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 paper introduces an improved bat algorithm (IBA) with a hybrid mutation strategy to improve its global search ability. In an effort to guide the evolution and reinforce the convergence efficiently, the spatial characteristics of the social and cognitive experience of each bat in the population with the differential evolution (DE) algorithm were developed. More specifically, it has been employed in original bat algorithm (BA) six DE mutation mechanisms, namely the explorative and the exploitative mutation operators. The mutation plays an important role to avoid trapping in a local optimal solution, to ensure the search efficiency of a near global optimal solution, and to increase diversity of population. Also, five unimodal and multimodal benchmark functions were used to test the performance of IBA. The results show that the new bat algorithm performs better than the original bat algorithms for each of the test functions. In addition, IBA could keep the diversity of bats and have a better glob...

Development of cities, destruction of forests and pastures, population growth and other factors have increased suspended sediment load in rivers of developing countries. Measurement of suspended sediment load is a challenging issue for engineers at flood conditions. The case study of this research is Idenak hydrometric station on the Marun River in the south west of Iran. The used data consists of 42 years (1968 to 2009) flood discharge. A perceptron artificial neural network is trained and validated by observed data. For training of Artificial Neural Network (ANN), momentum and Levenberg-Marquardt training methods are applied. For decreasing of Normalized Mean Square Error (NMSE) and increasing of correlation coefficient (R), parameters of ANN are optimized by Genetic Algorithm (GA) method. GA method optimizes the number of nodes of hidden layers of ANN that is trained by Levenberg-Marquardt training method while it optimizes the number of nodes and momentum of ANN that is trained by momentum training method. GA method can reduce NMSE to 80% while GA method doesn’t increase R significantly. In order to predict flood condition, the Markov chain method is applied. The results show that suspended sediment load may be increased almost from 400000 ton/day to 800000 ton/day at future.

In recent years, greenhouse gas emissions have caused extensive changes in the global climate. Climate change leads to extreme events, such as droughts. The present study investigates precipitation and temperature variations and the past and future drought characteristics in Iran through data from 40 synoptic stations and 33 general circulation models (GCMs) under RCP4.5 and RCP8.5 scenarios. As a first step, the country of Iran was classified into different climatic regions based on De Martonne aridity index. The GCMs were ranked using TOPSIS in four climatic regions and an ensemble of top ten GCMs was used in each region. Furthermore, the homogeneity of monthly precipitation was studied in the baseline and future periods. Meteorological drought was calculated through the standardized precipitation index (SPI), deriving drought severity, peak, and duration based on run theory. The results revealed that precipitation will reduce in future periods in the majority of Iran and temperature will reduce in the south and southeast and will increase in the northwest and north of Iran. Furthermore, the highest drought severity and peaks will occur in semi-arid and arid regions, while the longest drought duration will happen in the southeast and west of Iran. Overall, future droughts are found to have higher severity, duration, peaks, and standard deviation than the baseline period. Also, the results showed a reducing trend of the SPI values in northwestern regions, while the other stations indicated no significant trend.

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 reservoir hedging rule curves are used to avoid severe water shortage during drought periods. In this method reservoir storage is divided into several zones, wherein the rationing factors are changed immediately when water storage level moves from one zone to another. In the present study, a hedging rule with fuzzy rationing factors was applied for creating a transition zone in up and down each rule curve, and then the rationing factor will be changed in this zone gradually. For this propose, a monthly simulation model was developed and linked to the non-dominated sorting genetic algorithm for calculation of the modified shortage index of two objective functions involving water supply of minimum flow and agriculture demands in a long-term simulation period. Zohre multi-reservoir system in south Iran has been considered as a case study. The results of the proposed hedging rule have improved the long-term system performance from 10 till 27 percent in comparison with the simple hedging rule, where these results demonstrate that the fuzzification of hedging factors increase the applicability and the efficiency of the new hedging rule in comparison to the conventional rule curve for mitigating the water shortage problem.