Assessment of riverbank filtration performance for climatic change and a growing population

Data availability statement The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding authors.Copyright © 2023 Abd-Elaty, Kuriqi, Ganayem, Ahmed, Saleh and Garrote. Riverbank filtration (RBF) consists of green drinking water production in many regions and is used as a pre-treatment phase. This study investigates the performance of the RBF in the Nile delta, Egypt, for climate change and population growth scenarios of 2030, 2040, and 2050. This study presents a new method for predicting the sharing of riverbanks considering three cases: i) the river stage controlling the water levels in the river, ii) increasing RBF pumping, and iii) changing the groundwater levels. This last scenario is achieved by changing the general head in the MODFLOW model. The results showed that RBF sharing (RBFS) is a proportion of the river leakage inflow, in which the decrease of the river stage due to the influence of climate change reduced the river leakage inflow and RBFS. In addition, increasing RBF pumping, decreasing RBF pumping, and lowering the groundwater levels due to the increase in the future drinking water pumping for the population growth increased the river leakage inflow and RBFS. Finally, combining the three cases decreased RBFS in the coming years of 2030, 2040, and 2050, respectively, due to more groundwater sharing than the river inflow. The results show that the water budget is a good tool to investigate RBFS compared with MT3D results. This technique can reduce the cost of water quality collection and analysis; moreover, it will help with the estimation of RBF and save time compared with solute transport modeling.AK is grateful for the Foundation for Science and Technology’s support through funding UIDB/04625/2020 from the research unit CERIS

Hazards of sea level rise and dams built on the River Nile on water budget and salinity of the Nile Delta aquifer

Data availability: No data was used for the research described in the article.Code availability: Upon request.Supplementary material is available online at: https://www.sciencedirect.com/science/article/pii/S2214581823002872#sec0135 .Copyright © 2023 The Authors. Study region: The Nile Delta region consists of flat, low-lying areas, where most areas are used for agriculture. It covers an area of 22,000 km2, which is 2.20% of the total area of Egypt. Study focus: This study evaluates the water budget and the salinity due to the Sea Level Rise (SLR) and the reduction in the river water flow caused by the Grand Ethiopian Renaissance Dam (GERD) using the numerical code SEAWAT. Three filling scenarios were considered for the GERD reservoir at elevations 600 m, 621 m, and 645 m above mean sea level (AMSL) for the storage volumes of 17 billion cubic meters (BCM) (scenarios #1), 37.30 BCM (scenarios #2), and 74 BCM (scenarios #3). The impact of these fillings scenarios was combined with SLR of 25 cm, and increasing the abstraction rates from the Nile Delta aquifer by 25%, 50%, and 100%, respectively. New hydrological insights for the region: The study findings indicated that the SLR and the GERD reservoir filling with increasing pumping rates, especially during the filling periods, would influence the groundwater resources in the Nile Delta. The GERD reservoir filling could alter the freshwater, in which the aquifer salinity increased by 4.47%, 11.48%, and 29.99% for the three scenarios, respectively. The methodology and findings presented in this study might be useful for investing and comparing the impact of SLR and upstream dam projects on the downstream water budget and salinity at other coastal regions.Alban Kuriqi is grateful for the Foundation for Science and Technology's support through funding UIDB/04625/2020 from the research unit CERIS

Complementarity of wind and solar power in North Africa: Potential for alleviating energy droughts and impacts of the North Atlantic Oscillation

With growing gas and oil prices, electricity generation based on these fossil fuels is becoming increasingly expensive. Furthermore, the vision of natural gas as a transition fuel is subject to many constraints and uncertainties of economic, environmental, and geopolitical nature. Consequently, renewable energies such as solar and wind power are expected to reach new records of installed capacity over the upcoming years. Considering the above, North Africa is one of the regions with the largest renewable resource potential globally. While extensively studied in the literature, these resources remain underutilized. Thus, to contribute to their future successful deployment and integration with the power system, this study presents a spatial and temporal analysis of the nature of solar and wind resources over North Africa from the perspective of energy droughts. Both the frequency and maximal duration of energy droughts are addressed. Both aspects of renewables’ variable nature have been evaluated in the North Atlantic Oscillation (NAO) context. The analysis considers the period between 1960 and 2020 based on hourly reanalysis data (i.e., near-surface shortwave irradiation, wind speed, and air temperature) and the Hurrel NAO index. The findings show an in-phase relationship between solar power and winter NAO index, particularly over the coastal regions in western North Africa and opposite patterns in its eastern part. For wind energy, the connection with NAO has a more zonal pattern, with negative correlations in the north and positive correlations in the south. Solar energy droughts dominate northern Tunisia, Algeria, and Morocco, while wind energy droughts mainly occur in the Atlas Mountains range. On average, solar energy droughts tend not to exceed 2–3 consecutive days, with the longest extending for five days. Wind energy droughts can be as prolonged as 80 days (Atlas Mountains). Hybridizing solar and wind energy reduces the potential for energy droughts significantly. At the same time, the correlation between their occurrence and the NAO index remains low. These findings show the potential for substantial resilience to inter-annual climate variability, which could benefit the future stability of renewables-dominated power systems.Graphical abstrac

Characterizing the 2014 Indus River flooding using hydraulic simulations and satellite images

