THE IMPACTS OF SEA- LEVEL RISE ON
THE CALIFORNIA COAST
A Paper From:
California Climate Change Center
Prepared By:
Matthew Heberger, Heather Cooley,
Pablo Herrera, Peter H. Gleick, and Eli
Moore of the Pacific Institute
DISCLAIMER
This paper was prepared as the result of work funded by the California Energy Commission, the
California Environmental Protection Agency, Metropolitan Transportation Commission, California
Department of Transportation, and the California Ocean Protection Council ( collectively “ the funding
agencies”). It does not necessarily represent the views of the funding agencies, their respective
officers, agents and employees, or the State of California. The funding agencies, the State of
California, and their respective officers, employees, agents, contractors, and subcontractors make
no warrant, express or implied, and assume no responsibility or liability for the results of any actions
taken or other information developed based on this paper; nor does any party represent that the
uses of this information will not infringe upon privately owned rights. This paper is being made
available for informational purposes only and has not been approved or disapproved by the funding
agencies, nor have the funding agencies passed upon the accuracy, currency, completeness, or
adequacy of the information in this paper. Users of this paper agree by their use to hold blameless
each of the funding agencies for any liability associated with its use in any form. This work shall not
be used to assess actual coastal hazards, insurance requirements or property values, and
specifically shall not be used in lieu of Flood Insurance Studies and Flood Insurance Rate Maps
issued by the Federal Emergency Management Agency ( FEMA).
FINAL PAPER
Arnold Schwarzenegger, Governor
May 2009
CEC- 500- 2009- 024- F
i
Acknowledgments
Major funds for this report were made through the California Energy Commission’s Public
Interest Energy Research ( PIER) Program. Additional support was provided by the
Metropolitan Transportation Commission and the Ocean Protection Council. We thank them for
their generosity and foresight.
The scientists and engineers at Philip Williams and Associates provided us with information
and analysis on coastal flood and erosion hazards. Thanks to Dr. David L. Revell, Robert
Battalio, Jeremy Lowe, Justin Vandever, Brian Spear, and Seungjin Baek. For additional
information about their work on this project, please see www. pwa‐ltd.
com/ resources/ resource_ publications. html.
Many individuals, organizations, and agencies helped make this work possible by providing
data, information, and input and review of the final report. We owe thanks to Will Travis,
director of the Bay Conservation and Development Commission, for initiating the study and
suggesting our involvement and to staff members Leslie Lacko, Tim Doherty, Adam Parris, and
Steve Goldbeck, who worked closely with us as we prepared this report.
We thank Dr. Noah Knowles, Dr. Dan Cayan, Mary Tyree, and Dr. Peter Bromirski of Scripps
Institution of Oceanography for much of the oceanographic data. Dr. Reinhard Flick at Scripps
also provided useful data on historical tide trends.
Thanks to Doug Kimsey and his staff at the Metropolitan Transportation Commission for
providing accurate transportation data. Thanks to Reza Navai, Vahid Nowshiravan, and Barry
Padilla at the California Department of Transportation for many helpful conversations.
Special thanks to the staff at the National Oceanic and Atmospheric Administration’s ( NOAA)
Coastal Services Center, Kirk Waters and Keil Schmid, for helping us obtain several gigabytes of
LIDAR data. Abby Sallenger at the United States Geological Survey gave additional advice and
insights about the Coastal Change program’s LIDAR data. Thanks to Mark Sanchez, geographic
information system ( GIS) wizard at the State of Oregon, for help in figuring out how to handle
all those gigabytes!
Brian Fulfrost at the University of California ( UC) Santa Cruz, now at DCE Planning, helped us
locate several helpful GIS datasets. We thank Robert Colley, GIS Manager for Santa Clara
County, for providing data and for recognizing that the free and open sharing of public data is
so valuable to researchers and the public. Ray McDowell, GIS Data Coordinator at the
California Resources Agency, helped locate and obtain still more GIS data. At the Federal
Emergency Management Agency ( FEMA), Eric Simmons and Ray Lenaburg engaged us in
helpful discussion and gave pointers to the spatial data from recent FEMA mapping studies.
Staff at California’s Resources Agency engaged us in a number of insightful and provocative
discussions. Thanks to Sam Schuchat, Brian Baird, Tony Brunello, John Ellison, and Abe
Doherty. Special thanks to Christine Blackburn at the Ocean Protection Council ( OPC) for
ii
seeing the importance of this issue to the entire state, for helping to coordinate OPC’s
participation in the project, and for many valuable conversations.
Johanna Fenton, formerly head of the Earthquake and Tsunami Program in the Governor ʹ s
Office of Emergency Services, provided early guidance and advice. Leslie Ewing, Mark
Johnsson, and Greg Benoit of the California Coastal Commission provided data or suggestions.
We were especially thankful to discover the excellent work of Jennifer Dare, a NOAA Coastal
fellow, who compiled the Coastal Armoring Database.
Thanks to Philip Pang in the South Pacific Division of the U. S. Army Corps of Engineers for his
work estimating levee construction costs. Thanks to Jos Dijkman, flood management engineer at
Deltares/ Delft Hydraulics in the Netherlands, for a great deal of detailed information on the
construction of dikes and flood defenses. Walt Crampton, principal engineer at TerraCosta
Consulting Group, also provided seawall construction costs for California.
We wish to thank ESRI. A grant to the Pacific Institute in 2007 through their Conservation
Grants program allowed us to expand the range and sophistication of our analysis.
Special thanks go to the leader of the PIER Research Team, Guido Franco, not only for skillfully
overseeing this complex set of studies, but also for a number of insightful comments and
suggestions. Technical editor Susie Moser provided insightful comments on an early draft.
Thanks to editor Mark Wilson for skill and patience in making all of this readable.
Finally, we are especially grateful for our reviewers: Michael Hanemann, Arlene Wong, June
Gin, and two anonymous reviewers, who provided thoughtful and insightful comments. We
also received several public comments during the open comment period that helped improve
the final report. All conclusions and errors are, of course, our own.
iii
Preface
The California Energy Commission’s Public Interest Energy Research ( PIER) Program supports
public interest energy research and development that will help improve the quality of life in
California by bringing environmentally safe, affordable, and reliable energy services and
products to the marketplace.
The PIER Program conducts public interest research, development, and demonstration ( RD& D)
projects to benefit California’s electricity and natural gas ratepayers. The PIER Program strives
to conduct the most promising public interest energy research by partnering with RD& D
entities, including individuals, businesses, utilities, and public or private research institutions.
PIER funding efforts focus on the following RD& D program areas:
• Buildings End‐ Use Energy Efficiency
• Energy‐ Related Environmental Research
• Energy Systems Integration
• Environmentally Preferred Advanced Generation
• Industrial/ Agricultural/ Water End‐ Use Energy Efficiency
• Renewable Energy Technologies
• Transportation
In 2003, the California Energy Commission’s PIER Program established the California Climate
Change Center to document climate change research relevant to the states. This center is a
virtual organization with core research activities at Scripps Institution of Oceanography and the
University of California, Berkeley, complemented by efforts at other research institutions.
Priority research areas defined in PIER’s five‐ year Climate Change Research Plan are:
monitoring, analysis, and modeling of climate; analysis of options to reduce greenhouse gas
emissions; assessment of physical impacts and of adaptation strategies; and analysis of the
economic consequences of both climate change impacts and the efforts designed to reduce
emissions.
The California Climate Change Center Report Series details ongoing center‐ sponsored
research. As interim project results, the information contained in these reports may change;
authors should be contacted for the most recent project results. By providing ready access to
this timely research, the center seeks to inform the public and expand dissemination of climate
change information, thereby leveraging collaborative efforts and increasing the benefits of this
research to California’s citizens, environment, and economy.
For more information on the PIER Program, please visit the Energy Commission’s website at
www. energy. ca. gov/ pier/ or contract the Energy Commission at ( 916) 654‐ 5164.
iv
v
Table of Contents
Preface........................................................................................................................ ......................... iii
Abstract ............................................................................................................................... ............... xi
1.0 Introduction................................................................................................................... ...... 1
1.1. Key Findings .................................................................................................................... 2
2.0 Methods ............................................................................................................................... 4
2.1. Study Area........................................................................................................................ 5
2.2. Sea‐ Level Rise Projections.............................................................................................. 5
2.2.1. Mean Water Levels and Extreme Events............................................................... 5
2.3. Expected Risk to the Coast............................................................................................. 8
2.3.1. Coastal Inundation Risk........................................................................................... 8
Pacific Coast ........................................................................................................................ 9
San Francisco Bay............................................................................................................... 13
2.3.2. Erosion Risk ............................................................................................................... 15
2.3.3. Limitations of the Analysis...................................................................................... 17
2.4. Resources Threatened by Sea‐ Level Rise..................................................................... 20
2.4.1. Population.................................................................................................................. 20
2.4.2. Impacts on the Built Environment.......................................................................... 23
2.4.3. Natural Resources..................................................................................................... 27
Spatial Extent of Wetlands ................................................................................................ 27
Economic Value of Wetlands............................................................................................ 28
Impact of Sea‐ Level Rise on Wetlands ............................................................................ 30
2.4.4. Limitations ................................................................................................................. 33
2.5. Determine the Protective Responses Appropriate for the Region ........................... 33
2.5.1. Structural Coastal Protection Measures................................................................. 34
Beach Nourishment............................................................................................................ 34
Groins…........................................................................................................................ ...... 34
Seawalls, Bulkheads, and Revetments ............................................................................ 34
Breakwaters.................................................................................................................... .... 35
Dikes and Levees................................................................................................................ 35
Raise Existing Structures ( Roadways, Railroads, and Other Structures) ................... 35
2.5.2. Cost of Structural Protection Measures ................................................................. 35
vi
2.5.3. Estimating Needed Coastal Defenses .................................................................... 37
3.0 Results ............................................................................................................................... ... 38
3.1. Flood‐ Related Risks ........................................................................................................ 38
3.1.1. Population at Risk..................................................................................................... 39
Environmental Justice Concerns ...................................................................................... 43
3.1.2. Emergency and Healthcare Facilities at Risk ........................................................ 51
3.1.3. Hazardous Materials Sites ....................................................................................... 52
3.1.4. Infrastructure at Risk................................................................................................ 54
Roads and Railways........................................................................................................... 54
Power Plants........................................................................................................................ 58
Wastewater Treatment Plants........................................................................................... 62
Ports…......................................................................................................................... ........ 62
Airports....................................................................................................................... ........ 65
3.1.5. Wetlands..................................................................................................................... 65
3.1.6. Property at Risk......................................................................................................... 74
Pacific Coast ........................................................................................................................ 76
San Francisco Bay............................................................................................................... 78
3.1.7. Saltwater Intrusion to Groundwater Aquifers...................................................... 80
3.1.8. Cost of Protection...................................................................................................... 81
3.2. Erosion‐ Related Risks..................................................................................................... 82
3.2.1. Population at Risk from Erosion............................................................................. 82
3.2.2. Emergency and Healthcare Facilities at Risk from Erosion................................ 84
3.2.3. Infrastructure at Risk from Erosion........................................................................ 84
Roads and Railways........................................................................................................... 84
3.2.4. Property at Risk from Erosion................................................................................. 86
4.0 Conclusions and Recommendations................................................................................. 87
4.1. Conclusions.................................................................................................................... . 87
4.2. Recommendations........................................................................................................... 87
4.2.1. Principles for Adaptation......................................................................................... 88
4.2.2. Recommended Practices and Policies .................................................................... 88
4.2.3. Additional Research and Analysis ......................................................................... 91
5.0 References..................................................................................................................... ....... 92
6.0 Acronyms and Abbreviations............................................................................................ 99
vii
List of Figures
Figure 1. Trend in monthly mean sea level at the San Francisco tide station from 1854– 2006....... 6
Figure 2. Scenarios of sea‐ level rise to 2100............................................................................................ 8
