Mitigating Sediment and Nutrient Pollution through Best Management Practices under Future Climate and Land Use Scenarios

Abstract Water quality in a watershed is influenced by both climatic variability and human-induced changes. Best management practices (BMPs) are targeted strategies designed to improve water quality within watersheds and river basins. This study focuses on identifying critical areas for sediment and nutrient loading under projected climate and anthropogenic changes in the Rengali catchment of the Brahmani River basin in India. The effectiveness of three BMPs was assessed using the Soil and Water Assessment Tool (SWAT). Future land use and land cover changes were projected using three hybrid machine learning models: artificial neural network with cellular automata (ANN-CA), multilayer perceptron with cellular Automata and Markov chain (MLP-CA-MC), and logistic regression with cellular Automata and Markov chain (LR-CA-MC). Climate change projections were obtained from 10 general circulation models (GCMs) under the CMIP6 framework, with the most suitable models for the region selected using the compromise programming method. The three BMPs—filter strips, stone/soil bunds, and contouring—were applied in erosion-prone areas, and their effectiveness in reducing sediment and nutrient loads was evaluated at the subbasin scale. The study estimated the annual sediment yield, nitrogen loading, and phosphorus loading to be <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="1,449.02 normal t divided by h a divided by y e a r" display="inline" overflow="scroll"> <mml:mn>1,449.02</mml:mn> <mml:mtext> </mml:mtext> <mml:mi mathvariant="normal">t</mml:mi> <mml:mo stretchy="false">/</mml:mo> <mml:mi>ha</mml:mi> <mml:mo stretchy="false">/</mml:mo> <mml:mi>year</mml:mi> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="925.52 k g divided by h a divided by y e a r" display="inline" overflow="scroll"> <mml:mn>925.52</mml:mn> <mml:mtext> </mml:mtext> <mml:mi>kg</mml:mi> <mml:mo stretchy="false">/</mml:mo> <mml:mi>ha</mml:mi> <mml:mo stretchy="false">/</mml:mo> <mml:mi>year</mml:mi> </mml:math> , and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="1,141 k g divided by h a divided by y e a r" display="inline" overflow="scroll"> <mml:mn>1,141</mml:mn> <mml:mtext> </mml:mtext> <mml:mi>kg</mml:mi> <mml:mo stretchy="false">/</mml:mo> <mml:mi>ha</mml:mi> <mml:mo stretchy="false">/</mml:mo> <mml:mi>year</mml:mi> </mml:math> , respectively. Based on these findings, seven subbasins were identified as erosion hotspots due to their high levels of sediment and nutrient export. Implementation of best management practices resulted in notable reductions in sediment and nutrient loads. Stone or soil bunds reduced sediment by 48.70%, nitrogen by 41.01%, and phosphorus by 46.26%. Filter strips achieved reductions of 31.96% for sediment, 32% for nitrogen, and 34.02% for phosphorus. In comparison, contouring showed minimal impact relative to filter strips. These findings can inform the selection of appropriate BMPs for field-scale water resource management, contributing to the reduction of sediment and nutrient concentrations in surface waters.