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This text presents an updated state-of-the-art for managed aquifer recharge (MAR) for MENA regions. MENA regions are home to 6% of the world's population but only possess 1.4% of its water resources with almost absolute scarcity. Groundwater is the primary source of water in 54% of MENA countries. Therefore, the MENA regions seek sustainable management solutions amid its arid climate and rising demands from urbanization and agriculture. MAR aims to help sustain groundwater resources. This book explores MAR as a strategic approach to reducing water security by enhancing groundwater supplies. Utilizing techniques such as soil aquifer recharge, aquifer storage and recovery, rainfall harvesting, and riverbank filtration. The presented case studies offer deep insights into MAR methods, their implementation, and MAR technologies.

In the coming years, climate change is predicted to impact irrigation water demand considerably, particularly in semi-arid regions. The aim of this research is to investigate the expected adverse impacts of climate change on water irrigation management in Saudi Arabia. We focus on the influence of climate change on irrigation water requirements in the Al Quassim (97,408 ha) region. Different climate models were used for the intermediate emission SSP2-4.5 and the high emission SSP5-8.5 Coupled Model Intercomparison Project Phase 6 (CMIP6) scenarios. The FAO-CROPWAT 8.0 model was used to calculate reference evapotranspiration (ETo) using weather data from 13 stations from 1991 to 2020 and for both the SSP2-4.5 and SSP5-8.5 scenarios for the 2040s, 2060s, 2080s, and 2100s. The findings indicated that, for the 2100s, the SSP2-4.5 and SSP5-8.5 scenarios forecast annual average ETo increases of 0.35 mm/d (6%) and 0.7 mm/d (12.0%), respectively. Net irrigation water requirement (NIWR) and growth of irrigation water requirement (GIWR) for the main crops in the Al Quassim region were assessed for the current, SSP2-4.5, and SSP5-8.5 scenarios. For SSP5-8.5, the GIWR for the 2040s, 2060s, 2080s, and 2100s are expected to increase by 2.7, 6.5, 8.5, and 12.4%, respectively, compared to the current scenario (1584.7 million m3). As a result, there will be higher deficits in 2100 under SSP5-8.5 for major crops, with deficits of 15.1%, 10.7%, 8.3%, 13.9%, and 10.7% in the crop areas of wheat, clover, maize, other vegetables, and dates, respectively. Optimal irrigation planning, crop pattern selection, and modern irrigation technologies, combined with the proposed NIWR values, can support water resources management. The findings can assist managers and policymakers in better identifying adaptation strategies for areas with similar climates.

Jazan province on Saudi Arabia’s southwesterly Red Sea coast is facing significant challenges in water management related to its arid climate, restricted water resources, and increasing population. A total of 180 groundwater samples were collected and tested for important hydro-chemical parameters used to determine its adaptability for irrigation. The principal components analysis (PCA) was applied to evaluate the consistency/cluster overlapping, agglomeration in the datasets, and to identify the sources of variation between the 11 major ion concentrations (pH, K+, Na+, Mg2+, Ca2+, SO42−, Cl−, HCO3−, NO3−, TDS, and TH). The EC values ranged from excellent to unsuitable, with 10% being excellent to good, 43% permissible, and 47% improper for irrigation. The SAR classification determined that 91.67% of groundwater samples were good to excellent for irrigation, indicating that they are suitable for irrigation with no sodium-related adverse effects. Magnesium hazard values showed that 1.67% of the samples are unsuitable for irrigation, while the remaining 98.33% are suitable. Chloro-alkaline indices signify that most groundwater samples show positive ratios indicating that ion exchange is dominant in the aquifer. The Gibb’s diagram reflects that evaporation, seawater interaction, and water–rock interaction are the foremost processes impacting groundwater quality, besides other regional environmental variables. A strong positive correlation was declared between TDS and Na+, Mg2+, Ca2+, Cl−, SO42− in addition to TH with Mg2+, Ca2+, Cl−, SO42−, TDS, and also Cl− with Na+, Ca2+, Mg2+ were major connections, with correlation coefficients over 0.8 and p < 0.0001. The extracted factor analysis observed that TH, Ca2+, TDS, Cl−, and Mg2+ have high positive factor loading in Factor 1, with around 52% of the total variance. This confirms the roles of evaporation and ion exchange as the major processes that mostly affect groundwater quality, along with very little human impact. The spatial distribution maps of the various water quality indices showed that the majority of unsuitable groundwater samples were falling along the coast where there is overcrowding and a variety of anthropogenic activities and the possible impact of seawater intrusion. The results of the hierarchical cluster analysis agreed with the correlations mentioned in the factor analysis and correlation matrix. As a result, incorporating physicochemical variables into the PCA to assess groundwater quality is a practical and adaptable approach with exceptional abilities and new perspectives. According to the study’s findings, incorporating different techniques to assess groundwater quality is beneficial in understanding the factors that control groundwater quality and can assist officials in effectively controlling groundwater quality and also enhancing the water resources in the study area.

