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Selection of Catchment Descriptors for the Physical Similarity Approach. Part I: Theory

机译:物理相似性方法的集水区描述词选择。第一部分:理论

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This paper focuses on a description of the method used for the identification of optimal catchment descriptors for the physical similarity approach consisting of a scheme for the identification of optimal catchment descriptors and the procedure for finding hydrologically homogeneous regions using inverse clustering. Andrews' curves are used as the basis for homogeneity checking. The identification of an optimum catchment descriptor is based on the assumption that the addition of an optimal catchmentdescriptor to a predefined set of catchment descriptors improves the accuracy of model parameter estimation within a set of tested catchments. Two criteria are proposed for the selection of optimal catchment descriptors - a criterion evaluating estimatesof model parameters on the basis of different potentially optimal groups of catchment descriptors, MIN, and a criterion evaluating the improvement in model parameter estimation after the addition of a potentially optimal catchment descriptor into the group of preliminarily identified optimal catchment descriptors, MAX. The proposed method provides an alternative to the trial-and-error method for the identification of optimal catchment descriptors.
机译:本文重点介绍了用于物理相似性方法的最佳集水量描述物识别方法的描述,该方法包括用于识别最佳集水量描述物的方案以及使用逆聚类法找到水文均质区域的过程。安德鲁斯曲线用作同质性检查的基础。最佳集水量描述符的识别基于以下假设:将最佳集水量描述符添加到预定义的集水量描述符集会提高一组测试集水区中模型参数估计的准确性。提出了两个用于选择最佳集水量描述符的标准-一个基于不同潜在最佳集水量描述符组MIN评估模型参数估计的标准,以及一个在添加潜在的最佳集水量后评估模型参数估计的改进的标准初始识别的最佳集水区描述者MAX的描述。所提出的方法为反复试验法提供了一种替代方法,用于识别最佳集水区描述符。

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