Figure 3. Determining future flood elevations ...................................................................................... 9
Figure 4. Rates of change of tidal datums, San Francisco from 1900– 2000...................................... 11
Figure 5. Simple schematic of USGS San Francisco Bay hydrodynamic model ............................. 14
Figure 6. Historical and projected carbon dioxide emissions scenarios, 1990– 2010....................... 15
Figure 7. Comparison of 100‐ year flood elevations ( in meters NAVD88)....................................... 18
Figure 8. Limitations of the computer’s ability to accurately map coastal flooding in areas
protected by seawalls or levees or natural barriers..................................................................... 19
Figure 9. Relationship between demographics and vulnerabilities.................................................. 23
Figure 10. Distribution of census‐ block average replacement costs for single‐ family homes from
HAZUS.......................................................................................................................... ................... 25
Figure 11. Flooding of a coastal road in Santa Cruz, California........................................................ 26
Figure 12. National Wetlands Inventory wetlands classified as “ coastal” are below or adjacent to
the MHHW line ............................................................................................................................... 28
Figure 13. Assumed wetland area defined by the intertidal range................................................... 31
Figure 14. An example of coastal armoring leading to the disappearance of beach ...................... 37
Figure 15. Estimated current and future 100‐ year coastal flood risk areas around Santa Cruz ... 39
Figure 16. Population vulnerable to a 100‐ year coastal flood with a 1.4 m sea‐ level rise, by
county ............................................................................................................................... ................ 41
Figure 17. Total county population and population vulnerable to a 100‐ year flood with a 1.4
meter sea‐ level rise along the Pacific coast, by race .................................................................... 45
Figure 18. Percentages of low‐ income households among the population vulnerable to a 100‐
year flood with a 1.4 m sea‐ level rise compared with the county total.................................... 47
Figure 19. Roadways vulnerable to a 100‐ year coastal flood with a 1.4 m sea‐ level rise............... 55
Figure 20. Railroads vulnerable to a 100‐ year coastal flood with a 1.4 m sea‐ level rise ................ 56
Figure 21. Power plants vulnerable to a 100‐ year coastal flood with a 1.4 m sea‐ level rise.......... 59
Figure 22. San Francisco Bay power plants vulnerable to a 100‐ year coastal flood with a 1.4 m
sea‐ level rise........................................................................................................................... .......... 60
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Figure 23. Southern California power plants vulnerable to a 100‐ year coastal flood with a 1.4 m
sea‐ level rise........................................................................................................................... .......... 61
Figure 24. Wastewater treatment plants on the Pacific coast vulnerable to a 100‐ year flood with
a 1.4 m sea‐ level rise ........................................................................................................................ 63
Figure 25. Wastewater treatment plants on the San Francisco Bay vulnerable to a 100‐ year flood
with a 1.4 m sea‐ level rise ............................................................................................................... 64
Figure 26. Existing coastal wetlands ..................................................................................................... 66
Figure 27. Viability of potential wetland migration area in response to a 1.4 m sea‐ level rise in
Northern California ......................................................................................................................... 70
Figure 28. Viability of potential wetland migration area in response to a 1.4 m sea‐ level rise in
the San Francisco Bay ...................................................................................................................... 71
Figure 29. Viability of potential wetland migration area in response to a 1.4 m sea‐ level rise in
Central California..................................................................................................................... ....... 72
Figure 30. Viability of potential wetland migration area in response to a 1.4 m sea‐ level rise in
Southern California..................................................................................................................... .... 73
Figure 31. Replacement value of buildings and contents vulnerable to a 100‐ year coastal flood
with a 1.4 m sea‐ level rise ............................................................................................................... 75
Figure 32. Replacement value ( in billions of year 2000 dollars) of buildings and contents at risk
of a 100‐ year flood event with a 1.4 m sea‐ level rise, by region ................................................ 76
Figure 33. Replacement value of buildings and contents at risk of 100‐ year flood event with a 1.4
m sea‐ level rise along the Pacific coast, by major economic sector........................................... 78
Figure 34. Replacement value of buildings and contents at risk of a 100‐ year flood with a 1.4 m
sea‐ level rise on San Francisco Bay, by major economic sector................................................. 80
Figure 35. Saltwater intrusion ................................................................................................................ 81
Figure 36. Road erosion along Highway 1 with deployment of erosion mitigation strategy....... 85
List of Tables
Table 1. Elevation datasets used for mapping coastal flood risks..................................................... 12
Table 2. Recurrence intervals of inundation estimates ....................................................................... 14
Table 3. Year and estimated mean sea‐ level for inundation estimates under the A2 and B1
scenarios ............................................................................................................................... ............ 14
Table 4. Miles and fraction of coastline studied for the erosion hazard study, by county............ 20
ix
Table 5. Approaches for estimating ecosystem values ....................................................................... 30
Table 6. Mean higher high water ( MHHW) for long‐ term tide stations on California’s Pacific
coast.......................................................................................................................... ......................... 32
Table 7. Costs ( in year 2000 dollars) for building new levees, raising existing levees, and
building new seawalls..................................................................................................................... 36
Table 8. Population vulnerable to a 100‐ year flood along the Pacific coast, by county ................. 42
Table 9. Population vulnerable to a 100‐ year flood along the San Francisco Bay, by county....... 43
Table 10. Total county population and population vulnerable to a 100‐ year flood with a 1.4‐
meter sea‐ level rise along the Pacific coast, by race .................................................................... 44
Table 11. Key demographics of populations vulnerable to a 100‐ year flood event with a 1.4 m
sea‐ level rise........................................................................................................................... .......... 48
Table 12. Schools and emergency and healthcare facilities along the Pacific coast that are at risk
from a 100‐ year flood event in 2000 and with a 1.4 m sea‐ level rise......................................... 52
Table 13. Schools and emergency and healthcare facilities along San Francisco Bay that are at
risk of a 100‐ year flood event in 2000 and with a 0.5 m, 1.0 m, and 1.4 m sea‐ level rise. ...... 52
Table 14. U. S. EPA‐ regulated sites within areas vulnerable to 100‐ year flood event in 2000 and
with a 1.4 m sea‐ level rise ............................................................................................................... 53
Table 15. Miles of roads and railways vulnerable to a 100‐ year flood in 2000 and with a 1.4 m
sea‐ level rise along the Pacific coast, by county and type.......................................................... 54
Table 16. Miles of roads vulnerable to a 100‐ year flood along San Francisco Bay, by county and
type........................................................................................................................... ......................... 57
Table 17. Miles of railways vulnerable to a 100‐ year flood along San Francisco Bay, by county. 57
Table 18. Existing California coastal wetland area by county ........................................................... 67
Table 19. Wetland migration frontier area classified by land cover type and conversion potential
............................................................................................................................... ............................ 68
Table 20. Land area available for wetland migration, by county, in square miles, with percent of
county total in italics........................................................................................................................ 69
Table 21. Replacement value of buildings and contents ( millions of year 2000 dollars) at risk of a
100‐ year flood event along the Pacific coast, by county............................................................. 77
Table 22. Replacement value of buildings and contents at risk of a 100‐ year flood on San
Francisco Bay, by county ( in millions of year 2000 dollars)....................................................... 79
Table 23. Estimated length ( in miles) and capital cost of required defenses needed to guard
against flooding from a 1.4 m sea‐ level rise, by county.............................................................. 82
x
Table 24. Erosion with a 1.4 m sea‐ level rise, by county. ................................................................... 83
Table 25. Average and maximum erosion distance in 2000 for cliffs and dunes, by county ........ 83
Table 26. Population vulnerable to flood and erosion from a 1.4 m sea‐ level rise along the Pacific
coast, by county......................................................................................................................... ...... 84
Table 27. Miles of roads and railways vulnerable to erosion and flood from a 1.4 m sea‐ level rise
along the Pacific coast, by county and type ................................................................................. 85
Table 28. Number of properties within the erosion zone hazard zone with a 1.4 m sea‐ level rise,
by county......................................................................................................................... ................. 86
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Abstract
Over the past century, sea level has risen nearly eight inches along the California coast, and
general circulation model scenarios suggest very substantial increases in sea level as a
significant impact of climate change over the coming century. This study includes a detailed
analysis of the current population, infrastructure, and property at risk from projected sea‐ level
rise if no actions are taken to protect the coast. The sea‐ level rise scenario was developed by the
State of California from medium to high greenhouse gas emissions scenarios from the
Intergovernmental Panel on Climate Change ( IPCC) but does not reflect the worst‐ case sea‐ level
rise that could occur. We also evaluate the cost of building structural measures to reduce that
risk. If development continues in the areas at risk, all of these estimates will rise. No matter
what policies are implemented in the future, sea‐ level rise will inevitably change the character
of the California coast.
We estimate that a 1.4 meter sea‐ level rise will put 480,000 people at risk of a 100‐ year flood
event, given today’s population. Among those affected are large numbers of low‐ income people
and communities of color, which are especially vulnerable. Critical infrastructure, such as roads,
hospitals, schools, emergency facilities, wastewater treatment plants, power plants, and more
will also be at increased risk of inundation, as are vast areas of wetlands and other natural
ecosystems. In addition, the cost of replacing property at risk of coastal flooding under this sea‐level
rise scenario is estimated to be nearly $ 100 billion ( in year 2000 dollars). A number of
structural and non‐ structural policies and actions could be implemented to reduce these risks.
For example, we estimate that protecting some vulnerable areas from flooding by building
seawalls and levees will cost at least $ 14 billion ( in year 2000 dollars), with added maintenance
costs of another $ 1.4 billion per year. Continued development in vulnerable areas will put
additional areas at risk and raise protection costs.
Large sections of the Pacific coast are not vulnerable to flooding, but are highly susceptible to
erosion. We estimate that a 1.4 meter sea‐ level rise will accelerate erosion, resulting in a loss of
41 square miles ( over 26,000 acres) of California’s coast by 2100. A total of 14,000 people
currently live in the area at risk of future erosion. Additionally, significant transportation‐related
infrastructure and property are vulnerable to erosion. Statewide flood risk exceeds
erosion risk, but in some counties and localities, coastal erosion poses a greater risk. This report
also provides a comprehensive set of recommendations and strategies for adapting to sea‐ level
rise.
Keywords: sea‐ level rise, coastal impacts, climate change, California, San Francisco Bay, flood,
erosion, climate adaptation, climate impacts, levees, seawalls, greenhouse effect
xii
1
1.0 Introduction
California’s coastline, which includes more than 2,000 miles of open coast and enclosed bays, is
vulnerable to a range of natural hazards, including storms, extreme high tides, and rising sea
levels resulting from global climate change. Development along California’s coast is extensive.
In 2000, 26 million Californians lived in coastal counties, and by 2003, this number had grown to
nearly 31 million ( U. S. Census Bureau 2000; NOAA 2004). Indeed, six of the ten fastest growing
coastal counties in the United States between 1980 and 2003 were in California ( NOAA 2004).
Major transportation corridors and other critical infrastructure are found along the California
coast, including oil, natural gas, and nuclear energy facilities, as well as major ports, harbors,
and water and wastewater plants. The California coast is also an extraordinary cultural and
ecological resource and offers extensive tourism and recreational opportunities.
Flooding and erosion already pose a threat to communities along the California coast and there
is compelling evidence that these risks will increase in the future. Based on a set of climate
scenarios prepared for the California Energy Commission’s Public Interest Energy Research
( PIER) Climate Change Research Program, Cayan et al. ( 2009) project that, under medium to
medium‐ high emissions scenarios, mean sea level along the California coast will rise from 1.0 to
1.4 meters ( m) by the year 2100.1 Rising seas put new areas at risk of flooding and increase the
likelihood and intensity of floods in areas that are already at risk. In areas where the coast
erodes easily, sea‐ level rise will likely accelerate shoreline recession due to erosion. Erosion of
some barrier dunes may expose previously protected areas to flooding.
National studies on the economic cost of sea‐ level rise suggest that while adapting to climate
change will be expensive, so are the costs of doing nothing, as substantial investments are
already at risk and vulnerable. 2 Because the economic costs of flooding are highly site‐ specific,
regional analyses are critical for guiding land‐ use decisions and evaluating adaptive strategies.
The Pacific Institute published one of the earliest comprehensive regional assessments of sea‐level
rise ( Gleick and Maurer 1990), concluding that a one‐ meter sea‐ level rise would threaten
existing commercial, residential, and industrial structures around San Francisco Bay valued at
$ 48 billion ( in year 1990 dollars). Building or strengthening levees and seawalls simply to
protect existing high‐ value development was estimated to require an immediate capital
investment of approximately $ 1 billion ( in year 1990 dollars) and would require an additional
$ 100 million per year in ongoing maintenance. 3 The report also noted that substantial areas of
the San Francisco Bay, especially wetlands and marshes, could not be protected and would
likely be damaged or lost.
1 It is important to note that most climate models fail to include ice‐ melt contributions from the
Greenland and Antarctic ice sheets, and as a result, the potential increase in mean sea level may be much
higher.
