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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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The Karkheh basin is one of Iran's largest and most waterlogged rivers. In this study, we aim to analyze the impact of Climate change and landuse change on the Karkheh basin using the Soil and Water Assessment Tool (SWAT). In this research, the considered land-use change is based on Iran's future policies, and climate change is studied by employing the RCP series and GCM in Mid-term (2040–2060) and Long-term (2080–2100). Firstly, changes in climate and land use are separately examined, and then the simultaneous impact of these two parameters is investigated. The results of the climate change study illustrate that the highest rate of change, which is a decrease of 14.3–22.8%, is achieved from the RCP 8.5 series in the long term. The results obtained from investigating the land-use change based on future policies in Iran show a maximum reduction of 7% in the average monthly runoff. The flow rate also decreases further when considering the simultaneous effects of both changes in the basin. In this case, the RCP 8.5 series shows a reduction of up to 39% in the long term. This study suggests that the effects of climate change are more significant than changes in landuse.

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