This study assesses snow water equivalent (SWE) simulation uncertainty in the National Water Model (NWM) due to forcing and model parameterization, using data from 46 Snow Telemetry (SNOTEL) sites in the Upper Colorado River Basin (UCRB). We evaluated the newly developed Analysis of Record for Calibration (AORC) forcing data for SWE simulation and examined the impact of bias correction applied to AORC precipitation and temperature. Additionally, we investigated the sensitivity of SWE simulations to choices of physical parameterization schemes through 72 ensemble experiments. Results showed that NWM driven by AORC forcings captured the overall temporal variation of SWE but underestimated its amount. Adjusting AORC precipitation with SNOTEL observations reduced SWE root-mean-square error (RMSE) by 66%, adjusting temperature trimmed it by 10%, and adjusting both decreased it by 69%. Among the physical processes, the snow/soil temperature time scheme (STC) demonstrated the highest sensitivity, followed by the surface exchange coefficient for heat (SFC), snow surface albedo (ALB), and rainfall and snowfall partitioning (SNF), while the lower boundary of soil temperature (TBOT) proved to be insensitive. Further optimization of the parameterization combination resulted in a 12% SWE RMSE reduction. When combined with the bias-corrected AORC precipitation and temperature, this optimization led to a remarkable 78% SWE RMSE reduction. Despite these enhancements, a persistent slow and late spring ablation suggests model deficiencies in snow ablation physics. The study emphasizes the critical need to enhance the accuracy of forcing data in mountainous regions and address model parameterization uncertainty through optimization efforts.

中文翻译：

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强迫和参数化在使用国家水模型改进科罗拉多河流域上游积雪模拟中的作用


本研究使用来自科罗拉多河上游流域 (UCRB) 46 个雪遥测 (SNOTEL) 站点的数据，评估了国家水模型 (NWM) 中由于强迫和模型参数化而导致的雪水当量 (SWE) 模拟不确定性。我们评估了新开发的用于 SWE 模拟的校准记录分析 (AORC) 强迫数据，并检查了应用于 AORC 降水和温度的偏差校正的影响。此外，我们还通过 72 个集成实验研究了 SWE 模拟对物理参数化方案选择的敏感性。结果表明，由 AORC 强迫驱动的 NWM 捕获了 SWE 的总体时间变化，但低估了其数量。使用 SNOTEL 观测值调整 AORC 降水量可将 SWE 均方根误差 (RMSE) 降低 66%，调整温度可将其降低 10%，同时调整两者可将其降低 69%。在物理过程中，雪/土壤温度时间方案（STC）表现出最高的敏感性，其次是表面热交换系数（SFC）、雪面反照率（ALB）和降雨和降雪分配（SNF），而土壤温度下限（TBOT）被证明是不敏感的。参数化组合的进一步优化使 SWE RMSE 降低了 12%。当与偏差校正的 AORC 降水和温度相结合时，这种优化使 SWE RMSE 显着降低了 78%。尽管有这些增强，但持续缓慢且晚春的消融表明雪消融物理学的模型存在缺陷。该研究强调迫切需要提高山区强迫数据的准确性，并通过优化工作解决模型参数化的不确定性。