Introduction of NOAA's Community Hydrologic Prediction System

NOAA's National Weather Service (NWS) is responsible for the production of the nation's river flood forecasts and warnings in support of the protection of life and property and enhancement of national commerce. For the past thirty years, NWS hydrologists have used the NWS River Forecast System (NWSRFS) as the core infrastructure for their hydrologic operations. NWSRFS is remarkable in that it has met most of NWS needs for such a long time. With increasing operational needs and escalating support costs, NWS has decided to retire NWSRFS and introduce the Community Hydrologic Prediction System (CHPS).

CHPS has been developed by the NWS in collaboration with Deltares (formerly Delft Hydraulics) in the Netherlands. The Delft-Flood Early Warning System (FEWS) serves as the infrastructure for CHPS with NWS hydrologic models and United States Army Corps of Engineers hydraulic models providing the forecast science.

The migration to CHPS realizes significant improvements for NWS and its hydrologic forecast offices. First, CHPS provides all of the “standard” benefits of a legacy system replacement – lower support costs, better use of modern computing technology, and increased flexibility. Second, the plug-and-play nature of CHPS expands our ability to partner with Federal, state, local and university researchers. Third, CHPS allows NWS to re-examine its hydrologic forecast concept-of-operations, and consider new ways to meet its mission requirements. A fourth benefit is the immediate connection between the NWS and the international flood forecasting community through the Delft-FEWS user community. The NWS anticipates being able to exchange forecast techniques with many others very easily.

The paper and oral presentation will delve more deeply into the topics mentioned above, including a more comprehensive description of the CHPS infrastructure, a comparison of pros and cons of CHPS versus NWSRFS, the migration project at the NWS River Forecast Centers, anticipated benefits, and lessons learned.