To enhance the current dam break capabilities, the National Weather Service (NWS) is in the process of developing alternative forecasting tools and processes that will enable River Forecast Center (RFC) and Weather Forecast Office (WFO) forecasters of different skill levels to evaluate the consequences of a potential dam failure in a relatively short period of time.
Dam break flood hydrographs are dynamic, unsteady flow events that should be modeled using full unsteady flow hydraulic routing models (such as the NWS FLDWAV model). However, since the model has to be developed and calibrated by someone who has experience in hydraulics, and the whole process may take a significant amount of time. The currently in place system has been proven to be difficult to be used in real-time forecasting operations.
This project is designed to address the limitations of the current dam inventory data and dam break modeling approaches. Two goals should be achieved with this project, the first is to provide RFC and WFO forecasters access to quality-controlled, up-to-date dam inventory information and the second goal is to develop a system that will integrate several dam break modeling approaches. Results from these approaches will assist the forecasters in selecting the tool that could be the most appropriate one, given the length of time needed for execution, how quickly the results are needed, and the availability of data.
To address the limitations discussed above, the NWS previously initiated the Dam Analysis Tool (DamAT) project. The goal of the DamAT project was to develop a methodology and the supporting software to allow forecasters to generate a dam failure forecast in a relatively short period of time especially when data are sparse. DamAT used the existing simplified hydraulic model (SMPDBK) developed by the NWS and Geographical Information Systems (GIS) tools to estimate dam break flows. We are continuously updating dam information and testing dam break scenarios to improve the overall forecast.
Project Term: To be determined.