Abstract:
Water resource development planning is usually referred as the process to define how to
utilize the available water resource to meet the desired objectives. In order to
compensate for the scarcity of data for the available water resource, regionalization is
found to be one of the best methods of transferring information from the gauged station
to ungauged sites. This study analyses regional low flow frequency for Omo-Gibe River
Basin. Rapid water resources development and population growth in this basin have
caused serious concerns over the adequacy of the quantity and quality of water
withdrawn from the Rivers. Information on the magnitude and frequency of low flows
in the basin is needed for planning of water resources at present and in the near future.
Hydrological homogeneous low flow regions are formed by the cluster analysis of site
characteristics, using the hierarchical clustering and Ward's method. Statistical tests for
regional homogeneity are utilized, by Discordancy and Heterogeneity measure tests. In
compliance with results of the tests, the area of the Omo-Gibe River Basin has been
divided into four homogeneous regions. The findings are supported by Monte Carlo
simulations proposed to evaluate stability of the test results and the uncertainty of estimates.
The L-moment and LL-moment method is used to analyses the regional frequency of
low flows, since recent studies have shown that both methods are superior to other
methods that have been used previously. In this study, frequency analysis of low flows
using 7-day minimum low flow series is conducted using six distributions: Generalized
logistic (GLOG), Generalized extreme value (GEY), Pearson type III (P-III),
Generalized Pareto (GPAR), Weibull (WEI) and Power (PR) distributions. Flow model, providing a simple and effective method for estimation of low flows of desired
return periods for ungauged catchments. Generally, Physiographic and drainage
characteristics are related to low flow characteristics of watersheds to identify and
delineate homogeneous pools and to derive best regression models for ungauged sites.
