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首页> 外文期刊>Journal of Water Resource and Protection >Flood Change Detection and Attribution Using Simulation Approach in Data-Scarce Watersheds: A Case of Wabi Shebele River Basin, Ethiopia
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Flood Change Detection and Attribution Using Simulation Approach in Data-Scarce Watersheds: A Case of Wabi Shebele River Basin, Ethiopia

机译:利用数据稀缺流域模拟方法的洪水改变检测和归因:埃塞俄比亚Wabi Shebele River盆地的案例

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摘要

Flood events vary with sub-regions, sites and time and show complex characteristics. This study investigated temporal variabilities in flood discharges and relationships with principal driving factors in data scarce Wabi Shebele River Basin. The preliminary analysis using exploratory data analysis (EDA) on annual and seasonal maximum discharge reveals that there are cycles of extreme flows at five- and ten-year intervals respectively throughout the basin. The statistical verification using the Mann-Kendall test and Quantile perturbation method indicates a significant trend in flood magnitude and frequency entire the basin in the early 21st century. For longest period (1980-2010) annual maximum stream flow shows significant positive trend (p-value < 0.05) in middle catchments and negative trend (p-value < 0.05) in eastern catchments. The years: 1986-1995, 2006-2010 are the years in which positive significant anomalies occurred in all seasons, while the years: 1980-1985, 1996-2005 are the occurrence years of significant negative anomalies. Rainfall from climate drivers; DA, BE, VS and fraction of sand from environmental background drivers; fraction of forest and population density from external factors were identified as the powerful driving factors of flood variabilities in the Wabi Shebele River Basin.
机译:洪水事件因子区域,站点和时间而异,并显示复杂的特征。本研究调查了数据稀缺性沃比斯河流域的主要驾驶因子的洪水排放和关系的时间可变性。使用探索性数据分析(EDA)对年和季节性最大放电的初步分析表明,在整个盆地的五个和十年间隔中存在极端流动的周期。使用MANN-KENDALL测试和分位式扰动方法的统计验证表明了21世纪初的洪水幅度和频率整个盆地的显着趋势。对于最长的时间(1980-2010),年度最大流流程显示在东部集水区中的中间流域和负趋势(P值<0.05)中显示出显着的正趋势(P值<0.05)。多年来:1986-1995,2006 - 2010年是各个季节发生的阳性显着异常的多年,而多年:1980-1985,1996-2005是发生的显着阴性异常年份。从气候司机降雨; DA,来自环境背景司机的沙子,vs和分数;来自外部因素的森林和人口密度的分数被确定为WABI Shebele River盆地洪水变量的强大驾驶因素。

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