Distributed Hydrologic Modeling for Flood Mitigation—San Francisco Bay Area Advanced Quantitative Precipitation Information System

San Francisco Bay is a highly urbanized estuary and the surrounding communities are susceptible to flooding in inland rivers and creeks that drain to the Bay, and along the Bay shoreline. An integrated forecast system, called the Advanced Quantitative Precipitation Information (AQPI) system, that involves watershed and oceanic domains is being developed for predicting flooding in Bay area tributaries and estuaries.

This poster focuses on the AQPI watershed flood prediction modeling using the NWS National Water Model (NWM). The NWM is a distributed hydrologic model (DHM) which computes the hydrologic balance on a 250 m grid, aggregates excess precipitation to a 1 km grid, and routes these flood flows using the NHD-PlusV2 stream network. The system updates to include USGS gaged flows (-3 to 0 hrs), along with three forecast configurations (short- (0 to 18 hrs; 1-hr update), medium- (0 to 10 days; 6-hr update) and long-range (0 to 30 days; 1-day update). The DHM can provide a variety of flood forecast products, including hydrographs at any location (peak flow, time-to-peak, duration of high flow), and grid displays of streamflow, soil moisture, streamflow anomaly, snowpack, and ponded water depth. Other anticipated products include grids of flood recurrence levels, at-risk bridge crossings and flood inundation. Various verification analyses are being conducted to characterize DHM accuracy.

Coordination with Bay area flood response agencies is intended to help assure that the AQPI system products are acceptable and usable. To this end, we are asking local agency leaders to describe how they currently conduct their flood awareness, warning and response activities; how these procedures could be supported by the DHM products; and to review DHM outputs and recommend how these products could be formulated to support their jobs. Results of these reviews are presented to reflect how users’ assessments are informing design of the real-time AQPI system.