This paper provides a framework to evaluate how policymakers interact with information, make decisions, and act upon policy-related information. To explore the influence of information in bridging water policy knowledge boundaries and linking policy decision making and action, the authors conducted a grounded theory study of key congressional legislative staff in the US House and Senate involved in federal water policy development and oversight. Federal legislative water policies are largely shaped and developed by senior congressional legislative staff, whose policy priorities, decisions, and actions are influenced by policy-related information. Three conceptual themes emerged from the study as common priorities for legislative staff: (1) developing trusted relationship-information networks; (2) prioritizing relevant stakeholder interests; and (3) maximizing efforts to achieve desired results. While the use of policy information is largely determined by the staff's multiple principal-agent roles, competing interests and other constraints, results of this study suggest that information quality criteria can be useful as heuristic tools for both intuitive judgments and reasoning of legislative decision makers and for transferring knowledge across science-policy action boundaries.
Riverine flood event situation awareness and emergency management decision support systems require accurate and scalable geoanalytic data at the local level. This paper introduces the Water-flow Visualization Enhancement (WaVE), a new framework and toolset that integrates enhanced geospatial analytics visualization (common operating picture) and decision support modular tools. WaVE enables users to: 1) dynamically generate on-the-fly, highly granular and interactive geovisual real-time and predictive flood maps that can be scaled down to show discharge, inundation, water velocity, and ancillary geomorphology and hydrology data from the national level to regional and local level; 2) integrate data and model analysis results from multiple sources; 3) utilize machine learning correlation indexing to interpolate streamflow proxy estimates for non-functioning streamgages and extrapolate discharge estimates for ungaged streams; and 4) have time-scaled drill-down visualization of real-time and forecasted flood events. Four case studies were conducted to test and validate WaVE under diverse conditions at national, regional and local levels. Results from these case studies highlight some of WaVE's inherent strengths, limitations, and the need for further development. WaVE has the potential for being utilized on a wider basis at the local level as data become available and models are validated for converting satellite images and data records from remote sensing technologies into accurate streamflow estimates and higher resolution digital elevation models.
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