Geospatial Decision Support for Rainwater Harvesting in Arid Regions
Summary
Arid and semi-arid regions worldwide face chronic water scarcity driven by low and erratic precipitation, land degradation and growing demand. Geospatial decision support systems combine remote sensing, geographic information systems (GIS), spatial data analysis and multi-criteria decision analysis to identify optimal locations and designs for rainwater harvesting (RWH) interventions. By integrating thematic layers such as rainfall distribution, topography, soil texture, land cover and socio-economic factors, these tools generate suitability maps at micro, meso and watershed scales. Recent advances incorporate machine learning algorithms, fuzzy logic and high-resolution satellite imagery to refine runoff estimation and automate weightings of decision criteria. Participatory GIS approaches further engage local communities, blending indigenous knowledge with technical data to ensure social acceptance and practical feasibility. Applications range from small-scale rooftop and micro-catchment systems to large-scale storage dams and recharge structures. Beyond locating sites, geospatial decision support informs design parameters, estimated yield and potential ecosystem benefits, including soil conservation, vegetation restoration and aquifer recharge. Such systems offer decision-makers rapid, reproducible and spatially explicit guidance, enhancing resilience to drought and supporting sustainable land and water management across regions from the Middle East and North Africa to Central America and South Asia.
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Geospatial Decision Support for Rainwater Harvesting in Arid Regions publication trend
The graph below shows the total number of articles in geospatial decision support for rainwater harvesting in arid regions across all publications each year (not limited to Nature Index journals).
Technical terms
Geographic Information System (GIS): A framework for capturing, managing and analysing spatial and geographic data to support decision-making.
Multi-Criteria Decision Analysis (MCDA): A set of methods for evaluating and ranking alternative sites or interventions using weighted criteria.
Analytic Hierarchy Process (AHP): A structured decision technique that decomposes a problem into a hierarchy of criteria, assigns weights through pairwise comparisons and synthesises scores.
Curve Number (CN): A parameter in hydrological modelling representing land cover, soil type and antecedent moisture to estimate runoff potential.
Normalized Difference Vegetation Index (NDVI): A remote sensing index measuring vegetation greenness and biomass by comparing near-infrared and red light reflectance.
References
- Suitability mapping of micro and meso scale rain water harvesting for vegetation-Based restoration in arid degraded areas of Jordan. Catena (2024).
- Integrating GIS-Based MCDA Techniques and the SCS-CN Method for Identifying Potential Zones for Rainwater Harvesting in a Semi-Arid Area. Water (2021).
- Potential Water Harvesting Sites Identification Using Spatial Multi-Criteria Evaluation in Maysan Province, Iraq. ISPRS International Journal of Geo-Information (2020).
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