Using Social Science to Understand User Needs for National Weather Service Water Prediction Map Services

To meet the varied needs of National Weather Service (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 ongoing engagement to discuss innovative ideas and best practices among Service Coordination Hydrologists at NWS River Forecast Centers. 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. 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.

To demonstrate how new map services would fit into this model and to test the proposed framework, conceptual national mock-ups visualizing data and information services informed by the National Water Model (NWM) and the Hydrologic Ensemble Forecasting Service (HEFS) were developed and demonstrated to four focus groups across the country. To begin to demonstrate new types of information that could be made available through map service, a Story Map was created with conceptual NWM maps at the national and river basin scales and local information such as hydrographs and flood inundation.

To address water supply map services, prototype Water Resources Monitor and Outlook maps and other complementary datasets and visualizations used in water management decisions were shown to participants. Feedback from these focus 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 which bring 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. The results of these engagements will continue to refine the logic model and inform and prioritize new water prediction map services.