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Multiobjective sensitivity analysis to understand the information content in streamflow observations for distributed watershed modeling

机译：多目标灵敏度分析以了解用于分水岭建模的流量观测中的信息内容

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摘要

In a previous paper, van Werkhoven et al. (2008b) demonstrated that the information content of streamflow observations at a watershed outlet is a dynamic entity and is dependent on the spatiotemporal dynamics of the causal precipitation event. This result has important consequences for distributed hydrological model calibration strategies and for the design of observation networks. However, the conclusions drawn were based only on the analysis of the model parameter sensitivities to the hydrograph peak fit because of the use of the root-mean-square error objective function. An unanswered question is how will the previous result change if alternative objective functions are used? Here we extend the earlier analysis by adding low-flow and water balance objective functions. We study their impact on how much information can be extracted during calibration overall and for specific model components (parameters) using a synthetic rainfall-runoff event. Results suggest that both vertical (within a model cell) and spatial (across cells) sensitivities vary greatly with the objective function used. Timing-related objective functions show sensitivity largely focused on the area close to the outlet, while a volume-based objective function shows sensitivity distributed more evenly across the watershed. These results demonstrate the importance of using multiple evaluation metrics when assessing distributed model predictions. The resultant multiobjective sensitivity maps provide helpful tools for assessing the actual information provided by gauges in observation networks and motivate the need for a new generation of dynamic calibration strategies that would consider how the spatial parameter controls on the model response of interest vary in time.
机译：在上一篇论文中，van Werkhoven等人。 （２００８ｂ）证明了分水岭出口处水流观测的信息内容是一个动态实体，并取决于因果降水事件的时空动态。该结果对分布式水文模型校准策略和观测网络的设计具有重要意义。但是，由于使用了均方根误差目标函数，因此得出的结论仅基于模型参数对水位图峰拟合的敏感性分析。一个未解决的问题是，如果使用替代目标函数，先前的结果将如何改变？在这里，我们通过添加低流量和水平衡目标函数来扩展先前的分析。我们研究了它们对使用合成降雨径流事件在整体校准过程中以及特定模型组件（参数）中可提取多少信息的影响。结果表明垂直灵敏度（在模型单元内）和空间灵敏度（在跨单元格中）都随所使用的目标函数而有很大差异。与时间相关的目标函数显示灵敏度主要集中在出口附近区域，而基于体积的目标函数则显示灵敏度在流域上更均匀地分布。这些结果证明了在评估分布式模型预测时使用多个评估指标的重要性。由此产生的多目标灵敏度图提供了有用的工具，可用于评估观测网络中仪表提供的实际信息，并激发了对新一代动态校准策略的需求，这些策略将考虑空间参数如何控制感兴趣的模型响应随时间变化。

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  • 来源
    《Water resources research》 |2009年第2期|485-489|共5页
  • 作者

    Thorsten Wagener; Kathryn van Werkhoven; Patrick Reed; Yong Tang;

  • 作者单位

    Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, Pennsylvania, USA;

    Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, Pennsylvania, USA Now at Systech Water Resources, Inc., Walnut Creek, California, USA;

    Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, Pennsylvania, USA;

    Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, Pennsylvania, USA Now at Bechtel Corporation, Houston, Texas, USA;

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