Overview of the

ARkStorm Scenario

Open File Report 2010-1312 

U.S. Department of the Interior 

U.S. Geological Survey
This page intentionally left blank
Overview of the Arkstorm Scenario

Prepared for the U.S. Geological Survey Multihazards Demonstration Project

Lucile Jones, Chief Scientist

Dale Cox, Project Manager

By

Keith Porter18, Anne Wein17, Charles Alpers17, Allan Baez1, Patrick Barnard17, James 

Carter17, Alessandra Corsi7, James Costner20, Dale Cox17, Tapash Das14, Michael 

Dettinger17, James Done12, Charles Eadie8, Marcia Eymann5, Justin Ferris17, Prasad 

Gunturi20, Mimi Hughes13, Robert Jarrett17, Laurie Johnson10, Hanh Dam Le-Griffin16, David 

Mitchell11, Suzette Morman17, Paul Neiman13, Anna Olsen18, Suzanne Perry17, Geoffrey 

Plumlee17, Martin Ralph13, David Reynolds13, Adam Rose19, Kathleen Schaefer6, Julie 

Serakos20, William Siembieda4, Jonathan Stock17, David Strong17, Ian Sue Wing2, Alex 

Tang9, Pete Thomas20, Ken Topping15, and Chris Wills3. 

1 B.A. Risk Management 11 M-Cubed

2 Boston University 12 National Center for Atmospheric 

Research

3 California Geological Survey 13 National Oceanic and Atmospheric 

Administration

4 California Polytechnic State University 14 Scripps Institute of Oceanography

5 Center for Sacramento History 15 Topping Associates International

6 Federal Emergency Management Agency 16 TTW, Inc. 

7 Instituto de Pesquisas Tecnologicas, Brazil 17 U.S. Geological Survey

8 Hamilton Swift Land Use and Development 18 University of Colorado at Boulder

9 L&T Consulting 19 University of Southern California

10 Laurie Johnson Consulting 20 Willis Group
ii

U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia 2011

For product and ordering information:

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

Telephone: 1-888-ASK-USGS

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

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

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

Telephone: 1-888-ASK-USGS

Suggested citation:

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

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

Charles, Eymann, Marcia, Ferris, Justin, Gunturi, Prasad, Hughes, Mimi, Jarrett, Robert, 

Johnson, Laurie, Dam Le-Griffin, Hanh, Mitchell, David, Morman, Suzette, Neiman, Paul, Olsen, 

Anna, Perry, Suzanne, Plumlee, Geoffrey, Ralph, Martin, Reynolds, David, Rose, Adam, 

Schaefer, Kathleen, Serakos, Julie, Siembieda, William, Stock, Jonathan, Strong, David, Sue 

Wing, Ian, Tang, Alex, Thomas, Pete, Topping, Ken, and Wills, Chris; Jones, Lucile, Chief 

Scientist, Cox, Dale, Project Manager, 2011, Overview of the ARkStorm scenario: U.S. 

Geological Survey Open-File Report 2010-1312, 183 p. and appendixes

[http://pubs.usgs.gov/of/2010/1312/].

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

endorsement by the U.S. Government.

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

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

Acknowledgments

This document reflects the contributions of a large interdisciplinary team of lifeline experts 

and agencies, including:

California Department of Public Health: Clifford Bowen and Joseph Crisologo 

California Department of Transportation: Roy Bibbens, Mandy Chu, John Duffy, Jim Morris, 

Gustavo Ortega, Cliff Roblee, Earl Sherman, Loren Turner, and Bill Varley

California Department of Water Resources: Bill Croyle, Sonny Fong, Andy Mangney, Michael 

Mierzwa, Ricardo Pineda, and Tawnly Pranger

California Emergency Management Agency: Robert Mead

California Environmental Legacy Project: Kit Tyler

California Environmental Protection Agency: Duncan Austin and Antonia Vorster

California Extreme Precipitation Symposium: Gary Estes

California Geological Survey: Dave Branum and Ante Perez

California Institute of Technology: Margaret Vinci

California Polytechnic State University: Bud Evans, Robb Moss 

California State University at Sacramento Police: Kelly Clark

California Utilities Emergency Association: Don Boland

Coachella Valley Water District: Mike Herrera

Contra Costa County Health: Eric Jonsson 

Contra Costa County Sheriff: Marcelle Indelicato and Rick Kovar 

East Valley Water: Gary Sturdivan

Golden State Water: John Spitter

Institute for the Future: Jason Tester

Los Angeles City Building and Public Works: Tom Cotter 

Los Angeles City Bureau of Sanitation: Chuck Turhollow 

Los Angeles City Department of Water and Power: Craig Davis, Melinda Rho, Minas Sirakie, 

and Julie Spacht

Los Angeles County Department of Public Works: Ben Willardson 

Los Angeles Department of Water and Power: Kevin Garrity 

Metropolitan Water District of Southern California: Christopher Hill and George del Toro

Michael Baker Corporation: Travis Clark and Jagdeep Sidhu 

National Oceanic and Atmospheric Administration: Rob Hartman and Alan Hayes
iv

Sacramento County Environmental Management Department: Val Sielal

Sacramento County Water Resources: George Booth

Sacramento Municipal Utility District: Selby Mohr

San Bernardino County Department of Public Works: Erwin Fogerson 

San Diego County Water Authority: Lorrie Teates 

San Francisco Department of Public Works: Greg Braswell

San Francisco Public Utilities Commission: Bruce McGurk

San Joaquin County Environmental Health Department: Linda Turkatte 

San Jose Water Company: Jim Wollbrinck

Santa Clara Valley Water District: Michael Hamer

Southern California Edison: John A. Bocka, Tom Botello, Don Conner, and Roger Lee 

University of California at Davis: Robert Lim

URS Corporation: Paul Jacks 

U.S. Army Corps of Engineers: Sean Mann 

U.S. Environmental Protection Agency: Harry Allen, Pete Guria, Bruce Macler, Rich Martyn, 

and Dan Meer

U.S. Geological Survey: Gerald Bawden, Sue Cannon, Rick Champion, Laura Dinitz, Dawn 

MacCarthy, and Tanja Wolfmeyer

Water Forum: Mark Roberson 
v

Abstract 

 The U.S. Geological Survey, Multi Hazards Demonstration Project (MHDP) uses hazards 

science to improve resiliency of communities to natural disasters including earthquakes, tsunamis, 

wildfires, landslides, floods and coastal erosion. The project engages emergency planners, 

businesses, universities, government agencies, and others in preparing for major natural disasters. 

The project also helps to set research goals and provides decision-making information for loss 

reduction and improved resiliency. The first public product of the MHDP was the ShakeOut 

Earthquake Scenario published in May 2008. This detailed depiction of a hypothetical magnitude 

7.8 earthquake on the San Andreas Fault in southern California served as the centerpiece of the 

largest earthquake drill in United States history, involving over 5,000 emergency responders and 

the participation of over 5.5 million citizens. 

This document summarizes the next major public project 

for MHDP, a winter storm scenario called ARkStorm (for 

Atmospheric River 1,000). Experts have designed a large, 

scientifically realistic meteorological event followed by an 

examination of the secondary hazards (for example, 

landslides and flooding), physical damages to the built 

environment, and social and economic consequences. The 

hypothetical storm depicted here would strike the U.S. 

West Coast and be similar to the intense California winter 

storms of 1861 and 1862 that left the central valley of 

California impassible. The storm is estimated to produce 

precipitation that in many places exceeds levels only 

experienced on average once every 500 to 1,000 years. 

Extensive flooding results. In many cases 

flooding overwhelms the state’s flood-protection system, 

which is typically designed to resist 100- to 200-year 

runoffs. The Central Valley experiences hypothetical 

flooding 300 miles long and 20 or more miles wide. 

Serious flooding also occurs in Orange County, Los 

Angeles County, San Diego, the San Francisco Bay area, 

and other coastal communities. Windspeeds in some places reach 125 miles per hour, hurricaneforce winds. Across wider areas of the state, winds reach 60 miles per hour. Hundreds of landslides 

damage roads, highways, and homes. Property damage exceeds $300 billion, most from flooding. 

Demand surge (an increase in labor rates and other repair costs after major natural disasters) could 

increase property losses by 20 percent. Agricultural losses and other costs to repair lifelines, 

dewater (drain) flooded islands, and repair damage from landslides, brings the total direct property 

loss to nearly $400 billion, of which $20 to $30 billion would be recoverable through public and 

commercial insurance. Power, water, sewer, and other lifelines experience damage that takes 

weeks or months to restore. Flooding evacuation could involve 1.5 million residents in the inland 

region and delta counties. Business interruption costs reach $325 billion in addition to the $400 

billion property repair costs, meaning that an ARkStorm could cost on the order of $725 billion, 

which is nearly 3 times the loss deemed to be realistic by the ShakeOut authors for a severe 

southern California earthquake, an event with roughly the same annual occurrence probability. 

K Street, Sacramento, looking east

1861-1862
vi

 The ARkStorm has several public policy implications: (1) An ARkStorm raises serious 

questions about the ability of existing federal, state, and local disaster planning to handle a disaster 

of this magnitude. (2) A core policy issue raised is whether to pay now to mitigate, or pay a lot more 

later for recovery. (3) Innovative financing solutions are likely to be needed to avoid fiscal crisis 

and adequately fund response and recovery costs from a similar, real, disaster. (4) Responders and 

government managers at all levels could be encouraged to conduct risk assessments, and devise the 

full spectrum of exercises, to exercise ability of their plans to address a similar event. (5) ARkStorm 

can be a reference point for application of Federal Emergency Management Agency (FEMA) and 

California Emergency Management Agency guidance connecting federal, state and local natural 

hazards mapping and mitigation planning under the National Flood Insurance Plan and Disaster 

Mitigation Act of 2000. (6) Common messages to educate the public about the risk of such an 

extreme disaster as the ARkStorm scenario could be developed and consistently communicated to 

facilitate policy formulation and transformation. 

 These impacts were estimated by a team of 117 scientists, engineers, public-policy experts, 

insurance experts, and employees of the affected lifelines. In many aspects the ARkStorm produced 

new science, such as the model of coastal inundation. The products of the ARkStorm are intended 

for use by emergency planners, utility operators, policymakers, and others to inform preparedness 

plans and to enhance resiliency. 
vii

Table of Contents

Acknowledgments ...........................................................................................................................................iii

Abstract ............................................................................................................................................................... v

Index of Figures ................................................................................................................................................. x

Index of Tables ................................................................................................................................................xv

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

ARkStorm Meteorology.................................................................................................................................... 3

FLOODING....................................................................................................................................................... 7

WINDSPEED .................................................................................................................................................11

Coastal Inundation ..........................................................................................................................................12

RESEARCH NEEDS RELATED TO COASTAL INUNDATION..................................................................16

Landsliding ........................................................................................................................................................16

RESEARCH NEEDS RELATED TO LANDSLIDING...................................................................................22

Three Approaches to Estimating Damage..................................................................................................23

Lifeline Panel Process....................................................................................................................................23

Highway Damage ............................................................................................................................................24

HIGHWAY FACILITIES AND SOURCES OF DAMAGE............................................................................24

HIGHWAY DAMAGE AND RESTORATION SCENARIO .........................................................................24

LIFELINE INTERACTION INVOLVING HIGHWAYS.................................................................................33

LIMITATIONS OF HIGHWAY RESTORATION TIMELINE ......................................................................34

OPPORTUNITIES TO ENHANCE HIGHWAY RESILIENCY .....................................................................34

Power ................................................................................................................................................................35

POWER FACILITIES AND SOURCES OF DAMAGE.................................................................................35

POWER DAMAGE AND RESTORATION SCENARIO..............................................................................38

LIFELINE INTERACTION INVOLVING POWER........................................................................................41

OPPORTUNITIES TO ENHANCE POWER RESILIENCY..........................................................................41

LIMITATIONS OF ESTIMATES WITH RESPECT TO POWER................................................................42

Wastewater Treatment..................................................................................................................................42

WASTEWATER FACILITIES AND SOURCES OF DAMAGE...................................................................42

WASTEWATER DAMAGE AND RESTORATION SCENARIO................................................................44

LIFELINE INTERACTION INVOLVING WASTEWATER TREATMENT..................................................47

RESILIENCY AND RESEARCH NEEDS FOR WASTEWATER SERVICE................................................47

Water Supply....................................................................................................................................................48

WATER FACILITIES AND SOURCES OF DAMAGE.................................................................................48

WATER SUPPLY DAMAGE AND RESTORATION SCENARIO ..............................................................48

LIFELINE INTERACTION INVOLVING WATER SUPPLY ........................................................................56

ENHANCING RESILIENCY FOR WATER SUPPLY...................................................................................56

RESEARCH NEEDS FOR WATER SUPPLY ...............................................................................................57

Dams..................................................................................................................................................................57

DAM DAMAGE SCENARIO........................................................................................................................58

LIFELINE INTERACTION RELATED TO DAM DAMAGE.........................................................................59
viii

Levees ...............................................................................................................................................................59

THE DELTA LEVEE DAMAGE AND REPAIR SCENARIO........................................................................59

Flooded Islands ........................................................................................................................................62

Repair Costs and Timing.........................................................................................................................62

MITIGATION OF LEVEE FAILURE ..............................................................................................................64

Telecommunications ......................................................................................................................................64

TELECOMMUNICATIONS ASSETS EXPOSED TO LOSS.......................................................................65

MECHANISMS FOR TELECOMMUNICATIONS SERVICE INTERRUPTION .......................................66

TELECOMMUNICATIONS SERVICE RESTORATION SCENARIO.........................................................67

LIFELINE INTERACTION INVOLVING TELECOMMUNICATIONS........................................................70

OPPORTUNITIES TO ENHANCE TELECOMMUNICATIONS RESILIENCY..........................................70

Agricultural Damages and Losses ...............................................................................................................70

HISTORIC AGRICULTURAL DAMAGES FROM CALIFORNIA FLOODS................................................71

METHODOLOGY ...........................................................................................................................................73

Location, Extent, Depth, and Duration of Flooded Agricultural Land.............................................74

Field Cleanup and Repair Costs ............................................................................................................75

Damages to Annual Crops .....................................................................................................................75

Damages to Perennial Crops.................................................................................................................76

Damages to Livestock.............................................................................................................................77

ARkSTORM AGRICULTURAL DAMAGE ESTIMATES............................................................................78

Damaged Land .........................................................................................................................................78

Statewide Agricultural Damages..........................................................................................................78

County Agricultural Damages ...............................................................................................................81

Effects of Delays in Delta Island Dewatering.....................................................................................86

SUMMARY ................................................................................................................................................87

MITIGATING AGRICULTURAL DAMAGES ..............................................................................................87

RESEARCH NEEDS RELATED TO AGRICULTURAL DAMAGES............................................................88

Building and Content Repair Costs ..............................................................................................................89

PROPERTY-DAMAGE METHODOLOGY....................................................................................................89

PROPERTY-DAMAGE SCENARIO .............................................................................................................91

RESILIENCY AND RESEARCH NEEDS FOR BUILDING DAMAGE........................................................95

DEMAND SURGE .........................................................................................................................................97

COMPARISON WITH PAST EVENTS........................................................................................................98

Insurance Impacts ..........................................................................................................................................98

INTRODUCTION...........................................................................................................................................98

CALIFORNIA FLOOD AND WIND INSURANCE COVERAGE ................................................................. 99

INSURANCE LOSS FROM THE ARKSTORM SCENARIO.....................................................................100

HISTORICAL PRECEDENT AND ECONOMIC RESILIENCY..................................................................102

RESEARCH NEEDS RELATED TO INSURANCE.....................................................................................102

Evacuation ......................................................................................................................................................103

POPULATION LIVING IN FLOODED AREAS ..........................................................................................103

SOCIAL INDICATORS................................................................................................................................105
ix

SHELTER REQUIREMENTS ......................................................................................................................111

RESEARCH NEEDS RELATED TO EVACUATION ..................................................................................113

Business Interruption Costs........................................................................................................................114

INTRODUCTION.........................................................................................................................................114

HAZARD LOSS ESTIMATION ..................................................................................................................114

BASIC CONSIDERATIONS ...................................................................................................................114

CONDUITS OF ECONOMIC SHOCKS ..................................................................................................116

THE DYNAMIC COMPUTABLE GENERAL EQUILIBRIUM MODEL ....................................................117

METHODOLOGICAL DETAILS FOR INDIVIDUAL LOSS CATEGORIES ..............................................118

RESILIENCE ................................................................................................................................................120

ADJUSTMENT FOR MULTIPLE SOURCES OF BUSINESS INTERRUPTION ....................................121

MACROECONOMIC IMPACTS ................................................................................................................124

SUMMARY OF RESULTS ..........................................................................................................................137

CONCLUSION.............................................................................................................................................138

Truck Traffic Economic Impacts from Reduced Highway Capacities.................................................139

Method of Analysis ...................................................................................................................................139

Truck travel time, distance, cost, and economic impacts ................................................................. 141

RESEARCH NEEDS RELATED TO TRAFFIC ECONOMIC IMPACTS ...................................................146

Environmental and Health Issues ...............................................................................................................148

PLAUSIBLE ENVIRONMENTAL-HEALTH ISSUES AND IMPACTS ...................................................148

APPROACH FOR ENVIRONMENTAL AND HEALTH IMPACTS..........................................................149

Public Policy Issues......................................................................................................................................151

OVER-ARCHING POLICY CONSIDERATION..........................................................................................151

PRIORITY POLICY ISSUES: MITIGATION..............................................................................................157

PRIORITY POLICY ISSUES: PREPAREDNESS AND RESPONSE........................................................160

PRIORITY POLICY ISSUES: RECOVERY..................................................................................................162

PRIORITY POLICY ISSUES: RISK AWARENESS...................................................................................166

POSSIBLE COURSES OF ACTION ...........................................................................................................169

Summary .........................................................................................................................................................171

KEY FINDINGS............................................................................................................................................171

CONCLUSION.............................................................................................................................................172

References Cited...........................................................................................................................................173
x

Index of Figures

Figure 1. K Street, Sacramento, looking east, in January or February 1862. (Photographers 

Lawrence and Houseworth, The Bancroft Library Pictorial Collection, University of California, 

Berkeley) .............................................................................................................................................................3

Figure 2. This map of the Pacific region shows an Atmospheric River originating over the central 

Pacific on February 16, 2004, indicated by high (green) vertically-integrated water-vapor contents, 

in grams per square centimeter of water vapor, in the atmosphere extending from around Hawaii 

to the central California coast near the town of Cazadero (CZD). ...........................................................4

Figure 3. Weather Research and Forecasting (WRF) model domains used in simulations of 

ARkStorm meteorology, with nested model domains indicated by boxes and the topography 

resolved by the grids in each domain indicated by contours. Grid spacings are 2 km in smaller 

black box, 6 km in larger black box, 18 km in blue area, and 54 km in red area. ...................................5

Figure 4. ARkStorm stitched-storm calendar, with moderately wet conditions of autumn and early 

winter “preconditioning” the watersheds of California for rapid flood generation indicated by blue 

shading, followed by the two intense-storm periods that were combined to make up the ARkStorm 

scenario indicated in red shading. These calendars represent actual storms of 1969 and 1986, 

which provided the basis for the ARkStorm modeling. ..............................................................................6

Figure 5. For locations around Califonria, colors depict recurrence intervals in years of maximum 

3-day runoff during the ARkStorm scenario.................................................................................................7

Figure 6. This snapshot of available flood models shows that various models cover the Central 

Valley (gridded area) but not the rest of the state (California Department of Water Resources, 

2010a)...................................................................................................................................................................9

Figure 7. Hydrologic Unit Code 6 watershed boundaries, with ARkStorm flooding parameter 

values, depth of flooding and length of time flooded................................................................................10

Figure 8. Blue areas indicate ARkStorm flooding as projected by models used in the scenario. ...11

Figure 9. ARkStorm peak 10-meter elevation, 3-second gust wind speeds.........................................12

Figure 10. Extent of the CoSMoS model applied to the ARkStorm scenario. See Barnard and 

others (2009) for details and additional maps. ...........................................................................................13

Figure 11. CoSMoS-estimated coastal inundation at the Ports of Los Angeles and Long Beach. ..14

Figure 12. CoSMoS-estimated coastal inundation at Seal Beach. ........................................................14

Figure 13. CoSMoS-estimated coastal inundation at Mission Bay, San Diego...................................15
xi

Figure 14. CoSMoS-estimated coastal inundation at Coronado and Imperial Beaches. ..................15

Figure 15. Categories of landslide treated by ARkStorm (Wills and others, 2001 modified after 

Varnes, 1958 and Colorado Geological Survey, 1988)...............................................................................18

Figure 16. Deep-seated landslide in Ventura Calif., January 2005 (left, photo credit: J. Stock, 

USGS), and 1982 debris flow in Pacifica, Calif., that killed 3 (right, photo courtesy of Woodward 

Clyde Consultants). .........................................................................................................................................19

Figure 17. Deep-seated Landslide Susceptibility Map of California. Black lines denote county 

boundaries. Higher numbers and hotter colors indicate greater susceptibility to deep-seated 

landslides. .........................................................................................................................................................20

Figure 18. Quaternary, Pliocene, and Miocene sedimentary rocks (hachured area) superimposed 

on the forecast for landslide abundance resulting from the ARkStorm. Cells in the hachured area 

have rock types similar to the calibration area at Santa Paula. Cells outside these zones likely 

overestimate the number of landslides because (1) rock units produce stronger soils and( 2) 

different processes (for example, rock fall) dominate erosion. Gray areas have no unstable cells 

in 10-meter data. Calibration (Santa Paula) and test (Sunland) areas shown by white polygons. ..21

Figure 19. Deep-seated Landslide susceptibility expressed as the median value for each census 

tract, which can be related to loss ratio for that tract. ............................................................................22

Figure 20. Causes of ARkStorm cumulative highway damages. Red indicates debris flow, blue 

indicates flooding, green indicates flooding and erosion, yellow indicates landslide other than 

debris flow. .......................................................................................................................................................26

Figure 21. Highway route capacity on January 30, 2011..........................................................................28

Figure 22. Route capacity on February 10, 2011. .......................................................................................29

Figure 23. Route capacity on February 26, 2011. .......................................................................................30

Figure 24. Route capacity on April 27, 2011. ...............................................................................................31

Figure 25. Route capacity on July 27, 2011. ................................................................................................32

Figure 26. Route capacity on January 27, 2012..........................................................................................33

Figure 27. ARkStorm winds. Yellow indicates maximum windspeed in excess of 70 miles per hour. 

Red lines indicate electrical transmission lines........................................................................................36

Figure 28. Power plants (green diamonds) overlain with flooding (blue areas) in (a) San Francisco 

Bay Area, (b) Sacramento and Stockton, (c) Los Angeles and Orange Counties, and (d) San Diego.

............................................................................................................................................................................38
xii

Figure 29. Power restoration curves at a few key locations, showing percentage of customers 

capable of receiving power at selected times. .........................................................................................41

Figure 30. The larger water districts among the 324 federal, regional, state, and municipal water 

districts in California. Colors of districts varied to improve visibility.....................................................51

Figure 31. Major state water projects (modified from California Department of Water Resources, 

2005)...................................................................................................................................................................52

Figure 32. Locations of California dams (yellow triangles). Only a fraction is for water-supply 

purposes. ..........................................................................................................................................................53

Figure 33. Locations of 35,000 water wells in California (Department of Water Resources 

Integrated Water Resources Information System)...................................................................................54

Figure 34. Scenario water service restoration per county, showing percentage of customers with 

service at different times. ..............................................................................................................................56

Figure 35. St. Francis Dam before (left) and after (right) collapse (both images: public domain)....58

Figure 36. South Fork Dam near Johnsontown, Pa., (left, image courtesy of Johnstown Area 

Heritage Association) and aftermath of the May 31,1889, Johnstown flood (right, public domain 

image). ...............................................................................................................................................................58

Figure 37. Sacramento-San Joaquin Delta levee analysis zones. .........................................................61

Figure 38. Switch on wheels (left) and cellular on wheels (right) (Photograph taken by A. Tang, L & 

T Consulting).....................................................................................................................................................66

Figure 39. Microwave dish blown off the tower mount (left); flooded central office (right). 

(Kwasinski, 2006; public domain images)....................................................................................................67

Figure 40. Damage to fiber optic cables: flooding degraded transmission when water leaked into a 

splice (left); ground failure damaged a cable laid along a railbed that washed out (right). .............67

Figure 41. Early flooding along a levee (photograph taken by National Oceanic and Atmospheric 

Administration, date and location unknown). ............................................................................................71

Figure 42. Flooded vineyards, Guerneville, Calif., 2006, because of Russian River flooding 

(Photograph by A. Dubrowa for Federal Emergency Management Agency). .....................................73

Figure 43. State agricultural damages to annual and perennial crops and livestock. .......................79

Figure 44. County annual crop field repair costs and income loss. Shading of county indicates 

estimated dollar losses. Colors on pie charts indicate percentage of losses from each of the listed 

crops. ................................................................................................................................................................. 82
xiii

Figure 45. County perennial crop replacement and income loss (first year). Shading of county 

indicates estimated dollar losses. Colors on pie charts indicate percentage of losses from each of 

the listed crops. ...............................................................................................................................................83

Figure 46. County perennial crop income loss (subsequent years). Shading of county indicates 

estimated dollar losses. Colors on pie charts indicate percentage of losses from each of the listed 

crops. ................................................................................................................................................................. 84

Figure 47. Livestock replacement cost. Shading of county indicates estimated dollar losses. 

Colors on pie charts indicate percentage of losses from each of the types of livestock listed.......85

Figure 48. Time required to dewater 30 flooded Delta islands. ..............................................................86

Figure 49. Sample HAZUS-MH flood vulnerability functions showing damage ratio for different 

types of single-family dwellings. ..................................................................................................................90

Figure 50. ARkStorm hypothetical flooding in Sacramento.....................................................................92

Figure 51. ARkStorm hypothetical flooding in Stockton...........................................................................92

Figure 52. ARkStorm hypothetical flooding in the San Jose area..........................................................93

Figure 53. ARkStorm hypothetical flooding in Los Angeles and Orange Counties..............................93

Figure 54. Basic wind speed map for California (American Society of Civil Engineers, 2006, figures 

6-1) and adjoining states, showing zones where basic wind speed for design is 85 miles per hour 

(38 meters per second) and 90 miles per hour (40 meters per second). ...............................................94

Figure 55. Fraction of building square footage affected by ARkStorm flooding for buildings in the 

occupancy classes agriculture, commercial, education, governmental, industrial, religion or 

nonprofit, and residential...............................................................................................................................95

Figure 56. Contribution to $200 billion in flood-related building repairs (left), and $6 billion in windrelated building repairs (right) for buildings in the occupancy classes agriculture, commercial, 

education, governmental, industrial, religion or nonprofit, and residential. ........................................96

Figure 57. Estimates of number of people, per county, in areas flooded by the ARkStorm. Blue is 

fewer than 10,000 people, red is more than 400,000. ............................................................................ 104

Figure 58. Evacuation vulnerability index in northern inland region (Zone 1)....................................108

Figure 59. Evacuation vulnerability index in Delta counties (Zone 2). .................................................109

Figure 60. Evacuation vulnerability index in southern inland region (Zone 3). ..................................110

Figure 61. Percentage of people in top two quantiles of the evacuation index relative to the total 

population (2009) by county.........................................................................................................................111
xiv

Figure 62. Aggregate value added losses because of various components of storm damage for (A) 

flood damage. (B) wind damage (C) utility service interruptions (electricity, water, wastewater 

and telecommunications). (D) crop and livestock damage. (E) evacuation losses. ........................128

Figure 63. Employment and wage impacts of various components of storm damage for (A) flood 

damage, (B) wind damage, (C) utility service interruptions (electricity, water, wastewater and 

telecommunications), (D) crop and livestock damage, (E) evacuation losses. ................................. 135

Figure 64. TransNIEMO economic impact modeling framework. ........................................................140

Figure 65. National highway network volume change for southern California storm on day 3......143

Figure 66. National highway network volume change for northern California storm on day 14. ...144
xv

Index of Tables

Table 1. Power restoration (percent of customers receiving power). .................................................40

Table 2. Wastewater treatment plants (WWTP) in scenario flooding areas, per county.................43

Table 3. Sewer service restoration per county over time after the ARkStorm. ..................................46

Table 4. Water service restoration per county over time. ......................................................................55

Table 5. Dewatering time of flooded Delta islands and tracts..............................................................63

Table 6. Summary of Delta island repair costs and times......................................................................64

Table 7. Landline and internet network restoration showing percentage of customers with power 

service by date. ...............................................................................................................................................68

Table 8. Cellular network restoration showing percentage of customers with power service by 

date. ...................................................................................................................................................................69

Table 9. Field cleanup and repair cost assumptions. ..............................................................................75

Table 10. Acres of significantly damaged agricultural lands.................................................................78

Table 11 Statewide damages to annual crops for low, mid and high flood durations.......................80

Table 12. Statewide damages to perennial crops over 5-year reestablishment................................80

Table 13. Statewide damages to livestock. ...............................................................................................81

Table 14. ARkStorm property loss by county. ...........................................................................................97

Table 15. National Flood Insurance Program flood insurance coverage..........................................100

Table 16. Insured loss estimate for different levels of insurance. ......................................................101

Table 17. Estimates of county population living in flooded areas. ......................................................105

Table 18. Scaling of social indicators.for social vulnerability index...................................................107

Table 19. Indicators for flood variables used in scaling of social vulnerability................................107

Table 20. Estimates, per county, of numbers of people in flood area.................................................112

Table 21. Summary of Business Interruption double counting adjustments.....................................123
xvi

Table 22. Present discounted aggregate impact of various components of storm damage on value 

added. ..............................................................................................................................................................129

Table 23. Present discounted sectoral impacts of flood losses..........................................................136

Table 24. Summary of ARkStorm costs and business interruption.....................................................137

Table 25. Change in highway network truck travel time.......................................................................142

Table 26. Change in highway network truck travel distance. ..............................................................142

Table 27. Change in trucking costs. ..........................................................................................................145

Table 28. Truck traffic economic impact. ................................................................................................146

Table 29. Generalized framework for U.S. disaster policy at the federal level.................................153

Table 30. Generalized framework for U.S. disaster policy at the state level. ...................................154

Table 31. Generalized framework for U.S. disaster policy at the local level.....................................155

Table 32. Generalized framework for U.S. disaster policy at the private level. ................................156
1

Introduction 

 Naming the ARkStorm. A California storm is known by the year in which the storm 

occurred. To equate the storm in a person’s mind requires some visceral knowledge of the event, or 

some understanding of history, meteorology, hydrology, engineering, or other relevant technical 

discipline. Even with that knowledge, the naming convention does not communicate the magnitude 

of the event. Therefore, the ARkStorm scenario was named so as to be independent of time, to 

acknowledge the meteorological phenomena behind most large storms on the U.S. West Coast, 

namely Atmospheric Rivers (ARs), and to provide some future scale to compare past and future 

events. The hypothetical scenario would be an Atmospheric River, AR with a value of 1,000 (k), or 

an ARkStorm. 

The Multihazards Demonstration Project. The ARkStorm Scenario is the second major 

project of the U.S. Geological Survey (USGS) Multi-Hazards Demonstration Project (MHDP). The goal 

of the MHDP is to improve community resiliency to natural hazards through the application of 

science from a variety of disciplines. Early in the project, the MHDP gathered together decision 

makers and emergency managers in southern California and asked them what they wanted from 

science. Larry Collins, a Captain in the Los Angeles County Fire Department, Urban Search and 

Rescue, said: 

“In California, the emergency services deal with disasters as a matter of course. It's the catastrophic 

events that push us to our limits. We look to science to help us better prepare for catastrophes. By 

preparing for catastrophes, we can deal with disasters that much better.”

 There is much to learn from hypothetical catastrophes. To address southern California's 

catastrophic vulnerability to earthquake, the MHDP created the ShakeOut Earthquake Scenario. 

This scenario was the most comprehensive earthquake scenario ever devised, postulating a 

hypothetical magnitude 7.8 earthquake on the southern section of the San Andreas Fault. The 

scenario document (Jones and others, 2008) examined in detail the geophysical, physical, and social 

implications of a massive earthquake. This scenario was created by a team of more than 300 

scientists and other experts. The ShakeOut scenario served as the centerpiece of the 2008 Great 

Southern California ShakeOut, the largest earthquake preparedness drill ever, with over 5.4 million 

participants. The ShakeOut is now an annual statewide event and the basis of the federal and state 

Catastrophic Earthquake Plan. 

 By postulating a hypothetical catastrophe, scientists and engineers can better examine the 

interdependencies in our social structure and infrastructure and expose the choke-points and 

vulnerabilities. In one of many examples in the ShakeOut scenario, we learned that all lifelines into 

and out of southern California cross the San Andreas Fault, most notably electrical transmission 

lines, oil and natural gas lines, water conveyance, telecommunications, highways, and railroads. 

Everyone talks about The Big One, but what exactly does "The Big One" mean?

Californians understand to some extent their vulnerabilities to earthquake. The idea of “the Big 

One” is ubiquitously understood to mean a very large earthquake that California will eventually 

experience. For many people this event exists in imagination extrapolated from movies and 

possibly from personal experience in damaging earthquakes they have actually lived through, such 

as the 1989 Loma Prieta or 1994 Northridge earthquake. Having personal experience and an 

awareness that a much larger earthquake has and will occur, helps people understand the need for 

earthquake risk mitigation. Both elements—personal experience and a cinematic or other basis for 

extrapolation—are largely missing from the public’s understanding of catastrophic winter storms. 

Storms in the public’s own experience have caused inconvenience but not major societal impacts. 
2

So although potentially catastrophic storms have occurred in the past, these storms are beyond 

living memory, and so are less real to many people. Storms also are less sudden, less dramatic, and 

thus loom smaller than earthquakes do 

in the imagination of risk. But the 

evidence shows these storms do pose a 

real risk to California, in some ways far 

greater than that of earthquakes. One 

sequence occurred almost 150 years 

ago. 

Winter storms of 1861-1862. 

Beginning in early December 1861 and 

continuing into early 1862, an extreme 

series of storms lasting 45 days struck 

California. The storms caused severe 

flooding, turning the Sacramento Valley 

into an inland sea, forcing the state 

capitol to be moved temporarily from 

Sacramento to San Francisco, and 

requiring Governor Leland Stanford to 

take a rowboat to his inauguration. 

William Brewer, author of "Up and 

down California," wrote on January 19, 

1862, "The great central valley of the 

state is under water-the Sacramento 

and San Joaquin valleys-a region 250 to 

300 miles long and an average of at 

least twenty miles wide, or probably 

three to three and a half millions of 

acres!" 

 The 1861-62 series of storms 

were the largest and longest California 

storms in the historic record, but were 

probably not the worst California has 

experienced. Geological evidence 

indicates that floods that occurred 

before Europeans arrived were bigger. 

Scientists looking at the thickness of sediment layers collected offshore in the Santa Barbara and 

San Francisco Bay areas have found geologic evidence of megastorms that occurred in the years 

212, 440, 603, 1029, 1418, and 1605, coinciding with climatological events that were happening 

elsewhere in the world. There is no scientific evidence to suggest that such extreme storms could 

not happen again. 

 To demonstrate and prepare people for the risks associated with an event analogous to the 

1861-62 series of storms, the MHDP began the ARkStorm scenario on October 28, 2008. As with the 

ShakeOut earthquake scenario, the MHDP and its many contributing scientists created a 

hypothetical, but scientifically defensible storm scenario and then in detail examined the risks 

associated with that storm, including the potential impact on our buildings, infrastructure, water 

supply, transportation, agriculture, environment, and economy. 

About the storms of 1861-62, Marcia Eymann, History 

Manager, Center for Sacramento History, writes:

Some capital-city residents opted to ignore the obvious danger 

and attempted to enjoy the perceived novelty of the event. 

Historians Thompson and West write that “every balcony was 

crowded with spectators, and mirth and hilarity prevailed. 

However hard these citizens tried to enjoy the flood, they soon 

found it difficult to do so in the face of so much destruction. 

The levees remained intact, trapping flood waters inside the 

city. Residents were subject to hurricane-force winds and icecold, muddy water. The chain gang was charged with the 

dangerous task of breaching the R Street levee to relieve 

Sacramento of the excess water. Once it was breached, the 

force of the rushing water was so great that it took twenty-five 

homes with it, some of which were two stories tall. Sacramento 

remained under water for three months while four hundred 

families were left homeless and five thousand people were in 

need of aid. 

San Francisco preacher S.C. Thrall explained that the great 

storm’s visitation to California was simply God’s way of 

punishing the nation for the sins of greed and pride. In early 

1862 he proclaimed, “He who visited the nation with war, has 

smitten us with flood ... That this calamity is our part of the 

punishment of national sin seems especially evident from the 

fact that the visitation is so precisely coincident with the portion 

of our inhabited territory which has escaped the consequences 

of war.”

In southern California lakes were formed in the Mojave Desert 

and the Los Angeles Basin. The Santa Ana River tripled its 

highest-ever estimated discharge, cutting arroyos into the 

southern California landscape and obliterating the ironically 

named Agua Mansa (Smooth Water), then the largest 

community between New Mexico and Los Angeles. The storms 

wiped out nearly a third of the taxable land in California, leaving 

the State bankrupt. 
3

 This is the ARkStorm. This document summarizes the environmental effects, physical 

damages, economic and other losses in California as a result of the hypothetical flooding and high 

winds associated with the ARkStorm scenario. ARkStorm is an emergency planning scenario 

associated with a hypothetical severe winter storm striking California, imagined to begin on 

January 19, 2011. The scenario was designed by a collaborative group led by the U.S. Geological 

Survey, California Geological Survey, and others, under the authority of the U.S. Geological Survey 

Multi-Hazards Demonstration Project for Southern California. 

ARkStorm Meteorology 

 We begin with a brief history of extreme weather in California, touching on the historic 

precedent supporting the ARkStorm realism, especially the 1861-1862 severe storms that caused 

inundation throughout northern and southern California (fig. 1). These storms, and indeed most 

severe precipitation in California, were probably the result of a phenomenon termed atmospheric 

rivers, jets of warm moist air that originate over the mid-latitude north Pacific Ocean and transport 

that moisture to California where much of the moisture turns to rain and snow that falls on the 

state (fig. 2; http://www.noaanews.noaa.gov/stories2005/s2529.htm) 

 

Figure 1. K Street, Sacramento, looking east, in January or February 1862. (Photographers 

Lawrence and Houseworth, The Bancroft Library Pictorial Collection, University of California, 

Berkeley) 
4

Figure 2. This map of the Pacific region shows an Atmospheric River originating over the central 

Pacific on February 16, 2004, indicated by high (green) vertically integrated water-vapor contents, 

in grams per square centimeter of water vapor, in the atmosphere extending from around Hawaii to 

the central California coast near the town of Cazadero (CZD). 

 These atmospheric rivers, the meteorological conditions that produce them, and the 

resulting precipitation and winds that affect California, can be simulated by using computer models. 

These models are based on observations of atmospheric conditions, plus laws of fluid dynamics and 

thermodynamics that allow us to fill in the gaps between observations. 

 This technique was done for ARkStorm. The modeling was led by Mike Dettinger and Marty 

Ralph of the USGS and National Oceanic Atmospheric Administration (NOAA), respectively, along 

with a team of 13 others from Scripps Institution of Oceanography, the National Weather Service, 

the California Extreme Precipitation Symposium, Golden Gate Weather, San Francisco State 

University, the Western Regional Climate Center, and the California Department of Water 

Resources. For technical details of this modeling, Dettinger and others have a paper in progress (M. 

Dettinger, written commun., 2009). 

 The modelers employed the Global Climate Model (GCM)—a computer model that depicts 

the climate of the world over time at a fairly large scale, on the order of 150 kilometer (km) 

horizontal grid—and nested within a portion of the model over California they used a detailed 

climate model termed the Weather Research and Forecasting (WRF) model, which depicts weather 

in nested domains each resolving smaller scales (fig. 3). From innermost to outermost boxes, the 

grid spacings are 2 km (black box), 6 km (black), 18 km (blue) and 54 km (red) (Dettinger and 

others written commun., 2009). Encoding the laws of fluid dynamics and thermodynamics in 
5

equations that operate on meteorological parameters such as temperature, pressure, moisture 

content, and windspeed, the model calculates these parameter values at each grid point in the 

model and at each time step (here, about 30-second increments) during whatever duration is of 

interest. One can record all the parameter values at each grid point and time step, but for practical 

purposes, it is generally only necessary to record certain key parameters at larger time steps for 

later use, such as to make the maps shown later. 

 

Figure 3. Weather Research and Forecasting (WRF) model domains used in simulations of 

ARkStorm meteorology, with nested model domains indicated by boxes and the topography 

resolved by the grids in each domain indicated by contours. Grid spacings are 2 km in smaller black 

box, 6 km in larger black box, 18 km in blue area, and 54 km in red area. 

 To use the GCM and WRF models requires one to establish what are called boundary 

conditions, meaning the constraints or inputs at the temporal and spatial boundaries of the model. 

To have any faith in the boundary conditions and the model results, it also helps to have some 

actual physical observations with which one can compare the model's output. Because the 1861-

1862 storm occurred at a time before extensive detailed and generally reliable measurement of 

precipitation, barometric pressure, and wind speeds, we did not attempt to model the 1861-1862 

storms directly, which would have required too many arbitrary assumptions, and instead simulated 

a repetition of two actual storms in recent history for which boundary conditions are known. In 

particular, the ARkStorm is a hybrid of a storm that struck southern California from January 19-27, 

1969, followed without delay or interruption by a repetition of the storms that struck northern 

California from February 8-20, 1986, (fig. 4). That is to say, the GCM and WRF models were used to 

calculate and record the windspeed, barometric pressure, precipitation, and other weather 

parameters at each grid point on an hourly basis for 217 hours of a hypothetical storm that merges 

these two real events. The ARkStorm adds to these two storms a 24-hour period at the height of the 
6

January 1969 storm in which the storm is imagined to stall, so as to produce a sufficient amount of 

precipitation to approximately match the limited observations of 1861-1862. 

Figure 4. ARkStorm stitched-storm calendar, with moderately wet conditions of autumn and early 

winter “preconditioning” the watersheds of California for rapid flood generation indicated by blue 

shading, followed by the two intense-storm periods that were combined to make up the ARkStorm 

scenario indicated in red shading. These calendars represent actual storms of 1969 and 1986, 

which provided the basis for the ARkStorm modeling. 

 Using the modeled ARkStorm precipitation and temperatures, along with a macro-scale 

hydrology model termed the variable infiltration capacity model, the research team estimated the 

runoff generated by the ARkStorm, on an approximately 8-km grid throughout most of the state. 

Here, runoff means the rainfall that neither seeps into the ground or flora nor evaporates, but 

instead runs overland toward streams and ultimately the Pacific Ocean. 

 The meteorology team compared this modeled ARkStorm runoff with extreme-value 

statistics of runoff generated by the model for water years 1916-2003. In particular, the team fit a 

type-III log-Pearson parametric distribution to the yearly maximum 1- 3- and 7-day runoff volume 

in each grid cell, by using the statistics of these 87 years of simulation. One can compare the 

ARkStorm runoffs to this distribution to find the approximate return period of ARkStorm runoff in 

each grid cell. By return period, we mean the average number of years one would have to wait to 

observe storms generating at least that level of runoff. The calculation requires one to assume that 

the period 1916-2003 is representative of the future (reasonable, although climate change makes 

the assumption increasingly questionable the farther into the future we project), and that the type-
7

III log-Pearson parametric distribution is a reasonable approximation of the true probability 

distribution of runoff volume (a common assumption). Given this model, as shown in figure 5, 

ARkStorm produces runoff with a return period that varies between 10 years and 1,000 years, 

depending on location, relative to an historic simulation of water years 1916-2003 (Dettinger and 

others, written commun., 2009). Bear in mind that a storm can produce very high, rare runoff in one 

location and very low, commonly observed runoff in another, and no directly produced runoff in a 

third, so the runoff return period varies spatially for any given storm. 

 

Figure 5. For locations around California, colors depict recurrence intervals in years of maximum 

3-day runoff during the ARkStorm scenario. 

FLOODING 

 The runoff map was interpreted to produce a map of flooding. The map was generated by a 

team led by Justin Ferris of the USGS, with 14 others from the USGS, University of Colorado, Federal 

Emergency Management Agency (FEMA), the Michael Baker Corporation, California Department of 

Water Resources, and NOAA. Note that the FEMA and Michael Baker Corporation representatives 

are currently responsible (at least during the period October 2009-April 2010) for accrediting 

levees in California for purposes of establishing digital flood information rate maps (dFIRMs). 

 Although a statewide analysis of the expected runoff is estimated by the meteorology 

modeling effort, this runoff is calculated on an 8 km grid, which is insufficient to estimate the runoff 

at specific locations with a great level of confidence. Ideally we would have performed a detailed 

statewide hydrological and hydraulic analysis for the storm to estimate flooding. However, there 

are two key challenges to such an approach that we could not overcome during this project: (1) no 

such model currently (2010) exists, and we could not create one within the available time and 
8

budget, and (2) a number of variables cannot be modeled: for example, levee breaks occur in real 

storms, but the first such occurrence invalidates the current (2010) routing models. Existing 

models (2010) are of (1) such a comparatively small scale, (2) mutually incompatible in terms of 

input/output, and (3) not designed to function with such large and supercritical flows as to render 

their inclusion in this study effectively useless. 

 Figure 6 illustrates the first point: this figure shows the California Department of Water 

Resources web-based Integrated Water Resources Information System (IWRIS). The mesh shows 

the extent of the C2VSIM hydrologic model, which encompasses the Central Valley of California and 

is used for water planning purposes. The model covers the other three hydrologic and hydraulic 

models encoded in IWRIS. Note the lack of coverage of San Diego, Orange, Los Angeles, San 

Bernardino, and Santa Clara Counties, which are potentially among the most seriously flooded. 

However, even if the C2VSIM model had a hydraulic component and could handle the volume of atsite runoff produced by the meteorological models (that is, non-steady, supercritical flow 

conditions), the few other smaller models we identified would not be able to process the output 

from the C2VSIM model. The lack of a common set of input/output parameters between the 

existing models for the State of California prevented the usage of those models for even a part of the 

state. In other words, the numeric models we identified could not communicate with each other, 

and illustrated a need for either a statewide hydrologic and hydraulic models, or at least the 

establishment of a common protocol for model inputs and outputs that would allow smaller, local 

models to work together to simulate larger flood events. 

