水文气候变量的同质性和变化点检测——以埃塞俄比亚Ghba河流域为例

Mehari Gebreyohannes Hiben, Admasu Gebeyehu Awoke, A. Ashenafi
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引用次数: 2

摘要

在大多数关于水文气候变率和趋势的研究中,没有考虑到时间序列数据变化点检测分析的概念。了解该系统对于未来可持续地管理水资源至关重要,因为它意味着现状的改变。如果发生这种情况,当我们单独看趋势分析时,很难区分时间序列数据在各个领域的上升或下降趋势。本研究的主要目标是描述、量化和确认水文气候变量的同质性和变化点检测,包括年、季节和月平均降雨量、气温和流量。方法采用四齐性检验,即Pettitt检验、Buishand检验、标准正态齐性检验和5%显著性水平下的von Neumann比值检验。为了选择同质站,将测试输出分为三类:“有用”、“可疑”和“可疑”。结果表明,大部分台站的年降雨量和气温具有均匀性。结果表明,有68.8%的气象站和56.2%的气象站是有用的。然而,流量站被归类为100%有用。总的来说,变化点检测分析时间是在月、季、年时间尺度上发现的。在降雨时间序列中,没有检测到年变化点。在气温时间序列中,除Edagahamus站外,其余站均呈现增加变化点,而除Agulai站和Genfel站外,其余站均呈现减少变化点。而径流时间序列的变化没有明显的降雨时间序列的变化表明,变化是由降雨以外的变量引起的。最可能的是,观测到的水流突变可能是由集水区特征的变化引起的,比如次流域的土地利用和覆盖。这些研究结果为研究区域的气温、降雨量和河流流量的均匀性和变化点检测提供了重要的细节,这对于规划人员、决策者、水文学家和工程师制定更好的水资源分配策略、影响评估和趋势分析是必要的。
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Homogeneity and change point detection of hydroclimatic variables: A case study of the Ghba River Subbasin, Ethiopia
In most studies on hydroclimatic variability and trend, the notion of change point detection analysis of time series data has not been considered. Understanding the system is crucial for managing water resources sustainably in the future since it denotes a change in the status quo. If this happened, it is difficult to distinguish the time series data’s rising or falling tendencies in various areas when we look at the trend analysis alone. This study’s primary goal was to describe, quantify, and confirm the homogeneity and change point detection of hydroclimatic variables, including mean annual, seasonal, and monthly rainfall, air temperature, and streamflow. The method was employed using the four-homogeneity test, i.e., Pettitt’s test, Buishand’s test, standard normal homogeneity test, and von Neumann ratio test at 5% significance level. In order to choose the homogenous stations, the test outputs were divided into three categories: “useful,” “doubtful,” and “suspect.” The results showed that most of the stations for annual rainfall and air temperature were homogenous. It is found that 68.8% and 56.2% of the air temperature and rainfall stations respectively, were classified as useful. Whereas, the streamflow stations were classified 100% as useful. Overall, the change point detection analyses timings were found at monthly, seasonal, and annual time scales. In the rainfall time series, no annual change points were detected. In the air temperature time series except at Edagahamus station, all stations experienced an increasing change point while the streamflow time series experienced a decreasing change point except at Agulai and Genfel hydro stations. While alterations in streamflow time series without a noticeable change in rainfall time series recommend the change is caused by variables besides rainfall. Most probably the observed abrupt alterations in streamflow could result from alterations in catchment characteristics like the subbasin’s land use and cover. These research findings offered important details on the homogeneity and change point detection of the research area’s air temperature, rainfall, and streamflow necessary for the planers, decision-makers, hydrologists, and engineers for a better water allocation strategy, impact assessment and trend analyses.
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