ORIGINAL RESEARCH article
Front. Water
Sec. Water Resource Management
This article is part of the Research TopicHydrological Simulation and Uncertainty Analysis Methods Based on Data Assimilation and Deep LearningView all 3 articles
Multi-Scale Geospatial Assessment of Water Reuse Potential in the Contiguous U.S.
Provisionally accepted
- Iowa State University, Ames, United States
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Integrated water resources management faces significant challenges due to water scarcity and declining water quality. In the context of growing global water demand, efficient management and reuse have become essential for maintaining adequate supplies and ensuring sustainability. Consequently, access to accurate data and advanced tools is vital for informed decision-making in water reuse strategies. This study addresses this need by developing a water inventory for the contiguous U.S. and an interactive application with advanced visualization tools to analyze water availability across multiple spatial scales. The inventory categorizes water into two traditional sources (surface runoff and recharge) and four sources for reuse (rainwater, stormwater, treated wastewater, and agricultural runoff). Multiple reanalysis datasets and geospatial databases were utilized to estimate water volumes, covering 48 states, 3,108 counties, and 31,099 communities (incorporated and unincorporated). The WaterWise application allows users to interactively analyze water availability, generating charts of water volumes by source and aggregating data at community, county, state, and watershed levels. This work demonstrates that water reuse is a sustainable solution to U.S. water scarcity: favorable climate conditions in the eastern U.S. make water reuse highly feasible, while in the west, strategies must be adapted regionally to optimize available resources and enhance resilience to future water challenges.
Keywords: Water reuse, Water inventory, integrated water resources management, Rainwaterharvesting, Stormwater management, wastewater reuse, agricultural runoff, Geospatialanalysis
Received: 01 Oct 2025; Accepted: 17 Nov 2025.
Copyright: © 2025 Cintura, Arenas, Ikuma, Liu and Rehmann. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Ingrid Cintura, icintura@iastate.edu
Antonio Arenas, aarenas@iastate.edu
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