2 See, for example, Titus et al. ( 1992) and Yohe et al. ( 1996).
3 This estimate does not include the cost of protecting and restoring wetlands, groundwater aquifers, etc.
2
This assessment updates and expands our 1990 analysis using more comprehensive data, new
climate scenarios, and modern computerized analytical tools. We made extensive use of
geographic information system ( GIS) software and updated sea‐ level rise scenarios from the
Scripps Institution of Oceanography to estimate the population, infrastructure, ecosystems, and
property at risk. We also estimate some of the cost of armoring the coast, one potential
adaptation strategy to reduce that risk. This work is part of a larger set of research projects by
the California Climate Action Team to understand the impacts of climate change to
Californians, funded by the California Energy Commission’s Public Interest Energy Research
( PIER) program. The Pacific Institute also received significant financial support from two other
state agencies: the Ocean Protection Council and the Metropolitan Transportation Commission,
part of the Department of Transportation.
1.1. Key Findings
Over the past century, sea level has risen nearly eight inches along the California coast, and
general circulation model scenarios suggest very substantial increases in sea level as a
significant impact of climate change over the coming century. This study includes a detailed
analysis of the current population, infrastructure, and property at risk from projected sea‐ level
rise if no actions are taken to protect the coast, and the cost of building structural measures to
reduce that risk. We find the following:
• Under medium to medium‐ high greenhouse‐ gas emissions scenarios, mean sea level
along the California coast is projected to rise from 1.0 to 1.4 meters ( m) by the year 2100.
Maps for the entire coast of California demonstrating the extent of the areas at risk are
posted at www. pacinst. org/ reports/ sea_ level_ rise. 4
• A 1.4 meter sea‐ level rise will put 480,000 people at risk of a 100‐ year flood event, given
today’s population. Populations in San Mateo and Orange Counties are especially
vulnerable. In each, an estimated 110,000 people are at risk. Large numbers of residents
( 66,000) in Alameda County are also at risk.
• A demographic analysis identified large numbers of people at risk with heightened
vulnerability, including low‐ income households and communities of color. Additionally,
adapting to sea‐ level rise will require tremendous financial investment. Given the high
cost and the likelihood that individuals, the State, and local agencies will not protect
everything, adaptation raises additional environmental justice concerns.
• A wide range of critical infrastructure, such as roads, hospitals, schools, emergency
facilities, wastewater treatment plants, power plants, and more will also be at increased
risk of inundation in a 100‐ year flood event. This infrastructure at risk includes:
4 These maps are not the result of detailed site studies and were created to quantify risk over a large geographic area.
They should not be used to assess actual coastal hazards, insurance requirements or property values, and specifically
shall not be used in lieu of Flood insurance Studies and Flood Insurance Rate Maps issued by the Federal
Emergency Management Agency ( FEMA). Local governments or regional planning agencies should conduct
detailed studies to better understand the potential impacts of sea- level rise in their communities.
3
o nearly 140 schools;
o 34 police and fire stations;
o 55 healthcare facilities;
o more than 330 U. S. Environmental Protection Agency ( U. S. EPA)‐ regulated
hazardous waste facilities or sites, with large numbers in Alameda, Santa Clara,
San Mateo, and Los Angeles counties;
o an estimated 3,500 miles of roads and highways and 280 miles of railways;
o 30 coastal power plants, with a combined capacity of more than 10,000
megawatts;
o 28 wastewater treatment plants, 21 on the San Francisco Bay and 7 on the Pacific
coast, with a combined capacity of 530 million gallons per day; and
o the San Francisco and Oakland airports.
• Vast areas of wetlands and other natural ecosystems are vulnerable to sea‐ level rise. An
estimated 550 square miles, or 350,000 acres, of wetlands exist along the California coast,
but additional work is needed to evaluate the extent to which these wetlands would be
destroyed, degraded, or modified over time. A sea‐ level rise of 1.4 m would flood
approximately 150 square miles of land immediately adjacent to current wetlands,
potentially creating new wetland habitat if those lands are protected from further
development.
• We estimate that nearly $ 100 billion ( in year 2000 dollars) worth of property, measured
as the current replacement value of buildings and contents, is at risk of flooding from a
100‐ year event with a 1.4 m sea‐ level rise if no adaptation actions are taken. An
overwhelming two‐ thirds of that property is concentrated on San Francisco Bay. The
majority of this property is residential.
• Coastal armoring is one potential adaptation strategy. Approximately 1,100 miles of new
or modified coastal protection structures are needed on the Pacific Coast and San
Francisco Bay to protect against coastal flooding. The total cost of building new or
upgrading existing structures is estimated at about $ 14 billion ( in year 2000 dollars). We
estimate that operating and maintaining the protection structures would cost
approximately 10% of the initial capital investment, or around another $ 1.4 billion per
year ( in year 2000 dollars).
• Large sections of the Pacific coast are not vulnerable to flooding, but are highly
susceptible to erosion. We estimate that a 1.4 m sea‐ level rise will accelerate erosion,
resulting in a loss of 41 square miles of California’s coast by 2100. A total of 14,000
people live in areas at risk of erosion. In addition, significant transportation‐ related
infrastructure and property are also at risk. Throughout most of the state, flood risk
exceeds erosion risk, but in some counties, coastal erosion poses a greater risk.
4
• Continued development in vulnerable areas will put additional areas at risk and raise
protection costs.
2.0 Methods
Numerous studies have attempted to quantify the cost of sea‐ level rise and have been based
primarily on a framework developed in Yohe ( 1989) and refined in Yohe et al. ( 1996) and Yohe
and Schlesinger ( 1998). That framework employs a cost‐ benefit model to evaluate the property
at risk and the cost of protecting or abandoning that property. Property is protected if the value
of the property exceeds the protection cost at the time of inundation, and the protection cost is
equal to the construction cost of the protective structure. If the value of the property does not
exceed the cost of protection, then the property is abandoned, with the cost equal to the value of
the land and structure at the time of inundation. The total economic cost is then the sum of the
protection cost plus the value of the lost property.
To determine the value of lost property, the Yohe approach considers land and structure values
separately. In most locations, coastal land commands a premium price, with the price declining
as one moves inland. With inundation, the Yohe method assumes that land values will simply
migrate inland, and thus, the economic value of lost land is equal to the economic value of
interior land. The value of structures is calculated under two conditions: with and without
foresight. With perfect foresight, the economic value of structures is assumed to depreciate over
time as the “ impending inundation and abandonment become known” ( Yohe and Schlesinger
1998), approaching $ 0 at the time of inundation. Without foresight, the structure value does not
depreciate.
Despite its wide application, the Yohe method has a number of limitations, many of which are
discussed in Hanemann ( 2008):
• First, it ignores any transfers among property owners and looks only at the net social
cost. In reality, there will be winners ( those who had inland property that is now closer
to the coast and thus more valuable) and losers ( those who have lost their property), and
the gross social cost “ could be enormous” ( Yohe et al. 1996).
• Second, it assumes that coastal protection will be constructed just in time to avoid
damage from flooding. This is unlikely. If coastal protection is constructed too late, then
the property would incur some damage, thereby increasing the cost. If constructed too
early, then the discounted net present value of the cost of building the structure would
be higher ( Hanemann 2008).
• Third, it only examines changes in mean sea level ( eustatic change), thereby ignoring
damage from storm surge and extreme events.
• Fourth, by focusing on property values, it ignores other potentially expensive costs. For
example, the flooding of transportation infrastructure essential for moving people or
goods, e. g., highways and ports, could cause major interruptions to the local economy.
Flooding also causes impacts on the health and well‐ being of the affected individuals
and environmental damage, including erosion, oils spills, and discharge of pollution
5
from coastal industry ( Hanemann 2008). Over the long‐ term, flooding can lead to the
loss of wetlands.
• Fifth, prioritization of protection based on property value may directly undermine an
environmental justice framework for protection.
This study used a different approach to estimate the economic impact of sea‐ level rise. We
adopted the scenarios developed for the PIER studies and mapped the extent of inundation
from a 100‐ year flood event that is likely to occur with rising sea levels. We also identified areas
at increased risk from erosion as a result of rising seas. The inundation and erosion geodata
were overlaid with other geospatial data using GIS to produce quantitative estimates of the
population, infrastructure, and replacement value of property at risk from sea‐ level rise, as well
as the impacts on harder‐ to‐ quantify coastal ecosystems. We also produced an initial estimate of
the cost of adaptation measures, specifically building seawalls and levees in high‐ valued coastal
zones to protect against future flooding. Greater detail on the methods is provided below.
2.1. Study Area
The study area spans approximately 1,100 miles of California’s Pacific coast and 1,000 miles of
shoreline along the perimeter of the San Francisco Bay. The San Francisco Bay study area
extends from the Golden Gate in the west to Pittsburg, California, in the east and San Jose in the
south. The eastern boundary of the San Francisco Bay study was set according to where United
States Geological Survey ( USGS) researchers were able to extract reliable flood elevations from
the Bay hydrodynamic model. We provide estimates for a number of scenarios for San
Francisco Bay due to the ready availability of high‐ resolution geographic data provided by the
USGS.
The study area of the erosion analysis extended from Santa Barbara to the Oregon border,
covering about 930 miles ( 1,450 kilometers, km). Much of the Southern California coast was
excluded from the erosion analysis due to myriad ongoing initiatives focused on climate change
and hazards mapping.
2.2. Sea- Level Rise Projections
2.2.1. Mean Water Levels and Extreme Events
Sea levels are constantly in flux, subject to the influence of astronomical forces from the sun,
moon, and earth, as well as meteorological effects like El Niño. A worldwide network of more
than 1,750 tidal gages continuously collects data on water levels relative to a nearby geodetic
reference, and new satellite‐ based sensors are extending measurements. Tide gage data indicate
that the global mean sea level is rising. Water level measurements from the San Francisco gage
( CA Station ID: 9414290), shown in Figure 1, indicate that mean sea level rose by an average of
6
2.01 millimeters ( mm) per year from 1897 to 2006, equivalent to a change of eight inches in the
last century. 5
Figure 1. Trend in monthly mean sea level at the San Francisco tide station from
1854– 2006
Source: NOAA Sea Levels Online,
http:// tidesandcurrents. noaa. gov/ sltrends/ sltrends_ station. shtml? stnid= 9414290
Sea levels are expected to continue to rise, and the rate of increase will likely accelerate. In order
to evaluate climate change impacts, the Intergovernmental Panel on Climate Change ( IPCC)
developed future emission scenarios that differ based on assumptions about economic
development, population, regulation, and technology ( see Box 1 for a description of the
scenarios). Based on these scenarios, mean sea level was projected to rise by 0.2 m to 0.6 m by
2100, relative to a baseline of 1980– 1999, in response to changes in oceanic temperature and the
exchange of water between oceans and land‐ based reservoirs, such as glaciers and ice sheets
( Meehl et al. 2007).
More recent research by leading climate scientists, which includes more accurate sea‐ level
measurements by satellites, indicates that sea‐ level rise from 1993– 2006 has outpaced the IPCC
projections ( Rahmstorf et al. 2007). The authors suggest that the climate system, particularly sea
levels, may be responding to climate changes more quickly than the models predict.
Additionally, most climate models fail to include ice‐ melt contributions from the Greenland and
Antarctic ice sheets and may underestimate the change in volume of the world’s oceans.
5 The solid vertical line shows the earthquake of 1906. NOAA researchers fit separate trendlines before
and after an apparent datum shift ( vertical movement of the land surface) that occurred in 1897,
disrupting consistent measurements.
7
To address these new factors, the PIER projects used sea‐ level rise forecasts developed by a
team at the Scripps Institution of Oceanography led by Dr. Dan Cayan. Using a methodology
developed by Rahmstorf ( 2007), Cayan et al. ( 2009) produced global sea‐ level estimates based
on projected surface air temperatures from global climate simulations for both the IPCC A2 and
B1 scenarios using the output from six global climate models: the National Center for
Atmospheric Research ( NCAR) Parallel Climate Model ( PCM); the National Oceanic and
Atmospheric Administration ( NOAA) Geophysical Fluids Dynamics Laboratory ( GFDL)
version 2.1; the NCAR Community Climate System Model ( CCSM); the Max Planck Institute
ECHAM3; the MIROC 3.2 medium‐ resolution model from the Center for Climate System
Research of the University of Tokyo and collaborators; and the French Centre National de
Recherches Météorologiques ( CNRM) models.