We, therefore, used the FEMA dFIRMs as proxies. Two kinds of proxies are available: one 

that shows the boundaries of flooding with 500-year return period, and one that shows boundaries 

of flooding with 100-year return period. The reader should understand that “return period” refers 

to the average number of years between similar events. Return period does not mean that these 

levels of flooding happen like clockwork regularly every 100 or 500 years. Moreover, the return 

period does not mean that danger is over if a 100- or 500-year flood has just occurred. Rather, a 

100-year return period simply means that the probability that such flooding will occur next year is 

estimated to be 1 in 100, or 1percent probability. Similarly, flooding with 500-year return period 

has an estimated 0.2 percent probability of occurrence next year. 

 In hydrologic-unit-code-6 watersheds (HUC6, fig. 7) where the runoff map indicated runoff 

on the order of 250 to 1,000-year return period, we hypothesized that the ARkStorm could 

realistically generate flooding that fills the 500-year dFIRM boundaries. In HUC6 watersheds where 

the ARkStorm runoff map shows runoff with return period between 25 and 250 years, we 

hypothesized that the ARkStorm could realistically generate flooding that fills between 10 and 30 

percent of the 100-year dFIRM boundaries. 

 The 10 to 30 percent of the 100-year boundary that is to be considered inundated is often 

that part of the floodplain that is closest to the river, but subject to some judgment, based in part on

FEMA staff's knowledge of the state flood protection system. Given that this method is an 

approximation, admittedly the designation of certain parts of the floodplain as inundated will be 

viewed by some as arbitrary. We readily acknowledge this shortcoming, but feel this model gives 

the best available approximation of the inundated area. A more-rigorous determination of 

inundated area would require the translation of the estimated runoff value at each location to an 

area of inundation based on detailed analysis of local hydrology and hydraulics, which was beyond 

the funding and time scope of this exercise. 
9

Figure 6. This snapshot of available flood models shows that various models cover the Central 

Valley (gridded area) but not the rest of the state (California Department of Water Resources, 

2010a). 

 We added the 500-year dFIRM floodplains in HUC6 watersheds to those areas with 

approximately 500-year runoff, producing the flooding map shown in figure 8. This and other 

Google Earth maps were made available for interactive inspection by project participants. FEMA 

and USGS personnel reviewed the resulting map and found it to be realistic. 

 Other experts disagree with the flooding map we generated, one particularly with regard to 

flooding in Sacramento, another with regard to flooding in the California Delta. We found their 

concerns to be valid, especially with regard to the need for more thorough hydrologic and hydraulic 

analyses, though as discussed earlier such analyses were not practical for this study. However, after 

considering the particulars of their concerns, which are not detailed here, we judged their concerns 

valid but not compelling enough to invalidate the ARkStorm flood map that we had previously 

generated. 
10

 After the areas of inundation were decided upon, the flooding panel applied its knowledge 

of the local hydrology and hydraulics to estimate, for each HUC6 watershed, peak depth and 

duration of flooding (fig. 7). The panelists split two watersheds (Lower Sacramento and San 

Joaquin) into 3 smaller zones each, and estimated depth and duration for each of the 6 zones. Flood 

extents, depths, and durations were documented in ARCGIS and Google Earth KMZ files and other 

media, and used in later discussions and analyses

 

Figure 7. Hydrologic Unit Code 6 watershed boundaries, with ARkStorm flooding parameter 

values, depth of flooding and length of time flooded. 
11

 

Figure 8. Blue areas indicate ARkStorm flooding as projected by models used in the scenario. 

WINDSPEED 

 The windspeed time series generated by using the Weather Research and Forecasting 

(WRF) model were processed as follows. The time series contain the 30-second average windspeed 

at the beginning of each hour, at 10-meter elevation, at each of 59,580 grid points, over a 9-day 

period. For each grid point, the maximum value of the hourly samples is interpreted as the 

maximum 30-second gust velocity at that site. (The calculated maximum probably occurred during 

the hour.) Most structures tend to be sensitive to shorter-duration and more-intense gusts, 

therefore, we multiplied the maximum 30-second gust velocity by the ratio of 3-second gusts to 30-
12

second gusts, based on the work of Vickery and Skerlj (2005, fig 2). This ratio is approximately 1.18. 

The results were converted into Google Earth (KMZ) files as shown in figure 9. The figure shows 

peak gusts of 50 mph throughout much of the state, reaching as high 125 mph in mountainous 

regions. 

 

Figure 9. ARkStorm peak 10-meter elevation, 3-second gust wind speeds. 

Coastal Inundation 

 The time-series of sea surface pressure, wind speed, and wind direction generated by the 

ARkStorm meteorological model provided boundary conditions for a complex, process-based 

numerical modeling system for simulating the impact to the Southern California coast, stretching 

473 km from the Mexican border to Point Conception (fig. 10; KMZ project file available on 

request). The objective was to use a physics-based approach to identify the location and magnitude 

of the potential coastal hazards during the simulated storm. The Coastal Storm Modeling System 

(CoSMoS) was developed for the ARkStorm to incorporate atmospheric information (that is, wind 
13

and pressure fields) with a suite of state-of-the-art physical process models (that is, tide, surge, and 

wave) to enable detailed prediction of currents, wave height, wave runup, and total water levels for 

mapping the distribution of coastal flooding, inundation, erosion, and cliff failure. The Google Earthbased product output of CoSMoS is designed to provide emergency planners and coastal managers 

with critical information to increase public safety and mitigate damage associated with powerful 

coastal storms. Further details of the CoSMoS framework can be found in Barnard and others 

(2009). The Digital Elevation Model (DEM) serving as the morphological boundary condition is 

described in Barnard and others (2009). 

Figure 10. Extent of the CoSMoS model applied to the ARkStorm scenario. See Barnard and others 

(2009) for details and additional maps. 

 Findings. Figure 10 highlights locations of moderate and high wave damage potential 

(yellow and red squares, respectively) and moderate and high cliff failure potential (yellow and red 

triangles). The summary map shows that severe wave damage potential is predicted on the mostly 

west-facing beaches in Los Angeles and northern San Diego Counties, and the oil platforms in the 

western part of the Santa Barbara Channel. The coastal infrastructure that appears most at risk of 

severe wave damage includes the Manhattan, Hermosa, Venice and Imperial Beach piers, as well as 

coastal structures (for example, groins, jetties, seawalls) in the Los Angeles International Airport 

(LAX) region, and along Highway 1 in northern San Diego County. Sewage infrastructure near LAX 

(for example, Hyperion Treatment Plant) also appears vulnerable. Coastal flooding, resulting from 

the combined factors of tidal elevation, storm surge, and wave set-up, is most extensive and 

potentially damaging for southern Oxnard and Mugu Naval Air Station, Marina Del Rey, the Ports of 

Los Angeles and Long Beach (fig. 11), Seal Beach (fig.12), Del Mar (note: race track flooding), 

Mission Bay (fig.13) and Coronado and Imperial Beaches (fig. 14). Drastic shoreline change (beach 

erosion) induced by the ARkStorm conditions could lead to significant damage to public and private 

infrastructure, including the following regions: Imperial Beach, La Jolla, Del Mar, Solana Beach, 
14

Carlsbad, Malibu, Santa Clara River mouth (for example, McGrath State Park), Rincon Parkway, 

Carpinteria, and Isla Vista (for example, University of California at Santa Barbara). The cliff failure 

pilot project in Santa Barbara only identifies a few sites with major cliff failure potential, but one of 

those sites is immediately adjacent to the Summerland Water Treatment Facility.

Figure 11. CoSMoS-estimated coastal inundation at the Ports of Los Angeles and Long Beach. 

Figure 12. CoSMoS-estimated coastal inundation at Seal Beach. 
15

Figure 13. CoSMoS-estimated coastal inundation at Mission Bay, San Diego. 

Figure 14. CoSMoS-estimated coastal inundation at Coronado and Imperial Beaches. 
16

RESEARCH NEEDS RELATED TO COASTAL INUNDATION 

 Elevation data. The model relies on high-resolution, coastal elevation data (LIDAR and 

multibeam bathymetry). The available data could be brought up to date and expanded to cover a 

greater area. 

 Model development. We believe it would be beneficial to test and validate physical process 

models for the U.S. West Coast. It would also be beneficial to develop integrated modeling systems 

for easily assimilating atmospheric forcing data. 

Landsliding 

 Landslides in California, triggered by historic storms, have caused hundreds of millions of 

dollars in damage and numerous casualties. Buildings, roads, pipelines, and other structures have 

been damaged by being entrained in, deformed by or as a result of the inertial impact of material 

flowing or sliding downslope. Landslides can be classified by the type of earth material moving and 

by the mode of movement (Cruden and Varnes 1996). For the ARkStorm scenario, we focus on two 

general modes of landsliding: (1) Large, deep-seated, slow-moving landslides usually classified as 

either rock slides or earth flows; and (2) Small, shallower, fast-moving landslides, usually classified 

as debris slides or debris flows. Figures 15 and 16 illustrate these end-member divisions of 

landslides. The simple division of landslides into two end member types also can be applied to 

landslide damage and risk, because large, slow, deep-seated landslide cause damage to structures 

and infrastructure while small, shallow, fast-moving landslides often threaten lives. 

 To characterize the potential for landslides in the ARkStorm scenario, we developed two 

maps of landslide susceptibility for California, one for deep-seated landslides and the other for 

shallow slides. We evaluated susceptibility to larger, deep landslides by combining estimates of 

rock strength with slope. Hundreds of mapped geologic units from numerous sources were 

generalized into three rock strength classes according to the approach presented by Wieczorek and 

others (1985). Three rock strength units, which we call hard rock, weak rock, and soil, are 

combined with slope to derive 10 landslide susceptibility classes following the procedures 

developed by Ponti and others (2008) for the ShakeOut earthquake scenario. Locations where past 

landslides have occurred are assigned the lowest value of rock strength. The resulting map of 

susceptibility to deep-seated landslides (fig. 17) shows those areas most likely to experience deepseated landslide damage in the ARkStorm. 

 There are no established techniques to estimate the number of shallow landslides a storm 

will trigger across broad regions. Shallow landslides tend to occur during the most intense rainfalls 

of the storm in steep convergent areas, and involve failure of the soil and weathered bedrock layers. 

A fraction of these failures mobilize as debris flows, mixtures of soil, water, and rock, that sweep 

down steep valleys at high velocities and destroy homes and infrastructure with little warning. 

Historical accounts indicate that storms with more intense, longer lasting rainfalls trigger more 

shallow landslides. Shallow landslide susceptibility models, however, vastly overpredict the 

number of failures actually triggered by historic storms, and do not allow us to forecast the number 

of shallow landslides for a given storm. To evaluate susceptibility to shallow landsliding for the 

ARkStorm simulation, shallow landslide abundance was assumed to increase with rainfall intensity, 

duration, and number of unstable model cells in topographically-based landslide susceptibility 

models. Rainfall intensity and duration were calculated from hourly rainfall records, and the 

shallow landslide susceptibility model SHALSTAB (Dietrich and others, 1995; Dietrich and 

Montgomery, 1998; Dietrich and others, 2001) was used to estimate the number of unstable cells in 
17

a 10-m DEM. Digital maps of historic shallow landslides in southern California (Santa Paula and 

Sunland quadrangles) and northern California (Montara Mountain Quadrangle) were used to locate 

landslide initiation points, and to calculate the fraction of unstable cells that actually failed for a 

given rainfall metric. Landslide abundance for southern California sites was found to increase as a 

power law function of 6-hour rainfall intensity. The hourly rainfall data from the ARkStorm 

simulation was then used to calculate the maximum 6-hour rainfall intensities across the Southern 

California landscape, which was then used to estimate the abundance of shallow landslides that 

would likely be generated by the ARkStorm simulation (fig. 18). 

 Landslides on hill slopes damage buildings and other structures on those slopes, and in 

some circumstances can cause significant damage beyond the hills. Alluvial fans, which underlie 

many urban and suburban areas of California, are built up by repeated deposits transported from 

the mountains in floods. A significant part of those deposits may be the result of debris flows. 

Projecting the run-out areas that could be impacted by debris flows is a developing science. 

Scientists with the California Geological Survey are preparing maps showing areas where relatively 

recent alluvial fan deposits are found, and so could be subject to alluvial fan flooding. This includes 

debris flows and more conventional flood flows and gives a maximum extent of the potential 

hazard. The USGS has developed another approach to estimate the volume of material that could be 

mobilized as debris flows in rainfall following a fire and how far material could flow down channels. 

This approach gives a much more detailed look at a specific aspect of the debris flow hazard, but is 

unavailable to model debris flows over an area of the size that could be impacted by the ARkStorm. 

Although we have not attempted to estimate the amount of damage that could result from debris 

flow runout and alluvial fan flooding, the evaluations prepared for the ARkStorm scenario show the 

areas potentially impacted by this hazard. 

 Having established statewide susceptibility maps for deep-seated and shallow landslides, 

this study considered the risk these potential landslides pose to the constructed environment. 

However, landslide damage cost information is very limited, and this part of the ARkStorm 

evaluation was projected from a few small datasets. The most detailed and complete available data 

on landslide damage to buildings was gathered by the City of Los Angeles following the 1978 

storms. The damage cost reported in that dataset is estimated for each locality, and the type of 

landslide involved is described. This dataset shows the majority of the building damage from 

“surface slump” or “rotation,” with a relatively small fraction due to “mudflow.” Though limited, 

these data were extrapolated to the rest of the state and compared with susceptibility maps for 

deep-seated landslides. 

 The susceptibility map for deep-seated landslides was generalized to give a single value of 

susceptibility for each census tract. A “loss ratio” was calculated for each census track as the cost of 

landslide damage from the 1978 storms divided by the value of light wood frame structures within 

the census tract. Using the median susceptibility for each census tract provides three general 

categories of damage:(1) tracts with a median susceptibility of 0 to 3 have no landslide damage, (2) 

tracts with median susceptibility of 5 or 6 have loss ratios of about 0.016 percent, (3) and tracts 

with median susceptibility of 7 or above have loss ratios of 0.096 percent. The susceptibility map 

was converted to a landslide loss ratio map by using these values, (fig. 19). These loss ratios are for 

the average storm intensity and landslide vulnerability of Los Angeles in 1978; projected statewide. 

Generalization to other storm intensities is possible by using data from the San Francisco Bay Area. 

Damage from the 1982 storm in Santa Cruz County was over 3 times our loss ratio projection from 

the 1978 data, while damage in Sonoma County was only 5 percent of our projection. This probably 

reflects regional variation in the intensity of the 1982 storm, which had a recurrence interval of 

over 100 years in Santa Cruz, but only about 10 years in Sonoma County. Using this tentative 
18

relationship, the ARkStorm is estimated to cause at least three times our projections of building 

damage from the 1978 data, similar to the relatively rare, very intense 1982 storm in Santa Cruz 

County. 

 Data on landslide damage to roads, pipelines, and other infrastructure is similarly sparse 

and inconsistent. The relatively complete records kept by California Department of Transportation 

(Caltrans) over the past decade give some perspective on the cost of landslides to roads. Based on 

records of “emergency opening” costs, Caltrans spends $20-40 million on landslide repairs in a 

typical year. In wet years the cost jumps to about $150 million. Projecting from these data to the 

amount of rainfall in the ARkStorm suggests costs of about $300 million. In addition to “emergency 

opening,” Caltrans plans and budgets for long-term landslide repair projects. The average cost of 

these projects over the past decade has been about equal to the cost of “emergency opening”. 

Landslide damage to local roads, pipelines, electric transmission lines and other infrastructure has 

been greater than the damage to state highways in past storms (Crovelli and Coe, 2009). 

 Despite the major data gaps and the broad generalizations that resulted, our best estimate 

is that the ARkStorm could cause tens of thousands of landslides, the vast majority of them debris 

flows, and cost on the order of $3 billion. This estimate includes about $1 billion in damage to 

private property, $1 billion to state highways, and $1 billion to other infrastructure. Costs because 

of debris flow runout, which cannot be estimated at this time, and indirect costs because of 

disruption of infrastructure and other indirect damage will multiply these direct losses. 

Figure 15. Categories of landslide treated by ARkStorm (Wills and others, 2001 modified after 

Varnes, 1958 and Colorado Geological Survey, 1988) 
19

 

Figure 16. Deep-seated landslide in Ventura Calif., January 2005 (left, photo credit: J. Stock, USGS), 

and 1982 debris flow in Pacifica, Calif., that killed 3 (right, photo courtesy of Woodward Clyde 

Consultants). 
20

Figure 17. Deep-seated Landslide Susceptibility Map of California. Black lines denote county 

boundaries. Higher numbers and hotter colors indicate greater susceptibility to deep-seated 

landslides. 
21

Figure 18. Quaternary, Pliocene, and Miocene sedimentary rocks (hachured area) superimposed 

on the forecast for landslide abundance resulting from the ARkStorm. Cells in the hachured area 

have rock types similar to the calibration area at Santa Paula. Cells outside these zones likely 

overestimate the number of landslides because (1) rock units produce stronger soils and (2) 

different processes (for example, rock fall) dominate erosion. Gray areas have no unstable cells in 

10-meter data. Calibration (Santa Paula) and test (Sunland) areas shown by white polygons. 
22

Figure 19. Deep-seated Landslide susceptibility expressed as the median value for each census 

tract, which can be related to loss ratio for that tract. 

RESEARCH NEEDS RELATED TO LANDSLIDING 

 Our ability to understand and model landsliding and its consequences would benefit from: 

Accurate digital mapping of historic landslides; 

Accurate digital mapping of historic and pre-historic storm damage; and 

Methods for estimating damage from debris-flow runout and alluvial fan flooding. 
23

Three Approaches to Estimating Damage 

 To help inform our damage estimates, we compiled an extensive database of historic storm 

impacts in California, including summaries of news reports and newspaper images from 1861-

1862, 1938, 1969, 1986, and 1997. Many of these reports and images were geolocated and placed in 

Google Earth files for interactive examination. The database is too extensive to copy in this report; 

the database was made available for examination by ARkStorm panelists and scenario developers. 

 We estimated physical damages in the ARkStorm scenario by three different approaches. To 

produce a realistic outcome at the aggregate societal level, we employed the data and methods of 

the FEMA flagship emergency-planning software HAZUS-MH. To examine the effects of the 

ARkStorm on lifelines such as the highway network, power, and water, a series of 12 panel 

discussions were held with engineers, operators, and emergency planners from the various lifelines 

at risk. The panel participants were presented with the meteorological and flooding inputs, and 

offered their judgments about the resulting damage and restoration efforts that they would 

undertake if ARkStorm really occurred. Finally, three studies were performed by individual experts 

on topics requiring highly specialized knowledge: demand surge, telecommunications, and 

insurance. 

 To be clear: we did not apply all three approaches to each type of asset at risk. Only one 

approach was used for each particular kind of asset: lifelines damage was estimated based only on 

the expert opinion of the lifeline operators themselves, building damage was estimated by using 

only the HAZUS flood methodology, and three consultant reports were commissioned on specific 

topics. We begin by presenting lifeline panel findings, then present HAZUS-based property loss 

estimates, and conclude with the special studies. 

Lifeline Panel Process 

 In January and February 2010, scientists and engineers developing the ARkStorm scenario 

convened 12 half-day meetings in Pasadena, Sacramento, Menlo Park, and San Francisco with 

officials of 43 federal, state, local, and private agencies, utilities, and universities listed in the 

Acknowledgments section of this report. They were from departments of transportation, water and 

wastewater service providers, power utilities, public-health and public-safety agencies, and entities 

that own, maintain, or regulate dams and levees. 

 Panels were convened on four topics: roads and highways; dams and levees; power; and 

water and wastewater. Each topic was addressed by a different panel in each of these general 

locations: Pasadena (for participants based in southern California), Menlo Park and San Francisco 

(for the San Francisco Bay Area), and Sacramento (for Central Valley participants). We sent 229 

invitations; approximately 85 people attended the panel discussions. 

 The purpose of the lifeline panels was fourfold: (1) to educate lifeline operators of the 

potential for and impacts of an event like an ARkStorm, and thus indirectly to stimulate discussion 

within their organization about enhancing disaster resiliency; (2) to gather panelists’ expert 

opinion as to the physical damage of the hypothetical storm and other impacts to the facilities they 

operate; (3) to gather their expert opinion about the time it realistically could take to restore their 

facilities, considering among other things lifeline interaction; and (4) to identify important research 

needs highlighted by the ARkStorm. 

 Participants were invited by email and follow-up phone calls, generally beginning 2 to 4 

weeks in advance. The invitations also included a brief abstract summarizing the ARkStorm 
24

scenario and URL and credentials for downloading the Google Earth maps of windspeed, flooding, 

and historic impacts. 

 Each meeting generally followed the same agenda: 1.5 hours in which the ARkStorm 

development team summarized the material presented above, plus findings of panels that had 

already met, followed by 2.5 hours in which the group discussed the assets exposed to damage, the 

agents of damage, a realistic damage scenario, a realistic restoration scenario, and a discussion of 

opportunities to enhance resiliency either by strengthening facilities or by better responding to 

damage so as to minimize the negative impacts of damage. We made audio recordings and took 

notes of the discussion. After the panel meetings, we synthesized the notes and filled gaps through 

literature searches and additional analysis, as discussed below. 

 A note regarding methodology: We considered but dismissed the notion of following a 

formal process for eliciting expert opinion such as a Delphi Process (Dalkey and others, 1970). 

Considering the quantity of information we needed, such a process appeared likely to be 

excessively time consuming and likely to be off-putting to panelists. An important advantage of the 

Delphi process is that it prevents a few individuals from dominating the discussion. On the other 

hand, the same end can be achieved if the moderator is trained in group communication and 

deliberately seeks discussion from all panelists, including the quieter ones. Our less-formal 

approach seemed more suited to meet our objectives, particularly of stimulating discussion with 

panelists’ organizations, although this approach may lack scientific rigor. 

Highway Damage 

HIGHWAY FACILITIES AND SOURCES OF DAMAGE 

 The state of California has more than 50,000 miles of highway and freeway lanes, managed 

by Caltrans. According to the National Bridge Inventory, there are more than 23,000 bridges in the 

state. The primary perils to highways are landslides either burying or undermining them; floods 

inundating them; and clogged culverts causing flooding and erosion where the water washes over 

the roadway and onto the soil beyond. The primary causes of bridge damage in severe winter 

storms are scour undermining the foundations of bridge piers or abutments and hydrodynamic 

pressure at the upstream edge of the bridge superstructure (the girders, driving surface, and crash 

barriers). 

HIGHWAY DAMAGE AND RESTORATION SCENARIO 

 Since this slope-stability research was ongoing at the time of the panels, we applied the 

judgment of ARkStorm research staff and of Caltrans panel participants to select locations where 

landslides bury or undermine roadways, or block culverts and cause flooding and erosion. Likewise, 

because streamflows were not calculated at each bridge location, we applied judgment to select 

bridges to hypothesize as damaged by scour at the substructure or hydrodynamic pressure on the 

superstructure. Locations of inundation were identified by overlaying the flooding map on the 

roadway network. The resulting hypothetical damage is mapped in figure 20, which distinguishes 

highway impacts as resulting from either: (1) debris flow; (2) flooding, (3) both flooding and 

erosion, and (4) landsliding. 
25

 Restoration time was estimated depending on the type and extent of damage. Hypothesized 

duration of flooding dictates how long roads are inundated and impassible. Debris flows can be 

cleared relatively quickly, within hours of equipment and repair crews arriving. However, on routes 

with large numbers of debris flows the time to clear each flow adds up, and the importance of the 

route—how much traffic normally carried and the availability of alternate routes—matters greatly 

to how routes are prioritized for clearing. Bridges with severe foundation damage or displaced 

spans can take months to repair or replace. Roads over deep-seated landslides can be partially 

restored quickly by regrading and reducing the speed limit, but permanent repair could take 

months. 

 With these considerations in mind, the highway panels estimated the number of days 

required restore each route to 25 percent, 75 percent and 90 percent capacity, beginning after the 

peak of each storm, that is, January 27, 2011, in southern California, and February 9, 2011, in 

northern California. For present purposes, southern California comprises counties including and 

south of Santa Barbara, Ventura, Los Angeles, and San Bernardino. Northern California comprises 

all the counties north of these counties. 
26

 

Figure 20. Causes of ARkStorm cumulative highway damages. Red indicates debris flow, blue 

indicates flooding, green indicates flooding and erosion, yellow indicates landslide other than 

debris flow. 

 To present route capacity at arbitrary points of time, we fit smooth curves to the 3 assigned 

capacity points (25 percent, 75 percent, and 90 percent), on a route-by-route basis. For example, 

we found values a and b to most closely fit a curve of the form y = aln(x) + b where y is highway 

route capacity as a fraction of full capacity, x is time in days after the storm (that is, after January 27 

or February 9, in southern and northern California, respectively), and a and b are adjustable 

parameters fit to the 3 restoration points. Because 25 percent capacity approximately represents 

opening of the first lane of a highway, any calculated capacity less than 25 percent is taken as 

closed, that is, 0 percent capacity. The 90 percent capacity is representative of normal conditions 

because highways rarely operate at 100 percent capacity. The results are shown in the following 

maps. Figure 21 shows route capacity 3 days after the peak of the southern California part of the 
27

storm, on January 30, 2011. Figure 22 depicts capacity on day 14, February 10, 2011. The capacity 

after 30 days (February 26, 2011) is shown in figure 23. Figure 24 is capacity after 3 months; Figure 

25 after 6 months; and Figure 26 is route capacity 1 year after ARkStorm, showing that some routes 

(especially Route 1) are still undergoing repairs because of large deep-seated landslides. A 

spreadsheet and GIS files of these data were made available on a password-secured website for 

panelists’ use in interpreting and revisiting highway damages.

 Note that the figures show that the storm largely cuts off traffic from Los Angeles to the 

north and east for 1-2 weeks, with gradual recovery. The same is true of Sacramento: traffic to the 

north, south, and west is largely cut off for 1 week or so, with gradual recovery thereafter. 
28

 

Figure 21. Highway route capacity on January 30, 2011. 
29

Figure 22. Route capacity on February 10, 2011. 
30

 

Figure 23. Route capacity on February 26, 2011. 
31

 

Figure 24. Route capacity on April 27, 2011. 
32

 

Figure 25. Route capacity on July 27, 2011. 
33

 

Figure 26. Route capacity on January 27, 2012. 

LIFELINE INTERACTION INVOLVING HIGHWAYS 

 Several panels noted how restoring their facilities depend on access. A large water district 

noted that it has critical pipelines that run through steep terrain, vulnerable to landslide, and that 

access to some of these lines is limited. Highway and roadway damage from landslides and bridge 
34

damage could hinder repairs. Wastewater treatment near Long Beach likewise is affected by access, 

primarily by flooding: rising water might force employees at Terminal Island to abandon the 

treatment plant for their own safety. Furthermore, the southern California panel discussing water 

and wastewater noted that a number of water-supply pipelines are carried on highway or roadway 

bridges, some of which are subject to scour and hydrodynamic pressure. 

 Water-supply and wastewater treatment panels noted that they rely on supplies of 

chemicals that are carried on railway and trucks every few days. In the Sacramento panels, for 

example, it was observed that most of the chemicals used by water and wastewater treatment 

facilities are carried over I-80, which would be temporarily cut off. 

LIMITATIONS OF HIGHWAY RESTORATION TIMELINE 

 Three important limitations of this scenario were recognized during the initial panel 

discussions. First, these restoration times may underestimate competition for limited resources. 

Second, these restoration timelines were laid out without full consideration of the needs for 

evacuation. Third, the landslide assessment for northern California was not available at the time of 

the panel discussions. New landslide information might affect the assessment of both damage and 

restoration. Other considerations that were overlooked during the panel discussions also may affect 

restoration. 

OPPORTUNITIES TO ENHANCE HIGHWAY RESILIENCY 

 In discussions with highway panelists, the following possibilities were raised as 

opportunities to prevent highway damage or to recover faster: 

Caltrans could purchase and stockpile Bailey bridges. Bailey bridges are portable, pre-fabricated trusses, 

primarily used by military engineering units to bridge up to 60-meter gaps (200 feet). These bridges do 

not require heavy equipment for construction; can be brought to the jobsite in trucks; and are strong 

enough for heavy traffic. Caltrans owns a few, probably not enough for the need suggested by ARkStorm. 

One panelist suggested that railroad flatcars might serve a similar purpose. 

Preplan detours to enhance redundancy more quickly. 

Prepare to move Caltrans equipment away from vulnerable areas in response to forecasts of severe 

weather. 

Engage the contractor community to prepare for severe storms, for example, beginning with 

conversations with the Association of General Contractors in preparation for Golden Guardian 2011. 

Have contracts in place for rental and repair of pumps, especially large pumps. 

Consider dense string of webcams or CCTV cameras covering the roadway network for real-time virtual 

inspections. 

Install strain gages and pore-water-pressure gages or other monitoring of known, deep-seated landslides. 

Construct a system for 2-way 511 information with the public. USGS is in early conceptual planning of 

such an application called Did You See It? The application or Caltrans would require heavy filtering to 

keep the labor requirements for such a system affordable. 

Plan for emergency housing of Caltrans workers and possibly their families. 

Engage FEMA to allow post-disaster repair to higher standards than original construction. 

Enhance evacuation procedures—consider benefits of earlier evacuations and of identifying evacuation 

centers based on weather reports. 
35

Characterize and communicate uncertainty for better decisions by using weather and climate forecasts. 

Enhance education and outreach—encourage people to have a family evacuation plan. 

Coordinate with construction contractors to pre-position repair equipment. 

Address growing labor limitations, for example, by partnering with cities, cross-training labor force for 

repairs, and identifying potential sources of repair crews. (One panelist mentioned crews from San 

Quentin State Prison) 

Power 

POWER FACILITIES AND SOURCES OF DAMAGE 

 The following power scenario is based on panel discussions with Southern California 

Edison, Los Angeles Department of Water and Power, the Sacramento Municipal Utility District, and 

California Utilities Emergency Association. In addition to these discussions, one of these entities, 

provided a detailed but confidential write-up following its internal considerations of the 

meteorology, flooding, windspeed, and landslide information provided to the panel. These materials 

are supplemented by data provided by utilities in follow-up conversations, and with data available 

in the Homeland Security Infrastructure Program (HSIP) Gold 2007 database, including the 

locations of essential facilities such as substations, power transmission routes, and wastewater 

treatment plants. 

 Note that representatives from Pacific Gas and Electric (PG&E) stated that they were unable 

to contribute at that time and that they would inform us later whether they would be able to 

participate; as of this writing they have not done so. The following, therefore, does not reflect the 

opinions or judgment of PG&E personnel. It does reflect our initial interpretation of the statements 

made during the other meetings, subsequent conversations with representatives of all the other 

lifeline service providers, and a fairly exhaustive review of newspaper accounts of 1986 and 1997 

storm impacts on PG&E facilities, found in the Los Angeles Times, Sacramento Bee, and San 

Francisco Chronicle. 

 Several other power utilities were unable to attend panel meetings or were otherwise 

unable to estimate scenario damage and restoration. To estimate power outage and restoration for 

these remaining service areas, we make several assumptions, based on the panel discussions and 

other evidence cited below. 

Sources of wind damage. According to panel participants, wooden crossbars and polemount transformers on distribution-voltage utility poles can be damaged by wind speeds as low as 

60 miles per hour (mph). Moderate winds also can cause lines to sway, touch, and cause crossphase shorting. Another common cause of wind damage is moderate wind speeds with windborne 

debris such as palm fronds blown onto lines causing shorts. Where winds are stronger, damage is 

more severe. Hurricane-force winds (75 mph and higher) can cause transmission lines to sway and 

cause cross-phase shorting, or cause electrical transmission towers or poles to collapse. 

 The panels did not postulate damage in high-wind regions (75 mph and higher). In Alpine, 

Inyo, Mono, and parts of El Dorado, Placer, Riverside, San Bernardino, and Tulare Counties, winds 

reach 75 to 125 mph. Figure 27 shows where high winds could threaten transmission lines. Note 

especially how high winds on the eastern side of the Sierra Nevada range coincide with the location 

of transmission lines. 
36

 Some documentation is available regarding power outage and restoration in south Florida 

in Hurricane Andrew (Porter and others, 1996). Peak gust velocities in a few Florida locations 

reached 170 mph, but in many places between Miami and Homestead peak gusts were in the range 

100 mph to 125 mph (http://www.nhc.noaa.gov/prelims/1992andfig5.gif), similar to the most 

strongly affected regions in ARkStorm. About 55 percent of Florida Power and Light transmission 

lines were out of service because of Hurricane Andrew, (including 80 percent of the 230 kilovolt 

system and 60 percent of the 138 kilovolt system), along with about 70 percent of the distribution 

circuit miles. It took approximately 5 days for Florida Power and Light to restore service to 90 

percent of its customers, and 30 days to reach 99 percent. Note that electrical facilities in hurricane 

country may be built to different standards in consideration of higher wind loads. 

Figure 27. ARkStorm winds. Yellow indicates maximum windspeed in excess of 70 miles per hour. 

Red lines indicate electrical transmission lines. 
37

 Damage to wooden crossbars and pole-mount transformers was one of the more significant 

causes of system interruption. One panel estimated that 0.2 percent of customers in areas with peak 

gust velocities generally in the range of 45 to 75 mph could lose power because of wind damage to 

distribution poles. (Customers, as used here, are counted in electric-service meters, not inhabitants 

of residences or office buildings. A single-family dwelling, for example, would typically count as one 

customer.) Damage to poles in moderate windspeed areas is restored within 7 days of the storm. 

 We assume that 75 percent of customers in counties with higher winds - in the range of 75 

to 125 mph - lose service, and that repairs would take 7 days to restore to 90 percent of customers 

and 4 weeks to restore power to almost all customers. This restoration curve, shown 

mathematically in Equation (1), is comparable to the restoration estimated in the confidential 

utility study for areas with similar, strong winds. The restoration curve is of the form: 

0

f t C r t 1 exp

(1) 

where 

f(t) is the fraction of customers with power at time t, 

C0 is the initial fraction of customers without power (for example, 0.75), 

t is time in days after the peak of the storm (January 27, 2011, in southern California, February 9, 

2011, in northern California), and 

r is a constant reflecting speed of restoration: 0.05 for very slow restoration and 0.30 for fast 

restoration. A value of 0.25 is used here. 

Sources of flooding damage. Power plants, high-voltage substations (also called bulk 

substations) and control facilities can be sensitive to flooding damage in at least two important 

ways. Flooding can damage control equipment. High-voltage substations and generating plants 

have high-voltage transformers (50 to 200 megawatts (MW) at high-voltage substations and 300 to 

500 MW at generating plants) that also can be damaged by flooding, for example, by flood-borne 

debris impacting the transformer and ancillary equipment. A problem is that these transformers 

are custom made, designed to match impedance at the facility it serves, and each location serves 

fairly large populations: a high-voltage substation for example can serve in excess of 200,000 

people. The transformers are not interchangeable and are too expensive to stockpile backups 

beyond those available for normal operational redundancy. If one of these large transformers were 

damaged, it could take 6 months or more to replace. There is typically some redundancy, enough so 

that at any given high-voltage substation, for example, one of these transformers can be inoperative 

and the substation can still operate. In addition, agreements between utilities allow for the loan or 

sale of surplus or idle equipment in an emergency situation. However, flooding is a common-cause 

failure mode, the implication being that a flood can damage several components simultaneously, 

potentially damaging two or more of these transformers. Were this to happen, the utility would 

have to reroute power around the inoperative substation, which could take a few days, and 

immediately attempt to repair or replace the transformer once dewatering is completed. The 

reliability of the temporary grid layout would be reduced, meaning greater likelihood of power 

outages in the affected area. 

 Flooding also can damage equipment at generation facilities. If demand were high (less 

likely in the winter months in which the ARkStorm is postulated to occur), temporary emergency 

generation such as diesel generators—and the necessary fuel supplies—might have to be brought 

in to serve the affected areas. Figure 28 shows where power plants are located in relation to 
38

flooded areas in four metropolitan areas of the state. While most power plants are located out of the 

flooded areas, some are inside, especially in Santa Clara County and Los Angeles. 

 (a) (b) 

 (c) (d) 

Figure 28. Power plants (green diamonds) overlain with flooding (blue areas) in (a) San Francisco 

Bay Area, (b) Sacramento and Stockton, (c) Los Angeles and Orange Counties, and (d) San Diego. 

POWER DAMAGE AND RESTORATION SCENARIO 

 One panel concluded that power is shut off to all flooded areas, and that electric-power 

utilities will restore power to customers who are affected by flooding shortly after the buildings are 

determined by local authorities to be safe to enter and occupy. We assume that this adds between 1 

and 14 days to the downtime of all facilities with nonzero flood-related downtime. A median value 
39

of 7 days is used throughout. However, since according to the HAZUS-MH flood module the time 

required to restore flooded buildings to operation is measured in months rather than days, 

restoration of power to flooded buildings is judged not to add significantly to their downtime. 

 However, it seems likely that there will be large areas that are not flooded, but that flooding 

of at least a few feet above grade (perhaps 3 or 4 feet) could damage the electrical and electronic 

equipment in the substations that serve the main stations. Of special concern are the high-voltage 

substations, discussed above, which tend to serve a large geographic area (on the order of 200,000 

people). This damage could isolate those neighborhoods, either because the neighborhoods are 

near the end of a transmission or distribution line and lack a redundant path, or because all 

transmission to that area seems to run through substations that are flooded. 

 Using the HSIP Gold geospatial database of transmission lines and substations, we created 

an approximate inventory of the affected substations, and roughly approximated the fraction of the 

population of each county affected by the damage. We assumed that this fraction is without power, 

and that restoration takes up to 4 weeks, and applied the following restoration curve: 

0

0

1

1 exp

f t C t d

C r t d t d

(2) 

where 

C0 is the fraction of services assumed to be affected by substation flooding, 

t is the number of days after the end of the storm (January 27 and February 9, in southern and 

northern California, respectively), and 

d is the duration of flooding. 

 The foregoing considerations lead to the power restoration curves shown in table 1. The 

first column contains the county name, the second is Federal Information Processing Standard 

(FIPS) code. The column labeled peril denotes whether wind (W) or flood (F) dominates the cause 

of power failure to customers able to receive power. Column 4, labeled C0, denotes the estimated 

percentage of customers initially without power after the storm. The remaining columns reflect the 

estimated percentage of customers able to receive power that do have power service, by date. 

Figure 29 illustrates these curves in a few key locations. 

 It is estimated that the material and labor required to repair power facilities could cost 

between $300 million and $3 billion. For present purposes, the scenario posits the cost as the 

geometric mean (meaning the square root of the product) of these two figures, that is, $1 billion. 
40

Table 1. Power restoration (percent of customers receiving power). 

[FIPS, Federal Information Processing Standard; W, wind; F, flood; C0

County

, percentage of customers initially 

without power after the storm; %, percent]

FIPS

code

Peril C0 Power

1/27/2011

2/3/20112/10/20112/17/20112/26/20113/13/20113/28/20114/27/20115/27/20116/26/20117/26/20118/25/20119/24/2011

Alameda 06001W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Alpine 06003W 25.0% 100% 100% 81% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Amador 06005W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Butte 06007W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Calaveras 06009W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Colusa 06011W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Contra Cos 06013F 5.0% 100% 100% 96% 99% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Del Norte 06015F 10.0% 100% 100% 92% 99% 100% 100% 100% 100% 100% 100% 100% 100% 100%

El Dorado 06017W 50.0% 100% 100% 61% 93% 99% 100% 100% 100% 100% 100% 100% 100% 100%

Fresno 06019W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Glenn 06021W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Humboldt 06023F 5.0% 100% 100% 96% 99% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Imperial 06025F 8.0% 92% 99% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Inyo 06027W 50.0% 50% 58% 60% 78% 87% 88% 89% 91% 93% 94% 96% 98% 100%

Kern 06029F 5.0% 100% 100% 95% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Kings 06031W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Lake 06033W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Lassen 06035W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Los Angel. 06037F 30.0% 70% 91% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Madera 06039F 10.0% 100% 100% 90% 90% 99% 100% 100% 100% 100% 100% 100% 100% 100%

Marin 06041F 50.0% 100% 100% 59% 93% 99% 100% 100% 100% 100% 100% 100% 100% 100%

Mariposa 06043W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Mendocino 06045F 10.0% 100% 100% 92% 99% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Merced 06047W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Modoc 06049W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Mono 06051W 100.0% 0% 5% 13% 16% 30% 37% 45% 61% 79% 87% 95% 97% 100%

Monterey 06053W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Napa 06055F 10.0% 100% 100% 92% 99% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Nevada 06057F 50.0% 100% 100% 50% 50% 95% 100% 100% 100% 100% 100% 100% 100% 100%

Orange 06059F 21.0% 79% 94% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Placer 06061W 20.0% 100% 100% 84% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Plumas 06063W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Riverside 06065W 21.0% 79% 94% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Sacramento06067F 25.0% 100% 100% 75% 75% 75% 98% 100% 100% 100% 100% 100% 100% 100%

San Benito 06069W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

S. Bernard. 06071W 21.0% 79% 94% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

San Diego 06073F 15.0% 85% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

S Francisco 06075F 50.0% 100% 100% 59% 93% 99% 100% 100% 100% 100% 100% 100% 100% 100%

S Joaquin 06077F 25.0% 100% 100% 75% 75% 75% 98% 100% 100% 100% 100% 100% 100% 100%

S L Obispo 06079W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

San Mateo 06081F 50.0% 100% 100% 59% 93% 99% 100% 100% 100% 100% 100% 100% 100% 100%

Sta Barbara 06083F 69.0% 31% 44% 63% 63% 84% 100% 100% 100% 100% 100% 100% 100% 100%

Santa Clara 06085F 10.0% 100% 100% 92% 99% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Santa Cruz 06087F 10.0% 100% 100% 92% 99% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Shasta 06089W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Sierra 06091W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Siskiyou 06093W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Solano 06095F 5.0% 100% 100% 96% 99% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Sonoma 06097F 10.0% 100% 100% 92% 99% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Stanislaus 06099W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Sutter 06101W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Tehama 06103W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Trinity 06105W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Tulare 06107W 5.0% 50% 58% 60% 78% 87% 88% 89% 91% 93% 94% 96% 98% 100%

Tuolumne 06109W 0.2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Ventura 06111F 69.0% 31% 44% 63% 63% 84% 100% 100% 100% 100% 100% 100% 100% 100%

Yolo 06113F 10.0% 100% 100% 90% 90% 90% 99% 100% 100% 100% 100% 100% 100% 100%

Yuba 06115F 10.0% 100% 100% 90% 90% 99% 100% 100% 100% 100% 100% 100% 100% 100%
41

Figure 29. Power restoration curves at a few key locations, showing percentage of customers 

capable of receiving power at selected times. 

LIFELINE INTERACTION INVOLVING POWER 

 As with ShakeOut, damage to other lifelines can impact the restoration of power. As 

examples, roadway damage can hinder electric utilities from getting to affected areas and 

performing repairs. Telecommunications failure can hinder the reporting of any damage to the 

utilities, as well as hindering repair coordination. 

OPPORTUNITIES TO ENHANCE POWER RESILIENCY 

 One panelist observed that all California electric utilities are required to perform preventive 

maintenance under California Public Utilities Commission (CPUC) general order 165. He also noted 

that all California electric utilities are familiar with FEMA reporting and the appropriate forms for 

financial relief. The following methods for enhancing power resiliency were identified during panel 

discussions or in subsequent conversations and correspondence. 

Convert overhead bare conductors to underground cable in conduit. However, this conversion can be 

very costly (up to $1 million per mile in urban areas, or 5 times the cost of new overhead construction). 

This conversion raises new issues and challenges such as coordinating and routing several congested 

utilities, providing an adequate cathodic protection and protection from water. Lifecycle costs might be 

different and would have to be investigated to determine if the economical feasibility to perform this 

conversion. 

Elevate transformers and control equipment at substations to protect from flooding. 

Pre-position repair crews based on severe weather forecasts. 

0%

25%

50%

75%

100%

1/27/2011 3/28/2011 5/27/2011 7/26/2011 9/24/2011 11/23/2011 Power service (% of customers capable of receiving power)

Los Angeles

San Francisco

Sacramento

San Diego

Mono

(High winds)

(Lower winds)
42

Send utility inspection crews to check for hazardous conditions at substations. 

Train utility workers to document damage in the detail required for government assistance. 

Promote the design and acquisition of a mobile, high-voltage transformer that has ability for the user to 

easily change the internal impedance and the turns ratio. This innovation would mitigate the problem of 

long lead times to replace damaged high-voltage transformers. One panelist acknowledged the potential 

value of such a transformer; another expressed skepticism that such a design is, in fact, feasible. 

One panelist suggested promoting the design and acquisition of a mobile, modular substation complete 

with all the relays, circuit breakers, and controls capable to service one distribution station or a receiving 

station. 

Finally, there seemed to be broad consensus that mutual assistance among power utilities is key in any 

major disaster. Having the right spare equipment also is imperative. 

LIMITATIONS OF ESTIMATES WITH RESPECT TO POWER 

 This approach was a best estimate for a highly uncertain situation. It is particularly 

uncertain for the areas not addressed by panel discussions. Damage to power system components 

from landslides is not accounted for, nor is any special consideration made of shutdown of nuclear 

power plants or of other generating facilities not in the flooded areas. PG&E and some other utilities 

were unable to participate in developing the scenario. Had these utilities participated, the scenario 

would have been more accurate. These limitations argue for a more thorough assessment by PG&E 

and other utilities. 

