Development and Application of Spatially Parameterized Depth Duration Frequency Model for Estimation of Design Rainfall for Oromia State, Ethiopia
AbstractThe magnitude and frequency of extreme rainfall events are required for planning, design and operation of many hydrological and water resources projects. Design rainfall depth is often used to estimate the severity and rarity of floods in areas where flow records are not sufficient enough to warrant direct flood estimation. The design of hydraulic structures on un-gauged streams and creeks, such as bridges, culverts, spillways, water harvesting and flood defense mechanisms depends upon proper estimation of extreme rainfall events. Quantification of design rainfall is generally done by using information contained in Depth-Duration-Frequency (DDF) relationships. Depth Duration Frequency relationships are currently constructed based on at site frequency analysis of rainfall data separately for different durations. These relationships are not accurate and reliable since they depend on assumptions such as distribution selection for each duration; they require a large number of parameters, experience intensive equations and regionalization is also very poor and coarse. In this study a DDF model with gridded set of parameters is developed for estimation of point rainfall frequencies for a range of duration for any location in Oromia regional state. A DDF model was fitted to series of annual maxima and its parameters were determined by a least squares method and these parameters were interpolated and mapped on a 1km grid. The model allows for a parsimonious and efficient parameterization of DDF relationships, and its performance is shown to improve the reliability and robustness of design storm predictions as compared with those achievable by interpolating the quantile predictions of extreme rainfall data for specific durations. Moreover, design rainfall estimates found from the scaling DDF model are comparable to estimates obtained from traditional techniques; however, the scaled approach was more efficient and gives more reliable estimate compared with the observed rainfall depth at all stations.
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