Box 1: IPCC Climate Change Scenarios
The impacts of climate change will ultimately depend on future greenhouse gas
concentrations. Future greenhouse gas emissions remain uncertain and are influenced by a
variety of demographic, socio‐ economic, and technological factors. Scenarios can be a useful
tool for examining how changes in these driving factors affect greenhouse gas concentrations.
These scenarios can be useful for evaluating impacts associated with climate change as well as
assessing adaptation and mitigation activities. The Special Report on Emissions Scenarios
( SRES) outlines four storylines that differ according to demographics, social, economic,
environmental, and technological factors and lead to different levels of greenhouse gas
emissions. Each storyline has a number of different scenarios, referred to as a family. A total of
40 scenarios have been developed.
The four storylines are described below:
The A1 storyline is characterized by “ a future world of very rapid economic growth, global
population that peaks in mid‐ century and declines thereafter, and the rapid introduction of
new and more efficient technologies. Major underlying themes are convergence among
regions, capacity building, and increased cultural and social interactions, with a substantial
reduction in regional differences in per capita income” ( IPCC 2000). The A1 family is further
divided into three subgroups that are differentiated according to energy source: fossil
intensive ( A1FI), non‐ fossil sources ( A1T), and a mix of fossil and non‐ fossil sources ( A1B).
The A2 storyline is characterized by “ self‐ reliance and preservation of local identities” ( IPCC
2000). Population is expected to continuously increase, but economic growth and technological
development are expected to be slow.
The B1 storyline has the same population projections as the A1 storyline but “ rapid changes in
economic structures toward a service and information economy, with reductions in material
intensity, and the introduction of clean and resource‐ efficient technologies” ( IPCC 2000).
The B2 storyline is characterized by “ a world with continuously increasing global population
at a rate lower than A2, intermediate levels of economic development, and less rapid and more
diverse technological change than in the B1 and A1 storylines” ( IPCC 2000).
8
Additionally, Cayan et al. ( 2009) modified the sea‐ level rise estimates to account for water
trapped in dams and reservoirs that artificially reduced runoff into the oceans ( Chao et al. 2008).
Absolute sea‐ level rise along the California coast was assumed to be the same as the global
estimate. Based on these methods, Cayan et al. ( 2009) estimate an overall projected rise in mean
sea level along the California coast for the B1 and A2 scenarios of 1.0 m and 1.4 m, respectively,
by 2100 ( Figure 2). The more severe A1FI scenario, which assumes a continued high level use of
fossil fuels, was not used in this analysis, but is shown for comparative purposes.
1.38
1.02
1.46
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
2000 2020 2040 2060 2080 2100 2120
Sea Level
Rise ( m)
A1- FI
A2
B1
Figure 2. Scenarios of sea- level rise to 2100
Source: Dan Cayan, Scripps Institution of Oceanography, NCAR CCSM3 simulations, Rahmstorf method.
The majority of studies on climate change have emphasized changes in average conditions, yet
the greatest socio‐ economic impacts tend to occur as a result of extreme events. Coastal flooding
is often caused by storm surges, which are caused by high winds and pressure differentials
associated with storms. Along the California coast, wave‐ induced storm surge can exceed 1.5 m
( Cayan et al. 2006), flooding low‐ lying areas and eroding coastal bluffs. Increases in mean sea
level are expected to increase the frequency and intensity of these extreme events. Although this
study does not explicitly account for changes in storm surge, we do account for higher flood
elevations associated with extreme events, as described below in Section 2.3.
2.3. Expected Risk to the Coast
2.3.1. Coastal Inundation Risk
Sea‐ level rise increases the risk of flooding in low‐ lying areas. For this study, we considered
coastal flood risks only, e. g., flooding caused by rising seas along the Pacific Ocean and San
Francisco Bay. Higher sea levels, however, can also worsen flooding in nearby rivers as higher
water surface elevations at the downstream end of a river causes water to back up and increase
upstream flooding. These impacts are not evaluated here.
9
For the California coast, we used GIS to produce maps of the areas at risk of inundation from a
1.4 m sea‐ level rise. For the San Francisco Bay, we produced maps of the areas at risk of
inundation under three different sea‐ level rise scenarios: 0.5 m, 1.0 m, and 1.4 m. Below, we
describe the methods used to determine the areas at risk of flooding along the Pacific coast and
in the San Francisco Bay. Erosion is discussed in Section 2.3.2.
Pacific Coast
A flood is often described by its recurrence interval, which is the period of time between floods
of a particular intensity that is based on historic conditions for a given area. The terminology
used to describe the recurrence interval, however, can be misleading and is often
misinterpreted. A “ 100‐ year flood” does not refer to a flood level that occurs every 100 years.
Rather, it refers to a flood that has a 1/ 100, or 1%, chance of occurring in any year. Thus, over a
typical 30‐ year mortgage period, a 100‐ year flood has a 1‐ in‐ 4 chance of occurring ( see Box 2).
For the Pacific coast, we approximate the potential future flood impact by adding projected sea‐level
rise estimates to water levels associated with a 100‐ year flood event; that is, current flood
elevations for the 100‐ year flood are increased by 1.4 meters, the projected increase in sea level
by 2100 under the A2 scenario ( Figure 3).
Figure 3. Determining future flood elevations
Note: The solid line represents the current tide frequency. The dotted line represents the future flood frequency. As can be seen,
an increase in water surface elevation increases the frequency and intensity of flood events. For example, a 100- year flood event
could become an annual flood event. The flood frequency estimates shown are for demonstration purposes only and are not
based on actual data. See the Glossary for definitions of the abbreviations MLW, MSL, MHW, and MHHW.
This approach assumes that all tide datums, e. g., mean high tide and flood elevations, will
increase by the same amount as mean sea level. There is some evidence that this assumption
may not always hold true. Flick et al. ( 1999) found that in San Francisco, mean higher high
water ( MHHW) was increasing at a rate of 258 mm per century, while the mean sea level
hour day week month year decade century millennium
500- yr flood
100- yr flood
Annual Max.
( 1- yr flood)
MHHW
MHW
MSL
MLW
Exceedance Frequency
Water Surface
Elevation
Current Tide Frequency
Future Tide Frequency
Current 100- yr flood elevation
Future 100- yr flood elevation
10
increased at a lower rate of 217 mm per century ( Figure 4). Thus, while the overall trend is one
of rising seas, the intertidal range, i. e., the difference between MHHW and mean lower low
water ( MLLW), also seems to be widening. In addition, an increase in storminess due to climate
change might cause more frequent storm surges and an increase in the frequency of high water
events, although there is not yet consensus among climate scientists on changes in storm
intensity or frequency, and such changes are not included here explicitly.
Box 2: Estimating Flood Risk
What are the chances that a 100‐ year flood will occur during a 30‐ year period?
To make this determination, we must apply basic probability theory. Flooding is a random
event, i. e., the odds of it occurring in any year are independent of past conditions. Thus the
odds of a storm not occurring over a 30‐ year period can be calculated using the following
methodology.
If an event has an X percent chance of occurring in a given year, then the odds that the event
will not occur in a given year are
1‐ X
The odds that an event will not occur in two successive years is
( 1‐ X)( 1‐ X) = ( 1‐ X) 2
And the odds of an event not occurring over y number of years is
( 1‐ X) y
Let’s now calculate the odds that a 100‐ year flood event will not occur over 30 years.
In this case,
X = 1/ 100 = 0.01 and y = 30
( 1‐ X) y = ( 1‐ 0.01) 30 = 0.74
Thus there is a 74% change that a 100‐ year storm will not occur over a 30‐ year period; and a
26%, or approximately a 1 in 4 chance that it will occur.
11
Figure 4. Rates of change of tidal datums, San Francisco from 1900– 2000
Source: Flick et al. 1999
Existing flood levels were based on estimates of the 100‐ year flood elevation ( also called the base
flood elevation or BFE) from Flood Insurance Studies published by the Federal Emergency
Management Agency ( FEMA). The FEMA BFEs, however, only cover a part of the coast. We
contracted with Philip Williams and Associates ( PWA) to provide estimates of BFEs where none
exist. Their work consisted of the following:
1. Compiled available coastal flood BFEs published by FEMA for the California coast.
2. Estimated BFEs where FEMA estimates are not available using professional judgment.
3. Converted elevations to the North American Vertical Datum of 1988 ( NAVD88).
4. Adjusted elevations to nearest half foot based on observed sea‐ level rise to present day.
Further information on the methods used by PWA is available in a separate technical
memorandum ( Battalio et al. 2008).
We used automated mapping methods in GIS to delineate areas inundated by the current and
future flood elevations. The key inputs to this analysis are digital elevation models ( DEMs),
gridded datasets that contain values representing elevations of the Earth’s surface. We used the
most accurate, high‐ resolution terrain data available. NOAA’s Coastal Service Center assisted
12
us in processing and obtaining each of these data sets. The elevation datasets used for this
project are summarized in Table 1.
For much of the Central and Northern California coast, high‐ accuracy Light Detection and
Ranging ( LIDAR) data were available from Airborne LIDAR Assessment of Coastal Erosion
( ALACE) project, a partnership between NOAA, the National Aeronautics and Space
Administration ( NASA), and USGS. The ALACE project emphasized shoreline change, and so
the data were available for a relatively narrow swath of the coast. The coverage did not always
extend inland far enough to fully map the coastal floodplain. In addition, there were several
gaps in coverage along the entire coast.
We supplemented the LIDAR data, filling in gaps in coverage with topographic information
from the USGS National Elevation dataset. Although these data are at a lower resolution and
accuracy, they allowed us to map the entire coast. For portions of the Southern California coast,
Interferometric Synthetic Aperture Radar ( IFSAR) data were available from NOAA. The IFSAR
data are of coarser resolution than the LIDAR data described above ( i. e., they are 3‐ meter pixel
resolution compared to 2‐ meter resolution), and they have less vertical accuracy ( i. e., ± 2.2 m
compared to ± 0.07 m for the LIDAR data).
Table 1. Elevation datasets used for mapping coastal flood risks
Dataset
National
Elevation
Dataset ALACE 1998 ALACE 2002
So. Cal.
IFSAR
Source/ Mission USGS NASA, NOAA,
USGS
NASA, NOAA,
USGS NOAA
Geographic Coverage National Stinson Beach to
Santa Barbara
Northern border
of California to
Stinson Beach
Santa
Barbara
to
Mexican
border
Data Collection Method Various LIDAR LIDAR IFSAR
Resolution 10 m 3 m 2 m 3 m
Year Collected Various 1998 2002 2003
Stated Vertical Accuracy ± 7.5 m ± 0.07 m ± 0.07 m ± 2.2 m
13
GIS raster math tools were used to compare the elevation of land surfaces with the adjacent
flood elevation to determine the extent of flooding. Because of the large file sizes, and the large
area being studied, we worked with the terrain datasets in over 600 tiles. Pacific Institute
researchers wrote scripts to automate the processing steps on each of these tiles. The resulting
inundation grids were boundary‐ smoothed and small isolated ponds and islands were
removed. The raster datasets were then converted to vector polygons and merged so they could
be used in the social and economic analyses. A separate technical memorandum is available at
www. pacinst. org/ reports/ sea_ level_ rise that describes the GIS flood delineation methodology in
greater detail.
San Francisco Bay
While our study looks at the entire California coastline, we also produced more detailed
estimates of coastal flood risk in San Francisco Bay. In total, we estimated impacts along
approximately 1,100 miles of Pacific Coast from Oregon to Mexico, and an additional 1,000
miles inside of San Francisco Bay. Inundation maps generated from the climate scenarios were
provided to the Pacific Institute by Dr. Noah Knowles of the United States Geological Survey
( Knowles 2008). These estimates are described in Knowles 2009.
To estimate inundated areas in the Bay, “ the highest resolution elevation data available were
assembled from various sources and mosaicked to cover the land surfaces of the San Francisco
Bay region. Next, to quantify high water levels throughout the Bay, a hydrodynamic model of
the San Francisco Estuary was driven by a projection of hourly water levels at the Presidio. This
projection was based on a combination of climate model outputs and empirical models and
incorporates astronomical, storm surge, El Niño, and long‐ term sea level rise influences”
( Knowles 2009). The Bay computer model simulates the water surface elevation for each hour
from 2000– 2009. Inputs to the model include both upstream inflows and downstream water
surface elevations ( Figure 5).
