Performance Evaluation of Statistical Downscaling Model (SDSM) in Forecasting Precipitation in two Arid and Hyper arid Regions

One of the most important problems in the management and planning of water resources is to forecast long-term precipitation in arid region and hyper arid regions. In this study, statistical downscaling model (SDSM) is used for study of climate change effects on precipitation. The data used as input to the Model are daily precipitation of Kerman and Bam synoptic stations, NCEP (National Centers for Environmental Prediction) data and the A2 and B2 emission scenarios HadCM3 for the reference period (1971-2001). Using HadCM3 A2, B2 data the precipitation for three period (2010-2039), (2040-2069) and (2070-2099) are predicted and compared with the reference period. We used the first 15 years data (1971-1985) for the calibration and the second 15 years data (1986-2001) for model validation. Research results showed that the precipitation will change and Change directions are positive in some months and negative in other months. After the examination function Indexes results from SDSM model shown that this model has better accuracy and a high ability to predict precipitation in arid region than hyper arid region.