Precipitation is a key input variable in distributed surface water-groundwater models, and its spatial variability is expected to impact watershed hydrologic response via changes in subsurface flow dynamics. Gridded precipitation datasets based on gauge observations, however, are plagued by uncertainty, especially in mountainous terrain where gauge networks are sparse. To examine the mechanisms via which uncertainty in precipitation data propagates through a watershed, we perform a series of numerical experiments using an integrated surface water-groundwater hydrologic model, ParFlow.CLM. The Kaweah River watershed in California, USA is used as our virtual catchment laboratory to characterize watershed response to variable precipitation forcing from headwaters to groundwaters. By applying the three cornered hat method, we quantify the spatially distributed uncertainty in four publically available precipitation forcing datasets and their simulated hydrology. Simulations demonstrate that uncertainty in the simulated groundwater storage is primarily a result of topographic redistribution of uncertainty in precipitation forcing. Soil water redistribution is the primary pathway that redistributes uncertainty downslope. We also find that topography exerts a larger impact than variable subsurface parameters on propagating uncertainty in simulated fluxes. Finally, we find that improvement in model performance metrics is higher for a single simulation forced with the mean precipitation from the available datasets than the averaged simulated results of separate simulations forced with each dataset. Results from this study highlight the importance of topography-moderated flow through the critical zone in shaping the groundwater response to climate variability.

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