Lifeline Infrastructure and Collocation 

Exposure to the HayWired Earthquake 

Scenario—A Summary of Hazards and 

Potential Service Disruptions

By Jamie L. Jones, Anne M. Wein, Amy E. Schweikert, and Laurel R. Ballanti

Chapter T of

The HayWired Earthquake Scenario—Societal Consequences

Edited by Shane T. Detweiler and Anne M. Wein

Scientific Investigations Report 2017–5013–R–W

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

DAVID BERNHARDT, Secretary

U.S. Geological Survey

James F. Reilly II, Director

U.S. Geological Survey, Reston, Virginia: 2019

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Suggested citation:

Jones, J.L., Wein, A.M., Schweikert, A.E., and Ballanti, L.R., 2019, Lifeline infrastructure and collocation exposure to 

the HayWired earthquake scenario—A summary of hazards and potential service disruptions, chap. T of Detweiler, 

S.T., and Wein, A.M., eds., The HayWired Earthquake Scenario—Societal Consequences: U.S. Geological Survey 

Scientific Investigations Report 2017–5013–R–W, 104 p., https://doi.org/10.3133/sir20175013V3.

ISSN 2328-0328 (online)
iii

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................2

Lifeline Infrastructure Exposure to HayWired Earthquake Scenario Hazards....................................4

HayWired Hazard Descriptions and Classifications.......................................................................4

Fault Rupture.................................................................................................................................5

Ground Shaking............................................................................................................................8

Mainshock Ground Shaking..............................................................................................8

Aftershock Ground Shaking............................................................................................10

Liquefaction ................................................................................................................................12

Landslides ...................................................................................................................................12

Fire Following Earthquake........................................................................................................12

Individual Lifeline Exposure to Individual Hazards .......................................................................15

Transportation Infrastructure ..................................................................................................18

Water Supply and Wastewater Infrastructure .....................................................................19

Oil and Gas Infrastructure........................................................................................................19

Electric Power Infrastructure..................................................................................................20

Telecommunications Infrastructure .......................................................................................20

Combinations of Hazards............................................................................................................................21

Methods................................................................................................................................................21

Results for Hazard Combinations.....................................................................................................23

Results for Lifeline Infrastructure Exposure to Three or More Hazards ...................................23

Exposure of Individual Lifeline Infrastructure to Multi-hazard Intensities ........................................23

Multi-hazard Intensity Classification...............................................................................................27

Results for Individual Lifeline Infrastructure Exposure to Multi-hazard Intensities................28

Collocated Lifeline Infrastructure .............................................................................................................28

Method for Identifying Collocated Infrastructure .........................................................................30

Density of Collocated Infrastructure ...............................................................................................31

Exposure of Collocated Infrastructure to Multiple Hazards........................................................35

Relative Importance of Lifeline Infrastructure...............................................................................36

Classification of Criticality of Lifeline Infrastructure ...........................................................36

Lifeline Infrastructure Interactions...........................................................................................................42

Interaction and Interdependencies Among Lifeline Infrastructure Systems...........................42

Illustration of Interactions Among Lifeline Infra-structure Systems in the HayWired 

Scenario..................................................................................................................................44

Lifeline Infrastructure System Damage and Downtime Assessments ...............................................48

Damage Impact Assessments..........................................................................................................48

HayWired Lifeline Infrastructure Restoration Times ....................................................................49

Knowledge Gaps and Modeling Limitations............................................................................................53

Summary and Conclusion...........................................................................................................................54

Acknowledgments.......................................................................................................................................57

References Cited..........................................................................................................................................57
iv

Appendix 1. HayWired Scenario Liquefaction Modeling Expansion Methodology..........................61

Appendix 2. Individual Lifeline Exposure to Individual HayWired Scenario Hazards ......................66

Appendix 3. Electric Power Assessment using Hazus-MH..................................................................81

Appendix 4. Bay Area Rapid Transit Damage Assessment to HayWired Scenario Ground 

Shaking.............................................................................................................................................88

Appendix 5. California Department of Transportation Bridge Damage Assessment to HayWired 

Scenario Ground Shaking.............................................................................................................97

Figures

1. Schematic illustration showing the flow of analyses in this chapter..................................4

2. Maps of the San Francisco Bay region, California, showing transmission 

 infrastructure relative to rupture of the Hayward Fault during the HayWired 

earthquake scenario mainshock................................................................................................6

3. Maps of the San Francisco Bay region, California, showing ground shaking as a 

 result of the HayWired earthquake scenario mainshock on the Hayward Fault...............9

4. Map of the San Francisco Bay region, California, showing areas affected by 

overlapping aftershock ground shaking for the HayWired earthquake scenario...........11

5. Map of the San Francisco Bay region, California, showing the liquefaction 

 probability modeling extent and classification for the HayWired earthquake 

 scenario mainshock on the Hayward Fault............................................................................13

6. Map of the San Francisco Bay region, California, showing the landslide 

 probability modeling extent and classification for the HayWired earthquake 

 scenario mainshock on the Hayward Fault............................................................................14

7. Map of the San Francisco Bay region, California, showing the fire-following-earthquake modeling extent and classification for the HayWired earthquake scenario 

mainshock on the Hayward Fault.............................................................................................17

8. Map of the San Francisco Bay region, California, showing areas exposed to one 

 or more high-intensity HayWired earthquake scenario mainshock hazards...................22

9. Schematic illustration showing how overlapping hazards are merged into one 

 multiple hazard output ...............................................................................................................27

10. Map of the San Francisco Bay region, California, showing the composite 

 multi-hazard exposure as a result of HayWired scenario mainshock hazards ...............30

11. Simplified maps showing the steps of the process used to identify collocated 

 lifeline infrastructure..................................................................................................................31

12. Maps of the San Francisco Bay region, California, showing a density assessment 

 of collocated infrastructure ......................................................................................................32

13. Maps of the San Francisco Bay region, California, showing collocated infrastructure 

points and exposure level to multiple HayWired earthquake scenario mainshock 

hazards (three or more collocated infrastructure assets only)..........................................34

14. Maps of the San Francisco Bay region, California, showing lifeline infrastructure 

collocation analyses ..................................................................................................................40

15. Line graph showing the electric power restoration curve by county for the 

 HayWired earthquake scenario mainshock...........................................................................50
v

16. Line graph showing the voice and data telecommunications restoration curve by

county for the HayWired earthquake scenario mainshock .................................................50

17. Line graph showing the water distribution restoration curve by county for the

HayWired earthquake scenario mainshock ..........................................................................51

18. Line graph showing the Bay Area Rapid Transit (BART) facility restoration curve by

county for the HayWired earthquake scenario mainshock .................................................51

19. Line graph showing the California Department of Transportation highway bridge

restoration curve by county for the HayWired earthquake scenario mainshock ...........52

20. Line graph showing the lifeline restoration curves for Alameda County for the

HayWired earthquake scenario mainshock ...........................................................................52

Tables

1. Summary of hazards resulting from the HayWired earthquake scenario in the San

Francisco Bay region, California ................................................................................................5

2. Shaking-potential hazard intensity classification for the HayWired earthquake

scenario mainshock in the San Francisco Bay region, California. .......................................8

3. Modeled aftershocks with moderate or high ground shaking for the HayWired

earthquake scenario in the San Francisco Bay region, California. ....................................11

4. Liquefaction hazard intensity classification for the HayWired earthquake scenario

mainshock in the San Francisco Bay region, California ......................................................12

5. Landslide hazard intensity classification for the HayWired earthquake scenario

mainshock in the San Francisco Bay region, California ......................................................12

7. Lifeline infrastructure types in California’s San Francisco Bay region, scales of

analysis, and data sources. ......................................................................................................15

6. Fire-following-earthquake hazard intensity classification for the HayWired

earthquake scenario mainshock in the San Francisco Bay region, California. ...............15

8. Geographic extent of hazard combinations from the HayWired earthquake scenario

mainshock in the San Francisco Bay region, California ......................................................21

9. Amount of lifeline infrastructure exposed to three, four, or five overlapping hazards

resulting from the HayWired earthquake scenario mainshock in the San Francisco

Bay region, California. ................................................................................................................24

10. Multi-hazard classifications used in this study for hazards resulting from the

HayWired earthquake scenario mainshock in the San Francisco Bay region,

 California ......................................................................................................................................27

11. Single-lifeline exposure to multi-hazard intensity distribution for the HayWired

earthquake scenario mainshock in the San Francisco Bay region, California. ...............28

12. Number of collocations of surface streets with other lifeline infrastructure, organized

by exposure to multi-hazard intensity classifications for the HayWired earthquake

scenario mainshock in the San Francisco Bay region, California. .....................................37

13. Lifeline infrastructure types and assignment of societal criticality by infrastructure

class or attribute .........................................................................................................................37

14. Top ten lifeline infrastructure types collocated in high and very high multi-hazard

intensity areas for the HayWired earthquake scenario mainshock in the San

Francisco Bay region, California ..............................................................................................41

15. Matrix of potential lifeline infrastructure interdependencies for the San Francisco

Bay region, California.................................................................................................................45

16. Summary of four representative examples of lifeline system interdependencies in the

San Francisco Bay region, California ......................................................................................46

The HayWired Earthquake Scenario—Societal Consequences

Edited by Shane T. Detweiler and Anne M. Wein

Scientific Investigations Report 2017–5013–R–W

[Also see https://doi.org/10.3133/sir20175013 ]

Chapter T

Lifeline Infrastructure and Collocation Exposure to the 

HayWired Earthquake Scenario—A Summary of Hazards 

and Potential Service Disruptions

By Jamie L. Jones, Anne M. Wein, Amy E. Schweikert, and Laurel R. Ballanti

Abstract

This chapter provides a comprehensive exposure 

analysis of five interacting lifeline infrastructure systems 

(comprised of facilities and conveyances) to six different 

HayWired earthquake scenario hazards. The lifeline 

infrastructure systems examined are transportation, water 

supply and wastewater, oil and gas, electric power, and 

telecommunications. The hazards considered are the 

HayWired mainshock ground shaking, surface rupture 

(represented as coseismic slip and afterslip), liquefaction, 

landslides, fire following the earthquake, and aftershocks. New 

geographic information system methods were developed to: 

(1) create a more detailed liquefaction probability map for the 

San Francisco Bay region that is not limited to a census-tract 

scale; (2) map relative levels of earthquake hazard intensities 

into a single multi-hazard intensity map; and (3) map 

collocation and density of infrastructure systems in a multihazard environment. Using these new methods, we look at the 

interaction between lifeline infrastructure systems and hazards 

in several ways: the exposure of single lifeline infrastructure 

types to single hazards; the exposure of single lifeline 

infrastructure systems to combinations of multiple hazards; 

and potential interactions between systems in hazardous areas 

after an earthquake. Finally, we compile lifeline infrastructure 

restoration times that have been acquired from various 

collaborations with providers and comment on the capabilities 

to consider multiple hazards and interactions between systems.

Hazard exposures vary by lifeline infrastructure. Eleven 

internet exchange points and five oil refineries are the most 

exposed (comprising 27 and 20 percent of sites, respectively) 

to high shaking intensity (defined as above 50 percent of the 

ground motion in the low-rise building design maps of the 

International Building Code). Liquefaction hazards are the 

primary hazard at the three international airports, seaports, 

water treatment plants, and one refinery in terms of affected 

area and severity during the HayWired mainshock (and 

likely for some nearby aftershocks). Oil and gas pipelines 

run parallel to and cross various transportation infrastructure 

and fiber optic cables in some areas with high liquefaction 

probability. Across all infrastructure, only 22 facilities were 

found in the fault zone, but hundreds of roadways and fiber 

optic cables cross the surface rupture and (or) are further 

exposed to afterslip. Also, there is infrastructure in the fault 

zone that is potentially exposed to all five hazards at once: 

these include a few bridges and cellular sites, and less than 

a few kilometers of roadways, fiber optic cables, and oil and 

gas pipelines. Contrary to the outcome of the 1989 Loma 

Prieta earthquake in California, safe egress from the east bay 

(especially away from ensuing fires) might well be west across 

the trans-bay bridges rather than to the east owing to fault 

rupture and landslide hazards. Because of the urban nature 

of fire following an earthquake in the HayWired scenario, 

Bay Area Rapid Transit (BART) facilities are relatively more 

exposed to higher fire-density areas. Long-haul fiber optic 

cables are relatively more present in high and very high 

multi-hazard areas. Five percent or more of petroleum, oil, or 

lubricant terminals, storage facilities, or tank farms; internet 

exchange points; fiber-lit buildings; cellular sites; natural 

gas compressor stations; microwave towers; and AM radio 

antennas are potentially exposed to three hazards (shaking, 

liquefaction, and fire) simultaneously. Most commonly, 

collocated infrastructure in hazardous areas involves surface 

streets or interoffice fiber (distribution infrastructure), 

although transmission infrastructure collocations among 

highways, railways, natural gas and petroleum pipelines, and 

high-voltage electric infrastructure may be a higher priority 

to repair following an earthquake. More societally critical 

infrastructure collocations occur around ports (specifically, the 

Port of Oakland and San Francisco International Airport) and 

in high building-density areas.

Service restoration time estimates indicate that BART 

stations and train yards will take longer (a few years) than 

highway bridges (as long as 10 months) to restore in the 

most heavily impacted areas, and water supply restoration 

times (as long as 6 months) extend beyond gas and electric 

power (weeks to a month) and voice and data (days to weeks) 

service restoration times. Restoration times could be extended 

by additional damages from fires following the earthquake, 

aftershocks, collateral damage, and repair delays from 

collocated infrastructure and other restoration dependencies.
2 The HayWired Earthquake Scenario—Societal Consequences

This chapter offers an analytical approach to support 

planning and coordination among organizations of lifeline 

infrastructure providers, communities, government officials, 

businesses, and public safety. These results could assist 

agencies with identifying areas where they may benefit from 

working together to minimize damage and system disruption 

in several ways. This research provides: (1) comprehensive 

and standardized hazard exposure analyses to complement 

shaking-only or unavailable damage assessments of lifeline 

infrastructure systems, (2) a set of data layers for use in the 

development of information sharing tools, (3) potential exposure 

of collocated infrastructure to multiple hazards to inform 

and facilitate dialogue among organizations about increased 

societal risks for cascading failures and complex restoration 

interactions, and (4) windows into potential interactions that 

could arise following a major earthquake to suggest emergency 

management exercises among organizations. Finally, this work 

aligns with the proposed goals of a Regional Lifeline Council 

for the San Francisco Bay region.

Introduction

The economically vibrant, densely populated area 

around the Hayward Fault in the San Francisco Bay region in 

California presents a challenge in preparing and planning for 

a large earthquake. Lifeline infrastructure systems, such as 

transportation, electric power, oil and gas, water supply and 

wastewater, and telecommunications systems are the backbone 

infrastructure for economic and social activities (Perkins 

and Hutchings, 2010; The Lifelines Council, 2014; Applied 

Technology Council, 2016). Furthermore, economic and social 

activities increasingly rely on connected and complex systems 

of infrastructure. Electric power and telecommunications 

infrastructure are prime examples of interconnected 

infrastructure, as are transportation routes that also serve as 

right-of-ways for lifeline infrastructure (for example, pipes and 

conduits for water, wastewater, telecommunications, electric 

power, oil, and natural gas) (Applied Technology Council, 

2016). Furthermore, the efficiency of systems has increased 

with more recent technological dependencies, such as remote 

monitoring of water and natural gas systems using sensors that 

require electricity and telecommunications infrastructure.

Interactions within and between lifeline infrastructure 

systems are vulnerable during and after an earthquake (The 

Lifelines Council, 2014): there are functional dependencies 

when one infrastructure relies on another (for example, 

telecommunications use electric power), geographic 

dependencies where collocated infrastructure can damage 

or delay the restoration of another (for example, utility lines 

beneath surface streets), substitution dependencies where 

capacity loss for one shifts demand to another (for example, 

roads and rails can both transport people and goods), and 

restoration dependencies when one needs the other to complete 

repairs (for example, transportation networks are required 

to access damaged infrastructure). Lifeline infrastructure 

interactions can also cause unexpected cascading failures. For 

example, in 1989, a train derailment in the Cajon Pass, north of 

the City of Los Angeles, damaged a nearby gasoline transmission 

pipeline and the subsequent explosion resulted in many 

destroyed homes and lost lives (Applied Technology Council, 

2016). This example elucidates the importance of being able to 

anticipate, plan, and prepare for less predictable and less visible, 

potentially disastrous, lifeline infrastructure system interactions 

related to damages, services, and restoration. Arellano and others 

(2003) and Scalingi (2015) provide other examples and find 

that many of the impacts of seismic events have been caused by 

unforeseen (or previously unknown) interactions between lifeline 

systems. Furthermore, The Lifelines Council (2014) highlight 

hubs of collocated infrastructure systems as important areas of 

vulnerability and in need of further consideration in emergency 

management planning.

