Comparative evaluation of various parameter estimation methods for extreme rainfall in Kelantan River Basin

• Published in: Theoretical and Applied Climatology
• Main Author: Ng J.L.; Huang Y.F.; Tan S.K.; Lee J.C.; Md Noh N.I.F.; Thian S.Y.
• Format: Article
• Language: English
• Published: Springer 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176101680&doi=10.1007%2fs00704-023-04723-7&partnerID=40&md5=f3758c8d63e0bc9d75ca1d92aaaecb81

Extreme rainfall analysis has been discussed frequently in these decades as it can provide a better understanding of extreme rainfall for various hydrological applications. However, it is difficult to choose the most suitable parameter estimation method for extreme rainfall series which include the partial duration series (PDS) and annual maximum series (AMS). Therefore, the research aims to select the best parameter estimation method for the PDS and AMS in Kelantan River Basin, Malaysia. The generalized Pareto (GP), generalized extreme value (GEV), lognormal (LN), lognormal 3 (LN3), log-Pearson 3, and Weibull were fitted to the AMS and PDS and their performances were assessed using the goodness-of-fit tests. Maximum likelihood estimation (MLE), method of moment (MOM), Bayesian Markov chain Monte Carlo, and L-moment (LMOM) were adopted to estimate the parameters of the best fit distributions and their performances were measured using root mean square error, relative absolute square error, and relative root mean square error. It was found that the best fit distributions for the PDS and AMS were GP and GEV distributions, respectively, due to their comprehensive three-parameter characteristics. Furthermore, the findings concluded that MLE and LMOM were the best parameter estimation methods for AMS and PDS, respectively, due to the normality of MLE, and the robustness of LMOM to outliers. Overall, the MLE and LMOM performed adequately well for the AMS and PDS, respectively, as they can provide better parameter fitting for the extreme rainfall distributions. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2023.