Energy and water generation and delivery systems are inherently interconnected. With demand for energy growing, the energy sector is experiencing increasing competition for water. With increasing population and changing environmental, socioeconomic, and demographic scenarios, new technology and investment decisions must be made for optimized and sustainable energy-water resource management. This also requires novel scientific insights into the complex interdependencies of energy-water infrastructures across multiple space and time scales. An integrated data-driven modeling, analysis, and visualization capability is needed to understand, design, and develop efficient local and regional practices for the energy-water infrastructure components that can be guided with strategic (federal) policy decisions to ensure national energy resilience.

To meet this need of the energy-water nexus (EWN) community, an Energy-Water Knowledge Discovery Framework (EWN-KDF) is being proposed to accomplish two objectives:

• Development of a robust data management and geo-visual analytics platform that provides access to disparate and distributed geospatial Earth science and critical infrastructure data, socioeconomic data, and emergent ad-hoc sensor data to provide a powerful toolkit of analysis algorithms and compute resources to empower user-guided data analysis and inquiries; and
• Demonstration of knowledge generation with selected illustrative use cases for the implications of climate change for coupled land-water-energy systems through the application of state-of-the art data integration, analysis, and synthesis.

Oak Ridge National Laboratory (ORNL), in partnership with Argonne National Laboratory (ANL) and researchers affiliated with the Center for International Earth Science Information Partnership (CIESIN) at Columbia University and State University of New York-Buffalo (SUNY), propose to develop this Energy-Water Knowledge Discovery Framework to generate new, critical insights regarding the complex dynamics of the EWN and its interactions with climate variability and change. An overarching objective of this project is to integrate impacts, adaptation, and vulnerability (IAV) science with emerging data science to meet the data analysis needs of the U.S. Department of Energy and partner federal agencies with respect to the EWN.

The first year of work will encompass scoping of activities for the second and third years based on a thorough understanding of community needs and science drivers, analytical and data needs, identification of existing capabilities, and determining the hardware and software requirements. The scoping activity will also include design and planning for phased system implementation. The second year will focus on the initial build-out of the software and hardware capability, the data and analytical capabilities, and the testing of community-driven analytical hypotheses, as outlined at the end of the first year of scoping activities. It is envisioned that an initial functioning system will be stood up by the end of the second year for the community. The third year of work will focus on the federation and data analysis capabilities of the system.

Engagement with user communities will continue to define the science drivers and use of the framework will create further case studies for knowledge discovery. A layered federated system capable of advanced data analytics will be stood up and released to the community at the end of the third year.