion at Sub-basin Level
	3.6 Sediment Delivery Ratio (SDR) and Sediment Yield (SY)
	3.7 Case Study: Assessing of Sediment Delivery Ratio (SDR) and Sediment Yield (SY) at Sub Basin Level of Kangsabati Basin—A Case Study
		3.7.1 Estimation of SDR Factors
			3.7.1.1 ß Coefficient and Travel Time (ti)
			3.7.1.2 LULC (à Coefficient)
			3.7.1.3 Slope Factor (si)
			3.7.1.4 Flow Velocity (vi)
			3.7.1.5 Length of Segments (li)
			3.7.1.6 Basin Specific Parameter (ß)
		3.7.2 Delineation of Sediment Delivery Ratio (SDR)
		3.7.3 Potential Annual SDR at Sub-basin Level
		3.7.4 Validation of SDR
			3.7.4.1 Validation Using Drainage Area
			3.7.4.2 Validation Using Topographical Factors
		3.7.5 Delineation of SY
		3.7.6 Potential Annual SY at Sub-basin Level
	3.8 Sink of Sediment Budget
	3.9 Case Study: Assessing of Sediment Sink and Sediment Budget in Kangsabati River
		3.9.1 River Sand Mining in Kangsabati River
			3.9.1.1 Instream Sand Mining
			3.9.1.2 Floodplain Sand Mining
			3.9.1.3 Shifting of Sand Mining Sites
		3.9.2 Estimation of Sediment Transport (QT)
		3.9.3 Estimation of Sediment Concentration (X)
		3.9.4 Estimation of Sediment Budget in Eight Segments of Kangsabati River
	3.10 Conclusions
	References
4 Sediment Grain Size Analysis and Mining Intensity: Estimation by GRADISTAT, G-STAT and LDF Techniques
	4.1 Introduction
	4.2 Sand Mining Response on SGD
	4.3 Sediment Grain Size Analysis
		4.3.1 Application of GRADISTAT Software for Measuring the SGD
	4.4 Case Study: Accessing the Relationship Between Stream Energy and Sediment Grain Size Distribution in Kangsabati River Using GRAD Stat
		4.4.1 Preparation of Sampling Process
		4.4.2 Textural Characterization
			4.4.2.1 Descriptions of Graphic Mean (Mz)
			4.4.2.2 Descriptions of Graphic Sorting (ó1)
			4.4.2.3 Descriptions of Graphic Skewness (SK1)
			4.4.2.4 Descriptions of Graphic Kurtosis (KG)
		4.4.3 Bivariate Scatter Graphs of Grain Parameters
			4.4.3.1 Graphic Mean Size Versus Sorting
			4.4.3.2 Graphic Mean Size Versus Skewness
			4.4.3.3 Graphic Mean Size Versus Kurtosis
	4.5 Case Study: Estimation the Transporting Mechanism and Depositional Environment in Kangsabati River Using G-STAT (Grainsize Statistics) Software
		4.5.1 Cumulative Weight Percentage Diagrams of Sediment Textural Ratio
		4.5.2 Analysis of Granulometric Properties Using Triangular Diagram
		4.5.3 Analysis of Transport Mechanism and Mode of Deposition Using CM Diagram
		4.5.4 Estimation of Tractive Current Deposits at Course Level
	4.6 Linear Discriminate Function (LDF)
		4.6.1 Case Study: Derivation of Sediment Depositional Environment in Kangsabati River Using LDF
		4.6.2 Bivariate Graph of Sediment Depositional Environment During Pre Monsoon and Monsoon
	4.7 Grain Size Related to Bed Shear Stress (τ0) and Critical Shear Stress (U*)
		4.7.1 Case Study: Erosion and Deposition Process in Relation to Mining Intensity During Pre Monsoon and Monsoon in Kangsabati River
		4.7.2 Erosion and Deposition Process in Relation to SGD
	4.8 Conclusion
	Supplementary Table
	References
5 Mining Response on Alluvial Channel Flow and Sediment Transport: Application of Hydro-Morphological Techniques and Principal Component Analysis (PCA)
	5.1 Introduction
	5.2 Mining Genesis Turbulence Flow and Its Affected Hydraulic Variables of Sediment Transport
		5.2.1 Measure of Hydraulic Variables of the Flow Regime
			5.2.1.1 Reynolds Number (Re)
			5.2.1.2 Froud Number (Fr)
			5.2.1.3 Chezy Coefficient (C) and Manning Coefficient (v)
		5.2.2 Measure of Hydraulic Variables of the Sediment Transport
			5.2.2.1 Shear Stress ( \tau_{o} )
			5.2.2.2 Critical Shear Stress ( \tau_{cr} )
			5.2.2.3 Shear Velocity ( u_{*} )
			5.2.2.4 Settling Velocity ( \omega_{0} )
