فهرست مطالب zaniar fatehi

The GCM models are widely used to assess the effects of global climate change, but they are not accurate enough to assess climate change locally and regionally. In this study, using the statistical model of SDSM in the study area, the output of the CanESM2 model was downscaled and compared with data from Sanandaj synoptic station, which has long-term statistics. Then, considering the release scenarios of RCP2.6, RCP4.5 and RCP8.5 for the future periods of 2025-2050 and 2051-2075, the effect of climate change on the temperature of Sanandaj city was evaluated. The minimum and maximum daily temperatures for the base period of 1979-2005 were considered as the input of the model. The results of the SDSM model show that the average monthly minimum temperature drops below zero for the periods 2025-2050 and 2051-2075 and decreases under all three scenarios, and when the average air temperature is less than zero for both future periods, it increases under all three scenarios. The average maximum temperature for the next periods is 2025-2050 and 2051-2075 and under all three scenarios RCP2.6, RCP4.5, RCP8.5 increase and this increase is mostly related to the warm months of the year. According to the obtained results, Sanandaj city will have warmer days and colder nights in the future.

The Soil and Water Assessment Model (SWAT) is a continuous and semi-distributed model. The SWAT model is capable of being connected to the Geographic Information System (GIS). Using a range of information such as basin physical information (soil, land use and slope) as well as meteorological information such as rainfall, temperature, wind, relative humidity and solar radiation, this model can monitor hydrological processes in catchments on a daily time scale., Monthly and yearly. Simulation of hydrological processes in watersheds is essential for predicting future events in those basins and identifying ways to deal with them. In this study, using SWAT hydrological model, the runoff outflow of Santeh hydrometric station in the watershed of Lake Urmia in Kurdistan province in Iran with an area of ​​5306 km2. Runoff simulation was performed over a seven-year statistical period 2007-2013. The first five years of this period 2007-2011 for model calibration using 21 different parameters and the final two years of 2011 and 2012 for validation using two statistical indices (NSE) and coefficient of determination (R2). it placed. According to the simulated hydrograph and the monthly runoff observations, the calculated statistical indices of SWAT model in the calibration period and in the monthly time scale validation period have acceptable results in the runoff simulation, with coefficient values ​​(NSE) and (R2) in the calibration period were 0.78 and 0.82 respectively and for the validation period 0.9 and 0.91 respectively.

In this study, the effect of climate change on temperature and rainfall data in the Kermanshah Sanjabi basin was investigated. For this purpose, using climate and temperature data from 1979-2005 (observation period), the values ​​of these variables in 2041-2100 (forecast period) were predicted under three scenarios RCP8.5, RCP4.5 and RCP2.6 and CanESM2 atmospheric general circulation model. The SDSM model was used to fine-tune the survey data in the study area. The results showed that the mean annual precipitation, maximum and minimum annual temperatures in the squared Ravansar basin will increase in the forecast period of 2041-2100. The most predicted values ​​of these variables belong to the RCP8.5 scenario so that according to this scenario, the average increase in precipitation in December, in the forecast periods 2041-2070 and 2071 2100 is 153 and 159.6%, respectively, compared with the average rainfall in the observation period. Based on the results of this study, it is expected that in order to properly manage the water resources of the RWC due to the impact of climate change, a proper perspective on the future status of water resources in the watershed will be presented to water resource managers in the future.