Predictive Performance Analysis of PDF – IDF Model Types Using Rainfall Observations from Fourteen Gauged Stations

The design of structures for flood mitigation depends on the adequate estimation of rainfall intensity over a given catchment which is achieved by the rainfall intensity duration frequency modelling. In this study, an extensive comparative analyses were carried out on the predictive performance of three PDF – IDF model types, namely: Gumbel Extreme Value Type 1 (GEVT – 1), Log-Pearson Type 3 (LPT – 3) and Normal Distribution (ND) in 14 selected cities in Southern Nigeria. This is to rank the order of best performance. The principle of general model development was adopted in which rainfall intensities at different durations and specified return periods were used as input data set. This is not same as return period specific model that involves rainfall intensities for various durations and a given return period. The predicted rainfall intensity values with the PDF – IDF model types indicate high goodness of fit (R2) and Mean Squared Errors (MSE) ranging from: (a) R2 = 0.875 – 0.992; MSE = 33.17 – 224.6 for GEVT – 1; (b) R2 = 0.849 – 0.990; MSE = 65.34 – 405.5 for LPT – 3 and (c) R2 = 0.839 – 0.992; MSE = 29.23 – 200.2 for ND. The comparative analysis of all the 42 general models (14 locations versus 3 model types) considered showed that the order of best performance is LPT – 3 1st, GEVT - 1 2nd and ND 3rd for each return period (10, 50 and 100 years). The Kruskal Wallis test of significance indicates that no significant difference exists in the predictive performance of the three General models across the board. This may be due to the fact that the fourteen locations of the study area are bordering with the Atlantic Ocean and seems to have similar climatology. These developed General models are recommended for the computation of intensities in the fourteen locations for the design of flood control structures; and the order of preference should be LPT – 3 > GEVT – 1 > ND.