Coastal Hazards Task of the USGS Multi-Hazards Demonstration Project in 

Southern California 

The Framework of a Coastal Hazards Model—A Tool for 

Predicting the Impact of Severe Storms 

By Patrick L. Barnard1, Bill O’Reilly2, Maarten van Ormondt3, Edwin Elias1,3, Peter Ruggiero4, Li H. 

Erikson1

, Cheryl Hapke5, Brian D. Collins6, Robert T. Guza2, Peter N. Adams7 and Julie Thomas2

1

USGS Coastal and Marine Geology Program, Santa Cruz, Calif. 

2

Scripps Institution of Oceanography, La Jolla, Calif. 

3

Deltares, Delft, The Netherlands 

4

Oregon State University, Corvallis, Oreg. 

5

USGS Coastal and Marine Geology Program, Woods Hole, Mass. 

6

USGS Earth Surface Processes Team, Menlo Park, Calif. 

7

University of Florida, Gainesville, Fla. 

Open File Report 2009–1073 

U.S. Department of the Interior 

U.S. Geological Survey 
ii

U.S. Department of the Interior 

KEN SALAZAR, Secretary 

U.S. Geological Survey 

Suzette M. Kimball, Acting Director 

U.S. Geological Survey, Reston, Virginia: 2009 

For product and ordering information: 

World Wide Web: http://www.usgs.gov/pubprod 

Telephone: 1-888-ASK-USGS 

For more information on the USGS—the Federal source for science about the Earth, 

its natural and living resources, natural hazards, and the environment: 

World Wide Web: http://www.usgs.gov 

Telephone: 1-888-ASK-USGS 

Suggested citation: 

Barnard, P.L., O’Reilly, Bill, van Ormondt, Maarten, Elias, Edwin, Ruggiero, Peter, Erikson, L.H., Hapke, Cheryl, Collins, B.D., 

Guza, R.T., Adams, P.N., and Thomas, J.T., 2009, The framework of a coastal hazards model; a tool for predicting the impact

of severe storms: U.S. Geological Survey Open-File Report 2009-1073, 21 p. [http://pubs.usgs.gov/of/2009/1073/]. 

Any use of trade, product, or firm names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government. 

Although this report is in the public domain, permission must be secured from the individual 

copyright owners to reproduce any copyrighted material contained within this report. 
iii

Contents 

Introduction.................................................................................................................................................................... 1

Objectives...................................................................................................................................................................... 2

Project Area................................................................................................................................................................... 3

Model Framework.......................................................................................................................................................... 3

Regional Models ........................................................................................................................................................ 5

Spectral Refraction Wave Model............................................................................................................................ 5

Delft 3D FLOW/WAVE Model................................................................................................................................. 6

Local Models.............................................................................................................................................................. 7

XBeach Cross-Shore Profile Model........................................................................................................................ 8

Delft3D High-Resolution 2D Morphological Models................................................................................................ 9

Empirical Run-up Models ......................................................................................................................................10

Longshore Transport Model ..................................................................................................................................10

Probabilistic Cliff-Failure Model.............................................................................................................................11

Products .......................................................................................................................................................................12

Field Testing .................................................................................................................................................................12

Model Applications .......................................................................................................................................................13

Winter-Storm Scenario with Multi-Hazards Demonstration Project in Southern California........................................13

Other Winter-Storm Scenarios ..................................................................................................................................13

Additional Considerations .............................................................................................................................................15

Model Transferability ....................................................................................................................................................15

Collaboration and Peer-Review ....................................................................................................................................15

Project Timeline............................................................................................................................................................16

Summary ......................................................................................................................................................................16
iv

Acknowledgments ........................................................................................................................................................17

References Cited..........................................................................................................................................................18

Appendix.......................................................................................................................................................................21

Important Websites ...................................................................................................................................................21

List of Acronyms .......................................................................................................................................................21

Figures 

Figure 1. Coastline and bathymetry in the project area, which extends from Pt. Conception, California, to the Mexican 

Border............................................................................................................................................................................ 2

Figure 2. Coastal-hazards model framework. ............................................................................................................... 4

Figure 3. Example of MOnitoring and Prediction (MOP) points adjacent to the City of Ventura, California. ................. 5

Figure 4. Regional Delft3D model grid with nested higher resolution grids................................................................... 6

Figure 5. Map of the proposed high resolution Delft3D model locations in Southern California.................................... 9

Figure 6. Example from Southwest Washington State of the relationship between winter wave height increases and 

100-year water-level events under various sea-level rise (SLR) scenarios. ................................................................ 14

Tables 

Table 1. Examples of high resolution data sets available for DEM construction in Southern California........................ 8

Table 2. Preliminary list of high resolution Delft3D model locations in Southern California. ........................................10 
1

The Framework of a Coastal Hazards Model—A Tool for 

Predicting the Impact of Severe Storms 

By Patrick L. Barnard1, Bill O’Reilly2, Maarten van Ormondt3, Edwin Elias1,3, Peter Ruggiero4, Li H. 

