Using Social Science to Gather Stakeholder Feedback on the National Water Model and Hydrologic Ensemble Forecast Services

To meet the varied needs of NWS deep core partners, a logic model for NWS water prediction map services was developed based on input from continued social science work, including previous stakeholder engagement meetings, five virtual focus groups of NWS subject matter experts, and two recent in-basin stakeholder forums. This conceptual model encompasses both existing water prediction information that is or could be delivered through map services as well as new or improved water information from emerging systems and tools, including the National Water Model (NWM) and the Hydrologic Ensemble Forecast Services (HEFS). The model represents the varied timescales of information needs (from observed or antecedent conditions out to seasonal forecasts) as well as different spatial scales at which information would be encountered from a broad national overview down to local information at specific points. The model is framed around suites of map services for three main use cases: flood risk map services for those users most concerned about high flow conditions, low flow risk map services for users with concerns around minimum thresholds and drought, and general map services for users making routine high value decisions.

Feedback from these groups, especially with respect to the spatial scale and timeframe of core partner water decisions, was used to better refine the logic model and the vision for NWS water map services and provided initial input on NWM and HEFS-informed data and information services prototypes. Cross-sector engagements in the Delaware and Penobscot River Basins which brought together diverse users to work through a tabletop exercise, have further validated the logic model for provision of water prediction services in a geospatial framework. Additional internal prioritization and discussion with National Weather Service subject matter experts will continue to refine the logic model and inform and prioritize new water prediction map services.