NOAA Technical Memorandum OAR PSD – 317

SAN FRANCISCO BAY INTEGRATED FLOOD FORECASTING PROJECT

SUMMARY REPORT

Jungho Kim, Lynn E. Johnson, Tim Coleman and Rob Cifelli 

NOAA Earth System Research Laboratory – Physical Science Division 

Liv Herdman, Rosanne Martyr-Koller, Juliette Finzi-Hart, Li Erikson and Patrick Barnard

USGS Pacific Coastal and Marine Science Center

Earth System Research Laboratory 

Physical Sciences Division

Boulder, Colorado 

December 2017

noaa Office of Oceanic and 

Atmospheric Research

NATIONAL OCEANIC AND ATMOSPHERIC

ADMINISTRATION /

https://doi.org/10.7289/V5/TM-OAR-PSD-317 

NOAA Technical Memorandum OAR PSD – 317

SAN FRANCISCO BAY INTEGRATED FLOOD FORECASTING 

PROJECT SUMMARY REPORT 

Jungho Kim, Lynn E. Johnson, Tim Coleman and Rob Cifelli 

NOAA Earth System Research Laboratory – Physical Science Division 

Liv Herdman, Rosanne Martyr-Koller, Juliette Finzi-Hart, Li Erikson and Patrick Barnard 

USGS Pacific Coastal and Marine Science Center

Earth System Research Laboratory

Physical Sciences Division

Boulder, Colorado 

April 2018 

UNITED STATES

DEPARTMENT OF COMMERCE

NATIONAL OCEANIC AND 

ATMOSPHERIC 

ADMINISTRATION

Office of Oceanic and 

Atmospheric Research

Secretary Penny Pritzker

Department of Commerce

Dr. Kathryn Sullivan

Under Secretary for Oceans and 

Atmosphere / Administrator

Craig McLean

Assistant Administrator for 

Oceanic and Atmospheric 

Research

https://doi.org/10.7289/V5/TM-OAR-PSD-317 

Notice 

Mention of a commercial company or product does not constitute an endorsement by NOAA/Earth System 

Research Laboratory. Use of information from this publication concerning proprietary products or the test 

of such products for publicity or advertising purposes is not authorized. 

The findings and conclusions on this report are those of the authors and do not necessarily represent the 

views of the funding agency. 

Citation 

Herdman, L. J. Kim, , L.E. Johnson, T. Coleman, R. Cifelli, R. Martyr-Koller, J. Finzi-Hart, L. 

Erikson and P. Barnard. 2018. San Francisco Bay Integrated Flood Forecasting Project - 

Summary Report. NOAA Technical Memorandum PSD-317, NOAA Printing Office, Silver 

Spring, MD, 37 pp. https://doi.org/10.7289/V5/TM-OAR-PSD-317 

Available at: http://docs.lib.noaa.gov/noaa_documents/OAR/PSD/TM_OAR_PSD_317.pdf
i 

Table of Contents

Abstract iii

Executive Summary 1 

1. Introduction 2 

1.1. Background 2 

1.2. Objectives 2 

1.3. Case Study – Napa River Basin and Estuary 2 

1.4. Outline of report 4 

2. Integrated Flood Forecast System - Hydro-CoSMoS 4 

2.1. Distributed Hydrologic Model 4 

2.2. CoSMoS 7 

3. Case Study 9 

3.1. Flood Scenario 9 

3.2. Watershed Storm Scenario 9 

3.3. Coastal Storm Scenario 13 

3.4. Coupling the Watershed and Coastal Models 15 

4. Tabletop Exercise 16 

4.1. Objectives of the Exercise 16 

4.2. Table Top Exercise Participants 16 

4.3. Clickable PDF 17 

4.4. Description of Tabletop Exercise 18 

5. User Survey of Watershed and Hydro-CoSMoS Products 21 

5.1. Background 21 

5.2. Watershed Distributed Hydrologic Model Survey 22 

5.3. Table Top Exercise Survey 25 

6. Summary and Conclusions 27 

6.1. Summary 27 

6.2. Conclusions 27 

7. Acknowledgments 28 

8. References 29 

9. Acronyms 31 

Appendix A - Watershed Reflective Assessment Survey Results 32 

Appendix B - Hydro-CoSMoS Tabletop Exercise Agenda 35 

Appendix C - Hydro-CoSMoS Tabletop Exercise Survey Results 36 

List of Figures 

Figure 1. Map of San Francisco Bay region with Napa River watershed. 3 

Figure 2. HVT displays animations of (a) grid surface flows, and (b) flood flow frequency. 5 

Figure 3. HVT displays time series of flows at a user selected grid. 6 

Figure 4. HVT user can interrogate (mouse click) a grid to identify road-stream intersections. 6 
ii

Figure 5. Cosmos Structure. 7 

Figure 6. CoSMoS simulated maximum depth for San Francisco Bay. 8 

Figure 7. Flowchart of the methodology to establish the watershed scenario. 10 

Figure 8. A sample Multi/Radar and Multi/Sensor rainfall field. 10 

Figure 9. Radar-based distributed rainfall fields in T-year recurrence intervals. 11 

Figure 10. Capacity of initial states associated with soil moisture contents in the SAC-SMC Model. 12 

Figure 11. Antecedent moisture contents (AMC) and its saturation in the driest and the wettest case. 13 

Figure 12. Spatial distribution of streamflow and a hydrograph for coupling with CoSMoS. 13 

Figure 13. Meteorological and hydrodynamic conditions for the 72-hour coastal storm scenario. 14 

Figure 14. CoSMoS simulated maximum depth for San Pablo Bay and upstream to City of Napa. 15 

Figure 15. Coastal storm impact as depth of flooding for Route 37. 15 

Figure 16. Flow chart of the coupling framework within FEWS. 16 

Figure 17. Clickable PDF. 18 

Figure 18. Tabletop exercise held at the CaDWR Flood Operations Center. 19 

Figure 19. User survey forms for tabletop exercise. 20 

Figure 20. Photo of flip chart page. 20 

Figure 21. Screen shot of Google Earth view of flood potential over HWY 37. 21 

Figure 22. Major weather event decision cycle. 21 
iii

Abstract 

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. 
1 

Executive Summary

A joint National Oceanic and Atmospheric Administration (NOAA) and U.S. Geological Survey (USGS) 

project linking a watershed distributed hydrologic model and a coastal hydrodynamic model for the Napa 

basin and San Pablo Bay region of San Francisco was completed in 2014-2017. The aim of the project 

was to demonstrate interoperability between the two modeling systems to assess the impact of tributary 

inflows on coastal storm flooding in and around the mouth of the Napa River. 

The project used the National Weather Service (NWS) Research Distributed Hydrologic Model (RDHM) 

and the USGS Coastal Storm Modeling System (CoSMoS) to simulate a flood scenario in the Napa and 

San Francisco Bay. The RDHM is a gridded version of the NWS legacy hydrologic model based on the 

so-called Sacramento procedures. It is a conceptual model which represents surface and subsurface 

hydrologic processes as a collection of tanks which store and release incident precipitation in a non-linear 

manner. The RDHM model reflects terrain characteristics of slope and direction in each grid, which for 

this application was approximately 4 km. It routes excess precipitation across the grid and onto adjacent 

downstream grids as it accumulates surface runoff throughout the watershed. Previous research involving 

the Russian-Napa Rivers has documented the accuracy of the RDHM when compared to gauged flows, 

with generally good results when the forcing precipitation fields are accurate.

CoSMoS is a fully dynamic hydraulic model which represents the combined influences of oceanic tides, 

atmospheric pressure and winds throughout San Francisco Bay. This project involved input of the Napa 

River watershed inflows to San Pablo Bay, which is a sub-embayment in the north portion of San 

Francisco Bay. CoSMoS is based on the hydrodynamic modeling software DFlowFlexibleMesh from 

Deltares (DflowFM) which implements a finite volume method of conservation of mass and conservation 

of momentum on a staggered unstructured grid. CoSMoS can represent the extent of land inundation 

arising from coastal storms and tributary inflows. Given a high-resolution DEM (~2 m) CoSMoS resolves 

water depths up into the tributary to the full extent of tidal and storm surge influence. 

The project successfully demonstrated the interaction of the two models, dubbed the Hydro-CoSMoS 

system, using a storm scenario of moderate precipitation occurring over the Napa watershed coincident 

with coastal storm surge and wind conditions over SF Bay. The scenario represented storm conditions 

that have historically occurred in the region and which have resulted in watershed and coastal flooding, 

but are not considered extreme. Coupling of the watershed flows as input to the coastal model was 

accomplished using standard file exchange procedures within the Flood Early Warning System (FEWS) 

context. Simulation of the watershed allowed portrayal of forecast flood hydrographs, peak flows and 

their frequency equivalent (e.g. 1 % recurrence level), soil moisture levels, and built facilities at risk (e.g. 

bridge crossings) for each grid. Simulation of the SF Bay and San Pablo Bay allowed portrayal of the 

time variability of water depths and currents, maximum depths, duration of flooding, and built facilities at 

risk. 

A Table-Top Exercise (TTE) was conducted as the culmination of the project to represent products of the 

Hydro-CoSMoS system as they might be with fully integrated real-time forecast operations. The TTE was 

held at the Flood Operations Center in Sacramento and included participants from a variety of local, state, 

and federal organizations. Participants used several types of computer interfaces, including a clickable 

PDF, to navigate the scenario and interpret the impacts and consider their responses. The TTE provided 

opportunity for users’ feedback on the type and format of information and how it was conveyed. This 

information is expected to be useful in the design and deployment of a near real-time, fully coupled 

watershed-coastal modeling system for San Francisco Bay.
2 

1. Introduction

1.1 Background

The San Francisco Bay (SF Bay) area is highly urbanized, and communities are susceptible to flooding 

along the bay shoreline and inland rivers and creeks that drain to the Bay. A prototype forecast system 

that couples watershed and oceanic models was developed to demonstrate capabilities for forecasting 

watershed and coastal flooding in this complex urban environment. This collaborative project involved

linkage of the National Weather Service (NWS) Distributed Hydrologic Models (DHM) with the United 

States Geological Survey (USGS) Coastal Storm Modeling System (CoSMoS) The coupled modeling 

system has been dubbed Hydro-CoSMoS. A prototype for the Napa River basin and Napa Estuary was 

demonstrated at a “Table-Top Exercise (TTE)” held June 13, 2017 at California Department of Water 

Resources (CaDWR) office in Sacramento. 

1.2 Objectives

Objectives of this demonstration project were to: 

• Apply the watershed and coastal flood forecasting models to the Napa River watershed and estuary, 

• Identify appropriate meteorological data for forcing the models, 

• Calibrate the models to establish that they accurately represent forecasts of flood runoff and coastal 

inundation areas and depths, 

• Couple the two models at an interface that allows flood depths to be predicted by the hydraulic 

influence of both tides and watershed driven flows, 

• Develop visualizations that portray the timing, magnitude and extent of flooding, 

• Communicate the flood forecast model outputs to flood forecasters and flood emergency response 

managers, and, 

• Assess the flood forecast products and system requirements to provide guidance for buildout of a 

complete San Francisco Bay flood forecasting system. 

