Mohamed El-Sayed El-Mahdy

Rivers respond directly to climate change, as well as incorporating the effects of climate-driven changes occurring within their watersheds. In this research, climate change's impact on the Atbara River, one of the main tributaries of the Nile River, was studied. Various statistical methods of analysis were applied to study the basic characteristics of the climatic parameters that affect the discharge of the Atbara River. The three hydrological gauging stations on the Atbara River, namely, the Upper Atbara and Setit reservoirs, Khashm el-Girba reservoir, and Atbara Kilo 3 station, were included in the study. The correlation between the meteorological parameters and the hydrology of the Atbara River and the prediction of the future hydrology of the Atbara River Basin was determined. Many hydrological models were developed and tested to predict the hydrology of the river. Finally, forecasting for river hydrology was built. No significant trend was found in the precipitation in the study area. The developed model simulates the observed data with a high coefficient of determination ranging from 0.7 to 0.91 for the three hydrological gauging stations studied. Results predicted a slight decrease in river discharge in future years.

This study was carried out to assess the performance of the activated sludge process (ASP) for the treatment of oil refinery wastewater. Seasonal variation for temperature and the total dissolved solids (TDS) of wastewater were the key parameters examined. The treatment system is a batch-laboratory column that is continuously fed with oil refinery wastewater after physical separation of the surface oil layer in the refinery (API Separator). The determination of optimum operating conditions was performed for the treatment system at different temperatures ranging from 20 °C to 35 °C. Two main groups were examined: fresh wastewater (Group A) had an average TDS of less than 3 g/L, and saline wastewater (Group B) had an average TDS of 10–15 g/L. Results indicated that removal percentages in fresh wastewater (Group A) for chemical oxygen demand (COD), biological oxygen demand (BOD), oil and grease (O&G), phenols, and total suspended solids (TSS) were (76%–83%), (80.9%–92%), (83.5%–100%), and (94%–100%), respectively, while removal percentages in saline wastewater (Group B) for COD, BOD, O&G, phenols, and TSS were (76%–81%), (85.3%–95.8%), (87.5%–90%), (100%), and (93%–92%), respectively. The highest removal efficiency for pollution parameters was obtained at an average temperature of 25–35 °C. In conclusion, the overall treatment efficiency of fresh wastewater is better than that of saline wastewater. The quality of treated effluents achieved complies with the permissible limits of Egyptian regulations. Finally, ASP is efficient for the oxidation of organic matter applied to oil refinery wastewater with similar characteristics.

This study used Artificial Intelligence (AI) techniques as a modeling tool to estimate the risk of Nile flooding in the cities of southern Sudan. Climatic records, and precipitation, from stations along the area were used between 2010 and 2019. To test how well the models worked, the forecast was done using a variety of stations. To determine the flood rate in southern Sudan with the highest degree of accuracy, various artificial neural network techniques were investigated. Six artificial neural network (ANN) models were created and compared to show flood prediction to reach the maximum level of accuracy and to improve the results (NN, GRNN, RNN, CFNN, PNN, FFNN). The artificial neural network (FFNN) produced the best results in the first test, reaching a 95 % accuracy rate. Three further strategies were evaluated by increasing the neural network's hidden layer count to ten. Tests with 15 and 25 hidden layers also showed that the accuracy changes with the increase of hidden layers. Also, six other algorithms were applied to reach the highest value expected from Using one of the artificial intelligence techniques (AI), in predicting the flood by machine learning methods (ML). The highest expected value of flooding was reached through the (Gradient Boosting) model, where it was Classification Accuracy (CA) 0.937, followed by (AdaBoost), (CA 0.916).

Drought is one of the most common natural disasters, affecting ecosystems, agricultural production, and water supplies. This study presents the trend analysis of annual rainfall and the effect of climate variation in Tunisia. The paper seeks to provide up-to-date information for the better management of climate change in Tunisia. The analysis is based on monthly rainfall over 16 stations for a total period 62 years from 1958 to 2020 in Tunisia. The overall purpose of this study is to investigate the possible trend of rainfall variation as well as the effect of climatic changes on the study area. The Standardized Precipitation Index (SPI) and the Reconnaissance Drought Index (RDI) were employed to find long-term drought trends as well as to examine the occurrence of droughts at a longer duration. Borma and Tabarka stations have the lowest and highest extent of rainfall fluctuation with 50 and 880 mm respectively. Based on average temperature Thala and Kairouan are the coldest and warmest station in the time period of 1958–2020 with the 8.8 and 26.9 °C mean, respectively. The last ten years (2010–2020) counted as the warmest decade in the past 62 years. The mean annual rainfall varies from 200 to 900 mm for the study area. During the last decades, the time trend in rainfall has generally been in low for the southern Tunisia with an annual rainfall does not exceed 200 mm. The results of MK test for annual rainfall data revealed decreasing the trend of annual rainfall had shown non-significant decrease at all stations except at seven stations (which has a non-significant trend increase). Annual mean minimum and maximum temperatures showed increasing trend with almost same rate (0.03 °C/year) and statistically significant at 1% significant level. From the long term analysis of PET, SPI and RDI at all stations in Tunisia, it may be concluded that the annual SPI exhibited increasing trend at all station except Borma station in south but which has a non-significant trend increase. In most Tunisian regions, PET increases ranged between 0.03 and 0.52 mm annually. The north and northwestern parts of Tunisia have observed large increases in PET at rates between 0.2 and 0.52 mm year–1, in all the stations, RDI showed decreasing trend and no change trends (0.00). RDI exhibited decreasing trend in North of Tunisia. Since, non-significant positive trend showed in RDI during all stations.

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.

