Probability distribution analysis of extreme rainfall events in a flood-prone region of Mumbai, India

Abstract

Mumbai, being a coastal city surrounded by sea and creeks, is vulnerable to severe flooding almost every year owing to intense rainfall. Therefore, it is vital to quantify the magnitude of extreme rainfall events, which mainly depends upon the suitability of the derived distribution and the selection of a technique for processing and analysing data. The magnitude is often estimated using window-based approach of rainfall, which does not represent actual rainfall events since the duration of the rainfall is prefixed in this approach. Hence, the goal is to determine the best-fit probability distribution for extreme storms using the ‘storm event analysis’ method to overcome this limitation. The distribution is analysed with two-parameter [Lognormal (LN), Gamma (G), Gumbel (GUM) and Weibull (W)] and three-parameter [generalised extreme value (GEV), generalised Pareto (GP), Log Pearson 3 (LP3) and Frechet (F)] distribution functions on two sets [maximum extreme volume (MEV) and the maximum peak intensity (MPI)] of extreme events. Goodness-of-fit tests are applied to the distribution functions of the extreme storm series (2006–2016) of 26 meteorological stations. The best score results show that the GEV distribution fits best for 29% of the stations, and 27% and 22% of the stations showed the best fit for the F and GP distributions, respectively. On a basin scale, MEV events are best described by the GEV distribution while GP or F distributions show the best fits for MPI events. However, each station must be analysed individually as none of GEV, GP or F is always the best-fitting distribution function.