A simulation model for part of the Mississippi River Valley alluvial aquifer in the Cache area, Arkansas, was coupled with an optimization model to determine maximum optimal pumping from irrigation wells in the areas where cones of depression exist. Groundwater Vistas and Groundwater Management software were used for simulation and optimization model, respectively. The Cache area was designated as a critical groundwater area in 2009 due to the decline in its water level to below 50 % of the saturated thickness of the aquifer. The optimization model was formulated with the objective of maximizing water production from wells subjected to minimum head constraints and drawdown constraints, while limiting groundwater withdrawals to a maximum of 100 and 200 % of the rate pumped in 2010. Four different sets of managed wells were tested in Scenarios 1, 3 (938 wells) and Scenarios 2, 4 (3870 wells). The optimal pumping rates from groundwater in the case of minimum head constraints were 0.59 and 2.43 Mm 3 /d for Scenarios 1 and 2, respectively. In the case of maximum pumping constraints of the managed wells specified as 200 % of the pumping rate of 2010, the optimal pumping rates from groundwater in the case of minimum head constraints were 0.88 and 3.28 Mm 3 /d for Scenarios 3 and 4, respectively. The average optimal pumping increased by 6-49 % in the case of the maximum pumping constraint specified as 200 % of the pumping rate of the year 2010.

Groundwater is one of the most important natural resources in the world. The presence of groundwater is the result of interaction of several factors such as: hydrology, geology, climate, ecology, and physiography. The purpose of this paper is to produce groundwater potential zones which are useful in determining the amount of groundwater available in Sulaimani Governorate, North of Iraq. Geographic information system database for six different thematic layers (digital elevation model, rainfall, soil texture, drainage density, slope and land use/land cover) were generated. The study approach involved integration of six layers carried out based on the multiplication of each data raster values with specific weight using weighted overlay analysis method. Raster maps of all the layers assigned a fixed score and weight using multi-influencing factor technique. Based on the resulted map the study area has been divided into four zones that had very high potential zone (1%), high potential zone (14%), moderate zone potential (79%) and low potential zone (6%). About 50% of the high groundwater potential zone were located in Halabja, Rania, and Pshdar districts. Obtained results can be useful in localizing areas of exploration, preventing excessive exploitation of groundwater and planning for suitable sites of artificial groundwater.

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The precise and accurate models of hydrological time series that are embedded with high complexity, nonstationarity, and non-linearity in both spatial and temporal scales can provide important information for decision-making in water resources management and environmental related issues. Hybrid wavelet transform (WT) and adaptive neuro-fuzzy inference system (ANFIS) has been used in this study to improve the forecasting capability of ANFIS model by decomposing the time series into sub-time series (approximation and details) using wavelet transform then combining the effective and significant time lags of sub-time series to form a set of input variables. The present study attempts to add the effective and significant time lags of original time series as extra variables to the input variables set. In addition, different combinations of variables, 1-3, from the set of input variables as inputs to the ANFIS model were used to forecast the time series.To examine the potential of the approach for practical applications, the model is applied to forecast, one step-ahead, the monthly data of hydrological time series (rainfall, evaporation, minimum and maximum temperature, average wind speed and reservoir inflow) for Kirkuk, Sulaimani, Dokan and Darbandikhan meteorological stations in Iraq. The best fit models were selected using the coefficient of determination ( ) and root mean square error ( ). Based on the results, the proposed model has high performance in forecasting the monthly minimum and maximum temperature, evaporation and reservoir inflow with values ranged from 0.93 to 0.99 and relatively good performances in forecasting the monthly rainfall and average wind speed with values ranged from 0.77 to 0.93.