The San Francisco Bay region has been receptive to 

and proactive about earthquake risk from damages to lifeline 

infrastructure (Brocher and others, 2018), but interactions 

among infrastructure systems remain a challenge to assess and 

address before, during, and after a large earthquake. Applied 

Technology Council (2016) summarizes interdependency 

modeling capabilities at multiple scales that include 

optimization of restoration, functional dependencies, and 

simulations (both in the market and economy), especially 

regarding lifelines paired with power. They observe that 

infrastructure recovery models tend to neglect collocated 

infrastructure, resource limitations, and multiple and extended 

time horizons.

In practice, coordination among lifeline infrastructure 

systems that are affected by multiple earthquake hazards 

requires a cross-system, cross-organizational, and integrated 

approach to planning that is difficult to implement (Applied 

Technology Council, 2016). However, each lifeline 

infrastructure operator and provider has distinctive institutional 

procedures, operations, and operating areas. They lack a 

common set of service and restoration goals, scenarios for 

exercises, and comparable methods of assessment using 

standardized units. Sharing data for regional planning 

and preparedness can be cumbersome as a result of these 

inconsistencies (Scalingi, 2015). Exercises and disaster events 

are further limited by the inability of infrastructure owners 

and managers to easily and securely share infrastructure 

data (Scalingi, 2015; Applied Technology Council, 2016). 

Consequently, many areas and details of physical collocation 

are unknown.

According to Grain (2014), there is great value in welldesigned, informative exercises that can test the efficacy of 

contingencies and preparedness plans. A post-event workshop 

on the 2014 South Napa (California) earthquake (Scalingi, 

2015) gathered stakeholders and experts from across many 

jurisdictions and identified several immediate priorities. 

More dialogue between lifeline infrastructure managers 

about interdependencies and interactions was identified as the 

first priority. The second priority was a common approach, 

process, and standard for assessing vulnerabilities. The final 

priority identified during the 2014 workshop was to address 

the constraints on sharing information (including a secure 

information tool) to better coordinate restoration and allocating 

scarce resources (including personnel, fuel, electricity, 

communications, and transportation).
Chapter T. Lifeline Infrastructure and Collocation Exposure 3

In the San Francisco Bay region, earthquake scenarios 

have been used as a common approach to assess and facilitate 

dialogue about the vulnerabilities of lifeline infrastructure 

systems and the interactions among them. The Lifelines Council 

(2014) chose a San Andreas Fault earthquake as the basis for 

their study for the City of San Francisco. Germeraad and others 

(2014) used publicly available lifeline infrastructure data with 

three important and representative earthquake scenarios in the 

bay region to investigate potential transportation and utility 

system vulnerabilities.

The HayWired scenario provides the most comprehensive 

set of hazard data to date for an earthquake scenario. The 

HayWired mainshock is a hypothetical moment magnitude 

(Mw) 7.0 earthquake on the Hayward Fault in the east bay part 

of California’s San Francisco Bay area occurring at 4:18 p.m. 

on April 18, 2018. The HayWired earthquake hazards are: fault 

rupture and slip (during and after the event), ground shaking 

intensity, liquefaction probability, landslide probability, and 

fire following the earthquake for the mainshock, and two years 

of aftershocks (Detweiler and Wein, 2017; Scawthorn, 2018). 

We determined that the HayWired scenario can be used to 

illustrate a standardized assessment of multiple types of lifeline 

infrastructure (including collocated infrastructure) exposed to 

multiple hazards.

We approached the assessment by assembling critical 

San Francisco Bay region lifeline infrastructure stakeholders 

in 2014. We proposed that each stakeholder evaluate the 

performance of their infrastructure for the HayWired 

earthquake scenario hazards and share estimates of service 

restoration times and assumptions behind their estimates, 

including the use of shared, but scarce, resources (for example, 

labor, material, and accommodations). Each stakeholder’s 

contribution was affected by availability of analytical tools, 

sensitivity of information, and uneasiness with appearing 

to promise restoration times that are dependent on many 

uncertain factors (including lifeline interactions). Thus, lifeline 

infrastructure damage and restoration analyses varied. They 

were: (1) conducted by stakeholders using in-house tools but 

not made publicly available, (2) conducted by the Federal 

Emergency Management Agency using Hazus-MH 2.1 

(hereafter referred to as Hazus), (3) conducted by stakeholders 

for some hazards (dictated by assessment tool capabilities) 

and shared, and (or) (4) produced with additional assistance 

through HayWired collaborations. Hazus electric power, 

Bay Area Rapid Transit (BART), and California Department 

of Transportation (Caltrans) assessments are documented 

in appendixes 3–5 of this chapter. HayWired collaborative 

infrastructure damage and restoration assessments for water 

supply and telecommunications are in Porter (2018) and Wein, 

Witkowski, and others (in prep.; planned to be published as 

part of this volume), respectively.

Although Hazus was our contingency plan for examining 

electric power, the tool was not used as a standardized 

assessment because it does not support all infrastructure and 

hazards (for example, fiber optic cables crossing surface 

rupture) and coarsely evaluates hazards at the scale of census 

tracts rather than utilizing more detailed hazard maps. Thus, our 

approach constructs a comparable and standardized 

multi-hazard exposure analysis for lifeline infrastructure 

systems. Available data for transportation, water supply and 

wastewater, oil and gas, electric power, and telecommunications 

systems were mapped against each mainshock hazard and 

aftershock shaking intensities. We contend with multiple 

mainshock hazards by reducing ground shaking, surface rupture, 

liquefaction, landslides, and fire following the earthquake into 

low, moderate, and high hazard intensity and combining them 

into a multi-hazard intensity landscape. Results for each system 

provide counts of facilities and lengths of conveyances exposed 

to each hazard intensity as well as the mainshock multi-hazard 

intensity. These results complement infrastructure damage 

assessments that were conducted for a subset of the hazards.

From the baseline infrastructure hazard exposure analyses, 

we were able to proceed with the next level of complexity; 

that is, density and multi-hazard exposure of collocated 

lifeline infrastructure of different types to show geographical 

interactions. We produced maps of collocations involving all 

lifeline infrastructure, transmission infrastructure, and possibly 

more societally critical infrastructure with respect to multiple 

hazards. We referred to The Lifelines Council (2014) framework 

for lifeline infrastructure interactions (general [within systems], 

functional, substitution, and restoration) as well as geographical 

interactions. We updated The Lifelines Council (2014) 

lifeline interaction table with input from HayWired workshop 

discussions, and we illustrated interactions between collocated 

infrastructure in hazardous sites of the HayWired scenario.

Utility and transportation service restoration times 

were compiled from other parts of the HayWired scenario 

(Porter, 2018, for water supply; Wein, Witkowski, and others, 

in prep. [planned to be published as part of this volume], 

for telecommunications) and appendixes of this chapter 

(stakeholder analyses from BART and Caltrans; Hazus analysis 

for electric power). We reflect on the extent to which these 

estimates embody effects of multiple hazards and lifeline 

interactions.

In summary (also illustrated as a graphic in fig. 1), the 

following steps are taken.

1. Identify individual lifeline infrastructure systems

A. Analyze exposure of each infrastructure system to 

each earthquake hazard and fire following 

 the earthquake

B. Analyze exposure of each infrastructure system to 

multiple hazards

2. Identify collocated lifeline infrastructure

A. Determine density of collocation infrastructure

B. Analyze exposure of collocated infrastructure 

 (all, transmission, and potentially more critical 

 for society) to multiple hazards

3. Assess lifeline infrastructure interactions

A. Update lifeline interactions informed by 

HayWired activities

B. Provide illustrations of lifeline infrastructure 

interactions

4. Compile lifeline infrastructure service 

 restoration information.
4 The HayWired Earthquake Scenario—Societal Consequences

In addition to the scientific uncertainties (Detweiler and 

Wein, 2017), the knowledge and data gaps in our work pertain 

to hazard mapping thresholds, infrastructure data and attributes, 

infrastructure fragilities, and collocated infrastructure. We 

conclude with a summary of potential lifeline infrastructure 

system exposures to earthquake hazards in the San Francisco 

Bay region. We posit that the methods and data developed here 

can be used to facilitate deeper analyses of lifeline interactions 

in a multi-hazard setting and to provide situations for exercises 

and materials for developing information-sharing tools. Finally, 

this demonstration of lifeline infrastructure analysis informs the 

current policy initiative for a proposed San Francisco Bay area 

regional lifeline council.

Lifeline Infrastructure Exposure 

to HayWired Earthquake Scenario 

Hazards

The first level of lifeline exposure analysis looks at 

how individual lifeline infrastructure systems (composed of 

facilities and conveyances) are exposed to each HayWired 

earthquake scenario hazard. This single lifeline-single hazard 

assessment serves as a baseline for understanding potential 

impacts to lifeline infrastructure; hazard exposure analysis 

offers a comprehensive and standardized approach, but it does 

not estimate damages to infrastructure.

First, we describe and classify each of the HayWired 

earthquake scenario hazards (surface rupture and slip, 

ground shaking, liquefaction, landslides, and fire following 

the earthquake for the mainshock; and ground shaking for 

aftershocks) and data sources for the infrastructure systems 

(transportation, water supply and wastewater, oil and gas, 

electric power, and telecommunications). We follow with 

results of the exposure of each lifeline infrastructure system 

to each hazard intensity. Subsequent sections of this chapter 

expand on this baseline analysis to draw additional conclusions 

about multiple hazards affecting interdependent lifeline 

infrastructure in the San Francisco Bay region.

HayWired Hazard Descriptions and 

Classifications

The main source of earthquake hazard data is HayWired 

v. 1 (Detweiler and Wein, 2017), as well as an expanded 

liquefaction probability map (appendix 1) and a fire density 

map (explained below). The hazards and data sources are 

summarized in table 1. Each hazard (except fault rupture) 

is classified into low-, moderate-, and high-intensity 

exposure. These classifications are designed to simplify the 

complexities of multi-hazard analyses. These are relative 

classifications of hazard intensity within our study area for 

the HayWired scenario.

Individual lifeline infrastructure

Transportation

Water supply and wastewater

Oil and gas

Electric power

Telecommunications

Individual hazards

Fault rupture and slip

Ground shaking intensity

Liquefaction probability

Landslide probability

Fire following earthquake

Aftershock shaking

Multiple hazards

Fault rupture and slip

Ground shaking intensity

Liquefaction probability

Landslide probability

Fire following earthquake

Collocated lifeline infrastructure

All lifeline systems

Transmission systems

Societally critical systems

Lifeline infrastructure interactions

General (within systems)

Functional

Substitution

Restoration

Geographical

1

2 3 4

6

5 7

Collocation density

All lifeline systems

7

7

Lifeline restoration curves

Electric power

Telecommunications

Water distribution

BART

Caltrans bridges

7

Analytical steps

1. Individual lifelines are assessed relative to individual 

hazards.

2. Individual hazards are combined to create a multi-hazard 

dataset.

3. Individual lifelines are then assessed relative to multiple 

hazards.

4. Individual lifelines are combined to identify collocations for 

all lifeline infrastructure systems, transmission systems, and 

societally critical systems.

5. All lifeline collocations are aggregated to areas of 500 

square meters to create collocation density.

6. Collocations are assessed relative to multiple hazards.

7. Collocations and density are used in conjunction with 

multi-hazard data and lifeline restoration curves (when 

available) to identify lifeline 

infrastructure collocations. 

EXPLANATION

Direct connection

Secondary or ancillary 

connecting items

Figure 1. Schematic illustration showing the flow of analyses 

in this chapter. BART, Bay Area Rapid Transit; Caltrans, California 

Department of Transportation.
Chapter T. Lifeline Infrastructure and Collocation Exposure 5

Table 1. Summary of hazards resulting from the HayWired earthquake scenario in the San Francisco Bay region, California.

[km, kilometers; km2, square kilometers; m2, square meters; cm/s, centimeters per second]

Hazard Original data source Summary description

Ground shaking 

(mainshock)

HayWired v. 1, chap. C—“HayWired scenario mainshock ground motions” by Aagaard, Boatwright, 

and others (2017)

Magnitude of mainshock measured in 0.3 second period of 

vibration for spectral acceleration at approximately 1.5 

km2

 resolution (1.2 km2 longitudinal, 1.8 km2 latitudinal). 

Calculated for 51,372 km2

.

Fault rupture

(mainshock)

HayWired v. 1, chap. D—“ HayWired scenario mainshock coseismic and postseismic surface fault slip” 

by Aagaard, Schwartz, and others (2017)

Fault rupture that caused coseismic slip (during an 

earthquake) or afterslip (following an earthquake) at 

the ground’s surface. Total fault length that experienced 

surficial coseismic slip or afterslip is 83 km, of which 63 

km experienced surficial coseismic slip.

Liquefaction

(mainshock)

HayWired v. 1, chap. E—“HayWired scenario 

mainshock—Liquefaction probability mapping” by 

Jones and others (2017)

HayWired v. 3, this chap., appendix 1—“HayWired 

liquefaction modeling expansion methodology” 

Probability of liquefaction estimated at 50 m2

 resolution in 

Jones and others’ (2017) study area (susceptibility data 

used at same 50-m2

 resolution). Total area of modeled 

extent with non-zero occurrence probability is 3,144 km2

, 

of which 1,181 km2

 is based on Jones and others’ findings.

Landslide 

(mainshock)

HayWired v. 1, chap. F—“HayWired scenario mainshock—Earthquake-induced landslide hazards” by 

McCrink and Perez (2017)

Probability of landslide estimated at 10 m2 geographic scale 

modeled in areas where peak ground velocity exceeds 

20 cm/s. Total area of modeled extent with non-zero 

occurrence probability is 2,915 km2

.

Ground shaking 

(aftershocks)

HayWired v. 1, chap. G—“HayWired scenario aftershock sequence” by Wein and others (2017)Seven of the 16 largest aftershocks occur within 2 years of the 

mainshock event, and are the only ones that have ground 

shaking of moderate and (or) high-intensity classification.

Fire (following the 

mainshock)

HayWired v. 2, chap. P—“Fire following the HayWired scenario mainshock” by Scawthorn (2018)Density of burned buildings estimated in developed land 

tracts. Total area of modeled extent with non-zero density 

is 1,220 km2

.

Fault Rupture

The surface slip caused by a fault rupturing is described 

as coseismic slip (the displacement that occurs during an 

earthquake) and afterslip (the subsequent slip of the fault 

occurring over the days, weeks, and months following a large 

fault rupture). Surface slip disturbs any lifeline infrastructure 

crossing the fault. Coseismic slip followed by afterslip may 

require a series of repairs to facilities and networks that 

straddle the fault. This situation recently arose, for example, 

after the 2014 South Napa earthquake in California (Hudnut 

and others, 2014). Scientists observed that afterslip tended 

to occur along the fault rupture where coseismic slip was 

less pronounced (Hudnut and others, 2014; Lienkaemper and 

others, 2016).

