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Characterization of runoff generation in a mountainous hillslope according to multiple threshold behavior and hysteretic loop features

机译:基于多阈值行为和滞后回线特征的山区山坡径流生成表征

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摘要

This study explores the rainfall and soil moisture thresholds for runoff using a classification and regression tree (CART) algorithm for a large dataset (146 events) of hydrologic variables collected from a humid hillslope. The common CART structure for the runoff coefficient successfully separated rainfall events with zero runoff generation and further identified five distinct clusters with distinct runoff generation and hysteretic loop features. The total amount of rainfall was the primary driver of runoff, with the highest relative importance in the CART model. In contrast, the antecedent soil moisture and rainfall intensity helped explain the hysteretic loop features as well as exceptional events through hysteretic loop analysis. The multinomial logistic regression model was introduced to determine the probability of different hysteretic loop variations according to the rainfall characteristics. By identifying the distinctive hysteretic loop patterns between upslope and downslope regions, these findings demonstrate the sequential development of hydrological processes from zero runoff to extreme runoff events.
机译:本研究使用分类和回归树 (CART) 算法对从潮湿山坡收集的水文变量的大型数据集(146 个事件)探索径流的降雨和土壤水分阈值。径流系数的通用CART结构成功地分离了零径流产生的降雨事件,并进一步识别了5个具有不同径流生成和滞后回线特征的不同聚类。降雨总量是径流的主要驱动因素,在CART模型中相对重要性最高。相比之下,先前的土壤湿度和降雨强度有助于通过滞后环分析解释滞后环特征以及异常事件。引入多项Logistic回归模型,根据降雨特征确定不同滞后环变化的概率。通过识别上坡和下坡区域之间独特的滞回环模式,这些发现证明了从零径流到极端径流事件的水文过程的连续发展。

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