Simulation of karst aquifer water level under climate change in Lali region, Khouzestan Province, SW Iran

Climate change impacts on karst aquifers have not been mainly studied due to the difficulty of modeling these aquifers in relation to alluvial aquifers. However, it is possible to make a relationship between the climatic variables as part of the hydrologic cycle and the groundwater level using the Artificial Neural Networks (ANN). In this study firstly, precipitation and temperature data has been obtained using NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). Secondly, the groundwater level of three limestone wells, i.e. W1, W2 and W3, has been predicted using ANN in Lali region, Khouzestan Province. The correlation coefficients (R) demonstrate good ability of the groundwater model in simulating the climate change impacts on the karst aquifer. The groundwater level probably decreases for W1 and W2 during the future time period (2021-2050) in comparison with the present time period (1961-1990), while no important changes are predicted for W3.