Agricultural Runoff Models

Abstract Agricultural runoff is a major environmental concern contributing to nonpoint source pollution, soil erosion, and water quality degradation in both developed and developing countries. In regions like India, the issue is further exacerbated by intense monso on rainfall, changing land use, and widespread use of fertilizers and pesticides. Hydrological and watershed models play a vital role in simulating and managing these runoff processes, especially in areas where direct field measurements are limited. This r eview analyzes various agricultural runoff models, including SWAT, AnnAGNPS, RZWQM, PRZM, SWMM, LAPSUS, and emerging hybrid models. These tools vary in their design, data requirements, and suitability across different environmental conditions. A comparativ e analysis highlights their strengths and limitations in predicting runoff, sediment loss, and nutrient transport. The study also identifies key research gaps such as insufficient calibration for Indian monsoon systems, underrepresentation of subsurface flow in tropical soils, and limited application of machine learning models in regional contexts. Future research should focus on developing integrated, region -specific hybrid models that combine physical process -based understanding with machine learning tech niques. The findings aim to guide researchers, policymakers, and watershed managers in selecting appropriate models based on data availability, spatial scale, and specific objectives, ultimately contributing to more sustainable and climate -resilient agricu ltural practices. Keywords: Agricultural runoff, Hydrological modeling, SWAT, Monsoon rainfall, Machine Learning