Climate change represents an overwhelming challenge that demands urgent intervention for effective resolution. Among the devastating consequences of climate change, flash floods stand out as one of the most catastrophic repercussions. This research focuses on two primary objectives. Firstly, it aims to evaluate the existing state of flash flood intensity (FFI) in a specific area of Hamamatsu city, Japan, which frequently experiences flash flood incidents. Secondly, it seeks to develop a mitigation plan to alleviate the adverse impacts of flooding on buildings within the area. To accomplish these objectives, four parameters related to FFI (namely, runoff depth, runoff velocity, runoff duration, and affected portion) were selected and estimated through the implementation of hydrological and hydrodynamic models. Additionally, a hydrological model was employed, utilizing a storm event with a return period of 100 years as input. During this simulated storm event, FFI values were calculated and categorized into four distinct levels. The results revealed that more than one-tenth of the examined buildings encountered the highest scale of FFI (category 4), while categories 3 and 4 combined accounted for nearly three-quarters of all buildings in the study area. Moreover, two mitigation strategies were adopted to prevent flooding within the buildings’ vicinity. Finally, this study provides a valuable framework and guidance for decision-makers and insurance companies, enabling them to assess the flood hazard status of buildings and make informed decisions accordingly.

Flash floods in the Sinai often cause significant damage to infrastructure and even loss of life. In this study, the susceptibility to flash flooding is determined using hydro-morphometric characteristics of the catchments. Basins and their hydro-morphometric features are derived from a digital elevation model from NASA Earthdata. Principal component analysis is used to identify principal components with a clear physical meaning that explains most of the variation in the data. The probability of flash flooding is estimated by logistic regression using the principal components as predictors and by fitting the model to flash flood observations. The model prediction results are cross validated. The logistic model is used to classify Sinai basins into four classes: low, moderate, high and very high susceptibility to flash flooding. The map indicating the susceptibility to flash flooding in Sinai shows that the large basins in the mountain ranges of the southern Sinai have a very high susceptibility for flash flooding, several basins in the southwest Sinai have a high or moderate susceptibility to flash flooding, some sub-basins of wadi El-Arish in the center have a high susceptibility to flash flooding, while smaller to medium-sized basins in flatter areas in the center and north usually have a moderate or low susceptibility to flash flooding. These results are consistent with observations of flash floods that occurred in different regions of the Sinai and with the findings or predictions of other studies.

The Nubian sandstone aquifer is the only water source for domestic use and irrigation in the Kharga oasis, Egypt. In this study, 46 pumping tests are analyzed to estimate the transmissivity of the aquifer and to derive a spatial distribution map by geostatistical analysis and kriging interpolation. The resulting transmissivity values are log-normally distributed and spatially correlated over a distance of about 20 km. Representative values for the transmissivity are a geometric average of about 400 m2/d and a 95% confidence interval of 100–1475 m2/d. There is no regional trend in the spatial distribution of the transmissivity, but there are local clusters with higher or lower transmissivity values. The error map indicates that the highest prediction accuracy is obtained along the central north-south traffic route along which most agricultural areas and major well sites are located. This study can contribute to a better understanding of the hydraulic properties of the Nubian sandstone aquifer in the Kharga oasis for an effective management strategy.

Landslides and flash floods are significant natural hazards with substantial risks to human settlements and the environment, and understanding their interconnection is vital. This research investigates the hazards of landslides and floods in two adopted basins in the Yamaguchi and Shimane prefectures, Japan. This study utilized ten environmental variables alongside categories representing landslide-prone, non-landslide, flooded, and non-flooded areas. Employing a machine-learning approach, namely, a LASSO regression model, we generated Landslide Hazard Maps (LHM), Flood Hazard Maps (FHM), and a Composite Hazard Map (CHM). The LHM identified flood-prone low-lying areas in the northwest and southeast, while central and northwest regions exhibited higher landslide susceptibility. Both LHM and FHM were classified into five hazard levels. Landslide hazards predominantly covered high- to moderate-risk areas, since the high-risk areas constituted 38.8% of the study region. Conversely, flood hazards were mostly low to moderate, with high- and very high-risk areas at 10.49% of the entire study area. The integration of LHM and FHM into CHM emphasized high-risk regions, underscoring the importance of tailored mitigation strategies. The accuracy of the model was assessed by employing the Receiver Operating Characteristic (ROC) curve method, and the Area Under the Curve (AUC) values were determined. The LHM and FHM exhibited an exceptional AUC of 99.36% and 99.06%, respectively, signifying the robust efficacy of the model. The novelty in this study is the generation of an integrated representation of both landslide and flood hazards. Finally, the produced hazard maps are essential for policymaking to address vulnerabilities to landslides and floods.

