Impact of uncertainty in precipitation forcing datasets on the hydrologic budget of an integrated hydrologic model in mountainous terrain

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 ...