## 4 Results and Discussion  
Figure 4 shows a comparison of the simulated maximum and minimum temperatures versus the observed data. Temperatures are average values for the period 2046 to 2065. Notably, the observed and simulated data coincide, and Table 1 shows a statistical analysis of the simulated temperatures. Different indices in Table 1 reflect the high accuracy of the climate change model in simulating temperatures. Figure 5 shows the simulated temperatures for the future period (2046-2065). Temperatures for the coming period were compared to those for the base period (1980-1999). The average minimum  
temperature in the winter season increased by 33% compared to that in the base period. Additionally, the average minimum temperature in the spring season increased by 20% compared to that in the same season in the base period. Moreover, the average minimum temperatures in summer and autumn increased by 4% and 9.64%, respectively. Thus, the minimum temperature increased overall. The average maximum temperature increased by 14% for the winter season and by 17% for the spring season. Additionally, the average maximum temperatures in the summer and autumn seasons increased by 5% and 1.6%, respectively. Thus, the maximum temperature increased overall for the future period. Figure 6 shows the simulated values of precipitation. Results highlighted the accuracy of the proposed climate change model because the observed and simulated data coincided. Also, the R\({}^{2}\), RMSE and MBE values of the climate change model were 0.97, 4, and 3 mm, respectively, for the precipitation prediction. These results reflected the high accuracy of the applied model. Figure 7 shows simulated precipitation for the future period. Average precipitation totals in the winter and spring seasons decreased by 3.6% and 0.75%, respectively, compared to those for the base period. Moreover, the average precipitation totals in summer and autumn decreased by 3.7% and 1.6%, respectively. Thus, the precipitation totals decreased overall for the future period  
Figure 5: Prediction of temperatures for the future period (2046–2065). Figure 6: Simulated precipitation for the base period (1980–1999). Figure 7: Simulated precipitation for the future period (2046–2065). The total period of the data set that has been used to examine the IHACRES model was 20 years monthly (\(12\times 20=240\) records). The IHACRES model has a few parameters that needed to be tuned to achieve accurate prediction for the runoff value (reservoir's inflow). In order to identify the correct values for these IHACRES model's parameters, there was a need to calibrate and validate the model. Therefore, the collected data set was split into different data sets; 156 records were used to calibrate the model and achieve the correct value of the IHACRES model's parameters. Then, using the calibrated IHACRES model to predict runoff using the unseen 84 records to validate the achieved accuracy. The primary purpose of this step was to assure that when the model will be used for future records (downscaling records in future period), the accuracy of the runoff (reservoir's inflow) would be near to the real value. Figure 8 illustrates the performance of the IHACRES model in simulating runoff volume. A total of 156 periods were considered in the calibration of this model, and 84 periods were used for verification. Figure 9 shows the predicted runoff volumes for the base and future periods. Runoff volumes in the winter and spring seasons decreased by 4% and 23%, respectively, compared to those in the base period. In addition, runoff volumes in summer and autumn decreased by 4.7% and 3.3%, respectively, compared to those in the base period. Figure 8: Simulated runoffs for (**a**) calibration; (**b**) verification. Table 2 shows a statistical analysis of simulated runoff. Since the coefficient of determination for the runoff volume was 0.96 in the calibration stage, IHACRES was shown to be effectively applied for runoff simulation. Additionally, the RMSE and MBE values were negligible.