Dynamic Population Mapping to Advance Energy-Water Resilience

Dynamic population mapping, combined with household-level energy and water demand profiling, enables precise, actionable forecasting and resilience planning for U.S. energy-water systems under variable population and climate conditions.

Energy and water systems face growing stress from population growth, seasonal mobility, and high-demand industries, all under increasingly variable climate conditions. Household composition, socioeconomic characteristics, and migration patterns strongly influence local consumption patterns. Static population estimates limit the ability to anticipate peak loads and manage infrastructure effectively. High-resolution, temporally explicit population data integrated with household demand profiles can improve forecasting and reveal at-risk populations.

Near-Term Opportunities: Oak Ridge National Laboratory's LandScan datasets and synthetic population methodologies provide a foundation for dynamic population mapping at high spatial and temporal resolution. Integrating seasonal and temporary population flows enables modeling of demand spikes in regions with transient populations, such as tourist or second-home destinations. Scenario-based simulations combining population dynamics, household demand, and infrastructure data support climate-aware planning, operational optimization, and risk mitigation. Collaborative efforts with utilities, municipalities, and academic partners can validate models and guide investment decisions.

Success Measures: Success is reflected in improved demand forecasting accuracy and reduced peak energy and water loads. Enhanced system efficiency and operational flexibility lower outage risk and support equitable access for vulnerable populations. Broader adoption by utilities and municipalities demonstrates practical value and scalability. The approach strengthens resilience to climate and population-driven stressors while informing investment and policy decisions. Dynamic population-informed modeling offers a scalable, data-driven framework to enhance energy-water system resilience across the United States.