The aim of this study is to select the best model (combination of different lag times) for predicting the standardized precipitation index (SPI) and the standardized precipitation and evapotranspiration index (SPEI) in next time. Monthly precipitation and temperature data from 1960 to 2019 were used. In temperate climates, such as the north of Iran, the correlation coefficients of SPI and SPEI were 0.94, 0.95, and 0.81 at the time scales of 3, 12, and 48 months, respectively. Besides, this correlation coefficient was 0.47, 0.35, and 0.44 in arid and hot climates, such as the southwest of Iran because potential evapotranspiration (PET) depends on temperature more than rainfall. Drought was predicted using the random forest (RF) model and applying 1–12 months lag times for next time. By increasing the time scale, the prediction accuracy of SPI and SPEI will improve. The ability of SPEI is more than SPI for drought prediction, because the overall accuracy (OA) of prediction will increase, and the errors (i.e., overestimate (OE) and underestimate (UE)) will reduce. It is recommended for future studies (1) using wavelet analysis for improving accuracy of predictions and (2) using the Penman–Monteith method if ground-based data are available.

This study evaluates the impact of climate change (CC) on runoff and hydrological drought trends in the Hablehroud river basin in central Iran. We used a daily time series of minimum temperature (Tmin), maximum temperature (Tmax), and precipitation (PCP) for the baseline period (1982–2005) analysis. For future projections, we used the output of 23 CMIP5 GCMs and two scenarios, RCP 4.5 and RCP 8.5; then, PCP, Tmin, and Tmax were projected in the future period (2025–2048). The GCMs were weighed based on the K-nearest neighbors algorithm. The results indicated a rising temperature in all months and increasing PCP in most months throughout the Hablehroud river basin's areas for the future period. The highest increase in the Tmin and Tmax in the south of the river basin under the RCP 8.5 scenario, respectively, was 1.87 °C and 1.80 °C. Furthermore, the highest reduction in the PCP was 54.88% in August under the RCP 4.5 scenario. The river flow was simulated by the IHACRES rainfall-runoff model. The annual runoff under the scenarios RCP 4.5 and RCP 8.5 declined by 11.44% and 13.13%, respectively. The basin runoff had a downward trend at the baseline period; however, it will have a downward trend in the RCP 4.5 scenario and an upward trend in the RCP 8.5 scenario for the future period. This study also analyzed drought by calculating the streamflow drought index for different time scales. Overall, the Hablehroud river basin will face short-term and medium-term hydrological drought in the future period.

The purpose of this study was to investigate the impact of climate change on the water level and shrinkage of Lake Urmia. To achieve this, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm was used to select the top 10 general circulation models (GCMs) among 23 CMIP5 GCMs in the baseline period (1951–2005). Based on the K-nearest neighbors (KNN) method, 10 GCMs were combined and their uncertainties were quantified. Also, the future period (2028–2079) data were generated by using the LARS-WG model. According to the results, the temperature increased in all seasons of the future period. Under the RCP4.5 scenario, the precipitation decreases by 10.4 and 27.8% in spring and autumn, respectively, while it increases by 18.2 and 3.4% in summer and winter, respectively. Moreover, the RCP8.5 scenario lowers the precipitation by 11.4, 22.7, and 4.8% in spring, autumn, and winter, respectively, while it rises by 26.5% in summer. Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were used to calculate the short-, medium- and long-term meteorological droughts of the baseline and future periods. The occurrence number and peaks of droughts increase, while their durations decrease, in the future period. In general, the SPEI has a robust relationship than the SPI with changes in the water level of Lake Urmia.

In this study, five hydrological models, including the soil and water assessment tool (SWAT), identification of unit hydrograph and component flows from rainfall, evapotranspiration, and streamflow (IHACRES), Hydrologiska Byråns Vattenbalansavdelning (HBV), Australian water balance model (AWBM), and Soil Moisture Accounting (SMA), were used to simulate the flow of the Hablehroud River, north-central Iran. All the models were validated based on the root mean square error (RMSE), coefficient of determination (R2), Nash-Sutcliffe model efficiency coefficient (NS), and Kling-Gupta efficiency (KGE). It was found that SWAT, IHACRES, and HBV had satisfactory results in the calibration phase. However, only the SWAT model had good performance in the validation phase and outperformed the other models. It was also observed that peak flows were generally underestimated by the models. The sensitivity analysis results of the model parameters were also evaluated. A hybrid model was developed using gene expression programming (GEP). According to the error measures, the ensemble model had the best performance in both calibration (NS = 0.79) and validation (NS = 0.56). The GEP combination method can combine model outputs from less accurate individual models and produce a superior river flow estimate.

The present study aims to investigate the homogeneity of runoff time series and also to review the existence of trends in Tale Zang hydrometric station (the hydrometric station at the inflow into the Dez reservoir) runoff, using 61 years (1956–2016) daily recorded data. The Pettit test, which is a common method in investigating the homogeneity of time series, was used to identify change points. Both Mann-Kendall and auto-correlated Mann-Kendall tests were applied to analyze the existence or non-existence of trends in each annual, seasonal, and monthly time series observed in a runoff. In time series, significant trends at the 95% level of confidence were recognized, upper and lower limit values were presented for Sen's slope and it was tested for the increasing or decreasing trends in nature. Based on the results of this study, the significant change point at the 95% level of confidence was recognized in the annual, spring, summer, autumn, March, May, June, July, August, September, and October data sets in 1997,1997, 1999, 1997, 1999, 1999, 1999, 1997, 2000, 2007 and 2008, respectively. Analyzing the existence of a trend at the 95% level of confidence indicated that in spring, and in March, September, and October, for both Mann-Kendall and auto-correlated Mann-Kendall tests, the trend is significant and additive until the change point.