Authors: Divyansh Singh Nikhil
Abstract: Hydrological modeling plays a pivotal role in addressing contemporary challenges of water resource management, particularly in regions facing rapid urbanization, climate variability, and increasing anthropogenic pressures. Among the widely adopted modeling frameworks, the Soil and Water Assessment Tool (SWAT) and its ArcGIS-integrated version, ArcSWAT, stand out as versatile, semi-distributed, process-based tools designed for simulating the impacts of land use, climate, and management practices on watershed hydrology. ArcSWAT has been extensively applied across continents, from small agricultural watersheds to large river basins such as the Mississippi, Nile, and Ganga, providing insights into surface runoff, evapotranspiration, groundwater flow, sediment transport, and water quality dynamics. Its integration with Geographic Information Systems (GIS) enables seamless spatial analysis, making it particularly suited to data-scarce basins in developing regions. This review synthesizes the development, structure, and functionality of the ArcSWAT model, with particular emphasis on its global and Indian applications. The analysis highlights key advances in calibration and validation approaches, including the use of SWAT-CUP and the SUFI-2 algorithm, as well as emerging practices of multi-site calibration to enhance model robustness. Special attention is given to case studies from India, including the Gomti River basin, where urbanization, agricultural intensification, and climate variability necessitate advanced modeling frameworks for sustainable management. The review identifies major advantages of ArcSWAT, such as its ability to handle large heterogeneous basins, perform scenario-based analyses, and integrate global datasets (e.g., SRTM DEM, Landsat LULC, FAO soils). However, limitations are also recognized, including data dependency, underrepresentation of water quality processes in many studies, and insufficient scenario-based applications in Indian contexts. Future research directions are outlined, focusing on coupling ArcSWAT with machine learning approaches, integrating climate change projections, enhancing parameter sensitivity and uncertainty analysis, and expanding hydrology–water quality modeling. By critically assessing past applications and current research gaps, this review establishes ArcSWAT as both a proven tool and an evolving framework for hydrological research. Its continued development and integration with emerging technologies hold the potential to transform watershed management and policy-making in the era of climate change and increasing water stress.
DOI: https://doi.org/10.5281/zenodo.17066923