1.3 Case Study – Napa River Basin and Estuary

San Francisco Bay is an urbanized estuary that opens to the ocean at the 2km wide mouth of the Golden 

Gate (37.8° N, 122.5° W) and extends inland to the Sacramento and San Joaquin rivers and deltas. The 

Bay is at a maximum depth near the opening of the Golden Gate (113m) and shallows as the channel goes 

inland to San Pablo Bay (with channel depths of 11 to 24 m) (Barnard et al 2013). The Napa River drains 

into this shallow sub-embayment, which is dominated by tidal mud flats. The Napa River watershed is 

1,100 km2

 and is a mix of urban (9%), agricultural (35%), grassland (15%) and forests (40%) with the 

area adjacent to the bay being restored to wetland habitat. It extends from the Mayacamas Mountains to 

the north and empties into the San Pablo Bay west of the Carquinez straight. The watershed is bounded by 

relatively steep terrain surrounding the long narrow valley that is 43 km long and 8 km wide at its widest 

point (Dusterdorf et al 2014). The City of Calistoga is in the northern end of the watershed and the city of 

Napa sits at the southern, tidally influenced end, with Vallejo, CA located on the eastern side of the river 

where it meets the Bay (Figure 1). 
3 

The case study conducted on the 

Russian-Napa River basins in 

California has involved forcing a 

distributed hydrologic model 

with California Nevada River 

Forecast Center (CNRFC) 

gridded precipitation fields. The 

Russian River watershed 

encompasses 1,485 sq. mi. 

within Sonoma and Mendocino 

Counties, California. The Napa 

River basin, located just east of 

the Russian River basin, 

encompasses approximately 426 

square miles. These are two of 

the most flood-prone rivers in 

the State of California because 

of their unique geography and 

proximity to the coast, which 

together produce 

climatologically heavy 

wintertime rainfall. Total 

population in the study area is 

approximately 700,000. Primary 

water uses are for domestic 

supply and irrigation of 

(predominately) vineyards. Also, 

there are efforts to re-establish 

endangered fisheries habitat in 

the basins. 

For the Napa River basin, the DHM involved forecasted precipitation and surface runoff computations for 

each grid, and routing of the surface flows to the basin outlet. A real-time prototype has been established 

and operated during the past two winter storm seasons. The grid flows can be visualized as the flood 

recurrence interval equivalent (e.g. 100-year flood level), and on-line users can click on a grid to obtain 

the forecast runoff hydrograph. Flood impact features, such as road-stream crossings, could also be 

identified as a warning aid for emergency responders. The river basin outflows are then input to the 

CoSMoS model which forecasts estuary flood inundation depths. 

For the coastal application, we utilized Delft3D-FM, a hydrodynamic model based on a flexible mesh 

grid, and SWAN, a spectral wind-wave model, to calculate water levels that account for tidal forcing, 

seasonal water level anomalies, storm surge and in-Bay generated wind waves derived from the wind and 

pressure fields of a NWS forecast model. The flooding extent is determined by overlaying the resulting 

maximum water levels onto a 2-m digital elevation model of the estuary that resolves the extensive levees 

and tidal marshes. 

The Table Top Exercise was intended to inform staff with the CaDWR, NWS, and Bay area counties 

about the capabilities of the coupled watershed and coastal flood prediction system, and to seek feedback 

on how they would like to see forecast products per their needs. The feedback will help design the system 

for the upcoming Advanced Quantitative Precipitation Information (AQPI) system to be deployed for the 

entire 9 county San Francisco Bay area.

Figure 1 Map of San Francisco Bay region with Napa River 

watershed draining from the north into San Pablo Bay. 
4 

1.4 Outline of report

This report summarizes the demonstration project. The watershed hydrologic and coastal flood 

forecasting models are described in Section 2. Section 3 presents the case study, which involves a 

scenario of hydrometeorological conditions representative of storm events in the San Francisco Bay area. 

2. Integrated Flood Forecast System - Hydro-CoSMoS

2.1 Distributed Hydrologic Model 

2.1.1 Research Distributed Hydrologic Model (RDHM) 

The NWS-Office of Hydrologic Development (OHD, now the Office of Water Prediction (OWP)) 

Research Distributed Hydrologic Model (RDHM) was applied to develop the prototype SF Bay Integrated 

Flood Forecast System (Hydro-CoSMoS). RDHM was used to simulate the tributary flows and the overall 

movement of water through the watershed. Advantages of the distributed model are associated with the 

spatial detail of flow predictions at any location throughout the basin, which can inform efforts for flood 

mitigation, water supply, irrigation and ecosystem management. RDHM represents the general 

functionality of the class of distributed hydrologic models (DHMs) operating on a gridded data structure. 

The overall objective of this project, supported through NOAA’s Physical Sciences Division and the

California Department of Water Resources (Ca-DWR), was to assess the accuracy of the distributed 

hydrologic modeling approach in representing surface hydrologic processes, including flood events and 

low flows (Johnson et al 2016). It was also intended to examine how the DHM approach may be applied 

in support of NWS hydrologic forecasting services, as well as related water management purposes. 

The RDHM is a conceptual hydrologic prediction model which can be used to account for runoff, 

streamflow, soil moisture, snowmelt, evapotranspiration, and various hydrologic states during storm 

events and inter-storm periods. The required inputs are precipitation and temperature. RDHM computes 

the water balance between precipitation and infiltration for each grid, and routes both surface and 

subsurface water flow based on conceptual representations of terrain, soils, vegetation, and the influences 

of these on infiltration and evapotranspiration. 

The OHD provided base data sets on terrain and channel networks, soils, and the default parameters for 

the RDHM model. The CNRFC provided the primary datasets on precipitation fields. Additional 

precipitation data was derived from the Multi Radar – Multi Sensor (MRMS), an operational system that 

provides a suite of gridded quantitative precipitation estimation (QPE) products at ~ 4km spatial and 

hourly temporal resolution. 

Results of the DHM modeling activities are summarized in general terms here (after Johnson et al 2016). 

• The default data sets for defining the RDHM grid structure and model parameter values provide a 

workable foundation for the simulation model. 

• RDHM has been shown to provide so-called “natural” surface flow estimates that are reasonably 

accurate when the precipitation forcings are accurate (e.g. location, timing and intensity), the land 

surface and subsurface parameters are tuned to portray the hydrologic response (e.g. soil moisture and 

evapotranspiration dynamics), and water management influences are minimal. 

2.1.2 RDHM in CHPS-FEWS

The RDHM was implemented in the NWS CHPS-FEWS (Community Hydrologic Prediction SystemFlood Early Warning System) computing environment, which allows a streamlined, near-real time data 

ingest and simulation capability. The CHPS-FEWS has been configured to ingest multiple QPE and 

Quantitative Precipitation Forecasts (QPF) forcings; including radar-rainfall products generated by the 

MRMS system. 
5 

Implementation within a CHPS-FEWS computing environment facilitates automated RDHM model 

execution and near-real time data ingest. The DHM model executes without assimilation using the 

gridded CNRFC QPE as the observed precipitation forcing and with forecasts based on the HRRR 

dataset, extended to 48 hours with Weather Prediction Center (WPC) and Global Forecast System (GFS) 

precipitation fields. A simulation is automatically run every hour using 48-hour old states and forecast 

out to 48 hours. 

2.1.3 Hydrometeorological Visualization Tool

A Hydrometeorological Visualization Tool (HVT, Figure 2) produces web-oriented displays of RDHM 

output that provide animations of precipitation, flood runoff and soil moisture, and ancillary GIS 

mappings of flood impact features. 

The HVT was developed using a Google Maps interface so that it can be widely deployed and accessed 

using a commonly available platform familiar to most users. HVT builds on existing functionality for 

loading RDHM grid data into Google Maps. This functionality loads the RDHM grid data from the 

NetCDF format using routines written in Python scripts and NCL to convert the data into a raster image 

and KML file, which is then displayed in Google Maps in continuous animation using Javascript. An 

itemized description of HVT functions is summarized in general terms here: 

• The tool was made available online via the web, without requiring software downloads by the user. 

The tool displays GIS layers along with DHM grid results in raster KML format overlaying a Google 

Maps view of the study region. 

• RDHM data were automatically loaded into the interface as data are made available by CHPS-FEWS, 

which was available 24-7 during the prototyping periods. 

• Data available in the HVT were obtained by import from the CHPS-FEWS and include: a) Gridded 

surface flows for the DHM domain (i.e. Russian-Napa Rivers) for each time step (4-km grid, 1-hr), b) 

Gridded soil moisture levels for DHM domain (4-km grid, 1-hr), c) Gridded precipitation amounts for 

Figure 2 HVT displays animations of (a) grid surface flows, and (b) 

flood flow frequency equivalent. 
6 

the DHM domain (4-km grid, 

1-hr), and d) Gridded surface 

flows were converted to their 

flood frequency equivalent 

(e.g. 20-yr flood frequency 

level, Figure 3). 

• By default, the tool animates 

RDHM results over the past 

24 observations and 48-hour 

forecast hours in an endless 

loop until users specify a 

specific range of dates. These 

animations provide a quick 

snapshot of precipitation and 

other specified variables in the 

region. The user can select 

which variable they want to 

see animated in this fashion. 

• Users can look at specific 

day/time combinations and 

interact with the RDHM data 

for specific grids. Popups 

provided for specific grid points allow users to view and interact with the data in graph and tabular 

format. 

• Overlay of grids of flood frequency levels can provide locations where flash flood emergency 

responders are needed. This capability is provided for all streams, including small tributaries, which 

currently have no flood information or flow gage rating curves. 

• The HVT displays at-risk 

road crossings (Figure 4) and 

other flood impact features 

(e.g. schools and health care 

facilities) on user request. 

2.1.2 National Water Model

During this project, the National 

Water Model (NWM) was 

introduced as the new DHM for 

the NOAA NWS. Because of the 

timing of the NWM 

implementation relative to the 

project timeline, it was deemed 

prudent to continue with RDHM. 

In the future development of 

Hydro-CoSMoS, we will 

conform with the standards of the 

NWM to enhance forecasting 

capability for operational 

purposes. The NWM delivers 

streamflow forecasts on the 2.7 

Figure 3 HVT displays time series of flows at a user selected grid. 

The hydrograph indicates the flood frequency levels associated with 

that grid.

Figure 4 HVT user can interrogate (mouse click) a grid to identify 

road-stream intersections at risk for flooding.
7 

million river reaches of the USGS National Hydrography Dataset (NHDPlus v2) as well as gridded 

analyses of a host of other hydrologic variables across the Nation. NWM is a hydrologic model that 

simulates observed and forecast streamflow over the entire continental United States (CONUS). The 

NWM will provide complementary hydrologic guidance at current NWS River Forecast Center (RFC)

forecast locations and significantly expand guidance coverage and type in underserved locations 

(http://water.noaa.gov). 

2.2 CoSMoS

2.2.1 Overview 

The Coastal Storm Modeling System (CoSMoS, https://walrus.wr.usgs.gov/coastal_processes/cosmos/) is 

a dynamic modeling approach that has been developed by the USGS to allow more detailed predictions of 

coastal flooding due to both future sea level rise and storms integrated with long-term coastal evolution 

(i.e., beach changes and cliff/bluff retreat) over large geographic areas (100s of kilometers). In general, 

CoSMoS is a framework that takes large scale oceanic conditions and scales them using regional and 

local models to generate high 

resolution hazard predictions (P. 

Barnard et al 2014, Figure 5).

CoSMoS represents all the 

relevant physics of a coastal 

storm (e.g., tides, waves, and 

storm surge), which are then 

scaled down to local flood 

projections for use in communitylevel coastal planning and 

decision-making. Rather than 

relying on historic storm records, 

CoSMoS uses wind and pressure 

from global climate models to 

project coastal storms under 

changing climatic conditions 

during the 21st century. 