The HayWired scenario rupture of the Hayward Fault 

begins beneath the city of Oakland and extends from San 

Pablo Bay in Contra Costa County to southern Fremont in 

Alameda County over a total distance of 83 kilometers (km) 

(Aagaard, Schwartz, and others, 2017). The coseismic surface 

slip ranges from no measurable surface slip to about 2.1 

meters (m), whereas afterslip from the HayWired scenario 

could measure between 0.5 and 1.5 m. Afterslip may cause 

rupture at the surface along the entire fault rupture zone 

even where coseismic slip only occurred beneath the surface 

(Aagaard, Schwartz, and others, 2017). Figure 2 shows the 

fault rupture used for the HayWired scenario along with 

the linear transmission lifeline infrastructure assessed in 

this chapter—figure 2A is the full study area and figure 

2B is focused on the area where fault rupture is modeled 

to occur. See appendix 2 for an illustration of clusters of 

lifeline infrastructure crossing the fault along its length. The 

amount of offset that may cause damage varies widely across 

infrastructure types exposed—for example, brittle pipelines 

may be damaged by even a few millimeters of offset 

(K. Porter, University of Colorado at Boulder, written 

commun., 2017). Therefore, the classification of the fault 

rupture hazard intensity is binary: either the area has 

measurable (non-zero) coseismic surface slip (classified 

as high hazard intensity) or does not. However, damage 

from fault offset varies widely. For example, electric power 

transmission lines are included in the fault offset hazard for 

consistency, although such damage is likely minimal. In 

this case, transmission lines are generally suspended with 

flexibility between supporting towers that are not located 

on faults and the natural horizontal frequency for electric 

transmission towers is low to withstand high winds (which are 

generally stronger forces than earthquake shaking) (Federal 

Emergency Management Agency, 1990). In contrast, the 

building of structures in the fault zone is constrained by the 

Alquist-Priolo Act of 1972 (Perkins and Hutchings, 2010; 

California Department of Conservation, 2017). This reduces, 
6 The HayWired Earthquake Scenario—Societal Consequences

"

"

"

"

"

" "

"

"

"

"

"

Sacramento

Stockton

Fremont

Modesto

San Jose

Hayward

Oakland

SAN FRANCISCO Alameda Livermore

Richmond

Foster City

Palo Alto

SANTA

CLARA

SANTA

CRUZ

SAN

MATEO

MARIN

ALAMEDA

CONTRA

COSTA

SOLANO

SONOMA NAPA

MONTEREY

SAN

BENITO

MERCED

STANISLAUS

SAN

JOAQUIN

SACRAMENTO

YOLO

MENDOCINO

LAKE

COLUSA

SUTTER

PLACER

EL

DORADO

AMADOR

CALAVERAS

TUOLUMNE

P A C I F I C O C E A N

San Francisco Bay

San

 Pablo

 Bay

A

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

0 20 MILES

0 20 KILOMETERS 10

10

123° 122° 121°

39°

38°

37°

Map CALIF.

area

Study area

Fault-infrastructure crossing point

Hayward Fault rupture

MW 7.0 epicenter

Transmission infrastructure

Highway

Natural gas or petroleum pipeline

Long-haul fiber optic cable Electric transmission line

EXPLANATION

Secondary road

Water transmission conveyance

Map area

in B

Figure 2 (pages 6–7). Maps of the San Francisco Bay region, California, showing transmission infrastructure relative to rupture of the 

Hayward Fault during the HayWired earthquake scenario mainshock. Many transmission infrastructure lifelines cross the fault, including 

natural gas and petroleum pipelines, water supply transmission systems, highways and secondary roads, electric power transmission lines, 

and long-haul fiber optic cables. These maps also illustrate the density of certain infrastructure around more urbanized areas in the study 

region (defined by the black bounding box in A). Where linear features cross the fault, the intersection points are colored to be consistent 

with the linear infrastructure they correspond to (for example, a yellow point denotes where a natural gas or petroleum pipeline crosses 

the fault). A, Regional view of the Hayward Fault rupture and infrastructure relative to the study region. B, Zoomed-in view of the locations 

where infrastructure assets cross the Hayward Fault rupture. Mw, moment magnitude.
Chapter T. Lifeline Infrastructure and Collocation Exposure 7

"

"

"

"

"

"

HAYWARD FAULT

Alameda

Hayward

Oakland

Fremont

SAN FRANCISCO

Richmond

Foster City

S a n F r a n c i s c o B a y

CONTRA COSTA

ALAMEDA

MARIN

SAN

MATEO

San Pablo Bay

B 122°30' 122°20' 122°10' 122° 121°50'

38°

37°

50'

37°

40'

37°

30'

10 MILES

0 5

0 5

10 KILOMETERS

Fault-infrastructure crossing point

Hayward Fault rupture

MW 7.0 epicenter

Transmission infrastructure

Highway

Natural gas or petroleum pipeline

Long-haul fiber optic cable Electric transmission line

EXPLANATION

Secondary road

Water transmission conveyance

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

Figure 2 (pages 6–7).—Continued
8 The HayWired Earthquake Scenario—Societal Consequences

but does not eliminate, the presence of lifeline infrastructure 

facilities in the fault zone. The whole length of the fault is 

considered a high-intensity fault rupture hazard with respect to 

total surface slip (coseismic slip and afterslip).

To use this information in an assessment of lifeline 

infrastructure exposure, the coseismic slip from Aagaard 

and others’ (2010) model for the HayWired scenario is 

transferred to the most recent representation of the fault trace 

(Lienkaemper, 2007). The mapped fault trace provided in 

these geographic information system (GIS) data was aligned 

with surface slip data (discussed in Aagaard, Schwartz, and 

others, 2017). This was done by associating the centroid of 

each fault segment in the Lienkaemper (2007) dataset to the 

closest segment of the HayWired surface slip dataset using 

a spatial join in the GIS software. The fault trace dataset 

includes a field specifying the positional uncertainty of the 

mapped fault trace; the trace was buffered in the GIS using 

the positional uncertainty values to define a zone for the 

location of the Hayward Fault. The end result was the surface 

slip along the fault trace within a zone of uncertainty around 

the Hayward Fault. This means that parallel segments of the 

Lienkaemper (2007) fault trace were all assigned the same 

surface slip value from the HayWired surface slip dataset. 

In reality, the slip on parallel segments could be loaded on 

one segment or distributed across parallel segments. In our 

fault-crossing analyses, we assume some of the coseismic slip 

occurs on all parallel segments up to the amount for the zone 

of uncertainty.

Ground Shaking

Ground shaking is typically the most extensive 

earthquake hazard and occurs both as mainshock and 

aftershock shaking. In the HayWired scenario, ground shaking 

was modeled using two methods—a largely physics-based 

model for the mainshock (Aagaard, Boatwright, and others, 

2017) and ShakeMap for the aftershocks (Wein and others, 

2017). The implications of the differences between the two 

methods are explained in Porter (2017). The study area for 

this analysis is the extent used in the physics based model 

(Aagaard and others, 2010), which covers a total area of 

51,372 square kilometers (km2

).

The measurement of ground shaking used in this 

chapter is spectral acceleration (SA) at a 0.3 second period 

of vibration. This measurement of SA corresponds to the 

amplitude of earthquake shaking that resonates most with 

low-rise buildings (buildings between 1 and 4 stories in 

height, which represent the vast majority of structures in the 

region) and is used in this analysis because it is consistent 

across a range of geographic comparisons, accounts for 

differences such as soil type, and is an appropriate measure for 

infrastructure asset owners and operators to assess potential 

damages (Federal Emergency Management Agency, 2006). 

The thresholds for moderate and high shaking are defined 

by shaking that is at least 25 and 50 percent, respectively, of 

the ground motion in the design maps of the International 

Building Code (K. Porter, University of Colorado at Boulder, 

oral commun., 2014). For example, in the formulas in table 

2, 0.25 corresponds to 25 percent of the design map motions; 

Ss

 is the ground motion in the International Building Code 

for the seismic design of low-rise buildings on rock sites; 

and Fa(Ss) is a factor to account for the way soil tends 

to amplify earthquake motion above what a site on rock would 

experience. So, a building that experiences motion of 0.25 ×

Fa(Ss) × Ss is experiencing motion that is at least 25 percent of 

the very rare motion shown in the design maps in the building 

code. Fewer than 1 building in 2,000 would collapse below 

this level of motion, but about 1 building in 500 might be 

damaged to the extent of being unfit for human occupation 

(red-tagged), and perhaps as many as 1 in 40 could be 

identified as habitable for only short periods (yellow-tagged) 

at 25 percent of the mapped motion. More information about 

the International Building Code design maps and the formulas 

can be found in Federal Emergency Management Agency 

(2003) and Federal Emergency Management Agency (2009).

Mainshock Ground Shaking

Figure 3A displays the relative shaking intensity using 

SA classifications at a 0.3 second period of vibration, as 

described in table 2. As shown in figure 3A, most areas with 

high-intensity shaking exist within approximately 30 km of 

the HayWired scenario epicenter and include most of Alameda 

County as well as some areas in Contra Costa, Santa Clara, 

and San Joaquin Counties. Figure 3B is the same HayWired 

ground-shaking scenario intensity but shown relative to the 

U.S. Geological Survey (USGS) ShakeMap representation 

of the Modified Mercalli Intensity (MMI) scale. As shown in 

figure 3B, most areas of high-intensity ground shaking have 

MMI values of VIII or IX and small areas experience highintensity ground shaking associated with MMI values of VI–

VII. MMI values of VIII and IX are considered “severe” and

Table 2. Shaking-potential hazard intensity classification for the HayWired earthquake scenario mainshock in the San Francisco Bay 

region, California.

[km2, square kilometers; Ss, short-period 5 percent damped maximum considered earthquake spectral response acceleration; Sa(0.3), 0.3 second period 5 percent 

damped earthquake spectral acceleration; Fa

, short period average spectral amplification factor]

Intensity classification Formula Estimated geographic extent (km2

)

Low Sa

(0.3) ≤ 0.25 × Fa(Ss) × Ss 31,665

Moderate 0.50 × Fa

(Ss) × Ss > Sa(0.3) > 0.25 × Fa(Ss) × Ss 18,052

High Sa

(0.3) > 0.50 × Fa(Ss) × Ss 1,655
Chapter T. Lifeline Infrastructure and Collocation Exposure 9

“violent,” with moderate to heavy damage predicted. Some 

areas of moderate-intensity ground shaking (fig. 3A) have 

corresponding MMI values of VIII and IX as well (shown 

in bright yellow in fig. 3B). Most areas of low-intensity and 

some areas of moderate-intensity ground shaking correspond 

to MMI values of IV and V (light to moderate perceived 

shaking), indicating none to very minimal damage, as shown 

in figure 3B as the lightest shades of yellow and green (MMI 

values based on Aagaard, Boatwright, and others, 2017). 

Please note that SA is not directly comparable to MMI; SA 

approximates the shaking a building experiences, whereas 

MMI is a representation of an earthquake’s intensity.

Study area

"

"

"

"

"

" "

"

"

"

"

"

Stockton

Fremont

Modesto

Oakland

Livermore

Alameda

SAN FRANCISCO

Richmond

Palo Alto

SANTA

CLARA

SANTA

CRUZ

SAN

MATEO

MARIN

ALAMEDA

CONTRA

COSTA

SOLANO

SONOMA

NAPA

MONTEREY

SAN

BENITO

MERCED

SAN JOAQUIN

SACRAMENTO

YOLO

MENDOCINO

LAKE

COLUSA

SUTTER

PLACER

EL

DORADO

AMADOR

CALAVERAS

TUOLUMNE

San Francisco Bay

San Pablo

Bay

San Jose

Sacramento

Hayward

Foster City

STANISLAUS

A

P A C I F I C O C E A N

123° 122° 121°

39°

38°

37°

Mainshock ground shaking

Low

Moderate

High

MW 7.0 epicenter

Hayward Fault

EXPLANATION

Map CALIF.

area

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

0 20 MILES

0 20 KILOMETERS 10

10

Figure 3 (pages 9–10). Maps of the San Francisco Bay region, California, showing ground shaking as a result of the HayWired 

earthquake scenario mainshock on the Hayward Fault. A, Ground-shaking classifications used in this chapter. B, Ground shaking 

classifications compared to ShakeMap Modified Mercalli Intensity (MMI) instrumental intensity values. Mw, moment magnitude.
10 The HayWired Earthquake Scenario—Societal Consequences

Aftershock Ground Shaking

The HayWired earthquake scenario sequence includes 

a simulation of two years of aftershocks following the 

mainshock, although aftershocks after a large Hayward Fault 

earthquake could continue for many additional years (Wein 

and others, 2017). A total of 16 aftershocks in the HayWired 

aftershock sequence were modeled to have a magnitude (M) 

of at least 5. In this assessment of lifeline exposure, only 

seven of the 16 M5 or greater aftershocks are used—only 

these seven aftershocks cause at least moderate ground 

shaking and they occur within one year of the mainshock 

(table  3). Only one aftershock (M6.4 in Cupertino) produces 

high-intensity ground shaking.

Particularly important for the analysis of aftershocks 

is the timing, magnitude, and number of aftershocks that 

occur in the hours, days, weeks, and months following 

the mainshock. Figure 4 maps how many earthquakes that 

produce moderate or high ground-shaking intensity affect a 

single geographic region, indicating repeated ground shaking 

"

"

"

"

"

" "

"

"

"

"

"

Stockton

Fremont

Modesto

Oakland

Livermore

Alameda

SAN FRANCISCO

Richmond

Palo Alto

SANTA

CLARA

SANTA

CRUZ

SAN

MATEO

MARIN

ALAMEDA

CONTRA

COSTA

SOLANO

SONOMA

NAPA

MONTEREY

SAN

BENITO

MERCED

SAN

JOAQUIN

SACRAMENTO

YOLO

MENDOCINO

LAKE

COLUSA

SUTTER

PLACER

EL

DORADO

AMADOR

CALAVERAS

San Francisco Bay

San Pablo

Bay

San Jose

Sacramento

Hayward

Foster City

B

STANISLAUS P A C I F I C O C E A N

0 20 MILES

0 20 KILOMETERS 10

10

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016, Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection, North American Datum of 1983

123° 122° 121°

39°

38°

37°

EXPLANATION

MMI instrumental intensity

Low ground shaking

 IV–V

 VI–VII

 VIII–IX 

Moderate ground shaking

 IV–V

 VI–VII

 VIII–IX

High ground shaking

 IV–V

 VI–VII

 VIII–IX

Hayward Fault

MW 7.0 epicenter

Study area

Figure 3 (pages 9–10). —Continued
Chapter T. Lifeline Infrastructure and Collocation Exposure 11

Table 3. Modeled aftershocks with moderate or high ground shaking for the HayWired earthquake scenario in the San Francisco Bay 

region, California.

[Locations listed are approximate based on nearest major city, see figure 4. M, magnitude]

Aftershock description Number of days 

after mainshock

M6.2, May 28, 2018, 4:47 a.m., Palo Alto 40

M5.7, May 28, 2018, 11:53 p.m., Palo Alto 40

M5.2, June 23, 2018, 8:27 p.m., Palo Alto 66

M5.3, July 1, 2018, 11:19 a.m., Palo Alto 74

Aftershock description after mainshock

M6.0, September 30, 2018, 8:16 p.m., 

Mountain View

165

M6.4, October 1, 2018, 12:33 a.m., Cupertino 166

M5.4, October 1, 2018, 2:24 a.m., Sunnyvale 166

Number of days 

"

"

"

"

"

"

"

"

Alameda

Hayward

Oakland

Fremont

SAN FRANCISCO

Livermore

San Jose

Foster City

Palo Alto

CONTRA COSTA

ALAMEDA

SANTA CLARA

SAN MATEO

SANTA CRUZ

S a n F r a n c i s c o B a y

P A C I F I C O C E A N

MARIN

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

0 5 10 MILES

0 5 10 KILOMETERS

122°

37°

40'

37°

20'

122°40' 122°20' 121°40'

Map

area

CALIF.

Number of earthquakes with at

least moderate shaking

2

3

4

5

6

7

<2

MW 7.0 epicenter

Hayward Fault

EXPLANATION

Figure 4. Map of the San Francisco Bay region, California, showing areas affected by overlapping aftershock ground shaking for 

the HayWired earthquake scenario. Some areas experience multiple aftershocks in addition to the mainshock, especially the bay side 

areas near Palo Alto, and some areas experience moderate and (or) high ground shaking that occurs five, six, or seven times in the year 

following the HayWired scenario mainshock. Mw, moment magnitude.
12 The HayWired Earthquake Scenario—Societal Consequences

over time. As shown in figure 4, all the aftershocks that result 

in moderate or high ground shaking occur in the southern part 

of San Francisco Bay, where some areas experience multiple 

events of at least moderate-intensity ground shaking (indicated 

in dark red).

Liquefaction

Liquefaction is a form of ground failure induced by 

an earthquake in areas where loose, water-saturated ground 

composed of sand or silt becomes temporarily softened or 

liquefied, behaving like quicksand (Perkins and Hutchings, 

2010; Jones and others, 2017). In areas where intense 

ground shaking occurs and specific hydrologic and geologic 

conditions exist, liquefaction is probable (though not 

likely to occur in all areas with liquefaction susceptibility). 

When liquefaction occurs, it can cause severe damage to 

infrastructure above and below ground, including structures, 

roads, water pipes, and electric power lines. Historically, 

liquefaction from earthquakes has been a source of damage in 

the San Francisco Bay region, including the 1868 Hayward, 

1906 San Francisco, and 1989 Loma Prieta earthquakes 

(Knudsen and others, 2000).

We expanded upon the liquefaction probability map 

in Jones and others (2017) to map liquefaction probability 

within census tracts where Hazus results were used. Census 

tract-level Hazus results were modified based on liquefaction 

susceptibility data from Knudsen and others (2000)—tract 

liquefaction probabilities were estimated by susceptibility 

class and then mapped to each susceptibility class in each 

tract. The final liquefaction probability dataset covers 

3,144 km2

 in area (with non-zero liquefaction probability). 

