Climate change may cause profound changes in the regional water cycle causing negative impacts in many sectors, such as agriculture or water resources. In this study, projected changes of the terrestrial water cycle are investigated based on the simulations from 47 regional climate model ensemble members of the COordinated Regional Downscaling EXperiment (CORDEX) project’s EURO-CORDEX initiative, which downscale different global climate models of the CMIP5 experiment over a 12km resolution pan-European model domain. We analyze climate change impacts on the terrestrial water budget through changes in the long-term annual and seasonal cycles of precipitation, evapotranspiration, and runoff over 20 major European river catchments (Guadalquivir, Guadiana, Tagus, Douro, Ebro, Garonne, Rhone, Po, Seine, Rhine, Loire, Maas, Weser, Elbe, Oder, Vistuala, Danube, Dniester, Dnieper, and Neman) for near (2021-2050) and far future (2070-2099) time spans with reference to a historical period (1971-2000) for three Representative Concentration Pathways (RCPs), RCP2.6, RCP4.5, and RCP8.5. The analysis shows substantial differences between the projected changes in precipitation, evapotranspiration, and runoff for the twenty European catchments. For the near future RCP8.5 scenario, the long-term average of the annual sum precipitation increases over most of Europe by up to 10% in the ensemble mean over central European catchments; but also decreases up to 10 % are found, e.g. over the Iberian Peninsula. For the far future, the long-term average ensemble means of the annual precipitation sum increases from 30% for eastern, 15% for central to 7% for western European catchments, and further decreases up to 25% over the Iberian Peninsula, which will likely cause water stress situations. These first order changes in precipitation lead to ensuing changes in evapotranspiration and runoff, that cause altered hydrological regimes and feedback processes in the water cycle in the catchments.

Predication of temporal trends of aerosol optical depth (AOD) within the numerical climate models with enabled chemistry module is very challenging and computationally expensive. In this work, new predication model is introduced based on artificial neural networks (ANN) in order to estimate average AOD over Egypt. Long short-term memory (LSTM) algorithm which is artificial recurrent neural network (RNN) architecture, is selected to construct the predication model. Seven input datasets for LSTM algorithm are from NASA’s Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis within period (1980-2017). The seven variables are pressure (PR), temperature (T), wind speed (W), dust surface particulate matter (PM2.5), surface (SO2) and (SO4) concentrations and (CO) concentration. AOD is the output of the trained and validated model. Effects of changing the number of both hidden layers and number of neurons per layers were evaluated. The results of increasing the number of neurons per one hidden layer revealed that increasing the number of neurons leads to three main finding (a) leads to faster convergence of loss function. (b) Produces more realistic AOD estimation (c) RMSE is reduced by increasing number of neurons. It was also found that, the model with one hidden layer and 50 neurons is the best model setup with RMSE (0.06). However, our studies showed also that increasing the number of hidden layers has no dominant effect on model RNN performance. The proposed LSTM model showed a very high level of accuracy with percentage 99.94 %. Future work can include more variables that has direct effect on AOD calculations. Both ensemble algorithms and different datasets can have more positive impact on the current proposed model.