One of the most critical stages for developing groundwater resources for drinking water use is assessing the water quality. The use of a Water Quality Index (WQI) is considered an effective method of evaluating water quality. The objective of this research was to evaluate the performance of six multiple artificial intelligence techniques, i.e., linear regression (stepwise), support vector regression SVM (linear and polynomial kernels), Gaussian process regression (GPR), Fit binary tree, and artificial neural network ANN (Bayesian) to predict the WQI in Jizan, Southern Saudi Arabia. A total of 145 groundwater samples were collected from shallow dug wells and boreholes tapping the phreatic aquifer. The WQI was calculated from 11 physicochemical parameters (pH, TDS, Ca2+, Mg2+, Na+, K+, Cl−, SO42−, HCO3−, NO3−, and TH). The spatial distribution results showed that higher values of Cl− and SO42− were recorded in the places close to the coastline, indicating the occurrence of seawater intrusion and salinisation. Seven wells had a WQI of greater than 300, indicating that the water was unfit for consumption. The results showed that the GPR, linear regression (stepwise), and ANN models performed best during the training and testing stages, with a high correlation of 1.00 and low errors. The stepwise fitting model indicated that pH, K+, and NO3− were the most significant variables, while HCO3− was a non-significant variable for the WQI. The GPR, stepwise regression, and ANN models performed best during the training and testing stages, with a high correlation and low errors. In contrast, the SVM and Fit binary tree models performed the worst in the training and testing phases. Based on subset regression analysis, the optimum input combination for WQI model prediction was determined as these eight input combinations with high R2 (0.975–1.00) and high Adj-R2 (0.974–1.00). The resultant WQI model significantly contributes to sustainable groundwater resource management in arid areas and generates improved prediction precision with fewer input parameters.

The estimation of reference evapotranspiration (ETo), a crucial step in the hydrologic cycle, is essential for system design and management, including the balancing, planning, and scheduling of agricultural water supply and water resources. When climates vary from arid to semi-arid, and there are problems with a lack of meteorological data and a lack of future information on ETo, as is the case in Egypt, it is more important to estimate ETo precisely. To address this, the current study aimed to model ETo for Egypt’s most important agricultural governorates (Al Buhayrah, Alexandria, Ismailiyah, and Minufiyah) using four machine learning (ML) algorithms: linear regression (LR), random subspace (RSS), additive regression (AR), and reduced error pruning tree (REPTree). The Climate Forecast System Reanalysis (CFSR) of the National Centers for Environmental Prediction (NCEP) was used to gather daily climate data variables from 1979 to 2014. The datasets were split into two sections: the training phase, i.e., 1979–2006, and the testing phase, i.e., 2007–2014. Maximum temperature (Tmax), minimum temperature (Tmin), and solar radiation (SR) were found to be the three input variables that had the most influence on the outcome of subset regression and sensitivity analysis. A comparative analysis of ML models revealed that REPTree outperformed competitors by achieving the best values for various performance matrices during the training and testing phases. The study’s novelty lies in the use of REPTree to estimate and predict ETo, as this algorithm has not been commonly used for this purpose. Given the sparse attempts to use this model for such research, the remarkable accuracy of the REPTree model in predicting ETo highlighted the rarity of this study. In order to combat the effects of aridity through better water resource management, the study also cautions Egypt’s authorities to concentrate their policymaking on climate adaptation.

Growing drought hazard and water demand for agriculture, ecosystem conservation, and tourism in the Hungarian Drava river floodplain call for novel approaches to maintain wetland habitats and enhance agricultural productivity. Floodplain rehabilitation should be viewed as a complex landscape ecological issue which, beyond water management goals to relieve water deficit, ensures a high level of provision for a broad range of ecosystem services. This paper explores the hydrological feasibility of alternative water management, i.e., the restoration of natural reservoirs (abandoned paleochannels) to mitigate water shortage problems. To predict the efficiency of the project, an integrated surface water (Wetspass-M) and groundwater model (MODFLOW-NWT) was developed and calibrated with an eight-year data series. Different management scenarios for two natural reservoirs were simulated with filling rates ranging from 0.5 m3 s−1 to 1.5 m3 s−1. In both instances, a natural reservoir with a feeding rate of 1 m3 s−1 was found to be the best scenario. In this case 14 days of filling are required to reach the possible maximum reservoir stage of +2 m. The first meter rise increases the saturation of soil pores and the second creates an open surface water body. Two filling periods per year, each lasting for around 180 days, are required. The simulated water balance shows that reservoir–groundwater interactions are mainly governed by the inflow into and outflow from the reservoir. Such an integrated management scheme is applicable for floodplain rehabilitation in other regions with similar hydromorphological conditions and hazards, too.