In this implementation of 

CoSMoS, the oceanic forcing is 

forced by astronomic tidal 

constituents as described by 

Topex/Poseidon 7.2 (Dushaw et 

al 1997), and additional regional water level anomalies are obtained from an operational 7-day forecast 

run of the Hybrid Coordinate Ocean Model (HyCOM) by the Naval Research Laboratory at 1/12-degree

resolution (Cummings and Smedstad, 2013; Cummings, 2005; Fox et al., 2002). The offshore wave 

conditions are found from an operational 3-day wave forecast furnished by the Coastal Data Information 

Program, Integrative Oceanography Division, operated by the Scripps Institution of Oceanography. The 

wave forecast is a combination of the NOAA Wavewatch III model for deep water and CDIP’s spectral 

refraction wave model for shallow water (O’Reilly et al., 2016; Tolman, 1998). The coupled 

hydrodynamic-wave model simulates the forecast period in approximately 40 minutes using 64 processors 

on a high-performance computing cluster. 

For the regional model, we used the hydrodynamic modeling software DFlowFlexibleMesh from Deltares 

(DflowFM) which implements a finite volume method of conservation of mass and conservation of 

momentum on a staggered unstructured grid (Kernkamp et al., 2011). Our set up for San Francisco Bay 

includes a domain that extends 90 km offshore of the Golden Gate and to Point Arena in the north and 

Figure 5 Cosmos Structure 
8 

Monterey in the south (300 km). There are 202,842 grid cells in the domain with the size of the grid cells 

ranging from 4 km at the ocean side to as small as 5 meters in the coastal region near the Napa watershed. 

(Figure ure 1) This model is coupled to a structured grid wave model, Simulating Waves Nearshore 

(SWAN). SWAN solves the spectral energy density balance and accounts for wave propagation in time 

and space, shoaling, refraction due to current and depth with frequency shifting due to currents and nonstationary depth. It allows for wave generation by wind, white capping, bottom friction and depth-induced 

breaking. It also computes wave-induced set-up. The SWAN grid has 134,200 cells and covers 

approximately the same area as the DFLOWFM grid. The currents and waves interact by having 

communication between SWAN and DFlowFM at a 20-minute interval.

The bathymetry in the regional models was determined by averaging the closest four depth points from a 

2-m digital elevation model (Foxgrover et al., 2011 and 2014). Fixed weirs are used to represent regional 

levees (SFEI, 2016) in the model. Fixed weirs are defined at the velocity points and block flow between 

the two adjacent computational cells, when water levels are below the specified height of the fixed weir, 

without reducing the total wet surface and the volume of the model. This allows us to represent levees 

which have sub-grid dimensions, but are large enough to change flow patterns and flood extents. Major 

river discharges are, unless directly simulated as in the Napa River case, set to typical winter (November

through March) values. Water levels are initialized from a non-storm condition. 

2.2.2 CoSMoS Products 

There were many products that resulted from the CoSMoS framework, including flooding depths and 

extents, currents, water level, timing and duration of flooding, wave heights and wave run-up potential, 

levee overtopping and an integrated hazard metric. Flooding extents and depths (Figure 6) were

determined by interpolating water levels (a direct output of the regional model) onto a 2m resolution grid 

and subtracting the DEM. The currents and wave heights are direct outputs of the regional model. These 

results were output on hourly intervals, from which we can determine the spatial extent of the timing of 

initial flooding, the timing of the maximum flooding and the duration of the flooding. Given 

computational time constraints we were unable to directly compute the wave run up, but by using our 

detailed bathymetry and some 

standard wave run- up formula 

from the engineering literature 

(van Der Meer 2002; EurOtop 

2016) we could make estimates 

of wave runup in the domain 

which can contribute to 

extended flooding. The water 

levels can be compared to the

digitized levee network (SFEI) 

to predict potential levee 

overtopping. Finally, we 

computed many integrated 

hazard metrics that included 

time integration of currents and 

water depth to find the locations 

which will be most impacted in 

a storm event, either through 

very fast currents, extremely 

deep water or extremely long 

flood duration, or some 

combination of the three. Figure 6 CoSMoS simulated maximum depth for San 

Francisco Bay. 
9 

Examples of these types of results are all shown in the Clickable PDF used in the TTE. 

In previous CoSMoS work, projections of multiple storm scenarios (daily conditions, annual storm, 20-

year- and 100-year-return intervals) were provided under a suite of sea-level rise scenarios ranging from 0 

to 2 meters (0 to 6.6 feet), along with an extreme 5-meter (16-foot) scenario. This allows users to manage 

and meet their own planning horizons and specify degrees of risk tolerance.

3. Case Study

3.1 Flood Scenario 

The project partners created a realistic storm scenario to demonstrate the use of the Integrated San 

Francisco (SF) Bay Coastal Flood Forecast Model (i.e. Hydro-CoSMoS) to emergency responders and 

planners for the Napa River region. Like the ArKstorm scenario (Porter et al 2010), this scenario is based 

on a combination of actual events that have occurred in the SF Bay area and consists of two components: 

the watershed and the coast. For the watershed, soil moisture and precipitation conditions were input into 

the watershed using a NOAA-NWS distributed hydrologic model (DHM), providing projections of 

fluvial-related flooding. For the coast, flooding was projected from waves, winds and atmospheric 

pressure modeled by using the USGS Coastal Storm Modeling System (CoSMoS) in SF Bay. The storm 

scenario combined these two components to provide projections for the watershed and coastal area for the 

Napa River basin and estuary. 

3.2 Watershed Storm Scenario

3.2.1 Background 

For the watershed storm scenario, we chose a storm event that happened over Napa County on December 

23, 2012. This storm event had 58.3 mm (2.29 in) rain corresponding to 1-to-2-year return period for 12-

hour precipitation. However, CNRFC issued a flood warning because of the high peak flow (13,100 cubic 

feet per second at 08:00 PST, December 24, 2012) corresponding to 5-year return period for streamflow. 

In addition, the soil moisture condition was 46% saturation, higher than normal soil moisture condition 

(23% saturation). This storm was then scaled-up to reflect a more significant flooding event, as described 

below. 

To scale this storm to a more extreme event we modified the rainfall and soil moisture conditions. We 

estimated the 25-yr recurrence interval rainfall fields through NOAA Atlas 14 and MRMS data based on 

the radar products and scaled the 2012 storm to reflect the spatial-temporal characteristics of rainfall 

fields. We also increased the maximum soil moisture condition to 51% based on an actual storm event 

happened on March 27, 2012. 

3.1.2 Watershed Scenario Pre-processing 

The methodology for this study involved pre-processing to generate a distributed rainfall field in T-year 

recurrence intervals, estimation of antecedent moisture condition (AMC) as soil moisture, and 

implementation of flood frequency analysis to calculate T-year recurrence intervals from the simulated 

runoff flows. Figure 7 shows a flowchart of the methodology. 
10 

Generation of the distributed rainfall fields in T-year recurrence intervals was the first step (Figure 7). 

Pre-processing of the rainfall input considered the spatial distribution of MRMS radar-based rainfall field 

data, which can provide the spatial distribution features of the actual rainfall field. It also considered

rainfall frequency to reflect various rainfall scales with T-year recurrence intervals. For the rainfall 

scaling, this study referenced NOAA Atlas 14 which contains rainfall frequency estimates for the United 

States with associated 90% confidence intervals. In this study, we selected the 25- year recurrence 

interval as the rainfall scale. 

To generate T-year distributed precipitations, a simple assimilation approach is applied. In the 

assimilation approach, the spatiotemporal distributions of the target storm (Figure 8) and T-year 

precipitations are required. The target storm plays a role in providing spatial and temporal distributions 

and its duration for generating T-year distributed precipitation fields. If the target storm has a duration of 

12 hours, for example, the 

specific T-year distributed 

precipitation in hourly time step 

consists of 12 of distributed 

precipitation fields equal to the 

target storm. Temporal 

distribution of precipitation, 

also, follows the target storm. 

The temporal distribution of the 

target storm is extracted using 

hourly areal average 

precipitation (AAP). The hourly 

AAPs are used to temporally

distribute T-year precipitation as

total amount of the precipitation 

is for 12 hours. The AAP is 

calculated from Eq. (1), and the 

temporal distribution of T-year 

precipitation is resolved as same 

as the target storm by using Eq. 

(2).

2. Runoff simulation

A. Generation of

distributed rainfall field in

T-year recurrence intervals

B. Estimation of

antecedent moisture

Condition (AMC)

C. Implementation of

flood frequency

(peak flow)

Selection of an appropriate 

rainfall field using PRISM 

and MRMS

Implementation of 

rainfall frequency

(NOAA Atlas 14)

Scaling-up

the rainfall to match the 

rainfall frequency

Fourteen events

Estimation of 

five states of soil water 

content

Classification of

AMS cases

(Dry-Nor-Wet)

Regional flood-frequency 

equations

(USGS, 2012)

Computation of 

peak flows in 

T-year recurrence intervals

Building-up 

hydrologic modeling 

Forcing the combinations of the T-year rainfall fields and AMCs

 Running the hydrologic model and organizing the hydrographs 

from the combination results

1. Pre-processing

Implementation of 

temporal distribution of

T-year rainfalls

Calculation of a scale factor to scale-up the MRMS rainfall 

field using temporal distributed T-year rainfalls

Generation of the distributed rainfall field by multiplying the 

scale factor and MRMS rainfall field

Figure 7 Flowchart of the methodology to establish the watershed scenario. 

Figure 8 A sample Multi/Radar and Multi/Sensor rainfall field.
11 

  1

AAP( m n

RG G ( )t ) = R (t,i, j)   ∑∑ × (1)   j i =1 1= n m×

AAP(R

R ( )t = R × G ( )t ) F F dur

∑

ation (2)

AAP(RG ( )i ) i=1

R (t i, , j) Fscale (t i, , j) = G (3)

AAP(RG (t))

RFG (t i, , j) = RF (t)× Fscale (t i, , j) (4)

where, AAP is the areal average precipitation, R t G ( ) is gridded precipitation field (e.g. MRMS QPE products) at 

time t , RG ( ,t i, j) is each grid value in the gridded precipitation field, and n and m indicate a number of grids in 

row and column. RF ( )t is the temporally resolved hourly T-year precipitation, RF is T-year precipitation 

corresponding to specific recurrence interval. Fscale is scale factor for each grid value at time t . RFG ( ,t i, j) is each 

grid value in T-year distributed precipitation fields.

The assimilation approach for distributed precipitation fields (using MRMS QPE products) and T-year 

precipitation is implemented using scale factors to match hourly precipitation amount of the distributed 

precipitation fields with the hourly T-year precipitation temporally resolved. In this process, the scale 

factor is calculated from Eq. (3) for each time step (t) and at each grid (i, j) of MRMS QPE data. From 

Eq. (3), MRMS QPE fields at each time step are normalized using the AAP value for whole girds. And 

then the MRMS QPE fields are scaled-up by multiplying the scale-factor to the normalized MRMS QPE 

field through using Eq. (4). So, the areal average value of the scaled-up precipitation field would be equal 

Figure 9 Radar-based distributed rainfall fields in T-year recurrence intervals (2-200 

years) in the NAPA River basin. 
12 

to the temporally resolved hourly T-year precipitation at the same time step. Through this step, T-year 

distributed precipitation can be generated for the duration of a target storm. Figure 9 shows the 

accumulated rainfall field generated for the 2-200 T-year recurrence intervals in the Napa River basin. In 

this study, the 25-year recurrence interval was used to simulate the hydrograph and generate flood runoff. 

As a second process in pre-processing, 14 sets of five states of soil water content were estimated from a 

total of 14 rainfall events. This study used soil moisture states in the SAC-SMA model to estimate soil 

moisture conditions for the actual storm events to create an Arkstorm-like scenario. The SAC-SMA 

model has five soil moisture states to reflect an antecedent moisture condition (AMC) according to soil 

layer and moisture. The five states represent the amount of water retained in the upper/lower zone. The 

amount varies over the depth and for different types of soil. In the upper zone, the soil moisture state is 

rapidly decreased due to subsurface runoff by free water, and the tension water is reduced by evaporation 

(or evapotranspiration). In the lower zone, free water content consists of both supplemental and primary. 

