Aims: The aim of this study was to the prediction and analysis of temporal pattern changes of runoff, maximum discharge, and Drought indexes in the Tehran-Karaj basin.
Materials & Methods: In this study, the temperature and precipitation data extracted from Statistical Downscaling Model (SDSM; 2021-2050 and 2051-2080) together with observational runoff data of the Sulghan hydrometric station (1986-2015) were used as input data for IHACRES rainfall-runoff model and discharge rate, runoff volume, and maximum discharge were extracted in the desired scales. Then, drought indexes (SPEI and SRI) were investigated.
Findings: In the period of 2021-2050 and 2051-2080, the mean of annual discharge, volume of runoff and annual precipitation will be decreased. While seasonal runoff, discharge, and precipitation will rise in the winter. Moreover, the maximum predicted discharge (In most scenarios) in the return periods less than 5 and more than 50 years is less than the observation period and in the Return Periods of 5 to 50 years it will be more than the observation period. Besides, 48-month SPEI with 48-month SRI (without delay) has a maximum correlation with each other at the level of 99%.
Conclusion: In the winter season and return periods of 5 to 50 years, the floods hazards and Rivers overflow in the Future periods (2021-2080) will be more than the observation period. Also, meteorological droughts often have their effect on the drought of surface waters during the same month.
 

Aims: Trend analysis of climatic variables has got a great deal of notice from researchers recently. This study aimed to investigate the Spatio-temporal variability of extreme temperature indices based on the station data and gridded dataset analyses over the Bakhtegan-Maharloo basin in Iran from 1980 to 2010.
Materials & Methods: Climatic data related to the Bakhtegan-Maharloo basin was extracted from AgMERRA dataset for the study period (1980-2010) using R software. Daily temperature data were also extracted from the Meteorological Archive of meteorological stations located in the basin during the study period. Warm nights (TN90p), maximum monthly value of daily minimum temperature (TNx), cold nights (TN10p), and cold spell duration indicator (CSDI) indices had been chosen from the indices recommended by the Expert Team for Climate Change Detection Monitoring and Indices (ETCCDMI) and calculated by RClimDex software package.
Findings: The results of AgMERRA and stations data revealed an increasing trend in warm extremes including TN90p and TNx with the trend changes ranging from 0.135 to 0.721 and 0.061 to 0.139, respectively, but a declining trend in cold extremes including TN10p and CSDI with the trend changes ranging from -0.517 to -0.125 and -0.987 to -0.167, respectively.
Conclusion: The results of this study may contribute to a better understanding of regional temperature behavior in the study area. The results indicated that the frequency and intensity of cold extremes have declined, though warm extremes increased. Due to the intensive impacts of temperature extremes on human life, it is essential to speculate the effects of these extreme climatic events in future plannings in various sections.