Journals Information
Environment and Ecology Research Vol. 13(5), pp. 657 - 667
DOI: 10.13189/eer.2025.130505
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L-Moments Approach for Modelling Maximum Daily Rainfall
Zahrahtul Amani Zakaria 1,2,*, Nur Syafiqah Suhaimi 3, Mohammad Amir Syahmi 1, Nor Aida Mahiddin 1,2, Raja Hasyifah Raja Bongsu 1,2, Siti Dhalila Mohd Satar 1,2, Elissa Nadia Madi 1, Nur Amalina Mat Jan 4, Basri Badyalina 5
1 Faculty of Computing and Informatics, Universiti Sultan Zainal Abidin, Besut Campus, Malaysia
2 Disaster Management Research Unit, East Coast Environmental Research Institute, Universiti Sultan Zainal Abidin, Malaysia
3 Faculty of Sciences (Mathematics), Universiti Teknologi Malaysia, Malaysia
4 Department of Physical and Mathematical Science, Faculty of Science, Universiti Tunku Abdul Rahman, Malaysia
5 Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Segamat Campus, Malaysia
ABSTRACT
Extreme rainfall events in Terengganu, Malaysia, particularly during the northeast monsoon, often cause severe flooding, infrastructure damage, and socioeconomic disruption. With climate change likely to increase rainfall intensity and frequency, accurate modelling of such extremes is essential for effective flood forecasting and water resource management. This study applies the L-moment method, a robust statistical technique suited to skewed hydrometeorological data and resistant to outliers, to analyse annual maximum daily rainfall from 20 monitoring stations across the state. Eight probability distributions鈥擭ormal, Lognormal (two- and three-parameter), Logistic, Generalized Logistic, Gumbel, Generalized Extreme Value (GEV), and Generalized Pareto (GPA)鈥攚ere fitted to the data. Model performance was evaluated using the Mean Absolute Deviation Index (MADI), Mean Squared Deviation Index (MSDI), and L-moment ratio diagrams. Results show that the GEV distribution provided the most accurate fit, ranking first or second at most stations, while Normal and Logistic distributions performed worst. The consistency between statistical indices and visual analysis confirms GEV's suitability for extreme rainfall modelling in Terengganu. This study contributes to hydrological modelling by offering a large-scale comparison of candidate distributions, integrating both statistical and graphical evaluations. Practically, the findings provide planners and policymakers with evidence-based guidance for designing flood mitigation infrastructure, refining early warning systems, and improving water management. Socially, adopting reliable models can help reduce disaster losses and enhance community resilience. Although based on historical records, future work should integrate climate change projections to strengthen predictive capability and ensure effective long-term flood risk management in the region.
KEYWORDS
Extreme Rainfall, L-Moments, Flood Forecasting, Probability Distributions, Regional Frequency Analysis
Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Zahrahtul Amani Zakaria , Nur Syafiqah Suhaimi , Mohammad Amir Syahmi , Nor Aida Mahiddin , Raja Hasyifah Raja Bongsu , Siti Dhalila Mohd Satar , Elissa Nadia Madi , Nur Amalina Mat Jan , Basri Badyalina , "L-Moments Approach for Modelling Maximum Daily Rainfall," Environment and Ecology Research, Vol. 13, No. 5, pp. 657 - 667, 2025. DOI: 10.13189/eer.2025.130505.
(b). APA Format:
Zahrahtul Amani Zakaria , Nur Syafiqah Suhaimi , Mohammad Amir Syahmi , Nor Aida Mahiddin , Raja Hasyifah Raja Bongsu , Siti Dhalila Mohd Satar , Elissa Nadia Madi , Nur Amalina Mat Jan , Basri Badyalina (2025). L-Moments Approach for Modelling Maximum Daily Rainfall. Environment and Ecology Research, 13(5), 657 - 667. DOI: 10.13189/eer.2025.130505.