For a more detailed description, please refer to appendix 1.

The liquefaction intensity classification is defined in 

table 4 and mapped in figure 5. The classification for low 

intensity (less than 5 percent probability) excludes areas 

with zero probability of liquefaction. The high-intensity 

liquefaction threshold is set at 25 percent; whereas some 

areas from Jones and others’ (2017) model see much 

higher liquefaction probabilities, 25 percent is the Hazus 

cap and thus used to capture the high range of liquefaction 

probability. Moderate intensity is between 5 and 25 percent 

probability of liquefaction.

Table 4. Liquefaction hazard intensity classification for the 

HayWired earthquake scenario mainshock in the San Francisco 

Bay region, California.

[Probability refers to the percentage of land in a given geographic area 

affected by liquefaction. Thresholds are designed for classification and 

discussion purposes only and are not intended to provide guidance.

 %, percent; km2

, square kilometers]

Intensity 

classification

Liquefaction 

probability (%)

Estimated geographic 

extent (km2

)

Low <5 1,438

Moderate 5 to <25 1,513

High ≥25 193

Table 5. Landslide hazard intensity classification for the 

HayWired earthquake scenario mainshock in the San Francisco 

Bay region, California.

[Probability refers to the percentage of land in a given geographic area 

affected by slope failure. Thresholds are designed for classification and 

discussion purposes only and are not intended to provide guidance.

 %, percent; km2, square kilometers]

Intensity

 classification

Landslide 

probability (%)

Estimated geographic 

extent (km2

)

Low <5 1,780

Moderate 5 to <25 344

High ≥25 791

Landslides

Landslides are induced by shaking in combination with 

geologic characteristics (for example, material strength) and 

slope gradient of the land. Landslides can cause damage to 

lifeline infrastructure above and below ground (roads, rail, 

pipelines, buried fiber optics, and so on). McCrink and Perez 

(2017) completed landslide modeling for ten counties in 

the region assessed in the HayWired scenario; the landslide 

modeling covers 2,195 km2. According to McCrink and Perez 

(2017), because uniform 1:24,000 scale geologic mapping 

does not yet exist for the entire HayWired scenario study 

region, an unpublished, statewide, generalized geologic 

compilation map was used to extract the 10-county study 

region used in their analysis. They used geologic material 

strength, slope gradient, ground shaking maps, and other 

variables to produce landslide probability maps in areas where 

the HayWired mainshock peak ground velocity (PGV) exceeds 

20 centimeters per second. See McCrink and Perez (2017) for 

modeling parameters, details, and a discussion of limitations.

The probability of landslides caused by the HayWired 

mainshock is mapped using the same probability threshold 

categories used for the liquefaction hazard in order to be 

consistent across both types of ground failure (table 5). 

The high hazard class for landslides covers a larger area than 

the moderate hazard class (that is scattered throughout the 

region and not as large pockets) owing to more area in hilly 

terrain with high probabilities (and little development). Similarly, areas with zero probability of landslide are omitted. 

Figure 6 displays the study region and landslide probability 

classifications.

Fire Following Earthquake

Fires following earthquakes are common, and large 

conflagrations are possible—a notable example is the 1906 

San Francisco earthquake. Approximately 80 percent of the 

total damage following the 1906 earthquake was attributed 

to post-earthquake fires, even when compared to ground 

shaking damage. Scawthorn (2018) lays out the conditions 

for fires following an earthquake in terms of the simultaneous 

correlated effects of a large earthquake: numerous ignitions, 
Chapter T. Lifeline Infrastructure and Collocation Exposure 13

degraded building fire-resistive features, overwhelmed and 

potentially damaged communication and transportation 

routes, low or no water pressure in water-supply mains for 

fire response, multiple fires beyond the capacity of local fire 

companies to respond to in a timely manner, and the effects 

of wind on spreading fires. These factors, combined with an 

aging and (or) wooden urban and suburban building stock, 

create the potential for costly fire following earthquake events 

and are acknowledged as a vulnerability in the San Francisco 

Bay region.

"

"

"

"

"

" "

"

"

"

"

"

Stockton

Fremont

Modesto

Oakland

Alameda Livermore SAN FRANCISCO

Richmond

Palo Alto

SANTA

CLARA

SANTA

CRUZ

SAN

MATEO

MARIN

ALAMEDA

CONTRA

COSTA

SONOMA SOLANO

NAPA

MONTEREY

SAN

BENITO

MERCED

SAN

JOAQUIN

SACRAMENTO

YOLO

MENDOCINO

LAKE

COLUSA

SUTTER

PLACER

EL

DORADO

AMADOR

CALAVERAS

San Francisco Bay

San Pablo

Bay

San Jose

Sacramento

Hayward

Foster City

STANISLAUS P A C I F I C O C E A N

Study area

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

0 20 MILES

0 20 KILOMETERS 10

10

Map CALIF.

area

123° 122° 121°

39°

38°

37°

Liquefaction intensity

Low

Moderate

High

MW 7.0 epicenter

Hayward Fault

EXPLANATION

Figure 5. Map of the San Francisco Bay region, California, showing the liquefaction probability modeling extent and classification for 

the HayWired earthquake scenario mainshock on the Hayward Fault. Liquefaction probability is highest in the areas adjacent to San 

Francisco Bay and the creeks feeding into the bay. This map combines two methods of assessment, which are further described in 

appendix 1. Mw, moment magnitude.
14 The HayWired Earthquake Scenario—Societal Consequences

"

"

"

"

"

" "

"

"

"

"

"

Stockton

Modesto

Oakland

Livermore SAN FRANCISCO Alameda

Richmond

Palo Alto

CLARA

SANTA

SANTA

CRUZ

SAN

MARIN

MATEO

ALAMEDA

CONTRA

COSTA

SOLANO

SONOMA

NAPA

MONTEREY

SAN

MERCED

BENITO

SAN JOAQUIN

SACRAMENTO

YOLO

MENDOCINO

LAKE

COLUSA

SUTTER

PLACER

EL

DORADO

AMADOR

CALAVERAS

San Francisco Bay

San Pablo

Bay

San Jose

Sacramento

Hayward

Foster City

TUOLUMNE

Fremont

STANISLAUS

Study area

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

P A C I F I C O C E A N

0

0

10 20 MILES

10 20 KILOMETERS

Map CALIF.

area

123° 122° 121°

39°

38°

37° EXPLANATION

Landslide intensity

Low

Moderate

High

Hayward Fault

MW 7.0 epicenter

Figure 6. Map of the San Francisco Bay region, California, showing the landslide probability modeling extent and classification for 

the HayWired earthquake scenario mainshock on the Hayward Fault. Landslide probability is highest in the areas surrounding the fault 

rupture as well as on the northwest side of San Francisco Bay and the southwest areas of Santa Cruz County. Mw, moment magnitude.
Chapter T. Lifeline Infrastructure and Collocation Exposure 15

Scawthorn (2018) simulates a fire following earthquake 

scenario for the HayWired mainshock to estimate the square 

footage of building stock burned in a series of Voronoi areas1

of varying sizes, each served by one fire station. The results 

are mapped by Voronoi area for the region (Scawthorn, 

2019). We convert the burned area to a density of burned 

buildings, that is, the square footage of burned buildings in 

each Voronoi area divided by the area containing the buildings 

(approximated using developed area). The developed area 

is taken from the 2011 National Land Cover Database 

(Homer and others, 2015) and includes low-, medium-, 

and high-intensity developed areas (“intensity” here refers 

to the amount of impervious surface in a pixel, where a 

more impervious surface corresponds to a higher intensity). 

Restricting the analysis to only developed areas refines the 

geographical distribution of fire affecting buildings. This 

assumes that burned buildings are in areas classified as 

developed in the National Land Cover Database. Buildings in 

very remote areas may be missed, but the biggest concern for 

fire following an earthquake are conflagrations when the fire 

spreads faster than it can be contained and blocks of buildings 

in developed areas of the National Land Cover Database are 

burned. The final burned-building density extent covers a 

1,220 km2 area.

Fire burns buildings and restricts access in surrounding 

areas. The fire density calculation is burned-building square 

footage divided by the area in which the buildings are located 

for each Voronoi area. The low-, moderate-, and high-intensity 

threshold classifications were selected by using the GIS to 

identify natural breaks in the data. The burned building density 

classifications are detailed in table 6 and mapped in figure  7. In 

contrast to the liquefaction and landslide data, the fire density 

Table 6. Fire-following-earthquake hazard intensity 

classification for the HayWired earthquake scenario mainshock in 

the San Francisco Bay region, California.

[Fire density refers to burned-building square footage (Scawthorn, 2018) 

relative to the developed area containing the fires for each Voronoi area. 

Thresholds are designed for classification and discussion purposes only and 

not intended to provide guidance about potential magnitude of impacts on 

lifeline infrastructure. km2, square kilometers; %, percent]

Intensity 

classification Fire density (%) Estimated geographic extent (km2)

Low >0 to <0.85 994

Moderate 0.85 to <2.3 159

High ≥2.3 67

breaks are low because the burned-building square footage 

covers only a small fraction of the total developed land area 

within a Voronoi area.

Individual Lifeline Exposure to Individual 

Hazards

Exposure analyses of a single lifeline infrastructure 

system and a single hazard is a straightforward GIS analysis, 

given the spatial data for lifeline facilities and conveyances 

(table 7) and the hazard maps. International air and seaports 

were examined visually in GIS with hazard data overlain 

on satellite imagery to estimate exposure to these systems. 

Key findings for the exposure of each lifeline infrastructure 

to each HayWired mainshock hazard in the San Francisco 

Bay region follow. The findings are derived from summary 

tables of hazard exposure in appendix 2, with one table for 

numbers of lifeline infrastructure fault crossings and lengths 

in the fault uncertainty buffer and a series of tables for each 

lifeline infrastructure category summarizing exposure to 

the other hazards. Additional detail from these analyses, 

including exposure for specific subcategories of these lifeline 

infrastructure types, can be found in Jones (2019).

Table 7. Lifeline infrastructure types in California’s San Francisco Bay region, scales of analysis, and data sources.

[“All hazards” includes shaking, surface slip, landslide, liquefaction, and fire. HSIP, Homeland Security Infrastructure Program; BART, Bay Area Rapid Transit; 

Caltrans, California Department of Transportation; HIFLD, Homeland Infrastructure Foundation-Level Data; EPA FRS, U.S. Environmental Protection Agency 

Facility Registry Service; POL, petroleum, oil, and lubricants; AM, amplitude modulation; FM, frequency modulation; NTSC, National Television System 

Committee]

Infrastructure type Scale of analysis Data source Year Hazards of importance

Transportation

Roadways (highway, secondary, surface) Kilometers HSIP Gold 2013 Shaking1, surface slip, landslide, 

liquefaction

Railways Kilometers HSIP Gold 2013 All hazards

BART facilities Number of stations/yards HSIP Gold 2013 All hazards

Highway bridges Number of structures Caltrans 2015 Shaking, surface slip, landslide, 

liquefaction

Ports Number of docks/berths HSIP Gold 2013 All hazards

1

 A Voronoi area is a polygon whose sides are equidistant between the point 

the Voronoi area is centered around and any neighboring points—a line is 

drawn between two points, the midpoint of that line is found, a perpendicular 

line is drawn through that midpoint, and this is repeated for all neighboring 

points to complete a Voronoi polygon for a specific point.
16 The HayWired Earthquake Scenario—Societal Consequences

Table 7.—Continued

Infrastructure type Scale of analysis Data source Year Hazards of importance

Water supply and wastewater

Transmission conveyance systems Kilometers Various2 2015 Surface slip, landslide, liquefaction

Dams Number of structures HSIP Gold 2013 All hazards

Drinking-water sources Number of sources HIFLD 2015 All hazards

Drinking-water treatment plants Number of plants HIFLD 2015 All hazards

Wastewater treatment plants Number of plants EPA FRS 2016 All hazards

Oil and gas

Pipelines (natural gas, petroleum) Kilometers HSIP Gold 2013 Surface slip, landslide, liquefaction

Natural gas compressor stations Number of stations HSIP Gold 2013 All hazards

Natural gas processing plants Number of plants HSIP Gold 2013 All hazards

Natural gas receipt and delivery points Number of points HSIP Gold 2013 All hazards

Natural gas storage facilities Number of facilities HSIP Gold 2013 All hazards

POL terminals, storage facilities, and 

tank farms

Number of facilities HSIP Gold 2013 All hazards

Oil refineries Number of facilities HSIP Gold 2013 All hazards

Electric power

Transmission lines Kilometers HSIP Gold 2013 Surface offset, landslide, liquefaction, 

fire3

Electric power generation plants Number of facilities HSIP Gold 2013 All hazards

Energy distribution control facilities Number of facilities HSIP Gold 2013 All hazards

Substations Number of facilities HSIP Gold 2013 All hazards

Telecommunications

Fiber routes (long-haul, interoffice) Kilometers GeoTel 2018 Surface slip, landslide, liquefaction, fire3

Cellular sites (macro and small cells) Number of units GeoTel 2018 All hazards

Fiber-lit buildings (data center, central 

office, point of presence)

Number of buildings GeoTel 2018 All hazards

Wireless switch offices Number of offices GeoTel 2018 All hazards

Internet exchange points Number of points HSIP Gold 2013 All hazards

Microwave towers Number of towers HSIP Gold 2013 All hazards

AM radio antennas Number of units HSIP Gold 2013 All hazards

FM radio antennas Number of units HSIP Gold 2013 All hazards

NTSC television transmitters Number of units HSIP Gold 2013 All hazards

Digital television transmitters Number of units HSIP Gold 2013 All hazards

 1Shaking is an indirect impact on surface streets owing to the potential for debris to fall on streets.

2

Water transmission conveyance systems are a combination of named aqueducts, canals, and tunnels in the U.S. Geological Survey National Hydrography 

Dataset (2013 version) and supplementation from digital maps available from the California Department of Water Resources and the Metropolitan Water District 

of Southern California. See Jones (2019) for more information.

3

Fire is a potential issue for aerial lines; available geographic information system data do not specify whether the lines are subterranean or aerial, so we do not 

distinguish between them here.
Chapter T. Lifeline Infrastructure and Collocation Exposure 17

"

"

"

"

"

" "

"

"

"

"

"

Stockton

Fremont

Modesto

Oakland

Alameda Livermore SAN FRANCISCO

Richmond

Palo Alto

SANTA

CLARA

SANTA

CRUZ

SAN

MATEO

MARIN

ALAMEDA

CONTRA

COSTA

SOLANO

SONOMA

NAPA

MONTEREY

SAN

BENITO

MERCED

SAN JOAQUIN

SACRAMENTO

YOLO

MENDOCINO

LAKE

COLUSA

SUTTER

PLACER

EL

DORADO

AMADOR

CALAVERAS

TUOLUMNE

San Francisco Bay

San Pablo

Bay

San Jose

Sacramento

Hayward

Foster City

STANISLAUS P A C I F I C O C E A N

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

0 20 MILES

0 10 20 KILOMETERS

10

Map CALIF.

area

Study area

123° 122° 121°

39°

38°

37°

Fire following earthquake intensity

Low

Moderate

High

MW 7.0 epicenter

Hayward Fault 

EXPLANATION

Figure 7. Map of the San Francisco Bay region, California, showing the fire-following-earthquake modeling extent and classification for 

the HayWired earthquake scenario mainshock on the Hayward Fault. The scale of analysis is determined by the location of each fire station. 

Analysis boundaries are drawn based on the closest distance to each fire station. Low, moderate, and high classifications are based on 

the percentage of developed land that is estimated to be burned. Areas considered undeveloped based on the 2011 National Land Cover 

Database were not considered in this analysis. Mw, moment magnitude.
18 The HayWired Earthquake Scenario—Societal Consequences

Transportation Infrastructure

Referring to Applied Technology Council (2016), 

the transportation system is composed of several types of 

infrastructure, including roadways (highways, secondary 

roads, and surface streets), bridges, railways (including local 

and regional commuter and freight systems), and ports (air 

and sea based). Whereas some redundancies are built into the 

highway transportation network (especially in urban areas), 

damage to highways, major arterials, and bridges (commonly 

the weakest links in the system) can cause significant impacts 

to traffic in the region (especially in rural areas where there 

is less redundancy in the system). The railway network has 

similar susceptibilities, but fewer redundancies than the 

highway network, such that a few failures can have major 

impacts on rail transportation. Airport structures (for example, 

terminals or control towers) may experience damage as a 

result of most hazards, whereas runways experience similar 

damage as other roadways; takeoff and landing safety 

concerns mean that runways are more difficult to repair 

than other infrastructure (Applied Technology Council, 

2016). Seaports serve various types of vessels that rely on 

waterfront structures (for example, wharves or piers), cargo 

handling equipment (for example, cranes), cargo storage 

facilities (for example, warehouses), control centers, and 

fuel facilities to run, but damage to one port can cause major 

delays and economic impacts on the port and its environs. The 

transportation system is multimodal—if one part of the system 

is impacted, another part of the system can pick up the slack. 

