Assessment of the spatial-temporal distribution of groundwater recharge in data-scarce large-scale African river basin

Groundwater potential delineation using geodetector based convolutional neural network in the Gunabay watershed of Ethiopia

Groundwater potential delineation is essential for efficient water resource utilization and long-term development. The scarcity of potable and irrigation water has become a critical issue due to natural and anthropogenic activities in meeting the demands of human survival and productivity. With these constraints, groundwater resource is now being used extensively in Ethiopia. Therefore, an innovative convolutional neural network (CNN) is successfully applied in the Gunabay watershed to delineate groundwater potential based on the selected major influencing factors. Groundwater recharge, lithology, drainage density, lineament density, transmissivity, and geomorphology were selected as major influencing factors during the groundwater potential of the study area. For dataset training, 70% of samples were selected and 30% were used for serving out of the total 128 samples. The spatial distribution of groundwater potential has been classified into five groups: very low (10.72%), low (25.67%), moderate (31.62%), high (19.93%), and very high (12.06%). The area obtains high rainfall but has a very low amount of recharge due to lack of proper soil and water conservation structures. The major outcome of the study showed that moderate and low potential is dominant. Geodetoctor results revealed that the magnitude influences on groundwater potential have been ranked as transmissivity (0.48), recharge (0.26), lineament density (0.26), lithology (0.13), drainage density (0.12), and geomorphology (0.06). The model results showed that using a convolutional neural network (CNN), groundwater potentiality can be delineated with higher predictive capability and accuracy. CNN based AUC validation platform showed that, 81.58% and 86.84% were accrued from the accuracy of training and testing values, respectively. Based on the findings, the local government can receive technical assistance for groundwater exploration, and sustainable water resource development in the Gunabay watershed. Finally, the use of a detector-based deep learning algorithm can provide a new platform for industrial sectors, groundwater experts, scholars, and decision-makers.

Identifying the suitable managed aquifer recharge (MAR) strategy in an overexploited and contaminated river basin

Managed aquifer recharge (MAR) is a promising adaptation measure to reduce vulnerability to climate change and hydrological variability. However, in areas where the basin is highly polluted, densely populated, and intensely cultivated, implementing suitable MAR strategies is a significant challenge. This study used a geographic information system-based multicriteria decision analysis (GIS-MCDA) approach to delineate the MAR potential sites using seven thematic layers describing surface and subsurface features. Further, basin-specific MAR approach was developed using information such as polluted water areas, canal network distribution for water supply, and cropping patterns. The results of this study indicate that only 17% of the area is highly suitable, while 54% and 29% were found moderately suitable and unsuitable for the MAR approach. Since most highly and moderately suitable sites were falling in the agricultural areas, agricultural-based MAR (AgMAR) was considered a preferred option. AquaCrop model for sugarcane was developed considering excess canal water supply during the grand growth stage to understand the AgMAR potential in the study area. It was observed that the potential recharge under normal irrigation scenarios varies from 135.5 to 272 mm/year, which can be increased through AgMAR up to 545 mm/year depending on the water availability for excess irrigations. This study provides an improved understanding of the parameters that should be considered for MAR site selection and post-GIS-MCDA analysis to assess the basin-specific MAR strategy.

Proxy modeling approach to evaluate groundwater recharge potentiality zones in the data scarce area of upper Blue Nile Basin, Ethiopia

Prioritization of groundwater recharge potentiality evaluation is critical for sustainable water resources management. Since recharge is a main source for enhancing groundwater availability. Water scarcity is extremely severe in the upper Blue Nile Basin (i.e., Gunabay watershed). Therefore, this study emphasizes groundwater recharge delineating and mapping 3920.25 km² in the data-limited area of the upper Blue Basin using proxy modeling (i.e., WetSpass-M model and geodetector model) and tools. The driving/influencing factors are rainfall, temperature, wind speed, evapotranspiration, elevation, slope, land cover, soil, groundwater depth, drainage density, geomorphology, and geology that control the movement of groundwater recharge. However, the first nine factors were used as inputs in the WetSpass-M model to evaluate groundwater recharge. To validate the groundwater recharge availability, water table fluctuation was established based on recorded groundwater levels. Furthermore, the major influencing factors and their interaction have been quantified using geodetector model. Spatiotemporal recharge distribution (in mm) is classified as very low (0-6), low (6-30), moderate (30-51), high (51-83), and very high (83-508) comprising 21%, 20%, 20%, 20%, and 19% of the total area, respectively. Very high groundwater recharge zone has been found in the northwest part of the area. The geodetector results showed that soil (0.841) and temperature (0.287) had larger individual contributions, but the interaction between soil and temperature (0.962) was more significant. It indicates that the interaction between climate and soil has the largest influence on groundwater recharge variability. Generally, the overall approach of this study can be applied to water sectors, policymakers, and decision-makers to overcome future water scarcity.

3D modelling of surface spreading and underground dam groundwater recharge: Egri Creek Subbasin, Turkey

This study investigated surface spreading and underground dam recharge methods to replenish groundwater in Turkey's Egri Creek Sub-basin of the Kucuk Menderes River Basin. A three-dimensional numerical model was employed for this purpose. Field and lab data are provided to the model for realistic simulations. Pumping test results were used to determine the aquifer parameters. The laboratory works involved sieve analysis, permeability tests, and porosity and water content prediction. The numerical model's boundary conditions were determined from the geological and hydrogeological characteristics of the study area. Initial conditions were expressed regarding water content and pressure head in the vadose zone. The numerical model was satisfactorily validated by simulating water levels in three different pumping wells in the study area. Seven different scenarios, each having a different pool size, were investigated for the surface spreading recharge method. The results showed that a pool size of 30 × 30 m with a 6-m depth basin was the most optimal choice, raising the groundwater level to about 29.3 m. On the other hand, it was found that an underground dam could raise the levels by an average of 9.5 m, which might not be significant to warrant the construction.