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Conditioning a Hydrologic Model Using Patterns of Remotely Sensed Land Surface Temperature

机译：利用遥感土地表面温度模式调节水文模型

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摘要

Hydrologic models are usually calibrated using observed river runoff at catchment outlets. Streamflow, however, represents an integral response of the entire catchment and is observed at a few locations worldwide. Parameter estimation based on streamflow has the disadvantage that it does not consider the spatiotemporal variability of hydrologic states and fluxes such as evapotranspiration. Remotely sensed data, in contrast, include these variabilities and are broadly available. In this study, we assess the predictive skill of satellite-derived land surface temperature (T-s) with respect to river runoff (Q). We developed a bias-insensitive pattern-matching criterion to focus the parameter optimization on spatial patterns of T-s. The proposed method is extensively tested in six distinct large German river basins and cross validated in 222 additional basins in Germany. We conclude that land surface temperature calibration outperforms random drawn parameter sets, which could be meaningful for calibrating hydrologic models in ungauged locations. A combined calibration with Q and T-s reduces the root mean squared error in the predicted evapotranspiration by 8% compared to flux tower observations but reduces the NSEs of the streamflow predictions by 6% on average for the six large basins. Our results show that patterns of Ts better constrain model parameters when considered in a calibration next to Q, which finally reduces parametric uncertainty.
机译：水文模型通常使用集水口的河流径流进行校准。但是，水流代表了整个集水区的整体响应，并且在全球的一些地方都可以观察到。基于流的参数估计的缺点是它没有考虑水文状态和通量（如蒸散量）的时空变化。相比之下，遥感数据包括这些差异并且可以广泛获得。在这项研究中，我们评估了卫星衍生的地表温度（T-s）相对于河流径流（Q）的预测能力。我们开发了一种偏向不敏感的模式匹配准则，以将参数优化的重点放在T-s的空间模式上。所提出的方法已在六个不同的德国大型流域中进行了广泛的测试，并在德国的222个其他流域中得到了交叉验证。我们得出的结论是，地表温度校准优于随机绘制的参数集，这对于校准无水位的水文模型可能是有意义的。与通量塔观测值相比，结合使用Q和T-s进行的校准将预测的蒸散量的均方根误差降低了8％，但六个大盆地的流量预测的NSE平均降低了6％。我们的结果表明，当在Q旁边的校准中考虑时，Ts的模式更好地约束了模型参数，最终减少了参数不确定性。

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  • 来源
    《Water resources research》 |2018年第4期|2976-2998|共23页
  • 作者

    Zink Matthias; Mai Juliane; Cuntz Matthias; Samaniego Luis;

  • 作者单位

    UFZ Helmholtz Ctr Environm Res, Dept Computat Hydrosyst, Leipzig, Germany;

    UFZ Helmholtz Ctr Environm Res, Dept Computat Hydrosyst, Leipzig, Germany;

    UFZ Helmholtz Ctr Environm Res, Dept Computat Hydrosyst, Leipzig, Germany;

    UFZ Helmholtz Ctr Environm Res, Dept Computat Hydrosyst, Leipzig, Germany;

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