环境科学
降水
事件(粒子物理)
星团(航天器)
鉴定(生物学)
层次聚类
高度(三角形)
气候学
气象学
计算机科学
聚类分析
统计
地理
地质学
数学
植物
量子力学
生物
几何学
物理
程序设计语言
作者
María C. Patino‐Alonso,José-Luis Molina,Fernando Espejo,Santiago Zazo,Abedin Mohammad-Hosseinpour,Fernando Silla
标识
DOI:10.1016/j.jhydrol.2023.130014
摘要
Given the increasing non-stationarity of hydrological processes largely due to Climate Change (CC), it is essential to create more accurate methods. The inherent spatiotemporal variability of hydrological processes reaches its maximum when it comes to rainfall process. Rainfall is typically divided up into events in hydrological studies, but this does not acquire the necessary analytical importance. The objective of the research is dual. First, to generate a robust method for the identification and characterization of homogeneous rainfall events, largely based on the geometrical analysis of clustered hyetographs. Second, the generation of more accurate hyetographs. The study was built on the idea of Inter-Event Time Definition (IETD) and thresholds for rainfall depth, duration, and intensity. The rainfalls were grouped due to the similarity of features as: duration, volume, intensity, time maximum pick and interevent using hierarchical cluster analysis. The study area is placed in the Muñogalindo station located in the province of Avila (Spain) and characterized by an altitude above 1,132 m. Hierarchical cluster analysis showed to be particularly effective for selecting precipitation based on hyetograph shape. Cluster method strongly revealed the identification of three rainfall eventś groups. HyetoClust is intended to provide the scientific and technical community with a new tool for characterization of rainfall eventś and the generation of more accurate hyetographs for supporting hydrological prediction.
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