Water in Crisis: AI, Governance and Human-Water Symbiosis

Climate change is intensifying droughts, floods, and cascading water‑related disruptions worldwide. These extreme events are not “natural” alone: their impacts are produced through feedbacks among hydrology, infrastructure, land use, and crucially, governance and decision‑making. At the same time, artificial intelligence (AI) is rapidly entering water monitoring, forecasting, operations, and planning. These two trends are converging: AI is becoming part of the governance and operation of human–water systems, potentially reshaping incentives, information flows, accountability, coordination, and risk—especially in the context of compound extremes and deep uncertainty.

In this Research Topic, AI refers broadly to data‑driven and computational approaches: machine learning/deep learning, hybrid physics–Machine Learning models, large‑model decision support, and increasingly agentic AI that can recommend, coordinate, or execute tasks. Governance is understood as institutional arrangements (rules and norms) and organizations that structure interactions among actors, including monitoring, enforcement, resource allocation, and conflict resolution across scales. This Topic focuses on how AI systems interact with these institutional arrangements to shape decision‑making and outcomes in the face of extreme water events—sometimes strengthening them (e.g., improved early warning), and sometimes destabilizing them (e.g., creating new failure modes).

A central motivation is that the next generation of human–water systems will be AI‑augmented governance and operation of human‑water systems. AI may provide pattern recognition, early warning, and decision support for droughts, floods, and compound hazards, but it may also become embedded in operational and governance processes (e.g., reservoir operations, drought restrictions, emergency response coordination, insurance and risk communication, infrastructure maintenance scheduling). These developments raise urgent questions: How do AI‑driven information and recommendations alter cooperation, compliance, and collective action under extreme conditions? Under deep uncertainty and disruption—compound extremes, infrastructure failures, conflict, or rapid policy shifts—when do AI‑enabled systems improve resilience, and when do they introduce fragility? How do human cognitive biases and heuristics interact with AI limitations and maladaptive behaviors (e.g., hallucination) to shape decisions and outcomes? What institutional designs, oversight mechanisms, and participatory approaches are needed to ensure AI supports legitimate, robust, and socially beneficial water governance in an era of intensifying extremes?

This Research Topic seeks interdisciplinary and transdisciplinary contributions that advance understanding and actionable pathways toward climate‑resilient water futures by explicitly integrating AI, governance, and human–water feedbacks. We welcome empirical studies, comparative institutional analyses, methodological innovations, and conceptual syntheses that connect mechanisms across scales—from communities and utilities to transboundary basins and global assessments—and that bridge technical advances (e.g., environmental indices, remote sensing, hybrid modeling) with social‑institutional understandings.

Submissions are invited on (but not limited to) the following tightly connected themes:

• AI in water governance and operations for extreme events
• Water institutions and AI in the context of droughts, floods, and compound hazards
• Robustness under disruption (compound extremes, infrastructure failure, policy shifts)
• Behavioral dynamics and failure modes in AI‑augmented decision‑making
• Equity, legitimacy, and participation in AI‑enabled water governance
• Risk communication and decision interfaces under deep uncertainty
• Hybrid modeling and data integration (remote sensing, big data, social structure)
• Conflict, cooperation, and coordination in human–water systems
• Socio‑ecological vulnerability and resilience to water‑related extremes
• Community adaptation, indigenous knowledge, and traditional water systems
• Gender, equity, livelihoods, and justice in water‑stressed regions
• Drought indices, multi‑hazard risk assessment, and early‑warning systems
• AI/ML/DL and hybrid physics–AI models for climate–water analysis

By foregrounding the coupling of AI capabilities and limitations with institutional design and human behavior in the context of intensifying water extremes, this Topic aims to move beyond purely technical advances toward implementable visions for resilient and legitimate water governance.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: Climate Change, Water Governance, Socio-Hydrological Resilience, Droughts And Floods, Compound Extremes, Drought Indices, AI-ML-DL, Risk Communication, Indigenous Knowledge, Equity And Livelihoods

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.