Erikson1

, Cheryl Hapke5, Brian D. Collins6, Robert T. Guza2, Peter N. Adams7 and Julie Thomas2

1

USGS Coastal and Marine Geology Program, Santa Cruz, CA 

2

Scripps Institution of Oceanography, La Jolla, CA 

3

Deltares, Delft, The Netherlands 

4

Oregon State University, Corvallis, OR 

5

USGS Coastal and Marine Geology Program, Woods Hole, MA 

6

USGS Earth Surface Processes Team, Menlo Park, CA 

7

University of Florida, Gainesville, FL 

Introduction 

 The U.S. Geological Survey (USGS) Multi-Hazards Demonstration Project in Southern 

California (Jones and others, 2007) is a five-year project (FY2007-FY2011) integrating multiple USGS 

research activities with the needs of external partners, such as emergency managers and land-use 

planners, to produce products and information that can be used to create more disaster-resilient 

communities. The hazards being evaluated include earthquakes, landslides, floods, tsunamis, wildfires, 

and coastal hazards. 

 For the Coastal Hazards Task of the Multi-Hazards Demonstration Project in Southern 

California, the USGS is leading the development of a modeling system for forecasting the impact of 

winter storms threatening the entire Southern California shoreline from Pt. Conception to the Mexican 

border (fig. 1). The modeling system, run in real-time or with prescribed scenarios, will incorporate 

atmospheric information (that is, wind and pressure fields) with a suite of state-of-the-art physical 

process models (that is, tide, surge, and wave) to enable detailed prediction of currents, wave height, 

wave runup, and total water levels. Additional research-grade predictions of coastal flooding, 

inundation, erosion, and cliff failure will also be performed. Initial model testing, performance 

evaluation, and product development will be focused on a severe winter-storm scenario developed in 

collaboration with the Winter Storm Working Group of the USGS Multi-Hazards Demonstration Project 

in Southern California. Additional offline model runs and products will include coastal-hazard hindcasts 

of selected historical winter storms, as well as additional severe winter-storm simulations based on 

statistical analyses of historical wave and water-level data. The coastal-hazards model design will also 

be appropriate for simulating the impact of storms under various sea level rise and climate-change 

scenarios. The operational capabilities of this modeling system are designed to provide emergency 

planners with the critical information they need to respond quickly and efficiently and to increase public 
2

safety and mitigate damage associated with powerful coastal storms. For instance, high resolution local 

models will predict detailed wave heights, breaking patterns, and current strengths for use in warning 

systems for harbor-mouth navigation and densely populated coastal regions where beach safety is 

threatened. The offline applications are intended to equip coastal managers with the information needed 

to manage and allocate their resources effectively to protect sections of coast that may be most 

vulnerable to future severe storms. 

Figure 1. Coastline and bathymetry in the project area, which extends from Pt. Conception, California, to the 

Mexican Border. 

Objectives 

• Develop a state-of-the-art model for predicting hazards caused by severe storms along the 

Southern California coast. 

• Develop Web products that are easily accessible and meet the needs of emergency planners and 

coastal managers. 

• Integrate model products into the framework of the USGS Multi-Hazards Demonstration Project 

in Southern California. 
3

Project Area 

 The project area extends 470 km from Pt. Conception, California, south to the Mexican border 

(fig. 1). The region includes microtidal basins, but has no significant inlets or narrow straits that would 

focus tidal currents in the nearshore. The coastline is highly variable in terms of its orientation (west to 

south facing), morphology (rocky to wide, flat beaches), structures (seawalls, jetties, groins, 

breakwaters, etcetera), exposure (open to significant island sheltering) and backbeach development (for 

example, rural coast to urban beach front). The continental shelf is narrow, so storm surge during even 

the largest winter storms approaches only 30 cm (D. Cayan, personal commun. 2009), and extreme 

coastal flooding is usually caused by waves. 

 Deep-water swell is primarily from the west and northwest, but long period southern swell can 

be important during the summer months and during El Niño years (O’Reilly, 1993). Wave energy is 

highly seasonal and episodic, with winter storms capable of significantly eroding local beaches 

(Shepard, 1950). To address nearshore wave variability adequately, Southern California wave modeling 

requires a high spatial resolution bathymetry grid in water depths less than 300 m, and high resolution 

directional wave spectra at the model-domain boundaries (O’Reilly and Guza, 1993; O’Reilly and 

others, 1993). 

Model Framework 

 An overview of the model framework is given in figure 2. The model framework can be divided 

into two primary components: regional models and local models. The regional models are driven by 

boundary conditions derived from a global wave model (WaveWatch III), regional seas and swell data 

for Southern California provided by the Coastal Data Information Program (CDIP) and the National 

Data Buoy Center (NDBC) wave buoy network, a global tide model by TOPEX/Poseidon satellite 

altimetry, atmospheric pressure and wind forecasts by the National Weather Service (NWS), and 

bathymetric data sets by the National Ocean Service (NOS). The required inputs are swell, seas, wind, 

atmospheric pressure, tidal constituents, and bathymetry. The local models are driven primarily by 

output from the regional models. 

