عنوان مقاله [English]
نویسندگان [English]چکیده [English]
The results of most climate models, under diffusion scenarios (RCPs), indicate an increase in global temperature by the end of the 21st century, compared with the period 1850-1900 (IPCC, 2014). Considering the perceptible effects of climate change on meteorological parameters and then water resources, simulation and prediction of runoff in watersheds is extremely important. When it is necessary to simulate only the flow at the outlet of the watershed, the conceptual rainfall-runoff models (such as the IHACRES model) are preferred; because, they require less computational effort and input data, as well as provide a good response. A review of several studies on the effects of climate change on different systems in future periods shows that although many sources of uncertainty affect the final results, usually these sources of uncertainty are ignored in the calculations, which reduces the accuracy of the results. In some studies, despite the attempt to consider the uncertainty of AOGCM models in the calculations, all the AOGCM models have been applied with the same weight. Also, in most studies on climate change, limited types (less than 10) of climate models has been used. Therefore in the present study, 23 climate models were used to predict future climatic conditions, in order to reduce the uncertainty of these models. The superior model was selected using the weighting method. Then, using the IHACRES model, the effects of climate change on the Shirin (Azam Jareh) river runoff, with emphasize on the uncertainty of AOGCM models was studied in the forthcoming period of 2020-2040.
Shirin River (Azam Jareh) is one of the main tributaries of Dalaki River, which originates about 70 km from the southern slopes of Arjan Plain and its tributaries include Sarkhoon and Tang-E- Gachi. The average long-term flow of Shirin River (Azam Jareh) is 11.3 m3/s and the average annual precipitation is 761 mm. In order to quantify the uncertainty in this study, the output of 23 AOGCM models was evaluated, using weighting method and calculation of performance criteria. The best selected models were GFDL-ESM2G, GISS-E2-R, MPI-ESM-LR, MPI-ESM-MR and MRI-CGCM3. Then, their outputs were obtained under three scenarios of RCP2.6, RCP4.5 and RCP8.5. According to the IPCC recommendation, the historical period of 1975 to 2005 was used to calibrate GCM models. In order to mitigate the uncertainty in estimating, the changes of climate variables due to the climate change were weighed by the Mean Observed Temperature Precipitation (MOTP) method, developed by Massah Bovani (2006). Then, the performance criteria were calculated. In the MOTP weighting method, AOGCM models are weighted based on the deviation of mean simulated temperature or rainfall in the base period from the average of the observed data. In this study, the precipitation was used to investigate the uncertainty, due to its importance and effect on runoff. The inputs of LARS-WG model were the time series of observational data of temperature and precipitation of the Shiraz station, along with the output of climate models for every month. With the help of this generator, a 30-year time series of temperature and precipitation data was generated for this station. After this stage, it was possible to use the output of climate change models to predict the runoff in Shirin River Basin (Azam Jareh), using the IHACRES rainfall-runoff model. In order to reduce the uncertainty in AOGCM models, weights were calculated to observe the accuracy of the models in estimating the precipitation parameter. Also, the performance criteria were calculated in order to evaluate the performance of the models. The results showed that different GCM models had different accuracy in predicting precipitation. The MPI-ESM-MR and MIROC-ESM-CHEM models with the weight of 0.166 and 0.010 had the highest and lowest accuracy in estimating precipitation, respectively. Comparing the evaluated performance criteria of each model with the observed data of corresponding precipitation values of the same month, it can be concluded that the use of several different models reduced uncertainty and significantly increased the accuracy of climate forecasts. Comparison of the results of climate change forecast in the future period of 2020-2040 with the base period indicated an increase in temperature and a decrease in precipitation. Performance evaluation of IHACRES model showed that the coefficient of determination (R2) for the calibration and validation period were 0.74 and 0.69, respectively. The corresponding Nash-Sutcliffe coefficients were 0.7 and 0.52, respectively. These results indicated the acceptable performance of the model in rainfall-runoff simulation. Based on the simulations performed, the future outlook of mean annual Shirin River (Azam Jareh) runoff in the period of 2020-2040 indicates a decrease in runoff under all three RCP scenarios. The minimum in runoff was due to the RCP2.6 scenario with 682 mm3 (much less than 3000 mm3 of the base period). Also, the results of the average monthly runoff during the period 2020-2040, compared with the base period, showed a decrease of runoff in the most of months (except May, June, July and August). The highest forecasted runoff reduction, under all three scenarios were belong to January, February, March, April, September, November and October. In general, it can be concluded that climate change would reduce the runoff volume of Shirin River (Azam Jareh) during the future period 2020-2040.