Data Integration and Visualization for Enhanced Resilience and Sustainability in Hydropower (DIVERS-H)

Hydropower generation faces unprecedented challenges, including reduced water supply due to changes in hydroclimate, growing demand from population growth and multi-sector water usage, and warmer streams causing inefficiencies. Coupled with challenges in maintaining environmental regulations, these factors endanger the reliability and sustainability of hydropower operations, necessitating innovative solutions. We propose building "Data Integration and Visualization for Enhanced Resilience and Sustainability in Hydropower (DIVERS-H)." This initiative will support data-driven research using artificial intelligence methods to capture the complex dynamics of water availability, demand, and climate change. DIVERS-H will leverage advanced AI-based data processing and modeling techniques with interactive data visualization to predict factors affecting water supply at much higher spatial resolutions than present-day models allow. Through collaboration, we will integrate changing climate and socioeconomic data with water supply and demand considerations to predict hydropower system resilience and sustainability into the future. We will innovate a new open-source decision support tool that will enable users to 1) ingest and browse climate, hydrology, and socio-economic data; 2) select a location and connect data from that location to AI and predictive models 3) run algorithms on the cloud at scale 4) visualize and compare results in a web browser. Key data sources include climate variable projections from the Coupled Model Intercomparison Project (CMIP6) and hydrologic parameters (Land Use Land Cover, Elevation, Stream Characteristics), along with socioeconomic data from the U.S. Census and public sources. To bootstrap the development, we will use the Kitware open-source software UVDAT, with in-house UX experts guiding the user interface development. The proposed system will aid planners, policymakers, and logistics engineers in grappling with data that is growing in size, complexity, and diversity. By placing climate data alongside other infrastructure, sensor, and demographic data in new ways with our proposed analytic and visualization tools, planners will make data-driven decisions that result in better holistic outcomes for all hydropower stakeholders.