L-Moments Approach for Modelling Maximum Daily Rainfall

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—Normal, Lognormal (two- and three-parameter), Logistic, Generalized Logistic, Gumbel, Generalized Extreme Value (GEV), and Generalized Pareto (GPA)—were 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.