Wastewater Treatment 

WASTEWATER FACILITIES AND SOURCES OF DAMAGE 

 Wastewater treatment systems typically comprise sewer pipes (laterals from customers to 

the street; collectors, truck sewers, interceptors, and outflow structures), pumping stations (also 

called lift stations), and wastewater treatment plants (WWTPs). Pipes tend to be relatively brittle, 

constructed of vitrified clay or reinforced concrete, but some structures are aging, brick-lined 

tunnels or tunnels lined with unreinforced concrete. Sewer flow is typically driven by gravity and 

aided by lift stations. Sewer pipes can be damaged by landsliding and in some cases by scour, 

especially external scour to shallow cut-and-cover tunnels. Sewer pipes also can be damaged by 

ground settlement and other movement aggravated by soil becoming saturated after multiple 

storms. The primary cause of damage to pumping stations and WWTPs discussed in panels was 

flooding damage to electrical equipment and sediment getting into pumps. We begin by discussing 

damage to WWTPs. 

 Although there is an incomplete spatial database of WWTPs, discussed below, we are 

unaware of any complete inventory or GIS system describing all or even a significant fraction of 

California sewer systems. Roughly 135 WWTPs and an unknown number of sewage pumping 

stations (also called lift stations) operate in California. Flooding can damage these facilities, 

especially by damaging electrical equipment or by floating or buckling storage tanks. The degree of 

damage to electrical equipment depends on whether the equipment is deenergized before flooding. 

The damage is worse if the equipment is not deenergized before being powered down, which can 

happen if flooding occurs without sufficient advanced warning or at night when minimal crews are 

on hand to shut down equipment. When a WWTP or lift station is flooded and shut down, untreated 

sewage may emerge from nearby maintenance holes and wetwells, and flow by gravity overland to 
43

nearby rivers or shorelines, contaminating a radius of up to 1⁄2 to 1 mile around the point of sewage 

discharge if the flooding reaches that far. Once the sewage enters a stream, creek, or river, the 

distance of contamination can far exceed the 1⁄2 to 1 mile distance. The contaminated area 

potentially can require evacuation of homes and businesses. In addition to the soft-term health 

impacts, any agricultural fields with sewerage in nearby streams may cause the crops to be deemed 

unsafe.

WWTPs tend to be in low-lying areas and, therefore, more subject to flooding than the 

population served. A spatial database of WWTPs is available in the HSIP Gold 2007 database. It 

shows 21 WWTPs in inundated areas (out of 113 California WWTPs shown in database). The HSIP 

Gold 2007 database appears to be incomplete, however, and is missing at least 24 WWTPs, 7 of 

which lie in the hypothetically flooded areas: 2 in Amador County (neither flooded), 1 in Colusa 

County (not flooded), 4 in Imperial County (none flooded), 3 in Madera County (none flooded), 1 in 

Modoc County (not flooded), 1 in Los Angeles County (flooded), 1 in Sacramento County (not 

flooded, because of floodwall protection designed for 500-year flooding), 2 in San Francisco 

(neither in the hypothetically flooded areas, however), 3 in Santa Cruz (none flooded), 1 in Sutter 

County (flooded), 3 in Tehama County (1 possibly flooded), 3 in Yolo County (all in the 

hypothetically flooded areas). The list of counties with known WWTPs in the scenario flooded area 

is shown in table 2. The table lists, by county, the peak flooding depth (anywhere in the county) 

along with flooding duration, number of WWTPS known to be in the hypothetically inundated area, 

both as a number and as a fraction of the WWTPs shown in the HSIP Gold 2007 database and 

supplemented here by Google Earth searches. (After the development of the table, we found that an 

U.S. Environmental Protection Agency database reflected much of the missing information, though 

not all, and not in a convenient format. The database would have required extensive interpretation 

to be useful, and so is not reflected here.) 

Table 2. Wastewater treatment plants (WWTP) in scenario flooding areas, per county. 

With maximum flooding, duration of flooding, number and percentage of flooded WWTPs. 

[ft, feet; WWTPs, wastewater treatment plants; %, percent; <, less than] 

County Peak depth, ft Duration, days Flooded WWTPs % Total WWTPs

Alameda 3-10 <0.5 1 25 

Butte <3 0.5-3 1 100 

Contra Costa 3-10 <0.5 1 10 

Los Angeles 3-10 <0.5 2 20 

Marin 3-10 <0.5 2 40 

Orange 3-10 <0.5 4 57 

Tehama <3 0.5-3 1 33 

San Diego 3-10 <0.5 1 17 

San Joaquin 10-20 14-28 2 67 

San Mateo 3-10 <0.5 2 67 

Santa Clara 3-10 <0.5 3 100 

Sonoma 3-10 <0.5 1 100 

Sutter 3-10 3-14 1 100 

Ventura 3-10 <0.5 1 25 

Yolo 10-20 14-28 3 100 

Yuba 3-10 3-14 1 100 

Total 27 
44

WASTEWATER DAMAGE AND RESTORATION SCENARIO 

 Following is a description of damage and restoration discussed in the Pasadena wastewater 

treatment panel. This description is offered as a pattern for emergency planning purposes. 

Flooding at pumping stations and WWTPs. In Los Angeles, the scenario imagines that the 

Donald C. Tillman and Terminal Island WWTPs are flooded. The former floods the central and 

western San Fernando Valley. Raw sewage emerges from the 50 nearest maintenance holes, and 

flows overland to the Los Angeles River, causing a hazardous material condition that could trigger 

evacuation of homes and businesses that were not otherwise flooded, and shutting down roads 

through the affected area. 

 At Terminal Island, the Terminal Way pumping plant is submerged, and sewage is not 

pumped to Terminal Island because of the loss of power. Terminal Island is in danger of being 

isolated, causing a life-safety threat to employees, and is evacuated. Raw sewage emerges near the 

Terminal Way pumping plant and runs untreated to the Pacific Ocean. Near Venice Beach, the 

Venice Pumping Plant is briefly submerged, and sewage is not pumped to the Hyperion WWTP. 

Instead the sewage emerges from wetwells and maintenance holes between Santa Monica and 

Venice and runs overland to the Pacific Ocean. Similar effects would occur near other flooded 

WWTPs. 

 Pipeline damage and restoration. Landslides cause localized damage to sewer pipes 

throughout the area. Flooding also can carry large amounts of sediment into sewer pipes; one panel 

estimated that 10-15 percent of pipes in flooded area will have large amounts of sediment that will 

need to be cleaned out. Sewer pipe damage will continue to emerge for several months after the 

storm; the Menlo Park panel estimated that repairs to sewer pipes in the San Francisco Bay area 

could cost on the order of $60 million, realized over the 6 months after the storm. Scaling up solely 

by population, this suggests statewide sewer pipe repairs costing on the order of $300 million, that 

is, roughly $9 per resident, on average. Cities with more intense rainfall and older sewer systems 

will experience greater-than-average sewer damage rates. 

 In Los Angeles, one important point of significant damage is the north outfall sewer, which 

zigzags under the Los Angeles River. This aging structure is partly brick-lined, partly lined with 

unreinforced concrete. It is a shallow, cut-and-cover structure, and it seemed possible to some 

panelists that the sewer could be damaged by external scour near the river. 

 It is uncertain how long repairs would take to restore damaged electrical equipment. One 

panel felt that, if the equipment is deenergized before being wetted, the equipment can be dried and 

reenergized within a day of floodwaters receding; otherwise short-circuited equipment might take 

weeks or more to replace. Another panel disagreed with the notion that deenergized equipment 

could be dried and quickly restored to service, and felt instead that flooded electrical equipment 

could be contaminated with silt and have to be replaced, which might take months (one panel 

suggested 3-6 months). As a middle ground, we have perhaps optimistically assumed that service is 

restored within 4 weeks after floodwaters recede from WWTPs. This assumption needs checking. 

 For purposes of estimating economic impacts of the failure of sewer service, the percentage 

shown in table 2 is taken as the fraction of customers whose sewer service is rendered unavailable 

beginning near the peak of the storm, then continuing for the duration of flooding noted in the 

table, with an additional 2 days to 4 weeks, to account for the time required to repair or replace 

electrical equipment. Service restoration is assumed to follow the same exponential curve form as 
45

before, that is, Equation (2), where now f(t) denotes the fraction of customers with sewer service, 

C0 denotes the fraction of services assumed to be affected by WWTP flooding, t again is the number 

of days after the end of the storm (January 27 and February 9, in southern and northern California, 

respectively), and d is the flooding duration. The parameter r is a constant, this time set to 0.15 to 

cause the calculated fraction of sewer services restored to be 65 percent of services within 1 week 

after flooding recedes, and 99 percent restored within 4 weeks after flooding recedes. 

 The results are tabulated in table 3. Note well that C0 includes all customers, including 

residences, businesses, and other facilities that are rendered unoccupiable by flooding or other 

damage. 
46

Table 3. Sewer service restoration per county over time after the ARkStorm. 

[FIPS, Federal Information Processing Standard; C0

County

, percentage of customers initially without power after the storm; %, percent]

FIPS C0 1/27/11 2/3/11 2/10/11 2/17/11 2/26/11 3/13/11 3/28/11 4/27/11 5/27/11 6/26/11 7/26/11 8/25/11 9/24/11

Alameda 06001 25% 100% 100% 78% 92% 98% 100% 100% 100% 100% 100% 100% 100% 100%

Alpine 06003 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Amador 06005 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Butte 06007 100% 100% 100% 0% 59% 89% 99% 100% 100% 100% 100% 100% 100% 100%

Calaveras 06009 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Colusa 06011 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Contra Costa 06013 10% 100% 100% 91% 97% 99% 100% 100% 100% 100% 100% 100% 100% 100%

Del Norte 06015 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

El Dorado 06017 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Fresno 06019 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Glenn 06021 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Humboldt 06023 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Imperial 06025 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Inyo 06027 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Kern 06029 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Kings 06031 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Lake 06033 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Lassen 06035 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Los Angeles 06037 20% 80% 93% 97% 99% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Madera 06039 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Marin 06041 40% 100% 100% 64% 87% 97% 100% 100% 100% 100% 100% 100% 100% 100%

Mariposa 06043 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Mendocino 06045 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Merced 06047 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Modoc 06049 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Mono 06051 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Monterey 06053 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Napa 06055 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Nevada 06057 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Orange 06059 57% 43% 79% 93% 97% 99% 100% 100% 100% 100% 100% 100% 100% 100%

Placer 06061 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Plumas 06063 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Riverside 06065 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Sacramento 06067 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

San Benito 06069 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

San Bernardino 06071 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

San Diego 06073 17% 83% 94% 98% 99% 100% 100% 100% 100% 100% 100% 100% 100% 100%

San Francisco 06075 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

San Joaquin 06077 67% 100% 100% 33% 33% 33% 87% 99% 100% 100% 100% 100% 100% 100%

San Luis Obispo 06079 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

San Mateo 06081 50% 100% 100% 55% 84% 96% 100% 100% 100% 100% 100% 100% 100% 100%

Santa Barbara 06083 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Santa Clara 06085 100% 100% 100% 11% 69% 92% 99% 100% 100% 100% 100% 100% 100% 100%

Santa Cruz 06087 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Shasta 06089 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Sierra 06091 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Siskiyou 06093 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Solano 06095 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Sonoma 06097 100% 100% 100% 11% 69% 92% 99% 100% 100% 100% 100% 100% 100% 100%

Stanislaus 06099 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Sutter 06101 100% 100% 100% 0% 0% 74% 97% 100% 100% 100% 100% 100% 100% 100%

Tehama 06103 33% 100% 100% 67% 86% 96% 100% 100% 100% 100% 100% 100% 100% 100%

Trinity 06105 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Tulare 06107 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Tuolumne 06109 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Ventura 06111 25% 75% 91% 97% 99% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Yolo 06113 100% 100% 100% 0% 0% 0% 81% 98% 100% 100% 100% 100% 100% 100%

Yuba 06115 100% 100% 100% 0% 0% 74% 97% 100% 100% 100% 100% 100% 100% 100%
47

LIFELINE INTERACTION INVOLVING WASTEWATER TREATMENT 

 Some lift stations may be equipped with backup power, although none were identified in 

our panels. Lacking commercial power or backup power these stations cannot operate. Many 

WWTPs are provided with onsite electrical power. A San Francisco panelist reported that all San 

Francisco WWTPs have emergency generators (generally somewhat elevated above grade) with 

sufficient capacity to power the WWTP during normal power interruptions, and believed the same 

to be true at East Bay Municipal Utility District (EBMUD) facilities. If the emergency generators run 

on diesel or other fuel other than piped natural gas, then the generators are susceptible to running 

out of fuel within 3-5 days if roadway damage or flooding were to affect fuel delivery. We, therefore, 

assume that, in general, when commercial power is unavailable, lift stations do not operate but 

many WWTPs may function. Furthermore, since sewer lines are co-located with streets, where lift 

stations and WWTPs cease to function, untreated sewage can emerge onto streets, causing hazmat 

conditions that cause road closures and hinder repairs to other lifelines. One panel estimated that, 

in Marin County, 2 to 5 percent of street miles might have raw sewage on roads. Cleaning up sewage 

spills often uses large amounts of fresh water. 

RESILIENCY AND RESEARCH NEEDS FOR WASTEWATER SERVICE 

 The following opportunities were discussed for enhancing the resilience of wastewater 

service, either by reducing damage, by speeding restoration, or by otherwise reducing the impacts 

of damage. 

Utilities (perhaps in collaboration with FEMA) anticipate and budget for the opportunity to restore 

wastewater systems to greater-than-pre-storm capacity. Some panelists believed that when using FEMA 

public assistance funds after federally declared disasters, facilities had to be restored to predisaster 

condition, that is, that FEMA would not provide the equivalent amount of funding if the utility restored to 

higher capacity, even if the utility made up the difference in cost itself. A FEMA program officer involved 

in the discussion was unsure of the accuracy of that statement, but nonetheless the subject is worth 

investigating. 

Consider adding or enhancing floodwalls around critical facilities such as WWTPs, or elevating sensitive 

electrical and mechanical equipment, conduit, and bus bar (a bar that conducts electrical current) above 

extreme flooding stage. 

Enhance backup power and onsite fuel capabilities where cost effective. Where FEMA hazard mitigation 

grants are available, consider among the benefits the avoided health and agricultural impacts and loss of 

functionality of evacuated dwellings and businesses. Onsite backup power can produce benefits in terms 

of losses avoided from earthquake as well as floods. 

Develop or review contingency plans for monitoring weather forecasts and the impacts on local facilities; 

and for deenergizing sensitive electrical equipment, especially considering night-shift crews, when there 

are fewer staff available; and facilities with limited ability to see rising rivers nearby. 

Stockpile common electrical equipment or create a common national database of backup electrical 

equipment and the means to transfer and install it quickly. 

One panelist (Craig Davis) recommended that “drains, conduits (storm channels and pipes), and storm 

detention/retention basins be inspected periodically (probably at least annually) and where needed, 

cleaned of debris and sediments to ensure they retain their design capacity. Drains or channels also 

include the benches placed on slopes to help protect the slope stability. This might sound like something 

that is obvious, which it is, but it costs money and time and many organizations and private property 

owners simply do not do this. We inspect and clean in some areas, but not all. Even where known, we 

cannot always find the labor or money to clean out some areas for every winter. In 2005, we learned that 
48

the lack of even some of the simplest cleaning of bench drains on our slopes can be critical to preventing 

storm related slope stability and debris flow problems. Once a problem arises it is easy to see that the 

cost of repair is far greater than the annual saving by not keeping the drains, conduits, and basins cleaned 

on an annual basis.... Some agencies do a pretty good job with the basins and major channels. One that 

stands out in my mind is LA County Flood Control District.”

 We encountered some limitations of available knowledge in preparing this scenario that 

suggest research needs: 

The HSIP Gold 2007 database has major gaps in data on the location of wastewater treatment facilities 

(WWTPs). Furthermore, there appears to be no information linking WWTPs to particular watersheds, nor 

is there data about the locations of major sewer lines or lift stations. There is no centralized information 

about WWTP capacities, provision of onsite power, elevation of electrical and sensitive mechanical and 

plumbing equipment is available. WWTP facilities and vulnerabilities vary enormously, which makes the 

performance of studies such as this one problematic especially if the studies cross district boundaries. 

There was a difference of opinion as to whether flooded electrical equipment needs to be replaced, and 

no information is readily available as to replacement times. (This latter point is changing with the 

development of ATC-58, a FEMA-funded effort to codify 2nd-generation performance-based earthquake 

engineering. Part of that effort involves compiling a database of repair and replacement time for common 

mechanical, electrical, and plumbing equipment.) 

Water Supply 

WATER FACILITIES AND SOURCES OF DAMAGE 

 About 95 percent of Californians get their water from a public, municipal source. About 324 

water districts in California and more than 8,000 small public water systems serve 6 to 7 percent of 

the population. Figure 30 shows the larger districts, including federal, regional, state, and municipal 

water districts. A map of the state's major water projects is shown in figure 31. A map of about 

1,500 dams in the state in the HSIP Gold 2007 database is shown in figure 32; these include the 

1,250 dams regulated by the California Division of Safety of Dams (DSOD). The other 250 dams are 

either federal dams, or exempt, or below the size threshold that would place the dams under DSOD 

jurisdiction. Between 60 and 65 percent of statewide water use comes from surface-water supplies; 

the balance from ground water, though nearly half of Californians rely to some extent on ground 

water. Ground-water use is most intense in regions with limited access to surface water. According 

to the California Department of Water Resources, over 80 percent of water use in the Central Coast 

region comes from ground water, as does about 70 percent of water use in the southern Sierra. The 

general geographic distribution of the state's 35,000 monitored wells is shown in figure 33, which 

shows wells for which the California Department of Water Resources maintains hydrographic data 

in its Water Data Library (WDL) system. The drinking water for about 1.6 million people comes 

from over 600,000 private domestic wells (not shown). 

WATER SUPPLY DAMAGE AND RESTORATION SCENARIO 

 During panel discussions with water-supply service providers, several significant damage 

modes were identified. One was contamination of wellheads and flood damage to electrical 

equipment serving pumps at the wellhead. The damage mode was identified by representatives of a 

southern California water district serving 65,000 people, who observed that the district had 

hundreds of wellheads in dry riverbeds, and that some might have poor sanitary seals. Within one 

basin, it seemed realistic that a limited plume of untreated sewage could contaminate a significant 
49

part of the aquifer in one basin, interrupting water-supply service to perhaps 25 percent of the 

utility’s customers. Restoration would involve repairing damaged electrical equipment and cleaning 

the wells. Repairs to that system could cost $100 million and take 2 to 4 weeks if the system were 

properly shut down before electrical shorts could occur; otherwise repairs could plausibly cost $1 

billion and take up to 6 months. Scaling up from the smaller figure for this relatively small water 

district, repair costs for water supply for a larger water district could plausibly range from $1-$10 

billion; we have estimated $3 billion here. 

 Two northern California water utilities that rely on ground water echoed the same concern, 

suggesting that it was realistic for half of their wells to be impacted. Representatives from one of 

the two utilities felt that its wellheads could be disinfected and water supply restored 

approximately 3 days after floodwaters receded and power was restored. Its wellheads are 

supplied by backup power—emergency generators powered by natural gas with some onsite 

storage—although because the electrical equipment is located at ground level, the generator and its 

electrical equipment would be damaged, rendered nonfunctional, and have to be replaced. 

 Other damage modes identified during panel discussions include: 

Flooding damage to electrical and other equipment at water treatment plants (WTP). Panelists' estimates 

for restoration time varied widely, from 2 days to 6 months, generally over the question of whether the 

flooded electrical equipment could be dried and reenergized, or would have to be replaced. One panelist 

suggested that WTP serving the entire city of Sacramento would be inoperative for up to 3-6 months. 

Loss of water transmission from northern to southern California because of damage from overtopping of 

levees in the Sacramento Delta or aqueduct damage caused by flash flooding. (For example, flash flooding 

in the Arroyo Pasajaro could disable the California Aqueduct between Tracy and Coalinga, as happens 

elsewhere periodically.) About half of southern California’s water comes from the Delta. Panel 

participants felt that it was realistic for levee repairs necessary to restore conveyance to southern 

California to take 3 months. (Not the same as the amount of time required to repair all levee breaches and 

dewater flooded islands, which would probably be several years.) Note, however, that between ground 

water and other local supplies, alternate routes, reduced winter demand, and conservation, panelists felt 

that southern California would not lack for water solely because of levee and aqueduct damage to 

northern California supplies. Panelists and others have noted that southern California has one of the most 

robust water-supply systems in the world. 

Greatly increased turbidity in surface water because of runoff carrying sediments into reservoirs and 

because of erosion of the banks of reservoirs. Panelists concluded that in southern California at least, 

water quality would be a far more significant problem than quantity, primarily in that filters would have 

to be flushed frequently, and that there would be concerns of contaminants from runoff potentially 

requiring extended boil-water orders. 

Loss of power. Many water service providers rely on power to operate pumps. Those pumps that have 

onsite backup power tend to have generators and electrical equipment at grade, meaning that if they are 

flooded, the generator and electrical equipment would be damaged, rendered inoperative, and have to be 

replaced. 

Landslides could damage tanks on hillsides. The example cited was tanks near the Seven Oaks Dam in 

Devils Canyon. 

Pipes that cross canyons could be damaged by debris flows. The example cited was debris flow out of the 

Santa Ana River into the pickup for North Fork Irrigation and the City of Redlands. The pipe serves about 

50,000 people, though it is not the sole source of water. 

Pipes collocated on bridges could be damaged if the bridges are damaged, for example, by foundation 

scour. 
50

 Debris flows could damage local water distribution systems, for example, by breaking 

enough service connections where the pipes rise into houses to reduce pressure locally until the 

damage can be valved off. Panelists and ARkStorm landslide experts estimated that perhaps 100-

200 neighborhoods of on the order of 20 to 50 homes each could lose water supply as a result of 

debris flows. 

 From these observations, producing a quantitative, statewide damage and restoration 

scenario for water supply is challenging. The damage and restoration scenario depicted here is, 

therefore, intended primarily for purposes of economic analysis, and relies on the following 

simplifications: Water supply impacts in Central Valley counties are primarily caused by flooding 

damage to electrical equipment at water treatment plants and wellheads, and contamination of 

wells. The Central Valley counties with the most severe flooding are Glenn, Butte, Colusa, Sutter, 

Yuba, Yolo, Sacramento, San Joaquin, Merced, and Kings; for these we assume that 50 percent of 

water supply is lost during the duration of flooding and for 14 days thereafter. Supply is then 

gradually restored, reaching 99 percent restoration within 3 months. The same restoration curve is 

applied to Orange County, because of severe flooding. In Central Valley counties with less flooding 

(Fresno, Madera and Kern), 15 percent of the county's water supply is assumed to be impacted 

during the duration of flooding plus 7 days, and is gradually restored, reaching 99 percent of prestorm capacity within 30 days. In southland counties with moderate flooding (Los Angeles and 

Ventura), we assume that the primary impacts are contamination of wells and damage to electrical 

equipment at wellheads, affecting 10 percent of water supply for the duration of flooding plus 7 

days, with 99 percent of capacity restored within 30 days. Loss of water supply in other counties is 

assumed to be negligible from a macroeconomic perspective. Results are shown in table 4 and 

figure 34. 
51

 

Figure 30. The larger water districts among the 324 federal, regional, state, and municipal water 

districts in California. Colors of districts varied to improve visibility. 
52

 

Figure 31. Major state water projects (modified from California Department of Water Resources, 

2005). 

Water Projects After 1998

Diamond Valley Lake

East Branch Extension

Crafton Hills Reservoir

Olivenhain Dam
53

Figure 32. Locations of California dams (yellow triangles). Only a fraction is for water-supply 

purposes. 
54

 

Figure 33. Locations of 35,000 water wells in California (Department of Water Resources 

Integrated Water Resources Information System). 
55

Table 4. Water service restoration per county over time.

[FIPS, Federal Information Processing Standard; C0

County

, percentage of customers initially without power after the storm; %, percent]

FIPS C0 1/27/2011 2/3/2011 2/10/2011 2/17/2011 2/26/2011 3/13/2011 3/28/2011 4/27/2011 5/27/2011 6/26/2011 7/26/2011 8/25/2011

Alameda 06001 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Alpine 06003 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Amador 06005 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Butte 06007 50% 100% 50% 50% 53% 81% 92% 99% 100% 100% 100% 100% 100%

Calaveras 06009 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Colusa 06011 50% 100% 50% 50% 50% 73% 89% 98% 100% 100% 100% 100% 100%

Contra Costa 06013 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Del Norte 06015 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

El Dorado 06017 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Fresno 06019 15% 100% 85% 85% 92% 99% 100% 100% 100% 100% 100% 100% 100%

Glenn 06021 50% 100% 50% 50% 53% 81% 92% 99% 100% 100% 100% 100% 100%

Humboldt 06023 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Imperial 06025 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Inyo 06027 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Kern 06029 15% 100% 85% 85% 92% 99% 100% 100% 100% 100% 100% 100% 100%

Kings 06031 50% 100% 50% 50% 50% 77% 91% 98% 100% 100% 100% 100% 100%

Lake 06033 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Lassen 06035 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Los Angeles 06037 10% 90% 96% 98% 99% 100% 100% 100% 100% 100% 100% 100% 100%

Madera 06039 15% 100% 85% 85% 88% 98% 100% 100% 100% 100% 100% 100% 100%

Marin 06041 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Mariposa 06043 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Mendocino 06045 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Merced 06047 50% 100% 50% 50% 50% 73% 89% 98% 100% 100% 100% 100% 100%

Modoc 06049 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Mono 06051 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Monterey 06053 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Napa 06055 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Nevada 06057 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Orange 06059 50% 50% 50% 67% 81% 92% 97% 99% 100% 100% 100% 100% 100%

Placer 06061 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Plumas 06063 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Riverside 06065 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Sacramento 06067 50% 100% 50% 50% 50% 50% 76% 96% 99% 100% 100% 100% 100%

San Benito 06069 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

San Bernardino 06071 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

San Diego 06073 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

San Francisco 06075 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

San Joaquin 06077 50% 100% 50% 50% 50% 50% 76% 96% 99% 100% 100% 100% 100%

San Luis Obispo 06079 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

San Mateo 06081 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Santa Barbara 06083 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Santa Clara 06085 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Santa Cruz 06087 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Shasta 06089 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Sierra 06091 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Siskiyou 06093 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Solano 06095 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Sonoma 06097 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Stanislaus 06099 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Sutter 06101 50% 100% 50% 50% 50% 73% 89% 98% 100% 100% 100% 100% 100%

Tehama 06103 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Trinity 06105 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Tulare 06107 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Tuolumne 06109 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Ventura 06111 10% 90% 96% 98% 99% 100% 100% 100% 100% 100% 100% 100% 100%

Yolo 06113 50% 100% 50% 50% 50% 50% 76% 96% 99% 100% 100% 100% 100%

Yuba 06115 50% 100% 50% 50% 50% 73% 89% 98% 100% 100% 100% 100% 100%
56

Figure 34. Scenario water service restoration per county, showing percentage of customers with 

service at different times. 

LIFELINE INTERACTION INVOLVING WATER SUPPLY 

 As noted above, some water-supply pipelines are on roadway or railway bridges, so bridge 

damage or failure (from scour) could interrupt water transmission. Furthermore, treatment 

facilities isolated by roadway or rail damage or flooding can run out of chlorine; some facilities 

receive chlorine shipments every 3-4 days. Many water treatment plants (WTPs) have onsite 

emergency generators, often elevated above grade, typically with 3-4 day supply of fuel (if not 

running on piped natural gas). These WTPs are, therefore, somewhat resistant to offsite (utility) 

power failure. Pumps in wellfields, however, are generally not equipped with emergency 

generators, so water supply to communities that rely on ground water or on water that requires 

pumping may be susceptible to loss of water resulting from power failure. 

ENHANCING RESILIENCY FOR WATER SUPPLY 

 Panelists recommended retrofitting facilities to make structures housing electrical 

equipment watertight, for example, with high berms or by elevating equipment. New equipment in 

new facilities could be elevated above grade, out of potential floodwaters. Another measure 

identified was to stockpile electrical equipment. This step would be problematic, since electrical 

systems at WTPs tend to be entirely custom designed. One panel suggested that more-critical 

facilities should be protected to higher standards than protection against 100-year flooding. 

Panelists also suggested that each water district should have programs to house, feed, or otherwise 

care for employees and their families, and know where the employees live. 

0%

25%

50%

75%

100%

1/27/2011 3/28/2011 5/27/2011 7/26/2011 9/24/2011 11/23/2011 Water service (% of all customers)

Butte Colusa 

Fresno Glenn 

Kern Kings 

Los Angeles Madera 

Merced Orange 

Sacramento San Joaquin 

Sutter Ventura 

Yolo Yuba 
57

 As with other lifelines, panelists also suggested that water agencies work with FEMA to 

loosen requirements that public assistance grants provide reimbursement only for in-kind repairs. 

FEMA could reimburse for the cost of in-kind repairs, but allow that reimbursement to be applied to 

repairs that restore facilities to higher than pre-storm standards. 

 They also suggested more mutual-aid arrangements with better communication to speed 

recovery, and noted that water-supply systems could plan to stockpile water treatment chemicals 

in anticipation of severe storms to facilitate business continuity. 

RESEARCH NEEDS FOR WATER SUPPLY 

 To perform a complete analysis of the damageability and resiliency of water supply would 

require a systems analysis. However, a system analysis would be impractical in part because there 

appears to be no central clearinghouse for much of the required data. Some of the basic data are 

difficult to acquire, such as the locations of WTPs, which appear in neither HSIP Gold 2007 database 

nor the Department of Water Resources (DWR) Water Data Library. Much of the available data is

dispersed among various agencies and tends to be limited to location, owner, and various 

identifiers, and lacks basic information on capacity, equipment, flood protection, backup power, and 

other relevant attributes. Some of the locational data appears to exist, but is publically available 

only as images. A basic system analysis would require a single integrated geospatial database 

showing nodes such as reservoirs, pumps, treatment plants, and intertie valves, and links such as 

pipelines and aqueducts, and relevant structural and functional attributes. A thorough system 

analysis would require one to treat water supply in connection with all interacting lifelines 

(especially power, roads, and telecommunications). Civil engineers and others refer to such 

interconnected networks as a "system of systems;" ideally one would perform a system-of-systems 

analysis to estimate lifeline damage, interaction, and restoration, if the necessary system data could 

be compiled. 

Dams 

 A map of the approximately 1500 dams in the state was previously shown in figure 32. The 

California Division of the Safety of Dams (DSOD) regulates about 1,250 of these dams. The 

ARkStorm scenario developers considered the possibility that the storm could damage some of 

these dams, and possibly result in downstream impacts. According to University of California at 

Davis Professor Jay Lund, 45 dam failures have occurred in California since 1883, the last failure in 

1965. All occurred in dams built before 1950. The most deadly of these failures occurred in March 

1928. The St. Francis Dam was a concrete gravity dam built between 1926 and 1927 about 40 miles 

northwest of Los Angeles, near the present city of Santa Clarita. On March 12, 1928, the dam 

catastrophically failed. The resulting flood killed between 425 and 450 people and led to State 

legislation for the creation of the world's first dam-safety program to protect people against loss of 

life and property from dam failure. Figure 35 shows the St. Francis Dam, circa 1926, and on March 

13, 1928, shortly after catastrophic failure. The failure is not associated with a severe winter storm. 

Figure 36 illustrates a famous dam overtopping failure caused by a severe storm and neglect, the 

South Fork Dam near Johnstown, Pa., which failed on May 31, 1889, killing 2,209 people. 
58

 

Figure 35. St. Francis Dam before (left) and after (right) collapse (both images: public domain). 

 

Figure 36. South Fork Dam near Johnsontown, Pa., (left, image courtesy of Johnstown Area 

Heritage Association) and aftermath of the May 31,1889, Johnstown flood (right, public domain 

image). 

 Less dramatic problems with dams in California have occurred. According to ARkStorm 

panelists, a small detention basin built in the 1930s or 1940s overtopped in a 2005 storm, then 

failed, and soil from the dam entered a covered reservoir. This dam is not under the regulation of 

DSOD. The result was damage to the cover and mud in the reservoir, which affected water quality. 

The reservoir had to be drained and cleaned, and the cover repaired. In another instance, a 1997 

landslide near the Franklin Canyon Reservoir caused stormwater to be diverted onto the dam, 

which threatened but did not actually damage the dam. This dam also is not to be under the 

regulation of DSOD. In other instances, the 1997 and 1998 floods caused significant debris flows 

into reservoirs downstream of the upper watersheds, causing increased levels of sediment, some 

loss of storage capacity, and very significant levels of floating debris such as trees and storage tanks. 

DAM DAMAGE SCENARIO 

 Controlled releases of large quantities of water from reservoirs could cause flooding in 

downstream creeks, channels, and floodplains. A DSOD panelist felt that, close to the reservoirs, this 

could cause minor spillway damage or erosion in downstream channels. A dam owner panelist felt 

that “minor spillway damage” may be an underestimate, saying "When spillways sit untested for 
59

years, then are subjected to continuous flow for an extensive period, damage is possible or even 

likely.” Panelists found it plausible that one or more events could occur in ARkStorm like the 1997 

landslide near Franklin Canyon Reservoir or the 2005 detention-basin failure. They also found it 

plausible that in an extreme event such as the ARkStorm for a dam in the San Francisco Bay Area to 

experience spillway damage or downstream erosion. However, a DSOD panelist pointed out that 

DSOD requires all state-regulated dams to have spillways that can safely pass a specified design 

storm without overtopping. Design storm requirements are determined on a sliding scale. Smaller, 

low-hazard dams (those with minimal downstream consequences if they were to fail) are designed 

for 1,000-year storms. Larger, high-hazard dams that would produce extreme downstream 

consequences if they were to fail are designed for a probable maximum flood, determined in 

accordance with Hydrometeorological Report 36 (U.S. Weather Bureau, 1961) or more recently 

Hydrometeorological Reports 58 and 59 (U.S. Department of Commerce. 1998, 1999). Hydrology 

studies are periodically updated and spillways are enlarged for dams as DSOD judges necessary, 

particularly if the downstream consequences of failure change for a dam. ARkStorm produces 

runoff with local return periods between 10 and 5,000 years (fig. 5), which is generally less than the 

design capacity of the state-regulated dams. As part of DSOD's program, DSOD staff inspect all 

jurisdictional dams at least annually. A component of this inspection is to verify spillways are 

unobstructed and fully functional. The ARkStorm, therefore, does not posit any dam overtopping 

failure to DSOD regulated dams. Minor spillway damage and downstream erosion is plausible, 

however, as well as occurrences similar to detention-basin damage, or similar to the 1997 landslide 

near the Franklin Canyon reservoir. Because of the extremely sensitive nature of a dam-damage 

scenario, the selection of a particular dam to imagine as hypothetically damaged in such a way is 

left to emergency planners. 

LIFELINE INTERACTION RELATED TO DAM DAMAGE 

 Release of large quantities of water from reservoirs through valves and spillways could 

damage roads and bridges, and any other lifelines such as water-supply pipelines or 

telecommunication cables carried on dams. Such lifeline interaction is hypothesized on one Bay 

Area highway. 

Levees 

 The expert panel convened in Sacramento to discuss damages to levees for the ARkStorm 

scenario felt that urban levees might be threatened or overtopped at 60 to 75 critical sites, and that 

15-20 breaches might realistically occur. The panel believed that 30 breaches of Delta levees were 

realistic, with 2-3 breaches occurring per island. The panel felt that a total of 50 levee breaches was 

realistic. 

 A scenario of levee breaches for the Sacramento-San Joaquin Delta (the Delta) in particular 

was developed by Jack R. Benjamin and Associates, Inc. Estimates of cost and time to repair levees 

and dewater islands also was provided. The analysis used the Emergency Response and Repair 

module developed for the Delta Risk Management Strategy project which evaluated the risk of levee 

failures and island flooding as a result of large inflows into the Delta (URS Corporation and Jack R. 

Benjamin & Associates, 2009). 
60

THE DELTA LEVEE DAMAGE AND REPAIR SCENARIO 

 In the event that a major flood in the Delta, occurs, the number and specific islands that may 

experience a levee breach and flooding is potentially quite varied. Given the number of islands 

(referred to as analysis zones) in the Delta (fig. 37), there are many possible combinations of levee 

breach/island flooding sequences that could occur (involving varying numbers of islands and 

varying combinations of islands) for a given flood. In the Delta Risk Management Strategy project, 

thousands of levee breach/island flooding sequences were generated to model the randomness in 

levee response during floods. 

 In this study, a single levee breach/island flooding scenario was generated for the 

ARkStorm flooding. The characterization of the projected flooding in the Delta is not based on an 

event-specific hydrologic analysis. Rather, this characterization is a result of a general assessment 

based largely on the FEMA Digital Flood Insurance Rate Maps. As a result, the input to this 

assessment does not provide an event-specific characterization of the spatial distribution of 

flooding that might occur in the Delta during a major hydrologic event. Another input to this 

analysis was the projected number of flooded islands as a result of levee failure. It was understood 

that approximately half of the islands in the Delta could be breached and flooded as a result of levee 

failure. 

 The historic record of island flooding since 1900 was reviewed to generate a levee 

breach/island flooding scenario. Historically, there have been multiple events where 10 or more 

islands have flooded as a result of levee failures. Also, there are islands that have experienced levee 

breaches on multiple occasions during flood events. In addition to reviewing the historic record, the 

amount of levee overtopping that occurs for a projected 500-year Delta inflow was reviewed. 

Based on the historic experience and projected levee overtopping for a 500-year flood event, a list 

of islands that could experience levee failures was generated. In recognition of the randomness of 

individual flood events (e.g., all 500-year flood events are not the same) and levee performance, the 

scenario is simply one possible realization of the possible outcomes during a large flood. 
61

Figure 37. Sacramento-San Joaquin Delta levee analysis zones. 
62

Flooded Islands 

 Table 5 lists the 31 flooded islands (analysis zones) for use in the ARkStorm scenario. In 

addition to these islands, 8 associated analysis zones are projected to be flooded. These are zones 

which have adjacent areas that are protected by interior levees (for example, Netherlands). 

Repair Costs and Timing

 Table 5 lists the time (in number of days after the flood) it takes for levee breaches to be 

repaired and islands to be dewatered. The repair and dewatering time estimates are based on 

assumptions for the Emergency Response and Repair module documented in URS Corporation and 

Jack R. Benjamin & Associates (2008). The primary repair time constraint is the supply production 

rate for the rock material. Dewatering immediately follows levee repairs and 42-inch pumps are 

assumed to operate at 80 percent capacity based on Jones Tract data. 

 Table 6 provides the cost of closing the levee breaches and dewatering flooded islands. The 

costs are $55per ton of material placed and $35 per acre-foot of water pumped (URS Corporation 

and Jack R. Benjamin & Associates, 2008). The total time to complete the breach closures and to 

dewater all islands also is given. Note that the costs in table 6 do not include the economic 

consequences associated with island cleanup, repair of damage to structures, replacement costs of 

structure contents, lost business revenue, and other costs. Values in table 5 and table 6 have been 

rounded to 2 significant figures. 
63

Table 5. Dewatering time of flooded Delta islands and tracts. 

No. Island/Tract Zone 

Identifier

Time to 

Dewater 

(Days)

1 Glanville 170 55

2 McCormack-Williamson Tract 169 54

3 Dead Horse 173 62

4 New Hope 172 140

5 Prospect 211 95

6 Tyler 62, 63 110, 200

7 Shima 187 140

8 McMullin Ranch 120 130

9 Paradise Junction 119 110

10 Pescadaro 118 140

11 River Junction 161 150

12 Medford 152 280

13 Netherlands 142, 137, 138, 

145

340, 170, 180, 

210

14 Sacramento Pocket Area 196 200

15 West Sacramento (North) 135 200

16 West Sacramento (South) 194 210

17 Sargent-Barnhart 191, 156 260, 230

18 Roberts (Upper, Lower, Middle) 115, 154 240, 360

19 Moore Tract 1 89 300

20 Moore Tract 3 88 290

21 Hastings Tract 2 83 320

22 Zone 148 148 320

23 Walthal 165 310

24 Ryer 210 430

25 Egbert Tract 70 410

26 Zone 171 171 370

27 Zone 76 - Elk Grove 76 380

28 Zone 75 75 380

29 Peter Pocket 72 370

30 Little Egbert 68 580

31 Elk Grove 61 430

32 Jones Tract - Upper and Lower 17 540

33 Shin Kee 182 450
64

Table 6. Summary of Delta island repair costs and times. 

Item Amount

Total Cost $480,000,000

Total Repair Costs $460,000,000

Cost for Island Dewatering $20,000,000

Time to Close All Breaches (Days) 440

Time to Dewater All Islands (Days) 580

Volume of Rock (tons) 8,400,000

MITIGATION OF LEVEE FAILURE 

 The above analysis assumed that levees would be repaired and islands restored. A cost 

benefit approach to levee upgrades favors repair and restoration of islands and netherlands that 

are urbanized and/or contain highly valued assets. Suddeth and others (2010) apply decision 

analysis to the non-project Delta Island levees and posit that some of the island levees in table 5 

may not qualify for an upgrade (for example, Deadhorse, Jones, Medford, Roberts, and Tyler Island 

levees) and, in the case of failure, levee repair costs for some islands may not be justified (for 

example, Deadhorse and Medford Islands). Another consideration is the criticalness of islands to 

water export quality (for example, Twitchell, Bradford, and Jersey Islands). Related to the 

protection of state water supply from high water flood flows and other threats (for example, sea 

level rise, earthquake, and subsidence) is the on-going preparation of the Bay Delta Conservation 

Plan to improve water-supply reliability and restore habitat. The Delta Habitat Conservation and 

Conveyance Program will conduct an environmental review of the Bay Delta Conservation Plan. 

 As a result of California Senate Bill 5 (Machado), the DWR is required to evaluate the 

current level of performance of the federal-state flood protection system in the Central Valley. The 

Urban and Non-Urban levee evaluations are appraising federal-state Project levees, including 

associated non-Project levees, to help flood managers understand the overall flood risks in the 

Central Valley and evaluate alternative changes to the flood management system (California 

Department of Water Resources, 2010b). 

 Panels in Pasadena, Sacramento, and Menlo Park agreed that during severe weather, the 

California Department of Water Resources focuses on keeping levees in place long enough to assure 

safe evacuation of the protected areas. (The objective is comparable to that which California 

building codes have historically held for the seismic resistance of buildings: that code-compliant 

buildings provide a reasonable degree of protection against life-threatening damage, not 

necessarily that the buildings should be earthquake proof. Therefore, the goals of DWR for levee 

protection and of the Uniform and International Building Codes for seismic resistance are to protect 

people, not necessarily to protect property.) 

Telecommunications 

 One can extrapolate to ARkStorm from the performance of telecommunications in the 

Midwest flood of 1993, Katrina Hurricane 2005, and the northwest Pacific storm of 2007. 
65

TELECOMMUNICATIONS ASSETS EXPOSED TO LOSS 

 We consider here the effect of the ARkStorm scenario storm on landline and cellular 

telecommunications systems. Tang (2010) gives details of this assessment. Landline refers here to 

the conventional telephone system often called POTS (Plain Old Telephone System). 

 Landline systems include buildings and the links between them. The buildings are where 

switching and data processing occurs, and include central offices, data centers, and remote offices. 

The buildings house telephone switching equipment (essentially computers, typically installed in 

metal racks); often microwave transceiver equipment; backup power systems (especially 

uninterruptible power supply—UPS—that is, batteries that can provide 8 hours or more of power 

for switching) and various other mechanical, electrical, and plumbing (MEP) equipment common to 

commercial buildings. The links in a landline system comprise fiber optic cable, copper cable, 

microwave transmission, and as addressed here, submarine cables that carry international voice 

and data connections. 

 Cellular systems (also called wireless) comprise base transceiver stations (BTSs, sometimes 

called cell sites), mobile telephone switching offices (MTSOs), the links connecting BTSs to MTSOs 

and BTSs to each other, the transmission network that connects the cellular equipment to the 

landline network. Most BTSs have less than 3 hours of battery reserve power (because of battery 

weight and limits to placing heavy equipment on rooftops where BTSs are often placed). 

 Telecommunications systems tend to be highly redundant. Links between central offices 

and remote switches are usually dispersed and often use both microwave and cable connections, so 

traffic through a damaged link can be routed through another, undamaged link. However, the 

distribution links via landlines to individual subscribers are usually not redundant, and are 

typically provided by a single copper cable line terminated at a pedestal (generally a metal box near 

the property line containing telephone switches or connections) with a cross-connect to the 

household (that is, a wire connecting the house to pedestal). 

 All major service providers in the U.S. have emergency response and restoration plans to 

further mitigate the potential for service interruption. These providers own mobile units of self 

contained switching equipment and cellular equipment with a quick physical connection capability 

to the interoffice connection terminals. The mobile switching office is called switching on wheels, 

(SOW), and the mobile BTS is called cell on wheels; (COW); these mobile units are illustrated in 

figure 38. Although these units do not have the same capacity as the damaged sites, these units are 

able to provide emergency service to allow 911, police, fire fighting, and medical services to access 

the telecommunication circuit. In California, many central offices and remote offices are upgraded 

with external quick power connection terminals. This connection allows the service crew to 

quickly connect external power source such as mobile power generator set to power the equipment 

without having to enter the building. For BTSs this feature is not usually available unless it is a big 

site. 
66

 

Figure 38. Switch on wheels (left) and cellular on wheels (right) (Photograph taken by A. Tang, L & 

T Consulting). 

MECHANISMS FOR TELECOMMUNICATIONS SERVICE INTERRUPTION 

 Telecommunications networks are susceptible to slowing or even being blocked when 

overloaded by call volume. California systems are capable of handling the common level of usage in 

the area (referred to as traffic pattern and dwell time). When a disaster strikes and people call each 

other to check on their safety and so on, the systems get overloaded and it can be difficult to get a 

dial tone. Also, extended power failure can exhaust UPS capacity and cause service interruption. 

These two mechanisms for service interruption do not require physical damage to 

telecommunications equipment or facilities. 

 Many of the components of the telecommunication system are susceptible to physical 

damage in a severe storm, damage that can slow or interrupt voice or data service. Some of the 

mechanisms of storm damage are: flooded manholes, toppled poles, misaligned microwave dishes, 

severed cables, inundated buildings, and damaged antennas. Two of these damage modes are 

illustrated in figure 39, which contains images from Hurricane Katrina. In one, wind damage to 

rooftop equipment interrupted microwave communications. In the other, flooding to the central 

office ground floor damaged power equipment and other central office components. (Many power 

systems of central offices are installed in the lower part of the building because of the weight of the 

power equipment. A flooded power room will shut down the facility.) Similar damage has been 

observed in other storms, such as the December 2007 storm in the Pacific Northwest. 

