Abstract
The frequency and intensity of flood quantiles and its attendant damage in agricultural establishments have generated a lot of issues in Ethiopia. Moreover, precise estimates of flood quantiles are needed for efficient design of hydraulic structures; however, quantification of these quantiles in data-scarce regions has been a continuing challenge in hydrologic design. Flood frequency analysis is thus essential to reduce possible flood damage by investigating the most suitable flood prediction model. The annual maximum discharges from six representative stations in the Upper Blue Nile River Basin were fitted to the commonly used nine statistical distributions. This study also assessed the performance evolution of the probability distributions with varying spatial scales, such that three different spatial scales of small-, medium-, and large-scale basins in the Blue Nile River Basin were considered. The performances of the candidate probability distributions were assessed using three goodness-of-fit test statistics, root mean square error, and graphical interpretation approaches to investigate the robust probability distribution for flood frequency analysis over different basin spatial scales. Based on the overall analyses, the generalized extreme value distribution was proven to be a robust model for flood frequency analysis in the study region. The generalized extreme value distribution significantly improved the performance of the flood prediction over different spatial scales. The generalized extreme value flood prediction performance improvement measured in root mean square error varied between 5.84 and 67.91% over other commonly used probability distribution models. Thus, the flood frequency analysis using the generalized extreme value distribution could be essential for the efficient planning and design of hydraulic structures in the Blue Nile River Basin. Furthermore, this study suggests that, in the future, significant efforts should be put to conduct similar flood frequency analyses over the other major river basins of Ethiopia.
Highlights
In hydrology, the quantification of design peak discharges on data-scarce catchments has been a continuing problem [1,2]
Precise estimates of flood quantiles are needed for the efficient design of hydraulic structures [3,4]; historical data that are required to quantify the flood statistics are usually unavailable at the site of interest or the available information may not be representative of the catchment studied because of the changes in the watershed characteristics, such as urbanization [2,5]
The Anderson–Darling (A2 ), Kolmogorov–Smirnov (D), and Chi-Squared (χ2 ) statistical tests are the commonly used methods to check the adequacy of the probability distribution functions for flood frequency analysis [27]; this study considered these three goodness of fit tests with the root mean square error (RMSE) method to investigate the robust probability distribution function for flood frequency analysis in the Blue Nile River Basin
Summary
The quantification of design peak discharges on data-scarce catchments has been a continuing problem [1,2]. Precise estimates of flood quantiles are needed for the efficient design of hydraulic structures [3,4]; historical data that are required to quantify the flood statistics are usually unavailable at the site of interest or the available information may not be representative of the catchment studied because of the changes in the watershed characteristics, such as urbanization [2,5]. A promising and elegant way to solve this problem is deriving the flood frequency. Several studies have reported the increasing trend of flood frequency and magnitudes in different parts of the world [16,17,18]. Investigation of the most suitable flood frequency analysis method is of great importance in improving the reliability of water infrastructures
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.