However, all parts of the system need to be working correctly 

for the multimodal component to be optimally efficient 

(Applied Technology Council, 2016).

Earthquakes have the potential to damage most 

components of the transportation system, with the exception of 

air and water vessels not on land and land vehicles on at-grade 

roadways. Minor earthquakes that generate shaking below 

system design levels do not generally have extensive impacts 

on the transportation system—some structures, roadways, 

and bridges may experience minor damage requiring simple 

repairs, which are usually completed within days of the 

earthquake. Major earthquakes produce shaking at or greater 

than design specifications for the system, however, and may 

severely damage bridges, seaport facilities, airport facilities, 

and railways (damaged rail alignment). Damages are not 

limited to ground shaking: airport runways and seaport 

structures may be impacted by liquefaction and lateral spread 

(Applied Technology Council, 2016). High-intensity heat from 

fire (which may follow earthquakes) may warp railway lines 

via thermal expansion or ignite any wooden ties. Moreover, 

flames can extend into roads and railways and flammable trees 

can fall into transportation corridors (Radke and others, 2018).

For the HayWired scenario, we find:

• Roadways cross the Hayward Fault rupture zone more 

than any other lifeline infrastructure, with highways 

crossing 37 times, secondary roads crossing 127 

times, and surface streets crossing 424 times. Whereas 

roadways have the most fault crossings, some of 

these crossings are points where a single road crosses 

multiple times (for example, part of Interstate 580 

[I–580], which runs parallel and adjacent to the fault). 

The cities of Fremont and Oakland have the most road 

segments crossing the fault rupture zone; Fremont 

roadways could be impacted by afterslip whereas 

Oakland roadways could be affected by coseismic slip 

and afterslip.

• Highways crossing the fault with estimated measurable 

coseismic slip include: Interstate 80 (I–80) through 

San Pablo, I–580 in Oakland, California Routes 13 

and 24 in Oakland, and the Richmond Parkway, which 

connects I–580 and I–80. Coseismic slip and afterslip 

could occur multiple times along the California Route 

13 freeway, as large lengths of the freeway run parallel 

and adjacent to the fault rupture, similar to I–580.

• Shaking damage to structures along roads may dump 

debris on the streets and (or) be closed for safety 

reasons; around 4,100 km of surface roads are in 

developed parts of high-intensity shaking areas.

• Owing to the concentration of BART infrastructure 

in the east bay, 13 percent (6) of BART facilities are 

exposed to high shaking intensity compared to 

 8 percent (292) of highway bridges in the region.

• Three to five percent of the region’s highways (nearly 

180 km; 4 percent), bridges (around 170; 5 percent), 

and railways (about 90 km; 3 percent) are found in 

high liquefaction intensity areas. Other roadways and 

BART facilities are less exposed (percentage-wise) to 

high liquefaction probability.

• About 325 km of roadways go through high landslide 

intensity areas and where there is less redundancy 

(landslides are more common in hilly areas, where 

road coverage is more sparse) and road repair may be 

more challenging. About 82 bridges are exposed to 

moderate-intensity landslide hazards, whereas 20 are 

exposed to high-intensity landslide hazards.

• Since the HayWired fire following earthquake analysis 

is focused in urban areas, BART facilities (which 

service urban areas) are relatively more exposed to fire 

than other transportation infrastructure.

• The three major San Francisco Bay region airports 

have terminals, gates, runways, and other structures 

used for airport operations (for example, hangars) 

exposed to high liquefaction hazards, particularly 

at the Oakland International Airport (including the 

majority of runways there). The San Jose International 

Airport has some operations structures that are 

impacted by high ground-shaking intensity. Travis 

Air Force Base in Solano County is exposed to lowto-moderate shaking from the mainshock but not to 
Chapter T. Lifeline Infrastructure and Collocation Exposure 19

any of the other earthquake hazards. Moffett Federal 

Airfield in Santa Clara County is exposed to moderate 

to high shaking from the mainshock (a small part of 

the northernmost part of the Moffett airstrip has high 

liquefaction potential) and would be in moderate or 

high shaking intensity areas for four or five aftershocks 

of magnitude 5 or greater.

• Most of the Oakland seaport and the southern half 

of the San Francisco seaport is exposed to high 

liquefaction. Six percent of seaport docks and (or) 

berths are in areas with high liquefaction intensity, 

and three percent of docks and (or) berths are in high 

shaking intensity areas.

Water Supply and Wastewater Infrastructure

Referring to Applied Technology Council (2016), water 

infrastructure is a complex series of systems, each comprising 

several subsystems. Water supply systems include the raw 

water supply (for example, aqueducts or wells), treatment 

(for example, plants), transmission (for example, systems 

that move raw or treated water between treatment plants and 

either the raw water source or the treated-water distribution 

plant), and distribution (for example, local water pipelines) 

systems. Wastewater systems include collection (for example, 

local sanitation pipelines), conveyance (for example, sewer 

systems), treatment (for example, plants), and discharge 

and (or) disposal (for example, bay drainage lines) systems. 

Stormwater and flood-control systems are a third component, 

devoted to collection, diversion, storage, and release of 

storm and flood runoff. Water delivery, if interrupted, 

adversely affects water and wastewater systems, public 

health, and firefighting capabilities. Stormwater systems rely 

on wastewater systems for drainage functionality, whereas 

wastewater systems can provide an alternative water source 

for nonpotable purposes (including firefighting) (Applied 

Technology Council, 2016).

Water system pipelines have no seismic design standard; 

leaks and breaks occur from shaking and ground failure 

(Porter, 2018). Dam failures may occur as a result of ground 

shaking caused by an earthquake, and the resulting flooding 

may impact numerous people in downstream communities. 

Water loss immediately after an earthquake (caused by 

ground shaking, liquefaction, or lateral spreading) can 

potentially lead to the spread of fires because firefighters 

may not have access to sufficient water to put out the fires. 

Sewer systems can also be damaged by liquefaction, which 

may then lead to contamination of potable water systems. 

Treatment plants could even be damaged by excess amounts 

of foreign materials (for example, sand or silt disturbed 

by liquefaction) introduced into plant filters (Applied 

Technology Council, 2016).

For the HayWired scenario, we find:

• Two major water-transmission conveyances (the San 

Pablo Tunnel and the Mokelumne Aqueduct) cross the 

fault rupture zone in three places where measurable 

coseismic slip occurs. The San Pablo Tunnel crosses 

the Hayward Fault at a location where two parallel 

fault segments are present.

• Ten percent of the dams in the study area are exposed 

to high shaking intensity.

• Wastewater treatment plants are the most exposed (of 

all water infrastructure) to high liquefaction intensity 

(9 percent of plants), followed by dams (5 percent 

 of dams).

• Drinking-water sources and drinking water treatment 

plants are generally located outside of high-intensity 

hazard areas. However, around 2 percent of these 

facilities are in high shaking intensity areas and 

1 percent or less of facilities are exposed to high 

liquefaction, landslide, or fire intensity.

Oil and Gas Infrastructure

Referring to Applied Technology Council (2016), oil 

and gas infrastructure moves and serves natural gas and 

liquid fuels. It includes transmission systems (for example, 

pipelines transporting unrefined products to processing, 

refining, and storage facilities) and distribution systems (for 

example, pipelines from distribution terminals that move 

refined products to households). Facilities like refineries and 

processing plants serve as nodes within and between the 

transmission and distribution systems (Applied Technology 

Council, 2016).

Ground deformation damages gas distribution systems, 

especially those with vulnerable lines. On the other hand, 

aboveground facilities and lines are more susceptible to 

ground shaking and acceleration (E. Hickey and G. Molnar, 

Pacific Gas and Electric Company, written commun., 2017). 

Pipelines are commonly buried and insulated but have 

aboveground appurtenances that are susceptible to exposure 

(Radke and others, 2018).

For the HayWired scenario, we find:

• Natural gas and petroleum transmission pipelines 

cross the Hayward Fault in locations with measurable 

coseismic slip 7 and 8 times, respectively, mostly in 

Contra Costa County (with one in Alameda County).

• One oil refinery out of five is in an area of highintensity ground shaking and another is in a highintensity liquefaction area.

• One third of petroleum, oil, and lubricant (POL) 

terminals, storage facilities, and tank farms are found 

in areas with high liquefaction intensity.
20 The HayWired Earthquake Scenario—Societal Consequences

• Fifteen percent of natural gas compressor stations are 

in areas with high shaking intensity, whereas nine 

percent are in high liquefaction intensity areas.

• No natural gas storage facilities are within highintensity ground shaking areas and no natural gas 

processing plants are in the study area.

Electric Power Infrastructure

Referring to Applied Technology Council (2016), the 

electric power infrastructure system is comprised of three 

main components. The first is the electric generation system 

(for example, power plants), the second is the electric 

transmission system (for example, high-voltage substations, 

towers, and lines), and the third is the electric distribution 

system (for example, distribution substations and low-voltage 

lines). Generation systems can either be self-contained 

(for example, a coal-fired power plant) or distributed (for 

example, a wind farm), which affects how a generation source 

is impacted after a hazard event. Substations work at multiple 

levels, in addition to serving as nodes in the transmission and 

distribution system to help reduce energy loss over distance. 

Some transmission substations reduce high voltages to 

subtransmission voltages for distribution substations and the 

distribution substations convert the voltage to full distribution 

voltages to serve individual customers.

Underground electric power distribution lines may 

be more susceptible to earthquake-induced ground failure 

(liquefaction and ground displacement), but aboveground 

lines are generally not directly at risk of earthquake damage. 

Instead, aboveground infrastructure is more commonly 

impacted by earthquake damage to supporting towers (for 

example, from landslides) or substations (for example, 

from ground shaking). Earthquake damage to distribution 

systems most commonly result from poor installation 

methods, vulnerabilities in surrounding entities (for example, 

nearby buildings), and aging system components (Applied 

Technology Council, 2016).

For the HayWired scenario, we find:

• Transmission lines cross the Hayward Fault 8 times 

in areas with measurable coseismic slip. The majority 

of these crossings are aboveground (and hence not as 

likely to be directly damaged); one of these crossings 

occurs on a subterranean transmission line that 

transects San Pablo Bay.

• Substations and electric power generation plants are 

exposed to high-intensity ground shaking (63 and 

18, respectively), high liquefaction probability (33 

and 12, respectively), and high fire density (7 and 

3, respectively). One substation is located in a high 

landslide probability area.

Telecommunications Infrastructure

Referring to Applied Technology Council (2016), 

the telecommunications infrastructure system is much 

more complex than other lifeline infrastructure systems. 

Whereas other systems tend to run in one major hierarchy 

(transmission to distribution), telecommunications has a 

number of subsystems running in parallel. Fiber infrastructure 

(for example, fiber optic cables and fiber-lit buildings 

[buildings with large-bandwidth connections to receive fiber 

optic cables for internet and (or) data service]) can coexist 

with cellular infrastructure (for example, cellular sites), 

and cellular infrastructure can be collocated with older 

telecommunications infrastructure (for example, 

AM and FM radio transmitters or microwave towers). This 

provides redundancy in the communications network but 

may also have cascading impacts if a facility that serves as 

a node for multiple subsystems (for example, both FM radio 

and cellular transmission) goes offline. Fiber optic cables may 

also be used by multiple carriers, so damage to one cable may 

disrupt service for several different providers simultaneously 

(Applied Technology Council, 2016).

Earthquake impacts on buried fiber cables are not well 

known; impacts likely depend on the amount of ground 

displacement (fault offset, liquefaction, and so on) and 

design (Wein, Witkowski, and others, in prep.; planned 

to be published as part of this volume). Aged cellular site 

infrastructure are more vulnerable to earthquakes and 

could disrupt cellular networks. For more information on 

the interrelated components of the telecommunications 

infrastructure system and how they are affected by 

earthquakes, please refer to Wein, Witkowski, and others (in 

prep.; planned to be published as part of this volume).

For the HayWired scenario, we find:

• 123 fiber optic routes cross the Hayward Fault (second 

only to the number of surface streets crossing the 

fault) in locations where measurable coseismic slip 

occurs. The majority of these crossings (102) are 

interoffice distribution fiber cables and the remainder 

(21) are long-haul transmission fiber cables. Similar 

to roadways, the crossings with measurable coseismic 

slip occur in the cities of Richmond and San Pablo 

in Contra Costa County and the cities of Hayward, 

Oakland, and Berkeley in Alameda County.

• Five cellular sites and eight point of presence fiber-lit 

buildings are located within the zone of uncertainty 

with measurable coseismic slip.

• Cellular sites (about 1,100) and fiber-lit buildings 

(more than 5,200) are the most exposed of all lifeline 

infrastructure to high shaking intensity (13 percent). 

Within fiber-lit buildings, data centers have slightly 

greater exposure to high shaking intensity at 15 percent. 

In addition, 5 to 6 percent of cellular sites and fiber-lit 

buildings are exposed to high liquefaction intensity.
Chapter T. Lifeline Infrastructure and Collocation Exposure 21

• In general, wireless switch offices are not very exposed 

to high-intensity hazards. Ground shaking is the only 

high-intensity hazard affecting these facilities 

 (6 percent of facilities).

• The majority of internet exchange points in the San 

Francisco Bay region are in the southern and peninsular 

areas of the region, but there are a small number of 

them. Whereas only three of these facilities are exposed 

to high shaking intensity, the percentage exposed is 

 27 percent.

• AM radio antennas are the most exposed facility to high 

liquefaction probability (14 percent of antennas) and 

 8 percent are in high shaking intensity areas.

• Microwave towers are exposed to high shaking intensity 

(8 percent), high liquefaction intensity (6 percent), and 

high landslide or fire intensity (1 percent or less each).

• Analog and digital television transmitters are not 

exposed to any high hazard intensities.

• Telecommunications infrastructure lifelines are 

the most affected by aftershocks, with 10 percent 

of microwave towers, 11 percent of cellular sites, 

17 percent of point of presence fiber-lit buildings, 

45 percent of internet exchange points, and 55 percent 

of data centers exposed to at least moderate shaking. 

In total, 108 and 226 data centers are exposed to at 

least moderate shaking during the first three months 

and between three months and one year after the 

HayWired scenario mainshock, respectively. This 

is consistent with the movement of the HayWired 

scenario aftershock sequence into the south bay—data 

centers and internet exchange points are located in 

greater abundance in the south bay than in other parts 

of the region.

Combinations of Hazards

The next level of detail identifies combinations of 

mainshock hazards affecting each lifeline conveyance or 

facility. Aftershocks are not included in the multi-hazard 

assessment of the mainshock because the exposure is additive 

over time and shown in the individual hazard exposure 

analyses in appendix 2. We describe the methods to estimate 

exposure of lifeline infrastructure to combinations of 

mainshock hazards, followed by results.

Methods

In GIS, the area for mainshock hazard combinations was 

calculated using all low, moderate, and high-intensity hazard 

areas. For each hazard combination, the extent of the area 

(in km2) that contained at least one hazard of high-intensity 

was calculated, as well as the percentage of the area where at 

least one hazard is high-intensity (table 8). Figure 8 highlights 

areas where one or more hazards are classified as highintensity. The GIS dataset is used to assess the exposure of 

lifeline infrastructure to combinations of hazards.

Table 8. Geographic extent of hazard combinations from the HayWired earthquake scenario mainshock in the San Francisco Bay 

region, California.

[The entire study region is exposed to ground shaking; therefore, all values in this table assume ground shaking as a hazard included in each value. For 

example, the geographic extent listed for landslide is the area exposed to landslide and ground shaking hazards. km2, square kilometers; %, percent]

Hazards in addition to ground 

shaking

Geographic extent for 

hazard combination1

 (km2)

Extent with at least one 

hazard at high-intensity 

(km2

)

Percentage of total hazard combination 

extent with at least one hazard at 

high-intensity (%) 

None 45,449 565 1

Surface slip2 <1 <1 100

Landslide 2,461 1,183 48

Liquefaction 2,034 295 14

Fire 245 24 10

Landslide and liquefaction 206 101 49

Liquefaction and fire 726 178 24

Liquefaction and surface slip2 <1 <1 100

Landslide and surface slip2 <1 <1 100

Fire and surface slip2 <1 <1 100

Landslide and fire 70 39 56

Three additional hazards 178 124 69

All four additional hazards <1 <1 100

1

Geographic extent for hazard combination includes all intensity levels for each hazard.

