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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.

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

Chapter - 1Introduction1.1 River sand mining 1.2 Past work on river sand mining1.3 Past works on river sand mining in India1.4 Sand: mineralogical structure, origin and types1.5 Environmental sensitivity of sand1.6 Economic significance 1.7 Global challenge for sustainable sand mining during 21st century1.8 Scope of the present study1.9 The study areaReferences......................................................................................Chapter - 2Geomorphological thresholds and sand mining2.1 Types of river sand mining2.2 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 sites2.5 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 sandReferences...........................................................................Chapter - 3Sediment budget and mining area detection using RUSLE and SDR models3.1 Introduction3.2 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 (a 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 segment3.5 Chapter findingsReferences................................................................................Chapter - 4 Sediment analysis and mining intensity using G-stat, Grad-stat, Sed-log, LDF techniques4.1 Introduction       4.1.1 Objective4.2 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 ( 0); 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 stress4.4 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 techniques5.1 Introduction       5.1.1 Objective5.2 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 ( _o)                5.2.2.4 Critical shear stress ( _cr)                5.2.2.5 Settling velocity ( _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 incisionReferences.....................................................................................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)6.1IntroductionObjective6.2 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 Results6.3.1 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 segments6.3.2 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 segments6.3.3.1 Model validation6.3.4 Meandering geometry incorporates with erosion and accretion process in mining and non mining sites6.3.5 Bank erosion vulnerability by BEHI (2002-2016)6.3.5.1 Model validation6.3.6 River bank erosion incorporates with mining and non mining sites6.3.7 Pool-riffle alterations incorporates with mining and non mining sites6.3.8 River bed degradation and thalweg dynamicity incorporates with mining and non mining sites6.3.9 Correlation between geomorphic response and riverine land cover change in                                     mining and non mining sitesReferences......................................................................Chapter -7 Impact of instream sand mining on river ecology using WQI, Biodiversity index, HSI, MLR         7.1 Introduction7.2 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 biota7.2.4 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 - 8Economic audit and Proposed sustainable sand mining using Optimization model and EIA8.1 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 course8.4 Discussion8.4.1 Demarcation of sustainable potential mining sites       8.4.1.1 Proposed instream sand mining sites in upper course8.4.1.2 Proposed instream sand mining sites in middle course8.4.1.3 Proposed instream sand mining sites in lower course8.4.2 Management and recommendation8.5 ConclusionIndex