Figure 10 shows the capacity of the five soil moisture states used in Napa River basin. 

Figure 11 shows the five soil moisture states for both dry and wet conditions in the Napa River basin. For 

this study, we used the wet AMC condition to simulate a hydrograph for coupling with the CoSMoS 

model. The third process in pre-processing was to decide the flood frequency equations to derive T-year 

recurrence intervals from peak flows. In this study, the regression equations developed for coastal basins 

of California were referenced (USGS 2012). These equations involve the drainage area and mean annual 

precipitation for each grid cell, and were applied to derive the T-year recurrence interval for the simulated 

peak flow. 

The flood runoff simulation was implemented using the 25-year recurrence interval of the generated 

rainfall fields and the wettest AMC condition. Therefore, one hydrograph arising from the combination 

was simulated, the peak flows were translated to their T-year flood frequency recurrence interval. 

According to the hydrograph result, maximum flood frequencies in the overall watershed were around the 

100-year level at time corresponding to 36 hours of the simulation. Figure 12 shows the spatial 

distribution of streamflow and the resultant hydrograph from a selected location (Figure 12, red circle).

Figure 10 Capacity of initial states associated with soil moisture contents in the 

SAC-SMC Model
13 

3.3 Coastal Storm Scenario

3.3.1 CoSMoS Storm Scenario 

For this scenario, we wanted to choose a storm that, while extreme, represented a reasonable and 

plausible event (Figure 13). The atmospheric and wave conditions were chosen to match historical 

conditions that created an observed 50-

year return period non-tidal water level 

at the San Francisco gage station located 

on the south-east side of the golden gate. 

Water level data were analyzed from 

1890 to present-day to determine the 

non-tidal residuals and the 50-year 

return period non-tidal water level was 

found to be near 44 cm (1.44 ft). Nontidal water levels reached a peak of 44.1 

cm at 4 pm on February 6, 1998. The 

wind, barometric pressure and wave 

conditions from that time were taken as 

representative conditions for a 50-year 

storm.

(a) Driest case (Event#10, UTC 11/19/2014 02:00) 

(b) Wettest case (Event#03, UTC 03/27/2012 06:00) 

Figure 11 Antecedent moisture contents (AMC) and its saturation in the driest and 

the wettest case. 

Figure 12 Spatial distribution of streamflow and a 

hydrograph for coupling with CoSMoS. 
14 

During this 50-year storm wind speeds ranged from 4-12 ms-1 (9- 27 mph) and were predominantly from 

the south-west direction. The atmospheric pressure reached a low of 984mb in this storm. Offshore waves 

ranged from 3.4-8.2 m (11.2-26.9 ft) significant wave height and had a peak period of 20s with most of 

the energy coming from the west. These atmospheric storm and wave conditions were applied to a spring 

tidal period from November 2010. These tides represented slightly higher high tides and slightly lower 

low tides (i.e. a larger tidal range) but they are not as significant as king tides which occur later in the 

winter. 

Additional effort was directed to defining the bathymetry of the Napa River near the City of Napa, as 

there has been recent construction of a flood bypass channel. Preliminary simulation indicated that tidal 

and storm surge influences would be felt at this location and further upstream. Field investigation 

provided details on the bypass channel configuration. 

3.3.2 CoSMoS Storm Scenario Results 

CoSMoS was used to simulate the coastal storm scenario to obtain water level and current variations for 

the 72-hour simulation period. Simulation results were generated for the SF Bay regional scale, and the 

San Pablo – Napa River local scale. Outputs included the spatial distribution of a) winds and pressure, b) 

wave heights, c) current speeds, and d) water depths. Figure 14 shows the maximum water depth for the 

local scale which extends up into downtown Napa. 

Figure 13 Summary of meteorological and hydrodynamic conditions at the Napa 

River mouth for the 72-hour coastal storm scenario.

0 6 12 18 24 30 36 42 48 54 60 66 72

Time (hr)

0.0

4.0

8.0

12.0

Water Level

(ft)

0.0

2.0

4.0

Wave Ht., Hs (ft)

0.0

1.0

2.0

Current Mag

(knots)

10.0

20.0

30.0

40.0

Wind Mag.

(mph)

990.0

1,000.0

1,010.0

Pressure

(mb)

0

100

200

300

Wind Dir.

( o )

0

100

200

300

Current Dir.

( o )

0

1

2

Wave Period

T

p

 (sec)
15 

CoSMoS also provided various flood hazard indices which involve CoSMoS and DHM model output data 

characterizing flood magnitude, extent and criticality. The Hazard Index involves time integration of 

currents and water depth to find the locations which will be most impacted in a storm event, either 

through very fast currents, extremely deep water or extremely long flood duration, or some combination 

of the three. Flood indices included: a) time to first flood, b) duration of flooding, c) time of maximum 

depth, d) maximum depth, and e) hazard index (a combination of depth and velocity). Critical facilities 

were mapped in conjunction with maximum flood depth, these included a) fire stations, b) schools, c) 

wastewater treatment plants, 

and d) roads (Figure 15). 

3.4 Coupling the 

Watershed and Coastal 

Models

The general framework for the 

coupling of the models is 

housed in the Flood Early 

Warning System (FEWS), 

which is an open shell system 

for managing input/output 

communications that is 

extensively used in NWS

forecasting products and for 

handling real-time time-series 

data (Figure 16) 

(http://oss.deltares.nl/web/delftfews/).The coupling process 

involved retrieval of weather 

forecasts which are required to 

run CoSMoS, defined below, 

and provide the inputs for the 

distributed hydrological model. 

Figure 14 CoSMoS simulated maximum depth for San Pablo Bay and upstream to City of Napa. 

Figure 15 Coastal storm impact as depth of flooding for Route 

37
16 

The hydrological model was run 

for each watershed to provide a 

time series of discharge for the 

river which is passed to CoSMoS. 

The CoSMoS framework 

incorporated weather predictions 

and river discharge to ultimately 

make real time predictions that 

can be compared to recent 

observations. For this 

implementation we were only 

including the Napa River 

watershed, but this framework 

using FEWS can easily be 

extended to include more 

watersheds.

4 Tabletop Exercise 

4.1 Objectives of the 

Exercise

A table-top exercise (TTE) was created to advance users’ understanding of the Hydro-CoSMoS modeling 

system, the various flood forecast products, and to inform design of a fully operational watershed-coastal 

flood forecasting system. TTEs are used in meetings to discuss a simulated emergency, in this case an 

event as defined by the watershed and coastal flood scenario (described above). They help increase 

understanding of technical details and information products, clarify roles and responsibilities, and identify 

additional mitigation and preparedness needs. These exercises typically result in action plans for 

continued improvement of the flood forecasting system. The Federal Emergency Management Agency 

(FEMA) has an Emergency Planning Exercises web page which provides guidance and resources 

(https://www.fema.gov/emergency-planning-exercises). For this TTE, members of the flood forecasting 

and emergency response community were asked to review and discuss the flood forecasting information 

and actions they would take, testing their understanding of the modeling outputs and emergency response 

plans in an informal, low-stress environment.

The TTE was held on June 13, 2017 at the CaDWR Flood Operations Center (FOC) in Sacramento, CA. 

The FOC is an advanced computing and networking facility used by the CaDWR and NWS CNRFC 

before and during flood events to assess risks, and coordinate flood response actions and communications 

with the various local, state and federal agencies and citizens. The FOC has a collection of networked 

computers and teleconferencing equipment by which to display hydrometeorological forecasts and current 

conditions. 

4.2 Table Top Exercise Participants

The exercise involved attendees from a variety of local, state, and federal agencies on-site, as well as 

webinar remote access for at-distance participants. Participants in the exercise included a collection of 

managers and staff of the following organizations; see the appendix for a complete list of participants. 

• Federal: 

o NWS CNRFC

o NWS WFOs at Monterrey, Sacramento and Eureka

o NOAA ESRL PSD Hydromet Modeling and Applications 

o USGS Pacific Coastal and Marine Science Center

Figure 16 Flow chart of the coupling framework within FEWS.
17 

• California DWR:

o Flood Planning Group 

o Flood Operations 

• California Dept. Transportation 

• County Flood Response Agencies 

o Contra Costa County 

o Marin County 

o Napa County 

o Sonoma County 

• Bay Area Flood Protection Agencies Association

• University of California - Davis 

4.3 Clickable PDF

To facilitate demonstration of the functionality of the Hydro-CoSMoS system a “clickable PDF” 

document was created to provide an interactive medium for exploration of watershed and coastal model 

data and forecasts (Figure 17). The document was provided to participants beforehand, and was presented 

and reviewed at the outset of the exercise sessions. Users of the PDF were able to browse through the 

various flood model products in a manner like how they might be exposed to flood forecasts in real time. 

For instance, hydrological and meteorological data and flood forecast information products were time 

sequenced over the forecast period of 72 hours as described in the scenario above. 

The clickable PDF had three main sections focused on the Watershed, Coast and Flood Impacts, depicting 

three regional scales: the entire SF bay, the smaller Napa – San Pablo Bay and the Napa watershed

(Figure 17). The SF Bay view shows the largest map with the entire SF Bay area including watersheds.

The Napa Bay map shows the model results on the map area focused on the estuary between watershed 

and bay. The Napa watershed shows of the entire Napa watershed with the spatial distributed cells based 

on the DHM gridding scheme. Users choose a map type depending on their preferred map scale and 

region of interest. The clickable PDF provided time series data at several specific locations and the model 

results were overlaid onto a geopolitical map of the San Francisco bay region. Over the watershed, the 

model results provide the map of precipitation, soil moisture condition, surface runoff, discharge and 

recurrence intervals, at 6 hourly intervals over a forecast from 0 to 72 hours. The coast region shows

meteorological data such as wind and pressure, as well as model results for waves, currents, and water 

depths. The impacts represent the flood impact results from the Hydro-CoSMoS system and provides

practical information to support decision making from users. 
18 

4.4 Description of Tabletop Exercise

During the workshop, workshop participants were guided through a tabletop exercise (TTE) that was 

designed to have them interact with the information and provide feedback on the usefulness and usability 

of the products developed and included in the clickable PDF. 

The flow of the TTE involved the following activities:

Step 1: The scenario (as described above) was outlined for the participants (Figure 18). 

Step 2: Participants in the room and on the phone, were given 15 minutes to review the data via the PDF. 

They were guided through reviewing the 400+ page PDF by using a format similar to the pre-exercise 

survey (Figure 19). Following the small group exercise, participants were asked to provide their feedback 

in a large group discussion. Notes were captured on flip charts (Figure 20). 

Step 3: Information was provided to participants about flooding that had occurred during the winter of 

2017 along a stretch of Route 37 in Napa County (Figure 21). Participants were then guided to assess the 

impacts to a stretch of Route 37 in Napa County based on Hydro-CoSMoS modeling and outputs. The 

participants were asked to consider the following questions to determine the usefulness of the example 

products: 

1. Have you used information similar to this information before? If yes, what is the source? 

Figure 17 Clickable PDF 

(a) Clickable PDF (b) Interface and contents

(c) Example: discharge in the watershed (d) Example: water depth in the coast
19 

2. What physical scale would you want this information?

3. Are the time intervals appropriate?

4. How else would you like to see this information displayed?

5. Does it matter the scale of the event? (i.e. Does a bigger event require different info than a 

smaller event) 

During group discussion, representatives from Napa County provided insight into how the 2017 flooding 

was addressed, which information they used at the time, and how the information provided by the HydroCoSMoS model could enhance their response capabilities in the future. 

