Hydrologic modeling in mountainous terrains with monsoonal climate: Do gridded precipitation data improve streamflow simulations?

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 Humaya River basin, a monsoon-dominated watershed in the Sierra Madre Occidental, in northwestern Mexico. The four precipitation inputs include rain gauge data (Gauge), Daymet gridded data, and two bias-corrected versions of Daymet: one corrected using quantile mapping (Qmap) and another using mean field bias correction (MFBC). Model calibration and validation were conducted independently for each input using monthly streamflow data from 1983 to 2001. An ensemble-based approach was applied, retaining the 100 best-performing simulations per configuration to account for parameter uncertainty and equifinality. This approach allowed hydrologic fluxes to be analyzed as probability distributions, enabling comparisons of how precipitation inputs influence water balance partitioning and uncertainty. Spatial analyses were also conducted to examine the spatial representation of hydrologic processes under each forcing. Overall, SWAT+ reproduced the basin streamflow dynamics at the outlet gauge with good to very good performance, despite data scarcity and complex terrain. The model-driven by Daymet-MFBC produced the best results (NSE = 0.81; PBias = 6.4 %), followed by Daymet and Gauge, whereas Daymet-Qmap underperformed. Daymet and Daymet-MFBC also provided more realistic spatial patterns of hydrologic processes. These findings highlight the value of high-resolution gridded precipitation datasets for hydrologic modeling in mountainous and data-limited regions.