 Soil failure and flooding also can damage cables. For example, in the same 2007 Pacific 

Northwest storm, an optical fiber cable was damaged by soil failure and a number of fibers were 

severed. The result was 3 days of internet congestion between Australia, New Zealand, and North 

America. Figure 40 shows damage to fiber optic cable. In one, water in a flooded utility tunnel 

entered fiber optic cable at a splice, causing signal degradation and transmission capacity 

reduction. In the other, cable laid along a railbed was damaged when the railbed washed out. 
67

Figure 39. Microwave dish blown off the tower mount (left); flooded central office (right). 

(Kwasinski, 2006; public domain images) 

Figure 40. Damage to fiber optic cables: flooding degraded transmission when water leaked into a 

splice (left); ground failure damaged a cable laid along a railbed that washed out (right). 

TELECOMMUNICATIONS SERVICE RESTORATION SCENARIO 

 With these considerations in mind, we estimated the service restoration times shown in 

table 7 (for landlines and internet service) and table 8 (for cellular service). The direct loss to 

service providers is estimated to be on the order of $100 million, including costs of material, 

logistics, and technical personnel. 

The table’s first column contains the county’s name, the second its FIPS code. The column 

labeled peril denotes whether wind (W) or flood (F) dominates the cause of telecommunication 

service failure to customers. Column 4, labeled C0, denotes the estimated percentage of customers 

initially without telephone service after the storm. The remaining columns reflect the estimated 

percentage of customers able to receive power that do have power service, by date. 
68

Table 7. Landline and internet network restoration showing percentage of customers with power service 

by date. The estimates are based on post earthquake and hurricane recovery data.

[FIPS, Federal Information Processing Standard; W, wind; F, flood; C0

County

, percentage of customers initially 

without power after the storm; %, percent]

FIPS Peril C0 1/27/11 2/3/11 2/10/11 2/17/11 2/26/11 3/13/11 3/28/11 4/27/11 5/27/11 6/26/11 7/26/11 8/25/11 9/24/11

Alameda 6001 W 3% 100% 100% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Alpine 6003 W 15% 100% 100% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Amador 6005 W 2% 100% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Butte 6007 W 2% 100% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Calaveras 6009 W 2% 100% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Colusa 6011 W 2% 100% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Contra Cos 6013 F 10% 100% 100% 90% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Del Norte 6015 F 10% 100% 100% 90% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

El Dorado 6017 W 15% 100% 100% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Fresno 6019 W 2% 100% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Glenn 6021 W 2% 100% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Humboldt 6023 F 15% 100% 100% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Imperial 6025 F 15% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Inyo 6027 W 15% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Kern 6029 F 15% 100% 100% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Kings 6031 W 2% 100% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Lake 6033 W 2% 100% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Lassen 6035 W 2% 100% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Los Angel. 6037 F 20% 80% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Madera 6039 F 15% 100% 100% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Marin 6041 F 20% 100% 100% 80% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Mariposa 6043 W 2% 100% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Mendocino 6045 F 10% 100% 100% 90% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Merced 6047 W 2% 100% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Modoc 6049 W 2% 100% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Mono 6051 W 15% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Monterey 6053 W 2% 100% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Napa 6055 F 10% 100% 100% 90% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Nevada 6057 F 15% 100% 100% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Orange 6059 F 20% 79% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Placer 6061 W 10% 100% 100% 90% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Plumas 6063 W 2% 100% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Riverside 6065 W 20% 79% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

S Francisco 6075 F 15% 100% 100% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

S Joaquin 6077 F 2% 100% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

S L Obispo 6079 W 15% 100% 100% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

S. Bernard. 6071 W 20% 79% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Sacramento 6067 F 20% 100% 100% 80% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

San Benito 6069 W 10% 100% 100% 90% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

San Diego 6073 F 20% 80% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

San Mateo 6081 F 15% 100% 100% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Santa Clara 6085 F 15% 100% 100% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Santa Cruz 6087 F 15% 100% 100% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Shasta 6089 W 2% 100% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Sierra 6091 W 2% 100% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Siskiyou 6093 W 5% 100% 100% 95% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Solano 6095 F 15% 100% 100% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Sonoma 6097 F 5% 100% 100% 95% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Sta Barbara 6083 F 15% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Stanislaus 6099 W 2% 100% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Sutter 6101 W 2% 100% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Tehama 6103 W 2% 100% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Trinity 6105 W 5% 100% 100% 95% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Tulare 6107 W 10% 90% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Tuolumne 6109 W 5% 100% 100% 95% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Ventura 6111 F 20% 80% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Yolo 6113 F 2% 100% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Yuba 6115 F 5% 100% 100% 95% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%
69

Table 8. Cellular network restoration showing percentage of customers with power service by date. The 

estimates are based on post earthquake and hurricane recovery data.

[FIPS, Federal Information Processing Standard; W, wind; F, flood; C0

County

, percentage of customers initially 

without power after the storm; %, percent]

FIPS Peril C0 1/27/112/3/112/10/112/17/112/26/113/13/113/28/114/27/115/27/116/26/117/26/118/25/119/24/11

Alameda 06001 W 2% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Alpine 06003 W 15% 100% 100% 81% 95% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Amador 06005 W 15% 100% 100% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Butte 06007 W 5% 100% 100% 95% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Calaveras 06009 W 5% 100% 100% 95% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Colusa 06011 W 3% 100% 100% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Contra Cos 06013 F 10% 100% 100% 90% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Del Norte 06015 F 10% 100% 100% 90% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

El Dorado 06017 W 20% 100% 100% 61% 93% 99% 100% 100% 100% 100% 100% 100% 100% 100%

Fresno 06019 W 3% 100% 100% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Glenn 06021 W 3% 100% 100% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Humboldt 06023 F 15% 100% 100% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Imperial 06025 F 15% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Inyo 06027 W 15% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Kern 06029 F 15% 100% 100% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Kings 06031 W 3% 100% 100% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Lake 06033 W 3% 100% 100% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Lassen 06035 W 3% 100% 100% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Los Angel. 06037 F 20% 80% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Madera 06039 F 15% 100% 100% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Marin 06041 F 15% 100% 100% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Mariposa 06043 W 5% 100% 100% 95% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Mendocino 06045 F 20% 100% 100% 80% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Merced 06047 W 3% 100% 100% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Modoc 06049 W 3% 100% 100% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Mono 06051 W 20% 80% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Monterey 06053 W 3% 100% 100% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Napa 06055 F 10% 100% 100% 90% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Nevada 06057 F 15% 100% 100% 85% 100% 199% 100% 100% 100% 100% 100% 100% 100% 100%

Orange 06059 F 25% 75% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Placer 06061 W 10% 100% 100% 90% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Plumas 06063 W 3% 100% 100% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Riverside 06065 W 15% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

S Francisco 06075 F 10% 100% 100% 90% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

S Joaquin 06077 F 10% 100% 100% 90% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

S L Obispo 06079 W 15% 100% 100% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

S. Bernard. 06071 W 10% 90% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Sacramento06067 F 20% 100% 100% 80% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

San Benito 06069 W 10% 100% 100% 90% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

San Diego 06073 F 15% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

San Mateo 06081 F 15% 100% 100% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Santa Clara 06085 F 15% 100% 100% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Santa Cruz 06087 F 15% 100% 100% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Shasta 06089 W 3% 100% 100% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Sierra 06091 W 3% 100% 100% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Siskiyou 06093 W 3% 100% 100% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Solano 06095 F 3% 100% 100% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Sonoma 06097 F 10% 100% 100% 90% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Sta Barbara 06083 F 15% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Stanislaus 06099 W 3% 100% 100% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Sutter 06101 W 3% 100% 100% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Tehama 06103 W 3% 100% 100% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Trinity 06105 W 5% 100% 100% 95% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Tulare 06107 W 5% 95% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Tuolumne 06109 W 5% 100% 100% 95% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Ventura 06111 F 15% 85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Yolo 06113 F 5% 100% 100% 95% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Yuba 06115 F 3% 100% 100% 97% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%
70

LIFELINE INTERACTION INVOLVING TELECOMMUNICATIONS 

 Telecommunications recovery can be limited by restoration of roads, bridges, and power. 

The most important among the three is electric power, because the equipment does not operate 

without power and backup power is limited, particularly BTSs with their 3 hours or less of battery 

power. 

 Loss of telecommunications hinders restoration of other lifelines such as water and 

wastewater because the lifelines can rely on cellular phones to dispatch and coordinate service 

calls. This scenario happened in the moment magnitude scale (Mw) 8.8 earthquake in Chile on 

February 27, 2010. According to a district director of civil defense in Cauquenes, Chile, it took 5 

days, to restore cellphone service in that city, during which time firefighters were the only ones 

able to communicate other than in person, because they had battery-powered portable radios. 

Restoration of electric power, highway, water, wastewater, and some emergency services 

throughout the affected area were hindered by telecommunications service disruption during the 

first several days after the earthquake. 

 It is very common for collateral damage to occur to telecommunications cables because of 

damage to bridges on which the cables are collocated. 

OPPORTUNITIES TO ENHANCE TELECOMMUNICATIONS RESILIENCY 

 Telecommunications resiliency could potentially be enhanced by the following means: 

1. Install redundant, links that are not co-located and do this between all nodes (central offices, BTSs, and 

MTSOs) with different technology. 

2. Improve flood containment for manholes, central offices, BTSs, and MTSOs in the flood prone areas, or 

locate equipment above the expected flood level. 

3. Increase battery reserves to at least 8 hours for all BTSs. 

4. Install backup generators with at least 2 days of fuel above expected flood levels for difficult-to-access 

sites (remote central offices and BTSs). 

5. Use separate radios systems for emergency service dispatching and coordination. 

6. Locate spare parts and components storage facilities within reasonable distance for groups of nodes. 

7. Harden storage facilities of spare parts and components. 

8. Participate in Government Emergency Telecommunications Services (GETS) and other federal initiatives 

on mitigation and emergency response. 

9. Establish an overall service restoration interval and use voice and text messaging and internet services to 

reduce general anxiety after a disaster. 

Agricultural Damages and Losses 

 Agricultural damages and losses during winter storms result from precipitation-runoff 

flooding, coastal flooding, strong winds, and landslides. The ARkStorm coastal process analysis 

suggests that some agricultural land in the wide, flat alluvial Santa Clara River plain of Ventura 

County may be vulnerable to coastal flooding. A spatial analysis of ARkStorm windspeeds above 50 

mph indicated negligible potential for wind damage to orchards across the state of California. 

Although, mudslides reportedly caused $1 million (1969 dollars) of agricultural losses in Los 

Angeles County, during the 1969 storm (Malnik, 1969), an analysis of agricultural damages from 

landslides was not attempted for the ARkStorm scenario. However, the extent of ArkStorm 
71

precipitation-runoff flooding would be the overwhelming cause of damages to the agricultural 

sector. 

 To analyze the agricultural damages from flooding, an historic perspective on agricultural 

damages from California storms was gleaned from newspaper and agency reports to provide the 

context for the estimation of agricultural damages from ARkStorm flooding. The analysis, 

conducted by David Mitchell (M-Cubed), produced statewide and county estimates of agricultural 

damages and losses to annual and perennial crops and of livestock at risk. The types of losses 

include field restoration costs, crop and livestock replacement costs, and forgone income. 

HISTORIC AGRICULTURAL DAMAGES FROM CALIFORNIA FLOODS 

 Floods have damaged agricultural systems for hundreds of years in California (fig. 41). 

Newspaper reports of the 1861-62 storm present the agricultural damages and losses as: 

Unprecedented losses of livestock described as swimming, swept away, drowning, floating, or starving 

(Daily Alta California,1861, 1862a, 1862b, 1862c, and Los Angeles Star, 1862a) 

Grain and potato crop losses (Daily Alta California, 1862b, 1862e) 

Grape vines, pasture, and orchard lands washed away and fruit trees uprooted (Los Angeles Star, 1862b, 

1862c) 

Farming tool losses (Daily Alta California, 1862f) 

Farmland covered in a thick layer of sand destroying hopes of vegetation for some time (Daily Alta 

California, 1862g) 

Figure 41. Early flooding along a levee (photograph taken by National Oceanic and Atmospheric 

Administration, date and location unknown). 

 A more recent damaging storm to the Californian agricultural sector occurred in 1997 when 

nearly 300 square miles of flooded agricultural land caused losses to 30 agricultural commodities in 
72

more than 30 counties (California Department of Water Resources, 1997). The Flood Emergency 

Action Team (FEAT) final report (California Department of Water Resources, 1997) lists the 

economic losses as: 

Crop losses totaled $107 million, with the largest losses being walnuts, wine grapes, winter wheat, and 

alfalfa. 

Crop damage costs added another $49 million, with peaches, plums/prunes, wine grapes, and walnuts 

incurring the largest damage. 

Damage to nurseries totaled $16 million 

Livestock costs were another $12 million 

Damages to farm infrastructure (irrigation systems, roads, buildings, and fences) totaled $109 million. 

 The most severely affected counties were Butte, Yuba, Stanislaus, Nevada, Sacramento, San 

Joaquin, and Sutter. 

 To summarize, five categories of agricultural damages can be identified from these reports: 

damages to annual crops, perennial crops (fig. 42), livestock, fields, and equipment. While flooding 

is the primary cause of these damages, reports on the 1986 storm (San Francisco Chronicle 8 Mar 

1986) indicate agricultural damages and losses are further exacerbated by: 

Bacterial diseases 

Delays in pollination and maturity 

Delays from pumping out agricultural fields. 

 For the ARkStorm scenario, we estimated field repair costs (that include costs of repair to 

roads, culverts, and irrigation systems), perennial and livestock replacement costs, and forgone 

annual and perennial crop income losses. Flood damage to agricultural building stock and contents 

(presumably including equipment and feed) was estimated at about $13 billion by using HAZUS-MH 

inventory and methodology. We were unable to consider the effects of bacterial diseases and 

delayed pollination. We were able to address the effects of further agricultural production delays 

resulting from the time to repair Delta levees and dewater islands. 
73

Figure 42. Flooded vineyards, Guerneville, Calif., 2006, because of Russian River flooding 

(Photograph by A. Dubrowa for Federal Emergency Management Agency). 

METHODOLOGY 

 ARkStorm agricultural damages and losses from flooding were estimated for the following 

commodity categories: 

Annual crops 

Oilseeds – which include seed crops such as sunflower. 

Grains – which include wheat, barley, rice and other grains. 

Vegetables and Melons 

Fruits – which include strawberries and other annual fruit crops 

Sugar Beets 

All other annual crops, which include other forage and field crops 

Perennial crops 

Fruit trees – which include orchards and vineyards 

Greenhouse and nursery – which include nursery stock, Christmas tree farms, and other greenhouse 

crops1

Tree nuts - which include almonds, walnuts, pistachios, and other tree nuts 

 

1 Greenhouse and nursery crops were included under perennial crops for simplicity. 
74

All other perennial crops 

Livestock operations 

Dairy 

Feedlot cattle 

Poultry 

 Livestock damages pertain to livestock confined to feedlots, dairy farms, and poultry 

operations; we did not address potential losses of open range livestock. 

 The following data and methodologies were used to overlay agricultural commodity 

production on the flood map, calculate field repair and cleanup costs, and estimate agricultural 

damages. 

Location, Extent, Depth, and Duration of Flooded Agricultural Land 

 DWR land-use survey data2 provided the spatial distribution of commodity production in 33 

counties. The data specified one agricultural commodity for each area even if the commodity could 

be one of multiple crop types. GIS techniques were used to intersect acres of commodity production 

with the ARkStorm flood map attributes to produce the following information by county: 

Total amount of county acreage by agricultural land use (for example. orchard, forage, feedlot) 

Amount of county acreage by agricultural land use inundated under the ARkStorm flood scenario 

Range of flood depth (in feet) and flood depth at midpoint of depth range by agricultural land use 

Range of flood duration (in days) by agricultural land 

 Flood depth was set to the midpoint of the depth range for each land use class in a county. 

Low, mid-, and high flood durations were considered. 

 

2 DWR land use surveys are done on a rotating basis. The survey data used for this study covered 

the period 1993 to 2006. These land use surveys were performed by using aerial photographs and, 

more recently, satellite imagery to define field boundaries. Most of the land use survey data are 

entered directly into a digital map by using geographic information system (GIS) software on a 

laptop computer. Georeferenced, orthorectified imagery is used as a backdrop, and the land use 

boundaries are visible on top of the imagery. DWR staff visit and visually identify land uses on over 

95 percent of the developed agricultural areas within each survey area. After the field work has 

been completed and the maps have been checked for errors, a digital composite map of the survey 

area is created from the work of individual surveyors. Using GIS software, digital maps of quads, 

counties, water districts, and the DWR hydrologic planning units (Detailed Analysis Units) can be 

overlaid on the land use data to develop acreage summaries of land use by areas. Digitized land use 

survey data used for this study were downloaded at 

www.water.ca.gov/landwateruse/lusrvymain.cfm. 
75

Field Cleanup and Repair Costs

 Floods may impact farmland by causing erosion and deposition of debris and sediment, 

damaging farm roads, and clogging drainage and irrigation ditches. Damages are likely to be highly 

variable, depending on the velocity of flood flows, as well as depth and duration of inundation. 

Field cleanup and repair costs used for this study are based on average per acre costs for cleanup 

and rehabilitation used by the U.S. Army Corps of Engineers (USACE) in the 2002 “Sacramento and 

San Joaquin River Basin Comprehensive Study” (U.S. Army Corps of Engineers, 2002). Costs were 

assumed to vary according to flood depth and duration as shown in table 9. Costs were updated to 

2009 dollars. 

Table 9. Field cleanup and repair cost assumptions. 

[%, percent; USACE, U.S. Army Corps of Engineers; ft, foot; >, greater than] 

Applied % of USACE Estimate

Flood Duration (Days)

Flood Depth (ft) 0-0.25 0.25-1.75 1.75-3 3-5 >5

0-2 0% 25% 50% 75% 100%

2-6 0% 50% 75% 100% 100%

>6 0% 100% 100% 100% 100%

$/Acre Cost

0-2 $0 $79 $157 $236 $314

2-6 $0 $157 $236 $314 $314

>6 $0 $314 $314 $314 $314

Damages to Annual Crops 

 Flooding of agricultural land can disrupt field preparation and planting, destroy crops in the 

ground, and disrupt or prevent harvesting. The extent and severity of impact depends on the 

season in which flooding occurs, the duration of inundation, and the type of crops inundated. 

Losses from spring and summer floods generally will be greater than from autumn and winter 

floods. Brief periods of inundation may result in only small damages if these floods occur after 

harvest and prior to spring planting. Longer periods of inundation occurring in the autumn and 

winter may prevent spring planting altogether. Spring and summer flooding, regardless of the 

duration of inundation, will destroy most field and row crops in the ground. 

 For this study, production losses for annual crops are based on the crop production loss 

models developed for the Delta Risk Management Strategy Phase 1 Report (California Department 

of Water Resources, 2008). The crop loss values in this model were calculated by using Delta crop 

revenue and cost estimates prepared by DWR and monthly distributions of crop production costs 
76

and revenues developed for the Sacramento and San Joaquin River Basins Comprehensive 

Study (California Department of Water Resources, 2005 U.S. Army Corps of Engineers, 2002). The 

model calculates the difference between lost harvest revenue and avoided production expenses as a 

function of the month in which a flood is assumed to occur. For this study, flooding was assumed to

occur primarily in February.3 In addition, the models make the following assumptions about crops 

in the ground and planting of crops for next season: 

Three months is required following dewatering for cleanup and rehabilitation before fields can be 

prepared for planting.4

Spring/summer crops will not be planted if a field has not been dewatered and repaired by the end of 

March. 

Flooding occurring in January and February will destroy winter grain crops. 

 Given the above assumptions about lead times required for dewatering and field cleanup 

and rehabilitation, the ARkStorm flood scenario was assumed to disrupt the planting of truck crops 

and processing tomato crops, and winter grain crops, but not the planting or harvest of rice, corn, 

and other field crops. Average losses in 2009 constant dollars for flooded processing tomato and 

truck crop acreage are $424 and $1,479 per acre, respectively. The average loss for winter grain 

crops is $285 per acre. Crop income losses and field repair costs are assumed to be incurred in the 

same six-month period in which the flood occurs. 

Damages to Perennial Crops 

 Prolonged inundation may result in extensive damage or death of permanent orchard, 

vineyard and hay crops. Damages result largely from anaerobic soil conditions. In addition anoxic 

conditions in the soil can lead to the release of toxic substances such as manganese. A review of the 

literature provided limited information on the effects of prolonged inundation on various crop 

types (California Department of Water Resources, 2008). 

 For this study, production losses for perennial crops are based on the crop production loss 

models developed for the Delta Risk Management Strategy (California Department of Water 

Resources, 2005). These models assume that perennial crops inundated for 14 days or more would 

be killed by anaerobic soil conditions caused by standing water. Estimated damages are equal to 

the replacement cost of the crop plus the foregone production net income from crop production 

during the period of crop reestablishment. Damages are calculated in six-month increments. We 

assume field repair costs are incurred in the first six months; crop reestablishment costs are 

incurred in the six month period following the flood in which the crop would typically be planted; 

and net crop income losses are incurred in the six monthly periods following the flood in which the 

crop would typically be harvested. 

 Orchard crops are assumed to have an average reestablishment cost of $9,100 per acre and 

to require 4 years to reestablish. Annual net crop income loss is assumed to average $3,900 per 

 

3 Under the ARkStorm scenario flooding commences in middle to late January in northern California 

and early February in Southern California. February damage estimates from the crop models were 

considered to be most representative. 

4 This assumption is based on the experience with Jones Tract in the Sacramento/San Joaquin Delta. 
77

acre in the flood year and $2,600 per acre thereafter.5 Vineyard crops are assumed to have an 

average reestablishment cost of $11,400 per acre and to require 3 years to reestablish. Annual net 

crop income loss is assumed to average $3,700 per acre in the flood year and $3,400 per acre 

thereafter. Forage crops (primarily alfalfa) are assumed to have an average reestablishment cost of 

$640 per acre and to require 1 year to reestablish. Annual net crop income loss is assumed to 

average $530 per acre. 

Damages to Livestock 

 Damages to livestock confined to feedlots, dairy farms, and poultry operations are based on 

the replacement cost of livestock at risk of death by drowning. The replacement costs estimation 

assume the livestock are lost, though some producers may be able to mitigate losses by temporarily 

relocating some or all of their stock. Livestock inventory and replacement cost estimates are taken 

from U.S. Department of Agriculture (USDA) Livestock Review reports for California. Livestock 

inventories were allocated across counties in proportion to each county’s share of statewide 

feedlot, dairy, and poultry farm acreage, which was determined from the DWR land use survey data. 

 Flood depths of 6 feet or greater were assumed to place feedlot, dairy, and poultry livestock 

at substantial risk of death by drowning.6 The number of head at risk was calculated by multiplying 

the fraction of feedlot, dairy, and poultry farm acreage in each county with a flood depth of 6 feet or 

greater by the county livestock inventory. This calculation yielded the estimated number of 

livestock potentially at risk under the ARkStorm flood scenario. 

 The value at risk is calculated by multiplying the number of livestock potentially at risk by 

the replacement cost per head. The average replacement cost for feedlot cattle is based on the 

inventory of cows, heifers, steers, and calves and the respective value per head. The average value 

used in this study is $678 per head.7 Replacement costs for dairy cows and poultry come directly 

from USDA. Dairy cow replacement cost used in this study is $1,300 per head. Poultry replacement 

cost is $2.60 per head. Both estimates are based on 2009 prices received by California farmers. 

Livestock replacement and field repair costs are assumed to be incurred in the same six-month 

period in which the flood occurs. 

 

5 The higher net income loss in the flood year is because of production costs incurred prior to the 

flood event. In subsequent years these production costs can be avoided. 

6 The rationale for a flood depth threshold of 6 feet is that livestock operators would have more 

opportunity to move livestock to safe ground or let livestock wait out the flood at lesser depths; 

whereas, depths of 6 feet or more would likely pose an existential threat to most livestock and 

likely would entail mandatory evacuations, which would limit the ability of operators to move 

livestock to safe ground. 

7 This is based on 2008 marketing year prices reported by USDA. 
78

ARkSTORM AGRICULTURAL DAMAGE ESTIMATES 

Damaged Land 

 Damaged lands were defined in relation to the following damage parameters: any flooding 

of annual crop land was assumed to result in damage to crops already in the ground; damage to 

perennial crop production is assumed in areas where flood waters do not recede for 14 or more 

days; and significant damage to livestock production is assumed in areas where flood depth is 6 feet 

or more. 

 The percent of land area in annual crops, perennial crops, and livestock production 

significantly damaged by flooding is summarized in table 10. Overall, the ARkStorm flood scenario 

results in significant damage to about 23 percent of acreage in annual crop, 5 percent of dairy, 

feedlot, and poultry livestock production, and 5 percent of perennial crop production. Comparable 

results by county and commodity category are available in appendix A. The percent damage varies 

greatly by county, with counties in the northern part of the San Joaquin Valley and southern part of 

the Sacramento Valley experiencing the most damage. Areas within the San Joaquin-Sacramento 

Delta are especially vulnerable to damage. 

Table 10. Acres of significantly damaged agricultural lands. Values pertain to flooded annual crop lands, 

perennial crop lands with more than 14 days of flooding, and livestock areas with flood depth greater than 

6 feet. 

[%, percent] 

Commodity Acres production

(thousands)

Acres significant damage

(thousands)

Percent

Annual crop 18,582 4,324 23% 

Perennial crop 5,673 261 5% 

Livestock 285 13 5% 

Statewide Agricultural Damages 

 Economic losses accrue from costs of field cleanup, and repair; perennial crop and livestock 

replacement; and forgone annual and perennial crop income. In the case of annual crops, foregone 

crop income is the difference between the harvest value of the crops destroyed and avoided 

production expenses. In the case of perennial crops (orchards and vineyards), foregone crop 

income is the sum of net crop income losses during the period of crop re-establishment. Figure 43 

presents the estimated state losses of $3.75 billion (2009 constant dollars) for the low-end flood 

duration. Although there is 16 times more significantly damaged annual crop land than significantly 

damaged perennial crop land, most of the losses pertain to perennial crops for two reasons: 

reestablishment of these crops incurs replacement costs and multiple years of forgone income until 

the crop bears fruit. The high-end flood duration estimate increases annual, perennial, and 

livestock losses by 25 percent, 100 percent, and 1 percent, respectively, revealing the sensitivity of 

perennial crops to longer flood durations. 
79

Figure 43. State agricultural damages to annual and perennial crops and livestock. 

 Table 11 through table 13 display the statewide damages by commodity category under 

annual and perennial crops and livestock for the low, mid-, and high flood durations. For the annual 

crops (table 11), about 50 percent of the losses pertain to grains and 25 percent to vegetables and 

melons. For the perennial crops (table 12), the damages are dominated by fruit tree damages; fruit 

tree (including vineyard) damages represent about 65 percent of the perennial crop damages. 

Damages to nut trees comprise about 25 percent of total damages to perennial crops and damages 

to forage crops (alfalfa) and nursery crops about 10 percent. Damages to perennial crops are 

spread over a 5-year reestablishment period, with about 70 percent of the total occurring in the 

first two years and 30 percent in the 3 subsequent years. The first two years of damages primarily 

are because of field damage and reestablishment costs while damages in the latter three years 

accrue from forgone crop income during the nonbearing period of reestablished orchards and 

vineyards. Differences in crop income losses across low, mid, and high flood duration occur when 

the flood duration causes further damage as is the case for perennial crop damage that is sensitive 

to greater than 14 days of flooding. For the livestock damages (table 13), just over 80 percent of the 

livestock replacement costs apply to dairy because the cost per head of dairy is twice that of feedlot 

herd and more dairy lands are significantly damaged. 
80

Table 11 Statewide damages to annual crops for low, mid and high flood durations. All amounts are in 

millions of 2009 dollars. 

[low, mid, high are flood duration designations] 

Crop Category Crop Income Losses Field Damages Total Damages

Low Mid- High Low Mid- High Low Mid- High

All Other Crops $0.0 $0.0 $0.0 $56.7 $79.0 $92.2 $56.7 $79.0 $92.2

Fruits $52.5 $53.0 $53.0 $30.1 $37.0 $38.9 $82.6 $90.0 $91.9

Grains $85.3 $85.3 $85.3 $188.5 $244.7 $253.0 $273.8 $330.0 $338.3

Oilseeds $0.0 $0.0 $0.0 $2.3 $3.1 $3.1 $2.3 $3.1 $3.1

Sugar Beets $0.0 $0.0 $0.0 $4.6 $5.3 $5.7 $4.6 $5.3 $5.7

Veg/Melon $110.9 $129.4 $129.4 $20.4 $25.6 $26.9 $131.4 $155.0 $156.3

Total $248.7 $267.7 $267.7 $302.6 $394.7 $419.8 $551.3 $662.4 $687.5

Table 12. Statewide damages to perennial crops over 5-year reestablishment. All amounts are in millions 

of 2009 dollars. 

[low, mid, high are flood duration designations] 

Crop 

Category

Crop income losses Field damages and crop 

replacement cost

Total Damages

Low Mid- High Low Mid- High Low Mid- High

Fruit 

Trees

$1,06

1.8

$1,06

1.8

$2,09

5.9

$871.2 $888.6 $1,591.0 $1,93

3.0

$1,95

0.4

$3,68

6.9

Nut Trees $464.

7

$464.

7

$1,27

4.6

$327.3 $340.2 $858.0 $791.

9

$804.

9

$2,13

2.6

Peren. 

Forage

$79.8 $79.8 $136.

1

$179.6 $214.0 $288.2 $259.

5

$293.

8

$424.

3

Greenhou

se

$9.4 $9.4 $19.3 $7.3 $7.9 $14.3 $16.7 $17.3 $33.6

Total $1,61

5.7

$1,61

5.7

$3,52

5.9

$1,385.4 $1,450.7 $2,751.5 $3,00

1.0

$3,06

6.3

$6,27

7.4
81

Table 13. Statewide damages to livestock. All amounts are in millions of 2009 dollars. 

[low, mid, high are flood duration designations] 

Livestock 

Category*

Livestock Losses Field Damages** Total Damages

Low Mid- High Low Mid- High Low Mid- High

Dairies $110.6 $110.6 $110.6 $1.5 $2.3 $2.4 $112.1 $112.9 $113.0

Feedlots $22.2 $22.2 $22.2 $0.2 $0.4 $0.4 $22.4 $22.6 $22.6

Poultry $1.3 $1.3 $1.3 $0.4 $0.6 $0.7 $1.6 $1.9 $2.0

Total $134.1 $134.1 $134.1 $2.0 $3.2 $3.5 $136.1 $137.3 $137.6

* Does not include possible damages to free range livestock. 

**Does not include damages to structures (for example, barns) or contents of structures (for 

example, milking equipment). 

County Agricultural Damages 

 County results of annual and perennial crop and livestock losses were mapped for the first 

year and subsequent years (fig. 44 through fig. 47). The losses extend beyond the first year for 

perennial crops only. In the case of perennial crops, field repair costs are assumed to occur in the 

first six months, but crop income losses and reestablishment costs extend into the future, 

depending on the season in which the crop typically would be reestablished and the number of 

years until the crop would be bearing. The maps show the variation of losses across counties and 

the distribution of losses to commodities. About $140 million of the annual crop losses (about one 

third of the total for annual crops) occur in San Joaquin County followed by $55 million of annual 

crop losses in Kings County. San Joaquin County incurs about half of the perennial crop damages, 

followed by Sacramento and Yolo Counties. For livestock damages, again, the damages are 

concentrated in San Joaquin with almost half of the livestock damages. In contrast to crops, 

southern California counties are relatively more affected by livestock damages with $22 million in 

Riverside County and $13 million in San Diego County for replacement of dairy and livestock 

feedlot cattle. See appendix A for further details on the field costs, replacement costs, and forgone 

income losses by commodity and by county. 
82

Figure 44. County annual crop field repair costs and income loss. Shading of county indicates 

estimated dollar losses. Colors on pie charts indicate percentage of losses from each of the listed 

crops. 
83

Figure 45. County perennial crop replacement and income loss (first year). Shading of county 

indicates estimated dollar losses. Colors on pie charts indicate percentage of losses from each of the 

listed crops. 
84

Figure 46. County perennial crop income loss (subsequent years). Shading of county indicates 

estimated dollar losses. Colors on pie charts indicate percentage of losses from each of the listed 

crops. 
85

Figure 47. Livestock replacement cost. Shading of county indicates estimated dollar losses. Colors 

on pie charts indicate percentage of losses from each of the types of livestock listed. 
86

Effects of Delays in Delta Island Dewatering

 The above results were used in the economic impact analysis. Subsequently, we received a 

Delta Island scenario of levee breaching and times to repair and dewater islands (see the Levee 

section of this report) that alters the flood durations in the Delta area. The flooding of half of the 

roughly 60 islands in the Delta would present major logistical and response challenges. Levee 

repair and island dewatering would need to be prioritized and likely would be constrained by the 

availability of equipment, material, and labor required to stabilize and close levee breaches, repair 

levees, and pump out flooded islands. The analysis estimated that it could take up to one and a half 

years to dewater the 31 islands assumed to flood under the ARkStorm scenario. The analysis 

estimated that less than 40 percent of the islands would be dewatered within six months of the 

flood. Figure 48 shows the percentage of flooded islands estimated to be repaired and dewatered 

by time interval. 

 The flood duration times estimated for flooded islands in the Delta are longer than the 

ARkStorm flood durations used for the economic impact analysis. More extensive and prolonged 

disruption of agricultural production is the practical consequence of longer flood durations in these 

areas. Whereas, the flood durations used for the ARkStorm economic impact analysis implied that 

production of annual crops would be disrupted for one season, the Delta island dewatering analysis 

indicates that for some islands agricultural production could in fact be disrupted for multiple 

seasons. This means that agricultural damages could be greater than estimated by the above 

analysis for San Joaquin, Yolo, Sacramento, Solano, and Yolo Counties, which have substantial 

agricultural land in the Delta. The extent of underestimation is not expected to be large, however, 

because the majority of agricultural damage estimated by the ARkStorm analysis is associated with 

orchard and vineyard crops, which are not extensively grown on Delta islands. 

Figure 48. Time required to dewater 30 flooded Delta islands. 

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8

Percentage of Flooded Islands Dewatered

Years Following Flood Event
87

SUMMARY 

 The ARkStorm winter storm agricultural damages are an order of magnitude greater than 

1997 flood damages. Statewide damages to crops, livestock, and fields were estimated to range 

between $3.7 and $7.1 billion.8 Destruction of perennial orchard and vineyard crops accounted for 

most of the estimated agricultural production losses. We note that these perennial crop losses may 

have been underestimated because of the use of old survey information that in some areas may be 

more than 10 years old. Recent crop shifts away from field and forage crops toward fruit and 

vegetable crops, and especially toward orchards and vineyards may have affected the estimate. A 

quick look at the county agricultural reports of the 3 most-affected counties (Sacramento, San 

Joaquin, and Yolo) indicates that perennial crop acreage has increased by more than 20 percent in 

the last 10 years (Stephen Hatchett, Western Research Economics, 2010, written commun.). 

Damages to farm structures and equipment, were estimated separately at $13 billion by using the 

HAZUS-MH inventory and methodology. 

 Like the 1997 storm, San Joaquin and Sacramento Counties are among the counties most 

severely affected by the ARkStorm scenario, but damages in Yola, Solano, and Kings Counties are 

relatively more severe. Agricultural production in the Delta, much of which occurs on deeply 

subsided islands protected by increasingly fragile levees, appears to be particularly at risk. 

MITIGATING AGRICULTURAL DAMAGES 

 It is not possible to move crops and farm structures out of harm’s way during a flood. It is 

possible to provide buffers and barriers between these assets and flood waters. Thus flood 

protection for most agricultural assets depends on a broader set of land use and flood risk 

mitigation policies. In the case of livestock, given adequate advance warning and livestock 

relocation contingency plans, mobilization and relocation may be possible. Dairy is the most 

problematic because of the need for milking facilities. The California dairy industry has made some 

effort in recent years, in conjunction with the California Department of Food and Agriculture, to 

implement a formal emergency alert system that could be used to provide early warning in the 

event of a fast moving disease outbreak9 or pending natural disaster, such as the threat of 

 

8 We have estimated repair and replacement costs of damaged assets and infrastructure. This 

estimate is consistent with damage estimates in the rest of the ARkStorm scenario report. Economic 

damages to landowners pertain to earlier replacement of perennial crops and livestock. Supposing 

perennial crops are replaced two-fifths through the useful life, and by using a real discount rate of 3 

percent, the present value reestablishment cost is about 70 percent of the cost calculated simply as 

a new capital cost plus several years of no income. 

9 For example, experts estimate that in the case of an outbreak of Foot and Mouth Disease in 

California, each hour that the disease goes unrecognized could cost the dairy industry between one 

and three million dollars. (Dr. Michael Payne, Program Director, California Dairy Quality Assurance 

Program, written commun., 2010.)
88

flooding.10 However, progress on the system has been stymied by industry concerns about how 

producer information needed to implement the system, some of which the dairy associations 

consider proprietary, would be safeguarded. 

 Even with a robust alert system in place, livestock producers also must be able to act on the 

information. Mobilizing and relocating livestock in large numbers presents obvious logistical 

challenges. Adequate transportation has to be available to move the livestock and the livestock have 

to have some place to go. The success of mobilizing and relocating large numbers of livestock on 

short notice will depend almost entirely on having emergency relocation plans and the resources 

necessary to implement those plans already in place. Our research suggests that planning in this 

area is not very far advanced. The level of contingency planning among livestock producers in not 

known and, likely, many do not have flood emergency response plans in place, according to at least 

one industry expert (M. Payne California Dairy Quality Assurance Program, written commun., 

2010). Floods are rare events and responses are too often handled on the fly.11 This may work 

when only a few isolated producers are affected, but such a strategy is sure to fail under a scenario 

such as ARkStorm. 

 Animal emergency response plans reside in county plans to varying degrees, but moving 

stock at the regional scale has not been examined. This response plan is on the agenda for the 

upcoming Golden Guardian 2011 exercise. After the 1997 storm, the California Animal Emergency 

Response System (CARES) was established. This system is being revitalized by the California 

Department of Food and Agriculture (CDFA).12 “CARES will coordinate resources and decisions 

once an incident escalates to a state-level emergency; the CARES Plan is not intended to supersede 

local government plans but to assist them as they exhaust their resources” (California Department 

of Food and Agriculture, 2000). 

RESEARCH NEEDS RELATED TO AGRICULTURAL DAMAGES 

 We neglected estimation of livestock disposal costs for livestock at risk. In addition, the 

agriculture sector will not only be directly affected by flood damages to crops and livestock, but also 

by associated power outages, well contamination, and access limitations. We capture sector 

interdependencies in the economic impact analysis. However, we note that further disruptions from 

 

10 The proposed alert system had the capacity to send out 48,000 emails in 2 minutes, and provide 

additional follow-up through automated phone message recordings. (Dr. Michael Payne, Program 

Director, California Dairy Quality Assurance Program, written commun, 2010.)

11 This situation is not unique to California. A recent news article tells the story of a Freeport, 

Illinois, dairy farm along the Pecatonica River that would have lost 500 head of cattle to rising flood 

waters but for the fact that a neighboring dairy in the process of doubling its operation had recently 

completed construction of new barns, which were temporarily standing empty. Had these barns 

not been available and had neighbors not pitched in to move the cattle, the herd may have been lost. 

The story illustrates the inherent challenges of moving even a relatively small number of livestock 

on short notice. (http://gazettextra.com/news/2010/jul/29/larson-acres-provides-home-floodedillinois-cows/ accessed on August 6, 2010.) 

12 John Rowden, California Department of Food and Agriculture,. written commun., August 17, 2010. 
89

aqueduct damage and salinity were not studied, although, it is believed that initially the quantity of 

precipitation runoff will keep salinity at bay. Conversely, agricultural losses would indirectly affect 

other industrial sectors. The effects of crop losses (not livestock losses) on other sectors were 

evaluated in the ARkStorm economic impact analysis which allows for commodity price responses. 

 Finally, insurance payments and federal disaster payments are resilience strategies that 

would enable the recovery of the California agricultural sector, but the breakdown of insured and 

uninsured losses was not evaluated. 

Building and Content Repair Costs 

PROPERTY-DAMAGE METHODOLOGY 

 We use the HAZUS-MH methodology, its building exposure data, and its flood and wind 

vulnerability functions to estimate wind and flood damage to the general building stock resulting 

from the ARkStorm. However, we performed the loss calculations outside the HAZUS-MH software 

because (1) the ARkStorm flood map could not be readily imported into the HAZUS-MH flood 

module and (2) because the HAZUS-MH wind module does not include California. We adapted the 

Florida wind vulnerability and fragility functions for use in California by shifting those functions to 

account for lower design wind speeds (and, therefore, likely higher wind vulnerability) in 

California. We estimate damage and loss at each census block and by each of the 33 occupancy 

classes in HAZUS-MH, and then we aggregated to the state level. The full methodology is 

documented in Olsen and Porter (2010). 

 The HAZUS-MH methodology begins with a description of the inventory of assets exposed to 

loss. The inventory is characterized by estimates of the number of buildings, building area, building 

replacement cost (the cost to build a functionally and architecturally similar building at the same 

location, if the existing building were not there), content replacement cost (including furnishings, 

fixtures, equipment, and business inventories), and the number of building occupants at three times 

of day, based on working patterns. HAZUS-MH provides these quantities by census block and 

occupancy class. The estimates begin with data from the Census of Population and Housing and 

from the Economic Census. These and other sources provide estimates of the number of people 

living or working in a census block. The estimated total building area by census block is the product 

of the number of people and estimates of the per person square footage of the buildings these 

people occupy. Companies that estimate construction costs provide estimates of the replacement 

costs of the buildings (again, the cost to construct the buildings new, often estimated on a persquare-foot basis in construction cost manuals). Using insurance and other rules of thumb, one can 

then estimate the content value as a multiplicative factor of building value. The distributions of 

these building inventory characteristics by structure type can be estimated by using engineering 

experience and, in some cases, information from building departments and the insurance industry. 

 The building inventory as defined by HAZUS-MH is then overlain on the ARkStorm scenario 

maps of flood depth and wind speed (fig. 8, and fig. 9). When there is an asset in the inventory that 

is exposed to flooding or damaging winds, we find the damage ratio from a vulnerability function 

for that asset’s structure type. A vulnerability function is a relationship between an environmental 

excitation—here, flood depth or wind speed—and the ratio of repair cost to replacement cost, 

known as the damage ratio. We use unmodified HAZUS-MH flood vulnerability functions, but we 

modify the HAZUS-MH wind vulnerability functions as described in a subsequent paragraph. Figure 

49 shows flood vulnerability functions for several structural types of residential occupancy. HAZUSMH vulnerability functions provide the mean, or expected value, of the damage ratio given the 
90

environmental excitation; the uncertainties in the vulnerability functions are not estimated. We 

calculate the expected loss by multiplying the damage ratio by the value exposed for each 

combination of census block, occupancy class, and structure type. Adding up the loss for each 

combination leads to an estimate of aggregate repair cost. 

Figure 49. Sample HAZUS-MH flood vulnerability functions showing damage ratio for different 

types of single-family dwellings. 

 Equation (3) states mathematically what we have just described. L represents the expected 

value of loss, such as building repair cost. The three large Greek sigma characters mean ”sum over” 

or ”add up” and the letters beneath them—i, j, and k—are counters, referring respectively to census 

blocks (i), occupancy classes (j), and structure types (k). The term 

Vi j k , ,

represents the estimated 

value exposed to loss in census block i, occupancy class j, and structure type k. 

Vi j k , , is multiplied 

by

j k i , y x

, which is the vulnerability function evaluated at location i. In other words, 

j k i , y x

is 

the mean damage ratio for an asset of occupancy class j and structure type k, when exposed to the 

environmental excitation x experienced by census block i. 

i j k j k i , , ,

i j k

L V y x

(3) 
91

 We modified the wind vulnerability functions encoded in HAZUS-MH for use in California. 

HAZUS-MH does not have a wind model for California, so we used vulnerability functions from its 

Florida hurricane model. The International Building Code defines the primary wind speed to be 

used for the design of buildings (known as the basic wind speed) as 85 mph for sites in California 

and as 130 mph ± 20 mph for sites in Florida (American Society of Civil Engineers, 2006). We 

assume that a California building exposed to 85 mph winds would be damaged to the same extent 

as a similar Florida building exposed to 130 mph winds. Thus, we shift the wind speed axis of the 

Florida vulnerability functions to account for the lower basic wind speed in California when 

compared to Florida. 

PROPERTY-DAMAGE SCENARIO 

 Based on the methodology described in the previous section, the estimated repair cost for 

flood-induced building damage is about $200 billion, which is equivalent to approximately 7 

percent of the HAZUS-MH estimate of the replacement cost of all buildings in California ($2.7 

trillion), or 2-3 years of statewide construction spending at 2006-2007 rates (about $75 to $100 

billion per year). Flood-related content losses are estimated to contribute another $100 billion. The 

reason for these very high flood-related losses is evident when examining the flood maps in detail. 

Figure 50 through figure 53 show extensive inundation of urban areas in Sacramento, Stockton, the 

San Jose area, and Los Angeles and Orange Counties. 

 Using the HAZUS-MH methodology, inventory, and vulnerability functions, we first 

calculated flood-related content losses as roughly equal to flood-related building losses. However, 

this result seemed unreasonable: the content losses seemed too high—perhaps by a factor of two—

relative to the building losses. To check whether the content losses ought to roughly equal building 

losses, we examined National Flood Insurance Program claims documented in a FEMA Flood 

Mitigation Assistance database. The Flood Mitigation Assistance grants in the database mostly were 

dated between 1996 and 2003. We examined all claims where there were nonzero building losses 

and nonzero content losses. There were 485 such claims, dated between 1977 and 2001. These 

claims totaled about $8.6 million for building losses and $5.1 million for content losses, or roughly a 

$0.60 content loss per $1.00 of building loss. These claims data reinforce our doubts about the 

content losses first estimated in the ARkStorm scenario. The 0.60:1.00 ratio implied by the Flood 

Mitigation Assistance data is close to an upper boundary because the database showed many 

properties with repetitive losses where some, but not all, of the claims included content losses. 