 The regional models consist of: 

• CDIP Spectral Refraction Wave Model—the primary swell model, and 

• Delft3D FLOW/WAVE Model—the primary water-level model. 

 The regional models are designed to provide outputs of the key physical parameters at each of 

the 4,729 MOnitoring and Prediction (MOP) points established for the Scripps CDIP Program in the 

study area (fig. 3). The nearshore MOPs are located along the 10-m depth contour and are paired with 

the closest corresponding backbeach MOP, with spacing every 100 m alongshore for the entire study 

area. The MOPs are the critical interface where the regional models provide output and scale down with 

the local models. Output parameters from the CDIP spectral refraction wave model (standard wave 

parameters) and Delft3D FLOW/WAVE model (water level) provide offshore boundary forcings that 

drive the local models. 
4

Figure 2. Coastal-hazards model framework. 
5

Figure 3. Example of MOnitoring and Prediction (MOP) points adjacent to the City of Ventura, California. 

Regional Models 

Spectral Refraction Wave Model 

 The CDIP Spectral Refraction Wave model uses measurements from a network of wave buoys 

and co-located point forecast spectra from the National Oceanic and Atmospheric Administration 

(NOAA) WaveWatch III global wave model (Tolman, 1997, 1999; NOAA, 2006) for its boundary 

conditions. The Spectral Refraction Model has been developed, calibrated, and extensively validated in 

Southern California since the early 1970s (O’Reilly and Guza, 1990, 1993, 1998; O’Reilly, 1993; 

O’Reilly and others, 1993). The CDIP program operates its coastal-wave MOP system on a 24/7 basis 

(Scripps Institution of Oceanography, 2009). CDIP will provide wave-parameter outputs at the 

aforementioned MOP sites to establish the wave boundary conditions for the local models. Regional 

bathymetry for the CDIP model is obtained primarily from the NOS hydrographic-survey archive 
6

(National Ocean Service, 2009) but it is augmented with more recent, higher resolution bathymetry 

when available. 

Delft 3D FLOW/WAVE Model 

 A framework of coupled model grids was built in the Delft3D FLOW and WAVE modules 

(Lesser and others, 2004; Delft Hydraulics, 2007) with sufficient resolution to predict stationary water 

levels at the MOPs (fig. 4). This framework allows (1) the flexible coupling of large-scale regional 

models and high-resolution models at key coastal locations, and (2) the exchange of input and output 

data with other models used in this project (for example, XBeach). The larger scale (regional) models 

will be forced with tidal constituents at the open boundary using the Oregon State University 

TOPEX/Poseidon tide data (Egbert and others, 1994) and swell through a link to the WaveWatch III 

wave model. The primary atmospheric-forcing parameters, surface winds and atmospheric pressure, will 

be obtained from the NWS real-time forecasts for Southern California (National Weather Service, 

2009). Bathymetry is obtained from the same sources as in the Spectral Refraction Model. Water-level 

output and wave data from this regional-model coupling will also be used to drive a series of highresolution Delft3D models at key coastal sites, such as harbor-mouth entrances, piers, and vulnerable 

beaches. 

Figure 4. Regional Delft3D model grid with nested higher resolution grids. 
7

Local Models 

Wave parameters from the CDIP Spectral Refraction Model and water levels from the Delft3D 

FLOW/WAVE model assigned to each MOP station will drive the following local, high-resolution 

models where coastal hazards will be determined. 

• XBeach Cross Shore Profile Model 

• Delft3D High Resolution 2D/3D Morphological Models 

 These models will make local predictions of water levels, run-up, wave heights, and currents. 

The primary model interface would be MATLAB-based, with parameters input into a script as a matrix 

and output as another matrix, with graphical results then produced in MATLAB, ArcGIS, Google Earth, 

and/or other software packages for offline or online presentation. 

 At a given time step, such as in the middle of a major winter storm, the following parameters 

will be available for model input at the MOP sites and along MOP profiles. 

Parameter Source

significant wave height (Hs) CDIP 

peak wave period (Tp) CDIP 

incident wave angle/peak direction (θ) CDIP 

stationary water level (Zo) Delft3D 

nearshore slope DEM 

foreshore slope DEM 

beach slope DEM 

berm-crest elevation DEM 

dune-toe elevation DEM 

dune-crest elevation DEM 

cliff-toe elevation DEM 

cliff-top elevation DEM 

extreme back-beach elevation DEM 

elevation of infrastructure DEM 

obliqueness of wave approach CDIP+DEM 

Given these model inputs, the local models can be used to estimate numerous parameters, including 

• breaking wave height and location, 

• maximum water level, 

• extreme run-up height (R2%), and 

• flow velocities. 

