Abstract:
Floods are among the most recurring and devastating natural disasters and are
responsible for significant loss of life and property throughout the world. It causes
physical suffering, economic losses, limit the efficiency of drainage, and disturb
existence of life in the study area. An evaluation of predictive accuracy of regional
flood frequency estimation methods has been the backbone of water resources project
planning, design of any structures and the economic analysis of flood control
projects. It is due to the fact that floods represent the most disastrous natural event
causing several damages to enormous economic and life losses in the study area. The
aim of this study was to evaluate the predictive fit of probability distributions to
annual maximum flood data, and in particular to evaluate which combination of
distribution and estimation method gives the best fit of Tekeze River Basin.
Subsequently, the probability distribution fits were evaluated according to several
goodness-of-fit measures and to the variability of the predicted flood quantiles. To
achieve this, based on 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 were accomplished using ArcGIS10.4.1. Discordancy of
regional data of the L-moment statistics was identified using Matlab2018a. All
regions have shown satisfactory results for discordance measures and homogeneity
tests. For the regions, best-fit distributions were selected. L-moment ratio diagrams
and Easy Fit statistical software was used to select best-fit probability distributions.
The performances of the distributions were evaluated using Kolmogorov Smirnov,
Anderson-Darling and Chi-Squared goodness-of-tests. After three goodness of fit
tests was carried out, generalized extreme value (GEV) with MOM for Region-A and
generalized Pareto (GPA) with PWM for Region-B and C were 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. 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. Consequently, statistical analysis of gauged sites was revealed an
acceptable method of regionalization. Finally, the study can be further extended into
flood hazard, risk and inundation mapping of identified regions of the study area.
