The impact of soil data on SWAT modeling: Effects, requirements, and future directions

Abstract

The precision of hydrological models, such as the Soil and Water Assessment Tool (SWAT) model, depends on the quality of input data. This review aims to: (1) systematically analyze the effects of soil data quality and resolution on SWAT model performance across different applications, (2) evaluate the implications of using various global and local soil datasets, and (3) identify knowledge gaps and future research directions in this field. Through a comprehensive analysis of 34 studies published between 2002 and 2024, we examine how soil data influences hydrological modeling, erosion, and water quality simulations. Our findings reveal that while global soil datasets have enhanced accessibility for hydrological modeling, they introduce new uncertainties that demand careful consideration. Key results show that: (1) the choice of global soil dataset minimally affects streamflow simulations, especially for monthly time steps and large-scale catchments, though different datasets often require distinct parametrizations to achieve similar performance; (2) for sediment and nutrient transport simulations, soil data selection becomes crucial for model accuracy; and (3) the relationship between soil data resolution and model performance is not linear, with high-resolution data not always guaranteeing better results. This review identifies critical research gaps, including the need to: understand dynamic soil conditions, explore SWAT's sensitivity to temporal resolutions, refine global soil datasets, and investigate groundwater flow dynamics. Our analysis provides essential guidance for hydrologists in selecting appropriate soil data for watershed modeling and highlights priority areas for future research to improve model reliability.