In Malaysia, extreme rainfall events are often linked to a number of environmental disasters such as landslides, monsoonal and flash floods. In response to the negative impacts of such disaster, studies assessing the changes and projections of extreme rainfall are vital in order to gather climate change information for better management of hydrological processes. This study investigates the changes and projections of extreme rainfall over Peninsular Malaysia for the period 2081-2100 based on the RCP 6.0 scenario. In particular, this study adopted the statistical downscaling method which enables high resolution, such as hourly data, to be used for the input. Short duration and high intensity convective rainfall is a normal feature of tropical rainfall especially in the western part of the peninsular. The proposed method, the Advanced Weather Generator model is constructed based on thirty years of hourly rainfall data from forty stations. To account for uncertainties, an ensemble multi-model of five General Circulation Model realizations is chosen to generate projections of extreme rainfall for the period 2081-2100. Results of the study indicate a possible increase in future extreme events for both the hourly and 24 h extreme rainfall with the latter showing a wider spatial distribution of increase.
Generalized Extreme Value (GEV) model is the combination of three types of distribution class namely Gumbel, Fréchet and Weibull distributions of the Extreme Value Theory (EVT). In hydrological studies, GEV model is widely applied in the modelling of extreme rainfall. The nature of hydrological variables is highly complex, especially with the changing climate and frequent occurrences of extreme events. As such, some rainfall models assume rainfall series as stationary, while some as nonstationary. In this study, GEV models based on stationary and nonstationary data are used to assess monthly maximum rainfall data within Sabah, Malaysia and performance comparison between both GEV models is conducted. Theoretically both stationary and nonstationary GEV models are based on the same foundation, however nonstationary GEV model allows the location and scale parameters to be expressed as cyclic function of time. In this study, the stationary and the nonstationary GEV models are individually fitted to rainfall data at selected stations. Monthly maximum rainfall is blocked, and the estimated location (μ), scale (σ) and shape (ξ) parameters are estimated by using Maximum Likelihood Estimation method. Performance of both GEV models are compared based on the Akaike Information Criterion, Bayesian Information Criterion and the likelihood ratio goodness of fit tests. Results showed that nonstationary GEV model is the best fit. The inclusion of cyclic covariates in the GEV model gives improvement on the stationary GEV model at the study region. It is also concluded that, Gumbel is identified as the significant distribution for monthly maximum rainfall in Sabah at 5% significance level.
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