Monthly rainfall data for Blue Nile Basin (BNB) were obtained from the Ethiopian Meteorological Authority (1950–2018). Long-term trends in the BNB annual and monthly rainfall are investigated in this study. The challenges of the paper were to explore the impact of climate change on the study area using sound practical methods. The paper used the widely used statistical methods to find precisely the significance of the impact of climate change rainfall variability and distribution both spatially and temporally in the BNB. The statistical significance of the trend in the study is calculated by the Mann-Kendall (MK) test. Data were analyzed using the coefficient of variation, anomaly index, and precipitation concentration index. The coefficient of variation is high in Kiremt rainfall which implies more inter-annual variability of Bega rainfall than Kiremt (Coefficient of Variation (CV): Bega˃ Belg˃ Kiremt season). Based on Precipitation Concentration Index (PCI) value, the number of moderate concentration years (89.9%) has been increasing through time and the study area has encountered successive years of drought. The results showed that the annual, Bega, Belg, and Kiremt precipitation over the whole of BNB is significantly decreasing except Bega season with a magnitude of 36.38, 3.8, 7.8, and 24.7 mm per decade respectively. The rainfall in the study area is characterized by a high CV. Moreover, prolonged droughts have become common which adversely affects the agricultural system. It was also found that very low values of rainfall anomalies that correspond to severe droughts were associated with El Niño Southern Oscillation (ENSO) events.

Water is exposed to various types of stressors. There are environmental stressors or anthropogenic stressors, which have direct and/or indirect impacts on climate change. The overall objective of this paper is to develop a pioneer simple tool that can measure the water resources vulnerability due to environmental and anthropogenic factors. The tool is developed under six stages. Stage-1: A list of 28 indicators pertinent to the present work are generated; Stage-2: The indicators are assorted into two main categories, namely, environmental and anthropogenic indicators; Stage-3: Indicator weights are suggested using expert elicitation procedure; Stage-4: Index formulation is performed consecutively; Stage-5: The proposed index is applied; Stage-6: An appropriate adaptation strategy is proposed to each specific country. The developed tool is applied to the Limpopo River basin using 28 indicators. Results show that both the environmentally and anthropogenically driven vulnerability of the four riparian countries are moderate ranging from 41.98% in Mozambique to 47.69% in South Africa. The value of indicators ranges from 0.93% to 98.81% in environmentally driven vulnerability and ranges from 0.00% to 97.66% in anthropogenically driven vulnerability. Climate change is thought to increase the vulnerability of water resources by 9.47% in Mozambique to 13.56% in South Africa. The developed tool can be applied to other river basins and countries elsewhere considering indicator weight.

Evaporation from free water surface is considered a very important constituent in both the energy and hydrologic cycles. Precise measurement of evaporation from the free water surface is almost impossible. This is why we need a calculation model for free water evaporation. In this study, a simple mass-transfer evaporation model was developed to be applicable over Lake Nasser in the hyper-arid region located in the south of Egypt. Measured meteorological data (2011–2014) at two stations, Aswan and Abu-Simbel, were used to calculate free water surface evaporation using Priestly–Taylor equation. Priestly–Taylor equation was used because it is the most appropriate equation for Lake Nasser evaporation according to the literature. Results from this model were used to develop a simple mass-transfer evaporation model. The statistical analysis for both calibration and validation periods were very good. The slope of the regression line is about 0.9, with a coefficient of determination of 0.98. The t value is 0.6, at p value of 0.544, which is much greater than 0.05. The developed model could be used with confidence at Aswan meteorological station or on the average of the two meteorological stations, while it should be used carefully on Abu-Simbel meteorological station.

To protect the dam body and downstream area, spillways should be built. Because the difference in water level upstream and downstream spillways is usually very big, energy dissipation facilities should be introduced. Stepped spillways were discovered to immensely enhance energy dissipation. V-notch end sills installed on stepped spillways were not investigated enough. The challenge in using this type of end sills is to know the ideal angle of the notch. The second challenge is to link the energy dissipation percent and the scour hole characteristics with the hydraulic parameters of the spillway and the soil properties. This paper aims to investigate the V-notch end sills in stepped spillways and to assess their effect on energy dissipation and scour. For this purpose, physical models of four steps were built on stepped spillways in the laboratory. V-notch end sills were installed at the end of each step of the spillway. The dimensional analysis was used to link the various parameters influencing the studied phenomena. All V-notch end sill angles used enhanced energy dissipation, reduced the relative scour depth, length, and volume. Minimizing the angle of V-notch end sills enhanced energy dissipation. Three scour equations were developed to predict scour characteristics from easy-to-record measurements.

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.

(Key Words: High Aswan Dam Lake, Evaporation, Models, Trend)
The process of evaporation is a complex phenomenon, which includes physical processes in several aspects of physics. Accurate estimation of evaporation is important for designing and planning projects in which water budget is a key factor as well as for conservation of water. Although considerable progress has been made in understanding and measuring evaporation, however the methods used in the past have serious short-comings measuring evaporation. Therefore, developing more accurate measurements and predictions is needed.
HADL evaporation prediction have been and still studied by many researchers to choose the best model of the Lake evaporation prediction. In this study many methods and models were tested and correlated to water budget to find the best model for Lake evaporation prediction. These methods namely water budget method, energy budget method, mass transfer method, radiation methods, temperature based methods and combination methods. Then trend analysis was performed on Lake Inflows and evaporation both monthly and annually. The results showed that energy budget method, especially Priestley-Taylor model is the best model representing the Lake Evaporation. Also the trend analysis showed that, there is no accountable change in Lake inflows, as well as evaporation at any month or even in annual basis.