An accuracy in the hydrological modelling will be affected when having limited data sources especially at ungauged areas. Due to this matter, it will not receiving any significant attention especially on the potential hydrologic extremes. Thus, the objective was to analyse the accuracy of the long-term projected rainfall at ungauged rainfall station using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model. The SDSM was used as a climate agent to predict the changes of the climate trend in Δ2030s by gauged and ungauged stations. There were five predictors set have been selected to form the local climate at the region which provided by NCEP (validated) and CanESM2-RCP4.5 (projected). According to the statistical analyses, the SDSM was controlled to produce reliable validated results with lesser %MAE (<23%) and higher R. The projected rainfall was suspected to decrease 14% in Δ2030s. All the RCPs agreed the long term rainfall pattern was consistent to the historical with lower annual rainfall intensity. The RCP8.5 shows the least rainfall changes. These findings then used to compare the accuracy of monthly rainfall at control station (Stn 2). The GIS-Kriging method being as an interpolation agent was successfully to produce similar rainfall trend with the control station. The accuracy was estimated to reach 84%. Comparing between ungauged and gauged stations, the small %MAE in the projected monthly results between gauged and ungauged stations as a proved the integrated SDSM-GIS model can producing a reliable long-term rainfall generation at ungauged station.
The interference of climate circulation and continuous rising of surface temperature every year has caused the atmosphere composition change which gives serious impact to water resource management. Pahang is among of the affected states by El Nino that hit Malaysia in recent years which led to water depletion at several water plants. Based on the current situation, this study focuses on 1) simulate the average rain pattern using statistical downscaling; 2) identify the severity index and dry duration occurrence in the catchment area. Predicting potential changes in the climate events is important to evaluate the level of climate change in the critical region. Therefore, the integration of Statistical Downscaling Model (SDSM) and Standard Precipitation Index (SPI) have been conducted to study the potential occurrence of the dry period due to climate change for year 2020s and year 2050s. The results reveal that the dry condition is high during the mid-year. The lowest SPI value is estimated to reach -2.2 which can be classified as extreme. The potential dry period is expected to increase 2.5% and 3.3% in 2020 and 2050, respectively.
This paper reviews the climate change impact on rainfall as well as extreme events occurrences. The global extreme weather contributes to the uncertainties of the climate trend and water scarcity problems to the whole world. Thus, numerical models such as General Circulation Models (GCMs) have been developed to simulate the response of the global climate system to the expected increment of the greenhouse gases concentrations. However, the GCM cannot be directly applied to climate change impact studies, hence downscaling is needed. A large number of downscaling methods are available but there is no universal method exists at present that performs best for all conditions, depending on the application and this remains a subject of investigation. Therefore, this paper compares the performances among statistical and dynamical downscaling models that have been applied by different researchers in various purposes. It can be concluded that the statistical downscaling has been widely used and able to provide reliable climate projected results especially for Malaysia's climate variables. This review is very significant especially to the policy maker in deciding the reliable climatic methods for the long term planning and management of water resources. Besides, the reliable projected rainfall will be very beneficial in estimating water availability and water resource policy.
Statistical downscaling model was used to generate 30-year climate trend of Kedah – the state which has the largest cultivation area in Malaysia, resulting from climate changes. To obtain a better predictors set, multicorrelation matrix analysis was added in the climate model as a screening tool to explain the multiple correlation relationship among 26 predictors and 20 predictands. The performance of the predictor set was evaluated statistically in terms of mean absolute error, mean square error, and standard deviation. The simulation results depict the climatic changing trend in this region in terms of temperature, rainfall, and wet and dry length compared to historical data captured from 1961 to 2008. Annual temperature and rainfall depth are expected to increase 0.2 °C per decade and 0.9% per year, respectively, from the historical record. The months of November and January are expected to receive the highest and lowest rainfall depth, respectively, because of the two monsoon seasons. The wet spell is estimated to be from May to November in the middle of Kedah. The annual dry spell shall be from January to March, and is expected to shorten yearly.
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