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

ISBN-13: 9783030722951 / Angielski / Twarda / 2021 / 376 str.

Raj Kumar Bhattacharya; Nilanjana Das Chatterjee

Spis treści

Chapter - 1 Introduction

1.1 River sand mining 

1.2 Past work on river sand mining

1.3 Past works on river sand mining in India

1.4 Sand: mineralogical structure, origin and types

1.5 Environmental sensitivity of sand

1.6 Economic significance 

1.7 Global challenge for sustainable sand mining during 21st century

1.8 Scope of the present study

1.9 The study area

References…………………………………………………………………………..

Chapter – 2 Geomorphological thresholds and sand mining

2.1 Types of river sand mining

2.2 Methods of sand mining 

2.3 Sand mining in alluvial Non-perennial River: process and generation 

2.4 Sand mining in Kangsabati River: Background and mining sites

2.5 Geographical setting of River Kangsabati: a tropical non perennial river 

       2.5.1 Drainage

       2.5.2 Geological setup

       2.5.3 Geomorphic setup

       2.5.4 Soil class

       2.5.5 Climate

       2.5.6 Slope and elevation

       2.5.7 Land use and land cover

      2.5.8 Demographic setup along the basin to show the demand for sand

References…………………………………………………………………

Chapter – 3 Sediment budget and mining area detection using RUSLE and SDR models

3.1 Introduction

3.2 Sediment source

       3.2.1 Soil loss estimation (RUSLE)

                3.2.1.1 Objectives

                3.2.1.2 Methodology and mapping

                3.2.1.3 RUSLE parameter Estimation

                            3.2.1.3.1 Rainfall Erosivity Factor (R)

                            3.2.1.3.2 Soil Erodibility Factor (K)

                            3.2.1.3.3 Slope Length and Slope Steepness Factor (Ls)

                            3.2.1.3.4 Cover Management Factor (C)

                            3.2.1.3.5 Support Practice Factor (P)

                3.2.1.4 Results and Discussions

                            3.2.1.4.1 Estimation of potential soil erosion

                            3.2.1.4.2 Sub basin wise potential annual soil loss estimation

                            3.2.1.4.3 Sub basin wise mean Soil erosion probability zones

                            3.2.1.4.4 Correlation of sub basin wise land use/land covers

                             (LULC) and basin area

                                            3.2.1.4.4.1 Compare the Land use wise mean soil 

                                         erosion of each sub basin during 2002 and 2016

                                            3.2.1.4.4.2 Basin area wise soil loss estimate during 

                                            2002 and 2016

                     3.2.1.5 RUSLE findings

       3.2.2 Sediment Delivery Ratio (SDR)

                3.2.2.1 Objective

                3.2.2.2 Methodology and Mapping

                             3.2.2.2.1 Estimation of ß coefficient and travel time (ti)

                              3.2.2.2.2 Land use and land cover (à coefficient)

                              3.2.2.2.3 Slope Factor (si)

                              3.2.2.2.4 Flow velocity (vi)

                              3.2.2.2.5 Length of segments (li)

                              3.2.2.2.6 Basin specific parameter (ß)

              3.2.2.3 Results and Discussions

                           3.2.2.3.1 Delineation of Sediment Delivery Ratio (SDR)

                           3.2.2.3.2 Sub basin wise potential annual Sediment Delivery 

                             Ratio

                            3.2.2.3.3 Validation of SDR Estimation

                                             3.2.2.3.3.1 Drainage area and Sediment Ratio

                                             3.2.2.3.3.2 Topographical factors and Sediment

                                             Delivery Ratio

       3.2.3 Estimation of delineation of Sediment Yield zone

                3.2.3.1 Sub basin wise potential annual Sediment Yield

       3.2.4 Findings of sources estimation of sediment

3.3 Sediment sinks

       3.3.1 Sand mining

                3.3.1.1 Objectives

                 3.3.1.2 River sand mining in the study area

                                  3.3.1. 2.1 Instream mining (Year wise extraction rate and

                                  Mining sites)