			5.2.2.5 Incipient Motion (ym)
			5.2.2.6 Total Sediment Transport ( Q_{T} )
			5.2.2.7 Sediment Concentration (X)
			5.2.2.8 Bedload Estimation (Qb)
	5.3 Case Study: Sand Mining Affected Interruption of Hydraulic Variables in Flow Regime of Kangsabati River
		5.3.1 Hydraulic Variables of Flow Regime and Mining Intensity
			5.3.1.1 Effects on Bankfull Discharge (Q)
			5.3.1.2 Effects on Flow Velocity (v)
			5.3.1.3 Effects on Flow Characteristics
			5.3.1.4 Effects on Flow Resistance
		5.3.2 Hydraulic Variables of Sediment Transport and Mining Intensity
			5.3.2.1 Effects on Particle Diameter (d50)
			5.3.2.2 Effects on Shield Parameters
			5.3.2.3 Effects on Settling Velocity (w°)
			5.3.2.4 Effects on Incipient Motion (Ym)
			5.3.2.5 Effects on Sediment Transport (QT)
			5.3.2.6 Effects on Sediment Concentration (X)
			5.3.2.7 Effects on Bed Load Transport (Qb)
		5.3.3 Bivariate Relation Between Hydraulic Variables of Flow Regime with Mining Intensity
		5.3.4 Bivariate Relation Between Hydraulic Variables of Sediment Regime with Mining Intensity
	5.4 Comparatively Supremacy of Hydraulic Variables of Bedload Transport and Their Clustering Using Principal Component Analysis (PCA)
		5.4.1 Principle of PCA
		5.4.2 Hydraulic Variables Set up for PCA
		5.4.3 Supremacy Execution Amongst the Hydraulic Variables of Sediment Transport in Quarried River Kangsabati Using PCA
	5.5 Deformation of Hydrodynamic Regime
	5.6 Conclusion
	Supplementary Table
	References
6 Sand Mining Consequences on Channel Morphology: Practical Use of Digital Shoreline Analysis System (DSAS), Geometrical Indices and Compound Factor (CF)
	6.1 Introduction
	6.2 Application of Hydro-Morphological Techniques to Measure the Mining Induced Geomorphic Responses (GRs)
	6.3 Case Study: Mining Induced Geomorphic Responses and Riverine Land Cover Changes in Kangsabati River
		6.3.1 Estimation and Prediction of Mining Affected River Bank Erosion Using Digital Shoreline Analysis System (DSAS)
			6.3.1.1 Demarcation of Bank Line, Reference Baseline and Transect Points
			6.3.1.2 Estimation of BLS Rate Using EPR Model
			6.3.1.3 Prediction of BLS Rate Using LRR Model
			6.3.1.4 Validation and Evaluation of EPR and LRR Models
		6.3.2 BLS and Erosion/Accretion Process
			6.3.2.1 Linear Regression Based River BLS Trend (2000–2016)
			6.3.2.2 EPR Based Periodic River Bank Shifting Trend (2000–2016)
			6.3.2.3 Future Prediction of BLS
			6.3.2.4 Validation of DSAS Model
		6.3.3 Others Mining Induced GRs
			6.3.3.1 Instability of Sandbar
			6.3.3.2 Shifting of Thalweg Line
			6.3.3.3 Alteration of Pool-Riffle Sequence
			6.3.3.4 Lowering of River Bed Level
		6.3.4 Channel Planform Change
		6.3.5 RLCs Change
	6.4 Prioritization of Mining Induced Geomorphic Consequences Using Compound Factor (CF)
	6.5 Case Study: Mining Affected Geomorphic Prioritization at Segment Level in Kangsabati River
	6.6 Conclusion
	References
7 Sand Mining Consequences on Habitat Ecology, Water Quality and Species Diversity: Implementing of HSI, MLR, WQI and ANN Methods
	7.1 Introduction
	7.2 Three Tier Habitat (TTH) Degradation or Alternation and Sand Mining
	7.3 Establishment of Habitat Suitability Index (HSI) for TTH Degradation or Alternation
	7.4 Application of Multiple Logistic Regression (MLR) for Assessment of Sand Mining Impact
		7.4.1 MLR Model Set Up
		7.4.2 Basic Principle of MLR
		7.4.3 MLR Set Up for TTH Alteration or Degradation
	7.5 Case Study: Multi Habitat Suitable Parameters Based TTH Alteration or Degradation in Quarried Kangsabati River
		7.5.1 Factor Affecting on Habitat Suitability
			7.5.1.1 Slope
			7.5.1.2 Elevation
			7.5.1.3 Channel
			7.5.1.4 Sandbar
			7.5.1.5 Moist and Dry Sand
