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García-Arostegui, J.; Molina González, JL.; Pulido-Velazquez, M. (2015). Assessment of future groundwater recharge in semi-arid regions under climate change scenarios (Serral-Salinas aquifer, SE Spain). Could increased rainfall variability increase the recharge rate?. Hydrological Processes. 29 (6) AbstractThe projected impact of climate change on groundwater recharge is a challenge in hydrogeological research because substantial doubts still remain, particularly in arid and semiarid zones. We present a methodology to generate future groundwater recharge scenarios using available information about regional climate change projections developed in European Projects.It involves an analysis of Regional Climate Model (RCM) simulations and a proposal for ensemble models to assess the impacts of climate change. Future rainfall and temperature series are generated by modifying the mean and standard deviation of the historical series in accordance with estimates of their change provoked by climate change. Future recharge series will be obtained by simulating these new series within a continuous balance model of the aquifer. The proposed method is applied to the Serral-Salinas Aquifer, located in a semi-arid zone of SE Spain. The results show important differences depending on the RCM used. Differences are also observed between the series generated by imposing only the changes in means or also in standarddeviations. An increase in rainfall variability, as expected under future scenarios, could increase recharge rates for a given mean rainfall because the number of extreme events increases., For some RCMs, the simulations predict total recharge increases over the historical values, even though climate change would produce a reduction in the mean rainfall and an increased mean temperature A method based on a multi-objective analysis is proposed to provide ensemble predictions which give more value to the information obtained from the best calibrated models.The ensemble of predictions estimates a reduction in mean annual recharge of 14% for the scenario A2 and 58% for the scenario A1B. Lower values of future recharge are obtained if only the change in the mean is imposed.

17Bayesian Networks (BNs) are powerful tools for assessing and predicting consequences of water 38

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