Rivers play an essential role to humans and ecosystems, but they also burst their banks during floods, often causing extensive damage to crops, property, and loss of lives. This paper characterizes the 2014 flood of the Indus River in Pakistan using the US Army Corps of Engineers Hydrologic Engineering Centre River Analysis System (HEC-RAS) model, integrated into a Geographic Information System (GIS), and satellite images from Landsat-8. The model is used to estimate the spatial extent of the flood and assess the damage that it caused by examining changes to the different Land Use/Land Cover (LULC) types of the river basin. Extreme flows for different return periods were esti-mated using a flood frequency analysis using a log-Pearson III distribution, which the Kolmogorov-Smirnov (KS) test identified as the best distribution to characterize the flow regime of the Indus river at Taunsa Barrage. The output of the flood frequency analysis was then incorporated into the HEC-RAS model to determine the spatial extent of the 2014 flood, with the accuracy of this modelling approach assessed using images from the Moderate Resolution Imaging Spectroradiometer (MODIS). The results reveal good agreement between simulated and obtained flood from the MODIS images, with an overall classification accuracy of more than 85%. The results also revealed that the most affected LULC in the watershed was crop/agricultural land, of which 50% was affected by the flood. This paper provides further evidence of the benefit of using a hydrological model and satellite images for flood monitoring and for flood damage assessment to inform the development of risk mitigation strategies

Assessing Salinity Hazards in Coastal Aquifers: Implications of Temperature Boundary Conditions on Aquifer-Ocean Interaction

......This study did not receive any funding. Alban Kuriqi acknowledges the Foundation for Science and Technology's support through funding UIDB/04625/2020 from the research unit CERIS

Efficiency of Shoreline physical subsurface dam for mitigating the flooding of Sea level rise and saltwater in coastal aquifers

Data availability: Upon request.Code availability: Upon request.Fresh groundwater in arid and highly populated regions is limited. In coastal aquifers, the deterioration of fresh groundwater is accelerated by saltwater intrusion, primarily occurring through lateral encroachment and vertical movements in the proximity of discharging wells. Coastal regions have high salinity due to saline intrusion, where many abstraction wells are turned off by this high salinity, which leads to increased freshwater supply costs. This study investigates the performance of new approach using the shoreline subsurface dams (SSDs) for mitigating the saline water wedge in coastal aquifers, where the dams are installed at the shoreline (distance from shoreline = 0). Specifically, the current study's novelty is testing the effectiveness of SSDs by different relative heights ranging from 0.05 to 0.50 in the test case (Henry problem) and from 0.09 to 0.53 relative to the aquifer thickness in the field scale aquifer (Biscayne aquifer, Florida, USA). The results showed an exponential increase in salt repulsion for increasing SSDs height, reaching a maximum of + 0.70%, + 1.80%, + 3.25%, + 5.80%, + 10.45%, and + 18.40% for the dam height to aquifer thickness ratios of 0.09, 0.18, 0.26, 0.35, 0.44 and 0.53, respectively, in the field scale case. The SSDs increase the freshwater storage at the coastal zones where the low salinity occurs and reduces the freshwater supply cost. Despite the positive impact of height on repulsion, important factors such as economics, construction aspects, geographical suitability, and environmental impacts must be considered for real applications. This is crucial to develop feasible solutions applicable globally under the growing pressure of sea level rise.Alban Kuriqi is grateful for the Foundation for Science and Technology’s support through funding UIDB/04625/2020 from the research unit CERIS

Enhanced Groundwater Availability through Rainwater Harvesting and Managed Aquifer Recharge in Arid Regions Applied Water Science

...Alban Kuriqi is grateful for the Foundation for Science and Technology's help through funding UIDB/04625/2020 from the research unit CERIS

The superiority of data-driven techniques for estimation of daily pan evaporation

In the present study, estimating pan evaporation (Epan) was evaluated based on different input parameters: maximum and minimum temperatures, relative humidity, wind speed, and bright sunshine hours. The techniques used for estimating Epan were the artificial neural network (ANN), wavelet-based ANN (WANN), radial function-based support vector machine (SVM-RF), linear function-based SVM (SVM-LF), and multi-linear regression (MLR) models. The proposed models were trained and tested in three different scenarios (Scenario 1, Scenario 2, and Scenario 3) utilizing different percentages of data points. Scenario 1 includes 60%: 40%, Scenario 2 includes 70%: 30%, and Scenario 3 includes 80%: 20% accounting for the training and testing dataset, respectively. The various statistical tools such as Pearson’s correlation coefficient (PCC), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and Willmott Index (WI) were used to evaluate the performance of the models. The graphical representation, such as a line diagram, scatter plot, and the Taylor diagram, were also used to evaluate the proposed model’s performance. The model results showed that the SVM-RF model’s performance is superior to other proposed models in all three scenarios. The most accurate values of PCC, RMSE, NSE, and WI were found to be 0.607, 1.349, 0.183, and 0.749, respectively, for the SVM-RF model during Scenario 1 (60%: 40% training: testing) among all scenarios. This showed that with an increase in the sample set for training, the testing data would show a less accurate modeled result. Thus, the evolved models produce comparatively better outcomes and foster decision-making for water managers and planners.Validerad;2021;Nivå 2;2021-06-29 (beamah)</p