جستجوی مقالات مرتبط با کلیدواژه « ریزمقیاس آماری » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

This study presents the predicted amount of precipitation, temperature and the climate simulation periods 2099-2070, 2049-2020 and 2008-1979 observation period under A2 and B2 SRES scenarios using the statistical downscaling model (SDSM) in Tehran, Zanjan, Qazvin and Rasht stations on the southern side and Ramsar, Babol and Gorgan in Northern side of the Alborz. Also, future climate was determined using the Domarten method. The main objective was to compare the climate change on two fronts in the north and south of the Alborz. The performance of model to predict the climatic parameters was evaluated based on the coefficient of determination (R2 ) and root mean squared estimation error (RMSE). The results of predicting the climate parameters indicate that the model simulated these parameters adequately. The results of the precipitation in all stations in period 2020- 2049 compared with the period 1979-2008 show an increasing trend in all stations and the period 2070-2099 compared to observation periods show an increasing trend in precipitation; while it has a decreasing trend compared to the periods 2020-2049. In 2070-2099 and 2020–2049 periods, the average, minimum and maximum temperature was increased relative to the observation period 1979-2008. The results of the climate determination by the Domarten method show that the climate in the Babolsar, Qazvin, Ramsar and Rasht stations will be change comparing with the observed climate in 1979-2008 in the future periods. In the Gorgan station at period 2070-2099 under the A2 scenario, the climate will change from semi-arid to the arid climate. In the Zanjan station at period 2020-2049 under the A2 scenario, the climate will change from semi-arid to Mediterranean climate and at period 2070-2099 climate from Mediterranean to semi-arid climate. This data can be simulated with high accuracy to better foresight the climatic conditions in future periods to help the future macro-management in providing better productivity of resources, particularly the water resources management.