Estimation of the long term dryness pattern for Pahang state using integrated SDSM-SPI model - Ahmad Saifuddin, Othman (2020)
Drought or long dry season is a global issue that adversely gives huge impact on the world. Plenty of calamities have been reported by Malaysian Meteorological Department (MET) since 1900s. Malaysia faced at least 12 times of extreme drought from 1951 to 1998, especially during southwest season changes. Although the formation of this calamity was influenced by the season changes, however frequent occurrence of this event majorly affected by the uncertainty of global climate changes and drastic emission of greenhouse gases (GHGs). In this study, the integrated model of Statistical Downscaling Model and Standardised Precipitation Index (SDSM-SPI) was applied to estimate the probability of extreme dry events in Pahang. SDSM is a statistical climate model that been used to understand the change on present or long-term future climate condition in response to the long-term dispersion of greenhouse gases and aerosols emission into the atmospheric system. The chosen of the best suitable atmospheric variables is vital in obtaining a good long-term future climate condition prediction. The projected rainfall and temperature pattern for the interval year of Δ2020, Δ2050, and Δ2080 were used as important data input to estimate the event pattern in the study region. Therefore, the identification of potential dryness event in the long-term become significant to monitor how frequent the event and how huge the impact on water resources efficiency. By AR5, all RCPs agreed the annual rainfall was predicted to decrease until end of century. RCP4.5 produced a larger decrement (−3.1 %) from the historical record compared to RCP2.6 (−2.7 %), and RCP8.5 (−2.9 %). The heaviest rainfall was predicted to occur at most regions in Kuantan, Pekan, and southern of Bentong. Due to non-uniform rainfall pattern, almost 42 % of Pahang was predicted to receive lower rainfall intensity. Meanwhile, the temperature was predicted to have small increment in April to June, reaches over 33°C which might be influenced by the season interchanges. The highest and lowest temperature was estimated to be in May (34°C) and January (22°C), respectively. Even the changes were not too high, however it will still contribute to the water scarcity problem when it frequently happens in a longer period. The SDSM-SPI result shows the probability percentage of a normal level pattern is about 84 – 85 %. However, about 2 – 3 % of probability extreme pattern were detected at this state. Several areas such as Janda Baik and Kg Manchis are exposed to the dryness in the certain year with the SPI value detected to drop to −2. The calibration and validation processes were conducted to identify the fundamental rules and the predictand-predictors relationships are suited to original data. The calibration results obtained a good agreement for temperature and rainfall stations with low RMSE ranges 0.01 - 0.04°C and 5.0 - 15.0mm, respectively. However, the error slightly increased in the validation part for temperature and rainfall intensity with ranges 0.3 - 0.4°C and 25.0 - 72.0mm, respectively. Thus, it is reliable to be significant information to the respective agencies for the long term planning and management of water resources.