Additional parameters that can be estimated but only as research products with a high level of 

uncertainty include 

• berm overtopping, 

• coastal flooding/inundation, 

• profile change, 
8

• cliff failure, 

• littoral transport (divergence and convergence), and 

• infrastructure impact.

Digital elevation models (DEMs) will be constructed from the latest available Lidar (Light 

detection and ranging) and swath bathymetry mapping surveys (table 1) to generate a continuous grid 

from the 20-m depth contour through the shoreline and to 500 m beyond the backbeach, a distance 

where, in most cases, the coastal-hazards risks have significantly dissipated. DEMs will be built with a 3 

m grid resolution using standard gridding techniques (for example, nearest neighbor, inverse distance 

weighting). The horizontal coordinate system will be the Universal Transverse Mercator system (UTM), 

Zone 11, north. The North American Vertical Datum of 1988 (NAVD88) will be used as the vertical 

reference point for all depths and elevations. Data gaps will be filled in with other available data, such 

as personal watercraft-collected single-beam bathymetry (for example, Barnard and others, 2007, 2009), 

NOS bathymetry, and NOAA IfSAR (Interferometric Synthetic Aperture Radar). Finally, cross-shore 

profiles will be extracted from the DEM along the MOP transects for direct input into XBeach, as well 

as for determination of morphological parameters for other local model inputs, such as slope, and for 

determining the elevation of important morphological features, such as dune and cliff toes. DEMs and 

profiles will be updated as data become available. Where new profile data are not available following 

several seasons of storms, typical winter/summer profiles may be used. Despite using the most recent, 

high-resolution data sets available, beach morphology changes daily and responds significantly 

seasonally and to individual storms. The storm response of beaches is highly dependent on the 

antecedent topography. Further, sediment-composition data and sediment-thickness data are available 

only for portions of the project area (for example, Mustain and others, 2007). Therefore, predictions that 

rely on highly accurate pre-storm beach shape (for example, erosion), elevations (for example, berm 

overtopping, flooding/inundation), and local sediment transport (for example, littoral transport) are 

highly speculative and, therefore, should be considered as qualitative/research products with high levels 

of uncertainty. 

Table 1. Examples of high resolution data sets available for DEM construction in Southern California. [The agency 

listed controls the data. USGS, U.S. Geological Survey; CSUMB, California State University, Monterey Bay]

County Survey Agency 

Horizontal 

resolution, 

in meters Date 

Santa Barbara/Ventura Lidar USGS 1 2005 

Santa Barbara/Ventura Multibeam USGS/CSUMB 1-2 2005-8 

Los Angeles Lidar Los Angeles County 1-2 2005 

Orange Lidar Scripps 1 2008 

San Diego Lidar Scripps 1 2007 

XBeach Cross-Shore Profile Model 

Instantaneous profile change, extreme run-up, and maximum water level will be determined 

using the XBeach profile model (Roelvink and others, 2008). Computation time for each of the 4,729 

XBeach cross-shore models is on the order of minutes allowing for near real-time prediction of profile 

changes only if a multinode computer cluster is available. If a multinode computer cluster is not 
9

available, selected profiles can be run for real-time applications. Calibration coefficients will be 

determined for MOP locations in defined littoral cells and subsets thereof using available data and data 

collected during the proposed 2009-10 field work. 

Delft3D High-Resolution 2D Morphological Models 

High-resolution Delft3D models will be coupled to the regional Delft3D models using a nesting 

procedure. These models will be constructed in areas where detailed information of coastal conditions is 

critical to identify areas where safe navigation, public safety, and high-value infrastructure are likely to 

be compromised during a storm. A preliminary list of these sites is presented in figure 5 and table 2. 

These high-resolution models will predict wave height, breaking patterns, water levels, and current 

strength to support warning systems for harbor-mouth navigation and densely populated coastal regions 

where beach safety is threatened. Sediment transport (for example, channel shoaling and migration) may 

also be predicted, but this product is associated with a high level of uncertainty. 

Figure 5. Map of the proposed high resolution Delft3D model locations in Southern California. 
10

Table 2. Preliminary list of high resolution Delft3D model locations in Southern California. [B, Beach Safety; I, 

Infrastructure; N, Navigation]

