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

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...

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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
id 2-s2.0-85176101680
spelling 2-s2.0-85176101680
Ng J.L.; Huang Y.F.; Tan S.K.; Lee J.C.; Md Noh N.I.F.; Thian S.Y.
Comparative evaluation of various parameter estimation methods for extreme rainfall in Kelantan River Basin
2024
Theoretical and Applied Climatology
155
3
10.1007/s00704-023-04723-7
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.
Springer
0177798X
English
Article

author Ng J.L.; Huang Y.F.; Tan S.K.; Lee J.C.; Md Noh N.I.F.; Thian S.Y.
spellingShingle Ng J.L.; Huang Y.F.; Tan S.K.; Lee J.C.; Md Noh N.I.F.; Thian S.Y.
Comparative evaluation of various parameter estimation methods for extreme rainfall in Kelantan River Basin
author_facet Ng J.L.; Huang Y.F.; Tan S.K.; Lee J.C.; Md Noh N.I.F.; Thian S.Y.
author_sort Ng J.L.; Huang Y.F.; Tan S.K.; Lee J.C.; Md Noh N.I.F.; Thian S.Y.
title Comparative evaluation of various parameter estimation methods for extreme rainfall in Kelantan River Basin
title_short Comparative evaluation of various parameter estimation methods for extreme rainfall in Kelantan River Basin
title_full Comparative evaluation of various parameter estimation methods for extreme rainfall in Kelantan River Basin
title_fullStr Comparative evaluation of various parameter estimation methods for extreme rainfall in Kelantan River Basin
title_full_unstemmed Comparative evaluation of various parameter estimation methods for extreme rainfall in Kelantan River Basin
title_sort Comparative evaluation of various parameter estimation methods for extreme rainfall in Kelantan River Basin
publishDate 2024
container_title Theoretical and Applied Climatology
container_volume 155
container_issue 3
doi_str_mv 10.1007/s00704-023-04723-7
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176101680&doi=10.1007%2fs00704-023-04723-7&partnerID=40&md5=f3758c8d63e0bc9d75ca1d92aaaecb81
description 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.
publisher Springer
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language English
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