عنوان مقاله [English]
نویسندگان [English]چکیده [English]
To cope with the current water resources issues in Iran which are going to pose a real threat on a national scale, taking into consideration of all determining factors causing these formidable water resources challenges is of paramount importance. Computer modelling has been increasingly developed over the last few decades for water resources management and planning. In the present study, climate variables included precipitation, relative humidity and temperature were predicted for the period of 2020-2049, using SDSM model. Then, impacts of climate change on hydrological conditions were evaluated via Soil and Water Assessment Tool (SWAT) in the Talvar river watershed located in the Kurdistan province, Iran.
The Talvar river watershed with an area of 2490 km2 is situated in longitudes of 47° 06' 09" E to 47° 45' 58" E and latitudes of 35° 03' 26" N to 35° 35' 26" N, located in Kurdistan province. Land use in this basin is mostly cropland and pasture. Cropland has accounted as approximately 85% of the total area, among which paddy fields and dry land farming account for 10% and 75%, respectively. Pasture cover has appraised as 14% of the study area. All other land use types (rural area, urban area, water) have made only 1% of the total study area. Mean elevation of the watershed is 1927 m above mean sea level. The SWAT model requires input on topography, soils, land use and meteorological data. Therefore, recently available GIS maps for the model inputs of the study area were used. The Talvar river watershed was discretized into 50 sub-basins and also, based on the land use, slope and soil classes the watershed was subdivided into 1151 HRUs. The climatic data were derived from 7 meteorological stations located in and out of the basin under study. Climatic data refer to daily precipitation, maximum and minimum temperature, relative humidity data. The calibration of the SWAT model was done manually based on physical catchment understanding and sensitive parameters and calibration techniques from the SWAT user manual. Sensitivity analysis has been performed using OAT (One Factor at a Time) method to evaluate and demonstrate the influences of the model parameters on water budget components included surface runoff, lateral flow, groundwater and evapotranspiration. Data from Qorveh synoptic station (1990-1999) were used for calibration of SDSM model. Climate variable include precipitation, relative humidity and temperature were predicted for the period of 2020-2049 using SDSM model. Simulated values due to considered scenarios (RCP26, RCP45 and RCP85) were compared with baseline period (1990-2005). The performance of the SWAT model was evaluated via coefﬁcient of determination (R2) and Nash–Sutcliffe efﬁciency (ENS), also the performance of the SDSM model was evaluated via coefﬁcient of determination (R2), Nash–Sutcliffe efﬁciency (ENS), Mean Absolute Error (MAE) and Percent Bias (PBIAS).
Based on the results of sensitivity analysis, the parameters of initial SCS runoff curve number for moisture condition, the parameters that hadthe greatest influence on water budget components (including surface runoff, lateral flow, groundwater and evapotranspiration) can be listed as :.Π (CN2), soil available water capacity (SOL_AWC), soil bulk density (SOL_BD), saturated hydraulic conductivity (SOL_K), maximum canopy storage (CANMX), soil evaporation compensation factor (ESCO), minimum melt rate for snow during the year (SMFMN), maximum melt rate for snow during the year (SMFMX), snowfall temperature (SFTMP) and snow melt base temperature (SMTMP) According to the results, a satisfactory agreement was observed between monthly simulated and measurement discharge (R2 and ENS were 0.65 and 0.44 for calibration and 0.77 and 0.59 for validation periods). The results of the SDSM model showed that the monthly mean of minimum and maximum temperatures would increase compared to the baseline period except for the months of September, October, November and December. Also monthly average of precipitation would decrease in winter and spring seasons but it would increase in the summer and autumn seasons. The results of runoff simulation showed that monthly average of runoff would increase in the months of January, February and December, compared with the baseline period. The weakness of the model to simulate flow for some months was probably due to poor characterization of snowmelt processes in the basin under study. Also, the model overestimats surface water in the beginig of summer, due to its defaults for transfer in layers.