Site County Issue 

Cardiff By the Sea/Solana Beach San Diego B 

Coronado/San Diego Harbor San Diego B,I,N 

Mission Bay Harbor/Ocean Beach San Diego B,I,N 

Oceanside Harbor San Diego B,I,N 

San Onofre Nuclear Power Plant San Diego I 

Scripps Pier San Diego B,I 

Dana Point Harbor Orange N 

Huntington Beach Pier Orange B,I 

Newport Beach Harbor Orange B,I,N 

Long Beach/Los Angeles Harbor Los Angeles I,N 

Malibu Beach Pier Los Angeles B,I 

Marina Del Rey/Santa Monica Beach Pier Los Angeles B,I,N 

Redondo Beach Pier/Harbor Los Angeles B,I,N 

Hueneme/Channel Islands Harbor Ventura B,I,N 

Santa Clara River/Ventura Harbor Ventura B,I,N 

Santa Barbara Harbor Santa Barbara I,N 

Empirical Run-up Models 

For validation purposes, and as a quick comparison with the run-up estimates from XBeach, the 

run-up height will also be estimated along each MOP profile using empirical run-up formulations to 

define the limit of landward inundation, extent of potential beach-width change, and frequency of 

exposure of dunes and cliffs to wave run-up. Although several formulations will be tested, the ones 

presented by Ruggiero and others (2001) and Stockdon and others (2006) are expected to be the most 

advantageous as they have both been tested against west coast data. The Ruggiero and others (2001) 

formulation ( β oLHC ), similar to Hunt’s (1959) relationship, relies on four parameters: deep-water 

wave height (Ho) and wavelength (L), which can both be back-calculated from the spectral wave model, 

surf-zone slope (β), which will be available from the DEM dataset and is expected to be somewhat 

constant over seasons, and an empirical coefficient (C) that is dependent on several variables, such as 

breaker type, sediment grain size (friction and percolation), and foreshore slope. These dependencies 

and run-up predictions will be tested against available data and data collected in the field during the 

winter 2009-10 field campaign. 

Longshore Transport Model 

 As an additional research application, we will use the quasi process-based Coastal Engineering 

Research Center’s (CERC) equation (U.S. Army Corps of Engineers, 1984) to compute longshore 

sediment transport along the Southern California coast. This formulation has been applied to shorelinechange prediction in other studies, with moderate success along sandy, open-ocean coasts. By 
11

evaluating the effectiveness of the CERC equation along the sediment-starved coast of the project area, 

we expect to demonstrate the need for a hydrodynamics-based sediment-transport model, as illustrated 

by List and others (2008). The CERC equation will be used to identify erosion hot spots during storms 

due to longshore transport gradients. The coast is “naturally protected” by the presence of a beach, 

which depends on the sediment mass balance within a littoral control volume and the nearshore wave 

field that drives longshore sediment transport. A recent study investigated changes in the 

Southern California deep-water wave climate during the past 50 years (Adams and others, 2008) and 

documented a relationship among the Pacific Decadal Oscillation, El Niño Southern Oscillation 

(ENSO), and deep-water wave height, period, and direction. Changes in deep-water wave conditions 

will affect nearshore wave conditions and, hence, drive a response in spatial distribution of beach 

sediment. Using modeled nearshore wave conditions at the MOP sites, we will use the CERC equation 

to calculate longshore sediment-transport potential at each MOP location for the imposed wave climate 

during the assigned time step. Where the first spatial derivative of the longshore sediment-transport 

potential (also known as the divergence of drift) is negative, an erosional hot spot emerges; conversely, 

positive divergence results in sediment accumulation and coastal accretion during that time 

step. However, it is important to note that this calculation is based on potential longshore transport; 

actual longshore transport is limited by sediment availability, which can only be known from 

geophysical surveys of the subbottom and reliable estimates of sediment supply from fluvial sources 

and sea-cliff retreat, data that is only available in a few areas. Erosional hot spots identified with the 

longshore transport model can be used to guide the cliff-erosion and failure models by identifying 

specific sites where sea cliffs are less frequently protected by a sandy beach and, hence, are more 

frequently vulnerable to direct wave attack. 

Probabilistic Cliff-Failure Model 

 Cliff failure is episodic and difficult to predict using real-time response models similar to those 

proposed elsewhere in this document due to the high temporal and spatial variability of coastal cliff 

retreat. However, if a particular area can be well constrained in terms of its geometry, material strength, 

and predominant failure mode, a process model can be applied to give reasonable estimates of expected 

cliff response (Collins and Sitar, 2008). In San Diego, for example, data sets currently exist that will 

provide a starting point for this type of work. Unfortunately, the effort required to perform this type of a 

study along the entire Southern California area precludes the extended application of a process-based 

method. For areas with large spatial extent, such as those being investigated here, the best approach to 

estimating regional cliff response to storm hazards will be to develop probabilistic models that are based 

on a likelihood of failure function and to use process-based models as a tool for model verification. 

The likelihood of failure model will incorporate estimated total water levels associated with 

various storm scenarios, cliff geometry and lithology parameters. In addition, historical data on regional 

cliff-failure patterns (Hapke and Reid, 2007; Hapke and others, in press) will be used to identify erosion 

hotspots for more focused analyses. We will use a Bayesian statistical-based probabilistic cliff-failure 

model to develop predictions for wave-induced coastal cliff failure along the Southern California 

coastline. Although precipitation also leads to cliff failures, at this time it will be assumed that all cliff 

forcing from model inputs is solely wave driven. Run-up elevations determined from the XBeach Crossshore Model, Lidar-based cliff-toe elevation and slope data, GIS datasets of regional geology, and cliff 

sediment strength parameters estimated from existing data (for example, California Geological Survey 

Seismic Hazard Mapping Act reports and California Coastal Commission permit reports) will drive the 

model using an iterative process to integrate accumulating information. Cliff-sediment strength will 
12

guide the model to those sections of the coastline most susceptible to wave-action induced cliff failure. 

