Abstract:
Floods are one of the most common and destructive natural disasters, causing substantial
loss of life and property all over the world. Assessment of predictive accuracy for regional
flood frequency distribution estimation method has been the backbone of water resources
project planning, design of any structures, and the economic analysis of flood control
projects. The goal of this study was to test the predictive fit of probability distributions to
yearly maximum flood data and to determine which distribution and estimation method
provide the best match for the Upper Omo-Gibe River Basin. Using a river basin as a case
study, the performance of nine probability distributions, three fitting tests, evaluation
processes, and selection procedures was examined. To achieve this, data from eleven stream
gauged sites, three hydrological homogeneous sub-regions were defined and delineated
based on L-moment homogeneity tests, namely Region-A, Region-B, and Region-C.
Delineation of homogeneous regions was accomplished using ArcGIS10.4.1. Discordancy of
regional data of the L-moment statistics was identified using Matlab2018a. The R
programming language was used in conjunction with a collection of the most recent
computer statistical programs in an integrated development environment. The most relevant
distribution models were identified using maximum likelihood estimation, goodness-of-fit
tests-based analysis, and information criteria-based selection techniques. The performances
of the distributions were evaluated using Kolmogorov Smirnov, Anderson-Darling, and Chi Squared goodness-of-tests. After three goodness of fit tests were carried out, the results
showed that the lognormal and gamma distribution models were the best-fit functions for
Region-A. Because they had the lowest Akaike Information Criterion (AIC) values of -
46.251 and -45.802, and Bayesian Information Criterion (BIC) values corresponding to -
43.320 and -42.870, respectively. Similarly, for both Region-B and Region-C the lognormal
and gamma functions were the best-fit distribution functions and identified as suitable
distributions for analyzing accurate annual maximum flows in the basin. Based on best-fit
distributions for the three regions, regional flood frequency curves were constructed. In this
study, the flood magnitude is estimated for 2, 5, 10, 15, 20, 25, 50, 75, 100, 200, 500, and
1000 years return period, and their respective extreme event for Region-A became 427.65,
547.24, 626.42, 671.1, 702.37, 726.46, 800.6, 800.68, 843.82, 874.35, 917.31, and 947.75.
1044.59. The derived flood frequency curves at a given return period suggested that how
important engineering decisions and actions such as design and operation of the water
resources project have to be undertaken carefully