                                   3.3.1.2.2 Floodplain mining

                  3.3.1.3 Shifting of sand mining sites

       3.3.2 Segment wise Sediment concentration (Gcr)

       3.3.3 Segment wise sediment transport (QT)

3.4 Sediment budget

       3.4.1 Sand mining vs. Replenishment-Khatra segment

       3.4.2 Raipur segment

       3.4.3 Lalgarh segment

       3.4.4 Dherua segment

       3.4.5 Mohanpur segment

       3.4.6 Kapastikri segment

       3.4.7 Panskura segment

       3.4.8 Rajnagar segment

3.5 Chapter findings

References……………………………………………………………………..

Chapter – 4 Sediment analysis and mining intensity using G-stat, Grad-stat, Sed-log, LDF techniques

4.1 Introduction

       4.1.1 Objective

4.2 Materials and method

       4.2.1 Sampling procedure and method analysis

       4.2.2 Grain size analysis using of G-stat, Grad-stat, Sed-log, and LDF

       4.2.3 Estimation of bed shear stress -Shear stress (τ0); critical shear 

        stress (u*)

4.3 Result

       4.3.1 Textural characteristics of sediments

                 4.3.1.1 Mean (MZ)

                 4.3.1.2 Sorting (ó1):

                 4.3.1.3 Skewness (SK1)

                 4.3.1.4 Kurtosis (KG)

                 4.3.1.5 Grain size parameters determined by bivariate scatter graphs  

       4.3.2 Course wise granulometric analysis of sediment through triangular

      Diagram

       4.3.3 Analyzing transporting mechanism and depositional environment detects by CM diagram

       4.3.4 Course wise Tractive current deposit

                 4.3.4.1 Linear Discriminate function

       4.3.5 Grain size related to bed shear stress and critical shear stress

4.4 Discussion

       4.4.1 Relation between erosion and deposition process in relation to grain size in 

        mining and non mining sites

       4.4.2 Relation between erosion and deposition process to mining intensity during 

       pre monsoon and post monsoon 

References………………………………………………………………

Chapter – 5 Interruption on alluvial channel flow and sediment transport in quarried alluvial river: Application of different hydraulic techniques

5.1 Introduction

       5.1.1 Objective

5.2 Materials and Methods

       5.2.1 Measures of fluvial regime

                5.2.1.1 Reynolds Number (Re)

                5.2.1.2 Froude Number (Fr)

                5.2.1.3 Chezy equation (V)

                5.2.1.4 Manning equation (v) 

                5.2.1.5 Chezy coefficient (C)

                5.2.1.6 Roughness coefficient (C)

       5.2.2 Measures of sediment transport hydraulics

                5.2.2.1 Bed load transport (Q_T)

                5.2.2.2 Sediment concentration (X)

                5.2.2.3 Shear stress equation (τ_o)

                5.2.2.4 Critical shear stress (τ_cr)

                5.2.2.5 Settling velocity (ω_0)

                5.2.2.6 Shear velocity (u_*)

                5.2.2.7 Incipient motion (ym)

       5.2.3 Statistical measures of principal Component Analysis (PCA) and 

       Prinsscore (Kothari, 2009)

5.3 Results

       5.3.1 Flow Regime

                5.3.1.1 Bankfull discharge (Q)

                5.3.1.2 Velocity (VC)

                5.3.1.3 Flow resistance

                5.3.1.4 Uniform to non-uniform flow- Reynolds (Re) and Froud 

                Number (Fr)