Given the model inputs, the current state of knowledge regarding cliff-retreat hazards will be assessed, 

with new data subsequently gathered to address remaining questions. Finally, the site-specific, processbased modeling will be used to guide and verify probabilistic model output. 

Products 

Inundation and other hazard-assessment maps will be designed to meet emergency planner (for 

example, Los Angeles County Fire Department) and coastal management (for example, National Park 

Service, California Coastal Commission) agency needs. The results will be presented in a Google Map 

interface displaying predictions at each MOP site of parameters such as wave height, maximum water 

level, and maximum run-up. In addition, 2-D maps of inundation and harbor-mouth conditions (for 

example, breaking waves and current strength), as determined from the high-resolution Delft3D models, 

will be presented in an interactive format. Threshold values for parameters such as predicted breaking 

wave height and maximum run-up will be assessed to determine a rating for coastal hazards along each 

of the profile locations. Operational modeling may include an e-mail or phone-alert system for 

emergency planners to get the information out quickly. Coastal vulnerability based on the offline model 

simulations of severe winter storms can be used by the coastal-management agencies to assist in longterm planning and regional sediment management. Extensive product development is not planned to 

begin until fall 2009, after the critical needs of end users are identified. 

Field Testing 

 The USGS is planning rapid storm-response surveys for October 2009 to March 2010 in Santa 

Barbara and Ventura Counties along predefined transects where semiannual monitoring has been 

conducted since October 2005 (Barnard and others, 2009). This will entail pre- and post-storm 

topographic and nearshore bathymetric surveys using personal watercraft to test profile evolution 

predicted by XBeach. A series of buried beach pressure sensors are planned for installation prior to the 

winter-storm season for analyzing run-up conditions. 

 The USGS will use continuous video imaging at the Goleta Pier, City of Carpinteria Beach, and 

Ventura Pier in the Santa Barbara area, and at Scripps Pier and Tijuana Estuary in the San Diego area, to 

monitor shoreline response, run-up conditions, and/or wave height during the winter-storm season for 

calibration and validation of the hazards-model package. 

In conjunction with these USGS efforts, CDIP has requested assistance from San Diego County 

coastal cities in identifying problem areas and documenting wave-flooding events. Local city 

engineering and planning departments have agreed to document the extent and severity of the flooding. 

This documentation will contribute to the validation of the inundation predictions and warning system. 

A 7-year data set at Torrey Pines Beach in San Diego relates beach-profile changes to the 

incident wave field (Yates and others, 2009). The performance of the XBeach model will be compared 

with field data to determine the capability of the model to predict profile evolution and shoreline change 

on high-energy, west coast beaches, and to assess the level of uncertainty in predictions of beach 

morphologic changes within the coastal-hazards model. 

 Field verification of the probabilistic cliff-failure model will focus on one to three areas 

extending up to one to two kilometers in length. Both ground-based and aerial surveys will be 

conducted to identify areas of incipient failure and to monitor their response. These studies will form the 
13

inputs for future process-based modeling and for probabilistic model verification. Geologic and 

topographic cliff mapping, along with geotechnical testing, will be incorporated with both seasonal and 

historical observations of cliff failure to develop a process model that links the effects of storm input to 

cliff failure. The process model output (predictions of cliff failure given particular storm scenarios) will 

provide a direct verification of the probabilistic model output for these particular locations. 

Model Applications 

 In addition to operational predictions, the modeling framework lends itself to a variety of offline 

applications, such as sea-level rise impacts and coastal vulnerability to rare, unusually powerful storms. 

Coastal Managers need information on sea-level rise, coastal vulnerability, and storm impacts for their 

short- and long-term planning needs. The following winter-storm scenarios will provide information 

critical for their resource planning. 

Winter-Storm Scenario with Multi-Hazards Demonstration Project in Southern California 

The USGS, in collaboration with the NWS, is designing a “massive but plausible” winter-storm 

scenario to serve as a preliminary test for the various tasks in the USGS Multi-Hazards Demonstration 

Project in Southern California. The storm is loosely modeled after the 1861-1862 winter flood, which is 

believed to have been a greater than 100-year recurrence-interval event, but one that is more likely to 

occur under future climate-change scenarios. A simulated hourly time-series of the key atmospheric 

components (for example, rainfall, surface wind speed and direction, and atmospheric pressure) will be 

provided for the western Pacific region. The event will be simulated using standard Atmospheric 

simulation Reanalysis tools and by stitching together two very large winter storms for which extensive 

historical data exists, probably storms from the winters of 1969 and 1986. Coastal-hazards model results 

from the winter-storm scenario will be provided to emergency planners, coastal managers, and structural 

engineers so that they can become familiar with, and plan appropriately for, the kinds of impacts the 

Southern California coast will experience during a storm of this magnitude. This exercise will also 

greatly aid in developing products that are appropriate for the end users, and identifying model 

components that need refinement. 

