Integrated Watershed Management

Integrated Watershed Management

Prioritization of suitable sites for subsurface water harvesting using the data envelopment analysis method (Case study: Kalat and Sarakhs border areas)

Document Type : Original Article

Authors
Mohammad Rostami Khalaj 1
Hamze Noor 1
Ali Bagheriyan Kalat 1
Mirmasoud Kheirkhah Zarkesh 2
1 Soil Conservation and Watershed Management Research Department, Khorasan Razavi Agricultural and Natural Resources Research and Education Center, AREEO, Mashhad, Iran
2 Agricultural Research, Education and Extension Organization (AREEO), Soil conservation and watershed management research institute, Tehran, Iran
10.22034/iwm.2025.2044609.1183
Abstract
Extended Abstract
Introduction: Groundwater resources in border areas have often been overlooked by managers and policymakers due to challenges such as wide geographical spread, and the high costs associated with their study. However, it is crucial to inform decision-makers about the potential of these resources to address water scarcity issues. In the border areas of Razavi Khorasan Province, many farmers and ranchers lack access to sufficient water for agriculture and livestock. Constructing underground dams in these regions can help store significant portions of subsurface water flow in alluvial reservoirs, making it available for use when needed. This study aims to identify and prioritize suitable sites for subsurface water harvesting using underground dams in the border areas of Kalat and Sarakhs in Khorasan Razavi Province.
 Materials and methods: In this study, Boolean logic was used to eliminate areas unsuitable for underground dam construction. Five criteria—slope, geology, land use, distance from villages, and distance from roads—were employed to locate potential sites. The Fuzzy-AHP method was then used to weight these factors and identify susceptible areas. To prioritize the proposed sites, the Data Envelopment Analysis (DEA) method was applied. DEA, a linear programming-based technique, categorizes suggested points into effective and ineffective categories based on inputs such as dam construction costs, watershed area, precipitation, water demand, and water supply costs. The output considered was the estimated volume of harvested water.
 Results and Discussion: The results indicate that 58.6% of the study area is unsuitable for underground dam construction, demonstrating the effectiveness of Boolean logic in reducing the workload of site selection. The Fuzzy-AHP analysis revealed that geology and slope are the most influential factors, with weights of 0.395 and 0.268, respectively, while land use and distance from roads have the least impact, with weights of 0.07 and 0.097, respectively. Slopes of 0–3% were found to be most suitable due to minimal water flow velocity, which facilitates the creation of effective reservoirs. Pasture land use was identified as ideal for dam construction. Approximately 6.8% of the area has very high potential, and 11.7% has high potential for underground dam construction. Using DEA, seven priority sites were identified for further investigation.
 Conclusion: The study concludes that: (1). Farmers and ranchers in the border areas of Khorasan Razavi Province, located on alluvial plains near dry rivers, lack access to sufficient water resources. Constructing underground dams can store significant groundwater flows in alluvial reservoirs, providing a reliable water source for agriculture and livestock. (2). Building underground dams in border areas can create employment opportunities, increase the working population, and enhance regional security. This, in turn, will boost the income and satisfaction of local residents. (3). Eliminating unsuitable areas for dam construction simplifies analysis, reduces fieldwork, and lowers costs. (4). The use of DEA for prioritizing dam construction sites represents a novel approach, offering a new perspective on site selection techniques.
Keywords
Potential detection
Fuzzy-AHP method
underground dam
Linear programming
border areas

Subjects

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  • Receive Date 03 November 2024
  • Revise Date 18 December 2024
  • Accept Date 01 March 2025