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Coastal areas globally suffer from serious erosion and sedimentation problems. The assessment of coastal erosion risk is fundamental for better coastal management. The main goal of the current study was to enhance coastal management by identifying the coastal erosion risk and suggest possible mitigation measures. The study was developed for the Egyptian Northern Coast. MIKE 21 was used to evaluate the erosion and test the proposed measures. First, a bathymetric survey of the study area was conducted. Then the tidal data, storm surge, and global sea rise data were collected. The offshore wave data, shoreline data were compiled. And then, the different nearshore wave modeling and wave propagation were modeled. The model was calibrated using measured data in 2014 and validated using measured data in 2016. Future shoreline changes for the years 2029, 2039, and 2049 were depicted. A suitable mitigation measure was used and tested for the same years. The groins with a specific size and arrangement were tested. The main goal of the study is to find a suitable solution to protect the coast and investigate its impact on the shoreline in the next 30 years. The proposed mitigation measure minimized the coastal erosion of the study area rigorously. [ABSTRACT FROM AUTHOR]

Understanding lake evaporation and the climate change role in evaporation is paramount for any water resources management system. The prediction of the climate's future changes is a very important step in planning lake future management decisions. This study analyzed Lake Nasser's evaporation in southern Egypt. Meteorological parameters were compiled from Aswan meteorological station near Lake Nasser. Also, CORDEX predicted climatological parameters from 2021 to 2050 were collected. Lake Nasser's evaporation prediction model using artificial neural networks technique was built. Statistics were calculated in the calibration and validation stages to find the most adequate model of the Lake evaporation calculation. The predictions of future evaporation were extracted from the model using predicted climatological data from CORDEX regional climate models. Trend analysis was done to assure the impacts of climate change on the lake. ANN model was developed and implemented successfully on Lake Nasser, which could be used to handle evaporation calculation over Lake Nasser. ANN model with training algorithm with 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22 neurons with 5 input variables were tested to find the best model evaporation estimation of the lake with the least number of neurons. ANN model with training algorithm with 20 neurons with 5 input variables performed the best for evaporation estimation of the lake. According to predicted climate data, about a 2% increase in lake Nasser evaporation could be predicted in the year 2050. It was noticed that the maximum predicted values of evaporation are in July to August months with a range from 7.04 mm/day to 9.64 mm/day. The peak value, the outlier of maximum evaporation, is about 11.16 mm/day. The minimum predicted values are in December to January months with a range from 3.50 mm/day to 6.81 mm/day. Trend analysis showed that the predicted climatological parameters are slightly higher than historical records. [Display omitted] • This paper developed Artificial Neural Network model to assess Lake Nasser evaporation. • The developed model is calibrated and validated for Lake Nasser. • The paper assessed the impact of climate change using the developed model on Lake Nasser. • The model could be tested for other dry regions with a good degree of trust. • The most influential meteorological parameters on Lake Nasser projected evaporation. [ABSTRACT FROM AUTHOR]