Advancing the identification and evaluation of distributed rainfall‐runoff models using global sensitivity analysis
Advancing the identification and evaluation of distributed rainfall‐runoff models using global sensitivity analysis
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DOI：
10.1029/2006wr005813
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发表时间：
2007-06
影响因子：
5.4
通讯作者：
Yong Tang;P. Reed;K. V. Werkhoven;Thorsten Wagener
中科院分区：
文献类型：
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作者：
Yong Tang;P. Reed;K. V. Werkhoven;Thorsten Wagener
本研究提供了一个概念性的基于网格的分布式降雨径流模型，美国国家气象局（US NWS）水文实验室研究分布式水文模型（HL‐RDHM）的逐步分析。它评估了模型参数对年度、月度和事件时间段的敏感性，旨在阐明影响分布式模型预测的关键参数。本研究证明了一种方法，该方法平衡了全局灵敏度分析所带来的计算约束，并需要充分表征HL‐RDHM的灵敏度。在宾夕法尼亚州中部Juniata河流域的两个案例研究流域中，分别使用分布强迫和相同的模型参数，对所有网格单元在24小时和1小时模型时间步长下的年度和月度进行了HL‐RDHM敏感性评估。本研究还提供了详细的空间分析的HL‐RDHM的敏感性的两个洪水事件的基础上选择的1小时模型的时间步长，以证明如何强烈的强迫的空间异质性影响模型的空间敏感性。我们对灵敏度分析方法的验证分析表明，该方法提供了稳健的灵敏度排名，并且这些排名可用于显著减少校准HL‐RDHM时应考虑的参数数量。总的来说，敏感性分析结果表明，存储变化，空间趋势强迫，和细胞接近测量流域出口是控制HL‐RDHM的行为的三个主要因素。
This study provides a step‐wise analysis of a conceptual grid‐based distributed rainfall‐runoff model, the United States National Weather Service (US NWS) Hydrology Laboratory Research Distributed Hydrologic Model (HL‐RDHM). It evaluates model parameter sensitivities for annual, monthly, and event time periods with the intent of elucidating the key parameters impacting the distributed model's forecasts. This study demonstrates a methodology that balances the computational constraints posed by global sensitivity analysis with the need to fully characterize the HL‐RDHM's sensitivities. The HL‐RDHM's sensitivities were assessed for annual and monthly periods using distributed forcing and identical model parameters for all grid cells at 24‐hour and 1‐hour model time steps respectively for two case study watersheds within the Juniata River Basin in central Pennsylvania. This study also provides detailed spatial analysis of the HL‐RDHM's sensitivities for two flood events based on 1‐hour model time steps selected to demonstrate how strongly the spatial heterogeneity of forcing influences the model's spatial sensitivities. Our verification analysis of the sensitivity analysis method demonstrates that the method provides robust sensitivity rankings and that these rankings could be used to significantly reduce the number of parameters that should be considered when calibrating the HL‐RDHM. Overall, the sensitivity analysis results reveal that storage variation, spatial trends in forcing, and cell proximity to the gauged watershed outlet are the three primary factors that control the HL‐RDHM's behavior.