Step 4. The entire group discussed overall impressions of the modeling system and outputs/products and 

identified next steps for the project. 

Figure 18 Tabletop exercise held at the CaDWR Flood 

Operations Center demonstrating the integrated coastal flood 

forecasting model. 
20 

Figure 19 User survey forms for tabletop exercise. (Left) Participants in the room were provided a 

hard copy printout in which they could indicate the usefulness of each model product. (Right) 

Those on the phone, were provided a link to an online survey in which they could also provide realFigure 20 Photo of flip chart page. These were 

used to record group discussion comments. 
21 

5. User Survey of Watershed and Hydro-CoSMoS Products

5.1 Background

Two user surveys were conducted to assess various aspects of the Watershed DHM and Hydro-CoSMoS 

functionality and products. The purpose of the surveys was to document user satisfaction with the models 

for flood and flash flood operations experimental products or services and to collect suggestions for 

improvements. 

The general context is that of a forecaster, or anyone, who is involved with determining flood and flash 

flood threats, issuing watches and warnings, and/or organizing for flood mitigation response. The process, 

illustrated in Figure 22 below, involves a sequence of information gathering, assessment, decision making 

and follow-up. With this guide in mind, the survey addresses information products that precede an event, 

including recent observed and modeled rainfall and soil moisture. Forecast rainfall would then be 

reviewed to identify magnitude and lead time. Then, the various DHM and Hydro-CoSMoS products 

would be reviewed to identify time and locations of flood and flash flood threats. The prototypes also 

provide products that identify at-risk facilities, such as bridge crossings, where mitigation actions might 

be directed. 

Some questions involved assignment of a numeric ranking to quantify whether the respondent thinks the 

product is useful or not. These questions are usually followed by an optional comment box, so the 

respondent can include supplementary remarks and questions. 

Figure 21 Screen shot of Google Earth view of flood potential over 

HWY 37 that was available for participants to explore and view 

during the Table Top Exercise.

Figure 22 Major weather event decision cycle (after Morss and Ralph 2007)

Identification of 

increased 

potential for 

event

Decision that 

event is likely or 

imminent

Decision 

to 

activate

Decision 

to 

de-activate

Completion 

of post-event 

wrap-up

General 

preparation & 

planning

Situational 

awareness & 

readiness

Event specific 

preparation

Emergency 

operations

Post-event 

recovery

General 

preparation & 

planning

Time
22 

5.2 Watershed Distributed Hydrologic Model Survey

For the Watershed DHM project we established an Advisory Panel (AP) comprised of NWS staff and 

other users having interest in the DHM for flash flood operations. Five intended user groups were 

identified and a user-centered approach involving identification of users’ needs and requirements was 

outlined. The user groups included the 1) NWS California-Nevada River Forecast Center (CNRFC), 2) 

NWS Weather Forecast Office – San Francisco-Monterrey (WFO-MTR), 3) Emergency Management 

Agencies (EMAs), 4) General Public, and 5) National Marine Fisheries Service (NMFS). A user-centered 

approach involving identification of users, and their needs and requirements, led to design of the interface 

and forums. 

Retrospective assessments were conducted where the basic functionality of our distributed hydrologic 

model for flash flood operations (DHM for FFO) was demonstrated and feedback solicited from the 

Advisory Panel. The demonstration involved a GoToMeeting forum whereby the various CHPS-FEWS 

and HVT functions were shown, and comments by participants recorded. Rough transcriptions of these 

comments have been extracted from the recording and organized according to the primary flash flood 

services elements identified by the Flash Flood Summit (OWP 2015), namely 1) Observation and 

Monitoring, 2) Modeling, 3) Forecasting and Characterization, 4) Communication, and 5) Concept of 

operations. 

An on-line survey was also conducted which obtained user ratings on the various gridded and related 

watershed forecast products, and comments on these. The forum comments are below, and the online 

survey results are tabulated in Appendix A. 

• Quantitative Precipitation Estimation (QPE)

o QPE data sources include WSR-88D, MRMS, CNRFC QPE, spotter reports, etc; all of which 

are collected and viewed in either AWIPS or internet browser. 

o Accuracy of precipitation fields is paramount. Forecasters’ need some way to assess accuracy 

of differing precipitation products used as input to DHM. The display needs some error bars 

or ground gage data to compare. 

o Local ALERT-type rain gage data, coop reports, CoCoRaHS, and local media weather reports 

(e.g. radar imagery) are helpful. Looking at stream gage data is also very helpful. 

o CNRFC QPE is rated Somewhat to Very Much Helpful for flash flood location and 

magnitude. QPE is useful for “warming up” the DHM to establish initial states. 

o The MRMS is a system with automated algorithms that quickly and intelligently integrate 

data streams from multiple radars, surface and upper air observations, lightning detection 

systems, and satellite and forecast models. MRMS QPE has high resolution and can be set up 

to use alternate field data as available. 

o Russian-Napa River basins are largely blocked from radar coverage, so radar-rainfall 

products are not very accurate unless ground truth involved. 

• Quantitative Precipitation Forecast (QPF)

o The GFS has quite coarse resolution making it a poor choice for flash flood forecasting. It has 

an approximate horizontal resolution of 13 km for the first 10 days and 27 km from 240 to 

384 hours (16 days). It produces forecast output every hour for the first 12 hours, three hourly 

through day 10 and 12 hourly through day 16. 

o HRRR QPF seems to be preferred but may have latency problems. The Rapid Refresh (RR or 

RAP) is a numerical weather prediction (NWP) model. The model is designed to provide 

short-range hourly weather forecasts for North America. The grid points are spaced every 13 

kilometers (8.1 mi). The model runs once each hour, with forecasts given hourly out to 18 

hours. 

o The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, 

convection-allowing atmospheric model, initialized by 3-km grids with 3-km radar 
23 

assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-hr period adding 

further detail to that provided by the hourly data assimilation from the 13-km radar-enhanced 

Rapid Refresh. 

o QPF rated Somewhat to Very Much helpful for FF location and magnitude; rated Not at All 

to Somewhat Helpful for FF lead time. 

o Availability of rain gage data is important as input to rainfall field mapping. 

o A concern is that rain gage data may be delayed in transmission so not available. 

o Local agencies’ ALERT gage readings are routinely obtained by the WFO-MTR, but there 

are few ALERT stations in the Russian-Napa River basins. 

• Soil Moisture Grid Display

o Soil moisture data was rated Very Much Helpful for assessing watershed initial conditions. 

o Some commented it is the most useful new data set available. However, others commented 

that the soil moisture data can be misleading and late. SM data is not being assimilated into 

DHM model. One commented that it is much better to monitor rate of runoff from particular 

stream gages to get a feel for how saturated the soils are. 

• DHM Discharge Grid Display

o The DHM approach is of great interest as it provides hydrologic forecasts for all grid 

locations, including ungaged locations. 

o Flash floods are events which occur in less than 6 hours of intense rain, thus making small 

tributaries having rapid response a primary concern. The CHPS-FEWS-DHM computes at a 

1-hr time step. Even for precipitation forecasts issued at larger time steps, the CHPD-FEWS 

interpolates the data down to a 1-hr time step. This contrasts with the official NWS CNRFC 

forecasts which are developed for selected locations on the main stem of the major rivers 

which have response times of greater than 6 hours. 

o DHM discharge rated Somewhat to Very Much helpful for FF location and magnitude. The (4 

km) grid cell seems very coarse to accurately predict flash flooding, but would be useful for 

predicting potential threats. DHM discharge had varied responses per helpfulness for FF 

impacts. Need rating curves or some other means to determine how deep the water is, which 

is only available at gages sites. 

• DHM Flow Frequency Grid Display

o DHM flow frequency grid was rated Somewhat to Very Much helpful for FF magnitude; less 

so for FF location and impacts. 

o The flow frequency grid display provides a more qualitative rendering of model output to 

show levels of flash flood threat 

o Most understood the TF concept very well, but some did not. 

o Provides context if user is not familiar with typical flow levels. Useful for impacts and for a 

check on the forecast (do I really want to forecast a 100-year event). 

o Normalizing the flow levels to their flood frequency equivalent, the threshold frequency (TF) 

was strongly endorsed. 

• DHM Discharge Hydrograph Displays

o The hydrograph provides a more quantitative rendering of model output to show flood flow 

levels and stages. Interrogation by mouse click rated Somewhat to Not at All difficult for 

obtaining hydrograph. 

o Most rated the DHM flow hydrograph display as Very Much helpful for FF location, 

magnitude, duration, and lead time. 

o Comparison with USGS gage flows was considered very helpful. Rating curves are available 

for all USGS and most other gaging stations which can provide information on water depth. 
24 

o Water level (flood inundation) would be even more helpful. Need to have some sort of E-19 

data with these gages or locations to know what the impacts are. The DHM does not yet 

provide detailed flood inundation mapping information. 

• DHM Impact Features Displays

o Overlay of the grid onto the Impact Features layer provides useful utility for identifying 

features at risk to public safety.

 The bridge crossings layer allows interrogation by users to get name and other 

metadata about the location. Color coding of bridge crossings based on the TF level 

was endorsed. Need to have some sort of E-19 data with these gages or locations to 

know what the impacts are.

 The 4-km grid may be too coarse for precise portrayal of impact feature risk. The 

demonstration showed one grid with 4 impact features interior. Questions arose 

pertaining to:

• Which impact feature should receive attention by responders? 

• What flow level is represented in the grid when there are multiple streams 

interior? 

 The prototype was not designed to portray flood inundation maps. The DHM outputs 

such as flow rate could be used as input to a more detail flood plain hydraulic model 

to accomplish this.

• DHM Accuracy 

o The DHM was rated Sometimes to Frequently accurate enough for flash flood magnitude, 

location, timing, and impacts. 

o It would be helpful for improved temporal resolution. 

o Better than not, all depends on accuracy of weather model inputs. 

o Need improved calibration, and regulation modeling. 

o All agree that DHM performance statistics can guide use of DHM products. 

• DHM Forecasting Operations 

o Adding this into a forecaster's arsenal will undoubtedly help improve situational awareness of 

a rainfall event.

o Maintenance of the data feeds requires continuing attention. Changes in data formats, 

upgrades to computer operating systems, and other software and system issues are 

commonplace. 

o The CHPS-FEWS-DHM system is complicated and designed to be operated by a highly 

trained expert having understanding of meteorology and hydrology, and the various 

capabilities for managing such data and the functions incorporated into the system. 

o There is not time for a WFO forecaster to work the CHPS-FEWS-DHM system to obtain 

flash flood information. The system must work automatically and provide forecast products 

in an automatic and timely manner. 

o The WFO-MTR Service Hydrologist has capability for using the CHPD-FEWS-DHM system 

during 2 to 3-day ahead preparations for flash flood operations, but not during an event. 

Preparations may involve setting up the DHM, selecting input and output datasets and 

conducting calibration/verification activities. 

o Procedures for working with DHMs have not been established. These include: 

 Establishing calibrations and QA/QC for precipitation and DHM precursors.

 Developing some way to “nudge” the DHM to obtain more accurate simulations?