2

Surface slip includes parts of the Hayward Fault that have measurable coseismic slip as well as areas without measurable coseismic slip (which may 

experience afterslip).
22 The HayWired Earthquake Scenario—Societal Consequences

"

"

"

"

"

"

Alameda

Hayward

SAN FRANCISCO

Foster City

S a n F r a n c i s c o B a y

CONTRA COSTA

ALAMEDA

MARIN

SAN MATEO

San Pablo Bay

Richmond

Oakland

Fremont

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

10 MILES

0 5

0 5

10 KILOMETERS

High intensity multi-hazard exposure

One high-intensity hazard (hazard combinations)

Two high-intensity hazards

Three high-intensity hazards

Four high-intensity hazards

MW 7.0 epicenter

One high-intensity hazard (shaking only)

EXPLANATION

122°30' 122°20' 122°10' 122° 121°50'

38°

37°

50'

37°

40'

37°

30'

Map

area

CALIF.

Figure 8. Map of the San Francisco Bay region, California, showing areas exposed to one or more high-intensity HayWired earthquake 

scenario mainshock hazards. Areas in dark red or black indicate exposure to multiple high-intensity hazards and are mostly found in the 

Hayward, Richmond, and Oakland urban areas as well as many areas directly surrounding San Francisco Bay (for example, Foster City). 

Mw, moment magnitude.
Chapter T. Lifeline Infrastructure and Collocation Exposure 23

Results for Hazard Combinations

Table 8 shows that all of the approximately 50,000 km2

study area is affected by ground shaking. Much of the area 

(about 90 percent) is affected by ground shaking hazard 

alone; of this area, only 1 percent is affected by high-intensity 

shaking (as defined in table 8). In the rest of the study area, 

the most common additional hazards are landslides and 

liquefaction. A high-intensity hazard is most likely to occur in 

the shaking and landslide hazard combination (in 50 percent 

of the shaking-plus-landslide area). After ground shaking, slip 

hazards most commonly occur with landslide hazards because 

parts of the Hayward Fault run along the base of the east bay 

foothills, where liquefaction and fire are less common. One 

hundred percent of the hazards combined with surface slip 

involve at least one hazard of high-intensity because surface 

slip is a high-intensity hazard by definition. Aside from ground 

shaking, fire following earthquake hazards most commonly 

occur in areas with liquefaction hazards—liquefaction occurs 

mostly on flat land, as does development, which is where 

urban fires and liquefaction damage to the water supply (for 

firefighting) are modeled to coincide. Four hazards cooccur in 

178 km2 of area. There is a small area (0.2 km2) along the fault 

trace where all five hazards could coincide.

Results for Lifeline Infrastructure Exposure to 

Three or More Hazards

Because all the additional ground failure and fire hazards 

occur with ground shaking, lifeline infrastructure is exposed to 

a minimum of two mainshock hazards. Lifeline infrastructure 

exposure to two hazards is detailed in appendix 2 and the 

findings are captured by the single hazard assessment of 

ground failure and fire hazards. Table 9 isolates the exposure 

of lifeline infrastructure to three or more hazards. In this case, 

we find that lifeline infrastructure is most commonly exposed 

to the combination of ground shaking, liquefaction, and fire 

(consistent with the prevalence of this hazard combination in 

the study area), but many fewer include a high-intensity hazard 

in this combination. From table 9, infrastructure exposed to 

ground shaking, liquefaction, and fire includes:

• Thirteen BART facilities (approximately 28 percent 

of all facilities), but only one facility (2 percent of 

all facilities) is exposed to at least one high-intensity 

hazard.

• Eight internet exchange points (about 73 percent of 

all facilities), but only one facility (9 percent of all 

facilities) is exposed to at least one high-intensity 

hazard.

• More than 13,000 fiber-lit buildings (about 32 percent 

of all facilities), more than 3,000 (8 percent of all 

facilities) of which are exposed to at least one highintensity hazard.

• Around 300 data centers (67 percent of all facilities), 

of which close to 50 (10 percent of all facilities) are 

exposed to at least one high-intensity hazard.

The prevalent combination of hazards affecting electric 

power lines and dams is liquefaction, landslide, and ground 

shaking. Fifty-two percent (approximately 64 km) of electric 

power transmission lines in areas with liquefaction, landslide, 

and ground shaking are exposed to at least one high-intensity 

hazard. For 6 of the 7 dams that are exposed to the combination 

of ground shaking, landslide, and liquefaction, at least one 

hazard in the combination is of high-intensity. We note that 

the Cull Creek dam, a dam that is being managed for seismic 

instability (Alameda County Flood Control and Water 

Conservation District, 2017), is exposed to high shaking 

intensity, high liquefaction probability, as well as moderate 

landslide probability.

Bridges straddle various multi-hazard combinations. 

Notably, 357 bridges are exposed to four hazard combinations; 

39 percent of these bridges include at least one hazard of 

high-intensity. Nearly three quarters of cellular sites exposed to 

four hazard combinations are exposed to at least one hazard of 

high-intensity (284 of 384), as are about two thirds of fiber-lit 

buildings that are exposed to four hazards (1,386 of 2,168). 

Whereas the majority of these facilities are in Alameda County, 

some are located in Contra Costa and Santa Clara Counties.

All five hazards could affect three bridges in Alameda 

County and two cellular sites in Contra Costa County. 

Conveyances running through areas with all five hazards sum 

to 3 km of fiber optic cables (1 km of long-haul fiber, 2 km 

of interoffice fiber), less than 2 km each of secondary roads, 

surface streets, and highways (mostly in Alameda County), and 

a combined length of less than 1 km of natural gas pipelines, 

petroleum pipelines, railways, and transmission lines (found 

in either Alameda or Contra Costa County). Additional 

detail from this analysis, including exposure for specific 

subcategories of these lifeline infrastructure types, can be 

found in Jones (2019).

Exposure of Individual Lifeline 

Infrastructure to Multi-hazard 

Intensities

This section collapses the multiple mainshock hazards to 

one layer of multi-hazard intensities to simplify an analysis of 

collocated infrastructure in hazardous places for the HayWired 

scenario. The intermediary results of lifeline infrastructure 

exposed to multi-hazard intensities are related to the above 

results for the specific combinations of hazards. We describe 

GIS methods developed to identify collocated infrastructure. 

Furthermore, the points of collocated infrastructure among 

all infrastructure types are spatially aggregated to show larger 

areas containing a high density of collocated infrastructure. 

This analysis of lifeline infrastructure and hazards is 

constructed to frame lifeline infrastructure interaction 

examples in the next section.
24 The HayWired Earthquake Scenario—Societal Consequences , or five overlapping hazards resulting from the HayWired earthquake scenario mainshock in the San Francisco otal” column that is exposed The “1+ high” columns indicate the total amount of infrastructure from the corresponding “T The results in this table are exclusive and can be considered with the numbers in table 2.7 of appendix 2, which summarizes the exposure of lifeline AM, amplitude modulation; FM, frequency ransit; POL, petroleum, oil, and lubricants; T Area Rapid , Bay Amount of lifeline infrastructure exposed to three, four [In each column, the total is presented in kilometers (km) or number of facilities. infrastructure to shaking and coincidence with one additional hazard. km, kilometers; BART to at least one hazard with a high-intensity classification. elevision System Committee] able 9. TBay region, California. modulation; NTSC, National T Lifeline Study area total Ground shaking + landslide + liquefaction Ground shaking + liquefaction + fire Ground shaking + landslide + fire Ground shaking + surface slip1 + liquefaction Ground shaking + surface slip1 + landslide Ground shaking + surface slip1 + fire Four hazards Five hazards Total 1+ high Total 1+ high Total 1+ high Total 1+ high Total 1+ high Total 1+ high Total 1+ high Total 1+ high Transportation Roadways, highway (km) 4,624 72 37 562 193 63 36 0 0 <1 <1 <1 <1 143 103 1 1 Roadways, secondary (km) 16,353 166 112 2,147 451 130 68 <1 <1 1 1 1 1 558 359 1 1 Roadways, surface (km) 69,899 682 452 7,920 1,779 756 414 <1 <1 4 4 5 5 1,890 1,307 2 2 Railways (km) 2,726 29 18 259 64 18 16 0 0 <1 <1 <1 <1 121 80 <1 <1 BART facilities 47 1 1 13 1 0 0 0 0 0 0 0 0 11 5 0 0 Highway bridges 3,520 162 67 349 108 85 26 0 0 0 0 0 0 357 141 3 3

Ports 696 1 1 7 5 1 0 0 0 0 0 0 0 0 0 0 0

Water supply and wastewater

Transmission 

conveyance systems (km)

879 21 10 41 5 1 <1 0 0 <1 <1 <1 <1 6 2 0 0

Dams 137 7 6 1 0 2 1 0 0 0 0 0 0 0 0 0 0

Drinking water sources 3,648 6 5 173 31 0 0 0 0 0 0 0 0 33 9 0 0

Drinking water

treatment plants

1,857 0 0 19 6 5 0 0 0 0 0 0 0 8 5 0 0

Wastewater treatment 

plants

92 0 0 8 2 0 0 0 0 0 0 0 0 1 1 0 0

Oil and gas

Pipelines, natural gas 

(km)

7,674 50 34 564 112 24 21 0 0 <1 <1 <1 <1 115 92 <1 <1

Pipelines, petroleum 

(km)

2,336 29 20 216 64 17 14 0 0 <1 <1 <1 <1 27 17 <1 <1

Natural gas 

compressor stations

34 0 0 4 2 1 1 0 0 0 0 0 0 0 0 0 0
Chapter T. Lifeline Infrastructure and Collocation Exposure 25

Table 9.—Continued

Lifeline

Study 

area 

total

Ground shaking + 

landslide + 

liquefaction

Ground shaking 

+ liquefaction 

+ fire

Ground shaking 

+ landslide + fire

Ground shaking 

+ surface slip1 + 

liquefaction

Ground shaking 

+ surface slip1 + 

landslide

Ground shaking 

+ surface slip1

+ fire

Four hazards Five hazards

Total 1+ high Total 1+ high Total 1+ high Total 1+ high Total 1+ high Total 1+ high Total 1+ high Total 1+ high

Oil and gas—Continued

Natural gas processing 

plants

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Natural gas receipt and 

delivery points

2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Natural gas storage 

facilities

6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

POL terminals, storage 

facilities, and tank farms

36 0 0 8 5 0 0 0 0 0 0 0 0 0 0 0 0

Oil refineries 5 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0

Electric power

Transmission lines 

(km)

13,413 123 64 443 162 17 10 0 0 <1 <1 <1 <1 63 34 <1 <1

Electric power 

generation plants

258 1 1 36 11 2 1 0 0 0 0 0 0 3 1 0 0

Energy distribution 

control facilities

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Substations 1,007 4 2 97 33 7 4 0 0 0 0 0 0 11 6 0 0

Telecommunications

Fiber routes, long-haul 

(km)

6,049 123 76 765 255 255 87 0 0 1 1 <1 <1 618 403 1 1

Fiber routes, interoffice 

(km)

46,793 676 482 6,368 1,540 321 240 0 0 1 1 1 1 1,536 1,077 2 2

Cellular sites 8,311 93 59 1,670 470 52 35 0 0 0 0 0 0 384 284 2 2Fiber-lit buildings, data 

centers

428 0 0 286 47 0 0 0 0 0 0 0 0 1 1 0 0

Fiber-lit buildings, 

central offices

673 1 1 136 9 5 2 0 0 0 0 0 0 33 15 0 0

Fiber-lit buildings, 

point of presence

40,728 344 266 12,974 3,282 387 198 0 0 0 0 3 3 2,135 1,370 0 0

Wireless switch offices 53 0 0 11 0 0 0 0 0 0 0 0 0 1 1 0 0
26 The HayWired Earthquake Scenario—Societal Consequences Table 9.—Continued Lifeline Study area total Ground shaking + landslide + liquefaction Ground shaking + liquefaction + fire Ground shaking + landslide + fire Ground shaking + surface slip1 + liquefaction Ground shaking + surface slip1 + landslide Ground shaking + surface slip1 + fire Four hazards Five hazards Total 1+ high Total 1+ high Total 1+ high Total 1+ high Total 1+ high Total 1+ high Total 1+ high Total 1+ high Telecommunications—Continued Internet exchange points 11 0 0 8 1 0 0 0 0 0 0 0 0 0 0 0 0 Microwave towers 3,807 27 19 799 235 20 11 0 0 0 0 0 0 103 67 0 0 AM radio antennas 78 2 2 9 4 0 0 0 0 0 0 0 0 0 0 0 0 FM radio antennas 326 0 0 4 0 1 1 0 0 0 0 0 0 2 1 0 0 NTSC television transmitters 37 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Digital television transmitters 51 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1Surface slip includes parts of the Hayward Fault that have measurable coseismic slip as well as areas without measurable coseismic slip (which may experience afterslip).
Chapter T. Lifeline Infrastructure and Collocation Exposure 27

Multi-hazard Intensity Classification

The single hazard layers are the foundations of the multihazard layer. The classifications for the multi-hazard layer 

range from low to very high and are defined in table 10. For 

example, at a location, low multi-hazard intensity indicates 

that all individual hazards are either classified as low intensity 

or no hazard, whereas a multi-hazard intensity of very highindicates that at least two of the hazards are classified as highintensity. The resulting multi-hazard layer provides the basis 

for a multi-hazard lifeline infrastructure collocation analysis.

The multi-hazard intensity map was created using GIS 

tools. We combined all of the individual hazard intensity 

vector polygon datasets iteratively in Esri ArcGIS version 

10.5 into a single dataset that contains a hazard intensity class 

for each hazard. Combining the individual hazard polygon 

datasets into one allowed us to identify which hazards at each 

intensity are present at each location across the study area. The 

criteria in table 10 are used to assign the final multi-hazard 

intensity (for example, two or more individual high-intensity 

hazards present in a polygon is classified as very high multihazard intensity). Figure 9 illustrates the hazard combination 

process, showing five individual hazards schematically 

overlayed and combined into one multiple hazard dataset.

Table 10. Multi-hazard classifications used in this study for 

hazards resulting from the HayWired earthquake scenario 

mainshock in the San Francisco Bay region, California.

Classification Description

Low All hazards low or nonexistent

Moderate One hazard moderate, all others low or 

nonexistent

Moderate-high One hazard high, all others low or nonexistent, or

Two or more hazards moderate, all others low or 

nonexistent

High One hazard high, at least one hazard moderate

Very high Two or more hazards high

Multi-hazard intensity

Hazard 1

Hazard 2

Hazard 3

Hazard 4

Hazard 5

None

Hazard intensity

Low

Moderate

High

Low

Moderate

Moderate-high

High

Very high

Combined hazards

Individual hazard layers

EXPLANATION

EXPLANATION

Figure 9. Schematic illustration showing how overlapping hazards are merged into one multiple hazard output. All 

individual hazards are combined into one dataset; the number and intensity of all overlapping hazards in a specific 

location are assessed in order to assign final multi-hazard values for each part of each overlapping polygon. For 

example, a polygon where two hazards are classified moderate and one hazard is classified high is assigned a multihazard value of high (see table 10).
28 The HayWired Earthquake Scenario—Societal Consequences

Results for Individual Lifeline Infrastructure 

Exposure to Multi-hazard Intensities

Table 11 shows the exposure of individual lifelines to the 

reclassified multi-hazard dataset (shown in fig. 10). One can 

work back from the multi-hazard intensity to find the hazard 

combinations. Dams have the highest percentage in very 

high multi-hazard exposure areas (5 percent; 7dams). We 

know (from the “Individual Lifeline Exposure to Individual 

Hazards” section) that dams are subjected to high shaking 

intensity and high landslide and liquefaction probabilities 

and (or) fault rupture. POL terminals, storage facilities, and 

tank farms have the highest percentage (31 percent) exposed 

to high multi-hazard intensity—all occur in areas with 

high liquefaction intensity and moderate shaking intensity. 

