Increasing greenhouse gas concentrations can cause future changes in the climate system that have a major impact on the hydrologic cycle. To realize and predict future climate parameters, the Atmosphere-Ocean Global Climate Models (AOGCMs) are common employed tools to predict the future changes in climate parameters. The statistical downscaling methods have been applied as a practical tool to bridge the spatial difference between grid-box scale and sub-grid box scale. This paper investigates the capability of Statistical Downscaling Model (SDSM) and Artificial Neural Network (ANN) with different complexities in downscaling and projecting climate variables in the tropical Langat River Basin. These two statistical downscaling models have been calibrated, validated and used to project the possible future scenarios (2030s and 2080s) of meteorological variables, which are the maximum and minimum temperatures as well as precipitation using the CGCM3.1 under A2 emission scenario. The statistical validation of generated precipitation as well as maximum and minimum temperatures on a daily scale illustrated that the SDSM is more accurate than the ANN with different learning rules. On the other hand, the SDSM showed more capability to catch the wet-spell and dry-spell lengths than the ANN model. The calibrated models show higher accuracy in simulating the maximum and minimum temperatures in comparison with the capture of the variability of precipitation. The trend analysis test of generated time series by the SDSM indicates an increasing trend by the 2030s and 2080s at most of the stations.
Key words: Statistical downscaling, multiple linear regression, nonlinear regression, artificial neural network, tropical area, Malaysia.
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