• Concept of Operations: 

o The HVT web service was considered a good way to provide flash flood information to NWS 

forecasters and a wider audience involving local emergency response agencies. 
25 

 Capability for a user to interact with the DHM outputs to obtain actionable 

information is required. The demonstration was useful in showing a user 

interrogation of the DHM grids to identify the flash flood criticality, and to obtain 

metadata about the specific location.

o We can help highlight areas of concern for excessive rainfall to EMs and the public through 

partner emails or social media messaging before the event. 

o I could see that a product like this would be useful for county emergency responders during 

to help flood response planning. (Helpful) to support personal decision-making. (Helpful to) 

identify areas of risk, timing of risk, and general level of event. 

o Another piece of the puzzle but not the answer. 

o Latency of the product can be very long and for flash flooding need to run hourly if needed 

with new gridded QPE and QPF. Can't have 1-3 hr latency of model data feeding the 

RDHM. 

o Need to have the model forecast and the obs for the gages in the display so we can see how 

the model is doing near real-time. 

o The “Who” for DHM forecasting has not been defined. 

o Migration of the RFC and WFO organizational structure and responsibilities for flash flood 

services has not been defined.

o The DHM soil moisture grid data are being used to inform development of RFC Guidance at 

several regions. 

o The CNRFC expressed interest in developing a new way to forecast flash flood threats 

without dependence on simplified RFC Guidance methods. 

o There is sensitivity to DHM flow predictions being made for the same main stem locations 

where the CNRFC also makes forecasts. This sensitivity can be mitigated using the TF 

approach for representing flash flood threats.

5.3 Table Top Exercise Survey

A second survey was conducted prior to and during the TTE workshop; attendees were provided access to 

the Clickable PDF as well as a link to a questionnaire. The goal of the questionnaire was to provide a 

guided format for participants to review the clickable PDF and provide initial responses to the initial 

modeling outputs developed. In the questionnaire, respondents were asked information about their 

organization, their needs for coastal, estuarine and watershed flooding information, and what types of 

flooding information and products they currently use. They were then asked to review the clickable PDF 

and provide initial first-level responses to the usefulness of the various products. 

The discussion throughout the TTE provided valuable information for the project partners. Some of the 

comments addressed the outputs provided in the clickable PDF, but most of the comments addressed how 

to make the Hydro-CoSMoS information relatable and usable by the largest number of end-users. Results 

of the survey are tabulated in the Appendix; we highlight some of the key findings below. 

• Needs for Coastal Flood Forecasts

• NWS forecasters use radar data, flash flood guidance, rainfall reports, and river data for their 

warning decision making.

• County-level staff are concerned with storm surge into flood control channels and protecting 

communities and ecosystem restoration projects thereby. Some counties track flood threats and 

issue warnings.

• Flood Data Sources: 

• Most use NWS warnings, including from the WFO and the CNRFC. Some track precipitation 

reports (public and private), spotter reports (phone, email,, social media, and news media), 

USGS stream gages. 

• Some county staff have their own precipitation and stream gage networks. 

• Watershed Forecasts:
26 

• Participants indicated that the timing needs to be linked to local time. Hourly projections at a 

minimum during an event are preferred. 

• The time series outputs (i.e. hydrographs) were generally deemed to be helpful. 

• Local responders want to see information at a finer scale (e.g. 250m) than currently provided 

by the Napa Watershed modeling. 

• Precipitation information rated Very Helpful.

• Soil moisture rated Somewhat Helpful. 

• DHM surface runoff rated Not Useful to Somewhat Useful.

• Recurrence interval rated Very Useful by most, but some rated Not Useful.

• Time series unanimous Very Useful. Would like to see flood stage. 

• Relate water flows to citizen experience.

o Use storms of record (preferably within a 10-year window) to allow some 

comprehension of projected storm event.

o Depth and velocity of flow relate to danger. 

• Coastal Flood Forecasts:

• Meteorological data rates Somewhat Useful. Helps establish context.

• Wave forecasts rated unanimous Very Useful.

• Currents forecast rated Somewhat Useful; some rated Not Useful.

• Water level forecasts rated Somewhat Useful to Very Useful. 

• Time series forecasts rated Very Useful. 

• Suggest overlay FEMA Floodplain for reference; also recent flood inundation levels.

• Coastal Flood Indices: 

• People primarily want to know - Where, How High and When. 

• Consider describing projections as “ankle deep” “knee deep” etc. Indicate flood stage. 

• Incorporate tide projections so that responders can be aware of the confluence of tides with 

flooding projections. 

• Need to consider audience – is it Emergency Managers and First Responders or someone 

else? 

• Need to consider point of products – newscast or police with blow horn saying need to 

evacuate? 

• (Would be good to have) local corroboration of projected flooding.

• Flood indices uniformly rated Somewhat to Very Helpful, including those for a) Start of 

Flooding, b) Duration of Flooding, c) Time of Max Depth, d) Max Water Depth, and e) 

Hazard Index (although some confusion on what it means). 

• Impacts – Critical Facilities: 

• Identification of fire stations, schools and wastewater treatment plants rated Somewhat 

Useful. Suggest include hospitals and airports. 

• Identification of roads and road crossings (bridges) rated Very Useful. 

• Identify key locations for each region...to help bring context to flood projections. 

• EOC locations could be included in an internally (non-public) available layer - but that would 

not be good to include a public layer. 

• Forecasters have hydro-database (E-19). 

• Develop database service of user-generated content. 

• Need to narrow in on the audience for each product or output. Are the outputs geared to first 

responders or others? 
27 

6. Summary and Conclusions

6.1 Summary

The San Francisco Bay Integrated Flood Forecasting Project to demonstrated the feasibility of linking 

tributary and coastal storm models in San Francisco Bay. The demonstration was based on a direct 

coupling of the discharge from the watershed model into the coastal model, a first step toward a more 

advanced coupling. A TTE was conducted at the culmination of the project, to engage potential 

stakeholders and end users and solicit feedback on the kinds of information needed from an integrated 

flood forecasting system in the Bay area. 

6.2 Conclusions

• The prototype Hydro-CoSMoS was deployed and demonstrated capability to provide watershed and 

coastal flood information at scales and at locations not currently served by NWS operations.

• The DHM was interfaced to WFO precipitation forcing datasets available in real-time at the WFO and 

NOAA Physical Sciences Division in Boulder, CO. RDHM outputs were generated using hydrologic 

modeling software such as CHPS/FEWS and DHM products disseminated using web services. These 

precipitation data feeds were used to assess potential improvements in runoff predictions with the 

distributed modeling approach. 

• The DHM prototype and interactions with our Advisory Panel supported examination a concept of 

operations, requirements specification, and toolkit for DHM implementation and application for NWS 

flash flood operations. 

• Coordination was accomplished with the NWS agencies involved with hydrologic operations in 

assessment of the RDHM for WFO operations, including the WFO-MTR, CNRFC, NWC and 

Western Region. 

• Assessment of the DHM implementation was conducted to determine how best to implement the 

model to support flood forecast operations, identify constraints and opportunities for enhanced flood 

and water management applications which could be sustained by WFO/RFC DHM operations. 

• The assessments provided useful feedback on local needs, which model outputs are useful and usable, 

and how best to communicate the various model outputs. Participants expressed interest in continued 

discussions as the Hydro-CoSMoS model continues to be built out for Napa and the other watersheds 

in the SF Bay. 

• Integrating coastal storm and fluvial events is of growing importance, and coastal models operate best 

at a regional scale making this multi-watershed with one coastal modeling system a highly efficient

approach. 

• The project was successful in showing how tributary flows could be used to inform the coastal storm 

model during a flooding scenario. 

• The demonstrations of prototypes provide a basis for a larger project to fully couple the models and 

provide forecasts in real-time for the entire SF Bay region. 

• By joining together regional resources a tool can be built that will help all areas, notwithstanding 

local staffing limitations.

• As with all operational models, we also confront the challenge of balancing desired resolution and 

computational resources. 

• Interaction with many local authorities in design of system is helpful in optimizing usefulness, and 

allows validation in locations with little data by tapping local knowledge base and experience. 
28 

• The assessments provided an opportunity for the model developers to interact with the end-users of 

their information, providing valuable information to help guide continued model development and to 

inform what model outputs are most relevant to end-users.

• Similarly, the assessments provided an opportunity for the end-users to become familiar with this 

emerging tool and to gain an initial level of understanding prior to advancement to real-time 

operations. This helps develop an engaged end-user who will be more likely to utilize the model 

products once it is running operationally. 

• The assessments also enabled the project team to learn about other potential end-users and leverage 

the results of this these exercises to engage subsequent end-users in the SF Bay region and other 

counties in CA. 

7. Acknowledgements 

This project was supported by a grant from the California Department of Water Resources under the 

guidance of Dr. Michael Anderson, State Climatologist. The FEWS implementation of RDHM and 

prototyping of near real-time simulations was part of a grant provided by the NOAA Office of Oceanic 

and Atmospheric Research (OAR), Office of Weather and Air Quality (OWAQ), FY2015 US Weather 

Research Program, Hydrometeorology Testbed (HMT) and Hazardous Weather Testbed (HWT) Grant 

Award # NA15OAR4590151. The NOAA Physical Sciences Division provided resources to supplement 

project support from DWR, ancillary data sets, and access to the CHPS-FEWS system for the DHM 

forecasts. 

Project personnel who have collaborated in developing the prototype flood forecast system include 

ESRL-PSD’s Jungho Kim, Lynn Johnson, Tim Coleman and Rob Cifelli. Riverside, Inc’s James Halgren 

and John Park contributed to implementation of the distributed hydrologic model. USGS coastal 

modeling staff include Liv Herdman, Rosanne Martyr-Koller, Juliette Finzi Hart, Li Erikson and Patrick 

Barnard. 
29 

8. References 

Barnard , Patrick L., David H. Schoellhamer, Bruce E. Jaffe, Lester J. McKee, 2013. Sediment transport 

in the San Francisco Bay Coastal System: An overview, Marine Geology, Volume 345. 

Barnard, P. L., van Ormondt, M., Erikson, L.H., Eshleman, J., Hapke, C., Ruggiero, P., Adams, P. N., and 

Foxgrover, A. C., 2014. Development of the Coastal Storm Modeling System (CoSMoS) for predicting 

the impact of storms on high-energy, active-margin coasts. Natural Hazards, 31 p., doi: 10.1007/s11069-

014-1236-y

Cummings, J.A., and O. M. Smedstad. 2013: Variational Data Assimilation for the Global Ocean. Data 

Assimilation for Atmospheric, Oceanic and Hydrologic Applications vol II, chapter 13, 303-343. 

Cummings, J.A., 2005: Operational multivariate ocean data assimilation. Quart. J. Royal Met. Soc., Part 

C, 131(613), 3583-3604. 

Dusterhoff, S. D.; Beagle, J.; Collins, J. N.; Doehring, C. 2014. Initial Protocol to Identify and Delineate 

the Head of Tide Zone in San Francisco Bay Tributaries. San Francisco Estuary Institute: Richmond, CA. 

EurOtop, 2016. Manual on wave overtopping of sea defences and related structures. An overtopping 

manual largely based on European research, but for worldwide application. Van der Meer, J.W., Allsop, 

N.W.H., Bruce, T., De Rouck, J., Kortenhaus, A., Pullen, T., Schüttrumpf, H., Troch, P. and Zanuttigh, 

B., www.overtopping-manual.com. 

Fox, D.N., W.J. Teague, C.N. Barron, M.R. Carnes, and C.M. Lee, 2002. The Modular Ocean Data 

Assimilation System (MODAS). J. Atmos. Ocean. Technol., 19, 240-252. 

Halgren, J., L. Johnson, T. Coleman and R. Cifelli. 2015: RDHM-CHPS Research-to-Operations 

Demonstration, Russian-Napa River Basins, CA. Poster for 6th NOAA Testbed and Operational Proving 

Ground Workshop. April 14. 

Johnson, L.E. 1995: DARE Hydrologic Evaluations (1990-1992) - Assessment of Hydrologic Forecasts. 

NOAA Technical Memorandum. Forecast Systems Laboratory. Boulder, Colorado. June. 70 pp. 

Johnson, L.E. 1995: DARE Hydrologic Evaluations (1990-1992) - Evaluation of Hydrologic Training. 

NOAA Technical Memorandum. Forecast Systems Laboratory. Boulder, Colorado. June. 50 pp. 