One oil refinery (20 percent of these facilities) is exposed 

to high multi-hazard intensity owing to high liquefaction 

intensity and moderate shaking intensity. AM radio antennas 

have 14 percent (11 antennas) exposure to high multihazard intensity, owing to high liquefaction probability and 

moderate shaking. Data centers have the highest percentage 

of exposure to moderate-high multi-hazard intensity, with 47 

percent (202 facilities) in areas with either high or moderate 

shaking intensity and moderate liquefaction intensity (rarely 

accompanied by moderate fire intensity). Internet exchange 

points follow, with 45 percent (4 facilities) exposed to 

moderate-high multi-hazard intensity; the contributing 

moderate- and high-intensity hazards are shaking and 

liquefaction. Point-of-presence fiber-lit buildings are also 

commonly located in areas of moderate-high exposure—

nearly 12,000 buildings (29 percent) fall in this category 

owing to the same moderate and high-intensity hazards 

affecting data centers. Additional detail from this multi-hazard 

analysis, including exposure for specific subcategories of these 

lifeline infrastructure types, can be found in Jones (2019).

Table 11. Single-lifeline exposure to multi-hazard intensity distribution for the HayWired earthquake scenario mainshock in the San 

Francisco Bay region, California.

[Units are in number of facilities, except where noted to be in kilometers. Owing to rounding, the sum of individual multi-hazard intensity exposure values 

may not add up to the study area total. km, kilometers; BART, Bay Area Rapid Transit; POL, petroleum, oil, and lubricants; AM, amplitude modulation; FM, 

frequency modulation; NTSC, National Television System Committee]

Lifeline Study area 

total

Multi-hazard intensity classification

Low Moderate Moderate-high High Very high

Transportation

Roadways, highway (km) 4,624 1,883 1,775 572 342 51

Roadways, secondary (km) 16,353 7,394 5,973 1,948 877 162

Roadways, surface (km) 69,899 29,941 27,600 7,791 3,829 739

Railways (km) 2,726 859 1,323 325 178 42

BART facilities 47 7 14 19 5 2

Highway bridges 3,520 1,272 1,360 553 272 63

Ports 696 88 527 34 47 0

Lifeline exposure patterns across multi-hazard classes 

emerge. Generally, lifelines have more exposure to moderate 

multi-hazard exposure than low multi-hazard exposure (for 

example, water supply transmission conveyance systems and 

electric substations). Only a few systems have the majority 

of their multi-hazard exposure in low intensity areas (for 

example, roadways and transmission lines). Data centers 

and BART facilities are unique in that the majority of these 

facilities are exposed to moderate-high multi-hazard intensity. 

All lifelines have no more than one third of their assets 

exposed to high or very high multi-hazard intensity classes.

Collocated Lifeline Infrastructure

Whereas the physical proximity and collocation of 

lifeline infrastructure can provide for enhanced efficiencies, 

during an earthquake event collocation of infrastructure 

increases the potential for one to damage another, cascading 

failures, and complex interactions during restoration 

(Applied Technology Council, 2016). For example, 

transportation infrastructure can serve as corridors for 

buried infrastructure (such as pipes and conduits for water, 

wastewater, telecommunications, power, and natural gas) 

(Applied Technology Council, 2016). An earthquake could, 

for example, damage a bridge and as a result cut off any or all 

infrastructure routed along it.

This section describes the GIS method to identify and 

map the density of collocated infrastructure. The numbers of 

different types of collocated infrastructure are mapped against 

the multi-hazard layer. Maps show collocated infrastructure 

for (1) all infrastructure of different types, (2) transmission 

infrastructure, and (3) potentially critical infrastructure (more 

or less critical to society). The criticality of infrastructure to 

society is explained in more detail in this section.
Chapter T. Lifeline Infrastructure and Collocation Exposure 29

Table 11.—Continued

Lifeline Study area 

total

Multi-hazard intensity classification

Low Moderate Moderate-high High Very high

Water supply and wastewater

Transmission conveyance systems (km) 879 295 412 122 40 11

Dams 137 73 39 13 5 7

Drinking water sources 3,648 2,072 1,354 179 39 4

Drinking water treatment plants 1,857 1,173 593 59 26 6

Wastewater treatment plants 92 31 35 18 8 0

Oil and gas

Pipelines, natural gas (km) 7,674 3,147 3,483 745 247 51

Pipelines, petroleum (km) 2,336 550 1,217 382 158 29

Natural gas compressor stations 34 7 17 6 3 1

Natural gas processing plants 0 0 0 0 0 0

Natural gas receipt and delivery points 2 0 2 0 0 0

Natural gas storage facilities 6 2 4 0 0 0

POL terminals, storage facilities, tank farms 36 2 18 4 11 1

Oil refineries 5 0 3 1 1 0

Electric power

Transmission lines (km) 13,413 6,309 5,568 1,092 363 81

Electric power generation plants 258 92 108 36 19 3

Energy distribution control facilities 1 0 1 0 0 0

Substations 1,007 391 446 111 49 10

Telecommunications

Fiber routes, long- haul (km) 6,049 2,008 2,254 902 657 230

Fiber routes, interoffice (km) 46,793 12,245 22,029 7,924 3,603 992

Cellular sites 8,311 2,620 3,126 1,681 729 155

Fiber-lit buildings, data centers 428 16 180 202 30 0

Fiber-lit buildings, central offices 673 195 300 148 23 7

Fiber-lit buildings, point of presence 40,728 8,464 14,947 11,879 4,809 629

Wireless switch offices 53 9 32 11 1 0

Internet exchange points 11 0 6 5 0 0

Microwave towers 3,807 1,254 1,506 661 338 48

AM radio antennas 78 26 35 4 11 2

FM radio antennas 326 218 90 15 1 2

NTSC television transmitters 37 31 6 0 0 0

Digital television transmitters 51 20 31 0 0 0
30 The HayWired Earthquake Scenario—Societal Consequences

Method for Identifying Collocated Infrastructure

GIS was used to determine areas of intersecting 

infrastructure based on the lifeline data in table 7. To identify 

collocation, we began by creating polygons using a 5 m buffer 

applied to point and line infrastructure as a standardized 

distance to account for varying sizes of facilities and 

conveyances. Using ArcGIS v. 10.5, these lifeline polygons 

were combined into a single feature of multiple polygons 

using the merge and feature-to-polygon tools. By creating 

centroid points inside each polygon and running a spatial 

join with the lifeline polygons, we were able to determine 

the number of intersecting lifeline features, as well as 

which types of infrastructure were collocated. The resulting 

"

"

"

"

"

" "

"

"

"

"

"

Stockton

Fremont

Modesto

Oakland

SAN FRANCISCO Alameda Livermore

Richmond

Palo Alto

CLARA

SANTA

SANTA

CRUZ

SAN

MARIN

MATEO

ALAMEDA

CONTRA

SOLANO

COSTA

SONOMA

MONTEREY

NAPA

SAN

BENITO

MERCED

SAN JOAQUIN

SACRAMENTO

YOLO

MENDOCINO

LAKE

COLUSA

SUTTER

PLACER

EL DORADO

AMADOR

CALAVERAS

TUOLUMNE

San Francisco Bay

San

 Pablo

San Jose

 Bay

Sacramento

Hayward

Foster City

STANISLAUS

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

0

P A C I F I C O C E A N

0

10 20 MILES

10 20 KILOMETERS

Map CALIF.

area

123° 122° 121°

39°

38°

37°

Moderate

EXPLANATION

Multi-hazard exposure

Moderate-high

Low

High

Very high

Hayward Fault rupture

MW 7.0 epicenter

Study area

Figure 10. Map of the San Francisco Bay region, California, showing the composite multi-hazard exposure as a result of HayWired 

scenario mainshock hazards. For classifications of the multi-hazard layer, see table 10. Mw, moment magnitude.
Chapter T. Lifeline Infrastructure and Collocation Exposure 31

point dataset required the removal of single points with no 

intersections, as well as further reduction of points where 

duplicate collocations were indicated. Figure 11 illustrates 

the collocation identification process: figure 11A shows the 

5-m buffers for the lifelines, figure 11B presents the merged 

buffer dataset and the original collocation points created from 

the merged buffer dataset, and figure 11C includes the final 

collocation points (two lifelines or more at the location) and 

the original lifeline datasets.

Where applicable, multiple intersections along the same 

infrastructure assets were removed. This was most common 

for collocations involving surface streets and fiber routes 

running parallel and adjacent to each other owing to the larger 

lengths of these infrastructures. Surface streets also represent 

other distribution infrastructure because many household-level 

access systems run along or underneath many streets in urban 

areas. In some cases, multiple fiber routes serve one location 

(such as a large building). These and similar situations were 

removed from the analysis as much as possible to identify 

unique collocations among different infrastructure asset 

types. This also means that intersections within the same 

infrastructure network (such as surface street intersections) 

were not counted. Where more than one type of infrastructure 

intersected, a collocation point was identified. In the study 

region, we found more than 208,000 collocation points. The 

majority of these collocations (more than 132,000) contain 

two types of infrastructure, nearly 66,000 points had three 

types, and approximately 9,800 points had four to seven types 

of collocated infrastructure. In the next section, we show 

the collocation results for all infrastructure, transmission 

infrastructure, and potentially societally critical infrastructure 

with respect to the multi-hazard layer to highlight collocated 

infrastructure in hazardous places for the HayWired 

earthquake scenario.

Density of Collocated Infrastructure

To expand the view to concentrations of collocated 

infrastructure, we set up a 500 square meter (m2

) fishnet 

grid over the study area using GIS. Each individual point of 

collocation between two or more lifeline infrastructure assets 

was aggregated within each grid square. The resulting grid of 

collocation point counts serves as a density map of collocated 

infrastructure. The grid resolution is coarser than the hazard 

mapping, but it is used to find contiguous areas with dense 

#*

Buffered infrastructure

A

B

C

Electric transmission line

Surface street

Secondary road and underlying

inter-office fiber route

Wastewater treatment plant

Merged lifeline buffer

Collocation points

No lifelines

One lifeline

Two lifelines

Three lifelines

Four lifelines

EXPLANATION

EXPLANATION

EXPLANATION

Lifeline infrastructure

Electric transmission line

Surface street

Secondary road and 

underlying inter-office

fiber route

*# Wastewater treatment plant

Collocation points

Two lifelines

Three lifelines

Four lifelines

0 100 200 FEET

0 20 40 METERS

Figure 11. Simplified maps showing the steps of the process used 

to identify collocated lifeline infrastructure. A, Buffers of 5 meter 

width overlaid on top of each lifeline infrastructure system. 

B, Merged lifeline buffers and resulting polygon centroids used 

to determine final collocations. Note that some collocation points 

have been moved from their respective polygon centroids to 

appear within the bounds of this figure. C, Final collocation points 

(spatially joined to initial lifeline infrastructure buffers) and original 

infrastructure data.
32 The HayWired Earthquake Scenario—Societal Consequences

collocation of infrastructure that would require intense 

coordination during restoration and recovery.

Figure 12A maps the density of collocated infrastructure 

as the number of points in 500 m2 areas: orange represents 

26–50 points of collocated infrastructure and red represents 

greater than 50 points. Predictably, the hot spots are in the 

larger cities of San Francisco, Sacramento, and Oakland, as 

well as scattered throughout the south bay in Santa Clara and 

"

"

"

"

"

" "

"

"

"

"

"

Stockton

Fremont

Modesto

Oakland

Livermore

Alameda SAN FRANCISCO

Richmond

Palo Alto

SANTA

CLARA

SANTA

CRUZ

SAN

MATEO

MARIN

ALAMEDA

CONTRA

COSTA

SOLANO

NAPA

SONOMA

MONTEREY SAN

BENITO

MERCED

SAN

JOAQUIN

SACRAMENTO

YOLO

MENDOCINO

LAKE

COLUSA

SUTTER

PLACER

EL

DORADO

AMADOR

CALAVERAS

TUOLUMNE

San Francisco Bay

San Pablo

Bay

San Jose

Sacramento

Hayward

Foster City

A

STANISLAUS

P A C I F I C O C E A N

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

0 20 MILES

0 20 KILOMETERS 10

10

Map CALIF.

area

Study area

123° 122° 121°

39°

38°

37°

Major highway

Infrastructure density, in 

number of collocation 

points per 500 m2

0 to 2

3 to 25

26 to 50

>50

MW 7.0 epicenter

Hayward Fault

EXPLANATION

37

5

5

505

5

80

99

99

101

101

101

152

17

280

92

237

580

680

12

4

24

80

580

85

Map area

in B

Figure 12 (pages 32–33). Maps of the San Francisco Bay region, California, showing a density assessment of collocated infrastructure 

per 500 square meters (m2). Red areas highlight exposure exceeding 50 unique collocation points and orange areas show 26–50 unique 

collocation points. This map is independent of the HayWired earthquake scenario and shows only the density of collocated infrastructure 

points. A, Regional view of the density of unique collocated infrastructure locations throughout the study region. B, Zoomed-in view of the 

density of unique collocated infrastructure locations around the Hayward Fault. Mw, moment magnitude.
Chapter T. Lifeline Infrastructure and Collocation Exposure 33

San Mateo Counties. They also run along major highway 

corridors, such as U.S. Route 101 between San Jose and San 

Francisco and Interstate 580 in Alameda County between 

Hayward and Livermore. When we zoom in (fig. 12B), we 

see spots of dense infrastructure in smaller cities, including 

San Rafael in Marin County, Walnut Creek in Contra Costa 

County, and Redwood City in San Mateo County. These hubs 

of infrastructure in smaller cities coincide with moderate 

or moderate-high multi-hazard intensity for the HayWired 

scenario mainshock.

"

"

"

"

"

"

Alameda

Hayward

SAN FRANCISCO

Foster City

S a n F r a n c i s c o B a y

CONTRA 

 COSTA

ALAMEDA

MARIN

SAN MATEO

San Pablo Bay

Richmond

Oakland

Fremont

"

"

"

Redwood City

Walnut Creek

San Rafael

B 122°30' 122°20' 122°10' 122° 121°50'

38°

37°

50'

37°

40'

37°

30'

5 MILES

0 1 2 3 4

0 1 2 3 4

5 KILOMETERS

101

280

92

13

580

680

4

24

580

84

101

880

80

80

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

Major highway

Infrastructure density, in 

number of collocation 

points per 500 m2

0 to 2

3 to 25

26 to 50

>50

MW 7.0 epicenter

Hayward Fault

EXPLANATION

Figure 12 (pages 32–33).—Continued
34 The HayWired Earthquake Scenario—Societal Consequences

SAN FRANCISCO

SANTA

CLARA

SANTA

CRUZ

SAN

MATEO

MARIN

ALAMEDA

CONTRA

COSTA

SOLANO

SONOMA

NAPA

MONTEREY

SAN

BENITO

MERCED

SAN

JOAQUIN

SACRAMENTO

YOLO

MENDOCINO

LAKE

COLUSA

SUTTER

PLACER

EL

DORADO

AMADOR

CALAVERAS

TUOLUMNE

San Francisco Bay

San Pablo

Bay

A

P A C I F I C O C E A N

STANISLAUS

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

0 20 MILES

0 10 20 KILOMETERS

10

Map CALIF.

area

Study area

123° 122° 121°

39°

38°

37°

Multi-hazard exposure

Moderate

Moderate-high

High

Very high

Low

4 collocations

3 collocations

Collocated infrastructure

5 collocations

6 collocations

7 collocations

EXPLANATION

Figure 13 (pages 34–35). Maps of the San Francisco Bay region, California, showing collocated infrastructure points and exposure level 

to multiple HayWired earthquake scenario mainshock hazards (three or more collocated infrastructure assets only). The symbol shape 

corresponds to the number of infrastructure assets collocated at each point, ranging from 3 (smallest) to 7 (largest). Colors correspond 

to level of exposure, ranging from low (cyan) to very high (magenta). A, Each unique point with three or more collocated infrastructure 

assets and corresponding multi-hazard exposure level in the study region. B, Each unique point with four or more collocated 

infrastructure assets and corresponding multi-hazard exposure level in the study region.
Chapter T. Lifeline Infrastructure and Collocation Exposure 35

Exposure of Collocated Infrastructure to 

Multiple Hazards

Figure 13 shows infrastructure collocation points and 

their exposure to multiple hazards in the San Francisco Bay 

region for the HayWired scenario mainshock. The color 

indicates the level of exposure to all hazards (cyan is low 

exposure and magenta indicates very high exposure) and the 

symbol shape indicates the number of infrastructure assets 

collocated at each unique point. Because more than half 

of all collocations are between two lifeline infrastructures, 

we limit our visualization and discussion to collocations of 

three or more infrastructure types. This excludes the many 

collocations where a facility is close to the street (owing 

to the buffer used). Figure 13A displays unique locations 

where three or more infrastructure types are collocated and 

figure 13B shows only points where four or more collocated 

infrastructure types occur.

