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Visualization Workflow for Quantifying Parameter Sensitivities and Uncertainties for Hydrologic Models
Journal of the American Water Resources Association ( IF 2.6 ) Pub Date : 2021-07-26 , DOI: 10.1111/1752-1688.12946
Catherine Finkenbiner 1 , Kyla Semmendinger 2
Affiliation  

From regional to continental scales, hydrologic processes are represented by modular modeling frameworks dependent on input datasets and parameter sets representing physical attributes. The hydrologic community needs a common procedure to evaluate model output based on parameter sensitivities and uncertainties compared to performance metrics (i.e., objective functions), especially for large parameter sets. We developed a reproducible workflow for evaluating hydrologic models to objectively analyze model outputs as a function of parameter choice using numerical and visualization techniques. Our workflow was implemented on three separate case studies, each with a different hydrologic model, and the results can be reproduced and visualized from a community github code repository. Model parameter sensitivity was evaluated using several global sensitivity indices and Bayesian theory. Uncertainty in parameter spaces was quantified to highlight the impact of unreliable input data on model output. Model parameter sensitivities and uncertainties were evaluated numerically and visually to provide a comprehensive perspective on their impacts on model output. For each case study, we provided a summary and interpretation of the workflow results. Our workflow can be integrated into hydrologic modeling frameworks for objective modular model and parameter set evaluations based on a data-driven approach appropriate for model selection.

中文翻译:

用于量化水文模型的参数敏感性和不确定性的可视化工作流程

从区域到大陆尺度,水文过程由依赖于输入数据集和代表物理属性的参数集的模块化建模框架表示。与性能指标(即目标函数)相比,水文界需要一个通用程序来评估基于参数敏感性和不确定性的模型输出,特别是对于大型参数集。我们开发了一个可重复的工作流程来评估水文模型,以使用数值和可视化技术客观地分析模型输出作为参数选择的函数。我们的工作流程是在三个独立的案例研究中实施的,每个案例研究都有不同的水文模型,结果可以从社区 github 代码库中复制和可视化。使用几个全局敏感性指数和贝叶斯理论评估模型参数敏感性。量化参数空间中的不确定性以突出不可靠的输入数据对模型输出的影响。对模型参数的敏感性和不确定性进行了数值和视觉评估,以全面了解它们对模型输出的影响。对于每个案例研究,我们提供了工作流程结果的总结和解释。我们的工作流程可以集成到水文建模框架中,以基于适合模型选择的数据驱动方法进行目标模块化模型和参数集评估。对模型参数的敏感性和不确定性进行了数值和视觉评估,以全面了解它们对模型输出的影响。对于每个案例研究,我们提供了工作流程结果的总结和解释。我们的工作流程可以集成到水文建模框架中,以基于适合模型选择的数据驱动方法进行目标模块化模型和参数集评估。对模型参数的敏感性和不确定性进行了数值和视觉评估,以全面了解它们对模型输出的影响。对于每个案例研究,我们提供了工作流程结果的总结和解释。我们的工作流程可以集成到水文建模框架中,以基于适合模型选择的数据驱动方法进行目标模块化模型和参数集评估。
更新日期:2021-07-26
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