Abstract
In arid and semi-arid area, groundwater is the most important water resources. Surface runoff harvesting is the most important process in the artificial recharge of groundwater that should increase groundwater quality and quantity. Urban impervious area provides an appropriate surface to produce adequate amounts of runoff. Groundwater recharge via recharge wells is one of the successful direct sub-surface methods. As many cities around the world face issues of water scarcity due to a fast and unsustainable urbanization, identify the best locations of groundwater recharge wells is an interesting relevant topic, especially in the arid and semi-arid area. Selection of a suitable area for groundwater recharge could increase efficiency of the recharge wells. In this study, the best location of recharge wells was investigated in Urmia city located in the North-west of Iran using fuzzy logic technique. In this study, locations of the drainage channel junctions with adequate potential of surface runoff were determined using SWMM. Appropriate locations for recharge wells were determined based on different layers including distance to runoff harvesting points, distance to the production water wells and depth of groundwater table. Hydraulic condition (hydraulic conductivity and specific recharge) was also used separately. The input layers were prepared using geostatistical interpolation techniques in ArcGIS 9.3 software. Mamdani fuzzy inference system was applied to incorporate the fuzzified input layers. Finally, in each area, pixels with the highest value were proposed as suitable locations for recharge wells. Based on the results, the number of pixels with “High” priorities increased when the hydraulic conductivity was used to site selection. Comparing hydraulic conductivity layer and selected location of the recharge wells shows that the area with low hydraulic conductivity and the area closed to the production water wells has not suitable priority for recharge wells.
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Acknowledgements
This work was funded through the University of Kashan in Iran as a PhD thesis and by the Iranian National Science Foundation (Grant No: 95850079). The authors appreciate the University of Kashan and the Iranian National Science Foundation for their generous support.
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Ghazavi, R., Babaei, S. & Erfanian, M. Recharge Wells Site Selection for Artificial Groundwater Recharge in an Urban Area Using Fuzzy Logic Technique. Water Resour Manage 32, 3821–3834 (2018). https://doi.org/10.1007/s11269-018-2020-7
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DOI: https://doi.org/10.1007/s11269-018-2020-7