Delineating MAR Sites Using GIS-MCDA for Nuweiba Alluvial Fan Aquifer, Sinai, Egypt

Abstract

In the last few decades, groundwater has been the main water supply to the Nuweiba alluvial fan. However, currently, the main water supply is a desalination plant. The desalination plant might be vulnerable to malfunctions resulting in a severe drought. In addition, the aquifer type in the fan is coastal. Hence, replenishing the groundwater is necessary on a long-term basis to overcome drought events in the case of emergency. To replenish the groundwater using flash-flood water, a Managed Aquifer Recharge (MAR) system could be installed. This study applies the Geo-Information System–Multi-Criteria Decision Analysis (GIS-MCDA) method to delineate the feasible locations for installing a MAR system. To delineate the feasible MAR sites via a potential map, four steps were performed: problem definition, constraint mapping, suitability mapping, and sensitivity analysis. The results show that nearly 52% of the study area is suitable for installing MAR. Additionally, around 6% of the study area shows high potential for installing MAR, whereas nearly 20% falls under the moderate potential class. The potential map shows that the high-potential MAR sites are located at the western portion of the study area, near the ephemeral stream outlet. The map could be utilized as a tool for decision-makers to plan a future sustainable development strategy.

Integrated Fuzzy AHP-TOPSIS Model for Assessing Managed Aquifer Recharge Potential in a Hot Dry Region: A Case Study of Djibouti at a Country Scale

“…Nevertheless, some scholars argue that a combined consistency check and sensitivity analysis are enough to validate proposed models [25,97]. In line with this, several sensitivity analysis approaches have been proposed so far, ranging from the adoption of different weighting schemes [53], the addition or removal of relationships in the case of the multi-influence factor (MIF) method [24], or changes in the linguistic quantifier when applying the ordered weighted averaging (OWA) technique [97,98]. While the fuzzy AHP algorithm incorporates the inherent ambiguity of human decision making, we also tested the robustness of our model by adjusting the degree of fuzziness within the fuzzy AHP framework.…”

Risk Mapping of Water Supply and Sanitary Sewage Systems in a City in the Brazilian Semi-Arid Region Using GIS-MCDA

“…Observing the vulnerabilities and strengths of the UWSS and SSS, in a second moment, this work proposed the generation of a mapping of areas with risks of water shortages and areas with a degree of insufficiency of sanitary sewage. Thus, to evaluate the most susceptible areas, maps of these two aspects were produced for the entire study area, where it is possible to carry out a spatial assessment and generate visual outputs that help in the understanding of the study and in the decision making by urban managers, as highlighted by [15]. When generating the map, it is shown that the high potential MAR sites are located in the western portion of the study area.…”

“…On the other hand, the neighborhoods with "Low"-risk areas were: São João da Escócia (37); Cidade Jardim (11); Petrópolis (30); Indianópolis (15); Divinópolis (14); Morro Bom Jesus (25); São Francisco (36); Riachão (33); Universitário (41). The areas with a "Very Low" risk were the following neighborhoods: Jardim Boa Vista (16); Distrito Industrial (13); Maria Auxiliadora (23); Serras do Vale (39); São José (38); Cedro (8).…”

“…The growth of society in the urban environment requires the adequate use of its natural resources such that that they are not exhausted. Therefore, the Geographic Information System based the Multi-Criteria Decision Analysis (GIS-MCDA) as the preferred approach in several studies because it involves a combination of multiple criteria in a weighted way and also produces visual results, which is important for decisions in the urban environment, being used to: (i) delineate MAR (Managed Aquifer Recharge) sites [15,16]; (ii) identify potential zones for rainwater harvesting in a semi-arid area [17]; (iii) select priority management programs for watersheds [18]; (iv) assess the risk of urban flooding [19,20]; (v) decide the multi-criteria support for water management [21]; (vi) optimize the layout of water supply pipeline systems [22]; (vii) carry out mapping of water scarcity risks [23]; (viii) create zoning of areas at risk of deforestation [24]; (ix) identify water distribution points [25]; (x) analyze the feasibility of supply systems with groundwater abstraction [26]; (xi) identify Concentrated Solar Power (CSP) and Photovoltaics (PV) points to assess the potential of solar energy [27]; (xii) identify potential areas for marina construction [28]; (xiii) select landfill sites using GIS-MCDA [29].…”