Abstract

Rainwater harvesting (RWH) stands as a strategy to mitigate the widespread challenge of water scarcity in arid and semi-arid regions worldwide. The practice of capturing and storing stormwater via different RWH technologies during wet periods can serve as a means of replenishing stressed local aquifers. However, identifying optimal locations conducive to the successful implementation of RWH initiatives accounts for a multi-tiered and intricate challenge. Furthermore, contemporary methodologies utilized to assess the potential of RWH often adhere to conventional paradigms, exhibiting a dearth of sophisticated mathematical foundations. Hence, this study proposes a hybrid approach that integrates the fuzzy analytic hierarchy process (AHP) and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) algorithms to delineate suitable RWH sites. The proposed framework was subsequently applied in the southeastern basin of Djibouti, a nation facing acute water stress. The assessment of RWH potential in this research is predicated upon twelve decision criteria, organized into three principal dimensions: physical, environmental, and socio-economic aspects. The results highlighted the vitality of environmental (47.67%) and physical (37.98%) dimensions, whereas the socio-economic (14.35%) cluster was deemed less significant according to the established decision framework. Furthermore, the resultant suitability map portrays that a substantial area of the basin, approximately 872.5 km2 (equivalent to 64.25%), exhibits moderate to very high suitability for the implementation of diverse RWH structures, including check dams, percolation tanks, and infiltration ponds. The reliability of the analysis is verified through sensitivity analysis, underscoring the robustness of the decision model. This contributes to the empowerment of decision-makers and the research community, facilitating the creation of accurate RWH potential maps, and thereby advancing effective water resource management strategies for water stressed regions.

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