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Depth Duration Frequency (DDF) 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, scaling properties of extreme rainfall depth series were examined to establish scaling behavior of statistical moments and quantile estimates over different durations. The annual extreme series of precipitation maxima for storm duration ranging from 0.5 to 24 hr observed at network of rain gauges sited in Oromia regional state were analyzed using an approach based on moments. The analysis investigated the statistical properties of rainfall extremes and detected that the statistics of the rainfall extremes follows a power law relation with its duration. Moreover, the variations of the distribution parameters with durations of annual maximum rainfall depth series were explored and found that the logEV1, EV1 and logistic distribution parameters exhibit a power law relationship with durations. Following the analysis, scale invariance of extreme rainfall depth series is investigated and dissipative (multiple scaling) nature of extreme rainfall depth series is considered, thus introducing a general distribution free framework to develop Depth Duration frequency (DDF) model. Keywords : Scaling Properties Depth Duration Frequency Rainfall Depth Series Multiple Scaling

Investigating the Scaling Properties of Extreme Rainfall Depth Series in Oromia Regional State, Ethiopia