Other Winter-Storm Scenarios 

 Coastal-hazard simulations of selected historical winter storms will also be performed using 

existing physical-forcing data, such as archived wind and wave parameter data and detailed hindcasts 

from the last decade. For instance, hourly MOP site wave-parameter hindcasts for Southern California 

date back to 2000. Additionally, 50-year wave hindcasts of the Western Pacific are available (Graham 

and Diaz, 2001) and may be utilized to simulate storms that predate 2000, such as the severe storms that 

occurred during the 1982-83 El Niño winter. These simulations will serve to validate the model where 

quantitative, or at least anecdotal, information exists on storm impacts in the project area, and will 

inform coastal managers of the stretches of coast that were most vulnerable to these events. 

 The Intergovernmental Panel on Climate Change (IPCC) conservatively projects up to 79 cm of 

global sea-level rise by 2100, barring more dramatic changes that my be induced by the collapse of the 

West Antarctic ice sheet and other influences (Intergovernmental Panel on Climate Change, 2007). 

Additionally, increasing storm intensity, frequency, and wave heights on the west coast (Allan and 

Komar, 2006) will subject many beaches to elevated coastal-hazards risks for the foreseeable future. 
14

Current statistical trends suggest that coastal storms that include the damaging combination of large 

waves and higher water levels will be more frequent in the future, resulting in greater risk of coastal 

inundation (fig. 6). Therefore, additional simulations of storms based on current trends in sea level and 

Figure 6. Example from Southwest Washington State of the relationship between winter wave height increases and 

100-year water-level events under various sea-level rise (SLR) scenarios. Each 100-year Total Water Level (TWL) 

results from 500 year Monte Carlo simulations of both wave runup and storm surge including the dependency 

between extreme wave heights and extreme storm surge. 

extreme wave heights are planned. We will take advantage of recent developments in extreme-values 

modeling to develop additional plausible severe winter-storm scenarios within a probabilistic 

framework. Our goal is to improve upon existing approaches, such as using worst-case benchmark 

events (for example, the largest measured wave height) as design parameters for assessing coastal flood 

and erosion hazards. For physical systems like those that govern coastal flood and erosion hazards, 

outcome severity involves two or more random variables (for example, wave heights, periods, and water 

levels), and the return period of outcomes (for example, flooding) is not equal to the forcing return 

period of a single variate (Hawkes and others, 2002). Unless wave height and still-water level are 

completely dependent or independent, multivariate extremes are difficult to predict directly from 
15

observational data, particularly in light of the relatively short wave- and water-level records that 

typically are available. Thus, our goal will be to produce design events that incorporate joint probability 

analysis based on distributions fitted to water levels and wave heights (and wave steepness), including 

the dependence between them. We then will apply a full simulation method using the Monte Carlo 

simulation technique and the fitted distributions. In this manner, hundreds to thousands of years of total 

water-level time series (or other structural functions, such as dune-erosion models) can be simulated and 

accurate design event return levels can be determined. This approach is readily modified to include the 

potential impacts of climate change and variability on coastal-flood and erosion hazards (Callaghan and 

others, 2008). 

Additional Considerations 

 There are additional coastal hazards that should be considered during severe storms, including 

wind damage, terrestrial flooding, and water-quality issues. However, due to funding constraints, these 

items are not going to be considered under this project. Only Oceanside Harbor (San Diego County), 

Marina Del Rey (Los Angeles County), and the Santa Clara River mouth (Ventura County) could have 

potentially significant terrestrial flooding issues during a major storm, so the omission of this 

component would not be significant for most of the project area. 

 The influence of shoreline armoring and the undercutting of structures during coastal storms also 

deserve special consideration, however, the detail required to accurately identify these issues is beyond 

the scope of this project. 

Model Transferability 

 The coastal-hazards model outlined here has been designed for efficient transferability and 

application to a variety of coastal settings where the magnitude and relative balance between waves, 

tidal currents, and surge may vary significantly during coastal storms. To test model transferability, a 

small pilot project is currently being planned for the mouth of San Francisco Bay in Northern 

California. This site was chosen because of the importance of understanding storm conditions at the 

entrance to a major harbor, and because the USGS has an extensive bathymetric and topographic data 

set for the area, including video-camera monitoring (Barnard and others, 2007), which will enable 

hindcast testing of the coastal-hazards model for a series of recent winter storms. 

Collaboration and Peer-Review 

The USGS has developed formal working relationships with the following institutions to collaborate in 

the development of this model: 

• Deltares (Maarten van Ormondt, Edwin Elias), 

• Oregon State University (Peter Ruggiero), 

• Scripps Institution of Oceanography (Bill O’Reilly, Julie Thomas and Robert Guza), and 

• University of Florida (Peter Adams). 

