Abstract By integrating disciplinary sub-models, coupled hydrological models allow the exchange of output and input fluxes among their sub-models. While such coupling of models can mirror the conceptual representation of the water-cycle, potential uncertainty propagation and aggregation across the sub-models may limit their overall performance. There are limited studies dealing with uncertainty in coupled hydrological models due to the high computational needs, the absence of detailed data, and the lack of efficient uncertainty propagation frameworks. This study presents an effective uncertainty propagation framework using a combination of statistical techniques, multi-variable calibration, and parallel computation. The framework was tested using a synthetic mathematical coupled model and a real‐world, coupled surface water (PRMS) and subsurface (MODFLOW) model. For the synthetic coupled model, the framework has shown its effectiveness to reveal the interplay of input variables, quantify the uncertainties within each sub-model and track their propagation through the coupled modeling system. For the PRMS-MODFLOW model, the framework has demonstrated how uncertainty in input precipitation, surface water, and subsurface water sub-models influences the different segments of a hydrograph. The results also indicate that improved predictions of high flow require a better quantification of input uncertainty. In contrast, baseflow and recession flow uncertainties largely depend on the subsurface and surface water sub-models, respectively. The presented framework, in addition to providing relatively comprehensive uncertainty information on the integrated model outputs, it helps to identify the potential sources of uncertainty that can be used for further model improvement and guide new data collection campaigns.

中文翻译：

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使用螺旋楼梯和零空间蒙特卡罗方法的耦合水文模型中的不确定性传播

摘要 通过整合学科子模型，耦合水文模型允许在子模型之间交换输出和输入通量。虽然模型的这种耦合可以反映水循环的概念表示，但跨子模型的潜在不确定性传播和聚合可能会限制它们的整体性能。由于计算需求高、缺乏详细数据以及缺乏有效的不确定性传播框架，在耦合水文模型中处理不确定性的研究有限。本研究结合统计技术、多变量校准和并行计算，提出了一种有效的不确定性传播框架。该框架使用合成数学耦合模型和真实世界进行了测试，耦合地表水 (PRMS) 和地下 (MODFLOW) 模型。对于合成耦合模型，该框架已显示出其在揭示输入变量的相互作用、量化每个子模型内的不确定性以及通过耦合建模系统跟踪它们的传播方面的有效性。对于 PRMS-MODFLOW 模型，该框架展示了输入降水、地表水和地下水子模型的不确定性如何影响水文过程线的不同部分。结果还表明，对高流量的改进预测需要更好地量化输入不确定性。相比之下，基流和衰退流的不确定性在很大程度上分别取决于地下和地表水子模型。提出的框架，