               5.3.2 Sediment regime

               5.3.2.1 Bed Material-Grain size (d50)

               5.3.2.2 Bed load (QS)

               5.3.2.3 Sediment transport (QT)

               5.3.2.4 Sediment concentration (Gcr)

               5.3.2.5 Shield parameters

       5.3.3 Sedimentation

                5.3.3.1 Incipient motion (ym)

                5.3.3.2 Settling velocity (w°)

5.4 Discussion                     

       5.4.1 Grain size vs. Sediment transport

       5.4.2 Bed load vs. Shear stress

       5.4.3 Velocity vs. Bed load sediment

       5.4.4 Bed roughness vs. Sediment concentration

       5.4.5 Flow resistance vs. Grain size

       5.4.6 Particle settling velocity vs. incipient motion

 5.5 interruptions of hydraulic variables of bedload transport impact on channel dynamic

       5.5.1 Migration of sand wave

       5.5.2 Channel bed deformation: physical characteristic and dynamic change

       5.5.3 Mining pit migration

       5.5.4 Channel incision

References………………………………………………………………………….

Chapter-6   Impact of instream sand mining on channel geomorphology: using digital shoreline analysis system (DSAS), end point rate (EPL), linear regression rate (LRR), bank erosion hazard index (BEHI)

6.1Introduction

Objective

6.2 Method

             6.2.1 Application of EPR and LRR model for estimating and predicting on river bank shifting

             6.2.2 Geometrical change of channel meandering in comparison between mining    and non mining sites (Meandering length, height, radius of curvature, Arc length, and Mean channel width)

             6.2.3 Application of DSAS for estimating and predicting on erosion and accretion in mining and non mining sites along the eight segments

   6.2.4 Estimation of bank erosion by bank erosion hazard index (BEHI) 

 6.3 Results

6.3.1 Trend of EPR and LLR predicted river bank shifting incorporates with mining and non mining sites (2002-2016) 

Future prediction on river bank trend of 

6.3.1.2 Model validation: Student’s t test on EPR and LLR in eight different segments

6.3.2 Trend of meandering geometry incorporates with mining and non mining sites (2002-2016)

6.3.3 Trend of DSAS estimated erosion and accretion along the eight segments

6.3.3.1 Model validation

6.3.4 Meandering geometry incorporates with erosion and accretion process in mining and non mining sites

6.3.5 Bank erosion vulnerability by BEHI (2002-2016)

6.3.5.1 Model validation

6.3.6 River bank erosion incorporates with mining and non mining sites

6.3.7 Pool-riffle alterations incorporates with mining and non mining sites

6.3.8 River bed degradation and thalweg dynamicity incorporates with mining and non mining sites

6.3.9 Correlation between geomorphic response and riverine land cover change in                                     mining and non mining sites

References…………………………………………………………….

Chapter –7 Impact of instream sand mining on river ecology using WQI, Biodiversity index, HSI, MLR         

7.1 Introduction

7.2 Water Quality Index (W.Q.I)

       7.2.1 Objectives

       7.2.2 Materials and Methods

                7.2.2.1 Sample estimate process and method adaptation

                7.2.2.2 Measurement of WQI

                7.2.2.3 Statistical application

     7.2.2.4 Measurement of instream biota

       7.2.3 Results- Determination of physicochemical parameters in water 

       Samples

                7.2.3.1 Variation in pH

                7.2.3.2 Measurement of DO

                7.2.3.3 Electrical conductivity and turbidity

                7.2.3.4 Total Dissolved Solids and Salinity

                  7.2.3.5 Segment wise mining effects on WQI in Kangsabati river water

                7.2.3.6 WQI of mining vs. non mining sites

                            7.2.3.6.1 Khatra segment

                            7.2.3.6.2 Raipur segment

                            7.2.3.6.3 Lalgarh segment

                            7.2.3 6.4 Dherua segment

                            7.2.3.6.5 Mohanpur segment

                            7.2.3.6.6 Kapastikri segment

                            7.2.3.6.7 Panskura segment

                            7.2.3.6.8 Rajnagar segment

       7.2.3.7 Cluster zoning of WQI-Prinsscore (Kothari, 2009)

       7.2.3.8 Instream biota

7.2.4 Discussions

        7.2.4.1 Correlations of estimated parameters and Instream biota                

 7.3 Habitat Suitable Index (H.S.I)

       7.3.1 Objective

       7.3.2 Materials and Methods

                7.3.2.1 Habitat Suitability Index prepared through the use of geospatial 

                technology (DEM, slope, Elevation, Aspect, Road distance and channel 

                distance)

