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 data sets 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 data sets 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 data sets than the averaged simulated results of separate simulations forced with each data set. Results from this study highlight the importance of topography‐moderated flow through the critical zone in shaping the groundwater response to climate variability.

中文翻译：

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降水强迫数据集的不确定性对山区地形综合水文模型水文预算的影响

在分布式地表水-地下水模型中，降水是关键的输入变量，预计其空间变化会通过地下流动力学的变化影响流域水文响应。然而，基于测量仪观测的网格化降水数据集受到不确定性的困扰，尤其是在测量仪网络稀疏的山区。为了检验降水量数据不确定性通过分水岭传播的机制，我们使用地表水-地下水综合水文模型ParFlow.CLM进行了一系列数值实验。美国加利福尼亚的Kaweah河流域被用作我们的虚拟集水室实验室，以表征流域对从源头到地下水的不同降水强迫的响应。通过应用三角帽子法，我们在四个公开可用的降水强迫数据集及其模拟水文学中量化了空间分布的不确定性。仿真表明，模拟地下水存储中的不确定性主要是由于降水强迫中不确定性的地形重新分布所致。土壤水分的重新分配是重新分配不确定性下坡的主要途径。我们还发现，地形对可变通量在模拟通量中的传播不确定性产生的影响大于可变的地下参数。最后，我们发现，对单个模拟强制进行的平均性能（来自可用数据集的平均降水量），其模型性能指标的改进要高于对每个数据集强制进行的单独模拟的平均模拟结果。