The establishment of stability in rivers is dependent on a variety of factors, and yet the established stability can be interrupted at any moment or time. One factor that can strongly disrupt the stability of rivers is the construction of dams. For this study, the identification and evaluation of morphological changes occurring to the Karkheh River, before and after the construction of the Karkheh Dam, along with determining the degree of changes to the width and length of the downstream meanders of the river, have been performed with the assistance of satellite images and by applying the CCHE2D hydrodynamic model. Results show that under natural circumstances the width of the riverbed increases downstream parallel to the decrease in the slope angle of the river. The average width of the river was reduced from 273 meters to 60 meters after dam construction. This 78% decrease in river width has made available 21 hectares of land across the river bank per kilometer length of the river. In the studied area, the average thalweg migration of the river is approximately 340 meters, while the minimum and maximum of river migration measured 53 and 768 meters, respectively. Evaluations reveal that nearly 56% of the migrations pertain to the western side of the river, while over 59% of these migrations take place outside the previous riverbed. By average, each year, the lateral migration rate of the river is 34 meters in the studied area which signifies the relevant instability of the region.

Use of general circulation models (GCMs) is common for forecasting of hydrometric and meteorological parameters, but the uncertainty of these models is high. This study developed a new approach for calculation of suspended sediment load (SSL) using historical flow discharge data and SSL data of the Idanak hydrometric station on the Marun River (in the southwest of Iran) from 1968 to 2014. This approach derived sediment rating relation by observed data and determined trend of flow discharge time series data by Mann-Kendall nonparametric trend (MK) test and Theil-Sen approach (TSA). Then, the SSL was calculated for future period based on forecasted flow discharge data by TSA. Also, one hundred annual and monthly flow discharge time series data (for the duration of 40 years) were generated by the Markov chain and the Monte Carlo (MC) methods and it calculated 90% of total prediction uncertainty bounds for flow discharge time series data by Latin Hypercube Sampling (LHS) on Monte Carlo (MC). It is observed that flow discharge and SSL will increase in summer and will reduce in spring. Also, The annual amount of SSL will reduce from 2,811.15 Ton/day to 1,341.25 and 962.05 Ton/day in the near and far future, respectively.

The observed radar reflectivity (Z) converts to rainfall intensity (R) by a transfer function. In the first stage, for calibration of collected data (with time step 15 minutes) by weather radar and determination of the best relation between Z and R, it applied a genetic algorithm (GA) to minimize the amount of root mean square error (RMSE). Although Z = 166R2 (the transfer function in the Khuzestan province of Iran) is an appropriate equation, the GA method distinguished that Z = 110R1.8 (from February to May) and Z = 126R2 (for other months) are the optimum transfer functions for the Abolabbas watershed in Iran. The mean of RMSE of optimum transfer equations is 0.59 mm/hr in the calibration stage and 0.85 mm/hr in the verification stage. In the second stage, the Hydrologic Modeling System (HEC-HMS model) used four types of precipitation data (extracted rainfall data from radar and the optimum transfer equations, Z = 166R2, Z = 200R1.6 and extracted rainfall data from rainfall gauging stations). The calibrated rainfall data by the optimum transfer equations can produce flood hydrographs in which their accuracy is similar to the accuracy of generated flood hydrographs by collected rainfall data of rainfall gauging stations. The mean of RMSE is 0.65 cubic metres per second and the mean or R2 is 0.89 for optimum transfer equations.

This study applies three methods, Gene Expression Programming (GEP), M5 tree (M5T) model and optimized Artificial Neural Network by Genetic Algorithm (ANN-GA) for estimation of reference evapotranspiration in Ahvaz and Dezful in the southwest of Iran. Comparison between results of the FAO Penman-Monteith (FPM) method and the mentioned three methods shows that ANN-GA with the Levenberg-Marquardt training method is the best method and the M5T model is the second appropriate method for estimation of reference evapotranspiration. In Ahvaz, R2 and RMSE of ANN-GA method are 0.996, 0.184 mm/day. For M5T method, these values are 0.997 and 0259 mm/day, and for GEP method, they are 0.979 and 0.521 mm/day. In Dezful, R2 and RMSE of ANN-GA method are 0.994, 0.235 mm/day. For M5T method, these values are 0.992 and 0265 mm/day, and for GEP method, they are 0.963 and 0.544 mm/day. In addition, sensitivity analysis shows that the maximum temperature is the most effective parameter, and the wind speed is second effective parameter. In Dezful, the effect of the maximum temperature is more than those of Ahvaz but the effect of wind speed is less than those of Ahvaz. Because Ahvaz is more flatter than Dezful (the movement of wind in Ahvaz is freer than those of Dezful). The third effective meteorological parameter is the average relative humidity in Ahvaz and the sunny hours in Dezful. The reason for this subject is the less distant of Ahvaz from the Persian Gulf (it is source of moisture).

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