                            7. 3.2.1.1 River Channel

                            7.3.2.1.2 Sandbar

                            7.3.2.1.3 Dry and moist sand

                            7.3.2.1.4 Riparian zone

                            7.3.2.1.5 Rocky outcrop

                            7.3.2.1.6 Slope and elevation factor

                7.3.2.2 Apply of Multiple Logistic Regressions (MLR) on probable 

                prediction

       7.3.3 Results

                7.3.3.1 H.S.I of river bank habitat dominant species in pre mining and post mining sites 

                7.3.3.2 H.S.I of riparian habitat dominant species in pre mining and post mining sites   

                7.3.3.3 H.S.I of aquatic habitat dominant species in the mining pits

       7.3.4 Discussion

                7.3.4.1 Logistic regression analysis of GIS data layers to derive 

               coefficient value

                7.3.4.2 Validation of HSI of river bank and riparian dominant species in pre mining 

                 and post mining sites

References………………………………….

Chapter – 8 Economic audit and Proposed sustainable sand mining using Optimization model and EIA

8.1 Introduction

       8.1.1 Objective

 8.2 Methodology

     8.2.1 Computation of annual optimal mining quantity

     8.2.2 Computation of proposed sand mining quantity during planning period

     8.2.3 Environment Impact Assessment (EIA) in instream sand mining sites through Simple matrix method

  8.3 Results

   8.3.1 Economic audit

    8.3.1.1 Estimation of annual river sand mining rate using optimization model

    8.3.1.2 Estimation of annual optimal quantity of river sand mining following the relationship between quantity and price

    8.3.1.3 Estimation of optimizing utilization of river sand mining during planning period

     8.3.1.4 Optimization analysis between quantity and price during planning period

   8.3.2 EIA of instream sand mining

                8.2.3.1 EIA of instream sand mining in upper course

     8.2.3.3 EIA of instream sand mining in middle course

                8.2.3.5 EIA of instream sand mining in lower course

8.4 Discussion

8.4.1 Demarcation of sustainable potential mining sites       

8.4.1.1 Proposed instream sand mining sites in upper course

8.4.1.2 Proposed instream sand mining sites in middle course

8.4.1.3 Proposed instream sand mining sites in lower course

8.4.2 Management and recommendation

8.5 Conclusion

Index

Blurb

Worldwide demand for sand and gravel is increasing daily, as the need for these materials continues to rise, for example in the construction sector, in land filling and for transportation sector based infrastructural projects. This results in over-extraction of sand from channel beds, and hampers the natural renewal of sediment, geological setup and morphological processes of the riverine system. 

In India, illegal sand mining (of alluvial channels) and gravel mining (of perennial channels) are two anthropogenic issues that negatively affect the sustainable drainage system. Along the Kangsabati River in India, the consequences of sand mining are very serious. The construction of Mukutmonipur Dam (1958) on the river causes huge sediment deposition along the middle and downstream areas, these same areas are also intensely mined for sand (instream and on the flood plain). Geospatial models are applied in order to better understand the state and the resilience of stream hydraulics, morphological and river ecosystem variables during pre-mining and post-mining stages, using micro-level datasets of the Kangsabati River. 

The book also includes practicable measures to minimize the environmental consequences of instream mining in respect to optimum sand mining. It discusses the threshold limits of each variable in stream hydraulics, morphological and river ecological regime, and also discusses the most affected variables. Consequently, all outputs will be very useful for students, researchers, academicians, decision makers and practitioners and will facilitate applying these techniques to create models for other river basins.