An automated ststistical downscaling approach for hydrological based climate change in humid tropical area

Abstract

Many Climate Models have been developed to assist scientist to forecast climate changes. The Automated Statistical Downscaling (ASD) model is considered a new recent model to perform this task. The aim of this study is to explore the applications of ASD model in the projection of the future climate changes in humid tropical region. Global Climate Models (GCMs) provides climate impacts information from higher resolution, but there are many impacts require climate information of a smaller resolution. Downscaling is a method to link global scaling prediction to regional prediction. NCEP and CGCM3.1 predictors are used to link these two scales. Multi Linear Regression approach in the ASD model is applied to calibrate NCEP predictors with rainfall data for the period 1961 to 1975 and the results shows that the three stations were calibrated with low RMSE values. The results are validated by using CGCM3.1 and NCEP data for the period 1976 to 1990. The geographic location of the predictors has an influence on the number of predictors available, since the selected station is located within a grid box near the equator making it affected by coriolis effect, limited set of predictors are available. A2 and A1B SRES emission scenarios are used for future projection scenarios for the periods 2011-2040, 2041-2070, and 2071-2100. Five statistical indices that are used namely: mean rainfall, Standard Deviation, 90th percentile, wet days and Consecutive Dry days has been evaluated. A2 and A1B scenarios results shows that certain months will experience an increase in rainfall in terms of intensity and frequency while other months will experience a significant decline.