Predicting Variables Climate, Temperature and Precipitation by Multiple Linear the Model SDSM (Case Study: Tehran Synoptic Station)

O   One criticism that has been leveled GCM models, large spatial scale is the simulated climate variables in which case for hydrological studies and water resources are not sufficiently accurate. So designing them through different methods of small scale. Then, using downscaled-exposed Output, these new models in hydrologic studies evaluated the effects of climate of the downscaling methods; statistical methods by the performance of most interest to hydrologists are quick and easy. The aim of this study was to evaluate the effect of climate change on a scale of SDSM station of the statistical model is used. The data used in this study included daily temperature and precipitation values synoptic station in Tehran, open data and analysis NCEP Output HadCM3 (data for third generation global climate model under A2 and B2) for the base period (1961-2001) respectively. Precipitation and temperature for three terms (2001-2031), (2032-2061) and (2062-2091) was predicted and compared with the base period. According to the statistical parameters, under the A2 and B2 scenarios for temperature and precipitation amounts, respectively: (RMSE = 1.48, MAE = 4.3, R2 = 0.99 and Nash = 0.86) and (RMSE =0.127, MAE =0.413, R2 =0.99 and Nash =0.99) was observed. The results showed that during 2031-2001, 2061-2032 and 2091-2062, the average rainfall respectively 0.1, 0.4 and 0.1 mm compared to the baseline decreased and the average temperature of respectively 4.9, 4.7 and 4.7 °C over the period in the study, increases base station.