Dr. Knowles performed statistical analyses on the Bay model output to determine flood
quantiles for various years and provided outputs in the form of GIS raster files to the Pacific
Institute. These files were provided for five flood recurrence intervals ( Table 2) for each of four
years between 2000 and 2099, for a total of 20 files. Based on this information, we estimated
risks due to inundation with a 0.5 m, 1.0 m, and 1.4 m sea‐ level rise, which for the A2 scenario
correspond to 2050, 2081, and 2099, respectively.
14
Figure 5. Simple schematic of USGS San Francisco Bay hydrodynamic model
It is important to note that we report results based on the vertical rise in sea level rather than a
particular year in which the rise is projected to occur. As shown in Table 3, the year in which a
0.5 m sea‐ level rise is projected to occur under the A2 and B1 scenarios differs by only three
years. Additionally, sea‐ level rise estimates are continuously updated as climate science
advances and greenhouse gas emissions change over time. Indeed, carbon dioxide emissions in
2005 and 2006 were well above even the highest future emissions scenario, as shown in Figure 6
( Raupach et al. 2007). Because the results of this analysis are driven by sea levels and are not
directly tied to any set of scenarios, the results of this study will be relevant even when climate
projections change.
Table 2. Recurrence intervals of inundation estimates
Flood Interval Annual probability
1- year 1
10- year 0.1
50- year 0.02
100- year 0.01
500- year 0.002
Table 3. Year and estimated mean sea- level for inundation
estimates under the A2 and B1 scenarios
Mean Sea- Level Year Reached
Rise ( m) A2 B1
0 2000 2000
0.5 2054 2057
1.0 2083 2098
1.4 2100 2125
- 150
- 100
- 50
0
50
100
150
Sea Level
( cm)
0
2
4
6
8
River
Stage
( ft)
Bay
Hydrodynamic
Model
Ocean
Boundary
Conditions
Delta Boundary
Conditions
15
Figure 6. Historical and projected carbon dioxide emissions scenarios, 1990– 2010
Note that actual emissions for 2004– 2006 exceed the highest IPCC scenarios.
Source: Raupach et al. 2007
2.3.2. Erosion Risk
Large sections of the Pacific coast, especially those with rocky headlands or sea cliffs, are not
vulnerable to flooding, but are highly susceptible to erosion. In areas where the coast erodes
easily, higher sea levels are likely to accelerate shoreline erosion due to increased wave attack.
In addition, erosion of some sand spits and dunes may expose previously protected areas to
flooding.
The amount of erosion can be estimated by several methods. The most widely applied method
of predicting shoreline recession based on a sea‐ level rise was developed by Bruun in 1962. This
is based on the concept that the depth of water near the coast remains constant with sea‐ level
rise, that the basic beach profile will remain the same, and that there is a well‐ defined offshore
limit of sediment transport. The sediment required to maintain the beach profile through water‐level
changes is derived from erosion of the shore material. Based on this, an approximate
estimate of the shoreline recession due to readjustment of the beach profile to an equilibrium
state is 1.0‐ to‐ 1.5 meters of shore recession per centimeter of sea‐ level rise.
Although once widely used, the Bruun rule has been largely abandoned because it makes
several assumptions that may not be accurate ( Pilkey and Cooper 2004). The formulation is
based on a two‐ dimensional concept, while the sediment transport along a shoreline is a three‐dimensional
process. The Bruun rule assumes a shoreline profile in equilibrium, which is
difficult to confirm at any site. Another problem is that this approach always predicts shoreline
recession with offshore sediment transport as sea‐ level rises, yet there are several cases where
shorelines have accreted as a result of sea‐ level rise due to the movement of sand onshore from
offshore deposits. Depending on local sources and sinks of sediment, wave climate, topography,
and other conditions governing sediment transport mechanisms, the predictions of shoreline
16
recession obtained using the Bruun rule can significantly over‐ or underestimate the future
recession. More specific methods are needed for particular sites, and should be conducted to
better evaluate the impact of sea‐ level rise on a given region.
A team of scientists and engineers at Philip Williams and Associates ( PWA) developed an
alternative approach to evaluate erosion risk. They evaluated potential future erosion by
examining changes to a time series of total‐ water level ( TWL) elevations. TWL is a water
elevation determined by the sum of mean sea level, tides, waves and wave run‐ up, other storm
components ( including surge), and El Niño ( Ruggiero et al. 1996; Ruggiero et al. 2001). Studies
suggest that erosion will accelerate as sea levels rise and the coast is exposed to higher waves.
Higher water levels result in greater wave energy being dissipated higher up on the shoreline
and directly onto the face of cliffs and dunes. The exceedance of TWL above the elevation of the
toe junction has been related to erosion ( Sallenger et al. 2002; Ruggiero et al. 2001; Hampton and
Griggs 2004; FEMA 2005).
To generate the TWL predictions, PWA used a 100‐ year time series of “ measured tides” and
deepwater waves from Dr. Dan Cayan and colleagues at Scripps ( Cayan et al. 2009). The
deepwater wave heights were transformed to 140 near‐ shore locations by the Coastal Data
Information Program to account for differences in wave exposure and shoreline orientation.
Finally wave run‐ up was calculated using the relationship between wave height, wave period,
and beach slope ( Stockdon et al. 2006). The combination of sea levels and wave run‐ up were
evaluated over time to estimate future elevations of TWL, which were then intersected with the
land elevations along 4,100 segments of the coast.
California’s coastline is geologically and morphologically complex and each major geologic unit
will exhibit differential response to rising sea levels. PWA classified the shoreline based on
geologic formations and type, such as sea cliffs and dunes. For each type of coast, slightly
different methods were used to project the response to rising seas. For sea cliffs, which
accounted for 720 miles of the study area, erosion was estimated based on an acceleration of the
historic erosion rate and a percent increase in TWL exceeding the elevation of the toe of the sea
cliffs. The historic sea cliff erosion data were obtained from the USGS National Shoreline
Change Assessment ( Hapke and Reid 2007). The data were averaged by geologic unit with an
additional factor of safety ( two standard deviations) included to account for subtle changes in
geology along the coast.
For the dune classified shorelines, which covered about 170 miles of the study area, erosion
rates were based on the following information:
• Recession based on changes in TWL from sea level‐ rise.
• Historic shoreline change trends from the USGS National Shoreline Change Assessment
( Hapke et al. 2006).
• The impact of a “ 100‐ year storm event” extracted from the TWL time series and
estimated using a storm‐ response geometric model of dune erosion ( Komar et al. 1999).
17
Based on this approach, PWA developed digital GIS shapefiles representing future coastal
erosion hazard zones for cliff‐ backed and dune‐ backed coastal areas for 2025, 2050, and 2100
under a low ( 1.0 m) and a high ( 1.4 m) sea‐ level rise scenario. For this analysis, we evaluate the
socio‐ economic impacts of erosion under the 1.4 m sea‐ level rise scenario for 2100. Note that for
erosion, the year is important because it includes a background erosion rate plus accelerated
erosion rates resulting from sea‐ level rise.
The study area of the erosion analysis extended from Santa Barbara to the Oregon border,
covering about 930 miles ( 1,450 km). Much of the Southern California coast was excluded due to
the myriad of ongoing initiatives focused on climate change and hazards mapping. Due to
insufficient data, however, PWA was only able to include 80% of the 930 mile study area ( see
Section 2.4 for additional discussion of the limitations).
The erosion analysis represents a first‐ order evaluation of coastal hazards based on currently
available projections of water levels and wave conditions and interpretations of sea‐ level rise,
shoreline change rates, and geomorphic conditions. Available methods and data are not
sufficient to model coastal erosion with high confidence. While the methodology used to
develop the hazard zones was kept relatively simple and modular to facilitate understanding
and future application with minimal effort, it represents one of the most comprehensive erosion
hazard assessments under conditions of climate change ever completed for the California coast.
For additional information, see PWA ( 2008).
2.3.3. Limitations of the Analysis
Researchers at Scripps Institution of Oceanography and USGS performed hydrographic
modeling of the San Francisco Bay Estuary to determine the flood elevations under climate
change scenarios. All models are subject to errors and inaccuracies. It was not possible to
directly calibrate or verify a model that predicts flood frequencies. We performed an
independent evaluation of USGS‐ predicted San Francisco Bay flood elevations and found that
the model estimates of the 100‐ year water surface elevation for the year 2000 were generally
similar to flood elevations predicted by the U. S. Army Corps of Engineers ( USACE 1984a). We
compared all 52 points on the San Francisco Bay shoreline shown on the 1984 Corps maps and
found that 75% of the flood elevations were within 0.25 feet of those predicted by USGS. Most
of the new estimates were slightly lower than the heights estimated by the Corps, as shown in
Figure 7.
18
2.0
2.5
3.0
3.5
4.0
2.0 2.5 3.0 3.5 4.0
2008
USGS/ Scripps
Study
1984 USACE Study
Figure 7. Comparison of 100- year flood elevations ( in meters NAVD88)
The location of the shoreline is inexact and probably subjective. Knowles used a “ mask” of open
water as a filter, so as to report only land areas that are flooded. However, the shoreline is
constantly in flux and difficult to map precisely. Further, there are errors and inaccuracies in the
terrain data. The digital terrain model creates a smoothed or average surface from the raw
elevation data, and it does not accurately depict breaks in elevation that occur at a vertical wall
such as a cliff or a curb.
19
Another limitation is that the automatic, computerized method classifies flooding by depth
only. The algorithm using depth alone to determine flooding does not factor in the presence of a
flow pathway. In some cases, the high ground may be a levee specifically designed to protect
adjacent low‐ lying areas. In other locations, there are simply depressions, but they are not really
at risk because there is no path for seawater to flow into them. This means low‐ lying objects or
features such as ditches, stormwater detention basins, subway tunnels, and empty swimming
pools are filled in inappropriately at times, as shown in Figure 8.
Figure 8. Limitations of the computer’s ability to accurately map coastal flooding in areas
protected by seawalls or levees or natural barriers
The study area for the erosion analysis was constrained by data availability. The erosion
analysis covered only the 11 counties north of Santa Barbara County. Furthermore, data
limitations limited the analysis to only 81% of the coast in the 11 counties ( Table 4). The three
counties with the least coverage include Humboldt County, Monterey, and Santa Barbara.
Humboldt County included the Kings Range and the Lost Coast, public lands with no
development. The Monterey County analysis was limited along the Big Sur coast where high
levels of erosion currently affect the major transportation corridor of Highway 1 and are
expected to continue. In Santa Barbara, missing data along the region between Pt. Conception
and Goleta and the ending of the erosion analysis south of Santa Barbara harbor explain the
missing erosion analysis. As a result, the vulnerability assessments underestimate the actual
economic impact from erosion. Note that the flood analysis covered the entire Pacific coast of
California and results for the erosion analysis were not adjusted to account for missing
segments of the coast.
Normal conditions
Flood Conditions
Reality: High seas can’t find path inland
Simulation: Naïve computer al-gorithm
fills basins based on their
elevation only
20
Table 4. Miles and fraction of coastline studied for the erosion
hazard study, by county
County Studied Total % Studied
Del Norte 42.7 49.7 86
Humboldt 72.9 123.3 59
Marin 69.5 75.2 93
Mendocino 145.5 151.4 96
Monterey 94.4 132.0 71
San Francisco 7.5 8.8 85
San Luis Obispo 77.0 102.6 75
San Mateo 57.8 59.6 97
Santa Barbara 84.4 116.5 72
Santa Cruz 46.0 46.0 100
Sonoma 63.0 68.9 91
Total 760.7 934.1 81
2.4. Resources Threatened by Sea- Level Rise
In any given area, rising seas pose a threat to many different types of resources. Among the
vulnerable coastal systems are transportation facilities such as roadways, airports, bridges, and
mass transit systems; electric utility systems and power plants; stormwater systems and
wastewater treatment plants and outfalls; groundwater aquifers; wetlands and fisheries; and
many other human and natural systems from homes to schools, hospitals, and industry. Any
impacts on resources within the affected area may lead to secondary impacts elsewhere.
Determining the types of resources threatened by sea‐ level rise is a crucial step toward choosing
an appropriate level of response and method of protection.
2.4.1. Population
Sea‐ level rise and increased coastal flooding will lead to disruption due to evacuations,
displacement from destruction of homes and property,
and possibly the loss of lives. To determine populations
at risk if no adaptation actions are taken, we overlay the
inundation and erosion hazard maps with year 2000
census block data. We use current population data
aggregated by census block, the highest resolution
available for California. We make an assumption
common in regional GIS analyses that the population is
distributed evenly within a block’s boundaries. So if our
mapping shows that 50% of a 500‐ person census block is inundated by a flood, we estimate that
250 people are at risk. This method may underestimate ( where the houses are clustered on the
coast) or overestimate ( when the houses are set back from the coast) the actual risk.
