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In this research, a number of paired three-dimensional Atmosphere-Ocean General Circulation Models (AOGCM) from CMIP (Climate Model Inter Comparison Project) 5 group with the base period of 1989–2005 have been evaluated and the output of these models was micro-scaled and calibrated by LARS-WG software. The appropriate model was selected to simulate temperature and rainfall data under the emission scenarios of RCP (Representative Concentration Pathway) 2.6, RCP4.5 and RCP8.5 for the future period of 2020–2050, and then to model the groundwater level of the region, GMS software for both stable and transient states for one water year was calibrated and then was validated by observation data. The results in the future periods showed an increase of 1–1.5 degrees in temperature and an increase in rainfall in the early months of the year to late spring season and a decrease in rainfall in autumn season. Generally, the RCP4.5 scenario showed slightly more annual rainfall increase over the next 30 years compared to the base period than the other two scenarios. The time series investigation of the average of groundwater level shows that the implementation of RCP 2.6, RCP 4.5 and RCP 8.5 scenarios respectively leads to an average monthly increase of 4.2, 4.3 and 4.6 cm of the groundwater level.

In the present study, the optimal place to excavate extraction wells as the drawdown gets minimized was investigated in a real aquifer. Meshless local Petrov-Galerkin (MLPG) method is used as the simulation method. The closeness of its results to the observational data compared to the finite difference solution showed the higher accuracy of this method as the RMSE for MLPG is 0.757 m while this value for finite difference equaled to 1.197 m. Particle warm algorithm is used as the optimization model. The objective function defined as the summation of the absolute values of difference between the groundwater level before abstraction and the groundwater level after abstraction from wells. In Birjand aquifer which is investigated in transient state, the value of objective function before applying the optimization model was 2.808 m, while in the optimal condition, reached to 1.329 m (47% reduction in drawdown). This fact was investigated and observed in three piezometers. In the first piezometer, the drawdown before and after model enforcement was 0.007 m and 0.003 m, respectively. This reduction occurred in other piezometers as well.

In the present research the aim was to prepare a spatial and temporal optimization model for allocating irrigation water and cropping pattern in the Maroon irrigation and drainage networks, which are located in the province of Khoozestan, under uncertainty. Hydrometrical data were gathered from the Maroon network station. Meteorological data were prepared from Idenak station in Behbahan City during 2006-2016. Therefore a model was designed and developed to maximize the total gross benefit of the irrigation networks of Maroon. The presented model is capable of adjusting the optimal water distribution among networks, crops and their different growing stages, determining water shortage, allocating surplus water, and the gross benefit under three scenarios of arid, normal and wet years in two sub-models of actual intra-network optimal management and optimal management from the reservoir output to the inside network by applying multi-stage stochastic programming under uncertainty. The findings show the priority of the second sub-model over the first run. In the upper and lower bounds model it was illustrated that the cropping areas were increased by respectively 33 and 19%, and of course the benefit amount had an increase of 67 and 7% in the second sub-model. K E Y W O R D S irrigation water allocation, multi-stage stochastic programming, spatial and temporal optimization, uncertainty Résumé Dans la présente recherche, l'objectif était de préparer un modèle d'optimisation spatiale et temporelle pour l'allocation de l'eau d'irrigation et le modèle de culture dans les réseaux d'irrigation et de drainage de Maroon, qui sont situés dans la province du Khoozestan, sous incertitude. Des données * Gestion optimale de l'allocation et de la distribution de l'eau dans les réseaux d'irrigation en cas d'incertitude par la méthode stochastique à plusieurs niveaux. Étude de cas: réseaux d'irrigation et de drainage de Marun.

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In this study, we have first studied the trend in meteorological data from the Harmaleh, Vanai and Farsesh stations in the 50-year period in the Dez catchment area. The meteorological data will be then forecasted using SWAT and Mann-Kendall. Forecasting the results in the Mann-Kendall and SWAT model has been done using the code written in MATLAB software and RCP (4.5, 8.5) scenarios, respectively. Studying the results of the trend in the data of meteorological stations in this catchment area indicated that these two parametric and non-parametric methods have been used to determine trends in meteorological data. The results of the parametric method are positive in all meteorological parameters. Non-parametric method over a period of 50 years shows the presence of trends in the data. The comparison on the forecasting results at maximum temperature suggested that during summer, we will see an increase in temperature compared to the ground state in all three forecasts. The results of the minimum temperature forecast show a decrease in the minimum increase during the winter and the precipitation forecast indicates that at the end of autumn (Nov) precipitation decreased by 20 mm in the Mann-Kendall and 4.5 RCP while RCP8.5 suggests the increase in precipitation compared to the ground state. Studying the runoff forecast results using SWOT show that at the end of winter (Feb) and almost all spring (Mar, Apr) a decrease of about 40%, 15% and 14% will be seen, respectively