San Francisco Bay Integrated Flood Forecasting Project summary report / Jungho Kim, Lynn E. Johnson, Tim Coleman and Rob Cifelli, Liv Herdman, Rosanne Martyr-Koller, Juliette Finzi-Hart, Li Erikson and Patrick Barnard.

Format:

Book

Government document

Author/Creator:

Kim, Jungho, author.

Johnson, Lynn E., author.

Cifelli, Robert, author.

Coleman, Tim, author.

Herdman, Liv, author.

Martyr-Koller, Rosanne, author.

Finzi-Hart, Juliette, author.

Erikson, Li, author.

Barnard, Patrick L., author.

Contributor:

Earth System Research Laboratory (U.S.). Physical Sciences Division, issuing body.

Series:

NOAA technical memorandum OAR PSD ; 317.

NOAA technical memorandum OAR PSD ; 317

Language:

English

Subjects (All):

Precipitation forecasting--Economic aspects--California--San Francisco Bay Area.

Precipitation forecasting.

Precipitation forecasting--California--San Francisco Bay Area--Simulation methods.

Flood forecasting--Economic aspects--California--San Francisco Bay Area.

Flood forecasting.

Flood forecasting--California--San Francisco Bay Area--Simulation methods.

Flood control--Economic aspects--California--San Francisco Bay Area.

Flood control.

Flood control--California--San Francisco Bay Area--Simulation methods.

San Francisco Bay Area (Calif.)--Environmental conditions.

San Francisco Bay Area (Calif.).

Ecology.

Flood control--Economic aspects.

Flood control--Simulation methods.

Flood forecasting--Simulation methods.

Precipitation forecasting--Simulation methods.

California--San Francisco Bay Area.

Genre:

Government publications

Online resources.

Physical Description:

1 online resource (iii, 37 pages) : color illustrations, color maps

Other Title:

San Francisco Bay Integrated Flood Forecasting Project

Place of Publication:

Boulder, Colorado : United States Department of Commerce, National Oceanic and Atmospheric Administration, Office of Oceanic and Atmospheric Research, Earth System Research Laboratory, Physical Sciences Division, 2018.

Summary:

San Francisco Bay is a highly urbanized estuary and the surrounding communities are susceptible to flooding along the bay shoreline and inland rivers and creeks that drain to the Bay. An integrated forecast model is being developed for predicting flooding in Bay area tributaries and estuaries. This project involves state-of-the-art coupling of a NWS Distributed Hydrologic Model (DHM) with the USGS Coastal Storm Modeling System (CoSMoS). Results presented here are for a prototype focused on the interaction of the Napa River watershed and the San Pablo Bay. Discharges from the DHM are meteorologically driven and dynamic, allowing for identification of flash flood threats for model grids interior to the Bay tributaries. The DHM tributary flows are input to the CoSMoS model which in turn simulates flooding extent in the receiving estuary. We utilize Delft3D-FM, a hydrodynamic model based on a flexible mesh grid, to calculate water levels that account for tidal forcing, seasonal water level anomalies, surge and in-Bay generated wind waves derived from the wind and pressure fields of a NWS forecast model. This report focuses on assessment of the various flood forecast information products generated by the integrated flood forecast modeling system. The tributary DHM generates forecast information for each grid that are portrayed as discharge, flow hydrographs (peak flow, time-to-peak, duration of high flow), soil moisture, and flood recurrence level. The CoSMoS portrays flood inundation and timing, and duration. Both models can help identify flood impact features such as road-stream crossings, and other critical facilities. A workshop was held with state, federal and local agency staff involved with flood forecasting and warning, and flood mitigation. As part of the workshop, we asked participants to review Hydro-CoSMoS outputs and rate how useful these products would be for theirs jobs. Results of these reviews are presented, and discussion is directed to how users' assessments could influence design of the real-time operational system to be implemented.

Notes:

"April 2018."

Online resource; title from PDF title page (NOAA, viewed June 22, 2018).

Includes bibliographical references (pages 29-30).

OCLC:

1041855896

Publisher Number:

doi:10.7289/V5/TM-OAR-PSD-317