Johnson, L.E., C. Hsu, R. Zamora, and R. Cifelli, 2016. Assessment and Applications of Distributed 

Hydrologic Model - Russian-Napa River Basins, CA. NOAA Technical Memorandum PSD-316, NOAA 

Printing Office, Silver Spring, MD, 101 pp. doi:10.7289/V5M32SS9 

(http://dx.doi.org/10.7289/V5M32SS9) 

Johnson, L.E., P. Kucera, C. Lusk, W. Roberts. 1998: Usability Assessments for Hydrologic Forecasting 

Decision Support System..Journal American Water Resources Association. April. 

Kernkamp, H. W. J., Van Dam, A., Stelling, G. S., De Goede, E. D., 2011: Efficient scheme for the 

shallow water equations on unstructured grids with application to the continental shelf. Ocean Dynamics 

61, 1175–1188. 

Lusk, C., L.E. Johnson, P. Kucera, W. Roberts. 1999: Process and Methods Used to Evaluate Prototype 

Operational Hydrometeorological Workstations. (with P. Kucera, C. Lusk, W. Roberts). Bulletin 

American Meteorological Society.v80, N1, pp. 57-66. 

Morss, R. E., and F. M. Ralph, 2007: Use of information by National Weather Service forecasters and 

emergency managers during CALJET and PACJET-2001. Weather and Forecasting, 22(3), 539-555. 

Nielsen, J., 1993: Usability Engineering. Academic Press, Inc.ISBN 0-12-518406-9. 

O'Reilly, W. C. et al., 2016. The California coastal wave monitoring and prediction system Coastal Eng., 

v116, Oct. 2016, pp118-132. 
30 

Pelle, A., L. Johnson, R. Cifelli, G. Tootle 2015: Threshold Frequency Estimation for the Russian-Napa 

Rivers, CA. Poster at American Geophysical Union Annual Conference. December. 

Porter, Keith, Wein, Anne, Alpers, Charles, Baez, Allan, Barnard, Patrick, Carter, James, Corsi, 

Alessandra, Costner, James, Cox, Dale, Das, Tapash, Dettinger, Michael, Done, James, Eadie, Charles, 

Eymann, Marcia, Ferris, Justin, Gunturi, Prasad, Hughes, Mimi, Jarrett, Robert, Johnson, Laurie, Dam 

Le-Griffin, Hanh, Mitchell, David, Morman, Suzette, Neiman, Paul, Olsen, Anna, Perry, Suzanne, 

Plumlee, Geoffrey, Ralph, Martin, Reynolds, David, Rose, Adam, Schaefer, Kathleen, Serakos, Julie, 

Siembieda, William, Stock, Jonathan, Strong, David, Sue Wing, Ian, Tang, Alex, Thomas, Pete, Topping, 

Ken, and Wills, Chris; Jones, Lucile, Chief Scientist, Cox, Dale, Project Manager. 2010: Overview of the 

ARkStorm scenario: U.S. Geological Survey Open-File Report 2010-1312, 183 p. and appendixes. 

https://pubs.usgs.gov/of/2010/1312/

Roberts,W.F., L.E. Johnson, P.C. Kucera, C.M. Lusk, and D.C. Walker. 1995: Real-time Forecast 

Exercise for WFO-Advanced., 1996: NOAA Tech. Memorandum ERL FSL-18, Forecast Systems 

Laboratory, Boulder, CO. 

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design and summary results of the western basin experiments. J. Hydrol., 507, 0, 300-329. 

Tolman, H. L., 1998: A New Global Wave Forecast System at NCEP. In: _Ocean Wave Measurements 

and Analysis, Vol. 2, (Ed: B. L. Edge and J. M. Helmsley), ASCE, 777-786. 

van der Meer J.W. 2002: Technical Report Wave Run-up and Wave Overtopping at Dikes, Technical 

Advisory Committee on Flood Defence, Delft, The Netherlands.
31 

9. ACRONYMS • AWIPS Advanced Weather Interactive Processing System

• CaDWR California Department of Water Resources

• CHPS-FEWS Community Hydrologic Prediction System-Flood Early Warning System 

• CNRFC California Nevada River Forecast Center

• CoSMoS Coastal Storm Modeling System

• DEM Digital Elevation Model

• DHM Distributed Hydrologic Model

• DHM-TF Distributed Hydrologic Model - Threshold Frequency 

• EM Emergency Manager/Management

• FEMA Federal Emergency Management Agency

• FEWS Flood Early Warning System

• FFG Flash Flood Guidance 

• FFO Flash Flood Operations 

• FOC Flood Operations Center 

• GIS Geographic Information System

• GFS Global Forecast System

• GUI Graphical User Interface

• HMT Hydrometeorology Testbed

• HVT Hydrometeorological Visualization Tool

• Hydro-CoSMoS Integrated San Francisco Bay Coastal Flood Forecast Model 

• HRRR High Resolution Rapid Refresh

• IWRSS Integrated Water Resources Science and Services

• MPE Multi-sensor Precipitation Estimation

• MRMS Multi/Radar and Multi/Sensor 

• NCEP National Centers for Environmental Prediction

• NEXRAD Next generation radar

• NHD National Hydrography Dataset

• NOAA National Oceanic and Atmospheric Administration

• NWM National Water Model 

• NWS National Weather Service 

• OAR Office of Oceanic and Atmospheric Research

• OHD Office of Hydrologic Development 

• OWP Office of Water Prediction

• QPE Quantitative Precipitation Estimation

• QPF Quantitative Precipitation Forecast

• RDHM Research Distributed Hydrologic Model 

• RFC River Forecast Center

• SF Bay San Francisco Bay

• TF Threshold Frequency

• TTE Table Top Exercise

• USACE U.S. Army Corps of Engineers 

• USGS U.S. Geological Survey 

• WFO Weather Forecast Office 

• WPC Weather Prediction Center
32 

Appendix A – Watershed Reflective Assessment Survey Results DHMforFFO Questionnaire Responses Not at all Some what Very much 1 3 5 Please describe your job and needs for flash flood forecasts. What QPE data source(s) do you use to assess current rainfall amounts? Please list the QPE product(s) you use and where you access it/them. Detecting the location of a flash flood threat? 0% 67% 33% 3.7 QPE rated as Somewhat to Very Much helpful for flash flood location. Determining the magnitude of a flash flood event? 0% 100% 0% 3.0 All rated QPE as Somewhat helpful for flash flood magnitude. Assessing the accuracy of precipitation forcings? 20% 40% 40% 3.4 Varied responses for QPE for precip accuracy assessment. Please provide additional comment(s) about the QPE data you may use. What QPF data source(s) do you use to assess current rainfall amounts? Please list the QPF product(s) you use and where you access it/them. Detecting the location of a flash flood threat? 0% 60% 40% 3.8 QPF rated Somewhat to Very Much helpful for FF location Determining the magnitude of a flash flood event? 0% 80% 20% 3.4 QPF rated Somewhat to Very Much helpful for FF magnitude Determining the lead time of a flash flood event? 33% 67% 0% 2.3 QPF rated Not at All to Somewhat helpful for FF lead time. Please provide additional comment(s) about the QPF data you may use. Detecting the location of a flash flood threat? 0% 67% 33% 3.7 SM rated Somewhat to Very Much helpful for FF location. Determining the magnitude of a flash flood event? 0% 100% 0% 3.0 SM rated Somewhat helpful for FF magnitude Determining the watershed conditions for flood runoff? 17% 17% 67% 4.0 Most rated SM rated as Very Much helpful for watershed conditions. Please rate how helpful the Please provide additional comment(s) about the soil moisture product. QPF grid displays are for flash flood detection and forecasting. Please rate how helpful the DHM soil moisture (SM) grid displays are for flash flood detection and forecasting. All respondents are members of Advisory Panel, predominately NWS WFO forecasters. A few are hydrologic modelers involved with R&D and local flash flood warning operations. WSR-88D, MRMS, CNRFC QPE, spotter reports, etc. All of which are collected and viewed in either AWIPS or internet browser. Other QPE sources mentioned include local ALERT-type rain gage data, coop reports, cocorahs, and local media weather reports (e.g. radar imagery). Standard 6-hr time increment has limited value for FF operations. Multiple responses note that need 1-hr (or less) precip data having short latency to assess flash floods. To assess the accuracy of precipitation forcings, the display needs some of errors or data to compare ground gage data. Looking at stream gage data is also very helpful. AWIPS has numerous QPF products. Examples include the CNRFC QPF, HRRR, and/or local media weather forecasts. Need very short term forecasts for flash floods. QPF is the most important element of FF forecast system. 6-hr time scale too coarse. Need real-time obs data to corroborate. Soil moisture seems to be driven in these distributed models by recent rains. They are not assimilating soil moisture data. Thus they can be off greatly. So give little credence to these. Much better to monitor rate of runoff from particular stream gages to get a feel for how saturated the soils are. Most useful new gridded dataset available. Currency/latency day to day must be reliable. Thinking about the CNRFC QPE display above, please rate how helpful the QPE grid displays are for flash flood detection and forecasting. % Ratings by Category Question Topic Subtopic Comments Weighted Score
33 

DHMforFFO Questionnaire Responses

Not at all Some what Very much

1 3 5

Detecting the location of a flash flood threat? 0% 67% 33% 3.7 DHM discharge rated Somewhat to Very Much helpful for FF location.Determining the magnitude of a flash flood event? 0% 83% 17% 3.3 Most rated DHM discharge rated as Somewhat helpful for FF magnitudeDetermining the impacts of a flash flood event? 17% 50% 33% 3.3 Varied responses for DHM discharge grids as helpful for FF impacts. 

Please provide additional comment(s) about the discharge grid display.

Detecting the location of a flash flood threat? 17% 50% 33% 3.3 Varied responses for DHM flow frequency grids as helpful for FF location.

Determining the magnitude of a flash flood event? 0% 50% 50% 4.0 DHM flow frequency rated Somewhat to Very Much helpful for FF magnitude.

Determining the impacts of a flash flood event? 0% 83% 17% 3.3 Most rated DHM flow frequency as Somewhat helpful for FF impacts. How well do you understand the flood frequency characterization? 0% 33% 67% 4.3 Most rated the DHM flood frequency grids are Very Much undertandable.

Please provide additional comment(s) about the flood frequency grid display.

Detecting the location of a flash flood threat? 20% 0% 80% 4.2 Most rated the DHM flow hydrograph display as Very Much helpful for FF location. Determining the duration of a flash flood threat? 0% 50% 50% 4.0 DHM flow hydrograph rated Somewhat to Very Muchhelpful for FF durationDetermining the magnitude of a flash flood event? 0% 0% 100% 5.0 DHM flow hydrograph rated Very Much helpful for FF impacts. Determining the lead time of a flash flood event? 17% 17% 67% 4.0 Most rated DHM flow hydrograph as Very Much helpful for FF lead time.

Is it difficult mouse clicking the flow grid to obtain a hydrograph? 60% 40% 0% 1.8 Interrogation by mouse click rated Somewhat to Not at All difficult for obtaining hydrograph. 

Please provide additional comment(s) about the flow hydrograph display.Please rate how helpful the DHM discharge hydrograph displays are for flash flood detection and forecasting.

If accurate this type of output seems very useful. Good fingertip references. Water level (flood inundation) would be even more helpful. Need to have some sort of E-19 data with these gages or locations to know what the impacts are. Flooded intersection or closed bridge or flooded houses. Not familiar with these locations makes it difficult to know impacts. 

Please rate how helpful the DHM discharge grid displays are for flash flood detection and forecasting.

