When severe weather strikes, the National Weather Service Office of Water Prediction relies on complex modeling to make critical safety forecasts. Despite improvements, these predictive capabilities have plateaued. To overcome these limitations, a University of Vermont scientist has collaborated with federal agencies to develop the Next Generation Water Resources Modeling framework, with the goal of significantly improving National Flood Prediction.
Water moves through landscapes in incredibly complex ways, traversing different terrains at varying speeds and routes. This inherent unpredictability creates massive challenges for hydrologists and operational forecasters. There is no single perfect model. The current operational systems suffer from biases that affect their predictive accuracy and tends to works best in the Pacific Northwest and Rocky Mountains.
To address these gaps, the new NextGen framework was designed with the following capabilities:
1) the use of user-defined, not model-specific, geospatial data to define model domains, initialize parameter values and model states, and to build stream networks for flow routing;
2) the execution of various models and modules in the same framework using the same configuration system and execution commands, and;
3) the production of model outputs in a standardized format over a consistent domain regardless of the chosen model.
The National Water Model is already slated to adopt this new framework for its next operational version. By deploying the right model in the right place at the right time, this software will eliminate redundancies and deliver a far more accurate National Flood Prediction to save lives during extreme weather events.
