Geospatial Approaches for Sustainable Wastewater Treatment and Reuse in Agriculture under Climate Change Scenarios

Abstract

The growing challenges of climate change, rapid urbanization, and water scarcity have intensified the global demand for sustainable wastewater management and reuse, particularly in agriculture. Geospatial technologies comprising Remote Sensing (RS), Geographic Information Systems (GIS), Geo-Artificial Intelligence (Geo-AI), and Internet of Things (IoT) integration offer innovative, cost-effective, and data-driven tools to optimize wastewater treatment, reclamation, and safe agricultural reuse. These technologies enable precise spatial mapping of wastewater treatment plants, pollution hotspots, and irrigation networks, improving decision-making for water allocation and environmental protection. Climate-adjusted geospatial models support the identification of optimal reuse sites, water demand assessments, and impact evaluations, ensuring adaptive responses to hydrological variability. The integration of geospatial intelligence with advanced treatment methods, such as electrochemical systems and constructed wetlands, enhances wastewater quality while minimizing ecological and health risks. However, challenges persist in data heterogeneity, policy alignment, and limited stakeholder awareness. Future advancements in Geo-AI, machine learning, and climate-smart GIS promise to strengthen wastewater reuse efficiency, promote precision agriculture, and build resilience against climate-induced water stress. This review underscores the transformative potential of geospatial approaches as a cornerstone for sustainable water resource management and circular economy practices in agriculture under changing climatic conditions.