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Assessing the detail needed to capture rainfall-runoff dynamics with physics-based hydrologic response simulation

机译：通过基于物理的水文响应模拟评估捕获降雨径流动力学所需的细节

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Concept development simulation with distributed, physics-based models provides a quantitative approach for investigating runoff generation processes across environmental conditions. Disparities within data sets employed to design and parameterize boundary value problems used in heuristic simulation inevitably introduce various levels of bias. The objective was to evaluate the impact of boundary value problem complexity on process representation for different runoff generation mechanisms. The comprehensive physics-based hydrologic response model InHM has been employed to generate base case simulations for four well-characterized catchments. The C3 and CB catchments are located within steep, forested environments dominated by subsurface stormflow; the TW and R5 catchments are located in gently sloping rangeland environments dominated by Dunne and Horton overland flows. Observational details are well captured within all four of the base case simulations, but the characterization of soil depth, permeability, rainfall intensity, and evapotranspiration differs for each. These differences are investigated through the conversion of each base case into a reduced case scenario, all sharing the same level of complexity. Evaluation of how individual boundary value problem characteristics impact simulated runoff generation processes is facilitated by quantitative analysis of integrated and distributed responses at high spatial and temporal resolution. Generally, the base case reduction causes moderate changes in discharge and runoff patterns, with the dominant process remaining unchanged. Moderate differences between the base and reduced cases highlight the importance of detailed field observations for parameterizing and evaluating physics-based models. Overall, similarities between the base and reduced cases indicate that the simpler boundary value problems may be useful for concept development simulation to investigate fundamental controls on the spectrum of runoff generation mechanisms.
机译：使用基于物理的分布式模型进行的概念开发模拟为研究跨环境条件的径流生成过程提供了一种定量方法。用于设计和参数化启发式仿真中使用的边值问题的数据集内的差异不可避免地会引入各种水平的偏差。目的是评估边界值问题复杂性对不同径流产生机制的过程表示的影响。基于物理的综合水文响应模型InHM已用于为四个特征明确的流域生成基础案例模拟。 C3和CB集水区位于陡峭的森林环境中，主要是地下暴雨。 TW和R5集水区位于以Dunne和Horton陆上水流为主的缓坡牧场环境中。在所有四个基本案例模拟中都很好地捕获了观测细节，但是每种情况下土壤深度，渗透率，降雨强度和蒸散量的特征都不相同。通过将每个基本案例转换为精简案例方案来研究这些差异，所有这些案例都具有相同的复杂度。通过对高空间和时间分辨率下的综合和分布式响应进行定量分析，有助于评估各个边界值问题特征如何影响模拟的径流生成过程。通常，基本案例的减少会导致流量和径流模式的适度变化，而主导过程保持不变。基本案例和简化案例之间的适度差异凸显了详细现场观察对于参数化和评估基于物理的模型的重要性。总体而言，基本情况和减少情况之间的相似性表明，较简单的边值问题可能对概念发展模拟有用，以研究径流产生机制频谱的基本控制。

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  • 来源
    《Water resources research》 |2012年第3期|p.W00H10.1-W00H10.18|共18页
  • 作者

    Benjamin B. Minis; Brian A. Ebel; Christopher S. Heppner; Keith Loague;

  • 作者单位

    Department of Geological and Environmental Sciences, Stanford University, Stanford, California, USA Now at U.S. Geological Survey, Menlo Park, California, USA;

    Department of Geological and Environmental Sciences, Stanford University, Stanford, California, USA Now at U.S. Geological Survey, Boulder, Colorado, USA;

    Department of Geological and Environmental Sciences, Stanford University, Stanford, California, USA Now at Erler and Kalinowski, Inc., Burlingame, California, USA;

    Department of Geological and Environmental Sciences, Stanford University, Stanford, California, USA;

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