These data suggest that there are properties with both structure and content coverage that 

experienced claims with structure loss but not content loss. Such claims are not included in the $8.6 

and $5.1 million loss totals. If these claims were added to the totals that would increase the $8.6 

million figure but not the $5.1 million figure. With this evidence in mind, we somewhat arbitrarily 

reduced by half the content losses estimated with the HAZUS-MH methodology and data, and we 

reported that value in the above paragraph. 

 HAZUS-MH data on California census blocks include classification as riverine or coastal. The 

loss from flood damage to building structures in all coastal census blocks is $29 billion, and the loss 

from flood damage to building structures in all riverine census blocks is $164 billion. This loss in 

coastal areas is 15 percent of the total flood loss from structural damage. Note, however, that we 

calculated all property losses from the flood map derived from rainfall runoff, not from the coastal 

inundation study in Southern California. 
92

Figure 50. ARkStorm hypothetical flooding in Sacramento. 

Figure 51. ARkStorm hypothetical flooding in Stockton. 
93

Figure 52. ARkStorm hypothetical flooding in the San Jose area. 

Figure 53. ARkStorm hypothetical flooding in Los Angeles and Orange Counties. 

 Wind-related building repair costs are estimated to be $6 billion. A $6 billion loss from wind 

damage alone would constitute a significant natural disaster, but in comparison with the $200 

billion loss from flood damage, the amount is relatively small. Wind damage contributes such a 

small fraction of the overall loss because the areas of highest winds also are sparsely populated. 

Furthermore, although the wind speeds in the ARkStorm are large (fig. 9), the current basic wind 

speed for design exceeds the scenario wind speeds by 25 mph or more in most areas of the 

ARkStorm. For comparison, figure 54 maps the current basic wind speed for California (American 
94

Society of Civil Engineers ,2006). In fig. 54, the basic wind speed is 85 mph (38 meters per second) 

in California. Shaded areas denote special wind regions where unusual wind conditions exist. 

 We do not calculate the loss to building contents from wind damage. The wind vulnerability 

functions for contents cannot be extracted from the HAZUS-MH software or documentation for 

most occupancy classes. Nonetheless, it seems reasonable that content losses caused by wind 

damage would be negligible in comparison to content losses from flood damage. Again, the areas of 

highest wind speeds have a low potential for property loss. 

Figure 54. Basic wind speed map for California (American Society of Civil Engineers, 2006, figures 

6-1) and adjoining states, showing zones where basic wind speed for design is 85 miles per hour 

(38 meters per second) and 90 miles per hour (40 meters per second). 

 Nearly one-quarter of the total building square footage in California is affected by flooding 

in ARkStorm, with little variation of this ratio between occupancy classes (fig. 55) Most flooded 

buildings are not a total loss, but rather experience damage requiring repair costs between 10 

percent and 50 percent of replacement cost. Residential buildings dominate the flood-related 

building repair costs, as shown figure 56. Residential buildings (labeled "RES" in Figure 56) account 

for 81 percent of the total estimated flood-induced building repair costs and 96 percent of windrelated losses. Commercial ("COM") buildings account for 13 percent and 2 percent of loss from 

flood and wind damage, respectively. The modest balance of loss is from damage to buildings with 

the following occupancy classes: industrial ("IND"), religion or nonprofit ("REL"), governmental 

("GOV"), education, ("EDU") and agriculture. ("AGR"). Table 14 lists property losses by county, 

divided according to flood- or wind-related loss. 
95

Figure 55. Fraction of building square footage affected by ARkStorm flooding for buildings in the 

occupancy classes agriculture, commercial, education, governmental, industrial, religion or 

nonprofit, and residential. 

RESILIENCY AND RESEARCH NEEDS FOR BUILDING DAMAGE 

 Although the purpose of the work described in these sections is to estimate property losses, 

a few obvious opportunities for research and improved resiliency present themselves: 

Consider mold in post-flooding inspections. This scenario disaster has a large number of flooded 

buildings, which raises questions about how safety inspections would be performed after a real-world 

California flood. A standard methodology from the Applied Technology Council (ATC)—known as ATC45—exists for performing these inspections and posting the safety of buildings (Applied Technology 

Council, 2004). The ATC-45 methodology establishes the level of safety based on observed damage 

resulting from: inundation; hydrostatic and hydrodynamic forces; waves; erosion and scour; and various 

kinds of wind damage. After Hurricane Katrina in 2005, building inspectors in New Orleans performed 

safety inspections for more than 100,000 buildings. This experience showed that mold also can be a 

source of damage resulting from flooding, and can endanger occupants’ safety. While mold is a more 

modest concern after a California winter storm, it may be worthwhile to consider procedures that 

address mold in the ATC-45 methodology. 

Expand training for ATC-45. A large cadre of structural engineers and others has been trained to 

perform post-earthquake safety inspections. Training for inspection of flood and wind damage in 

California has been more modest. 

Examine flood warning systems. Research in the late 1960s by the U.S. Army Corps of Engineers, New 

York District, suggested that 48 hours of advanced warning could reduce riverine flood damage in the 

Passaic River Basin in New Jersey by up to 35 percent. The developers of the HAZUS-MH flood module 

observed that this was an upper boundary and that in practice the savings would probably be less than 

this hypothetical maximum. If 48 hours of warning could reduce property losses by 10 percent in the 

ARkStorm. This reduction is equivalent to at least $30 billion, a significant avoided loss. 

Develop California wind vulnerability functions. 

0%

10%

20%

30%

40%

50%

AGRCOMEDUGOVINDRELRES

Occupancy group Fraction of area affected
96

Continue to improve the building inventory. Special attention is needed for the presence or absence of 

basements in single-family residences. 

 

Figure 56. Contribution to $200 billion in flood-related building repairs (left), and $6 billion in 

wind-related building repairs (right) for buildings in the occupancy classes agriculture, commercial, 

education, governmental, industrial, religion or nonprofit, and residential. 
97

Table 14. ARkStorm property loss by county. $1,000s, on replacement-cost basis. [values do not sum 

exactly to the total shown because of rounding] 

County Flood Wind County Flood Wind

Alameda 14,000,000270,000 Orange 50,000,000 22,000

Alpine 3,300 3,300 Placer 120,000 120,000

Amador 10,000 10,000 Plumas 19,000 20,000

Butte 320,000 61,000 Riverside 1,600,000 580,000

Calaveras 9,300 9,600 Sacramento 29,000,000 92,000

Colusa 230,000 9,100 San Benito 320,000 45,000

Contra Costa16,000,000430,000 San Bernardino 840,000 850,000

Del Norte 220,000 2,600 San Diego 25,000,000 24,000

El Dorado 120,000 120,000 San Francisco 990,000 180,000

Fresno 66,000 18,000 San Joaquin 22,000,000 72,000

Glenn 29,000 11,000 San Luis Obispo440,000 86,000

Humboldt 1,400,000 5,100 San Mateo 11,000,000 380,000

Imperial 5,500 6,100 Santa Barbara 1,300,000 73,000

Inyo 47,000 59,000 Santa Clara 40,000,000 59,000

Kern 220,000 140,000 Santa Cruz 66,000 62,000

Kings 44,000 13,000 Shasta 270,000 49,000

Lake 14,000 16,000 Sierra 5,300 5,400

Lassen 60,000 31,000 Siskiyou 7,400 7,700

Los Angeles 46,000,000580,000 Solano 7,000,000 130,000

Madera 29,000 190 Sonoma 5,500,000 86,000

Marin 8,500,000 72,000 Stanislaus 140,000 55,000

Mariposa 1,600 1,300 Sutter 2,400,000 41,000

Mendocino 1,400,000 26,000 Tehama 130,000 22,000

Merced 65,000 18,000 Trinity 990 79

Modoc 23,000 7,100 Tulare 17,000 2,200

Mono 44,000 47,000 Tuolumne 6,900 7,300

Monterey 280,000 280,000 Ventura 4,300,000 34,000

Napa 2,000,000 33,000 Yolo 3,800,000 47,000

Nevada 33,000 33,000 Yuba 1,000,000 24,000

Total 300,000,0005,500,000

DEMAND SURGE 

 If an event, such as an earthquake or flood, damages a very large number of properties, the 

cost to repair a given amount of damage at a particular property can be greater than to repair the 

same damage if fewer other properties were damaged. This increased repair cost is known as 

“demand surge,” and is a poorly understood socioeconomic phenomenon of large-scale natural 

disasters. Demand surge has caused estimated cost increases of 20 percent (for example, the 1994 

Northridge Earthquake; Kuzak and Larsen, 2005) to 50 percent (for example, Cyclone Larry in 

2006; Australian Securities and Investments Commission, 2007) in past catastrophes, and the 

ARkStorm would certainly measure among the largest of these catastrophes in terms of absolute 

dollar repair costs. However, two important mitigating circumstances are present in ARkStorm. 

First, there is a low penetration of flood insurance policies in California, and the small number of 

policies means many owners of damaged properties may not have access to immediate funds to pay 
98

for repairs. Consequently the demand for repair materials and labor might be spread over a longer 

time period as funds become available. Second, the anticipated low economic growth in early 2011 

suggests that demand for new construction will remain low. Construction laborers and contractors 

may not seek a premium for working in flooded areas if they do not have work on new construction 

projects. The research team estimates that demand surge would be on the order of 20 percent for 

ARkStorm. The estimated total property loss is about $306 billion, not including lifeline repairs, 

which could bring the total property loss after demand surge to perhaps $367 billion. Olsen (2010) 

estimated in detail the anticipated amount of demand surge in the ARkStorm. 

COMPARISON WITH PAST EVENTS 

 To judge whether the estimated property losses in the ARkStorm are reasonable, we 

compared this loss with the experience of Hurricane Katrina, which made landfall in Florida and 

Louisiana in August 2005. Katrina cost at least $81 billion in property damage (Blake and others, 

2007). As reported by St. Onge and Epstein (2006), the Bush Administration sought $105 billion for 

repairs and reconstruction in the region. An unknown—but large—fraction of the loss occurred in 

the City of New Orleans, which had a population at the time of about 1.2 million people and a land 

area of about 180 square miles. Of these totals, about 140 square miles and the homes of 

approximately 1 million people experienced flooding. If we assume that one-half to three-quarters 

of the total property loss in Katrina is because of flooding in the City of New Orleans and the 

reported losses are a depreciated value (at 75 percent of the replacement cost new value), then the 

property loss in Katrina is roughly $54,000 to $81,000 per person with flood damaged property. 

 For comparison, ARkStorm flooding covers about 4,000 square miles (much of it

agricultural) and occurs in census blocks containing a population of about 6.5 million. If we apply 

the loss-per-person values from Katrina, we would expect property losses in ARkStorm of $350-530 

billion, which agrees reasonably well with the estimated $370-440 billion total from building 

property damage, lifeline repair cost, and agricultural property loss estimated for ARkStorm. 

 An insurance expert (G. Michel, Willis Ltd, written commun., September 22, 2010) indicated 

that his back-of-the-envelope estimate for expected property losses from building damage in 

ARkStorm would have been on the order of $200 billion, before demand surge, for a similar-sized 

event, as opposed to the $300 billion estimated here. However, his back-of-the-envelope calculation 

assumes average residential claims of $20,000; whereas, a review of 485 claims discussed earlier 

suggests a figure closer to $28,000. Adjusting for this difference, the agreement between the 

insurer’s back-of-the-envelope calculation and that produced here is reasonable. Nonetheless, the 

difference emphasizes the uncertainty inherent in such modeling efforts. This discrepancy between 

the expectation of an insurance expert and the calculated ARkStorm property losses highlights the 

need for validation of the building inventory, vulnerability functions, and overall losses, for 

example, by comparing these results against recorded losses (insured and otherwise) from historic 

natural disasters. 

Insurance Impacts 

INTRODUCTION 

 California is a dynamic insurance market. Insurance coverage is provided for fire, wind, 

flood, and earthquake, among other natural perils. At year end 2009, there was a total of $21.5 

billion in direct written property insurance premiums in the state (Highline Data, 2009) accounting 
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for 12 percent of the U.S. premium volume. The physical damage to structures from wind is 

primary covered under standard residential or nonresidential insurance policies to protect the 

wealth and livelihood of inhabitants. The damaging effects of flood are generally not covered under 

residential or nonresidential property policies and must be purchased separately, in the case of 

residential coverage, or in addition to the coverage offered under the standard commercial 

property policy. 

 An individual or businesses decision to purchase flood insurance requires an analysis of the 

cost of insurance coverage, if coverage is even available, weighed against the benefits provided by 

the insurance policy and the risk of flooding. In the case of residential flood insurance, the 

homeowner may be required to purchase a flood policy as a requirement of securing a mortgage on 

the property. A policy premium is paid for the term of the insurance contract and policy coverage 

limits are offset by a deductible that must be met before the claim is paid. 

 Following is a brief summary of insurance coverage available in California for the perils of 

flood and wind. References are provided for more in-depth understanding of the insurance 

dynamics. Insurance loss estimates were derived from the damage estimates in this report and an 

estimate of the insurance availability. Because of limited data on insurance availability within the 

highest risk flood areas, we made broad assumptions across the counties affected to arrive at a 

range of loss estimates that we believe to be reasonable. 

CALIFORNIA FLOOD AND WIND INSURANCE COVERAGE 

 The primary cause of loss in the ARkStorm scenario is flooding. Flood insurance for 

residential homeowners in California is primarily covered by the National Flood Insurance Program 

(NFIP). The NFIP was established in 1968 with the passage of the National Flood Insurance Act. 

The NFIP was broadened and modified with the Flood Disaster Protection Act of 1973, the National 

Flood Insurance Reform Act of 1994 and the Flood Insurance Reform Act of 2004 (Federal 

Emergency Management Agency, 2010a). The NFIP is administered by the Federal Emergency 

Management Agency (FEMA), a component of the U.S. Department of Homeland Security. 

 The NFIP enables property owners in participating communities to purchase flood 

insurance as an alternative to disaster assistance. Communities participating in the NFIP must 

agree to enforce a floodplain management ordinance to reduce future flood risks in specially 

designated flood hazard areas. The federal government, through the NFIP, will make the insurance 

coverage available as a means of financial protection against flood losses. 

 To make insurance coverage available to property owners, insurance companies participate 

in a Write Your Own (WYO) program, and service the NFIP Standard Flood Insurance Policy in their 

name. The federal government retains the responsibility for the insurance coverage and pays the 

WYO carrier an administration fee to cover the expense of writing the policy and processing the 

claims. Currently (2010), about 100 insurance companies write flood insurance with FEMA. Top 

carriers in California include State Farm, Allstate, Hartford, Zurich, and Travelers. 

 NFIP insurance coverage (table 15) is provided for the building and contents of residential 

properties up to $250,000 and $100,000, respectively, and $500,000 for both building and contents 

coverage for nonresidential properties. Deductibles are applied separately to building and contents 

coverages and vary by policy and higher deductibles will result in a reduced premium for the 

policyholder. 
100

Table 15. National Flood Insurance Program flood insurance coverage. 

Building Coverage Limits

Single Family Dwelling $250,000

Two to Four Family Dwelling $250,000

Other Residential $250,000

Non-Residential $500,000

Contents Coverage Limits

Residential $100,000

Non-Residential $500,000

 As of December 31, 2009, WYO carriers in California wrote a total of $179.5 million 

(Highline Data, 2009) of premiums for a total of $17.7 billion of policy limits (National Flood 

Insurance Program, 2010). 

 For nonresidential properties (in this case, the commercial, industrial, religious, 

governmental, education, and agricultural properties included in the study), in addition to the NFIP, 

businesses have the opportunity to insure against flood loss in their standard property policy or 

Difference in Conditions (DIC) policy. Insurance companies writing these policies offer larger 

policy limits, various deductible options, and are tailored to the specific insurance needs of the 

commercial business and its property locations. These carriers include FM Global, CNA, Allianz, 

Chubb, Lexington, Westchester, Arrowhead, and RLI, among others. 

 For wind coverage, wind is typically a covered peril in the standard property insurance 

policy offered by insurance carriers for both residential and nonresidential properties. 

INSURANCE LOSS FROM THE ARKSTORM SCENARIO 

 According to this study, total flood-induced building damage is estimated at $200 billion. 

Flood related contents losses are estimated at $100 billion for a combined total of $300 billion. 

Flood related losses would only be covered by insurance policies that provide flood related 

coverage. In order to estimate the part of flood related damage that may be covered by insurance, 

we need to estimate the proportion of property exposure that is covered by insurance. In order to 

do this, we calculated an insurance penetration or take-up rate. Based on published NFIP policy 

limits by county (National Flood Insurance Program, 2010) and the exposed values provided with 

the HAZUS MH exposure data (National Institute of Building Sciences and Federal Emergency 

Management Agency, 2009) used in the flood-induced damage calculation, it is estimated that 2.4 

percent of the total residential property exposed values are covered for flood by the NFIP. The 

breakdown of residential to nonresidential NFIP coverage limits is unavailable. Therefore, for 

purposes of calculating the NFIP insurance penetration or take-up rate, we have conservatively 

assumed all NFIP policy limits written provide residential coverage. We have made no provision 

for nonresidential coverage offered by the NFIP. 
101

 Nonresidential flood coverage is provided by the NFIP standard commercial property 

policies or through Difference In Conditions policies, only if the flood risk is acceptable to the 

carrier. It is generally, expected that flood insurance will be purchased if the risk is acceptable and 

the insurance premium is reasonable. However, information about the actual take-up rate of 

nonresidential flood insurance coverage is not publicly available. For purposes of our analysis, we 

have assumed a range of take-up rates from 20 percent to 90 percent, varying by county. Our 

estimates vary by county in a similar proportion to the actual NFIP take-up rates with the highest 

coverage take-up found in the northern counties of Yuba, Sutter, and Colusa, just north of 

Sacramento. 

 For the calculation of an insured loss estimate, we have applied the penetration rates for 

residential and nonresidential flood insurance coverage to the flood damage estimates by county 

and line of business. For a flood damage estimate of $300 billion, we estimate the flood insurance 

loss to be in the range of $20 billion to $30 billion. 

For wind insurance loss estimates, we must cite the “Efficient Proximate Cause” language, 

which comes into play in California insurance law. The efficient proximate cause applies to an “All 

Risk” insurance policy when a loss is caused by a combination of a covered peril and a specifically 

excluded peril. The loss is covered only if the covered peril was the efficient proximate cause. The 

loss is excluded if the excluded risk was the efficient proximate cause of loss. This is subject to 

interpretation and there have been many cases involving efficient proximate cause language 

(Johnson 1999), the subject and review of which is beyond the scope of this analysis. Flood damage 

exceeds the wind damage estimates in all counties in the ARkStorm scenario; therefore, flood 

damage was assumed to be the proximate cause of loss for all properties. Accordingly, only the 

flood insurance policy would respond for insurance coverage. Litigation and judicial rule may 

require wind policies to respond; however, this is not contemplated explicitly in our estimates. 

 For purposes of estimating the insurance loss, it is estimated that the flood policy will 

respond to offer coverage, to the extent a policy covering flood is in place. If no flood policy is in 

place, the wind policy would not respond because of the efficient proximate cause language. 

 Table 16 shows the estimated residential flood insurance loss, covered by the NFIP, and the 

estimated range of nonresidential flood insurance loss assuming a moderate and high level of 

insurance take-up. 

Table 16. Insured loss estimate for different levels of insurance. 

Residential Nonresidential* Total

NFIP only

Moderate 

Insurance Take-up

High 

Insurance Take-up

Moderate 

Insurance Take-up

High 

Insurance Take-up

$2.1 billion $20.1 billion $26.4 billion $22.2 billion $28.5 billion

*Commercial, Industrial, Religious, Governmental, Education, and Agricultural. 

 Computing the NFIP loss as a percentage of the insurance available, $2.1B per $17.7B, or 12 

percent, is a reasonable damage ratio for the amount of insurance available. We are unable to 

perform the same calculation for nonresidential flood insurance because the amount of flood 

insurance written is unknown. However, we believe our estimates to be reasonable. 
102

HISTORICAL PRECEDENT AND ECONOMIC RESILIENCY 

 California is best known for its earthquake and wildfire events. California is not generally 

known for its windstorm or flood events. In fact, since 1952, there have been 63 windstorm-related 

natural peril events and only 9 of these have involved some form of flooding. Interestingly, all the 

flood related events have occurred since 1995. The largest of these was an insured loss of just 

under $400 million in 2008 (Property Claims Service, written commun.). This report, however, 

provides the evidence that significant wind and flood events have the potential to occur. 

 The largest natural peril wind and flood insured loss event was Hurricane Katrina. Katrina 

was a Category 3 hurricane that made landfall on August 29, 2005. Katrina impacted the Louisiana 

and Mississippi coastlines with storm surge as high as 25 feet and contributed to the levee failure in 

New Orleans. For this reason, Katrina is thought of as two catastrophic events; the hurricane winds 

and the flood that ensued from the failure of the levee system. While total economic losses are not 

tracked, the estimated economic losses for Katrina exceeded $100 billion (Insurance Journal, 2005). 

 The insurance loss to Katrina is estimated at $41.1 billion (Insurance Information Institute, 

2010) resulting from 1.7 million claims. This estimate does not include the total flood loss to the 

NFIP that is estimated at $16.1 billion from 167,000 claims (Federal Emergency Management 

Agency 2010b). Combined, the total insured loss is just under $60 billion. 

 Obviously, Katrina is not a direct comparison to the ARkStorm scenario but Katrina is the 

most relevant event in recent history. Katrina caused significant economic disruption in the New 

Orleans and Gulf coast region; disruption that remains today (2010); however, the impact to the 

U.S. economy was mitigated for a few reasons. First, the economic production capabilities as 

measured by the Gross State Product (GSP) of the Gulf region are lower than the national average. 

The GSP for Louisiana and Mississippi is $225 billion and $93 billion (Chantrill, 2010), respectively, 

compared to the national average of $292 billion. Next, the general population affected is small 

with 7.6 million people living in Louisiana and Mississippi or 2.1 percent of the U.S. population. 

Lastly, nearly 60 percent of the damages were insured that has provided the funds to allow people 

to rebuild, contributing to a faster recovery. 

 By contrast, for the ARkStorm scenario, the GSP of California is $1,870 billion or nearly 6 

times that of Louisiana and Mississippi combined. The population of California is 38.1 million or 

12.3 percent of the U.S. population. And it is, estimated that only 6 percent to 10 percent of the 

economic damages would be insured. Undoubtedly a repeat of the 1861-62 winter storm would 

have a significant impact not only on the California economy but on the U.S. economy as well. 

RESEARCH NEEDS RELATED TO INSURANCE 

 These are the two most compelling research needs from our viewpoint: 

1. Wind vs. Water: determining the primary cause of loss. Study ways to more quickly and efficiently 

determine if wind or flood is the primary cause of loss. Efforts in this area will help reduce litigation 

expense, reduce pressure on policyholders who may need to prove the cause of loss, and generally 

create a more efficient claims handling process that will benefit insurers and policyholders alike. 

2. NFIP reform. The current structure is not capable of handling the size of the loss potential today. 

The primary rate needs to be reviewed for adequacy. Exploration into giving the NFIP some 

authority to enforce appropriate construction to reduce risk or eliminate risk (by relocating 
103

homes/communities). In our opinion, simply renewing the current process is not a sustainable 

option. 

Evacuation 

 During winter storms, weather forecasts may trigger evacuation of areas threatened by 

flooding or landslide susceptibility where precipitation is persistent. Despite the advantage of 

forewarning, existing social conditions in the evacuation areas can determine the success of an 

evacuation procedure. Our analysis of the ARkStorm scenario identifies some of the social 

conditions that could create evacuation challenges caused by flooding in the Inland Region and two 

additional delta counties: Contra Costa and Solano (Fig. 57). First we estimated the number of 

people in the flooded areas in the Inland Region and the two additional Delta counties, then we 

examined social variables that have complicated other massive U.S. evacuations and analyzed these 

variables in the ARkStorm flooded areas, and finally we applied the HAZUS-MH formula (Federal 

Emergency Management Agency, 2010b) for shelter requirements. Estimates of county populations 

in flooded areas were passed to the economic impact analysis. Future refinement of the evacuation 

analysis for regional planning purposes will expand the social variables and incorporate other 

factors that affect evacuation (for examples, elevation, traffic routes, shelters). 

POPULATION LIVING IN FLOODED AREAS 

 We use the number of people living in the flood-hazard zones (designated as flooded in the 

ARkStorm scenario) as a proxy for evacuation numbers; we assume that these flooded areas are 

forecast with sufficient certainty and all occupants in flooded areas are evacuated. Typically, some 

people will refuse to evacuate (for example, because of pets), cannot evacuate (for example, 

because of disabilities), or try to hold out. Our estimate of people living in the flooded area indicates 

the order of magnitude of an ARkStorm scenario evacuation. 

 Review of contemporary newspaper coverage of previous storms with flood-related 

evacuations (James Carter, USGS, 2010, written commun.) revealed the following: 

A total number of evacuees for the 1861-62 storms was not reported, but some accounts tell of 6-7 

families sharing houses and 60 people residing in one room. Boats were “slapped together” in response 

to a scarcity of boats for evacuation. 

In 1938, apparently tens of thousands of people were evacuated. 

In 1969, helicopters evacuated sick and aged in isolated foothill areas. 

In 1986, about 45,000 people were reported to have evacuated. 

In 1997, the number of evacuees was on the order of 125,000 people. 

 The largest evacuation in the United States was in 2008 when over 3 million people were 

evacuated because of Hurricane Gustav (Global Risk Miyamoto, 2009). In 2005, the number of 

people older than 16 years old who evacuated from Hurricane Katrina was about 1.5 million 

according to U.S. Bureau of Labor Statistics (Groen and Polivka, 2008). Another important 

meteorological event was Hurricane Rita (2005). The emergency office in Harris County (Texas) 

envisioned an evacuation of 0.8-1 million people, but more than 2.5 million people fled from that 

county of 3 million citizens (Victoria Transport Policy Institute, 2006). 

 In order to obtain the number of people living in ARkStorm scenario flooded areas (Table 

17; Figure 57), we used the ESRI 2009 projections of census-block population (table 17; fig. 57). 

(The projection is relative to the United States Census 2000.) For blocks that were partially flooded, 
104

we calculated the proportion of the block population in the flooded area by using the National Land 

Cover Data (NLCD) of 2001. In partially flooded census block areas we calculated the area in high, 

medium, and low intensity of development and the proportion of developed land that was flooded. 

We assumed that the proportion of people living in flooded areas corresponds to the proportion of 

the developed area that is flooded. Our intent with this procedure is to avoid counting population 

that is concentrated outside of the flood zones. Despite the 8 years of difference between ESRI 2009 

projections and the 2001 NLCD, we justify use of this procedure with the observation that most of 

the partially flooded blocks are located in rural areas, where population tends to be dispersed with 

no major changes in the distribution of growth in recent years. Thus, we estimate that 1.5 million 

people13 reside in the flooded areas of the ARkStorm scenario. Most of these people are 

concentrated in Sacramento and San Joaquin Counties, but Sutter County has the highest 

percentage (97 percent) of population living in a flooded area. These 1.5 million people represent 

about 20 percent of the population in the evacuation study area and even though we cannot affirm 

that all these people would need to evacuate in the ARkStorm scenario, previous events like Katrina 

and Gustav have demonstrated that evacuations around this size will require federal, regional, and 

state resources beyond the county capacity. 

Figure 57. Estimates of number of people, per county, in areas flooded by the ARkStorm. Blue is 

fewer than 10,000 people, red is more than 400,000. 

 

13 Using the proportion of area flooded as the proportion of the population in flooded areas raises 

the total flooded population estimate by another 100,000 people. 
105

Table 17. Estimates of county population living in flooded areas. 

County Estimated 2009 

population

People in flooded

area

Population 

in flood area, in percent

Butte 221,957 33,467 15.08

Colusa 22,162 10,101 45.58

Contra Costa 1,063,951 85,445 8.03

Fresno 936,063 11,828 1.26

Glenn 29,084 3,424 11.77

Kern 830,563 22,020 2.65

Kings 155,116 9,632 6.21

Lassen 36,131 2,032 5.62

Madera 153,361 2,197 1.43

Merced 258,323 5,295 2.05

Modoc 9,662 1,197 12.39

Sacramento 1,432,760 527,885 36.84

San Joaquin 692,792 480,106 69.30

Shasta 183,135 22,043 12.04

Solano 426,258 76,486 17.94

Stanislaus 528,982 1,488 0.28

Sutter 97,353 94,024 96.58

Tehama 63,516 10,656 16.78

Tulare 440,882 1,755 0.40

Yolo 202,429 80,689 39.86

Yuba 75,240 56,262 74.78

Regional total 7,859,720 1,538,032 19.57

 In addition to the aforementioned assumptions, we were unable to distinguish depth of 

flooding less than 3 feet. HAZUS methodology suggests that 1 foot is a threshold flood depth for the 

floatation of cars (Federal Emergency Management Agency, 2010b). On the other hand, other 

causes for evacuation besides flooding such as landslides, utility (water and power supply) outages 

and environmental conditions (for example, sewage back up) were not considered. Therefore, the 

number of people in the flooded areas likely overestimates flood induced evacuation while 

neglecting other causes for evacuation. 

SOCIAL INDICATORS 

 According to studies of past evacuations (Cabinet Office Civil Contingencies Secretariat, 

2006) and (Victoria Transport Policy Institute, 2006), social variables affect the ability of people (as 

individuals and groups) to evacuate. We analyzed some of those variables that are supported by 

census block data from the 2009 ESRI projections and the 2000 Census (for English speaking only). 

The social indicators were defined and justified as follows: 

Population: Evacuations in more populated areas are more prone to problems associated 

with coordination of evacuees, traffic jams, mobilizing people with special needs, shelter availability 

and public transportation. The population indicator is measured as the number of people living in 

flooded areas of each census block. The population data are from the ESRI 2009 projection. 

Age: A person’s ability to evacuate depends on physical self-sufficiency. Research has 

shown that the majority of deaths during emergencies occur in people older than 65. For example, 

66 percent of the fatalities associated with Hurricane Katrina were older than 65 (Louisiana State 
106

University, 2005). Also, in most cases when a large-scale evacuation is taking place, the senior 

population suffers the majority of the consequences. They get left behind by their caretakers, 

families, and even the authorities do not plan for their particular conditions or impediments 

(Cherry and others, 2009). For our analysis, the age indicator is the percentage of people over the 

age of 65 years in census blocks with flooding. The data for this statistic were taken from the ESRI 

2009 projections. 

Income: Per capita income is an indicator of mobility in an evacuation in two ways. First, 

income represents a capacity to afford services, for example, renting a car or staying in a hotel for a 

long period during an emergency. Second, people with higher incomes have better access to private 

transportation (own cars). Those with lower incomes tend to rely more on public transportation. 

 Our income indicator is per capita income by block. We obtained these data from the ESRI 

2009 projections that are based on the block Per Capita Income of 2000. 

Population density: The population density indicator highlights the more densely 

inhabited census blocks with flooding. Various studies such as Committee on Role of Public 

Transportation (2008) and American Highway Users Alliance (2006) reveal several factors that 

influence evacuation procedures and timing; population density is among these factors. Also, areas 

with high density tend to be located in urban areas where poverty is more concentrated. 

Consequently, people in high-density areas may need more assistance with evacuation. 

 Population density by definition is the census block population (ESRI 2009 population 

estimate) divided by the horizontal projected area of the block. 

Diversity: The relationship between ethnic minority groups and response to an evacuation 

warning is a subject of research (Sorenson and Sorenson, 2006). In general, studies suggest that 

minority groups are less likely to evacuate, but Perry (1987) suggests that warning belief and 

personal risk are stronger determinants of evacuation compliance. However, he also notes that 

some minority groups perceive authority figures—particularly those from the government—

differently from majority groups. In this case, a greater effort is required to accommodate more 

minority groups into an evacuation plan. 

 We use the Diversity Index (D.I.) formulated by ESRI that measures the diversity of 

races/ethnicities of people in a block. The D.I. ranges from 0 (no diversity, the population is 

comprised of a single ethnic group or race) to 100 (complete diversity, each member of the 

population is from a different ethnic group or race). By way of example, the U.S. Diversity Index of 

61 means that there is a 61 percent probability that two people randomly chosen from the U.S. 

population belong to a different ethnic group or race (Environmental Systems Research Institute, 

Inc., 2009). Therefore, a block with a high D.I. has a variety and a balance of races/ethnicities. It is 

important to clarify that this indicator describes diversity and not the dominant presence of a 

specific ethnic group or race. The non-English speaking component of minority groups is isolated 

in the language indicator. 

Language: Another major factor affecting evacuation compliance is language. Local 

authorities need to be aware of the languages spoken in their communities and potential language 

barriers. For example, the majority of people that died in the 1987 Saragossa Tornado in Texas 

were Hispanic and this outcome was attributed to a failure to provide a good translation of the 

warning into Spanish (Aguirre and others, 1991). 

 For the language indicator, we used English-speaking data from the Census 2000; we 

calculated the percentage of people in each block that did not speak English at all or were described 

to have “low English skills”.
107

Evacuation vulnerability index: In anticipation of summing the indicators to highlight 

census blocks that may be relatively more challenged by an evacuation, we normalized the data for 

each indicator by dividing each value by the maximum value for that indicator. Subsequently, for 

each block we added the normalized indicators and divided the result by 6 (number of indicators) 

to create an evacuation index that encompasses all 6 indicators. We mapped each indicator and 

evacuation index by using quantiles (table 18). The lowest (highest) values represent low (high) 

presence of that indicator. The evacuation index is calculated for the study region, but mapped for 

three zones (figs. 58 through 60) to display more detail. 

Table 18. Scaling of social indicators for social vulnerability index. 

Range/Percentiles Quantile/Value Description

No datum presence 0 No data

Lower 20th 1 Very low

21st to 40th 2 Low

41st to 60th 3 Medium

6 1st to 80th 4 High

81st to 100th 5 Very high

 To capture the interaction between the social variables and severity of flooding, we scaled 

the flood variables of depth and duration by normalizing the data of each flood indicator (table 19), 

summing the results, and mapping the top 40 percent of the values to produced a spatial layer of 

more severe flooding—characterized by greater depth and longer duration. These areas with more 

severe flooding are shown in black shading (figure 56 through figure 59) to visualize the spatial 

relation between areas with a high evacuation index and areas with more severe flooding. Note that 

the flood variables are not incorporated into the evacuation index. 

Table 19. Indicators for flood variables used in scaling of social vulnerability. 

[<, less than] 

Depth

(feet)

Duration

(days)

<3 <0.5

3 - 10 0.5-3

10 – 20 3-7

3-14

14-28
108

 Our social variables are similar to those used by social vulnerability researchers. Our set of 

evacuation variables includes the four social vulnerability variables (income, diversity, age, and 

density) that explain most of the social variation in Burton and Cutter (2008). These authors also 

consider gender, number of renters, and number of medical services in the (larger) census tracts of 

a similar study region. Wood and others (2010) include employment as well as gender and housing 

in their analysis of social vulnerability. However, there are some differences between social 

vulnerability and factors that complicate evacuation: for example, typically female heads of 

household are considered to be more socially vulnerable yet females are more likely to evacuate 

than males (Bateman and Edwards, 2002). Although more sophisticated social indices exist (Wood 

and others, 2010), this simple approach was achievable in the time frame of this first report on the 

ARkStorm scenario. The method serves to highlight the coincidence of social variables that make 

evacuation more difficult and raises questions about the spatial variability of social characteristics. 

Figure 58. Evacuation vulnerability index in northern inland region (Zone 1). 

 Figure 58 shows high evacuation vulnerability indices in Sutter and Colusa Counties. The 

red zone in Colusa (next to Grimes) is a result of a high percentage of people with no or poor 

English skills, low incomes, and high diversity. The agricultural area in Sutter County (orange 

evacuation index) contains high percentages of people with no or poor English skills and low 

incomes. Sutter County is a low-density area, but all the other indicators (population in the flood 

area, percentage of people over 65 years old, and diversity) score medium or higher. 
109

 We also observe that the county seats of Sutter (Yuba City) and Yuba (Marysville) Counties 

are surrounded by areas with a very high and high evacuation index. 

 

Figure 59. Evacuation vulnerability index in Delta counties (Zone 2). 

 In Zone 2 (fig. 59) the majority of areas with a high evacuation index coincide with more 

severely flooded areas. The Sacramento-San Joaquin Delta area is lower lying land with a higher 

density population accounting for 81 percent of the population considered here. The larger and 

less densely populated areas with a high evacuation index are located inside the delta area (Bethel, 

McDonald, Liberty, and Ryer Islands and others such as Jersey, Venice, and Woodward Islands) 

where residents have medium incomes, no or poor English skills and high diversity. 

 Two main cities of San Joaquin County (Stockton and Lodi) contain blocks with high and 

very high evacuation indices. These areas present a high number of people in a flood area, high 

percentage of people with no or poor English skills, high population density, and high diversity, 

while the percentages of people over 65 or with low incomes scored between low and medium. 

 Sacramento is surrounded by areas with low, medium and high evacuation indices. The 

areas evaluated as high have a high number of people, high population density, high diversity, and 

low incomes. In contrast, the areas with a low evacuation indices contain people with high incomes, 

low diversity, and high English skills. 
110

Figure 60. Evacuation vulnerability index in southern inland region (Zone 3). 

 Zone 3 (fig. 60) exhibits the smallest amount of area with severe flooding. These areas are 

northwest of the zone. We identify two main areas of high evacuation indices. The first area is next 

to Firebaugh City. Blocks in this area contains a high percentage of people with poor English skills, 

high diversity, and low income while the number of people, and percentage of people over 65 years 

old scored lower (values between 2-3). Density score was very low (1). Kettleman City blocks 

contain a high percentage of people with no or poor English skills and low income while the 

number of people, percentage over 65 years old, and diversity fluctuated between 1and 3. Again, 

density scored the lowest possible value of 1. 
111

Figure 61. Percentage of people in top two quantiles of the evacuation index relative to the total 

population (2009) by county. 

 By way of summary, Figure 61 displays the percentage of people living in the flooded blocks 

with high and very high evacuation indices for each county. Except for Contra Costa and Solano 

Counties, the counties with the larger populations in flooded areas (table 17) also have a higher 

percentage of people living in blocks with high evacuation vulnerability according to our index. 

Consequently, both San Joaquin and Sacramento Counties have high absolute (over 300,000 people 

in both counties) and relative numbers of people residing in blocks with high evacuation indices. 

However, the less populated Colusa County also has a high percentage of people in the top two 

quantiles of the evacuation index. It is no surprise that Sutter County has the highest percentage of 

population in blocks, with high and very high evacuation indices, because 97 percent of the county 

population is in the ARkStorm flooded area. 

SHELTER REQUIREMENTS 

 We estimated the number of people who may need temporary shelter after ARkStorm by 

using methods in the HAZUS®MH MR4 technical manual (National Institute of Building Sciences 

and Federal Emergency Management Agency, 2010). This formula takes into account the following 

factors: 

Low income families that lack the means to find other shelter on their own. 

4.2%

29.6%

2.3%

1.5%

9.7%

2.3%

9.6%

1.3%

2.1%

27.5%

48.4%

5.8%

0.3%

59.4%

2.1%

0.5%

34.4%

48.3%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0%

Butte

Colusa

Contra Costa

Fresno

Glenn

Kern

Kings

Madera

Merced

Sacramento

San Joaquin 

Solano

Stanislauss

Sutter

Tehama

Tulare

Yolo

Yuba

Percentage

Counties
112

Young and elderly families that may have the means of finding temporary shelter on their own, but prefer 

to use publicly provided shelters. 

 The formula to calculate the number of people with shelter needs is the following: 

5 3

1 1

k m

k m

STP akm DP HI HA

(4) 

where 

STP= number of people using established 

shelters 

DP= Displaced population 

αkm= Constant for income and age class 

HIk= Percentage of population in the kth 

household income class 

HAm= Percentage of population in the mth age 

class 

 The 2009 income data were provided by the ESRI projections. The age data are from 

the 2000 census because the 2009 ESRI projection age data were not available. 

Table 20. Estimates, per county, of numbers of people in flood area. 

 Includes number and percentage of those needing short-term shelter. 

County Estimated total people 

in flood area

Estimated number of 

people in need of shortterm shelter

Percentage

Butte 33,467 4,767 14.24

Colusa 10,101 1,298 12.85

Contra Costa 85,445 10,227 11.97

Fresno 11,828 1,942 16.42

Glenn 3,424 257 7.51

Kerns 22,020 2,787 12.66

Kings 9,632 561 5.82

Lassen 2,032 257 12.65

Madera 2,197 468 21.30

Merced 5,295 740 13.98

Modoc 1,197 147 12.28

Sacramento 527,885 76,073 14.41

San Joaquin 480,106 76,118 15.85

Shasta 22,043 3,022 13.71

Solano 76,486 9,899 12.94

Stanislaus 1,488 272 18.28

Sutter 94,024 10,399 11.06

Tehama 10,656 1,658 15.56

Tulare 1,755 418 23.82

Yolo 80,689 11,502 14.25

Yuba 56,262 7,509 13.35

Total 1,538,032 220,321 14.32
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 The results in table 20 suggest that 8 percent (125,022) of the population in the 

Inland Region and Contra Costa and Solano Counties may need shelter in the short-term. 

San Joaquin and Sacramento County residents are in greater need of short-term shelters for 

about 100,000 people, representing 84 percent of all shelter needs. The concentration is 

caused by the combination of two main features of these counties: a large number of people 

residing in the flooded area (table 17) and a relatively high percentage of lower income 

population. (As another point of reference, according to the 2000 Census, 18 percent of San 

Joaquin and 14 percent of Sacramento County residents were living below the poverty line.) 

RESEARCH NEEDS RELATED TO EVACUATION 

 For census blocks with partially flooded areas, further refinement of flooded 

population estimates can be accomplished by identifying important features of the 

residential population. For example, Solano County has a high number of people living in 

houseboats. These people may not need to be evacuated. Also additional variables like car 

ownership and household gender could be considered to further inform the spatial 

variability of evacuation issues. Another key variable affecting people’s ability to evacuate is 

the number of people with disabilities and health issues. The Flood Emergency Action 

Team (FEAT) final report (California Department of Water Resources, 1997) concluded that 

warnings and shelters were not accommodating of people with disabilities. The location of 

people with disabilities and health issues is not readily available and is challenging to keep 

current. The Sacramento County evacuation plan (Sacramento County, California, 2008) 

describes various strategies for locating, alerting, and warning this vulnerable population. 

Elsewhere, the Florida ADA Hurricane and Disaster Center has partnered with the Center 

for Independent Living of Southwest Florida and emergency preparedness officials to track 

this information in four Florida counties (Cahalan and Renne, 2007). 

 An additional consideration is public transportation as an asset for massive 

evacuation. Issues around transportation are well illustrated by Hurricanes Katrina and 

Rita. -“If Katrina exposed what happened when many people have no cars to evacuate; Rita 

seemed to show the other side of the coin”- (Victoria Transport Policy Institute, 2006). 

During Katrina, the demand for transportation was massive, but the city was not ready for 

such demand. The lack of planning and concrete strategies for people with no private 

transportation provoked the public to take over infrastructures that were not designated to 

become shelters. Also, many people with special needs for transportation were left behind, 

increasing the number of fatalities. In contrast, during Hurricane Rita, emergency managers 

in Texas expected people in coastal areas who owned boats to take the boats with them by 

trailer and cause additional congestions on the roads. However, they had not anticipated 

families using all their cars to evacuate (rather than take one car per family) and in some 

cases residents took trailers and horse trailers as well. This evacuation led not only to very 

heavy congestion on the road but also to a shortage of gas (Cabinet Office Civil 

Contingencies Secretariat, 2006). Public transportation is an asset and is key to a successful 

evacuation, but the challenges include overcoming the stigma of public transportation, 

improving efficiency, identifying rally points, and educating residents (Victoria Transport 

Policy Institute, 2006). Overall, evacuation planning needs to consider a range of public 

services for the evacuees (such as gas stations, supermarkets, health care, public buses) and 

the management and retention of staff for those services. 

 The evacuation analysis can be enhanced with considerations of public 

infrastructure, location of shelters and dams, and options for public transportation. 
114

Elevation data can be used to identify suitable shelter sites. Integration of various spatial 

information will be useful for regional evacuation planning. One example of spatially 

integrated information at the local level is the evacuation mapping designed by Baldwin 

(2010) for San Joaquin County. 

 Finally, a concern of CalEMA is the level of complacency about evacuation in 

California (D. Owens, CalEMA, 2010, personal commun.). In contrast to the Gulf States, the 

residents of California are not routinely faced with flooding and evacuation warnings and 

orders. Tendencies to ignore official warnings and wait until the last minute to evacuate will 

put more people at risk. Changing passive perceptions toward flooding evacuations is a 

challenge for emergency managers because people’s risk attitudes and beliefs is what 

makes evacuation a reality. 

Business Interruption Costs 

INTRODUCTION 

 This section summarizes business interruption (BI) impacts of the ARkStorm 

Scenario. These impacts stem from a combination of damage to buildings, agricultural 

lands, and several types of infrastructure. Our BI estimates include not only direct impacts 

at the site of the damage but also indirect BI stemming from interdependencies among and 

between businesses and households throughout the economy. 

 Our direct BI estimates are based on calculations of loss of building function, loss of 

productivity on agricultural land, reduction of lifeline services from damaged infrastructure, 

and a reduction in the labor force because of evacuation. These estimates are translated 

into decreases in the capital stock or direct declines in output, as appropriate, into 100 

sectors of the economy. 

 Our indirect BI losses are based on the application of a dynamic computable general 

equilibrium (CGE) model of the California economy. CGE is a state of the art economic tool 

that is based on the behavioral response of representative producers and consumers to 

market price signals within the limits of available capital, labor, and natural resources 

(Shoven and Whalley, 1992). CGE captures both technical interdependence between 

sectors in terms of input linkages and also market actions and interactions through prices. 