While disasters do not
discriminate, the existing
societal and environmental
conditions before, during, and
after a disaster produce
differences in vulnerability
among groups within the
population affected.
21
It is critical to understand that our estimates of populations at risk are based on current
population data, not a projection of populations that might be at risk in the future. If no policies
are put in place to limit new exposure in areas at risk of rising seas, our estimates will be low—
perhaps substantially low. If, however, policymakers are proactive about reducing coastal risks
in coming decades, the levels of risk could be substantially reduced.
We also evaluate potential environmental justice impacts of sea‐ level rise. 6 As seen during
Hurricane Katrina, flooding and other natural disasters often do the greatest harm to low‐income
communities and communities of color. Hurricane Audrey, for example, struck the
coast of Louisiana in 1957 and had a death rate of 38 per thousand among whites and 322 per
thousand among blacks ( Bates et al. 1963, cited in Pastor et al. 2006). A study of all U. S. disasters
between 1970 and 1980 found that white households had $ 2,370 less of a financial burden
following a disaster than other racial groups ( Rossi et al. 1983). One year after Hurricane
Katrina, the black population of New Orleans had decreased 57% while the white population
had fallen 36% ( Frey 2007). Racial disparities are mirrored in economic disparities where low‐income
communities have shouldered a disproportionate burden of harm resulting from
disasters: reports following Hurricanes Hugo and Katrina pointed to a range of problems
related to the “ invisibility” of low‐ income communities before the disasters ( Pastor et al. 2006).
The uneven distribution of the harms of natural disasters highlights the same racial and
economic inequities present in the distribution of other environmental risks and benefits, which
in the 1980s catalyzed affected communities to develop the framework of “ environmental
justice.” This framework was ultimately affirmed by the United States Environmental
Protection Agency ( U. S. EPA) in its 1992 creation of what is now called the Office of
Environmental Justice, which holds that
“ no group of people, including racial, ethnic, or socioeconomic groups, should bear a
disproportionate share of the negative environmental consequences resulting from
industrial, municipal, and commercial operations or the execution of federal, state, local,
and tribal environmental programs” ( U. S. EPA).
Presidential Order 12898 of 1994 expanded the application of environmental justice principles in
its decree that “ each Federal agency shall make achieving environmental justice part of its
mission” ( Presidential Executive Order 12898).
We use the environmental justice framework in two analyses that are relevant to understanding
the full costs of sea‐ level rise in California. The first is a simple analysis looking for potential
inequities in who is likely to be directly exposed to sea‐ level rise, within the geographic units at
which relevant political decisions are made. In this case these geographic units include the state
of California as a whole and each county affected by sea‐ level rise. We urge further studies
looking at possible inequities at different spatial scales, e. g., within cities, neighborhoods, and
metropolitan regions. Our second environmental justice analysis focuses on the factors of
6 Here, we evaluate the environmental justice impacts of flooding but not erosion. Additional analysis
should examine erosion as well.
22
vulnerability and the differential vulnerability to the impacts of sea‐ level rise of people from
different demographic groups.
A third analysis, which is beyond the scope of this study, should focus on potential inequities in
the distribution of the resources invested to protect and adapt to sea‐ level rise. Here we focus
on completing a part of the first and second analyses, and leave the third analysis for future
studies.
Any analysis of populations affected by sea‐ level rise should include a broader discussion of
vulnerability to these events. According to the IPCC, “ Vulnerability to climate change is the
degree to which these systems are susceptible to, and unable to cope with, adverse impacts”
( Schneider et al. 2007). Vulnerability is a function of the magnitude of the impact, the sensitivity
of the system to that impact, and the system’s ability to adapt. Vulnerabilities, like lack of access
to a vehicle or other means of transportation, are shaped by “ intervening conditions” that are
not tied to a specific hazard but will greatly determine the human impact of the disaster and the
specific needs for preparedness, response, and recovery ( Hewitt 1997).
Here, we report key population characteristics that increase vulnerability to the adverse impacts
of flood events and disasters for low‐ income people and communities of color. We sort the
types of vulnerabilities and key demographics correlated with increased vulnerability,
according to the three phases of a disaster event: preconditions, disaster, and recovery and
reconstruction ( Hewitt 1997). Figure 9 offers a conceptual model of the relationship between
demographics, vulnerabilities, and human impact. Our analysis is limited to two factors: the
distribution of race and income. A more comprehensive analysis of the human impact of sea‐level
rise is needed for all vulnerable subgroups, including children, elderly, homeless, and
incarcerated residents.
23
Figure 9. Relationship between demographics and vulnerabilities
2.4.2. Impacts on the Built Environment
Extensive development has occurred in areas already threatened by erosion and floods along
the California coast. Residential homes along the California coast often draw a premium price
as a result of their location. Some homes in coastal zones are protected by levees and
revetments; many are not protected at all. Additionally, high‐ value commercial, industrial, and
transportation facilities are also located along the coast. Such facilities make use of the
waterfront for waste disposal, movement of goods or people, or commercial activities. Among
the most common coastal facilities are airports, railroad tracks and terminals, highways, power
plants, waste‐ disposal sites, waste‐ treatment plants, ports and docks, warehouses, salt ponds,
and marinas. Existing forms of protection for these facilities vary greatly, from bulkheads and
engineered seawalls to riprap and non‐ engineered levees. An increase in sea level will increase
the severity of possible damages in threatened areas and will expand the size of flood and
erosion zones.
Data on the replacement value of buildings and contents were taken from datasets supplied
with the HAZUS model, which was developed for FEMA’s Mitigation Division by the National
Institute of Building Sciences. HAZUS was designed to help planners estimate the potential
24
losses from natural disasters such as earthquakes, floods, and hurricane winds. HAZUS uses a
database called the “ General Building Stock Inventory” that contains the value of buildings and
contents based on data from a number of sources including the U. S. Census Bureau, Dun &
Bradstreet ( a business listing service), and the U. S. Department of Energy. HAZUS estimates
direct economic losses based on the repair and replacement of damaged or destroyed buildings
and their contents, and includes the following:
• Cost of repair and replacement of damaged and destroyed buildings
• Cost of damage to building contents
• Losses of building inventory ( contents related to business activities)
Replacement values are provided for residential, commercial, industrial, agricultural, religious,
governmental, and educational developments and are compiled at the census block level. See
Section 14.2 of the HAZUS technical manual for additional detail ( FEMA 2006). To determine
the replacement value for the areas at risk, we overlay the inundation maps with year 2000
census block data. We assume that if 50% of an area is affected, then 50% of its assets are at risk.
For inundation risks, we use replacement value, as described in more detail below, because
flooding does not usually destroy property and land value completely. In contrast, erosion often
completely destroys the property. As a result, replacement value is not appropriate for
evaluating the economic cost of erosion and was not used for that part of the study. For the
erosion analysis, we assume that the value of the average coastal property is about $ 1.4 million
( Heinz Center 2000).
We compared replacement costs and the market value of homes at a few locations along the
California coast and found that the replacement costs in HAZUS can substantially
underestimate actual market values for residential properties. According to the HAZUS
database, the median home replacement values range from $ 63,000 in Del Norte County to
$ 135,000 in San Mateo County ( Figure 10). In comparison, the median home price in California
was $ 286,000 in November 2008. In Northern California, the median price was $ 307,000, and in
the San Francisco Bay Area, the median price was $ 474,000. Of course, homes on the coast are
usually much more expensive.
25
$ 0
$ 50,000
$ 100,000
$ 150,000
$ 200,000
$ 250,000
$ 300,000
Del Norte
Humboldt
Mendocino
Los Angeles
San Diego
San Luis Obispo
Santa Barbara
Monterey
Orange
Ventura
Solano
Napa
Alameda
Sonoma
Contra Costa
Santa Cruz
San Francisco
Marin
Santa Clara
San Mateo
Min
75%- ile
Median
Max
25%- ile
90%- ile
10%- ile
Key:
Figure 10. Distribution of census- block average replacement costs for single- family homes
from HAZUS
The difference between the replacement value and the market value of a home is likely due to
several factors. Home values are determined by more than the cost to build the house, including
land value, neighborhood, school district, and dozens of other tangible and intangible factors. In
addition, the HAZUS documentation warns that replacement value is based on national‐average
construction costs, which are much lower than construction costs in California. Future
studies should include more detailed estimates of California construction costs.
Parcel data from each county assessor’s office provide higher spatial resolution, but there are
some significant limitations to using these data. First, we were unable to obtain complete
coverage for all coastal counties. In some counties, parcel data have not been converted to a
digital format, while others claimed that sharing these data was a threat to Homeland Security.
Second, even where parcel boundary files are available, these must be linked to the value of the
property. While obtaining a list of affected parcels is straightforward, most counties do not
readily share their tax rolls or tables with assessed value. This information is part of the public
record, and can legally be requested in person or by phone from a county assessor’s office, but
this approach is not feasible for a regional analysis where hundreds or thousands of parcels are
affected. Third, even if assessed value were readily available to us, it often bears little
relationship with the actual market value of a property. Finally, assessed value will not include
any publicly owned buildings, so it would exclude many police and fire stations, government
buildings, park buildings, schools, water treatment plants, and others.
26
Important transportation infrastructure is also at risk of flooding and erosion from projected
increases in sea‐ level rise ( Figure 11). We estimate the miles of roadways and railroads at risk
by overlaying the GIS inundation and erosion hazard layers with transportation data from Tele
Atlas. We note that because there are not elevations associated with the roadways, it is difficult
to infer the extent to which the roadway is at risk from flooding. Additionally, the railroad data
do not provide information on the number of tracks, e. g., single, double. We also do not provide
estimates of the value of this infrastructure because adequate data are not available. Thus, the
information on roads and railways is presented as miles of structures at risk rather than value,
but it provides an indication of the areas at risk and those warranting additional analysis.
Figure 11. Flooding of a coastal road in Santa Cruz, California
Photo courtesy of David L. Revell
A number of other facilities along the coast are also at risk of flooding and erosion. We evaluate
the sites and facilities at risk by overlaying the GIS inundation layer with the relevant spatial
data. Data on the locations of schools and emergency facilities come from the HAZUS
geographic database ( FEMA 2006). Data on licensed healthcare facilities come from the
California Office of Statewide Health Planning and Development ( 2006). Data on coastal power
plants were provided by the California Energy Commission.
27
Data on U. S. EPA‐ monitored hazardous materials sites were from the U. S. EPA Geospatial Data
Access Project 2008 and included Superfund sites, hazardous waste generators, facilities
required to report emissions for the Toxics Release Inventory, facilities regulated under the
National Pollutant Discharge Elimination System ( NPDES), major dischargers of air pollutants
with Title V permits, and brownfield properties. 7 The Pacific Institute developed a geographic
database of wastewater treatment plants based on data in the U. S. EPA’s Permit Compliance
System ( PCS) database, by interpreting aerial photos and by telephone and Internet research.
2.4.3. Natural Resources
Wetlands are among the Earth’s most productive ecosystems. Once abundant across the United
States, wetlands have been extensively drained and filled to make way for agricultural,
industrial, commercial, and residential development. Pollution and invasive species threaten
the health of the remaining areas. The U. S. EPA estimates that more than 220 million acres of
wetlands existed in the lower 48 states in the 1600s. By 2000, only 100 million acres of wetlands
remained ( U. S. EPA 2001). In some parts of the United States, wetland loss was even more
severe. In California, for example, more than 90% of the historic wetlands have been lost to
development. Growing recognition of their importance and concern about their rapid decline
has prompted wetland restoration efforts across the United States, including the San Francisco
Bay. A recent U. S. Fish and Wildlife Service report suggests that the net wetland acreage
actually increased between 1998 and 2004 for the first time as a result of restoration efforts and
the construction of engineered wetlands ( Dahl 2006).
While legislation has helped to protect wetlands from further destruction, rising seas threaten to
substantially modify or destroy remaining wetland habitat. Most coastal wetlands in the United
States are within one tidal range of mean sea level ( Titus 1988), i. e., between mean high tide and
mean low tide. Thus, as noted by Titus ( 1988), if sea levels rose by one tidal range overnight,
“ then all of the existing wetlands in an area would drown.” Rising seas, however, may also
inundate land that is now dry, thereby creating new wetlands. Wetlands may also be able to
adapt to rising water levels over time by trapping sediment or building on the peat the
sediment creates, a process referred to as vertical accretion. These compensatory mechanisms
may be hindered by coastal development that limits wetland migration or rates of sea‐ level rise
that exceed natural accretion rates.