This (4 km) grid cell seems very coarse to accurately predict flash flooding, but would be useful for predicting potential threats. A model like this needs to earn its way into the forecast process. One would have to look at this type of output for many events and self calibrate to see what the false alarms and detection rates are. Also will it translate to other areas readily with different precip regimes...convection. 

Please rate how helpful the DHM flow frequency grid 

displays are for flash flood detection and forecasting.

I am not sure I fully understand what this display is showing. Provides context if user is not familiar with typical flow levels. Useful for impacts and for a check on the forecast (do I really want to forecast a 100 year event). 

Question Topic Subtopic

% Ratings by Category

Weighted 

Score

Comments
34 

DHMforFFO Questionnaire Responses

Not at all Some what Very much

1 3 5

Detecting the location of a flash flood threat? 0% 33% 67% 4.3 DHM impact features rated Somewhat to Very Much helpful for FF location.Determining the impacts of a flash flood event? 0% 50% 50% 4.0 DHM impact features rated Somewhat to Very Much helpful for FF impactsIs it a burden to interrogate by mouse clicking the impact icon? 67% 33% 0% 1.7 Most rate interrogation by mouse click rated as Not at All difficult for obtaining impact features.Do you feel it is appropriate for NWS to provide this product/service? 17% 33% 50% 3.7 Varied responses on appropriateness for NWS to provide impact feature info. 

Please provide additional comment(s) about the flood impact features displays.

Magnitude of flood 17% 33% 50% 3.7 How often DHM forecasts accurate for flood magnitude rated Rarely, to Sometimes, to Frequently.Location of flood 17% 33% 50% 3.7 How often DHM forecasts accurate for flood location rated Rarely, to Sometimes, to Frequently.Time of flood 17% 50% 33% 3.3 How often DHM forecasts accurate for time of flood rated Rarely, to Sometimes, to Frequently.Impacts of flood 17% 33% 50% 3.7 How often DHM forecasts accurate for flood impacts rated Rarely, to Sometimes, to Frequently.

In general, do you believe the DHM forecasts are accurate enough to support your needs? Response 50% 0% 50% 3.0 DHM accuracy to support your needs rated No and Yes equally.What could be done to improve DHM accuracy and 

usability?

DHM performance statistics can guide use of DHM products. 0% 0% 100% 5.0 All agree that DHM performance statistics can guide use of DHM products. Need to provide options to modify DHM parameters. 17% 67% 17% 3.0 Need to provide options to modify DHM parameters - Most neither disagree or agree.Need capability to re-run DHM for local watersheds. 0% 67% 33% 3.7 Need capability to re-run DHM for local watersheds - Most neither disagree or agree, but some agree. 

Please tell us how much you agree or disagree with the statements below (check a single column box for each statement).Tell us how you would use the information provided in this product/service (e.g., information only, to support personal decision-making, to support flash flood response decision making, etc)?Do you have any additional input regarding the concept of operations or any other aspect of DHM use for support of FF operations?

We can help highlight areas of concern for excessive rainfall to EMs and the public through partner emails or social media messaging before the event. I could see that a product like this would be useful for county emergency responders during to help flood response planning. (Helpful) to support personal decision-making. (Helpful to) identify areas of risk, timing of risk, and general level of event. Another piece of the puzzle but not the answer. Latency of the product can be very long and for flash flooding need to run hourly if needed with new gridded QPE and QPF. Can't have 1-3 hr latency of model data feeding the RDHM. Need to have the model forecast and the obs for the gage in the display so we can see how the model is doing near real-time. No comments on ConOps

Please rate how helpful the DHM impact features 

displays are for flash flood and impact assessment.

Good fingertip references. Water levels and elevations of bridge decks would be even more helpful. Need to have some sort of E-19 data with these gages or locations to know what the impacts are. 

In your opinion, how often would you expect the DHM forecasts to be accurate enough to support your job?

It would be helpful for improved temporal resolution. Adding this into a forecaster's arsenal will undoubtedly help improve situational awareness of a rainfall event. Uncertainty analysis information about forcings and soil moisture. Need improved calibration, and regulation modeling. Better than not, all depends on accuracy of weather model inputs. Needs to be able to assimilate observations like radar rain rates and soil moisture. It needs a longer calibration to get a more distributed rainfall climatology. Ability to re-run allows better precip inputs in evolving real-time. 

Question Topic Subtopic

% Ratings by Category

Weighted 

Score

Comments
35 

Appendix B – Hydro-CoSMoS Tabletop Exercise Agenda

Tabletop Exercise: SF Bay Integrated Flood Forecast Model 

Location: Sacramento Flood Center, 3310 El Camino Avenue Room 200 

Date: Tuesday, June 13th, 2017 Time: 09:00 – 12:00 PDT

Objectives: 

o Present initial results of SF Bay Integrated Flood Forecast Model 

o What it is & how it works 

o Understand end-user information needs for products and services 

Check-In (08:45-09:00) 

Greeting (09:00-09:15) 

Part I Project Overview (09:15-10:30): 

o Opening Remarks – Mike Anderson (10 min.) 

o Prototype for SF Bay Advanced Quantitative Precipitation Information - Rob Cifelli (15 min.) 

o Watershed distributed hydrological model – Jungho Kim (20 min.) 

o CoSMoS model – Rose (20 min.) 

Q & A / Discussion (10 min.) 

Short Break (10 min) 

Part II Pilot Study Scenario: Napa Watershed – San Pablo Bay (10:40-12:00): 

 Model Scenario Exercise (65 min) 

• Introduction of scenario: Route 37 and Napa basin flood impacts due to confluence of heavy 

precipitation, river flooding, and storm surge (Liv) 

• Review impacts of recent January flooding that included long term closure of Route 37 (Juliette – 

very short 5 min and allow for some comments from Napa folks in the rooms) 

• Small group exercise: attendees use model products to plan their response before, during and after 

the event, each group will be facilitated by a NOAA/USGS team member 

• What information is most useful? 

• What information is mission critical? 

• Is there a different way that you would like to see the data presented? 

• How you might use this? What decisions would this inform? 

• Does the scale of the event matter (i.e. typical winter storm vs. extreme event)? 

• What other information do you use and are there other products you’d like to see 

developed? 

• Full group discussion: 

o Lessons learned from small group exercise 

 Next Steps and Wrap Up (15 min) 
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Appendix C – Hydro-CoSMoS Tabletop Exercise Survey Results Tabletop Exercise Questionnaire Responses Not Useful Some what Very Useful 1 3 5 Please describe your job and needs for flash flood forecasts. Please describe your needs for flood forecasts. What sources do you currently typically consult for this type of information? Precipitation 0% 40% 60% 4.2 Precipitation information rated Very Useful. Soil Moisture Content 0% 80% 20% 3.4 Soil moisture rated Somewhat Useful. Surface Runoff 40% 40% 20% 2.6 Grid runoff rated Not Useful to Somewhat Useful. Discharge 0% 40% 60% 4.2 Cumulative discharge rated Useful. Recurrence Intervals 20% 20% 60% 3.8 Recurrence interval rated Very Useful by most, but some rated is Not Useful. Time Series 0% 0% 100% 5.0 Time series unanimous Very Useful. Additional comments on the WATERSHED section. NWS forecasters use radar data, flash flood guidance, rainfall reports, and river data for their warning decision making. County-level staff are concerned with storm surge into flood control channels and protecting communities and ecosystem restoration projects thereby. Some counties track flood threats and issue warnings. Most use NWS warnings, including from the WFO and the CNRFC. Some track precipitation reports (public and private), spotter reports (phone, email,, social media, and news media), USGS stream gages. Some county staff have their own precipitation and stream gage networks. Please provide your first-level reaction for the information provided in the WATERSHED section. Note that the WATERSHED data included DHM input factors that contribute to flood magnitude, such as precipitation (current and forecast). Saturated soil moisture levels can exacerbate flood runoff. Surface runoff is the local runoff from a single grid, Discharge is the cumulative runoff at a grid from all upstream grids. Time series is the flow hydrograph which forecasts the peak flow and time-to-peak, and the duration of high water. Use local clock time for time series. Hourly time step preferred. Show flood stage. Refer to flood of record and/or recent flood levels (e.g. Napa River 1986 storm of record). DHM 4 km grid too coarse; need 250 m grid for watershed. Local responders used to more fine-scale information. Need to better define discharge and runoff. CFS is not communicable – make it more relatable. Question Topic Subtopic % Ratings by Category Weighted Score Comments Respondents included a mix of NWS forecasters, CaDWR flood managers, and several county-level flood response agency staff. A few are hydrologic modelers involved with R&D and local flash flood warning operations. 
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Tabletop Exercise Questionnaire Responses

Not 

Useful

Some 

what

Very 

Useful

1 3 5

Meteorological 20% 60% 20% 3.0 Met.data rated Somewhat Useful, with range of Not Useful to Very Useful.

Waves 0% 100% 0% 3.0 Wave forecasts unanimous rated Somewhat Useful. Currents 20% 80% 0% 2.6 Currents forecast rated Somewhat Useful; some rated Not Useful . Water Levels 0% 60% 40% 3.8 Water level forcasts rated Somewhat Useful to Very Useful. Time Series 0% 40% 60% 4.2 Time series forecasts rated Very Useful.

Additional comments on the COASTAL section. 

Start of Flooding 0% 50% 50% 4.0 Flood start time rated Somewhat to Very Useful.Duration of Flooding 0% 0% 100% 5.0 Flood duration rated unanimous Very Useful.Time of Max Depth of Flooding 0% 0% 100% 5.0 Time of Max Depth of Flooding rated unanimous Very Useful.Max Water Depth 0% 25% 75% 4.5 Max Water Depth rated Very Useful.Hazard Index 0% 50% 50% 4.0 Hazard Index rated Somewhat to Very Useful. Some confusion on what it means.

Additional comments on the FLOOD INDICES section. 

Fire Stations 0% 75% 25% 3.5 Fire stations rated Somewhat Useful.

Schools 0% 75% 25% 3.5 Schools rated Somewhat Useful. 

Wastewater Treatment Plants 0% 50% 50% 4.0 Wastewater treatment rated Somewhat to Very Useful.Roads 0% 20% 80% 4.6 Roads data rated Very Useful. 

Additional comment(s) about the CRITICAL FACILITIES Please provide your first-level reaction for the information provided in the FLOOD INDICES section. Note that FLOOD INDICES involve CoSMoS and DHM model output data characterizing flood magnitude, extent and criticality. The Hazard Index involves time integration of currents and water depth to find the locations which will be most impacted in a storm event, either through very fast currents, extremely deep water or extremely long flood duration, or some combination of the three.data you may use.

People primarily want to know - Where, How High and When. Consider describing projections as “ankle deep” “knee deep” etc. Need to consider audience – is it Emergency Managers and First Responders or someone else? Need to consider point of products – newscast or police with blowhorn saying need to evacuate? Local corroboration of projected flooding.

Please provide your first-level reaction for the information provided in the IMPACTS - CRITICAL FACILITIES section. These data involve identification of built facilities that are of concern to flood responders.

Suggest include hospitals and airports. Bridges subject to flooding. Identify key locations for each region...to help bring context to flood projections. EOC locations could be included in an internally (non-public) available layer - but that would not be good to include a public layer. Forecasters have hydro-database (E-19). Develop database service of user-generated content. 

Please provide your first-level reaction for the information provided in the COAST section. Note that COAST data include the CoSMoS model Meteorological forcing data (winds, barometric pressure), as well as gridded model forecasts such as Waves, Currents, and Water Levels. Time series show the forecast water level heights over time at a point, so the peak height, time-to-peak and duration of high water.

Show tide projections. Overlay FEMA Floodplain for reference; also recent flood inundation levels. 

Question Topic Subtopic

% Ratings by Category

Weighted 

Score

Comments