The dynamic feature links the 6-month time periods in the model (appendix B). CGE 

models are increasingly being used in disaster context (Rose and Liao, 2005; Rose and 

others, 2009; Dixon and others, 2010). This study further advances the methodology for 

application to disasters. 

HAZARD LOSS ESTIMATION 

BASIC CONSIDERATIONS 

 For many years, hazard loss estimation was dominated by engineers, and 

accordingly the focus was on property damage to structures. Other types of impacts 

(whether economic, sociological, psychological) were combined in a grab bag category 

termed "indirect" or "secondary" losses. By the mid-1990s, there was a growing 

appreciation of the role of business interruption (BI) losses, which refer to the reduction in 

the flow of goods and services produced by property (capital stock). This stock/flow 
115

distinction is a basic concept in economics and, in fact, flow measures, such as Gross 

National Product (GNP), have long held a dominant position. 

 Direct and indirect versions of both categories of losses are prevalent. Direct 

property damage relates to the effects of flooding, winds, and landslides; while collateral, or 

indirect, property damage is exemplified by toxic releases from HAZMAT facilities (those in 

the EPA facility registry system) damaged by flood debris. Such indirect property damages 

have been identified under environmental and health issues in this report, but we have not 

been able to evaluate the economic impacts of them. Direct BI refers to the immediate 

reduction or cessation of economic production in a damaged factory or in a factory cut off 

from at least one utility lifeline. Indirect BI stems from the interdependencies of the 

economy in the form of “multiplier" effects associated with the supply chain or customer 

chain of the directly affected business or through the general equilibrium effects of market 

interactions. Rose (2004a) explains these concepts and Rose and others (2009, National 

Research Council (2005, Multihazard Mitigation Council (2005a, 2005b), and European 

Union (2003), provide examples of the application. 

 An important consideration is that nearly all direct property and ancillary (or 

indirect) property damage takes place during the time span of the winter storm. An 

exception is damage from deep seated landslides: while some landslides, triggered by the 

heavy rainfall season including the ARkStorm, will begin to move during or shortly after the 

storm they may continue to move for months or years. Furthermore, other deep-seated 

landslides may not begin to move until weeks or months following the storm. BI, being a 

flow variable, manifests over a longer time period than storm related damage. BI begins 

when the damages from flooding, wind, and landslides occurs and continues until the built 

environment is repaired and reconstructed to some desired or feasible level (not 

necessarily predisaster status) and a healthy business environment is restored. As such, BI 

is complicated because it is highly influenced by the choices of private and public decision 

makers about the pattern of recovery, including repair and reconstruction. As in the 

ShakeOut scenario (Rose and others, written commun.; Jones and others, 2008,), the 

aggregate magnitude of BI can rival that of property damage. Also, technological progress 

implies that, over the long run, reconstruction investment that replaces old, less efficient 

capital with new, more efficient capital may generate a temporary increase in the aggregate 

productivity of capital and pay positive dividends offsetting some loses in the long-run. 

 More recently, the loss estimation framework has been expanded in two ways, and 

the term economic consequence analysis is being used to distinguish this breadth (Rose, 

2009). First is the incorporation of the loss reduction strategy of resilience, in both static 

and dynamic forms. We define static economic resilience as the ability of an entity or system 

to maintain function (continue producing) when shocked by the types of disruptions 

accounted for in this scenario (Rose, 2009; 2004b;). Static economic resilience is thus 

aligned with the fundamental economic problem of efficient resource allocation, which is 

exacerbated in the context of disasters. This aspect is interpreted as static because it can be 

attained without repair and reconstruction activities, which affect not only the current level 

of economic activity but also its future time path. Another key feature of static economic 

resilience is that it is primarily a demand-side phenomenon involving users of inputs 

(customers) rather than producers (suppliers). This is in contrast to supply-side 

considerations, which definitely require the repair or reconstruction of critical inputs. A 

more general definition of dynamic resilience is the speed at which an entity or system 

recovers from a severe shock to achieve a desired state. This also subsumes the concept of 
116

mathematical or system stability, as it implies the tendency of the system to “bounce back”. 

 This version of resilience is relatively more complex, because it involves a long-term 

investment problem associated with repair and reconstruction. Production may be affected 

by building damages, reduced lifeline services, and absent employees. 

 The second major consideration is extended linkages. One type is systems linkages, 

such as cascading infrastructure failures. Another is behavioral linkages, which refer to 

considerations like the effect of recent disasters on risk attitudes (Burns and Slovic, 2007). 

A good example is the fact that 85 percent of the BI loss following 9/11 stemmed from the 

nearly 2-year decline in air travel and related tourism because of heightened fear of flying 

(Rose and others, 2009). Note this category also has associated indirect effects. Thus it can 

increase BI losses by one or two orders of magnitude. In the following analysis, we take 

these various considerations into account to the extent possible within project limitations. 

Throughout our modeling, we carefully distinguish stock from flow effects and direct from 

indirect losses. We factored in BI associated with interdependent infrastructure failures. We 

included some major sources of resilience in the aftermath of disasters relating to static 

resilience strategies of substitution responses to price signals, the ability to recapture lost 

production through overtime or extra shifts, and the fact that not all aspects of economic 

activity require infrastructure inputs. However, we were not able to factor in behavioral 

linkages. 

CONDUITS OF ECONOMIC SHOCKS 

 We analyze the following conduits of shocks to the economic system stemming from 

damages to the built environment (Actual damage is not necessary in all cases to cause 

economic loss. Evacuation prior to disaster can cause even greater BI losses than a small 

version of the event. Also, some buildings can be closed for business because of proximity 

to damaged buildings. Some infrastructure services may be shut down as a precautionary 

measure as well.): 

Direct building and content damage from flood 

Direct building damage from wind 

Direct damage to crops, fruit and nut trees, and agricultural lands 

Direct lifeline service outages for: 

Electric power systems 

Water systems 

Wastewater treatment systems 

Highway networks 

Telecommunication systems 

Evacuation 

 An important additional consideration is the need to adjust for double-counting of 

the channels of losses. That is, a factory may suffer from a flooded first floor and loss of 

electricity simultaneously, either one being enough to cause it to shut down business 

operations. Our analysis does adjust for possible double-counting. 
117

 Finally, we note that our results could be presented in terms of several economic 

impact indicators. We first present them in terms of property damage (loss of asset values). 

We also calculate the results in terms of two types of flow variables relating to BI. The first 

is value added, a net measure that corresponds only to the cost of primary factors of 

production (labor, capital, and natural resources, and excludes the cost of intermediate, or 

processed goods). The second is Gross Domestic Product (GDP), which differs only slightly 

from value added by including some taxes. GDP at the state level is sometimes referred to 

as Gross State Product (GSP. The term "Gross" here refers to the fact that depreciation 

(wear-and-tear or obsolescence of fixed capital assets) is included, although intermediate 

goods are not. ) 

THE DYNAMIC COMPUTABLE GENERAL EQUILIBRIUM MODEL 

 A CGE model is a stylized computational representation of the circular flow of the 

economy (Shoven and Whalley, 1992). CGE solves for the set of commodity and factor 

prices and the set of activity levels of outputs from firms and household incomes that 

equalize supply and demand across all markets in the economy (Sue Wing, Boston Univ., 

written commun., 2009). The model developed for this study divides the California 

economy into 100 industry sectors, each of which is modeled as representative firm 

characterized by a constant elasticity of substitution (CES) technology to produce a single 

good or service. Households are modeled as a representative agent with CES preferences 

and a constant marginal propensity to save and invest out of income. The government also 

is represented in a simplified fashion. Its role in the circular flow of the economy is passive: 

collecting taxes from industries and passing some of the resulting revenue to the 

households as a lump-sum transfer, in addition to purchasing commodities to create a 

composite government good which is consumed by the households. Three factors of 

production are represented in the model: labor—which responds to changes in the wage 

rate, and two types of capital—intersectorally mobile, and sector-specific varieties of 

capital. These factors are owned by the representative agent and rented to the firms in 

exchange for factor income. California is modeled as an open economy that engages in trade 

with the rest of the U.S. and the rest of the world by using the Armington specification 

(imports from other states and the rest of the world are imperfect substitutes for goods 

produced in the state). 

 The static component of the model computes the prices and quantities of goods and 

factors that equalize supply and demand in all markets in the economy, subject to 

constraints on the external balance of payments. This equilibrium submodel is embedded 

in a dynamic process, which on a 6-month time-step specifies exogenous improvements in 

firms’ productivity and updates the capital endowments of the economy based on 

investment-driven accumulation of the stocks of capital. The impacts of a severe storm are 

modeled as exogenous shocks to the productivity of industries, and contemporaneous 

destruction of capital stock, with concomitant reductions in the California economy’s 

endowments of malleable and sector-specific capital input. 

 The model is formulated as a mixed complementarity problem using the MPSGE 

subsystem for the General Algebraic Modeling System (GAMS) software (Rutherford, 1999; 

Brooke and others, 1998) and is solved by using the PATH solver (Ferris and others, 2000). 

A more detailed and technical presentation of the model is presented in appendix B. The 

model is calibrated by using an IMPLAN social accounting matrix for the state of California 

for the year 2007 (Minnesota IMPLAN Group, 2006 in conjunction with values of the 
118

elasticities of substitution and transformation drawn from Rose and others, (2009) and 

Rose and Liao (2005). The latter parameters are summarized in appendix B, which also 

provides a list of the sectoring scheme. 

We model the consequence of the imposition of the storm’s shock as an array of 

initial declines in sector outputs, which induce intra- and intersectoral substitution 

adjustments by producers and consumers, as well as changes in the prices of commodities 

and factors. The result is a new equilibrium with reduced aggregate expenditure and 

investment, which generates contemporaneous losses of consumer welfare (relative to the 

baseline solution of the model), and slower growth of the capital stock that adversely affect 

the economy’s capital endowment and productive capacity in subsequent periods. The 

latter, dynamic impact of the initial capital stock destruction is an important source of 

hysteresis in the losses caused by a storm. Symmetrically, the principal channel through 

which repair and reconstruction investments dampen the persistence of losses is the 

output- and income-enhancing effect of restoring business productive capacity. 

METHODOLOGICAL DETAILS FOR INDIVIDUAL LOSS CATEGORIES 

 In addition to the IMPLAN social accounting matrix, other data are critical for 

evaluating economic impacts and resilience associated with disasters. These data include 

inventory data on the built environment (commercial and industrial property, residences, 

infrastructure) and on the natural environment. Also needed is a set of damage functions 

that relate changes in underlying conditions to property damage and loss of function. One 

such source is Hazards United States-Multi-Hazard (HAZUS-MH) System (Federal 

Emergency Management Agency, 2008a). HAZUS-MH is a large expert system that contains 

detailed data on the built environment at the small area level, a set of damage functions, and 

a GIS capability. Physical damage and business interruption are translated into direct dollar 

values of building repair costs and business downtime costs, respectively. 

 Estimation of the conduits of business interruption was as follows: 

 1. Flood damaged buildings. The flooded building damage estimates provided to 

us were calculated by using HAZUS-MH equations. (The HAZUS-MH building inventory was 

extracted from the HAZUS-MH software because of the size of the problem) The flow of 

goods and services emanating from damages to this productive capital stock (essentially 

equivalent to BI losses) is direct “output loss”, where output refers to "gross output," 

equivalent to gross sales revenue. We followed the procedures in Chapter 14 of the HAZUSMH flood technical manual (Federal Emergency Management Agency, 2010b) to calculate: 

(1) output losses for nonresidential occupancy classes and nursing homes and (2) rental 

and owner occupied losses for the remaining residential occupancy classes. We included the 

HAZUS-MH flooded building downtime add-ons of dry out and cleanup; inspection, 

permitting and ordinance approval; contractor availability; and hazmat delay. We used 

HAZUS-MH equations to calculate relocation costs. Flooded buildings and moderately 

damaged, severely damaged, and destroyed buildings will take longer to restore than the 

lifeline services, which are mostly recovered within two months. After two months, residual 

power restoration continues only in Mono, Inyo, and Tulare Counties. 

 2. Wind damaged buildings. Likewise, the building wind damages were provided 

to us by Keith Porter. We used the procedures in Chapter 7 of the HAZUS-MH Hurricane 

Technical Manual (Federal Emergency Management Agency, 2010d) to calculate output, 

owner-occupied dwelling, and rental losses. 
119

 3. Damages to agricultural commodities. An adaptation of the methodology 

developed for the Delta Risk Management Strategy (DRMS) (California Department of Water 

Resources, 2008) was used to estimate agricultural damages. Field repair costs were 

calculated for annual and perennial crops and livestock. In addition, forgone income was 

calculated for flooded annual crops; perennial crops flooded for 2 weeks or more incurred 

crop replacement costs and forgone income for up to 5 years; and the replacement value of 

livestock (dairies, feedlots, poultry) at risk was estimated in areas with at least 6 feet of 

flood depth. 

 4. Electric Power. One feature of the computations for most of the infrastructure 

categories involved is the timing of the disruptions. (For buildings this feature was internal 

to the HAZUS-MH computations.) The percentage of customers affected by the outages is 

not constant but decrease over time as services are restored. Like buildings, wind and flood 

damages to infrastructure were considered. The more dominant cause of damage was 

identified for each infrastructure in each county. Service reduction and restoration curves 

were based on panel discussions and expert opinion. Each infrastructure BI impact was 

simulated separately. 

 The power restoration pattern (percentage of power services recovered in 

individual restoration periods) differed by county and ranged from 0.2 percent to 69 

percent customers initially out of service with most restored within a month except for a 

couple of outlier counties needing 6 months to fully restore power to customer base. The 

power outages were localized to county because generation capacity that is located “high 

and dry” was not considered to be a limiting factor. Each county restoration curve was 

transformed into quarterly power shortages for each occupancy class by (1) integrating 

under the inverse of each county restoration curve to estimate percentage of county 

customers not served during each quarter, (2) weighting this percentage by the proportion 

of occupancy class square footage in the county and (3) summing up weighted county 

power shortages for each occupancy class. 

 5. Water. The estimation of BI losses stemming from disruption of the water 

system is similar to that of the power system except that flooding was the only cause of 

damage. Consequently, 42 counties were not affected by water supply disruptions. The 

remaining counties have disrupted water services to 10-60 percent% of customers with 

service restored within three months. 

 6. Wastewater. The estimation of BI losses stemming from disruption of the 

wastewater system is similar to that of the water system. Forty-one counties were not 

affected by wastewater treatment disruptions. The remaining counties presented disrupted 

wastewater services to 17-100 percent of customers with service restored within a month. 

 7. Telecommunications. The estimation of BI losses stemming from disruption of 

the telecommunications system is similar to that of the power system. All counties 

experience reduced telecommunication services affecting 2 to 25 percent of customers for 

up to 7 days. 

 8. Truck Transportation. The truck traffic economic impact analysis was 

conducted outside of the CGE model and is described in a separate section. We obtained 

technical support from various University of Southern California-affiliated independent 

consultants coordinated by Hanh Le Griffin of TTW, Inc. Their transportation model 

analyzes the effects of reduced highway capacities on the regional and national movement 
120

of goods and services by estimating changes in truck distance, travel time, and associated 

costs and the impacts of increased shipping prices on major economic sectors. 

 9. Evacuation. We were provided with county estimates of population in the 

ARkStorm flooded areas (based on GIS analysis by using ESRI 2009 population projections). 

About 1.5 million people reside in the ARkStorm flooded area of the Inland region and Delta 

counties. We assumed that the effective impact from evacuation extended for the duration 

of the flooding. IMPLAN county employment data (Minnesota IMPLAN Group, 2006 was 

used to allocate the evacuated population to sectors, that is, we assumed that the 

distribution of employment among evacuated residents is the same as the distribution of 

employment in the county. 

RESILIENCE 

 For the most part this study only addresses aspects of static resilience because we 

received one restoration time path for each BI conduit and because dynamic resilience, 

especially for infrastructure, is so strongly dependent on a series of public and private 

decisions regarding the timing of repair and reconstruction, which are complex and 

uncertain and hence highly variable. Moreover, only a limited number of static resilience 

options were incorporated, albeit they are by far the ones that have been found to have the 

greatest potential for reducing BI losses (Rose and others, 2009). 

The primary source of static resilience is “production rescheduling,” or the ability of 

firms to work overtime or extra shifts after they have repaired or replaced the necessary 

plant and equipment and their employees and critical inputs become available once more 

such that “loss of function” has been overcome. This is rather straightforward for the case 

of flood and wind damaged buildings. For infrastructure, it is more complicated. Just 

because electricity service has been restored does not mean that businesses can 

immediately turn on the assembly line; they must repair the necessary plant and equipment 

first (though this need not be 100 percent restoration to be fully operational). HAZUS-MH 

includes an adjustment for this consideration, referred to as the “Building Service 

Interruption Time Multiplier” for earthquakes and wind (hurricanes), but an adjustment 

has not yet been developed for the buildings damaged by flooding. Production rescheduling 

is incorporated in HAZUS-MH through the inclusion of production “recapture factors” (RFs), 

scaling parameters that represent the percentage of direct gross output losses that can be 

recovered at a later date. The original HAZUS-MH RFs range from 0.30 to 0.99. 

Manufacturing enterprises that produce nonperishable commodities are at the high end, 

while sectors producing perishables ( agricultural) or nonessential services (entertainment) 

are at the lower end of the scale. These RFs are subject to the caveat that they are 

applicable only for three months with no effect thereafter. This is meant to reflect the fact 

that customers and suppliers will grow impatient as their orders go unfilled. Accordingly, 

we adjusted the HAZUS-MH RFs downward by a linear decay rate of 25 percent for every 3-

month period during the first year, so that recapture becomes zero by the second year. In 

our view, this reflects a more realistic situation in which customers become increasingly 

impatient over time, canceling larger numbers of orders as delays mount. 

 The second type of resilience modeled was infrastructure “importance.” The term 

stems from Applied Technology Council-25 (1991), which convened a panel of experts to 

advance hazard loss estimation. One of the contributions was to identify the percentage of a 

sector’s business operations that does not depend on a specific category of infrastructure. 
121

Thus, even if a lifeline outage occurs, a part of the sector can keep operating. Examples are 

headquarters offices being less dependent than production lines in general, and some 

sectors being less dependent than others on lifeline services (the relatively low dependence 

of agriculture on the delivery of electricity and natural gas through the existing 

transmission and distribution infrastructure). Typically, the operation of industrial and 

commercial establishments is dependent on the availability of electricity, water, and natural 

gas, in that order. Like production rescheduling, this type of resilience also dissipates over 

time, though in a less dramatic manner. For example, if activities of headquarters or 

maintenance facilities are disrupted, other business functions may still be able to continue, 

but eventually inoperable headquarters and maintenance activities will disable the other 

functions of the enterprise. Unfortunately, no data were available to make adjustments that 

reflect this additional complication. 

 Rose (2009, 2004b) has emphasized that resilience has several key dimensions. 

One is that resilience can take place at the micro (individual business or household), meso 

(sector), and macro (economy-wide) levels. Another is the distinction between inherent 

and adaptive resilience. The former refers to features that exist in the economy under 

normal circumstances. The latter refer to adjustments that arise out of the ingenuity of the 

situation. A good example of the former is a dual-fired electric generation boiler, so that it is 

possible to substitute fuel oil for natural gas. An example of adaptive substitution would be 

to further modify the boiler after the flood to be able to burn waste products. 

 The market system is a major source of resilience. Price increases signal that 

resources have become more scarce, and, thereby, have a higher value, and that we should 

reallocate inputs accordingly. Note, all price increases do not represent gouging, and our 

CGE model is able to estimate what increases are warranted on the basis of economic 

efficiency. The CGE model also incorporates substitution possibilities as part of the 

production function of individual businesses. 

 Because of a lack of other information, we have often employed scalar or linear 

relationships to characterize resilience. At the same time, we must acknowledge that there 

is likely to be a threshold at which even resilience is eroded, beyond which the economic 

system will be overwhelmed and rendered much less able to return to pre-disaster 

equilibrium. 

ADJUSTMENT FOR MULTIPLE SOURCES OF BUSINESS 

INTERRUPTION 

 Many businesses and households will suffer disruptions from many sources. They 

may simultaneously incur building damage and loss of one or more lifeline services. Thus, 

each of our estimates when totaled may double-count some impacts—the same business 

establishment cannot be shut down more than once in any given period. We adjusted for 

these multiple causes of failure (table 21), using the following procedures: 

1. We identify uncorrelated conduits of economic shocks. Building damage because of wind 

occurs in higher elevation areas and building damage because of flood occurs in lower 

elevation areas. Therefore, we assume that the impacts from flood and wind building 

damage are additive (no double-counting). The economic impacts from crop and livestock 

damages also are treated as additive because they are calculated independently of building 

damages. 
122

2. We identified additional superfluous lifeline impacts. We observed that the times to 

restore flood damaged buildings and buildings with moderate wind damage or greater 

exceed the time to restore power, water, wastewater, and telecommunication services. 

Therefore, if a flooded building or moderately or severely wind damaged or destroyed 

building is subjected to lifeline outages, the lifeline service will most likely be standing by to 

provide service once the building repairs are completed. Therefore, we accounted for the 

additional economic impacts from lifeline service outages affecting operations in buildings 

that have no downtime from flooding or are not moderately or severely damaged or 

destroyed by wind. We estimated these additional economic impacts from lifeline service 

outages by the following means: 

a. First, we compared the percentage of building square footage with longer 

downtimes than lifeline service restoration times with the percentage of customers 

affected by each lifeline service outage. Across the building occupancy classes, 

about 21-36 percent of building square footage have downtimes exceeding lifeline 

service restoration times. The high technology HAZUS-MH occupancy class (IND 5) 

is an outlier with 70 percent of building square footage with downtimes exceeding 

lifeline service restoration times. On average, 25 percent of building square footage 

have downtimes longer than lifeline service restoration times. Weighted averages of 

initial lifeline service outages suggest that 12 percent, 16 percent, 21 percent, and 

24 percent of building square footage is subjected to water, telecommunications, 

power, and wastewater service outages, respectively. Therefore, it is possible that 

all or most lifeline service outages affect buildings with downtimes longer than 

lifeline service restoration. 

b. Second, at the other extreme, we considered the case of equal distribution of 

lifeline service outages across building square footage such that building damages 

and lifeline outages are essentially treated as independent of each other. Weighting 

initial county lifeline service outages with county building square footage, suggests 

that, on average, 60 percent of the water and wastewater outages could pertain to 

buildings with downtimes less than lifeline service restoration. Similarly, on 

average, 75 percent of the power and telecommunications outages could pertain to 

buildings with downtimes less than lifeline service restoration. The lower 

percentage for water and wastewater is not surprising given that flooded 

components of the water and wastewater systems yielded a greater percentage of 

service outages in those counties with more building flood damage. Thus, additional 

impacts from lifeline service outages after building damage could be in the range of 

0-60 percent of water and wastewater service economic impacts and 0-75 percent 

of power and telecommunication service economic impacts. 

c. Third, we selected a percentage within the above ranges by considering the likely 

spatial correlation between damaged buildings and lifeline service disruptions. 

Further data and GIS analysis is needed to assess the distribution of flooded building 

square footage relative to water and wastewater service areas. In the absence of this 

information, we surmise that these lifeline service reductions disproportionately 

affect flooded buildings because flooding is the cause of water and wastewater 

infrastructure damages. Within the possible range of 0-60 percent of additional 

economic impacts from water and wastewater outages, we assumed 10 percent of 

water and wastewater economic impacts qualified as additional. Power service 

outages, for the northern region at least, were calculated with double counting in 
123

mind (and extended to the whole area for the independent analysis of lifeline 

service economic impacts). Therefore, we retained 75 percent of the economic 

impacts from reduced power services. Telecommunication services are restored 

relatively quickly and thus will have relatively less of an economic impact. Similar to 

power, we retained 75 percent of the telecommunication economic impacts. 

d. Evacuation is correlated with building damages because of flooding. The 

additional economic impact from evacuation involves those evacuated residents 

that work outside of the flooded area. We were unable to assess the percentage of 

evacuees that work outside of the flooded areas; we assumed 50 percent in the 

absence of this information. 

e. California economic impacts from truck traffic affected by reduced capacity on the 

highways will be constrained by the ability of California industrial sectors to 

produce commodities (given building damages and other lifeline outages) that are 

shipped throughout the U.S. However, much of the truck traffic passes through 

California to and from the ports. We arbitrarily retained 70 percent of the economic 

impact from truck traffic while noting that the estimated impacts are relatively 

small compared to the other conduits of shock. 

 There will be further double counting between the lifeline services (for example, a 

building does not have power or water services, but we have ignored these as second order 

effects). 

Table 21. Summary of Business Interruption double counting adjustments. 

[%, percent] 

Conduit for Business Interruption Double Counting Adjustment

Building damages from flooding and wind Retain 100% of output losses – no adjustment

Agricultural damages Retain 100% of output losses – no adjustment

Power and telecommunication outages Retain 75% of output losses to capture 

economic impact to customers in functional 

buildings without power or telecommunication 

services.

Water and wasterwater outages Retain 10% of output losses to capture 

economic impact to customers in functional 

buildings in service areas affected by flooding.

Evacuation Retain 50% of output losses to capture 

economic impact from flooded residents 

working outside of the flooded area.

Highway truck transportation Retain 70% to capture economic impacts 

corresponding to truck traffic to and from 

otherwise operational facilities. 

Double counting of lifeline service 

reduction outside of flood and wind 

damaged areas

Adjust to zero as a second order effect.
124

MACROECONOMIC IMPACTS 

 We summarize the macroeconomic impacts of the ARkStorm Scenario estimated 

with the use of a computable general equilibrium model of California (appendix B for model 

details). The value added losses are presented for the pure damage effects and for the case 

where reconstruction spending is factored in. a In all cases, t The reductions in Gross State 

Product (GSP) exceed the losses in aggregate value added by 15-20 percent, with the former 

measure reflecting the attenuating effect of the shock on tax revenues. Looking at the 

magnitude of impacts as shown in the various panels of figure 62, by far the largest impacts 

are because of flooding, followed by utility service interruptions, crop losses, evacuation, 

and finally wind damage. 

0

20

40

60

80

100

120

140

160

180

0-6 6-12 12-18 18-24 24-30 30-36 36-42 42-48 48-54 54-60 Losses (Billion 2007 $)

Months Post-Storm

No reconstruction Reconstruction A
125

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0-6 6-12 12-18 18-24 24-30 30-36 36-42 42-48 48-54 54-60 Losses (Billion 2007 $)

Months Post-Storm

No reconstruction Reconstruction

B
126

0

20

40

60

80

100

120

0-6 6-12 12-18 18-24 24-30 30-36 36-42 42-48 48-54 54-60 Losses (Billion 2007 $)

Months Post-Storm

C No reconstruction Reconstruction
127

0.0

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1.0

1.5

2.0

2.5

0-6 6-12 12-18 18-24 24-30 30-36 36-42 42-48 48-54 54-60 Losses (Billion 2007 $)

Months Post-Storm

D No reconstruction Reconstruction
128

Figure 62. Aggregate value added losses because of various components of storm damage 

for (A) flood damage. (B) wind damage (C) utility service interruptions (electricity, water, 

wastewater and telecommunications). (D) crop and livestock damage. (E) evacuation 

losses. 

 The temporal pattern of impacts from flooding damages show large and fairly 

constant value added losses over the first one and one-half years after the storm. The 

pattern of impacts from the wind and crop loss components of damage shows value added 

losses in the first 6 months starting out at fairly low levels, then rising sharply toward the 

second half of the first year and peaking 12-18 months after the disaster before declining, 

sharply at first as they approach initial levels, and then very gradually falling toward zero. 

Physical damages and utility disruptions are highest in the initial period, and recovery 

activity begins immediately. In the case of wind and crop damage, a key reason that the 

losses peak in the second and third period is that the effect of the recapture factors is 

especially strong in the first 6-month period but then dissipates quickly thereafter. The 

recaptured production offsets the heavy losses as a consequence of productivity impacts 

significantly in the first six months, but this potential reduces in later periods. We assume 

that recaptured production takes place as soon as possible. For example, a large number of 

businesses are not severely damaged, but simply cannot operate because one of the utility 

lifelines is disrupted. Once the utility service comes back on line, the business can resume 

production immediately, unless there is a shortage of a critical material for which the 

business does not hold inventory or for which there are no substitutes. If there are 

0.0

0.5

1.0

1.5

2.0

2.5

0-6 6-12 12-18 18-24 24-30 30-36 36-42 42-48 48-54 54-60 Losses (Billion 2007 $)

Months Post-Storm

No reconstruction Reconstruction

E
129

significant lags in recapture, the time-path of BI losses would have an earlier peak and 

perhaps even be highest in the first period and decline thereafter. 

 The key initiating factors are the destruction of sector-specific and intersectorally 

mobile capital stocks in the initial semiannual period, combined with the chilling effect of 

output losses on economic sector investment, which conspire to reduce the economy’s 

endowment of capital (and therefore productive capacity) in the second semiannual period. 

Moreover, it is over this later period that the secondary impacts of storm damage on 

productivity exert the strongest influence, amplifying losses while contributing to further 

reductions in investment that perpetuate the effects of the shock. In the model, these 

fluctuations dampen out by 24-30 months post-storm, at which point the growth of the 

economy resumes. Nevertheless, the combined long-run effect is to move the economy to a 

lower growth path, so that without reconstruction through exogenous infusions of capital, 

business-as-usual levels of output and income are not regained. 

 Reconstruction is modeled as an exogenous replacement of 50 percent of the 

destroyed capital stock in the first semiannual period, followed by replacement of the 

remainder at a constant rate over the succeeding two 6-month periods. It is worth noting 

that although we assume reconstruction is paid for by insurance and outside aid and, 

therefore, incurs no drain on financial resources on the part of the California economy, the 

timing does little to mitigate the run-up in losses in the 6-12 months post-storm because 

most losses are accounted for by the persistent impacts on productivity as opposed to the 

initial damage to capital stocks. However, reconstruction does have an attenuating effect on 

peak losses, and its key benefit is to allow the economy to more rapidly converge to its 

business-as-usual trajectory, thereby dramatically mitigating the present value of losses 

over the long run. Table 22 indicates that the latter effect reduces the 5-year costs of utility 

disruptions by 30 percent and of flood damage by more than 35 percent. 

Table 22. Present discounted aggregate impact of various components of storm damage on 

value added. 

[%, percent; $, dollar] 

A. Flood damage % Billion 2007 $ Damage Multiplier

2 Year Horizon

Without Reconstruction -15.7 -508.8 1.10

With Reconstruction -14.9 -481.7 1.04

5 Year Horizon

Without Reconstruction -7.8 -591.5 1.27

With Reconstruction -6.6 -497.7 1.07

B. Wind damage % Billion 2007 $ Damage Multiplier

2 Year Horizon

Without Reconstruction -0.04 -1.4 1.02

With Reconstruction -0.04 -1.3 0.94

5 Year Horizon

Without Reconstruction -0.02 -1.7 1.23

With Reconstruction -0.02 -1.3 0.96
130

C. Utility service 

interruptions* % Billion 2007 $ Damage Multiplier

2 Year Horizon

Without Reconstruction -3.14 -101.4 1.29

With Reconstruction -3.14 -101.4 1.29

5 Year Horizon

Without Reconstruction -1.39 -105.1 1.34

With Reconstruction -1.39 -105.1 1.34

 (*electricity, water, wastewater and telecommunications) 

D. Crop and livestock 

damage % Billion 2007 $ Damage Multiplier

2 Year Horizon

Without Reconstruction -0.17 -5.3 1.82

With Reconstruction -0.15 -5.0 1.70

5 Year Horizon

Without Reconstruction -0.10 -7.3 2.02

With Reconstruction -0.08 -6.2 1.72

E. Evacuation losses % Billion 2007 $ Damage Multiplier

2 Year Horizon

Without Reconstruction -0.07 -2.3 0.56

With Reconstruction -0.07 -2.3 0.56

5 Year Horizon

Without Reconstruction -0.03 -2.4 0.58

With Reconstruction -0.03 -2.4 0.58

 By contrast, the temporal pattern of impacts for utility service and evacuation 

components of damage is much simpler, concentrated in the first 6 months after the storm 

and dissipating immediately thereafter. The driving forces that underlie this effect are 

different. Interruption of electricity, water, wastewater and telecommunications services 

imposes costs in the form of productivity reductions on the downstream firms that consume 

these utilities, while any capital stock losses are assumed to be confined to the 

corresponding upstream sector (for example, damage to cell phone towers, water and 

sewer mains, electric transmission and distribution assets). Evacuation losses affect 

industries by rationing the supply of labor, with no capital stock losses at all. The upshot is 

that at the aggregate level the persistent effect of capital stock losses on the change in 

overall value added is negligible, and for this same reason reconstruction has no effect on 

the corresponding economic impacts in this case. 

 Two features of the results warrant additional explanation. First, the peculiar 

temporal pattern of losses because of crop and livestock damage is the result of persistent 

productivity impacts associated with damage to perennial crops that recur on an annual 

basis, which give rise to a slowly decaying sequence of punctuated losses. This phenomenon 

arises even in the reconstruction scenario, though the losses there are reduced. Second, the 

present value of aggregate utilities losses is made up of $54.1 billion reduction in aggregate 
131

value added because of water outages, and $27.6 billion and $18.1 billion reductions in 

aggregate value added because of wastewater and electricity outages, with the remainder 

due to telecommunications outages. 

 The labor market effects of the components of damage shown in figure 63 have 

temporal patterns that resemble the losses in aggregate value added, primarily because of 

the influence of the aforementioned capital stock dynamics on labor’s marginal product in 

the various sectors of the economy. Flooded building damage incurs the most severe 

impacts, inducing a 14 percent reduction in wages and a 6 percent reduction in aggregate 

employment relative to the baseline trajectory of the economy. Utility service disruptions 

are a distant second, with reductions in wages and aggregate employment of 4 percent and 

7.5 percent, respectively. The magnitudes of the corresponding impacts for the other 

components of damage are all less than 1 percent. 

-16

-14

-12

-10

-8

-6

-4

-2

0

0-6 6-12 12-18 18-24 24-30 30-36 36-42 42-48 48-54 54-60 % Change from Baseline Level

Months Post-Storm

Employment - no reconstruction Employment - reconstruction

Wage - no reconstruction Wage - reconstruction

A
132

-0.09

-0.08

-0.07

-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0.00

0-6 6-12 12-18 18-24 24-30 30-36 36-42 42-48 48-54 54-60 % Change from Baseline Level

Months Post-Storm

Employment - no reconstruction Employment - reconstruction

B Wage - no reconstruction Wage - reconstruction
133

-8

-7

-6

-5

-4

-3

-2

-1

0

0-6 6-12 12-18 18-24 24-30 30-36 36-42 42-48 48-54 54-60 % Change from Baseline Level

Months Post-Storm

Employment - no reconstruction Employment - reconstruction

C Wage - no reconstruction Wage - reconstruction
134

-0.16

-0.14

-0.12

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0-6 6-12 12-18 18-24 24-30 30-36 36-42 42-48 48-54 54-60 % Change from Baseline Level

Months Post-Storm

Employment - no reconstruction Employment - reconstruction

Wage - no reconstruction Wage - reconstruction

D
135

Figure 63. Employment and wage impacts of various components of storm damage for (A) 

flood damage, (B) wind damage, (C) utility service interruptions (electricity, water, 

wastewater and telecommunications), (D) crop and livestock damage, (E) evacuation losses. 

 Table 23 compares the impact of the various components of damage on the 

discounted present value of aggregate value added. As was seen in Figure 62, the majority of 

losses are within the first two years after the storm, and the losses are concentrated in flood 

and utility service components of damage. The multiplier statistic is the ratio of ex-post 

value added losses computed by the CGE model to the ex-ante reduction in sectoral gross 

output that constitutes the direct business interruption impact. With the exception of losses 

due to evacuation, the multiplier computed on a 5-year horizon is above unity, indicating 

that the impacts of the shock on the economy’s productive capacity and general equilibrium 

inter-market price and quantity adjustments are between 27 percent and 129 percent 

larger than the direct productivity effects on the value of output. (The size of the latter 

figure, which pertains to crop and livestock damage, indicates the importance of indirect 

price-mediated substitution effects in downstream industries that use the outputs of 

agricultural sectors relatively intensively.) This multiplier also highlights the crucial role 

played by reconstruction in mitigating the economic costs of physical damage, which in the 

case of flood and wind damage reduces the magnitude of ex-post losses below that of the exante shock. However, this is not the case with components such as utility service 

disruptions, evacuation losses, and agricultural damage, where destruction of the capital 

stock plays a minor role and the bulk of the aggregate shock manifests through reductions 

in industries’ productivity.

-0.10

-0.09

-0.08

-0.07

-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0.00

0-6 6-12 12-18 18-24 24-30 30-36 36-42 42-48 48-54 54-60 % Change from Baseline Level

Months Post-Storm

Employment - no reconstruction Employment - reconstruction

Wage - no reconstruction Wage - reconstruction

E
136

 For many of the components of storm damage, the effects on individual sectors were 

small enough that it was difficult to discern with precision how the characteristics of the 

industries determined changes in value added that were observed in the results. Table 23 

identifies the big winners and losers in this regard, focusing on flood damage component, 

where the impact is large enough to be most transparent. Here we see that reconstruction 

activity not only has a substantial mitigating effect on the losses incurred by those sectors 

that are hardest hit, it also attenuates the gains enjoyed by sectors that would otherwise 

expand by re-absorbing displaced labor and intersectorally mobile capital. Though more 

analysis is necessary to pin down why the sectors that gain (or lose) the most, do so, a key 

attribute appears to be the fraction of the industry’s capital stock that is specific to that 

sector. In particular, industries with a larger share of intersectorally mobile capital see 

reallocation of assets to other parts of the economy where those assets can earn a higher 

rate of return. But while such intersectoral “capital churning” adversely affects these 

industries in which capital is more mobile, it lowers the costs of adjustment to the storm for 

the economy as a whole. 

Table 23. Present discounted sectoral impacts of flood losses. 

Computed on a 5-year time horizon by using a 5 percent discount rate. [%, percent; $, dollar] 

Without Reconstruction With Reconstruction

Change___ Change____

%

Billion 

2007 $ %

Billion 

2007 $

Sectors experiencing largest % gain in value added relative to baseline scenario

Nonmetal mineral 

prod 36.9 8.7

Nonmetal mineral 

prod 23.4 5.5

Natural gas 

distribution 35.3 20.1

Natural gas 

distribution 21.0 12.0

Internet publishing 

and broadcasting 25.1 13.7

Internet publishing 

and broadcasting 16.6 9.1

Paper Manufacturing 16.5 1.7

Warehousing & 

storage 14.6 2.8

Other information 

services 16.1 8.1 Paper Manufacturing 13.3 1.4

Sectors experiencing largest % loss in value added relative to baseline scenario

Residential 

Construction -24.2 -38.6

Residential 

Construction -21.0 -33.5

Non-store retailers -21.7 -7.4

Owner-occupied 

Dwellings -20.8 -67.6

Nonresidential 

Construction -21.1 -57.5

Nonresidential 

Construction -17.6 -47.9

Owner-occupied 

Dwellings -20.6 -66.8 Non-store retailers -16.4 -5.6

Gasoline stations -18.5 -4.2

Bldg materials & 

garden dealers -14.1 -4.6
137

SUMMARY OF RESULTS 

 The major economic results of our study are presented in table 24. The first part of

the table summarizes property damage calculated earlier in the report, while the second 

collates the business interruption losses. 

Table 24. Summary of ARkStorm costs and business interruption. With recapture and without 

reconstructiona) for California over a 5-year time horizon. 

[in billions of 2007 U.S. dollars] 

 

Property repair/ 

reconstruction cost 

Business 

Interruption 

Building Flood Damage $195.0b $591.5

 Related Content Damage 103.0

Building Wind Damage 5.6 1.7

Agricultural Damagec 3.6d 7.3

Power System Damage 1.0e 18.1

Telecommunication System Damage 0.1 5.2

Wastewater System Damagef 0.3 27.6

Water System Damageg 3.0h 54.1

Highway/road Damagei 2.5j 0.02k

Levee Repair and Island Dewateringl 0.5 n.a.m

Evacuation 2.4

Relocation 39.0n n.a.o

Total 353.6 707.9

Total After Double-Counting Adjustment 353.3 627.4

 

a Results for the case “without reconstruction” are used in this summary because they report the gross damage from 

the event; the “with reconstruction” case includes an offsetting stimulus and gives a misleading picture of 

losses from the hazard when most of the funding comes from outside of the region. 

b Weather and flood warning (of at least 48 hours) could reduce building damages by $30 billion, while demand surge 

could increase property repair costs by $70 billion. (See section on Building Content and Repair Costs). 

c

 Agricultural costs pertain to field damage, crop, and livestock replacement, and forgone income from crop losses. 

d Agricultural losses increase to $6.8 billion for high end range of flood duration estimate. 

e

 Power system repair cost estimates range from $0.3-$3 billion. 

f

 Wastewater system repair costs pertain to sewer pipe damage. 

g Water system repair costs pertain to electric equipment and well damage. 

h Water system repair cost estimate ranges from $1-10 billion. 

i

 Highway/road repair cost pertain to landslide damages. 

j

 Highway repair cost estimate ranges from $2-3 billion. 

k Economic impacts from reduced highway capacity pertain to truck traffic in California only. 

l

 Levee repair and dewatering costs pertain to the levees and islands in the Delta area only. 

m Potentially, levee repair and island dewatering time would increase business interruption losses through increased 

agricultural damages. 

n $39 billion relocation costs calculated by using HAZUS-MH formulas, $25 billion for relocation of residences and $11 

billion for relocation of commercial establishments, and the remainder for industry, education, religion, and 

agricultural occupancy classes. 

o The positive effects of relocation have not been evaluated; building service interruption time multipliers have not 

been developed for the flood module of HAZUS-MH
138

 The ARkStorm scenario presents a flood catastrophe and wind disaster. Hundreds of 

billions of dollars of building damages can cause even greater hundreds of billions of dollars of 

business interruption losses because of building downtimes of one to three years. These downtimes 

were lengthened by the inclusion of add-ons such as drying out the buildings, permitting and 

dealing with hazardous waste. In contrast, the shorter building downtimes from wind damages 

result in business interruption losses that are less than the wind damage property repair and 

replacement costs. 

 The business interruption losses from lifeline outages depend on the spatial scale of the 

outages, the time to restore service, the importance of the lifeline service to operations, and the 

availability of substitutes. Water and power lifelines cause the greatest business interruptions: the 

water supply system is presumed to have longer restoration times than the other lifelines and 

power system outages, affecting all counties, are more widespread than the other lifelines. 

However, consideration of double counting reduces the business interruption loss contribution 

from water such that power causes more business interruptions than water after adjusting for 

double counting. This loss is because of more spatial correlation of flooded building damages with 

water outages than power outages. The ratio of business interruption losses to replacement cost is 

highest among the lifelines reinforcing the importance of critical infrastructure to the macro 

economy. 

 Agricultural business interruption losses are only slightly larger than soil and crop damage. 

The impact of evacuation is quite small because we only isolated the flood duration component of 

evacuation time. We considered the impact of highway damages on truck traffic only, in terms of 

increased shipping costs because of increased travel time, though these costs are moderated by the 

redundancy in the highway network 

 The $327 billion in business interruption from ARkStorm would make this the costliest 

disaster in the history of the U.S. For example, business interruption from the ShakeOut 

Earthquake Scenario is estimated to be about $67 billion, from the World Trade Center attack a 

maximum of $100 billion, and from Hurricane Katrina currently (2010) $100 billion (though still 

rising because recovery has not been completed). The magnitude is a combination of the intensity 

of the storm, the vulnerability of the site in terms of a large asset base that is ill protected from this 

kind of event, and its large areal extent. 

CONCLUSION 

 We have estimated the economic impacts of ARkStorm to be several hundred billion dollars 

over a five-year period. At the same time, we offer many caveats to the economic analysis. There 

are limitations in scope (for example, levee repair estimates for the Delta region only, impacts of 

transportation for highways/roads and truck traffic only), gaps in knowledge (for example, lack of 

building service interruption time multipliers for flood damaged buildings), and uncertainties in the 

cost estimates (noted as ranges for lifelines and agricultural damages, and as adjustments for 

warnings and demand surge in the notes for (table 24)) are two. However, the relative order of 

magnitude of the results is likely representative of the domination of flooded building damages and 

economic impacts followed by lifeline services, water service in particular. Although agricultural 

and transportation damages and impacts are estimated as relatively light, they are on a much 

greater scale than experienced during previous California storms. Similarly, the scale of evacuation 

would be unprecedented for California. Sensitivity analysis will be used in the next stage of the 

research to explore the effects of the uncertainties in the damage and restoration estimates. 

 The novel aspect of this study is the use of a computable general equilibrium approach to 

systematically characterize and quantify the economic consequences of the full spectrum of 
139

individual but overlapping impacts of a large-scale natural disaster. Cost estimation based on the 

full spectrum of individual impact categories is not new in large-scale disaster research, as, for 

example the ShakeOut earthquake scenario study (Jones and others, 2008; Rose and others, written 

commun.). However, the input-output approaches utilized by ShakeOut and similar studies 

(Okuyama and Chang, 2004; Okuyama, 2007) have difficulty capturing the feedback effects of 

property damage, temporary interruptions in labor supplies, and hysteretic adverse productivity 

shocks on prices, producers’ and consumers’ substitution responses, and concomitant intersectoral 

supply-demand adjustments across the economy. Distinctly, prior CGE analyses of the effects of 

disasters either limit consideration of impacts to a fairly narrow range of damage categories (Rose 

and Liao, 2005; Rose and others, 2007), or express the shock to the economy in a highly aggregate 

fashion with little differentiation among different types of damage (Selcuk and Yeldan, 2001), 

potentially leading to under- or double-counting of impacts (respectively) and the associated 

macroeconomic costs. Bearing these issues in mind, our key contribution is the development of 

algorithms for translating the outputs of geospatial engineering models of disaster damage 

(HAZUS-MH) into sequences of shocks to capital stocks and productivity in various industry 

sectors, and to aggregate together the resulting general equilibrium economic cost impacts in a way 

that controls for the overlapping effects of different categories of damage. (A useful point of 

comparison is Rose and others (2009) alternative approach of specifying BI losses directly as 

constraints on the outputs of CGE model sectors.) By addressing several of the methodological 

concerns outlined in Rose (2004a) and Okuyama (2007), the current advance provides a roadmap 

for refining future estimates of both the macroeconomic costs of disasters and the mitigating 

influence of resilience. 