Spatial Extent of Wetlands
In this analysis, we use GIS data from the National Wetlands Inventory ( NWI) to determine the
current spatial extent of wetlands along the California coast and the San Francisco Bay. While
there is currently no single source that contains the boundaries of all existing wetlands, the NWI
is the best dataset available. It is important to note that all datasets likely underestimate the
actual wetland area. Wetland delineation is a time‐ and labor‐ intensive task requiring extensive
field work by experts; vast areas have never been subject to detailed study.
7 A brownfield is an abandoned industrial site available for redevelopment, often with environmental contamination.
28
The NWI does not make a clear distinction between coastal and upland wetlands. The datasets
are distributed in tiles, with each tile containing a mix of marine, estuarine, and freshwater
wetlands. We used a simple rule‐ based approach to decide which wetlands are coastal, or
“ coast‐ dependent”” we assume that coastal wetlands are generally limited to within 100 feet
( horizontally) of the mean higher‐ high water line ( Figure 12).
All NWI Wetlands
Coastal Wetlands
Mean Higher High Water
Figure 12. National Wetlands Inventory wetlands classified as
“ coastal” are below or adjacent to the MHHW line
Economic Value of Wetlands
Wetlands are highly diverse ecosystems that provide a variety of goods and services, including
flood protection, water purification, wildlife habitat, recreational opportunities, and carbon
sequestration. While there are rarely any direct market values for services provided by
wetlands, such as biodiversity and flood control, there is a growing recognition that these
services have real economic values and should be included in decision‐ making processes.
Methods for estimating the economic value of an ecosystem, including wetlands, can be done in
one of three ways: direct, indirect, and proxy ( Table 5). Each of these methods has strengths and
weaknesses; each fails to fully capture the value of ecosystems. The unacceptable alternative,
however, is to assign an economic value of $ 0— clearly acknowledged to be wrong. To put it
simply, “ we don’t protect what we don’t value” ( Myers and Reichert 1997).
In recent years, a number of studies have attempted to estimate the economic value of wetlands.
Based on a literature review and some original calculations, Costanza et al. ( 1997) estimate that
the value of tidal marshes is around $ 5,700 per acre per year ( in year 2007 dollars). In a meta‐analysis
of 39 wetland valuation studies, Woodward and Wui ( 2001) found that wetland values
varied considerably according to the methods used, the type and location of wetlands
evaluated, and the study characteristics. While the valuation method affected the value
29
obtained, the method was not the primary determinant of value. However, study quality was
not a strong determinant either; weak studies yielded wetland values similar to strong studies,
but with more error, suggesting that the quality of the study affects precision. The authors
conclude: “ From our analysis it is clear that the prediction of a wetland’s value based on
previous studies is, at best, an imprecise science. The need for site‐ specific studies remains”
( Woodward and Wui 2001).
For this analysis, we estimate the economic value of wetlands in California using recent cost
estimates for restoring wetlands. Numerous wetland restoration projects have been initiated in
the San Francisco Bay, with the cost of restoring these tidal marshes ranging from $ 5,000 to
$ 200,000 per acre ( Hutzel 2008). The South Bay wetland restoration project, for example, is
estimated to cost about $ 67,000 per acre ( Hutzel 2008). We note that these estimates represent
the public’s willingness to pay for these ecosystems rather than their actual value, but without a
more detailed site‐ specific analysis, the restoration costs are the best estimates available. We do
not evaluate the ability of wetlands to adapt to these changes through vertical accretion or
landward migration, but note that these processes could reduce damage to wetlands. We urge
more detailed wetland valuation studies be conducted to improve these estimates.
30
Table 5. Approaches for estimating ecosystem values
Approaches Description Example Weaknesses Strengths
Direct Surveys can be used
to ascertain people’s
willingness to pay for
benefits provided by
the wetland or the level
of compensation they
would expect for the
loss of those benefits.
Such surveys measure
the value of specific
benefits.
A survey that asks
users what they
would be willing to
pay to retain a
recreational area.
This approach
requires
sophisticated
survey design,
analysis and
interpretation.
This approach
can measure
relatively subtle
changes in
value and can
also be used to
calculate the
value of non-use
benefits.
Indirect Economists use
mathematical models
to estimate wetland
values based on the
market demand for
related goods and
services.
Expenditures and
the distance
traveled by people
visiting a wetland
are used as
indicators of the
value of the
wetland for
recreational
purposes.
Similarly, real-estate
price
differences could
be used to
estimate the value
of the wetland’s
aesthetic benefits.
This approach
cannot measure
non- use benefits
( e. g., option or
bequest benefits)
or benefits that
do not currently
exist ( e. g., the
benefits of an
enlarged
wetland).
This approach
is usually faster
and less
expensive, as it
can be based
on easily
accessible
data.
Proxy The values of other
goods and services are
used to approximate
the values of wetland
benefits.
The replacement
cost for a wetland
benefit ( e. g., water
filtration), such as
the cost of
installing a buffer
strip or building a
water treatment
plant, is used as a
measure of the
value of the
benefit.
This approach
may confuse
costs and
benefits. For
example, using
the cost of a
water treatment
plant estimates
the cost rather
than the value of
water filtration,
( i. e., people’s
willingness to pay
for clean water).
This approach
can be more
quickly
calculated, but
the result is
only a very
rough estimate
of value.
Source: Environment Canada 2001
Impact of Sea- Level Rise on Wetlands
Evaluating the impacts of sea‐ level rise on a particular coastal wetland area requires site‐ specific
data on various physical and biological factors, as described above. While this information is
clearly important for developing adaptation strategies, it is beyond the scope of this analysis. A
simple method to estimate wetland loss is to compare wetland elevations to future tide
elevations. If the areas are permanently inundated in the future, they will be converted to open
31
water and lose their value as wetland habitat. Data limitations, however, prevent us from
performing even this simple analysis: the existing digital elevation models ( DEMs) do not
include data below the shoreline and the modeled mean lower low water mark, even with 1.4 m
of sea‐ level rise, falls below this elevation. This means there are no data in the critical area
where the boundary must be drawn. We recommend additional work in this area to create a
DEM for the California coast that combines land surface elevations with accurate bathymetry to
allow for more detailed study of potential wetland responses to sea‐ level rise. Given these data
limitations, we evaluate the land cover adjacent to existing wetlands and the potential for these
areas to support suitable wetland habitat. We note that this simplified analysis does not take
into account erosion or accretion due to sediment movement, which is difficult to predict with
any accuracy.
Wetlands exist in areas that are frequently, but not permanently, inundated. In The Effects of Sea
Level Rise on US Coastal Wetlands, Park et al. ( 1989) assumed that all areas between mean lower
water ( MLW) and mean higher water springs ( MHWS) are tidal wetlands ( Figure 13). The
MHWS is only a few centimeters from the mean higher high water ( MHHW) datum, which is
more readily calculated and tabulated in tide reports. We assume that wetlands will migrate to
land areas that are below the future MHHW, which we estimate as current MHHW plus the
projected 1.4 m sea‐ level rise.
Figure 13. Assumed wetland area defined by the intertidal range
Adapted from Park et al. 1989.
The National Oceanic and Atmospheric Administration maintains tide stations along the
California coast that provide measurements of MHHW. We interpolated the high‐ water
elevation for the entire California Pacific coast using data from 12 long‐ term coastal tide gages.
Each of these NOAA tide stations has been in continuous operation for over 25 years. The
MHHW elevation for each of these stations is listed in Table 6. Using spatial interpolation tools
MLLW
MLW
MSL
MHW
MHHW
MHWS
Intertidal zone, or
mean range of tide
Beach and
tidal flats
Low marsh
Mangrove
swamp
High marsh
32
available in ArcGIS software, we developed a continuous grid or “ surface” of MHHW
elevations in year 2000.8 To estimate MHHW elevations with a 1.4 m sea‐ level rise for the Pacific
coast of California, we created a second surface by adding 1.4 m to each pixel in the year 2000
MHHW surface. The difference between the high water lines is the “ wetland migration zone”:
the land into which wetlands must migrate to survive.
Table 6. Mean higher high water ( MHHW) for long- term
tide stations on California’s Pacific coast
NOAA
Station ID
Station Name MHHW
9410170 San Diego, CA 1.61
9410230 La Jolla, CA 1.57
9410660 Los Angeles, CA 1.61
9410840 Santa Monica, CA 1.60
9411340 Santa Barbara, CA 1.61
9412110 Port San Luis, CA 1.60
9413450 Monterey, CA 1.67
9414290 San Francisco, CA 1.80
9415020 Point Reyes, CA 1.75
9416841 Arena Cove, CA 1.76
9418767 North Spit, CA 1.99
9419750 Crescent City, CA 1.98
Note: Elevations in meters above NAVD88 vertical datum. Tide datums
calculated by NOAA for the 1983– 2001 epoch.
Source: http:// tidesandcurrents. noaa. gov/
We analyzed the land cover in the potential wetland migration zone using 2001 land cover data
from NOAA’s Coastal Change Analysis Program ( C‐ CAP). 9 We rated each land cover type
according to its suitability to support wetland habitat in the future. We assume that natural
lands such as woodland, grassland, or shrub could provide suitable habitat for wetland plants
and animals in the future when they are in the new intertidal zone and are intermittently
wetted. Other land cover types may be viable for conversion to wetlands, but at a loss of some
direct value to humans, e. g., farmland or parks. The third and final category represents built‐ up
8 In some areas of Southern California, however, the available digital terrain data were not sufficiently
detailed to complete the analysis. The terrain data do not include points below an elevation of 1.5 m
NAVD88, and we could not map the current MHHW inundation extent for the entire coast. We mapped
about 49% of Santa Barbara County, 23% of Los Angeles County, and 65% of Orange County. The
coverage was 100% in the other 11 counties on the Pacific coast.
9 The C‐ CAP data layer classifies land cover based on an adapted version of the Anderson et al. ( 1976)
classification scheme and is estimated to have an accuracy of 85% ( NOAA Land Cover Analysis website
www. csc. noaa. gov/ crs/ lca/ ccap. html).
33
areas that will likely provide unsuitable habitat for wetlands in the future due to the presence of
buildings and other paved areas.
2.4.4. Limitations
Our analysis also has limitations related to the economic valuation methodology. For the flood
analysis, we estimate the economic cost of sea‐ level rise based on estimates of the replacement
value of buildings and their contents. We do not include estimates of the property or land value,
which are much higher and should be included if inundation is permanent or leads the
abandonment of property. Replacement values are also not appropriate for estimating the cost
of erosion because it typically results in the total loss of property and land. We make a rough
estimate of land values along the coast but note that additional study is needed.
Flooding and erosion can cause serious economic and social disruptions that are not captured in
estimates of the buildings and infrastructure. For example, flooding events can cause deaths
and injuries. Flooding or erosion of a major highway can prevent people from getting to work.
Thus, estimating the replacement value and even some wetland values substantially
underestimates the total cost of flood impacts and as a result, our findings should be considered
conservative. A more detailed analysis would include transportation risks, lost work days,
health issues, impacts on migratory bird habitat, and others.
We also do not factor in any expected changes in population density or the level of
development in the regions at risk over the next century: these are largely unknown and will be
determined by future policies. If policies are put in place to reduce development in regions of
future flooding, society could over time reduce the risks. While limiting coastal development
( an institutional adaptation) is likely the most effective way to reduce risk, this approach can
also incur costs. Development permits designed to provide flexibility for future generations to
address sea‐ level rise ( e. g., development permits that allow development but stipulate that the
area reverts to nature if seas rise a specified amount) may reduce today’s cost. Conversely, if
current development in coastal areas continues unchecked, a far larger population and far more
infrastructure will be vulnerable than at present. We make no estimates of these changes, but
future research could look at different scenarios for growth and coastal development and
integrate them into the assessment tools developed here.