The USGS is interacting with the following agencies to ensure that model products meet emergency 

planner and coastal management needs: 
16

• Bay Area Conservation and Development Commission (Carolynn Box), 

• Beach Erosion Authority for Clean Oceans and Nourishment (Jim Bailard), 

• California Coastal Commission (Lesley Ewing), 

• California Department of Boating and Waterways (Kim Sterrett), 

• California Ocean Protection Council (Doug George), 

• California Resources Agency (Brian Baird), 

• California State Parks (Sydney Brown), 

• Federal Emergency Management Agency (Kathleen Schaefer), 

• Los Angeles County Fire Department, Urban Search and Rescue Task Force (Larry Collins), 

• Los Angeles County Lifeguards (Angus Alexander), 

• National Park Service (Rebecca Beavers), 

• Southern California Coastal Ocean Observing System (Julie Thomas), 

• U.S. Army Corps of Engineers, Los Angeles District (Heather Schlosser), and 

• U.S. Army Corps of Engineers, San Francisco District (Peter Mull). 

The following coastal scientists are being consulted for evaluation of model development and 

performance: 

• Dan Hanes, USGS, Coastal and Marine Geology Program, Santa Cruz, CA, 

• Jeff List, USGS, Coastal and Marine Geology Program, Woods Hole, MA, 

• Nathaniel Plant, USGS, Coastal and Marine Geology Program, St. Petersburg, FL, 

• Bruce Richmond, USGS, Coastal and Marine Geology Program, Santa Cruz, CA, 

• Dano Roelvink, UNESCO-IHE Institute for Water Education, The Netherlands, 

• Hilary Stockdon, USGS, Coastal and Marine Geology Program, St. Petersburg, FL, 

• Dave Thompson, USGS, Coastal and Marine Geology Program, St. Petersburg, FL, 

• Dirk-Jan Walstra, Deltares, The Netherlands, and 

• John Warner, USGS, Coastal and Marine Geology Program, Woods Hole, MA. 

Project Timeline 

• Spring/Summer 2009—Set-up preliminary model and testing. 

• Summer 2009—Winter-Storm Scenario. 

• Fall/Winter 2009-10—Field testing, model improvements, and product development. 

• Spring 2010—Additional storm scenarios. 

• June 2010—Publish results of winter-storm scenario(s). 

• June 2010+—Full model implementation (online?). 

• September 2011—Project completion. 

Summary 

The USGS is developing a coastal-hazards model to determine the impact of severe winter 

storms, both in real-time and using prescribed scenarios. The model and forecasts in this project can be 

updated periodically to include new changes in sea level and other inputs (for example, wave climate, 
17

bathymetry, topography), and can be used as the basis for local hazard assessments and real-time 

warning systems in strategic partnership with numerous Federal, State, local, and academic partners. 

Once developed and validated in southern California, the modeling framework can readily be adapted 

and applied for other priority areas, such as coastal urban centers and threatened ecosystems, 

particularly on the west coast. 

Acknowledgments 

This project is supported by the USGS Multi-Hazards Demonstration Project in Southern 

California and the Coastal and Marine Geology Program. Thanks to Mike Carr and John Warner for 

providing helpful internal reviews. Thanks also for additional peer reviews provided by Dan Hanes, Jeff 

List, Jeff Hansen, Rebecca Beavers, Jodi Eshleman, Carolynn Box, Kate Dallas, Lesley Ewing, Dirk-Jan 

Walstra, Dano Roelvink, Jim Bailard, Doug George, and Dan Hoover. 
18

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21

Appendix 

Important Websites 

• Coastal Data Information Program: http://cdip.ucsd.edu (last accessed April 26, 2009) 

• USGS Coastal Hazards Project: http://walrus.wr.usgs.gov/coastal_processes/socalhazards/ (last 

accessed April 26, 2009) 

• XBeach: http://www.xbeach.org/ and http://www.unesco-ihe.org/About/Academicdepartments/Water-Engineering/Hydraulic-Engineering-Coastal-Engineering-and-PortDevelopment/Research/Research-Projects/Modeling-hurricane-impacts-on-beaches-dunes-andbarrier-islands-XBeach (last accessed April 26, 2009) 

• Delft3D Model: 

http://delftsoftware.wldelft.nl/index.php?option=com_content&task=view&id=109 (last 

accessed April 26, 2009) 

List of Acronyms 

BEACON—Beach Erosion Authority for Clean Oceans and Nourishment 

CDIP—Coastal Data Information Program 

CERC—Coastal Engineering Research Center 

CSUMB—California State University, Monterey Bay 

DEM—Digital Elevation Model 

MOP—Monitoring and Prediction 

NDBC—National Data Buoy Center 

NOAA—National Oceanic and Atmospheric Administration 

NOS—National Ocean Service 

USGS—U.S. Geological Survey