Truck Traffic Economic Impacts from Reduced Highway 

Capacities 

 Closed highways and reduced highway capacities because of landslide and flooding 

damages will affect emergency responder access and commuter and truck routes. Analytical 

methods to analyze effects of extreme weather on the performance of the transportation system is 

not well developed. For the ARkStorm scenario, we attempted an economic impact analysis of large 

truck traffic. We obtained technical support from various University of Southern Californiaaffiliated independent consultants through Hanh Dam Le Griffin (TTW, Inc.). Their transportation 

model analyzed the effects of reduced highway capacities on the regional and national movement of 

goods and services by estimating changes in truck distance, travel time, and associated costs and 

the impacts of increased shipping prices on major economic sectors. Despite the sophistication of 

this modeling system, we identified various issues with its application to a winter storm event and 

determined some of the research needs for transportation modeling. 

Method of Analysis 

 The state and national highway network was analyzed for 4 points in time following the 

southern California storm: on day 3, day 14 (one day after the northern California storm), day 90, 

and day 180. The highway capacities on these days are explained and mapped in the highway 

damage section of this report. Independent model runs were conducted for the southern and 

northern storm events —generating a total of seven model runs for the analysis. (Note that 

highway damages from the northern California storm have not yet occurred on day 3.) 

 A model run on the national highway network is comprised of three principal models: a 

national highway network model, a transportation cost model, and a demand-driven national 

interstate economic model (the National Interstate Economic Model (NIEMO) and an elaborated 

TransNIEMO) that have been developed by the consultant group (Park and others, 2005, 2007, 
140

2009). These models analyze the movement of truck traffic on the national highway network and 

the economic impact of any change in truck travel distance and/or time resulting from reduced 

highway capacities on the national highway network. The analysis assumed that the trucking 

industry is able to pass costs (of increased time and distance) to customers in the form of prices. 

The final users, mainly households and government, react to higher priced products and services by 

cutting back on consumption. Reduced demand for industry outputs prompts a new interindustry 

trade and production equilibrium. This framework (fig. 64) was used to estimate the truck 

transportation related economic impacts of the ARkStorm scenario throughout the nation. 

Figure 64. TransNIEMO economic impact modeling framework. 

 The 2002 Federal Highway Administration (FHWA) Freight Analysis Framework (FAF) 

dataset was used to construct highway network links on the national highway network. Percent 

capacity losses on the links were set accordingly for days 3, 14, 90, and 180. The FHWA FAF dataset 

provided large truck trips that have been converted from commodity flows (U.S. Department of 

Transportation, 2007). The truck traffic was assigned to the national highway network by using a 

standard link capacity constrained user equilibrium model and shortest-path travel time algorithm; 

truck traffic is forced to re-route in response to losses in highway capacity and road closures.29 The 

model selected best alternate routes for the concurrent users. The re-routing of truck traffic 

resulted in changes in time and distance traveled by trucks. Results were aggregated by state of 

 

29 The re-routing assumption neglects to consider resilience strategies of changing transportation 

modes or changing trucking schedules. 
141

origin and destination and reported for California, other states combined, and all of the national 

highway network. 

 Next, the effects of changes in total truck travel time and distance on trucking costs were 

assessed for (49) states across 29 commodity sectors30 by using the transportation cost impact 

model based on a cost-price input-output model. The difference in trucking time and distance was 

converted to dollars by summing the variable costs of time ($17per hour labor rate) and distance 

(for example, $0.48/mile) (Berwick and Farooq, 2003)31. The increased shipping costs from the 

state of origin (i) to the state of destination (j) for industry sector (k, k=1...29) were aggregated by 

the state of destination. Total truck costs (measured in millions of dollars per year were 

summarized for California, other states combined, and a total for the U.S. Finally, based on 

changes in total shipping costs for the 29 University of Southern California commodity sectors, a 

destination-state cost-price input-output model measured the effect of price increases on 47 

economic sectors in 49 destination states. The model assumed that 100 percent of increased 

shipping costs were passed on to customers as increased prices for goods and services at the 

destinations, which results in decreased consumer demand for goods and services. The NIEMO 

estimated direct and indirect economic damages associated with changes in consumer demand at 

the destination states. The economic impacts (measured in millions of dollars of industry output) 

were aggregated by destination state for each scenario and summarized for California, other states 

combined, and the U.S. 

Truck travel time, distance, cost, and economic impacts

 Change in truck travel time and distance: table 25 and table 26 present differences in truck 

travel time and distance, relative to the 2002 baseline, for each model run received from TTW, Inc. 

Overall, increases in truck travel time results from the re-routing that occurs in response to 

ARkStorm highway capacity reductions and closures. Most of the California truck travel time 

increases occur from the southern California storm reflecting the severe capacity losses of the 

highway network in southern California on day 3 and also the region’s significant trade volume and 

goods movement activities. On Day 14, the increase in truck travel time in the south is reduced 

following restored highway capacities on some routes affected by flooding and erosion and 

landslides. Truck travel is less affected by the northern storm, but as noted under the study 

limitations, route 99 was allowed to operate at full capacity when it should have been closed. Rerouting increases travel time throughout the rest of the country, but the use of alternative routes 

 

30 The models utilize common-denominator aggregations of standard economic classifications 

(including NAICS, SIC, and others), called the University of Southern California Sectors. There are 

47 University of Southern California Sectors, 29 of which are commodity sectors. The commodities 

are shipped, but the availability can affect the remaining sectors. Therefore, first-order network 

effects are reported for 29 sectors, but full effects are reported for all 47 sectors. 

31 The equation to calculate truckers’ labor cost per mile in appendix A of Berwick and Farooq 

(2003) assumes that the Labor (Wage) Rate per Hour (LRPH) is $10 per hour. For this analysis the 

current (2010) LRPH is updated to $17 per hour yielding a trucker’s labor cost of $0.09 per mile. 

Other variable costs are $0.48 per mile such that the estimated labor cost is 65 percent of the total 

variable cost (0.65 = 0.9 / (0.9+0.48)). 
142

actually decreases the total truck travel distance in the other states. Some routes experience less 

congestion and a corresponding gain in traffic flow. 

Table 25. Change in highway network truck travel time. 

2002 FHWA Freight Analysis Framework (FAF) data as baseline. 

Aggregated to spatial unit by network origin and destination. 

Region Truck Travel Time Increase (in Hours) 

Day 3 Day 14 Day 90 Day 180 

Southern California storm 231.0 17.0 0.3 zero

Northern California storm n/a 37.8 1.3 1.1

TOTAL California 231.0 54.8 1.5 1.1

OTHER STATES 899.3 410.5 44.3 4.5

TOTAL U.S. 1,130.3 465.4 45.8 5.6

Table 26. Change in highway network truck travel distance. 

2002 FHWA Freight Analysis Framework (FAF) data as baseline. 

Negative signs denote a decrease in truck miles. 

Aggregated to spatial unit by network origin and destination. 

Region Truck Travel Distance Increase (in Miles) 

Day 3 Day 14 Day 90 Day 180 

Southern California storm 8027 1,968 75 zero 

Northern California storm n/a 1,063 97 194

TOTAL California 8,027 3,031 173 194

OTHER STATES -63,312 -3,105 -2,215 -878

TOTAL U.S. -55,285 -74 -2,043 -684

 Figure 65 and figure 66 display the change of network volumes on day 3 from the southern 

California storm, and day 14 from the northern California storm, respectively. On Day 3, the 

southern California network link volumes experience substantial percentage changes, especially on 

the Ventura Freeway (U.S. 101 and route 134) east-west route through Ventura County and in the 

southern San Fernando Valley in Los Angeles County. However, the effects are not limited to 

southern California, as indicated by the percentage volume changes in the rest of country, albeit of a 

much smaller scale. On day 14, in northern and central California, there is a high percentage 

decrease in network link volumes in Sacramento, the Bay Area, and the Central Valley. Similar to 

the results of the southern California storm event, the impacts are not limited to just the northern 

and central regions of the state, but at a smaller scale to southern California and other states. For 

traffic volumes traveling between a specific pair of origin and destination points in the national 

network, ARkStorm highway capacity changes will result in some volume decreases along certain 

links, and countervailing increases in volume along alternative links. 
143

 Rough extrapolation of the 4 day results to the 6 month period following ARkStorm 

suggests that the total number of truck miles reduces by about 600,000 miles because of re-routing: 

the truck miles increase by 100,000 miles in California and decrease by 700,000 miles throughout 

the rest of the U.S. However, total truck travel time increases by 18,000 hours: truck travel time 

increase by 15,000 hours outside of California and most of the 3,000 hour truck travel time increase 

in California is associated with the southern California storm. These results suggest that while both 

truck travel distances and time would increase within California, trucking costs outside of 

California could involve a trade-off between fewer miles travelled and longer travelling times. 

Figure 65. National highway network volume change for southern California storm on day 3. 
144

Figure 66. National highway network volume change for northern California storm on day 14. 

 Truck traffic time and distance costs: outside of California, there are overall gains in 

trucking costs after an initial decrease in trucking costs (of -0.05 million dollars) on Day 3 (table 

27). This result reflects the dynamics of improved traffic flow on some routes in other states. 

Aggregate trucking costs for California destinations increase, relative to the baseline, but taper off 

as highway capacity is restored. The costs of commodity shipments, induced by ARkStorm highway 

capacity losses, would be felt throughout the U.S. These impacts vary substantially across the 29 

commodity sectors and 49 states as trade value and activity between California and other states 

vary, with some commodities experiencing increased trucking cost while others benefit from 

reduced shipping costs. 
145

Table 27. Change in trucking costs. 

Costs are aggregated to spatial unit by geographic destination. 

Time is valued at $17 per hour; distance is valued at $0.48 per mile. 

Impact on truck costs associated with 29 commodity sectors (excluding service sectors). 

Negative sign denotes a decrease in truck costs for commodity shipments. 

[n/a, not applicable] 

Region Total Trucking Costs (in millions of dollars)

Day 3 Day 14 Day 90 Day 180 

Southern California 2.2 0.7 0.0 0.00

Northern California n/a 0.6 0.1 0.03

TOTAL California 2.2 1.2 0.1 0.03

OTHER STATES 0.0 0.8 0.3 0.01

TOTAL US 2.2 2.0 0.4 0.04

 Rough extrapolation of the 4 days to 6 months suggests that trucking costs increase on the 

order of $60 million dollars: $38 million in California with 75 percent of the cost increases 

associated with the highway damages from the southern California storm and $22 million trucking 

cost increases in other states. 

 Truck traffic economic impact: The total (direct and indirect) economic impact, measured in 

terms of industry output losses (in millions of dollars), is associated with trucking cost changes 

only.32 Table 28 presents estimates of the economic impacts of reduced highway capacities from the 

ARkStorm scenario for each of the 4 days. Table 28 is not a simple and direct reflection of changes 

in trucking costs (table 27) because of consumer responses to changes in prices. 

 

32 Other economic impacts that may occur as a consequence of highway network disruption 

associated with California storm events were not accommodated. 
146

Table 28. Truck traffic economic impact. 

Aggregated by geographic destination. 

Time value at $17 per hour; distance value at $0.48 per mile. 

Negative signs denote a gain in economic output. 

[n/a, not applicable] 

Total impact (in millions of dollars of output)

Day 3 Day 14 Day 90 Day 180

Southern California 1.1 0.3 -0.03 0.00

Northern California n/a 0.3 0.03 -0.04

TOTAL California 1.1 0.6 0.00 -0.04

OTHER STATES 0.2 0.8 0.34 0.09

TOTAL US 1.3 1.4 0.34 0.06

 The economic impact of the ARkStorm highway capacity reductions is evident throughout 

the nation. The national aggregation of results suggests that there are output losses, although there 

are slight output gains in California (estimated at $30,000 for Day 90 in southern California and 

$40,000 for Day 180 in northern California) as highway conditions improve. Other states also 

experienced output gains (where substitution of pricier commodities favored some sectors in some 

states) that were offset by output losses in other states. Therefore, given the fixed interindustrial 

and trade relationships (of the NIEMO model) economic impact in some states can positively affect 

the economy in California through the industrial links and domestic trade activities of these states 

with California. For California, the largest gains in economic output were reported for the 

motorized and other vehicles sector. The sectors with the largest output losses included 

construction, other services, wood products, electronics, real estate, and accommodation and food 

services. 

 Rough extrapolation of the 4 day analyses suggest that output losses for California are on 

the order of $16 million and are mostly attributed to the southern California storm. Output losses of 

$27 million in the rest of the U.S. adds up to a $43 million economic impact to the U.S. economy 

from ARkStorm highway network disruptions. These economic impacts are an order of magnitude 

less than the $500 million estimates of delayed and lost automobile and truck trip costs calculated 

for the ShakeOut earthquake scenario in the southern California region (Jones and others, 2008). 

Conversely, the costs of ARkStorm highway repairs of $2.5 billion are an order of magnitude greater 

than the repair cost estimate of $400 million for the ShakeOut scenario. We conclude that while the 

highway truck transportation model accommodates the scale of California and the rest of the 

country, various compromises very likely have underestimated the economic impacts of the 

ARkStorm highway capacity reductions. 

RESEARCH NEEDS RELATED TO TRAFFIC ECONOMIC IMPACTS 

 Overall, despite the accomplishment of applying a state-of-the-art highway model to analyze 

the ARkStorm highway capacity reductions, budget and time constraints did not permit further 

investigation of the limitations and research gaps that were identified. First, the analysis was 
147

challenged by the hundreds of highway capacity alterations from ARkStorm landslides and flooding. 

These analyses were aggregated to reduce the burden on data entry. During this process, it appears 

that flood related capacity reductions along California 99 between the Grapevine and Sacramento 

were overlooked; Day 14 conditions would not likely allow the re-routing and volume increases 

along this highway. Highway closures and capacity reductions along California 99 would increase 

truck travel time, distance, and costs, and economic impacts. Second, independent model runs of 

the northern and southern storms have missed interactive effects of the damages from the two 

storms on truck travel and will have further underestimated the impacts of truck distance, time, 

associated costs and price feedbacks. Third, the analysis of four days captured the impacts as 

snapshots in time so that it was necessary to coarsely extrapolate to the six-month period following 

the winter storm scenario. Fourth, we took stock of highway damages and capacities at the 

conclusion of each of the southern and northern storms. Therefore, we have not captured all the 

phases of a severe storm. Precipitation, wind, and wave surge in coastal areas that precede 

inundation or actual closure of roadways would also effect highway operations and safety in the 

form of travel delays. Fifth, the model, as complex as it is, only implements the resilience strategy of 

re-routing. Other strategies such as change of travel time (for example, travelling through the 

night) or change of transportation modes (to rail or air) were not considered. Beyond a price 

response, industrial sector resilience strategies for supply chain disruptions were not 

incorporated. Sixth, the result of reduced truck distances and increase miles needs further 

exploration and explanation. Although equilibrium-based network models are appropriate for 

uncongested urban network when travel time is a function of volume, and traffic demand is spread 

over many alternative routes in such a way to equalize volume-sensitive travel times on each of the 

routes, the application to the interstate network requires caution. Furthermore, equilibrium–

based network models have been used to analyze investment scenarios that add capacity or 

enhance performance, rather than degrade it in the case of disasters. Reduced highway capacity can 

create impossible optimization problems if a subnetwork is totally isolated from the network. 

(Sungbin Cho, Southern California Association of Governments, written commun., 2010) Seventh, 

explanation of the effects on the different sectors would be insightful. Eighth, the models used to 

analyze economic impacts were developed to deal solely with truck traffic and movement on the 

national highway network. Therefore, for this and various other reasons, stated above, the results 

for the ARkStorm scenario (table 24) understate the potential economic impacts of reduced 

highway capacities from such storms. 

 The ARkStorm scenario would affect the mobility of automobile and emergency vehicles, 

and further studies and additional data would be required to assess these impacts in any detail. In 

principle, the impacts on automobile and emergency vehicles would reflect those estimated for 

truck traffic. Because of highway capacity losses and road closures, automobiles and emergency 

vehicles also would be forced to re-route to avoid congestion or to reach their destinations. As 

result of re-routing, travel delays would likely occur and be measurable on a value of time and 

distance basis. In addition to re-routing, depending on trip type and purpose, some commuters and 

travelers might switch to alternative modes, such as public transit; some may be able to 

telecommute; and some travelers may decide not to travel. These decisions could alter the number 

and timing of automobile trips on the national highway, and consequently traffic flows in some 

parts of the network may experience relief. The response of auto travelers to highway capacity 

losses and road closures are somewhat similar to those of trucks, although automobile and small 

truck trips have more flexibility than large truck trips that tend to be more time sensitive and have 

fewer routing and alternative modes of transportation. In addition, emergency vehicles and also 

some trucking companies may have designated alternative routes and may not react to time, cost, 

and congestion alone. The consequences of delay or an inability to travel or effectively respond to 
148

emergency situations, could potentially be substantial. Any quantitative measure and confirmation 

of these impacts would require further in-depth study. 

 Finally, for future analysis of storm events in the broader context of climate change, further 

development of transportation models for conditions of reduced highway capacities and closures 

are needed to inform planning decisions about industrial locations and emergency plans. Linkages 

to environmental impacts of extreme weather in terms of fuel use and greenhouse gas emissions 

could also be made. (Reza Navai, California Department of Transportation, written commun., 2010) 

Environmental and Health Issues 

 This section is an abridged version of a much more detailed report (G. Plumlee and C. 

Alpers, USGS, written commun., 2010). The rainfall, snowfall, erosion, flooding, landslides, winds, 

and resulting physical damage to infrastructure from an extreme storm such as that modeled by 

ARkStorm could result in substantial physical and chemical stresses on the environment, with 

resulting impacts on the health of affected ecosystems and humans. The framework developed for 

the ARkStorm meteorology and physical impacts can be integrated with a wide variety of 

information to infer plausible scenarios for environmental, ecological, and environmental-health 

impacts of the hypothesized storm. 

PLAUSIBLE ENVIRONMENTAL-HEALTH ISSUES AND IMPACTS 

 Severe storms can pose diverse and significant physical threats to human safety. Common 

results of these threats include, for example, drowning in flood waters and injuries or death caused 

by hypothermia, tornadoes, floodwater-borne debris, lightning strikes, rainfall-triggered landslides 

or rockfalls, avalanches, and wind-related damages (such as falling trees or power lines), and fires 

from petroleum products released into floodwaters. These hazards deservedly receive the bulk of 

public-health focus during active storms, and a detailed analysis of plausible ARkStorm impacts in 

this realm should prove extremely useful, but is beyond the scope of this study. The focus of our 

analysis from a public-health perspective is on plausible environmental-health impacts, meaning 

those shorter- to longer-term health issues that result from environmental exposures. 

 Our analysis is based in part on inferences that can be drawn from peer-reviewed 

publications summarizing environmental and environmental-health impacts documented or 

postulated from past storms and floods, both in California and elsewhere. Based on this type of 

analysis, a wide variety of plausible environmental issues must be considered for ARkStorm. 

 Storm runoff, particularly in those areas receiving high hourly rainfall rates and areas in 

which rain falls on pre-existing snow, would likely lead to widespread erosion, transport, and 

redistribution of soils, sediments, and rock materials. The physical impacts alone of these materials 

on riverine, floodplain, lacustrine, and coastal environments could be substantial and could lead to 

significant impacts on species habitat and loss of life in some aquatic or terrestrial organism 

populations. 

 Some rock types, and the soils developed on them, may contain naturally elevated levels of 

potentially toxic metals (such as selenium, zinc, copper, arsenic, and lead), environmentally 

deleterious minerals (such as iron sulfides that generate acid rock drainage when weathered), 

mineral toxicants (such as asbestos), or pathogens (such as Coccidioides Immitis, the soil fungus 

that causes Valley Fever). Storm-induced erosion or landslides affecting these rock types have the 

potential to disperse these materials in the environment. 
149

 Storm or flood damage to buildings, infrastructure, industrial facilities, or agricultural 

facilities (such as wastewater treatment plants, petroleum refineries, active or inactive mines, 

chemical manufacturing plants, animal feeding operations, and others) could release debris, 

contaminants, and microbial pathogens into the environment. Erosion or flooding of agricultural 

lands could lead to extensive loss or contamination of arable soils. Storm runoff from agricultural 

lands, residential areas, and urban areas could release a variety of sediment-borne or water-borne 

anthropogenic contaminants into the environment. Water supplies used for human consumption, 

livestock consumption, or agricultural irrigation, including surface water and shallow groundwater, 

could become contaminated by a wide variety of contaminants or pathogens. Following the storm, 

contaminated sediments and debris redistributed by landslides or floodwaters could then dry out 

and become available for further redistribution by human disturbance and (or) wind transport. 

 In addition to the acute physical threats to safety posed by the storm, the possibility exists 

for adverse health effects on humans and ecosystems. These effects could include, for example, 

potential outbreaks of infectious disease from exposure to contaminated floodwaters, consumption 

of contaminated drinking water, or exposure to dusts from landslide or flood deposits containing 

soil pathogens such as Coccidioides Immitis. In most developed countries, effective health hazard 

communication and preventive emergency response measures (such as warnings not to drink 

potentially contaminated water and providing access to uncontaminated water) commonly prevent 

or substantially lessen the magnitude of many infectious disease outbreaks. However, as shown in 

this report, widespread flooding and damage to infrastructure could reduce the ability of 

emergency responders to provide basic services in the timeframe needed to avoid at least some 

infectious disease occurrences; an example would be the challenges of providing fresh drinking 

water to large numbers of people across broad storm-affected areas. 

APPROACH FOR ENVIRONMENTAL AND HEALTH IMPACTS 

 While the list of plausible environmental and environmental-health issues that could result 

from ARkStorm is substantial, a much more detailed analysis of each issue is needed to determine 

the likelihood of whether it would actually occur and, if likely, the magnitude of the potential 

impacts. While detailed analyses of all potential issues are well beyond the scope of this study, we 

have outlined and begun to test an approach by which such analyses can be made. 

 Further insights about potential geogenic (natural) and anthropogenic sources of storminduced environmental contamination can be gleaned by linking ARkStorm precipitation and 

flooding maps to data extracted from diverse database sources with geologic, hazards, and 

environmental information. Guided by results of such analyses, site-specific or other types of 

investigations can be developed to assess in more detail the potential occurrences and magnitude 

of the issues in question. Two examples are discussed briefly here: linkages of ARkStorm 

precipitation and flooding data to (1) environmental lithology databases and (2) National/State 

scale environmental facilities databases. 

Linkages to environmental lithology maps. Environmental lithology maps can be derived 

from State digital geologic map databases to show the distribution of rock types that could serve as 

nonpoint sources for potentially hazardous materials released into the environment as a result of 

ARkStorm-triggered runoff and erosion. For example, certain types of rocks naturally contain high 

levels of iron sulfides (pyrite, FeS2, also known as fool’s gold) and trace metals such as copper and 

zinc. Weathering and oxidation of these rocks can produce natural acid-rock drainage with a wide 

variety of elevated metals that can be detrimental to aquatic organisms. Watersheds where 

abundant outcrops of these rock types coincide with high rainfall or snowmelt (such as ARkStorm) 

would, therefore, be at higher risk for storm-related release of acid-generating rocks by landslides 
150

or erosion into downstream areas, amplifying the environmental impacts of the natural acid-rock 

drainage. However, it is likely that such effects would be localized largely in the watersheds close to 

where the rocks outcrop. Another example that has occurred elsewhere is the storm-related 

dispersal of asbestos into the environment from landslides in ultramafic rocks; however, the effects 

likely would be restricted to areas relatively close to the ultramafic rock outcrops. 

Linkages to national- or state-scale facilities databases. A variety of state- to nationalscale facilities databases can provide useful insights into the locations and types of industrial or 

other facilities with some potential for storm-related releases of contaminants into the 

environment. Examples of useful databases include the U.S. Environmental Protection Agency 

(EPA) Facility Registry System (FRS) database, State databases of land use and facilities such as 

animal feeding operations, and various USGS databases on historic and active sites of mining or 

mineral processing. 

 Wastewater treatment plants (WWTPs) are an example of a facility type that can be 

examined linking state- or national-scale databases to ARkStorm flooding and precipitation maps. 

As discussed earlier , WWTPs are more likely to be located in flood-prone areas, because the need 

to minimize pumping dictates that these plants be located at the lower elevations of the respective 

sewage system service areas. An analysis of the EPA regulated facilities database for California, in 

which WWTPs were extracted by using the key words or phrases “sewage”, “sewerage”, or “WWT”, 

identified more than 900 facilities across the state. Of these, a substantial fraction are predicted to 

be either within the plausible zone of ARkStorm flooding, or in predicted zones of maximum daily 

precipitation rates well in excess of 4 inches per day. Either result would likely lead to release of 

raw or partially treated sewage or other wastewater (along with contained contaminants and 

pathogens). Therefore, release of contaminated wastewater from flooded, shut-down, or 

overloaded WWTPs is a plausible and substantial environmental impact that likely would result in 

some areas of the State; geospatial analysis of the results highlights areas that are of higher concern 

than others. Such releases are commonly observed as a result of floods in the U.S. and worldwide, 

but effective hazards communication and preventative actions by emergency responders can 

substantially reduce the potential human health impacts. Further examination is warranted, 

however of potential ecological impacts from flooded WWTPs and other environmentally 

significant facilities near flood-prone areas. Many of the lowland, riparian, or lacustrian areas 

where floodwaters accumulate also are home to a wide variety of aquatic and terrestrial wildlife 

that could be affected adversely by floodwater-borne contaminants or pathogens. 

Limitations: There are some substantial limitations in this approach that must be 

recognized and the appropriate caveats placed on interpretations and conclusions. A 

reconnaissance survey of a number of the databases used in the analysis reveals relatively common 

issues, such as incompleteness of coverage (for example, known facilities are missing), duplicate 

entries with different locations, and inaccuracy of geospatial location information for individual 

facilities of concern. Also, many databases lack specific information about the types of 

environmental contaminants that potentially could be released during an ARkStorm-scale event 

from specific source facilities. As a result, without extensive efforts to address such limitations, the 

use of state- to national-scale databases to map potential sources of storm-induced environmental 

contamination should be considered qualitative at best. Nevertheless, this qualitative approach 

does provide much useful information and also helps to fulfill a primary goal of this study, which is 

to identify areas where improvement in the approach for estimating plausible impacts is needed. 
151

Public Policy Issues 

 Stated simply, public policy can be described as a predisposition of governments to act in a 

predetermined manner, although in actuality it is “whatever governments choose to do or not to 

do” (Newell, 2004, p. 153). Public policymaking in the U.S. is the responsibility of elected bodies, 

such as city councils, state legislatures, and the U.S. Congress. Managers of federal, regional, state, 

county, and city agencies also participate in policymaking, helping conceptualize and create 

policies. The policymaking process tends to evolve in response to societal or community problems 

perceived by citizens, interest groups, and political leaders. In a governmental context, it can be 

described as a cycle of (Newell, 2004, p. 153): 

Agenda setting, when issues are brought to the attention of policymakers, 

Policy formulation, when options are considered and a course of action is adopted, 

Implementation, when adopted policies are put into action, and 

Evaluation, when a policy assessment is performed, and ways to modify or improve policies are provided. 

 In the context of this policymaking process description, the ARkStorm policy assessment 

largely corresponds with the agenda and policy formulation stages. This assessment looks at the 

priority public policy issues highlighted by the ARkStorm scenario. These priority issues are 

organized around the basic functions of disaster management—mitigation, preparedness, response, 

and recovery, as well as risk awareness—a major behavioral factor in disaster management. In 

addition, this assessment also identifies an overarching policy consideration and identifies possible 

courses of action. 

OVER-ARCHING POLICY CONSIDERATION 

The ARkStorm scenario hypothesizes a disaster of catastrophic proportions for which 

existing national and state disaster policies are ill suited. Over the last 60 years, U.S. disaster 

policy agenda setting and policy transformations have occurred mostly during relatively brief 

periods of time – usually following disastrous events (Birkland, 2006; Rubin 2007). As Birkland 

advises “a disaster can often do in an instant what years of interest group activity, policy 

entrepreneurship, advocacy, lobbying, and research may not be able to do” (Birkland, 2006, p. 5). 

But, having relatively few major urban disasters within this timeframe suggests that these eventrelated policy changes mostly reflect the learning from far more moderate disasters. 

 The modern U.S. disaster management system works as a shared system in which over 

88,000 local governments, special districts, and Native American tribal governments have primary 

responsibility for supplying disaster-related resources; federal, regional, and state agencies are to 

provide support as requested (Federal Emergency Management Agency, 2005). This shared system 

is triggered from the “bottom-up.” Local governments are aided, as needed, by states, and both are 

aided, in turn, by the federal government. By design, the system requires extensive coordination 

and cooperation among all levels of government, as well as the many private organizations involved 

in disaster management. This process is also an incentivized system of partnerships, in that states 

and localities are encouraged, but not required, to participate. 

 Table 29 generalizes the current (2010) disaster policy framework in the U.S. for the basic 

disaster management functions, combining preparedness and response, and adding risk awareness. 

The basic role and responsibility of each level of public policy making—federal, regional, state, 

county, and localities—plus the private sector are described briefly. Some of the key policies 

evaluated in this assessment are articulated in the following legislation, doctrine, and programs: 
152

 National Flood Insurance Program (NFIP) provides flood insurance through the private 

sector, with backing by the federal government. Insurance also is reinforced by 100-year and 500-

year floodplain mapping, together with rate reductions in relation to local government mitigation 

actions. Various types of flood mitigation and project grants also are administered. The NFIP 

represents the national position on “shared-risk.” The government will help, but the aim is to 

encourage local governments and individuals to share flood risk. 

 Robert T. Stafford Disaster Relief and Emergency Assistance Act (Stafford Act) provides for 

individual and household assistance post-disaster, Public Assistance grants to restore damaged 

public facilities and infrastructure, and mitigation grants that fund both local hazard mitigation 

planning and projects to help strengthen communities against future disaster losses. Interacting 

with Stafford Act programs are the National Incident Management System (NIMS), providing a 

standardized nationwide protocol for managing response at each level of government, and the 

National Response Framework (NRF), which coordinates disaster response among federal agencies. 

These directives help further define federal response, recovery, and preparedness doctrine. The 

Post-Katrina Emergency Management Reform Act (PKEMRA) of 2006 modified the Stafford Act. 

PKEMRA directed FEMA to develop both a National Disaster Recovery Strategy and National 

Disaster Housing Strategy. FEMA finalized the National Disaster Housing Strategy in January 2009 

(http://www.fema.gov/pdf/media/2009/ndhs.pdf). The National Disaster Recovery Framework 

(http://disasterrecoveryworkinggroup.gov/ndrf.pdf) is still under development. 

 Disaster Mitigation Act of 2000 (DMA 2000) requires local adoption of a FEMA-approved 

Local Hazard Mitigation Plans (LHMP) as a precondition for receipt of federal mitigation project 

grant funding. The act also provides a competitive Pre-Disaster Mitigation (PDM) grant program to 

fund local hazard mitigation planning efforts and mitigation projects. 

 State and local government programs include state and local emergency planning and use of 

the Incident Command System (ICS)/Standardized Emergency Management System (SEMS); 

statewide mandates for local safety element adoption; new requirements for linking floodplain 

mapping to land use, housing, safety and conservation elements; 200-year floodplain mapping in 

the Central Valley; and, various regional and local levee and water reclamation district formations. 

 In practice, current (2010) U.S. disaster policies and programs tend to work reasonably well 

in large-scale, noncatastrophic disasters, as well as in moderate and predominantly localized 

disasters. However, these policies and programs are not well-equipped to address catastrophic 

events that stretch societal resources. Disaster management tools and training tend to emphasize 

the delivery and management of federal and state assistance programs from a top-down 

perspective, rather than a more bottom-up view of communities as the “client.” There have been 

repeated calls for greater flexibility and streamlined processes for local governments are needed to 

obtain federal and state assistance and a recommended broader use of the more flexible block grant 

forms of public financing in disaster recovery (Rubin, 1985; Olshansky and others, 2006; Smith and 

Wenger, 2007). 
153

Table 29. Generalized framework for U.S. disaster policy at the federal level. 

Mitigation Preparedness and 

Response

Recovery Risk Awareness

Federal

-Provide federallybacked private flood 

insurance, 100- and 

500-year floodplain 

maps, rate reductions 

linked to mitigation, and 

mitigation grants 

-Require state-local 

adoption of FEMAapproved hazard 

mitigation plans for 

mitigation grant eligibility 

-Provide state-local preand post-disaster 

mitigation project grants 

-Make building-site 

mitigation improvements 

reducing risks to federal 

infrastructure

-Implement National 

Response Framework 

(NRF), based on Incident 

Command System (ISC) 

principles:

 +Engage in partnership 

with state and local 

agencies

 +Establish a tiered 

response

 +Seek scalable, flexible, 

adaptable response

 +Establish unity of effort/ 

command

 +Foster readiness to act

-Provide training and 

technical support for 

preparedness and 

response

-Establish and support 

communication and 

information mechanisms

-Provide state and local 

assistance as requested

-Provide assistance to 

state and local 

governments, Indian 

tribes or authorized tribal 

organizations, and 

certain specified private 

non-profit organizations 

for eligible emergency 

work and the repair, 

restoration, and 

replacement of public 

facilities and infrastructure 

damaged by a disaster 

event

-Provide grant and loan 

assistance to individuals, 

families, and businesses 

for damages and

economic losses caused 

by disaster

-Provide limited resources 

for long-term recovery 

planning

-Fund post-disaster 

mitigation to reduce future 

losses

-Provide residential and 

commercial resources for 

recovery via the NFIP

-Create and administer 

public education 

programs to promote 

risk awareness that 

work at a national 

level, and also fund 

regional, state, and 

locally specific efforts. 

Examples: National 

Earthquakes and Wind 

Hazard Reduction 

programs

-Limited funding for 

research and outreach 

on risk communication

-Flood risk awareness 

is part of the NFIP 

advertising campaigns
154

Table 30. Generalized framework for U.S. disaster policy at the state level. 

Mitigation Preparedness and 

Response

Recovery Risk Awareness

State

-Make building-site 

mitigation improvements 

reducing risks to state 

infrastructure 

-Require counties and 

cities to adopt general 

plans, including 

floodplain mapping 

-Require counties and 

cities to approve 

developments 

consistent with general 

plans, and pay fair 

share of liability linked to 

development approvals 

in areas impacted by 

state flood control 

project failures

-Provide 200-year 

Central Valley floodplain 

mapping, prepare 

Central Valley Flood 

Protection Plan, and 

map levee flood 

protection zones 

-Require Central Valley 

counties and cities and 

counties to amend 

general plans to 

conform with Flood 

Protection Plan and 

deny subdivisions in 

unprotected flood 

hazard zones 

-Implement State 

Emergency Plan 

framework for disaster 

preparedness and 

response

-Coordinate requests for 

federal assistance; utilize 

mutual aid regions and 

operational areas to 

support and coordinate 

federal and local 

response

-Provide training and 

technical assistance to 

local agency response 

organizations

-Coordinate NGO support 

for state and local 

response

-Provide and coordinate 

the flow of information 

internally and externally to 

the public

-Provide state financial 

assistance to affected 

local governments for the 

repair, restoration, and 

replacement of public 

facilities damaged by a

disaster

-Administer federal 

recovery programs to 

repair public facilities and 

infrastructure and provide 

necessary additional 

funds

-Coordinate federal 

assistance available for 

individuals, families, and 

businesses

-Help administer federal 

programs for post-disaster 

mitigation

-Participate in national 

risk awareness 

programs

-Create and administer 

public education 

programs to promote 

risk awareness, and 

also fund locally 

specific efforts. 

Example: Earthquake 

Country Alliance

-Have state floodplain 

managers (add more if 

they do risk awareness 

work)
155

Table 31. Generalized framework for U.S. disaster policy at the local level. 

Mitigation Preparedness and 

Response

Recovery Risk Awareness

Local 

-Make building-site 

mitigation improvements 

reducing risks to local 

infrastructure 

-Adopt state-mandated 

county and city general 

plans and building 

codes

-Adopt FEMA-approved 

Local Hazard Mitigation 

Plans 

-Form flood controllevee districts and 

geologic hazard 

abatement districts

-Have primary 

responsibility for disaster 

response

-Establish priorities for 

allocation of personnel 

and resources

-Provide information and 

locally-based 

assessments 

-Request assistance and 

mutual aid by using tiered 

relationships

-Obtain and disseminate 

information to the local 

public

-Coordinate laterally with 

local non-governmental 

organizations (NGOs), 

community-based 

organizations (CBOs), 

faith-based organizations 

(FBOs), medical 

providers, other local 

agencies, and private 

sector

-Coordinate vertically with 

federal and state 

agencies/responders

-Primary responsibility for 

recovery; request federal 

and state assistance as 

necessary

-Participate in national 

and state risk 

awareness programs

-Limited examples of 

locally created and 

administered risk 

awareness programs. 

-Disclose flood risk as 

part of land 

development and other 

locally controlled

development review.
156

Table 32. Generalized framework for U.S. disaster policy at the private level. 

Mitigation Preparedness and 

Response

Recovery Risk Awareness

Private

-Sell NFIP flood 

insurance and fire 

insurance with 

graduated mitigationbased costs

-Make building-site 

mitigation improvements 

reducing risks to private 

utility-owned 

infrastructure 

-Make building-site 

mitigation improvements 

reducing risks to private 

property

-Coordinate with local 

agency responders

-Provide resources for 

support of local and 

regional response efforts

-Provide information to 

emergency responders 

-Coordinate 

communication with local 

businesses and 

organizations

-Coordinate vertically with 

corporate parent and 

partner entities and 

organizations

-Businesses and 

individuals provide 

resources for recovery 

through insurance, grants, 

and reserves

-Non-governmental and 

philanthropic 

organizations provide 

resources for recovery 

through donations, 

services, and grants 

-Mortgage lenders 

disclose flood risk in 

NFIP mapped areas

-Private insurance 

promotes risk 

awareness as part of 

the underwriting 

process

-Limited examples of 

non-profit 

organizations that have 

been created to 

promote two-way risk 

communication for 

their particular hazard. 

 The ARkStorm scenario is an extreme event that, by its very nature, would not fit well 

within the current U.S. disaster management system and policy framework. Flooding from the 

ARkStorm scenario could inundate a great portion of California over 300 miles long by 20 miles 

wide, take several months to return basic levels of infrastructure service, seriously disrupt 

commerce and government, affect one in four homes over large areas, result in more than $300 

billion in damages, and affect millions of people. 

 Local governments—the “first responders” and the backbone of the federal and state 

disaster management system, would likely be overwhelmed in managing response and recovery 

from such a scenario. State response systems that are designed to share resources in a mutual aid 

system between the northern, southern, and Central Valley regions of California also would be 

challenged to meet the simultaneous statewide demands of this scenario that will have cascading 

impacts that can impede response and recovery efforts for weeks, even months. 

 The excessive damage and loss levels estimated for the ARkStorm scenario are also a good 

indicator of the current (2010) inadequacies in federal and state programs to mitigate potential 

disaster losses (pre- and post-disaster mitigation funding), as well as a general absence of risk 

awareness promotion for such a low-probability/high-consequence event at all policy levels. An 

overarching risk awareness challenge is to reach the general public, as well as the special interest 

sectors, and the multiple levels and types of governments that will be affected by such a scenario. 

The catastrophic disaster depicted by the ARkStorm scenario requires moving beyond the event 

horizon perspective in place and considering scenarios that challenge the currently acceptable 

levels of response and preparation. The levels of severe disruption depicted by the ARkStorm 

scenario may require new levels of acceptance of loss and more attenuated systems of response 

thinking. 
157

PRIORITY POLICY ISSUES: MITIGATION 

 Hazard mitigation is a disaster management function ideally preceding, and lessening the 

impacts of, disasters. FEMA defines hazard mitigation as “sustained action taken to reduce or 

eliminate long-term risk to people and their property from hazards.” Mitigation speaks to 

interrupting the expensive and often repetitive cycle of disaster losses and reconstruction. 

(http://www.fema.gov/plan/mitplanning/). 

 According to the State of California Multi-Hazard Mitigation Plan, mitigation generally 

involves reducing long-term risk from hazards to acceptable levels through measures modifying 

physical development to be more resilient. Examples include strengthening structures to withstand 

earthquake shaking, minimizing development in flood-prone areas, clearing defensible space 

around residences in wildland-urban interface (WUI) areas, or steering development away from 

geologically unstable hillsides. 

 Mitigation reflects governmental and private sector expenditure of varying sums of money. 

Mitigation has been shown to be a sound investment with the Multi-Hazard Mitigation Council 

(MMC) study revealing a 4:1 overall loss avoidance ratio obtained from FEMA grants from 1993-

2003 ( Multi-Hazard Mitigation Council, 2005; Rose and others, 2007). In short, every dollar 

invested in mitigation saved four dollars of potential losses; greater savings were estimated for the 

subset of grants that dealt with flooding. These findings can be stated simply through the principle 

“pay now to mitigate, or pay a lot more later on for recovery.”

 Mitigation policy issues are found at all geographic levels and in multiple sectors. National 

mitigation laws and authorities generally authorize financial support to state and local 

governments and, in the case of flood insurance, to the private market supporting mitigation 

actions geared to preventing or minimizing disaster losses in advance of disasters (pre-event 

mitigation), or reducing repetitive future losses after disasters (post-event mitigation). Primary 

federal legislation fostering mitigation includes the National Flood Insurance Act (1968), the 

Stafford Act (1988), and the Disaster Mitigation Act (2000). In turn, these federal laws tend to be 

mirrored in state laws, and in many states, in local mitigation laws and policies leading to 

strengthening community resiliency. 

 Mitigation policy issues tend to be rooted in the specifics of federal-state mitigation laws. 

However, key overarching mitigation policy issues include the following: (1) insufficient mitigation 

funding, (2) levee failure impacts,( 3) variable mitigation performance, (4) poor community 

impacts, and (5) landslide, mudslide, and debris flow issues. 

There is insufficient funding to effectively mitigate the potential impacts and losses 

associated with the ARkStorm scenario. The ARkStorm scenario suggests a long-range mitigation 

requirement in the tens of billions of dollars or more that would take at least many decades to 

mitigate at current funding levels. The general policy question raised is whether funds should be 

authorized to close that funding gap in fewer years? 

 How can pre-event mitigation funding be increased to more adequate levels? The primary 

pre-event policy question raised by this scenario is what level of mitigation funding would be 

sufficient to help prevent or substantially reduce a loss of this magnitude? Presently (2010), 

federal, state and local mitigation actions are under-funded in relation to potential aggregate loss 

exposure. For example, the Pre-Disaster Mitigation (PDM) grant funds, an innovation introduced 

under the Disaster Mitigation Act, are usually funded at an annual level of only $100 million for the 

entire nation, an infinitesimally small amount in relation to the entire need. 
158

 How can post-event mitigation funding be enlarged and better focused? A post-event 

recovery policy question would be how to fund mitigation in order to reduce or minimize repetitive 

losses in the future, while absorbing the post-disaster costs of the losses incurred? Linking this to 

recovery, a corollary policy question would be how to reconcile losses of such magnitudes with 

standard Stafford Act relief funding levels ($28,800 maximum per household), and other sources, 

such as USDA, Small Business Administration, and NFIP assistance that would most likely become 

available after the scenario event? 

Levee system vulnerability to potential failures and escalated losses from an 

ARkStorm scenario needs to be substantially reduced. The ARkStorm scenario includes 

multiple levee failures that would flood large areas in Flood Insurance Rate Map (FIRM) map Zone 

X previously thought to be protected from 100-year storms by levee systems. In 2005, Hurricane 

Katrina demonstrated the vulnerability of communities living behind levees built over time without 

sufficient attention to engineering standards assuring adequate performance under extreme 

conditions. 

 Extensive levee failure in New Orleans led to a nationwide reexamination of levees by the 

U.S. Army Corps of Engineers. In 2006, California it led to California voters’ approval of $4.9 billion 

in bonds primarily for strengthening levees in the San Francisco Bay – San Joaquin Delta (Bay 

Delta) area. New Orleans levee failure also led, among other things, to legislation promoting: state 

200-year floodplain mapping in the Central Valley; requirements for local governments to include 

floodplain mapping in general plans; and requirements that Central Valley communities deny 

subdivisions in unprotected flood hazard zones. 

 According to the California Multi-Hazard Mitigation Plan, substantial parts of the Bay Delta 

area are below sea level and currently reliant for flood protection on public and private levees built 

out of dredged sand for the purpose of protecting agricultural activities in the early 20th century, 

and are, therefore, susceptible to failure from earthquakes and other factors (California Governor’s 

Office of Emergency Services, 2007). 

 Primary mitigation policy questions associated with levee failures include the following: 

 Who pays the bill for levee strengthening? A primary pre-event policy question related to 

added levee protection is availability of additional funding. The $4.9 billion authorized by the 2006 

bond election is said to represent about 1/10th of the overall amount needed to help create a more 

stable levee system in the Delta. The question of additional sources of funding for levee 

strengthening so far has been addressed on a regional and local level through promotion in recent 

years of benefit-assessments placing additional fees on property for local levee improvements. The 

major policy question remaining is whether a California statewide fund for levee strengthening will 

be established. 

 What are the limits to development behind weak levees? An equally important policy 

question related to levee protection is whether development should be allowed to proceed behind 

levees that are susceptible to failure. This land use question is partially addressed by the new statemandated requirements for inclusion of floodplain mapping in general plans and the assumption of 

partial liability where state flood control projects fail. However, suburbanization behind weak 

levees is likely to continue through local financial mechanisms such as Mello-Roos district 

formation for new development to levee strengthening costs. The policy question is whether 

development should happen before levees are strengthened or after? 