2.5. Determine the Protective Responses Appropriate for the Region
Each of the resources and facilities described in Section 2.4 can be protected by some
combination of structural and non‐ structural measures. Some of the possible structural
measures include building or improving coastal defenses such as dikes and dunes, seawalls,
bulkheads, and other structures. Non‐ structural measures include abandoning property and
land and moving to less threatened areas and beach nourishment. Perhaps the most effective
non‐ structural response is to prohibit development in regions likely to be threatened in the
future. This choice, however, requires the most forethought and planning. Below, we describe
some of the structural measures and their associated costs.
34
2.5.1. Structural Coastal Protection Measures
Beach Nourishment
The addition of beach sand to a shoreline has been used to construct beaches where none had
previously existed and to replenish eroded sand. As a response to the expected increase in
erosion due to sea‐ level rise, the purpose of beach nourishment is to restore the width of an
eroding beach on a temporary basis, although nourishment can also provide long‐ term
restoration in certain types of areas. The rate at which the replenished beach erodes is a function
of wave action, the uniformity of placement of the sand, and the grain size ( U. S. Army Corps of
Engineers 1984b). The sand used for a beach nourishment project usually comes from offshore
dredging and pumping to the desired site; less frequently material is imported from an off‐ site
location. The cost of the material can vary greatly depending on its origin and associated
transportation costs.
Groins
One type of structure designed to lessen the impact of coastal processes on a shoreline is a groin
— a structure oriented perpendicular to the shore that serves to reduce the flow of sediment
along a shore ( the local littoral drift rate). Sand collects on the updrift side of the groin until it is
filled to capacity, when longshore drift is allowed to pass. Groins are often used in fields ( sets of
more than one groin) to protect a long section of coastline. The shoreline immediately
downfield of the groin field, however, is often subjected to accelerated erosion, especially when
the groins are not filled with sand during construction ( National Research Council 1987).
Sea‐ level rise can affect a groin by reducing its effectiveness due to “ flanking” or
“ submergence.” A groin typically extends landward to the dune line, and the dune line may
retreat due to sea‐ level rise, leaving the groin susceptible to flanking during high or storm tides,
allowing sand to bypass the groin. Submergence of the groin can lead to overtopping by the
longshore current, further decreasing the structures’ efficiency at stabilizing the area ( National
Research Council 1987).
Seawalls, Bulkheads, and Revetments
There are three principal forms of vertical shoreline walls used to protect upland areas from
storm surges and high tides: seawalls, bulkheads, and revetments. The differences between
seawalls, bulkheads, and revetments are in their protective function. Seawalls are designed to
resist the forces of storm waves; bulkheads are to retain the fill; and revetments are to protect
the shoreline against the erosion associated with light waves ( U. S. Army Corps of Engineers
1984b). These structures tend to fix the position of the coast. While this strategy may protect
upland development, there are two kinds of adverse consequences of these types of structures.
Placement loss refers to the loss of beach due to the footprint of the structure. For seawalls this is
not as great as a revetment, which is usually built at a 2: 1 ( horizontal: vertical) slope. The other
impact of these structures is called passive erosion. As sea level rises, and the structure fixes the
position of the shoreline, the beach in front of the structures can be “ drowned,” resulting in a
loss of recreation opportunities and habitat ( Griggs 2005).
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Breakwaters
Offshore breakwaters are above‐ water structures parallel to the shore that reduce both wave
heights at the shoreline and littoral drift. Sea‐ level rise will reduce the protective capacities of
breakwaters in two ways: rising water levels will effectively move the shoreline farther from the
breakwater, increasing the ability of the waves to diffract behind the structure and reducing the
sheltering and efficacy of the device; and the increased frequency of overtopping will diminish
the ability of the breakwater to reduce the wave energy in the sheltered region ( National
Research Council 1987).
Dikes and Levees
Dikes or levees are embankments to protect low‐ lying land. A sea‐ level rise can result in
reduced stability and increased overtopping of existing levees. New levees may be constructed
to protect developed areas ( National Research Council 1987). Whether existing levees can be
modified for a rise in sea level depends on the availability of material for raising the levee, the
suitability of the foundation material to support the additional weight of the material, the
stability of the levee with the increased water level, and the accessibility of additional area for
widening the base of the levee. Considerations for new levees also include issues such as land
condemnation and interference of the levee with navigation ( National Research Council 1987).
Raise Existing Structures ( Roadways, Railroads, and Other Structures)
In some regions, building levees or seawalls to protect a small number of structures may not be
cost effective. In these instances, raising the structures may be a better alternative. Roadways,
railroads, and other structures may be raised so as to avoid damage from flooding. Over time,
for example, we think it likely that important economic assets such as airports, transmission
lines, or roadways will be raised rather than protected with levees or seawalls.
2.5.2. Cost of Structural Protection Measures
The cost of flood defenses is site‐ specific and little reliable information is available to generalize
these costs. Gleick and Maurer ( 1990) developed cost estimates for building new coastal
protection structures and raising existing ones, as well as raising roadways, railroads, and
individual structures. We update these costs for this analysis based on a literature review
( Table 7). Costs are converted to year 2000 dollars. Given the site specificity of construction
costs, we relied on cost information from California where possible.
Data suggest that a new levee between 10 and 20 feet in height with a waterside slope of 3: 1
would cost about $ 1,500 per linear foot ( in year 2000 dollars). This represents a 320% increase
over the 1990 estimate, much higher than the rate of inflation. The increase is likely due to large
increases in construction and material costs in recent years. We estimate that raising existing
levees would cost about $ 530 per linear foot ( in year 2000 dollars). Seawalls, while providing
significant protection, are among the most expensive option, estimated at about $ 5,300 per
linear foot ( in year 2000 dollars).
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Table 7. Costs ( in year 2000 dollars) for building new levees, raising existing levees, and building
new seawalls
Cost
($ per linear foot) Location Sources
New Levee $ 725–$ 2,228 San Francisco, CA Pang ( 2008)
Average New Levee $ 1,500
Raise Levee $ 319 Central Valley, CA Mount and Twiss ( 2005)
$ 223–$ 1,085 San Francisco, CA Moffatt and Nichol
Engineers ( 2005)
$ 278–$ 944 Central Valley, CA Mount and Twiss ( 2005)
Average Levee Upgrade $ 530
New Seawall $ 1,292 New England Kanak ( 2008)
$ 3,828 Southern California Gustaitis ( 2002)
$ 2,646–$ 6,173 Northern California Stamski ( 2005)
$ 5,654–$ 8,078 Philadelphia PennPraxis ( 2008)
$ 4,847 California Crampton ( 2008)
Average New Seawall $ 5,300
Note: All costs are shown in year 2000 dollars. Costs shown for a new levee are based on a U. S. Army Corps of Engineers cost-estimation
model, for a levee between 10 and 20 feet in height with a waterside slope of 3: 1 and built using local materials.
In addition to the construction costs of the various structures described above, maintenance
costs are often significant. In general, the greater the engineering employed in the construction
of a shore protection scheme, the lower the proportion of maintenance costs. The maintenance
cost of engineered riprap‐ revetment, for example, can amount to 2%– 4% of the construction cost
per year over the life of the project. This can be compared with the maintenance cost for a non‐engineered
revetment of 5%– 15% of the construction cost per year ( Fulton‐ Bennett and Griggs
1986). Average maintenance costs for levees are about 10% per year of the costs of construction.
The estimated maintenance costs for seawalls run from 1%– 4% per year, reflecting the higher
level of engineering that goes into their construction. Because the majority of structures in our
study are levees, we assume here an annual operation and maintenance cost equal to 10% of the
capital cost of construction.
Levees, seawalls, and other structural methods have a number of environmental and social
costs that are not reflected in the cost estimates shown in Table 7. Armoring the coast prevents
natural movement and migration of the beach and associated ecosystems. In some areas,
beaches may disappear completely, as shown in Figure 14. Structural measures can also
increase vulnerability by encouraging development in flood‐ prone areas and giving those who
live behind the structure a false sense of security. According to the United Nations,
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“ protective works have a tendency to increase the level of development in floodprone
areas, as the assumption is made that it is now safe to build and invest in areas that are
protected. However, it must be recognized that at some point in the future the design
event will likely be exceeded and catastrophic damages will result” ( United Nations
2004).
In addition, structural measures require regular maintenance, a task that is often overlooked
due to budgetary constraints. Failure to maintain protective structures can lead to structural
failures and catastrophic damage.
Figure 14. An example of coastal armoring leading to the disappearance of beach
Source: David L. Revell
2.5.3. Estimating Needed Coastal Defenses
Details about what level of protection to choose are a function of the perception of the value of
the threatened property, the cost of alternative measures, and political and societal factors. In
this analysis, we evaluate one scenario: the cost associated with raising the height of existing
structures to maintain current flood protection levels and building new structures to protect
some development that will be at risk of flooding with a 1.4 m sea‐ level rise. We do not evaluate
coastal protection costs for erosion and urge additional studies on this topic.
In order to determine the cost of protecting development along the San Francisco Bay and
California coast, we first needed to determine the location and type of existing coastal
protection structures. Unfortunately, neither the U. S. Army Corps of Engineers nor any other
agency maintains a comprehensive database with this information. The California Coastal
Commission, however, recently compiled spatial data on the location and type of protective
structure along the Pacific coast, e. g., groins, revetments, levees, and seawalls. Similar data were
not available for the San Francisco Bay. Digital Flood Insurance Maps ( DFIRMs) that showed
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the presence of protective structures in the San Francisco Bay, however, were available in some
areas. We supplemented the DFIRMS with a visual assessment of aerial imagery of the region.
Because the DFIRMs do not distinguish between the types of structure, we assumed that
seawalls were located around high‐ density, highly valued areas and levees were located around
all other areas.
Geospatial data on the existing coastal protection structures were overlaid with the inundation
maps to determine where existing structures needed to be raised and new structures built. To
make this determination, we made the following assumptions:
• Existing coastal protection structures are strengthened and raised by 1.4 m with no
change in the type of protection, e. g., levees are raised but are not replaced by a seawall.
• New coastal protection structures are needed wherever built structures are at risk of
flooding. Agricultural land was not protected, unless a levee already existed.
• Seawalls are used in areas along the Pacific coast that are currently not protected but
will need protection in the future and in areas where space limitations due to
development prohibit the construction of new levees.
• Levees are used within enclosed areas, like the San Francisco Bay, that are currently not
protected but will need protection in the future. These bays are protected from wave
action, and we assume that levees will provide sufficient protection.
3.0 Results
Here we report on the results of our analyses for San Francisco Bay and the Pacific coast. In
particular, we report on the population, infrastructure, and property at risk from sea‐ level rise,
as well as the impacts on harder‐ to‐ quantify coastal ecosystems. We also provide an estimate of
the economic costs of building coastal protections of different types to protect lives and
property from flooding. All economic values are reported in year 2000 dollars. Results are
reported separately for the flood and erosion risks.
3.1. Flood- Related Risks
In this analysis, we use the 100‐ year flood levels to evaluate the vulnerability to inundation. The
100‐ year flood is used as a standard for planning, insurance, and environmental regulations. It
is important to note that people, infrastructure, and property are already located in areas
vulnerable to flooding from a 100‐ year event. Sea‐ level rise will cause more frequent and more
damaging floods to those already at risk and will increase the size of the coastal floodplain,
placing new areas at risk where there were none before. In Figure 15, for example, those areas
shown in light blue are currently vulnerable to a 100‐ year flood event in the Santa Cruz area.
With a 1.4 m sea‐ level rise, additional areas ( shown in dark blue) will be at risk. Thus, the
damage attributed to a 1.4 m sea‐ level rise is equal to the area currently vulnerable to a 100‐ year
flood event ( but now protected by levees, seawalls, etc.) plus new inundated areas, i. e., the areas
shown in light blue and dark blue in Figure 15.
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A series of maps for the entire coast of California demonstrating the extent of the areas at risk
are posted at www. pacinst. org/ reports/ sea_ level_ rise. It should be noted again that these maps
are not the result of detailed site studies, and were created to quantify risk over a large
geographic area. These maps should not be used to assess actual coastal hazards, insurance
requirements or property values, and specifically shall not be used in lieu of Flood Insurance
Studies and Flood Insurance Rate Maps issued by the Federal Emergency Management
Agency ( FEMA). Local governments or regional planning agencies should conduct detailed
studies to better understand the potential impacts of sea‐ level rise in their communities.
Coastal Flood Risk Area
Sea Level Rise Scenario
Base Flood + 1.4 meters ( 55 inches)
Current Base Flood
( approximate 100- year flood extent)
Figure 15. Estimated current and future 100- year coastal flood risk areas around
Santa Cruz
3.1.1. Population at Risk