Evaluation of spatial interpolation methods and spatiotemporal modeling of rainfall distribution in Peninsular Malaysia

Spatial interpolation is important for visualizing changes of parameters over space. Interpolation methods for the spatiotemporal analysis of rainfall pattern changes of Peninsular Malaysia due to climate change were evaluated. Inverse Distance Weighting (IDW) and Ordinary Kriging (OK), Geographical Weighted Regression (GWR) and Multi-scale Geographical Weighted Regression (MGWR) methods were used. Based on the statistic results of RMSE, MAE and R-2, the MGWR was the best performing model. To investigate the spatial interpolations with the MGWR, the period considered was arbitrarily and conveniently divided into six 5-year sub-periods for spatiotemporal analysis. Monthly Rainfall, Number of Wet Days and Maximum Daily Rainfall were parameters identified for the analyses. The results showed that the Peninsular Malaysia generally receives relatively higher rainfall amounts and intensities over the northern part of the East Coast region, while lower values were commonly received at the Central region. Changes in spatial pattern were also observed in the maps generated for the onset and withdrawal months of both the Northeast Monsoons and the Southwest Monsoons. The sub-period analyses also showed that Peninsular Malaysia has gradual increase of rainfall intensity due to climate change and is susceptible and vulnerable to El Nino/La Nina events. Hence, in the future Peninsular Malaysia is likely to have increasing occurrence of extreme rainfall events. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University.

Automated summary

This study evaluated spatial interpolation methods for rainfall pattern changes in the Peninsular Malaysia, with results indicating that Multi-scale Geographical Weighted Regression (MGWR) was the best performing model. The research showed that the rainfall intensity in Peninsular Malaysia is gradually increasing due to climate change and is vulnerable to El Nino/La Nina events.