HydroBench: Jupyter supported reproducible hydrological model benchmarking and diagnostic tool
HydroBench: Jupyter supported reproducible hydrological model benchmarking and diagnostic tool
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DOI：
10.3389/feart.2022.884766
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
E. Moges;B. Ruddell;Liang Zhang;J. Driscoll;P. Norton;Fernando Pérez;L. Larsen
中科院分区：
文献类型：
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作者：
E. Moges;B. Ruddell;Liang Zhang;J. Driscoll;P. Norton;Fernando Pérez;L. Larsen
评估水文模型是否出于正确的原因而正确，需要可重复的模型基准测试和诊断，不仅评估统计预测模型的性能，还评估内部流程。这种模式基准和诊断工作将受益于标准化方法和现成可用的工具包。利用HydroBench平台，本研究提出了HydroBench，一种与模型无关的基准测试工具，由三组指标组成：1）常见的统计预测措施，2）基于水文特征的过程指标，包括新的与时间相关的流量持续时间曲线和3）测量模型变量之间信息流的信息理论诊断。作为一个测试案例，HydroBench应用于比较两个模型产品（校准和未校准）的国家水文模型-降水径流模拟系统（NHM-PRMS）在雪松河流域，华盛顿州，美国。虽然未校准的模型具有最高的预测性能，特别是对于高流量，基于签名的诊断表明，该模型高估了低流量，并不代表衰退过程。解释为什么低流量可能被高估，信息理论诊断表明，在未校准的模型，信息流更直接地从降水到径流的校准模型相比，从降水到融雪到径流的信息流更高。该测试用例证明了HydroBench在过程诊断、模型预测和功能性能评估方面的能力，以及沿着的权衡。拥有这样一个模型基准工具不仅为建模者提供了一个全面的模型评估系统，而且还提供了一个可由水文界进一步开发的开源工具。
Evaluating whether hydrological models are right for the right reasons demands reproducible model benchmarking and diagnostics that evaluate not just statistical predictive model performance but also internal processes. Such model benchmarking and diagnostic efforts will benefit from standardized methods and ready-to-use toolkits. Using the Jupyter platform, this work presents HydroBench, a model-agnostic benchmarking tool consisting of three sets of metrics: 1) common statistical predictive measures, 2) hydrological signature-based process metrics, including a new time-linked flow duration curve and 3) information-theoretic diagnostics that measure the flow of information among model variables. As a test case, HydroBench was applied to compare two model products (calibrated and uncalibrated) of the National Hydrologic Model - Precipitation Runoff Modeling System (NHM-PRMS) at the Cedar River watershed, WA, United States. Although the uncalibrated model has the highest predictive performance, particularly for high flows, the signature-based diagnostics showed that the model overestimates low flows and poorly represents the recession processes. Elucidating why low flows may have been overestimated, the information-theoretic diagnostics indicated a higher flow of information from precipitation to snowmelt to streamflow in the uncalibrated model compared to the calibrated model, where information flowed more directly from precipitation to streamflow. This test case demonstrated the capability of HydroBench in process diagnostics and model predictive and functional performance evaluations, along with their tradeoffs. Having such a model benchmarking tool not only provides modelers with a comprehensive model evaluation system but also provides an open-source tool that can further be developed by the hydrological community.