			7.5.1.6 Riparian Sites
		7.5.2 Validation of Habitat Suitability Model
		7.5.3 Result of Habitat Suitable Parameters
		7.5.4 HSI of Koeleria Macrantha During Pre Mining and Post Mining Phase
		7.5.5 HSI of Cynodon Dactylon During Pre Mining and Post Mining Phase
		7.5.6 Validation of HSI of Koeleria Macrantha and Cynodon Dactylon Species
	7.6 Water Quality Deterioration
		7.6.1 Determination of Water Quality in Mined River Using Water Quality Index (WQI)
		7.6.2 Relative Weighted Arithmetic WQI Set Up
		7.6.3 Application of Artificial Neural Network (ANN) Model and MLR to Explain the Impact of Sand Mining on Water Quality
			7.6.3.1 ANN Model Set Up
			7.6.3.2 Basic Principle of ANN Model
			7.6.3.3 ANN and MLR Set Up for Predicting the Deterioration of WQI in Mined River
		7.6.4 Case Study: Water Quality Assessment in Quarried Kangsabati River
			7.6.4.1 Descriptive Statistic of PP
			7.6.4.2 Assessment of Water Quality in Mined River Using WQI
			7.6.4.3 ANN Model Predicted WQI in Kangsabati River
			7.6.4.4 MLR Predicted WQI in Kangsabati River
	7.7 Assessment of Sand Mining Impact on Instream Biota Using Biodiversity Index
		7.7.1 Case Study: Assessment of Instream Biota in Kangsabati River
		7.7.2 Correlations of Estimated Water Quality Parameters and Instream Biota
	7.8 Conclusion
	References
8 Sand Resource Estimation, Optimum Utilization and Proposed Sustainable Sand Mining: Recommending Sand Auditing, Optimization Model and EIA
	8.1 Introduction
	8.2 Audit of River Sand
		8.2.1 Adopted Methodology
			8.2.1.1 Estimation of Sand Resource
			8.2.1.2 Allocation of Sand Resource
			8.2.1.3 Accounting of Sand Resource
	8.3 Case Study: Utilizing Sand Audit Report to Estimate the Amount of River Sand Resource for Mining Plan of the Kangsabati River
		8.3.1 Estimation of Sand Resources in Possible Mining Sites
		8.3.2 Allocation of Mineable Sand in Possible Mining Sites
		8.3.3 Bed Level Lowering Estimates the Recorded and Non-recorded Sand Mining
	8.4 Optimal Sand Utilization
		8.4.1 Optimal Model Related Theories
		8.4.2 Optimal Model Premises Hypothesis
		8.4.3 Optimization Model Establishment
			8.4.3.1 Estimation for Annual Mining of Aggregate Sand Resources in the River
			8.4.3.2 Estimation for Annual Optimal Quarrying Amount of Sand and Gravel Based on Quantity and Price Relationship
			8.4.3.3 Establishment of Optimization Model for Optimal Utilization of Aggregate Sand Resource During Short or Long Term Planning Period
			8.4.3.4 Optimal Assessment for Planning Period Based on Linear Relationship Between Amount and Price of Aggregate Sand Resource
	8.5 Case Study: Optimal Sand Extraction or Sand Mining Plan for Kangsabati River
	8.6 Environmental Impact Assessment (EIA) for Propose Sand Mining Sites
		8.6.1 Methodological Set Up for EIA Through Analytical Hierarchy Process (AHP)
		8.6.2 Methodological Set Up for EIA Through Rapid Impact Assessment Matrix (RIAM)
	8.7 Case Study: EIA of Instream Sand Mining for Allocating of Sustainable Sand Mining Sites of the Kangsabati River
		8.7.1 Impact on Riverine Environment
			8.7.1.1 Impact on Flow and Sediment Hydraulics/Regime
			8.7.1.2 Impact on Instream and Floodplain Morphology/Landforms
			8.7.1.3 Impact on Habitat Ecosystem
			8.7.1.4 Impact on Water Quality
			8.7.1.5 Impact on Socio-economic and Operational Environment
		8.7.2 EIA for Proposing of Sustainable Sand Mining Sites in Upper Course
		8.7.3 EIA for Proposing of Sustainable Sand Mining Sites in Middle Course
		8.7.4 EIA for Proposing of Sustainable Sand Mining Sites in Lower Course
	8.8 Conclusion
	8.9 Key Suggestions for Sustainable Sand Mining
	8.10 General Conclusion
	Supplementary Tables
	References
Index