Abstract
Accurate hydrologic modeling in data-scarce mountainous regions remains challenging due to strong climatic variability, complex topography, and limited ground data. This study evaluates the influence of four precipitation inputs on the performance of the Soil and Water Assessment Tool Plus (SWAT+) model in the monsoon-dominated Humaya River basin, located in the Sierra Madre Occidental, northwestern Mexico. The evaluated inputs include: (1) rain gauge data (Gauge), (2) Daymet gridded data, and two bias-corrected Daymet products using (3) quantile mapping (Qmap) and (4) mean field bias correction (MFBC). Model calibration and validation were conducted independently for each input using monthly streamflow data (1983–2001). Model performance was assessed using NSE, PBias, and complementary diagnostics, including KGE, logNSE, regime-based PBias, and flow-duration curve metrics, enabling a more comprehensive evaluation of both high- and low-flow conditions. An ensemble-based approach was implemented by retaining the 100 best-performing simulations per configuration to account for parameter uncertainty and equifinality, allowing hydrologic fluxes to be analyzed as probability distributions. Spatial analyses, including a quantitative assessment using Moran's I, were conducted to evaluate the spatial representation of hydrologic processes under each forcing. Overall, SWAT+ reproduced basin streamflow dynamics with
good
to
very good
performance. The Daymet-MFBC configuration achieved the best overall results (NSE = 0.81; PBias = 6.4%; KGE = 0.85), followed by Daymet and Gauge, whereas Daymet-Qmap showed comparatively lower performance. Daymet and Daymet-MFBC also produced more heterogeneous spatial patterns of hydrologic processes, supported by lower Moran's I values. However, all configurations showed systematic deficiencies in low-flow simulation, as indicated by high PBias in low-flow conditions and negative logNSE values (−0.98 to −0.77), reflecting a known limitation of SWAT+ in representing baseflow dynamics. The ensemble analysis indicates that precipitation forcing influences not only model performance but also constrains parameter uncertainty and the range of hydrologically plausible internal process representations. These findings highlight the value of high-resolution gridded precipitation datasets for improving both the accuracy and spatial realism of hydrologic modeling in mountainous and data-scarce regions.