Building Resilient Energy-Water Systems: Integrated Modeling, Scenario Selection, and Near-Term Decision Support

U.S. energy and water systems are increasingly interdependent, often leading to cascading and compounding system failures when faced with acute and chronic hazards. Such stressors propagate across multiple spatial scales, typically starting with changing earth system dynamics (for example, droughts) that interact with the regional natural environment (for example, streamflow and temperatures), and affect the local built environment (for example, cities, reservoirs, hydropower generators, and conveyance pumps). Emerging water-intensive industries (e.g., advanced manufacturing, artificial-intelligence (AI) data centers, geothermal power, and hydraulic fracturing) further concentrate risk. Current modeling architectures rarely support this level of cross-sectoral coupling, with limited unifying and integrated tools for the joint planning and co-optimized operations of the sectors, or standardized selection of scenarios to stress test future conditions. While many modeling tools exist, they are typically focused on specific scales, time horizons, single hazard classes, and sectors in isolation, making it difficult to quickly identify joint vulnerabilities. Further, such models are often not actionable for decision-makers.

Across all three focal areas (energy for water, water for energy, and energy-water intersections) there is therefore a need for integrated modeling frameworks, co-produced between scientists and resource managers, that (i) include common scenario selection across sectors, (ii) connect existing water and energy system sector models, and global earth system and economic models, (iii) enable quick, practical risk screening to find the biggest vulnerabilities and early opportunities for action, before investing in deeper, more complex modeling, and (iv) provide tools for decision-support under deep uncertainty (DMDU).

In this white paper we describe the near-term opportunity to invest in model connection or "wrapper" tools that link data, scenarios, and assumptions between existing global and regional energy, water, and hazards models that were built for single sectors/hazards/time frames. Such connection tools, building on the advances in AI, can facilitate better communication between models across multiple scales and sectors, both vertical (global to regional to local) and horizontal (between energy and water sectors). Strategic investments in automated modules that link inputs and outputs across existing earth systems, integrated assessment, regional hydrology, water management, and grid models can enhance interoperability without requiring the development of new platforms from scratch. Practical triage tools are also needed so utilities can anticipate threats and prioritize actions without relying solely on heavy modeling, with deeper analyses available as needed.

The success of such model connection, triage, and decision-support tools can be measured in terms of reduced service interruptions and restoration time during system failures, decreased exposure to cascading failures, lower lifecycle and operations and maintenance cost, improved interoperability with data standards across agencies, decision-support and threat-triage tools incorporated by practitioners, and hazard-aware siting playbooks for utilities, operators, planners, and regulators.