Abstract
Climate change is considered to be one of the biggest threats faced by nature and humanity today. The goal of this study is to predict future climate change for rainfall in the Upper Kurau Basin. In this research, the applicability of statistical downscaling model (SDSM) in downscaling rainfall in the Upper Kurau River basin, Perak, Malaysia was investigated. The investigation includes calibration of the SDSM model by using large-scale atmospheric variables encompassing the National Centers for Environmental Prediction (NCEP) reanalysis data. Rainfall data were derived for three 30-year time slices, 2020s, 2050s and 2080s, with A2 and B2 scenarios. A2 is considered among the “worst” case scenarios, projecting high emissions for the future. Unlikely, B2 projected a lower emission for the future and it is considered as “environmental” case scenarios. Results from simulation showed that during the calibration and validation stage, the SDSM model was well acceptable in regards to its performance in downscaling of daily and annual rainfalls. Under both scenarios A2 and B2, during the prediction period of 2010–2099, changes of annual mean rainfall in the Upper Kurau River basin would present a trend of increased rainfall in 2020s; insignificant changes in the 2050s; and a surplus of rainfall in the 2080s, as compared to the mean values of the base period. Annual mean rainfall would increase by about 33.7% under scenario A2 and increase by 27.9% under scenario B2 in the 2080s. Most of the areas of the Upper Kurau River Basin were dominated by increasing trend of rainfall and will become wetter in the future.
Highlights
Global warming will have a significant impact on local and regional precipitation and hydrological regimes, which in turn will affect ecological, social and economic systems of human, such as health of ecosystems and fish resource management, industrial and agricultural water supply, resident living water supply, water energy exploitation, human health, etc
The downscale daily rainfall simulated by statistical downscaling model (SDSM) at Stations 5, 6, 7 and 8 as tabulated in Table 5, gives a higher value for R2 compared to other stations during calibration with 0.24, 0.30, 0.20 and 0.23 respectively
The result shows that the daily rainfall series simulated from National Centers for Environmental Prediction (NCEP) with the mean R2 values is less than 0.3, which is comparable with literature values [22]
Summary
Global warming will have a significant impact on local and regional precipitation and hydrological regimes, which in turn will affect ecological, social and economic systems of human, such as health of ecosystems and fish resource management, industrial and agricultural water supply, resident living water supply, water energy exploitation, human health, etc. These potential changes will affect some qualitative and quantitative estimation on the impact of climate change upon regional water resources [1]. Compared to other downscaling methods, the statistical method is relatively feasible to be used as it provides station-scale climate
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