Integrated watershed management relies on distributed hydrological models to simulate water transport processes and support decision-making. However, model reliability is often constrained by the resolution and quality of input data, particularly soil information. High-resolution soil datasets remain scarce in many regions of Sub-Saharan Africa, limiting the representation of spatial soil heterogeneity in hydrological simulations. This study evaluates the effect of detailed soil information derived using the Soil–Land Inference Model (SoLIM) on the performance of the Soil and Water Assessment Tool (SWAT) in the Sigi River watershed, a topographically complex watershed in northeastern Tanzania. Two model setups were compared: (i) a high-resolution SoLIM-based soil dataset and (ii) the coarser global ISRIC SoilGrids database. The SoLIM-informed model better reproduced hydrographs and flow duration curves and showed stronger parameter sensitivities, achieving superior calibration performance (NSE = 0.87, PBIAS = 8.7%) compared to SoilGrids (NSE = 0.86, PBIAS = 11.1%). Hydrological component analysis further revealed that SoLIM enhanced baseflow (181 vs. 60 mm/year) and percolation (349 vs. 135 mm/year) while reducing surface runoff (263 vs. 474 mm/year). These findings demonstrate that high-resolution soil data measurably improve the representation of subsurface processes and moderately improve streamflow performance, especially for baseflow and low-flow regimes; reduce model uncertainty; and improve the robustness of SWAT simulations, thereby supporting more effective watershed management in data-scarce and heterogeneous landscapes.
Chidodo et al. (Mon,) studied this question.