The ARkStorm scenario would severely impact communities that have made 

sustained mitigation efforts, and those that have not. All communities in California are required 

to adopt general plan safety elements, and are subject to the requirement for consistency with the 
159

general plan of zoning, subdivision, and capital improvements decisions. All communities must 

include floodplain mapping with the general plan (Assembly Bill 162 – 2006). Though local 

governments are equally subject to such state laws, their mitigation performances tend to vary. A 

review of 436 FEMA-approved Local Hazard Mitigation Plans (LHMPs) to qualify for federal hazard 

mitigation grant project funds showed variable degrees of conformance with Disaster Mitigation 

Act criteria (California Governor’s Office of Emergency Services, 2007). Policy issues regarding 

local mitigation performance revolve around the question of what to do about under-performing 

communities. 

 Should only good performers be rewarded? The current (2010) competitive mitigation 

grant funding systems under the Stafford Act and Disaster Mitigation Act tend to reward good 

mitigation performance through additional funding. However, the ARkStorm scenario suggests that 

flooding and other damages would affect homeowners and businesses both in communities that 

have performed well and those that have performed poorly in hazard mitigation. The policy 

question is to what extent should grant funds be awarded to localities that have made sustained 

mitigation efforts over time versus those that have made unwise development decisions or acted 

carelessly in areas susceptible to substantial flooding or storm-related hazards? 

 Should hazardous areas in under-performing communities be bought out? It may be 

cheaper in the long run to buy out flood or landslide prone land in under-performing communities 

to avoid greater recovery costs later. In either case, policy solutions should be devised by which 

such differences between well-performing and under-performing communities can be reconciled in 

advance of an ARkStorm event. An examination of Severe Repetitive Loss Communities under the 

National Flood Insurance Program (NFIP) may provide some clues. From another perspective, a 

community may want to undertake significant mitigation activities, but lack financial means to do 

so. This is definitely the case with a potentially large number of rural, Central Valley communities. 

This issue of disproportionate impacts on poorer communities is further addressed below. 

 How should local financial liabilities be shifted? In the ARkStorm scenario many cities and 

special districts may fail financially as there will be little or no license or sales tax revenue or other 

normal revenue flows. In an event of this magnitude, the federal and state governments may find it 

necessary to support local functions and services in many communities. Although the state would 

still have substantial capacity to provide support to localities, resources would be seriously 

stretched. Traditional local-to-local assistance normally deployed in emergencies through mutual 

aid agreements may help buttress and sustain certain shared local services. Such arrangements 

would probably survive and be operable to some degree. The costs of such actions would be far 

reaching. Some thought needs to be given to how to underwrite combined large-scale local 

government financial failure. One area of enhanced federal support might be the FEMA Community 

Disaster Loan Program, designed to provide assistance for local government revenue losses. This 

program has been used infrequently in California. One challenge is how to make local governments 

more aware of this program so they can use it in an effective manner. 

 Like other disasters, the ARkStorm scenario would have a harsher impact on poor 

communities than those that are better off. The effects of ARkStorm would be more devastating 

for poor communities, making it harder for those communities to recover. The 2007 review of 436 

FEMA-approved LHMPs indicated that communities that had not prepared an LHMP tended to be 

smaller and have higher percentages of households below the poverty line than those that had 

prepared one (California Governor’s Office of Emergency Services, 2007). Yet Stafford Act and 

Disaster Mitigation Act offer no subsidies to assist poorer communities with pre-disaster mitigation 

or with post-disaster mitigation or recovery funds. With a long-range mitigation need under this 

scenario representing tens of billions of dollars or more, poor communities are less likely to be 

successful in securing post-disaster mitigation funds and preparing LHMPs during the pre-disaster 
160

period. During the post-disaster period poor communities would have to struggle harder to prepare 

LHMPs to qualify for both mitigation and certain recovery project grants. 

 The primary policy issue regarding mitigation in poor communities centers on the question 

of whether to create explicit new efforts to help bring those communities along. 

 Should special consideration be given to poor communities? The policy issue raised would 

be whether poor communities should be provided special consideration in competing for pre- and 

post-event mitigation planning and project grants because of their economic circumstances? 

 Should poor communities be provided subsidies? Another policy issue raised is whether 

poor communities should be financially assisted to help prepare LHMPs before a disaster, thereby, 

equipping those communities with the means by which to more readily secure post-disaster 

mitigation grant funding with less delay. 

PRIORITY POLICY ISSUES: PREPAREDNESS AND RESPONSE 

 The structure of emergency response established at the federal level in the National 

Incident Management System (NIMS) and National Response Framework (NRF), and carried 

forward by the state and local agencies through use of the California Standardized Emergency 

Management System (SEMS), is predicated on these principles (Federal Emergency Management 

Agency. 2008b): 

Engaged Partnership 

Tiered Response 

Scalable, Flexible, and Adaptable Response 

Unity of Effort/Unity of Command 

Readiness to Act 

In practice, “Engaged Partnership” involves vertical coordination among federal, regional, 

state, and local agencies, along with lateral coordination among the various levels of public agencies 

with private sector and nongovernmental organizations (NGOs). The nature of a disaster is that all 

entities in an affected area instantly have to relate to one another; the intent of the “Engaged 

Partnership” principle is to anticipate and structure the relationships and interconnectedness of 

needs, roles, and responsibilities in advance. 

“Tiered Response” is built on the premise of avoiding overlap and establishing lines of 

authority and communication along the vertical continuum from the local up to the federal level. 

The idea is that the local actors are the most knowledgeable and should be in charge of the disaster 

response and then request upward for federal and state assistance. This premise also involves the 

“Unity of Command Principle”, the idea being to avoid duplication in effort, confusion and 

overlapping/overstepping actions. 

“Scalable” is a key aspect of the incident command system. The structure is intended to 

remain the same regardless of the size or type of event. Roles and relationships are clearly outlined 

and remain the same in any scale of disaster; all that needs to happen is to apply the right amount 

of resource to the level of the event. 

“Readiness to Act” refers to the need to train and prepare so that the emergency responders 

understand the system at their own level, and are prepared to coordinate through the chain of 

coordination envisioned in the “Engaged Partnership”.
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 What is apparent in practice is that coordination, information flow, and communication are 

the fundamental blocks determining the effectiveness of the response. Under normal 

circumstances, coordination and communication within and among government agencies is 

challenging; a disaster simply amplifies the challenges; however, the clarity of the Incident 

Command System embedded in disaster response is intended to highlight channels of 

communication and information flow. 

 A second key element is the high level of responsibility delegated to the local agencies to 

manage the disaster. This means that effectiveness is dependent first and foremost on the capacity 

and capability of the local actors on the scene. 

 The ARkStorm scenario unfolds a disaster of a huge scale, potentially affecting millions of 

people with a wide swath of devastation that would disrupt all societal components—

communications, transportation, and other infrastructure systems, in urban areas, rural areas, and 

in all manner of terrain. Secondary effects, such as debris flows and landslides would further 

complicate the picture. What this means for the principles and structures established for 

emergency response includes the following key considerations and issues: 

Partnership: The ARkStorm scenario promises major disruptions to communications 

systems that are necessary for effective coordination. 

What will happen when elements of the communication network are broken or diminished in capacity? 

What will be the “work-arounds” necessary for the coordination to take place?

What if the disruptions are for long periods of time, such as weeks or months? 

What are the implications for NGO and private sector partnerships and/or informal unstructured 

relationships that in a widespread disaster will place unprecedented demands for information and 

coordination? 

What are reasonable expectations for performance and resilience of communications systems in the 

event of a continuous series of disruptive storms? 

What are reasonably effective approaches to allow partial functionality of systems? 

What expectations and processes should be established to reverse the bottom-up management of a 

disaster when the local capacity is diminished or nonexistent? What are the appropriate shifting of roles 

of federal and state entities in such circumstances, and who would be empowered to make the decision to 

override fundamental local control? 

Tiered Response/Scalability: The ARkStorm scenario would place potentially 

unprecedented demands on every level of government than experienced in the past. The 

response effectiveness will depend on the effectiveness of operations at each tier in the 

system. A fundamental assumption embedded in the response framework is that in fact the system 

structure can be effectively scaled up to a disaster of any size. The system is well-proven in limited 

emergency events. There are not many instances, however, of events as widespread in impact as 

ARkStorm supposes, and, therefore, the scalability of the system is not confirmed in practice. The 

system will only be as strong as its weakest link. 

Will local governments have functional capacity to effectively activate and operate the emergency 

response system? 

Will they be stretched so thin, or be so limited in resources that they lack the capacity and ability to 

manage the disaster and reach up the tiered response ladder? In that scenario, how will an intervention 

strategy emerge? 

Will federal and state response structures be so overwhelmed by the breadth of the event that their 

effectiveness is compromised? 
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How will federal and state actors determine when and how to respond in a situation of multiple crippled 

local government agencies and/or a wide unevenness among various local actors? 

How will resources (equipment, supplies, personnel – all potentially scarce or not matched to need 

geographically) be allocated among multiple and competing needs? 

How will the need to reach far outside the area to mobilize and obtain needed resources affect the timing 

and delivery of support?

Unity of Command/Readiness to Act: The ARkStorm disaster, as anticipated, suggests 

that ad hoc organizational structures would be needed because of the widespread crippling 

effects of the devastation on the organizations and systems, such as mutual aid. Experience 

has shown that in some disasters, the responders established in the emergency response plan are 

not always the ones who end up with the key roles and responsibilities (for example, World Trade 

Center, Loma Prieta Earthquake). In these instances, ad hoc organizational structures were created 

by local leadership because - for any number of reasons - that proves to be the most effective way to 

proceed. 

How will unity of command be affected when people less trained are substituted into the response roles 

and systems? 

How will ad hoc structures be tethered to the command structure and tiered response system? 

What are effective ways of utilizing ad hoc community or private structures to take advantage of the 

capacities they provide? 

PRIORITY POLICY ISSUES: RECOVERY 

 This section presents 6 policy issues: 

1. Multi-governmental and agency communication and coordination challenges; 

2. Post-disaster recovery financing challenges; 

3. Gaps in policies and programs to handle the prioritization, funding, and execution of 

infrastructure recovery statewide; 

4. Lack of plans for dealing with large-scale mass evacuations; 

5. Absence of pre-event plans to handle short- and long-term housing needs and the 

restoration of community; and, 

6. Challenges of large-scale and widespread redevelopment and changes in land use following 

an ARkStorm disaster. 

The ARkStorm scenario would create a statewide catastrophic level emergency, with 

major issues of communication and coordination continuing far into recovery. A fundamental 

management underpinning of the disaster response and recovery framework in California is a 

shared mutual aid between northern and southern California. After the 1994 Northridge 

earthquake, northern California personnel supported mitigation and recovery functions of the state 

in southern California. Damage inspection also was supplemented after the 1994 earthquake with 

northern California inspectors working in southern California. This paradigm is well founded since 

there are few other natural disasters that would have catastrophic effects on a statewide basis. But, 

the ARkStorm scenario would pose significant response and recovery resource challenges 

statewide. It would require a great number of local districts and layers of agencies. In some cases, 

these organizations have not had adequate experience working in such extreme conditions as 

envisioned by this scenario. Breakdowns in communication and coordination are not going to be 
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limited to response; they will extend well into recovery as the “fog of war” will continue for many 

months, possibly years. 

The ARkStorm scenario would generate unprecedented recovery financing 

challenges as the existing private and public recovery programs and resources would be 

woefully inadequate. The ARkStorm scenario could generate building repair costs exceeding $200 

billion, which is equivalent to 5-10 years of construction at 2007 statewide building construction 

rates. There could also be flood-related content losses in excess of $100 billion, wind-related 

building repair costs of $6 billion, and hundreds of billions in business interruption losses. There 

would likely be significant challenges to recovery financing for all affected residential properties 

and tenants that do not have National Flood Insurance Program (NFIP) or other insurance 

coverage, small commercial and industrial business owners and property owners without NFIP or 

other insurance, small agricultural businesses, local governments, state government, nonprofits, 

and private utility providers. If the current (2010) economic recession and state and local budget 

crises are persisting when this scenario occurs, the catastrophic scale of this disaster could 

substantially accelerate these ex-ante conditions. 

 Scenario estimates are that NFIP penetration is quite low in the potential damage areas and 

would not cover the majority of private and public sector losses expected for the ARkStorm 

scenario. The NFIP is financially fragile as well, recovering from the multi-billion dollar shortfalls 

caused by the 2005 storms (Government Accountability Office, 2007a). Given the enormity of the 

potential ARkStorm-related losses, there could be post-disaster Congressional efforts to reduce the 

payouts on claims from such a scenario. Also losses associated with debris flows and landslides are 

not typically covered by the NFIP, so there would be significant unfunded gaps for these losses, too. 

 The Stafford Act provides federal supplemental assistance for individuals and families, 

government agencies, tribal organizations, and private nonprofit organizations. With millions of 

people potentially affected, an ARkStorm scenario could result in the largest-ever individual 

assistance payout. Public assistance is designed to cover emergency response costs and repair costs 

for public facilities and infrastructure, and tens of thousands of claims would be likely. Distribution 

of these funds could be a time consuming extensive process with the majority of funds being 

delayed until year two and three into the recovery, primarily because of the sheer volume of work 

created by a catastrophic event. 

 Also, the Public Assistance (PA) program that restores infrastructure is essentially a 

reimbursement-based program as administered by FEMA. State and local government agencies and 

other qualifying entities would be challenged, especially given the state’s ongoing fiscal crises, to 

front-end the costs until reimbursements are made. Similarly, it is unclear whether the state and 

local government agencies would have the necessary funds to meet the required 25 percent match 

of the Stafford Act programs. It should be recognized that while reimbursement is the preferred 

method for funding PA costs, regulations give FEMA the authority to provide advances for 

immediate needs, as was done following the Northridge Earthquake when FEMA provided more 

than $100 million within a few days after the federal disaster declaration. Similarly, FEMA now has 

the regulatory ability to waive the local share and provide 100 percent funding for a limited period 

of time, usually for a designated emergency period. 

The ARkStorm scenario also could result in a massive fiscal crisis for state and local 

governments as well as special districts struggling to meet the response and recovery needs of such 

a scenario, while simultaneously facing significant sales and property tax revenue reductions. The 

impacts could have cascading effects, as localities and special districts turn to state and county 

levels for financial assistance. While the state may be able to absorb costs of a few substate level 

units, a large group of failures may create a fiscal crisis for California. 
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 The ARkStorm scenario estimates that private insurance would provide some resources to 

key sectors, particularly the medium and large commercial and industrial businesses, for structural 

and contents related losses related to flooding (that is, in excess of NFIP) as well as the limited 

amount of wind damage estimated. Similarly, higher valued residential properties (condominiums, 

apartment complexes, properties owned by real estate investment trusts) also tend to have excessNFIP coverages. Local governments and private utility providers do carry some levels of insurance 

although the coverages are not likely to be sufficient. 

 But, long-term business and economic interruptions would largely be unfunded and 

restoration would be protracted. There would be significant and long-term transportation 

disruptions, interruption of supply lines and supply chains, and lost production (upstream and 

downstream) also would occur. Business interruption policies have time elements that would likely 

be exceeded by the ARkStorm scenario. Small Business Administration post-disaster loans would be 

a critical recovery resource for small businesses, and apartment owners, but these sectors would be 

challenged to accept additional debt burdens if real estate values are down and property owners 

are already upside-down on their loans. Businesses would be challenged to sustain themselves over 

a prolonged period of time with limited resources (both funds and supplies) and displaced markets. 

 Agricultural business recovery would face long-term changes, in restoring losses caused by 

floodwaters, addressing soil pollution and hazardous materials issues, and any longer-term delays 

in crop restoration and maturity. Post-disaster programs managed by the U.S. Department of 

Agriculture would be critical to this sector’s recovery. 

 It is important to realize, however, that agricultural assistance programs are severely 

limited. First, they are loan programs requiring repayment and, as such, have rather stringent 

rules. Second, although these programs benefit farmers, few benefits flow to farm workers. The 

reality of a Central Valley flood will be the displacement of thousands of farm workers. Making this 

particularly tragic is that most farm workers do not qualify for government assistance programs 

because of the undocumented status of a large percentage. This tragedy adds a substantial social 

dimension to the ARkStorm scenario, placing tremendous pressure on CBOs and FBOs to provide 

post-disaster services. Meeting the needs of this particular group of survivors will demand a great 

deal of creativity and coordination between the government agencies and organizations serving the 

farm worker community. 

 Overall, there would likely be pressure placed on Congress to pass supplemental legislation, 

as it did with the 2005 Hurricane Katrina and other disasters, to provide post-disaster recovery 

resources to the state and local governments to help address the many recovery financing gaps 

generated by an ARkStorm disaster. Over $11.5 billion was appropriated by the federal government 

to the state of Louisiana through the Community Development Block Grant (CDBG) program, to 

fund a statewide housing repair program and supplement local government recovery following 

Hurricane Katrina (Government Accountability Office, 2007b). But, as with the state and local 

budget crises, current (2010) federal fiscal challenges may make such political action difficult to 

achieve following an ARkStorm scenario. 

Current policies and programs are not adequate to handle the prioritization, funding, 

and execution of infrastructure recovery statewide. The ARkStorm scenario estimates floodrelated damage to the Sacramento-San Joaquin delta system could interrupt water supplies to 

Central Valley agriculture and the southern California populace for at least 3 months. The cost and 

timing of system restoration involves complex levee repairs, dewatering, and land use issues. A host 

of policy issues are likely to arise, including: determining and allocating alternative, interim water 

supplies (for example, lottery and rationing); considering long-term sustainability issues (for 

example, wetlands restoration or diversion projects) within the accelerated timeframes and 
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heightened pressures to restore systems quickly; prioritizing island restoration and dewatering; 

determining agency responsibilities and coordinating actions and decisions; and, compensating for 

lost agricultural and economic production and land buyouts in areas that are not restored. 

California would likely face political challenges as it attempts to build support to finance the tens of 

billions of dollars needed for repairs to levees, dams, and other flood-control systems, amidst the 

state's perennial land and water wars and ongoing fiscal problems. 

 Road repairs, electric system recovery, and storm water and wastewater systems recovery, 

all would present management, coordination, financing, and interdependency challenges given the 

widespread and unprecedented scale of an ARkStorm disaster. The current (2010) state of U.S. 

disaster recovery policy is hampered by a conventional definition of “disaster recovery” as a return 

to pre-disaster status quo. Many of the major programs, such as the Stafford Act, emphasize repairs 

to pre-disaster conditions, rather than promoting infrastructure renewal, risk reduction, or 

betterment. Areas with older infrastructure would generally be more vulnerable to damage and 

agencies challenged to find funds to complete upgrades or enhancements. Road reroutings would 

be a major regional event; statewide, coordination is needed to keep commerce moving short- and 

longer-term and to help stimulate economic recovery. 

There is a lack of policy and experience among state and local emergency responders 

and government managers in dealing with the complexity of mass evacuations, short- and 

long-term housing needs, and the restoration of communities statewide once the flood 

waters recede. The ARkStorm scenario could cause large-scale evacuations in the immediate wake 

of the storm that turn into long-term and protracted displacements similar to those following 2005 

Hurricane Katrina. Extended displacements can lead to looting and extensive blight. Blight 

conditions could be significant in areas hit-hard by the current (2010) real estate recession. 

Transient and highly mobile parts of the state population may not return. This occurred after the 

1994 Northridge earthquake, when apartment dwellers quickly vacated damaged buildings and 

neighborhoods resulting in a “ghost town” apartment situation in neighborhoods across the San 

Fernando Valley and central Los Angeles (Los Angeles Housing Department, 1995). It is also 

important to consider the effects that business relocations may have on community recovery. State 

and local governments need a mechanism to involve the business community in long-term recovery 

planning and implementation. 

 There would also be substantial need for both short-term and long-term housing following 

an ARkStorm . Impacts of the recent (2010) economic downturn on the housing market and 

available housing stock post-disaster are extremely unclear. In some communities, rental housing is 

at a premium and vacancy rates are quite low, while in others, there is housing surplus. State and 

local agencies might consider how the current stock of foreclosed properties could be used for 

short-term housing. There would be challenges in matching short-term housing resources with the 

needs and resources of inhabitants. A system for allocating temporary housing also may need to be 

developed to equitably distribute housing resources. 

 Helping displaced individuals and families to return and re-inhabit neighborhoods would 

require coordination and resources (maintaining information on displaced individuals, 

communications and outreach to the displaced, providing both short- and long-term housing 

options, and systems for allocation). Case management is currently handled through various postdisaster programs (FEMA individual assistance and temporary housing assistance, CDBG section 8 

housing vouchers) and generally does not provide an integrated means of tracking people longterm or helping restore their lives (General Accountability Office, 2009). While this lack of 

coordination has been a sore point for some time, this may be changing in that FEMA is now 

negotiating with states to cover some of the cost of case management activities. This assistance is 

particularly important when considering the complex work of long-term recovery. 
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 There may also be hazardous materials and public health issues to consider as part of the 

community restoration process. Contaminated water could affect soils and structures inundated by 

flooding. Policies and programs may need to be developed to address the removal of contaminated 

topsoil, and the detoxification of concrete and other affected building materials. There could also be 

policy issues of liability, litigation, and insurance coverages. 

Large-scale and widespread redevelopment and changes in land use may be quite 

significant following the ARkStorm scenario. Properties that are more than 50 percent damaged 

are supposed to have flood risk mitigation as part of rebuilding under the National Flood Insurance 

Program (NFIP). The ARkStorm scenario estimates that 25 percent of the buildings in the impacted 

area would have some flood damage (1 percent light damage, 22 percent extensive, and 2 percent 

complete). Policies would need to be developed by state and local agencies to handle the 

moderately damaged buildings and enforce the NFIP requirements for 50 percent or greater 

damaged structures. Local governments (county and city) may be pressured to make less than 50 

percent determinations so that people can rebuild to pre-disaster conditions. There may also be 

pressure to modify federal and state policies and make exceptions for an extreme event, like the 

ARkStorm disaster. There could also be challenges to fund mitigation options of relocation and 

redevelopment of buyout areas, structural elevation, or retrofitting of slab-on-grade foundations. A 

statewide or regional coordinating, or advising, body may be needed to help craft policy and 

coordinate financing and technical assistance to local governments. The state has some legal 

mechanisms in place (including state redevelopment legislation, geologic hazard abatement 

districts, and the Disaster Recovery and Reconstruction Act) that could aid in these kinds of efforts. 

PRIORITY POLICY ISSUES: RISK AWARENESS 

 This section presents the following policy issues: building awareness of catastrophic floods 

and atmospheric rivers; speaking with a common voice; and building constituencies that can carry 

the message forward over time. 

Building awareness of catastrophic floods and atmospheric rivers: “the big one.” The 

challenge is to include catastrophic disasters in the policy dialogue at all levels of government, and 

in national organizations. Risk awareness of catastrophic disasters receives little to no attention. 

Why? This is, in great part, because of the infrequency of the event, the local nature of such events, 

and the issues of estimating the event impacts in physical, social, economic, and government 

functions terms. When a hypothetical disaster is very large, people tend to discount its likelihood. 

(Paine, 2002). The effort to simply agree on common integrated multi-level government approach 

to moderately sized disasters is still being debated. 

 An important function of risk awareness is to enhance the capacity of a person, household, 

or governmental unit to make informed resource allocation choices. As people become more aware 

of the ARkStorm event, the likelihood is greater that they may decide to invest in resiliency actions, 

particularly if it is understood that a small investment made now in preparedness or mitigation will 

reduce post-event losses and potentially yield large benefits. For example, an individual may buy a 

rubber raft to keep in the garage. A fire department may invest in a bulldozer rather than another 

fire truck. Or a city council may see that open-space land acquisitions can be part of a larger 

groundwater retention or flood diversion strategy. The more coordinated and publicized such 

efforts, the more likely the efforts can contribute to a common awareness and belief in the benefits 

of risk reduction behavior. 

The “Risk Awareness” part of the FEMA Risk MAP (Mapping, Assessment, Planning) 

Program awaits implementation. Nowhere in the Risk MAP plan are catastrophic disasters 

emphasized as an area to be addressed. The Risk MAP theme to “clearly and effectively inform the 
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public of their flood risk and impacts” does exist, and forms the basis for including catastrophic 

atmospheric river and flood events (http://www.fema.gov/library/viewRecord.do?id=3587). 

One Risk MAP objective is to measurably increase the public’s awareness and 

understanding of risk. When operational, this objective can be used to support the policy needs of 

the ARkStorm scenario through providing map-based information on disasters of this scale and 

impact. Another federal effort—the draft National Disaster Recovery Framework– does recognize 

scalability and a potential for regional approaches; but does not speak to risk awareness. 

 At the national special interest group level, the Association of Flood Plain Managers, for 

example, does not mention such extreme events like the ARkStorm scenario in any of their national 

policy statements. At the state level, the California Department of Water Resources (DWR) Division 

of Statewide Integrated Water Management is seeking to provide consultant support on various 

themes including how to communicate statewide flood risk, but does not mention catastrophic 

disasters in any of its public documents. 

To build awareness of the “big one,” in this case the ARkStorm scenario, requires 

overcoming a common human tendency of “out of sight, out of mind.” An event with a low 

probability of occurrence in 200, 500, or 1,000 year timeframes is not part of everyday thinking. 

Large-scale floods, however, continue to be common in the U.S., with the 2010 Nashville floods that 

caused 30 deaths, the 2009 Iowa floods, and Hurricane Katrina as examples. But these are thought 

of in the popular mind as “rare” occurrences. While the policy formation process must account for

basic individual behavior it can also take a long-term view of awareness, as in the case of the 30-

year old National Earthquake Hazards Reduction Program (NEHRP) (http://www.nehrp.gov), 

whose goals include improving the earthquake resilience of communities nationwide. 

Speaking with a common voice: Getting the message straight is a key policy challenge. 

To raise awareness, the message must be consistent and clear. It is a challenge to get the message 

straight when the federal government does not control land use at the state level, but does work in 

partnership with states and cities, by providing information (for example, NOAA) and incentives for 

participation (such as the Disaster Mitigation Act of 2000). There is a “unity of effort” concept

embedded in the draft National Disaster Recovery Framework, and the Post-Katrina Emergency 

Reform Act of 2006. The latter does includes catastrophic events in the act text. 

The basic ARkStorm risk awareness message must go to people’s core survival values and 

tell them that they will suffer severe personal and economic losses directly and indirectly. At the 

household level, families would suffer along with their neighbors. To lessen the danger and impact, 

they would need to support local disaster mitigation actions, call for actions to be taken, and to 

prepare themselves (such as through self-training, obtaining insurance, flood-proofing their homes 

and businesses, knowing how to evacuate, having survival supplies at hand, and being able to 

communicate with local authorities and neighbors). As people and governments would be working 

to avoid and prepare for an event that has not been experienced in living memory, the message 

needs to be implanted in all levels of personal and civic life. Community-based disaster response 

has been on the upswing in recent years (for example, neighborhood Community Emergency 

Response Teams (CERT)). Outreach and communication issues extend from major media to 

individual volunteers. 

 Effective messaging must be consistent, unrelenting, and come from multiple sources. 

People respond better to graphic images than numerical data regarding risk. Thus message 

transmission needs to be more visual, and better maps and imagery do help. People have difficulty 

dealing with probabilistic information; low probability events become “zero probability events” in 

people’s minds (Kunreuther and others, 2004). They need a context in which to evaluate the 

likelihood of a disaster occurring. Obtaining and disseminating information is one of the most 
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critical aspects of any disaster, and how it is handled can either support or undermine other 

emergency response operations. 

 Risk perception and risk reduction actions tend to be influenced by culture (Weber and 

Hsee, 1998). People from different cultural backgrounds may perceive risk differently and act 

differently when a large loss is incurred. In shaping a common message it is important to 

understand how different groups perceive risk reduction. Thus, in more culturally diverse regions, 

such differentials should influence message formation and dissemination. Outreach needs to be 

done with heterogeneity in mind. 

 Observations of the Gulf Coast oil spill suggests that elected officials were not prepared to 

coordinate or to establish a common voice. This lack of preparedness leads to confusion, and raises 

the anxiety level for all involved. Such an event had not been anticipated, nor prepared for at the 

federal, state or local levels. For the ARkStorm message to be delivered with one unified political 

voice (at a federal, regional, or state level) some new programmatic efforts will be required, 

possibly championed by the California League of Cities and the California Congressional delegation. 

Building constituencies that can carry the message forward over time. The policy issue 

here is the need to build the constituencies that support long-term messaging. The question here is 

who needs to be aware? In the absence of any existing coordination framework, the answer is 

everyone from the national level down to the local private business person needs to be aware of the 

ARkStorm threat. Such an effort might be started by FEMA as called for in the Stafford Act, Section 

503 (b) to “lead the Nation's efforts to prepare for, respond to, recover from, and mitigate the risks 

of natural and man-made disasters, including catastrophic incidents” (Federal Emergency 

Management Agency, 2007, p. 94). 

 At the federal level, an opportunity exists to fashion a NEHRP-type effort to continually 

support science inputs. NEHRP, created by Congress in 1977, has among its objectives the 

collection, interpretation, and dissemination of information on earthquakes, and public hazard 

awareness; and to provide national and local leadership to engage communities in earthquake 

safety practices. A similar risk reduction program has been established for windstorm, but nothing 

yet exists for major flood hazards. Fashioning an ARkStorm policy effort along these lines would be 

useful and provide a legislative basis for action and funding. 

 The draft National Disaster Recovery Framework (NDRF) provides for catastrophic policy 

development. Recognition of “scalable” events provides a starting point. The NDRF is an emerging 

effort toward greater coordination among federal government recovery programs. NDRF also 

should be seen as a vehicle to improve risk awareness tools and build long-term consensus efforts. 

Additionally, the FEMA National Preparedness Directorate can play a role in increasing efforts to 

enhance risk awareness content in its member units. 

 At the state level, California has a variety of constituent building efforts for disasters. 

FloodSAFE (http://www.water.ca.gov/floodsafe) provides a starting point. The California approach 

addresses large flood danger at a regional level (for example, the Central Valley Flood Management 

Planning Program). These efforts do form the basis of constituency building. The FloodSAFE 

program in California, while focusing more on institutional units (such as local government, flood 

districts, irrigation districts), is building a message delivery system on a common theme. 

 People listen to other people who share common values and interests. In California, the 

FIRESafe council system (of which there are 150 local councils) is a model of people sharing 

common safety interests with other people. FIRESafe is a system of community based nonprofit 

organizations dedicated to reducing wildland fire hazard and improving fire-safety awareness 

(http://www.firesafecouncil.org). These organizations receive federal funding but are essentially 
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local based operations. Being local they have the advantage of talking directly to individuals about 

disaster fears and perceptions, and appropriate risk reduction actions. 

California’s Earthquake Country Alliance (ECA) is a form of coalition building along a 

common natural hazard danger (http://www.earthquakecountry.info). ECA is a statewide "alliance 

of alliances" linking the public information efforts of organizations and individuals that provide 

earthquake information and services. The main strategy of ECA is to coordinate the earthquake 

information, so that the public receives information that is consistent, from multiple sources they 

trust, through multiple channels, and serves all California residents. 

 Experience has shown that media statements, sloppy science, and inaccurate predictions 

easily distort catastrophic disaster awareness. A program of educating the media (including media 

meteorologists) can build pre-event alliances as part of the message delivery system. Lessons from 

the Pacific Tsunami Warning Center, which work on tsunami risk awareness, may yield lessons for 

ARkStorm (http://www.weather.gov/ptwc/).

If the saying “all recovery is local” has any validity, then the areas of highest ARkStorm 

impact require particular attention to promote a level of risk awareness that leads to risk reduction 

through mitigation and resiliency measures. Partnerships of awareness at the neighborhood level 

are required. These can be modeled after the Berkeley, Calif., program of neighborhood earthquake 

and flood training, and of disaster materials caches 

(http://www.ci.berkeley.ca.us/disasterresistant/). Such efforts would be focused on areas where the 

greatest chance of isolation might occur, and on making neighbors the first responders to the 

disaster. We also need to know more about how cities get motivated to become more “selfprotecting.” In Oklahoma, The Tulsa Partnership is an example of a nonprofit working in flood 

impact avoidance and preparedness that reinvents itself to assure continuous local involvement 

over time (http://www.tulsapartners.org).

POSSIBLE COURSES OF ACTION 

 The ARkStorm scenario aims to use science to inform decisions that enhance community 

resiliency, in this case resiliency against a winter storm causing a statewide disaster. ARkStorm 

represents an important wake-up call about the extensive devastation and long-term consequences 

an extreme event of this magnitude might cause. To actually enhance resiliency, however, will 

require significant disaster policy changes, programmatic adjustments, and organizational and 

individual behavioral adaptations will be required to face the immense challenges that such an 

extreme event poses. Some possible courses of actions that this assessment offers are as follows: 

1. The ARkStorm scenario raises serious questions about the ability of existing national, 

state, and local disaster policy to handle an event of this large magnitude. The potential for 

extended disruption of all levels of government calls into question basic assumptions about 

mitigation and disaster management in existing emergency operation plans, general plans, and 

hazard mitigation plans policies and programs; Incident Command System protocols and National 

Response Framework emergency support functions; National Flood Insurance Program 

requirements; and Stafford Act requirements and programs (including mutual aid, Individual 

Assistance, Public Assistance, pre- and post-disaster mitigation funds). Federal, state, and local 

agencies may need to redefine what constitutes reasonable and effective mitigation, as well as 

emergency preparedness, response, and recovery expectations. Response, recovery and mitigation 

goals may need to shift and be prioritized to accept a greater breadth and duration of disruption 

and resulting impacts than previously considered with other disaster scenarios. For example, 

several ARkStorm participants highlighted the need to reconsider FEMA limitations on PA grants 

that fund restoration of public infrastructure facilities to no greater than pre-disaster conditions. 
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Local governments may not have the capacity to sustain response and recovery management for an 

extended period, and shifts in control and/or responsibility to other levels of government may be 

needed. Minimum, stop-gap measures and more flexible, ad hoc systems of communications and 

emergency response and recovery operations also may need to be established. 

2. A core policy issue raised by this extreme event scenario is whether “to pay now to 

mitigate, or pay a lot more later for recovery.” The high level of damage and uninsured losses 

estimated for this scenario are good indicators of the current policy and program gaps for 

addressing a low-probability/high-consequence event like the ARkStorm scenario. Careful 

consideration needs to be given as to what programmatic and policy approaches can be reasonably 

pursued in advance of an event of this size for cost-effective and reasonable risk reduction. 

Research has shown that, in addition to reducing the potential for a flood catastrophe, flood risk 

preparedness and mitigation can be highly cost effective on a benefit-cost-ratio basis (Multihazard 

Mitigation Council, 2005a, 2005b). Some reasonable examples of locally cost-effective pre-disaster 

action might include: moving local emergency operations centers and critical facilities out of 

floodplains, guiding development away from floodplains, adding access-evacuation points for areas 

likely to be isolated, and modifying local flood works to reduce impacts. But addressing statewide 

levee system vulnerability to reduce potential failures and catastrophic losses anticipated with an 

ARkStorm scenario cannot be addressed on a regional and local level as has been tried in recent 

years; a California statewide fund for levee strengthening may be necessary. 

3. Innovative financing solutions are likely to be needed to avoid fiscal crisis and adequately 

fund response and recovery costs from an ARkStorm scenario disaster. The protracted and 

cascading effects of damage and impacts resulting from this scenario may lead to a massive fiscal 

crisis among affected localities and special districts struggling to meet the response and recovery 

needs of such a scenario, while simultaneously facing significant sales and property tax revenue 

reductions. The impacts could have cascading effects, as localities and special districts turn to 

county and state levels of government for financial assistance. 

4. Responders and government managers at all levels could be encouraged to conduct selfassessments, and devise table-top exercises, to consider how the intensity and breadth of the 

ARkStorm scenario could challenge current (2010) assumptions in emergency response and 

planning documents, as well as organizational structures and systems, and their abilities to 

scale up and meet the needs of such a disaster, and the unexpected new work such an event 

will demand. Such assessments and exercises could help to create more locally- and regionally 

specific scenarios of impacts, and also produce inventories of resources (for example, public and 

private sector, equipment and trained personnel) that may be needed to adequately respond to, and 

recover from, an extreme event, as well as gaps in public and private sector resources available. 

Political leaders, policy makers, and administrators could be involved in such assessment and 

exercises. 

5. ARkStorm can become a reference point for application of FEMA and California Emergency 

Management Agency guidance connecting federal, state, and local natural hazards mapping 

and mitigation planning under the NFIP and Disaster Mitigation Act of 2000. It identifies the 

importance of connecting this scenario to the evolving NFIP 100-year flood risk mapping, 

assessment and planning (Risk MAP), to California Department of Water Resources 200-year 

floodplain mapping, and to local hazard mitigation plans under the Disaster Mitigation Act of 2000; 

and the importance of examining California statutory requirements relating flood hazard mapping 

to local general plans. 

6. Common messages to educate the public about the risk of such an extreme event as the 

ARkStorm scenario could be developed and consistently communicated to facilitate policy 
171

formulation and transformation. Federal, state, and local models (such as for earthquake, fire, 

and windstorm) exist for crafting and executing a risk awareness program for an extreme event like 

the ARkStorm scenario. Multi-level, multi-actor involvement needs to be a core component of such 

an effort. 

Summary 

KEY FINDINGS 

1. Megastorms are California’s other “big one.” A severe California winter storm could realistically 

flood thousands of square miles of urban and agricultural land, result in thousands of landslides, 

disrupt lifelines throughout the state for days or weeks, and cost on the order of $725 billion. This 

figure is roughly 3 times that estimated for the ShakeOut earthquake, another planning scenario 

reflecting an earthquake with roughly the same annual occurrence probability as an ARkStorm-like 

event. The $725 billion figure comprises about $400 billion in property damage and $325 billion in 

business-interruption losses. An event like the ARkStorm could require the evacuation of 1,500,000 

people. Because the flood depths in some areas could realistically be on the order of 10-20 feet, 

without effective evacuation there could be substantial loss of life. These impacts are not exhaustive: 

they do not consider tourism and recreation, loss of cultural value as a result of damage to historic 

artifacts and buildings, Native American burial grounds, or museum contents. Quite significantly, we 

have not addressed many aspects of public health, such as drowning victims and mental-health 

effects of the storm, which would likely be substantial. 

2. An ARkStorm would be a statewide disaster. Extensive flooding is deemed realistic in the 

California Central Valley, San Francisco Bayshore, Los Angeles and Orange Counties, several coastal 

communities, and various riverine communities around the state. Both because of its large 

geographic size and the state’s economic interdependencies, an ARkStorm would affect all California 

counties and all economic sectors. 

3. An ARkStorm could produce an economic catastrophe. Perhaps 25 percent of buildings in the 

state could experience some degree of flooding in a single severe storm. Only perhaps 12 percent of 

California property is insured, so millions of building owners may have limited or no ability to pay for 

repairs. That degree of damage would threaten California with a long-term reduction in economic 

activity, and raise insurance rates statewide—perhaps nationwide or more—afterwards. 

4. An ARkStorm is plausible, perhaps inevitable. Such storms have happened in the California 

historic record (1861-1862), but 1861-1862 is not a freak event, not the last time the state will 

experience such a severe storm, and not the worst case. An ARkStorm would be unlike any storm that 

has occurred in living memory: 6 megastorms that were more severe than 1861-1862 have occurred 

in California during the last 1800 years, and there is no reason to believe similar storms won’t occur 

again. There may be no pattern that forces the storms to occur with clockwork regularity, so such an 

event could occur in any year. 

5. The ARkStorm is to some extent predictable. Unlike earthquakes, for the ARkStorm there exists a 

capability to partially predict key aspects of the geophysical phenomena that would create damages 

in the days before the storm strikes. While these predictive systems already have some important 

capabilities, there could be great benefit in enhancing their accuracy, lead time, and the particular 

measures they can estimate. This represents a great challenge scientifically and practically. A gamechanging attention to this problem is needed, likely of a scope similar to what is currently done for 

hurricanes and tornadoes. 

6. California flood protection is not designed for an ARkStorm-like event. Much has been done to 

protect the state from future flooding, but the state flood-protection system is not perfect. The 

existing systems are designed, among other things, to protect major urban areas from fairly rare, 

extreme flooding. The level of protection varies: some places are protected from flooding that only 

occurs on average once every 75 years; others, on average every 200 years. But the levees are not 
172

intended to prevent all flooding, such as the 500-year streamflows that are deemed realistic 

throughout much of the state in ARkStorm. 

7. Planning for ARkStorm would complement planning for earthquakes. The ShakeOut exercise 

has become an annual activity in California, with more than 6 million people participating each year. 

Many of the same emergency preparations are useful for a severe winter storm: laying in emergency 

food and water, shelter preparations, exercising emergency corporate communications, testing 

mutual aid agreements, and so on. 

8. Those considering flood mitigation should consider ARkStorm. Governments, businesses, public 

and private utilities, and individuals have the opportunity now to explore the costs and benefits of 

physical improvements to their infrastructure to reduce future damage. As shown by Multihazard 

Mitigation Council (2005a, 2005b), flood risk mitigation can be highly cost effective, with benefit-cost 

ratios on the order of 5.0 or more. For instance, although enhancing state flood protection is very 

costly; not doing so may be even more so. Enhancing urban sections of the state flood protection 

system to 500-year levels could realistically cost $10s of billions. Not doing so could realistically cost 

$100s of billions when such a storm occurs. 

9. Hurricane Katrina is a relevant, cautionary experience. Just under 1 year before Katrina, the 

USACE requested $4 million from Congress for a study on how to protect New Orleans from a 

category-4 hurricane, which, according to one recent estimate, would have cost on the order of $30 

billion. Congress deemed the cost of the study to be too high at the time. The actual storm ultimately 

cost the federal government in excess of $100 billion, resulted in perhaps $150 billion in total 

economic loss, and killed 1,800 people. The alarm over the Californian flood-protection systems has 

already been raised; this study echoes prior ones. 

10. There are many ways in which scientific improvements could help to manage risk from severe 

winter weather. Several research issues are raised by ARkStorm, such as the need for a statewide—

or even nationwide—end-to-end stochastic model of severe weather, physical impacts, and 

socioeconomic consequences. Researchers identified the need for a convenient way to talk about the 

size of such a California winter storm; better elevation data and historic landslide maps to improve 

coastal inundation and landslide models; better asset location data in HSIP Gold to improve our 

understanding of essential facilities exposed to risk; and various reforms to NFIP. 

CONCLUSION 

 The ARkStorm project was a primary focus of the Multi-Hazards Demonstration Project in 

2010. Some 120 scientists, engineers, lifeline operators, emergency planners, and others from the 

private and public sectors collaborated on this depiction of a hypothetical severe winter storm. The 

storm is akin to a real one that occurred in California in 1861-1862, and 6 more-severe, real events 

of the past 2,000 years. The scenario is intended to inform community decision-making and help 

communities increase their resilience to severe California winter storms. It describes in granular 

detail: the meteorological mechanisms and measures of the storm; the resulting coastal and 

riverine flooding; windspeeds and landslides; the physical damage to buildings and other aspects of 

the built environment; the disruption to and recovery of affected lifelines; the impacts on 

agriculture; hazardous material and other public-health impacts; the costs to the California 

economy resulting from business interruption; the limited nature of insurance recovery; the 

enormous demands of evacuation and sheltering; and the public-policy implications and issues 

raised by the real potential for such a storm. The ARkStorm project produced the present report, 

and the following notable outcomes. 

The Extreme Precipitation Symposium. The ARkStorm scenario was the theme of the 

2010 Extreme Precipitation Symposium at the University of California John Muir Institute of the 

Environment. About 200 experts in science, flood management, engineering, and policy attended 

the symposium where the ARkStorm scenario was presented and discussed by panels of experts. 
173

Largest HAZUS-MH loss estimate. Several groups have used the HAZUS-MH methodology 

to make loss calculations inside and outside of HAZUS-MH. However, the present study seems to be 

have produced the largest-ever building and content property loss ($305 billion) estimated using 

the HAZUS-MH methodology for a scenario natural disaster. It exceeds a $140 billion loss estimated 

for a hypothetical M 7.0 earthquake in the New York metropolitan area, and the losses in the 

ShakeOut scenario, which resulted in an estimated $33 billion in shaking-related damage to 

buildings and contents from a M 7.8 rupture of the Southern San Andreas fault. 

Design storm. Toward the end of the project, the Art Center College of Design brought 

together over 30 leaders in emergency response, flood management, engineering, and earth and 

atmospheric science in an activity coincidentally called a design storm (the term is generic, and has 

nothing to do with weather nor the storm dealt with here) to help product-branding professionals 

and students develop a communication plan to improve public awareness of major winter storms. 

ARkStorm Summit. An invitation-only conference will be held in cooperation with the 

California Emergency Management Agency (CalEMA) and Federal Emergency Management Agency 

(FEMA) in January 2011 in Sacramento where this report will be released. Participants will be 

policymakers, lifeline professionals, scientists, and executives from the public and private sectors 

whose responsibilities include community resiliency. 

CoSMoS Modeling System. The ARkStorm scenario led to the development of the CoSMoS 

(Coastal Storm Modeling System) a model for analyzing the impacts of severe storms at present day 

and under various climate change/sea level rise scenarios. The impacts include flood hazard zones, 

beach erosion, cliff failure location, and the location of damaging wave conditions co-located with 

infrastructure such as piers, jetties, and breakwaters. The effort broke new ground on the West 

Coast where no such physics-based, process-based system previously existed. It has attracted 

attention from numerous agencies such as the United States Army Corps of Engineers, United States 

National Park Service, and FEMA, many of which want to see the model applied to their own areas 

of interest. The model is being used at the mouth of the San Francisco Bay, to estimate the impact of 

sea level rise and severe storms on the outer coast. It is the subject of several talks at the 2010 

California and Worlds Oceans Conference. 

Statewide landslide-susceptibility maps. Prior to the ARkStorm scenario there had been 

few previous studies that mapped landslides triggered by individual storms, and even fewer that 

tallied the amount of damage done by landslides. Research for the ARkStorm scenario led to the 

development of two maps that show large areas of California that are susceptible to landslides. 

These landslide-susceptibility maps are the most detailed ever created for the state. Through 

ARkStorm, researchers gathered data on past landslides and damage from numerous sources, and 

estimated that the cost to repair damage resulting from landslides triggered by an ARkStorm. 

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