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From lumped to distributed via semi-distributed: Calibration strategies for semi-distributed hydrologic models

机译:从集总到半分布式:半分布式水文模型的校准策略

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Modeling the effect of spatial variability of precipitation and basin characteristics on streamflow requires the use of distributed or semi-distributed hydrologic models. This paper addresses a DMIP 2 study that focuses on the advantages of using a semi-distributed modeling structure. We first present a revised semi-distributed structure of the NWS SACramento Soil Moisture Accounting (SAC-SMA) model that separates the routing of fast and slow response runoff components, and thus explicitly accounts for the differences between the two components. We then test four different calibration strategies that take advantage of the strengths of existing optimization algorithms (SCE-UA) and schemes (MACS). These strategies include: (1) lumped parameters and basin averaged precipitation, (2) semi-lumped parameters and distributed precipitation forcing, (3) semi-distributed parameters and distributed precipitation forcing and (4) lumped parameters and basin averaged precipitation, modified using a priori parameters of the SAC-SMA model. Finally, we explore the value of using discharge observations at interior points in model calibration by assessing gains/losses in hydrograph simulations at the basin outlet. Our investigation focuses on two key DMIP 2 science questions. Specifically, we investigate (a) the ability of the semi-distributed model structure to improve stream flow simulations at the basin outlet and (b) to provide reasonably good simulations at interior points.The semi-distributed model is calibrated for the Illinois River Basin at Siloam Springs, Arkansas using streamflow observations at the basin outlet only. The results indicate that lumped to distributed calibration strategies (1 and 4) both improve simulation at the outlet and provide meaningful streamflow predictions at interior points. In addition, the results of the complementary study, which uses interior points during the model calibration, suggest that model performance at the outlet can be further improved by using a semi-distributed structure calibrated at both interior points and the outlet, even when only a few years of historical record are available.
机译:要模拟降水的空间变异性和流域特征对河流流量的影响,就需要使用分布式或半分布式水文模型。本文介绍了DMIP 2研究,该研究侧重于使用半分布式建模结构的优势。我们首先介绍了NWS SACramento土壤水分核算(SAC-SMA)模型的修订的半分布式结构,该模型将快速响应流和慢响应径流组件的路径分开,从而明确说明了这两个组件之间的差异。然后,我们利用现有优化算法(SCE-UA)和方案(MACS)的优势,测试四种不同的校准策略。这些策略包括:(1)集总参数和盆地平均降水量;(2)半集总参数和分布式降水强迫;(3)半集散参数和分布式降水强迫;(4)集总参数和盆地平均降水量,使用SAC-SMA模型的先验参数。最后,我们通过评估流域出口水文模拟中的收益/损失,探索了在模型标定中使用内部观测值的价值。我们的研究集中在两个关键的DMIP 2科学问题上。具体而言,我们研究(a)半分布式模型结构改善流域出口处的水流模拟的能力,以及(b)在内部点提供合理良好的模拟的能力。半分布式模型已针对伊利诺伊河流域进行了校准在阿肯色州的西洛阿姆斯普林斯,仅使用流域出口处的水流观测数据。结果表明,集中到分布式校准策略(1和4)既可以改善出口仿真,又可以在内部点提供有意义的流量预测。此外,补充研究的结果(在模型校准期间使用内部点)表明,通过使用在内部点和出口处均已校准的半分布式结构,可以进一步改善出口处的模型性能,即使仅使用已有数年的历史记录。

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