摘要
<span style="font-family:""><span style="font-family:Verdana;">Flood is a natural process generated by the interaction of various driving fac</span><span style="font-family:Verdana;">tors. Flood peak flows, flood frequency at different return periods, and potential driving forces are analyzed in this study. The peak flow of six gauging stations, with a catchment area ranging from 169 -</span></span><span style="font-family:""> </span><span style="font-family:""><span style="font-family:Verdana;">124,108 km</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> and sufficient observed streamflow data, was selected to develop threshold (3</span><sup><span style="font-family:Verdana;">rd</span></sup><span style="font-family:Verdana;"> quartile) magnitude and frequency (POTF) that occurred over ten years of records. Sixteen Potential climatic, watershed and human driving factors of floods in the study area were identified and analyzed with GIS, Pearson’s correlation, and Principal Correlation Analysis (PCA) to select the most influential factors. Eight of them (MAR, DA, BE, VS, sand, forest AGR, PD) are identified as the most significant variables in the flood formation of the basin. Moreover, mean annual rainfall (MAR), drainage area (DA), and lack of forest cover are explored as the principal driving factors for flood peak discharge in Wabi-Shebele River Basin. Fi</span></span><span style="font-family:""><span style="font-family:Verdana;">nally, the study resulted in regression equations that helped plan and design different infrastructure works in the basin as ungauged catchment empirical</span><span><span style="font-family:Verdana;"> equations to compute Q</span><sub><span style="font-family:Verdana;">MPF</span></sub><span style="font-family:Verdana;">, Q</span><sub><span style="font-family:Verdana;">5</span></sub><span style="font-family:Verdana;">, Q</span><sub><span style="font-family:Verdana;">10</span></sub><span style="font-family:Verdana;">, Q</span><sub><span style="font-family:Verdana;">50</span></sub><span style="font-family:Verdana;">, and Q</span><sub><span style="font-family:Verdana;">100</span></sub><span style="font-family:Verdana;"> using influential climate, watershed, and human driving factors. The results of these empirical equations are </span></span><span style="font-family:Verdana;">also statistically accepted with a high significance correlation (R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> > 0.9).
<span style="font-family:""><span style="font-family:Verdana;">Flood is a natural process generated by the interaction of various driving fac</span><span style="font-family:Verdana;">tors. Flood peak flows, flood frequency at different return periods, and potential driving forces are analyzed in this study. The peak flow of six gauging stations, with a catchment area ranging from 169 -</span></span><span style="font-family:""> </span><span style="font-family:""><span style="font-family:Verdana;">124,108 km</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> and sufficient observed streamflow data, was selected to develop threshold (3</span><sup><span style="font-family:Verdana;">rd</span></sup><span style="font-family:Verdana;"> quartile) magnitude and frequency (POTF) that occurred over ten years of records. Sixteen Potential climatic, watershed and human driving factors of floods in the study area were identified and analyzed with GIS, Pearson’s correlation, and Principal Correlation Analysis (PCA) to select the most influential factors. Eight of them (MAR, DA, BE, VS, sand, forest AGR, PD) are identified as the most significant variables in the flood formation of the basin. Moreover, mean annual rainfall (MAR), drainage area (DA), and lack of forest cover are explored as the principal driving factors for flood peak discharge in Wabi-Shebele River Basin. Fi</span></span><span style="font-family:""><span style="font-family:Verdana;">nally, the study resulted in regression equations that helped plan and design different infrastructure works in the basin as ungauged catchment empirical</span><span><span style="font-family:Verdana;"> equations to compute Q</span><sub><span style="font-family:Verdana;">MPF</span></sub><span style="font-family:Verdana;">, Q</span><sub><span style="font-family:Verdana;">5</span></sub><span style="font-family:Verdana;">, Q</span><sub><span style="font-family:Verdana;">10</span></sub><span style="font-family:Verdana;">, Q</span><sub><span style="font-family:Verdana;">50</span></sub><span style="font-family:Verdana;">, and Q</span><sub><span style="font-family:Verdana;">100</span></sub><span style="font-family:Verdana;"> using influential climate, watershed, and human driving factors. The results of these empirical equations are </span></span><span style="font-family:Verdana;">also statistically accepted with a high significance correlation (R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> > 0.9).
作者
Fraol Abebe Wudineh
Semu Ayalew Moges
Belete Berhanu Kidanewold
Fraol Abebe Wudineh;Semu Ayalew Moges;Belete Berhanu Kidanewold(School of Civil and Environmental Engineering, Addis Ababa University, Addis Ababa, Ethiopia;Department of Civil and Environmental Engineering, University of Connecticut, Connecticut, USA)