Figure 13 shows that many areas with high numbers of 

collocated infrastructure systems occur east of the Hayward 

SAN FRANCISCO

SANTA

CLARA

SANTA

CRUZ

SAN

MATEO

MARIN

ALAMEDA

CONTRA

COSTA

SOLANO

SONOMA

NAPA

MONTEREY SAN

BENITO

MERCED

SAN

JOAQUIN

SACRAMENTO

YOLO

MENDOCINO

LAKE

COLUSA

SUTTER

PLACER

EL

DORADO

AMADOR

CALAVERAS

TUOLUMNE

B

STANISLAUS San Francisco Bay

San Pablo

Bay

P A C I F I C O C E A N

0 20 MILES

0 20 KILOMETERS 10

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016 10

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

123° 122° 121°

39°

38°

37°

Study area

Multi-hazard exposure

Moderate

Moderate-high

High

Very high

Low

4 collocations

Collocated infrastructure

5 collocations

6 collocations

7 collocations

EXPLANATION

Figure 13 (pages 34–35).—Continued
36 The HayWired Earthquake Scenario—Societal Consequences

Fault (concentrated in Contra Costa County, as well as 

beyond the east bay foothills where multi-hazard exposure 

is low) and in San Francisco and, thus, have low exposure 

to multiple hazards caused by the HayWired scenario 

mainshock. However, collocated infrastructure is exposed 

to very high multi-hazard intensity along the Hayward Fault 

rupture and in developed areas of Alameda and western 

Contra Costa Counties. Collocated infrastructure is also 

exposed to high multi-hazard intensity in San Francisco, San 

Mateo, and Santa Clara Counties and along the southeastern 

edge of the Sacramento-San Joaquin Delta. The majority 

of these collocations highlight a pattern of conveyances—

surface streets, other roadways and railways, pipelines, 

transmission lines, fiber optic cables, and water supply 

transmission conveyances.

Underlying this picture of collocated infrastructure in 

hazardous areas is the unseen collocation of end-user utility 

distribution systems with surface streets. The exposure of 

surface streets to multi-hazard intensities is listed in table 

12 and as an underlying base dataset for highway bridge 

damage assessment results in appendix 5. By extracting 

collocations with surface streets out of the infrastructure 

collocation analysis (table 12), we found about 115,000 

collocation points involving surface streets, about 9,700 of 

which are in high and very high hazard intensity areas. More 

than 40 percent of the surface street collocations involve 

three or four types of infrastructure. The collocation in the 

high and very high-intensity areas involve other roadways, 

rails, and (or) fiber optic cables twice as much as pipelines 

and transmission lines. Additional detail from this analysis, 

including exposure for specific subcategories of these lifeline 

infrastructure types, can be found in Jones (2019).

Relative Importance of Lifeline Infrastructure

Within communities, collocation of surface streets 

and other distribution infrastructure will amount to a large 

number of repairs that may or may not be coordinated 

to reduce disruption, but the number overshadows the 

importance of the infrastructure in two senses: transmission 

infrastructure and societally critical infrastructure. Identifying 

transmission infrastructure is straightforward, but the 

concept of societal criticality of infrastructure is not well 

established (Theoharidou and others, 2009). We attempt to 

identify the criticality of infrastructure using attributes in the 

data. We then map collocated transmission and criticality of 

infrastructure with respect to the multi-hazard layer.

Classification of Criticality of Lifeline 

Infrastructure

Theoharidou and others (2009) discuss the criticality of 

infrastructure and compile various societal impact factors, 

including economic impact, interdependency with other 

infrastructure, public confidence, public order, continuity 

of government, safety, and defense. Impact criteria 

consider scope, severity, and timing of the factors. Such an 

assessment is beyond the scope of this analysis and would 

require in-depth knowledge of infrastructure in the region. 

At the asset level of infrastructure systems, we identified 

available attributes in infrastructure data that indicate societal 

criticality within the transmission systems in terms of 

potential economic impact inherent in capacity (for example, 

larger power generation facilities or number of licenses for 

microwave towers), service area (for example, broadcasting 

range), and safety (for example, seismic condition of dams or 

closeness of pipelines to buildings).

Many lifeline infrastructure systems had attributes 

included, which served as discrete classes with varying 

levels of societal criticality. For example, the AM and FM 

radio antenna datasets include a field that defines the station 

class, which indicates the broadcast reach. By referring to the 

metadata for the datasets (and filling in missing information 

from internet searches) we were able to break most of the 

lifeline data into some set of discrete classes. In the absence 

of class data for dams, microwave towers, and natural gas 

and petroleum pipelines, we used other societal impact 

indicators. Dams are classified into one of five classes, 

depending on their condition assessment as completed by the 

California Department of Water Resources Division of Dam 

Safety (California Department of Water Resources, 2017). 

Satisfactory and undefined conditions were treated as less 

societally critical and the remaining classes (unsatisfactory, 

poor, and fair) were considered more societally critical, 

owing to their increased likelihood of being damaged by 

an earthquake. Microwave towers were classified into two 

categories depending on the number of licensees for the 

tower; fewer than 10 was considered less societally critical 

and 10 or more was determined to be more societally critical. 

For natural gas and petroleum pipelines, we used a variant 

on the classification provided in Applied Technology Council 

(2016) to classify pipeline segments. We classified each as 

class 1, 2, or 3 based on the number of buildings within 220 

yards of a 220-yard pipeline segment (class 1 is fewer than 10 

buildings, class 2 is between 10 and 45 buildings, and class 

3 is more than 45 buildings). We limit our classes to three 

for pipelines, though a fourth class is also generally used 

(available GIS data were not sufficient to determine which 

pipelines were class 4). For this analysis, we generalized the 

pipeline classes further by assuming that classes 2 and 3 are 

more societally critical and class 1 is less so. For the lifeline 

infrastructure systems where discrete capacity data were 

the only practical attribute for defining criticality, Pareto’s 

principle (also known as the 80-20 rule; refer to Newman 

[2005] for more information) was used to identify the more 

societally critical facilities. We ranked each facility’s capacity 

as a proportion of the total capacity in the State from large 

to small; summing from the largest value down, the facilities 

whose cumulative proportion added up to the top 80 percent 

were considered most societally critical and the remainder 

classified as less so. Some datasets (noted by “N/A” in the 

“infrastructure class or attribute” column of table 13) did 

not have an attribute that could help discretize the data, so 

these were treated as equally important. Table 13 lists the 

importance assignment for each lifeline.
Chapter T. Lifeline Infrastructure and Collocation Exposure 37

lassifications for the HayWired Number of collocations of surface streets with other lifeline infrastructure, organized by exposure to multi-hazard intensity c Table 12.

earthquake scenario mainshock in the San Francisco Bay region, California.

Multi-hazard 

classification

Other 

transportation

Water supply and

 wastewater Oil and gas Electric power Telecommunications Two additional 

infrastructure 

assets

Three or more 

additional 

infrastructure 

assets Facilities Roads and rail Facilities Transmission conveyances Facilities Pipelines Facilities Transmission lines Facilities Fiber routes

Low 95 7,987 163 197 0 2,920 1 4,626 454 5,567 14,553 1,952

Moderate 115 9,039 101 457 1 4,075 0 4,676 649 7,431 17,603 2,526Moderatehigh 56 3,730 23 252 1 1,472 0 1,389 330 3,468 8,399 1,234

High 29 1,454 7 64 1 717 0 686 180 1,505 3,145 419

Very high 10 256 2 3 0 163 0 113 30 271 612 64

Total 305 22,466 296 973 3 9,347 1 11,490 1,643 18,242 44,312 6,195

ransportation; POL, petroleum, oil, and lubricants; 

Lifeline infrastructure types and assignment of societal criticality by infrastructure class or attribute.

T ransit; N/A, no applicable infrastructure subcategory; Caltrans, California Department of 

elevision System Committee] T

Area Rapid T T, Bay 

able 13.T

[BARAM, amplitude modulation; FM, frequency modulation; NTSC, National 

Infrastructure type Infrastructure class or attribute Criticality System type More Less Transmission Distribution

Transportation

Roadways Highways

X

X

Secondary roads

X

X

Surface streets

X

X

Railways Heavy rail

X

X

Light rail

X

X

BART facilities N/A

X

X

Highway bridges N/A

X

X

Ports Major ports (named by Caltrans1

) X

X

Other ports (not named by Caltrans1

) X

X

Water supply and wastewater

Transmission conveyance systems N/A

X

X

Dams Unsatisfactory condition2 X

X

Poor condition2 X

X

Fair condition2 X

X

Satisfactory condition2 X

X

Unrated condition2 X

X
38 The HayWired Earthquake Scenario—Societal Consequences Table 13.—Continued Infrastructure type Infrastructure class or attribute Criticality System type More Less Transmission Distribution Water supply and wastewater—Continued Drinking-water sources Permanent source X X Seasonal source X X Emergency source X X Other source X X Interim source X X Drinking-water treatment plants Community water plant X X Non-transient noncommunity water plant X X Transient noncommunity water plant X X Wastewater treatment plants N/A X X Oil and gas Pipelines (natural gas, petroleum) Class 3 X X Class 2 X X Class 1 X X Natural gas compressor stations N/A X X Natural gas processing plants N/A X X

Natural gas receipt and delivery points N/A X XNatural gas storage facilities Top 80 percent of statewide working capacity X X

Remaining statewide working capacity X X

POL terminals, storage facilities, tank farms N/A X XOil refineries Top 80 percent of statewide facility capacity X X

Remaining statewide facility capacity X X

Electric power

Transmission lines N/A X XElectric power generation plants Top 80 percent of statewide operating capacity X X

Remaining statewide operating capacity X X

Energy distribution control facilities N/A X XSubstations N/A X X

Telecommunications

Fiber routes Long-haul X X

Interoffice X X

Cellular sites Macro cell sites X X
Chapter T. Lifeline Infrastructure and Collocation Exposure 39

Table 13.—Continued

Infrastructure type Infrastructure class or attribute Criticality System type More Less Transmission Distribution

Telecommunications—Continued

Small cell sites X X

Fiber-lit buildings Data center X X

Central office X XCarrier hotel X XColocation X XPoint of presence or other X X

Wireless switch offices N/A X XInternet exchange points N/A X XMicrowave towers 10 or more licensees X X

Fewer than 10 licensees X X

AM radio antennas Class A X X

Class B X XClass C X XClass D X X

FM radio antennas Class B X X

Class B1 X XClass A X XClass D X XClass L1 X X

NTSC television transmitters CA (analog class A) service class X X

LD (digital low power3) service class X X

TX (analog auxiliary) service class X X

Digital television transmitters DT (digital full-power) service class X X

DC (digital class A) service class X X

DX (digital auxiliary) service class X X

.fcc.gov/ https://www

est Sacramento. , Richmond, San Francisco, Stockton, and W

ater Resources (2017) dam condition assessment. W

Though LD service class transmitters are primarily digital systems, these transmitters may still transmit analog signals at the time of this analysis (

As such, LD transmitters are included in the NTSC system.

Ports named by Caltrans include the Ports of Benicia, Oakland, Redwood City 1

Dam infrastructure subcategories obtained from California Department of 

). -television-lptv-service

23

consumers/guides/low-power
40 The HayWired Earthquake Scenario—Societal Consequences

Figure 14 shows the different perspectives of infrastructure 

collocation density (fig. 14A), and collocations among 

all types of infrastructure (transmission and distribution) 

(fig.  14B), transmission infrastructure (fig. 14C), and societally 

critical infrastructure as defined here (fig. 14D). The results 

offer windows into areas for further examination, which we 

demonstrate in the next section by broadening the consideration 

of collocation to other types of interactions. Please note that 

we do not highlight San Francisco or Fremont as examples. In 

the San Francisco case, there appears to be more potential for 

system redundancy in the roadways than at the San Francisco 

International Airport example. In the Fremont case, the area 

has the same types of collocations as the North Richmond and 

Hayward examples, so does not offer additional insight.

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"

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Alameda HAYWARD FAULT

Hayward

Oakland

Fremont

SAN FRANCISCO

Richmond

Foster City

ALAMEDA

CONTRA

COSTA

SAN

MATEO

MARIN

122°20' 122°

38°

37°

40'

North 

Richmond

Port of

Oakland

Hayward

San Francisco

International Airport

4 collocations

EXPLANATION

Collocated infrastructure

5 collocations

6 collocations

7 collocations

Infrastructure density, in 

number of collocation 

points per 500 m2

0 to 2

3 to 25

26 to 50

>50

MW 7.0 epicenter

Multi-hazard exposure

Moderate

Moderate-high

High

Very high

Low

Hayward Fault

0 5 10 MILES

0 5 10 KILOMETERS

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

A. Collocated infrastructure density B. Number of collocated infrastructure

C. Number of collocated transmission systems D. Number of collocated societally critical lifelines

Figure 14. Maps of the 

San Francisco Bay region, 

California, showing 

lifeline infrastructure 

collocation analyses. 

Each panel was used to 

select different examples 

of lifeline infrastructure 

interactions. Boxes 

outline four lifeline 

interaction examples that 

are discussed in the text 

and table 16. A, Density of 

all lifeline infrastructure 

collocations at a 500 

square meter scale. 

B, Collocations for all 

lifeline infrastructure 

systems, showing 

points with four or more 

collocated lifelines. 

C, Collocations for 

transmission systems, 

showing points with 

four or more collocated 

lifelines. D, Collocations 

for societally critical 

lifelines, showing 

points with four or more 

collocated lifelines. Mw, 

moment magnitude.
Chapter T. Lifeline Infrastructure and Collocation Exposure 41

To get a sense of the nature of collateral damage 

and (or) coordination involved around collocated lifeline 

infrastructure in high or very high hazard areas, we calculated 

the top ten types of collocated infrastructure for the three 

perspectives (table 14). Interoffice fiber optic cable and 

surface and secondary roadways are most commonly involved 

in lifeline collocations in high and very high hazard areas 

(9,000–10,000 for each infrastructure type, representing 8–9 

percent of their total collocations). Narrowing the focus to 

collocations among transmission infrastructure only in high 

and very high hazard areas, secondary roadways remain in 

the top ten (approximately 2,500 collocations), followed 

by other transportation (highways, bridges, and railways), 

electric transmission lines, natural gas and petroleum 

pipelines, and long-haul fiber optic cable. Comparing our 

societally critical infrastructure collocations with transmission 

infrastructure collocations, electric substations, water 

transmission conveyance systems, and macro cellular sites 

move into the top ten in place of highway bridges and less 

critical infrastructure (for example, secondary roads). As 

the importance of the infrastructure increases, the number 

of times infrastructure (of the same level of importance) is 

collocated in high and very high hazard areas is reduced from 

tens of thousands to thousands or hundreds of collocation 

sites. Societally critical infrastructure collocations with 

the highest percentage of occurrence in high and very high 

multi-hazard areas are natural gas and petroleum pipelines 

(15 and 20 percent, respectively). More broadly, long-haul 

fiber optic cable and heavy railway collocations also feature 

commonly in the most hazardous areas, likely reflecting that 

pipelines and long-haul fiber cables run along heavy railway 

corridors. Additional detail from these collocation analyses, 

including exposure for specific subcategories of these lifeline 

infrastructure types, can be found in Jones (2019).

Table 14. Top ten lifeline infrastructure types collocated in high and very high multi-hazard intensity areas for the HayWired 

earthquake scenario mainshock in the San Francisco Bay region, California.

[Collocations range from two to seven collocated lifeline infrastructure types. Top ten items for all lifeline collocations were identified by summing up all 

occurrences of collocation for each individual lifeline infrastructure type in high or very high multi-hazard intensity areas and selecting the ten infrastructure 

types with the most occurrences of collocation with other infrastructure. The percentage of high and very high collocations for each lifeline infrastructure type is 

calculated by dividing the number of high and very high collocations for each infrastructure type by the total number of collocations of the infrastructure type. 

%, percent; Caltrans, California Department of Transportation]

Lifeline infrastructure type

Number of high 

or very high 

hazard

 collocations

Percentage of 

high or very 

high hazard 

collocations

All lifeline collocations

Interoffice fiber routes 10,413 9

Surface streets 9,731 8

Secondary roads 9,284 8

Electric transmission lines 2,830 7

Class 1 natural gas pipelines 2,225 8

Highways 2,162 11

Heavy railways 1,745 12

Class 2 or 3 natural gas pipelines 1,456 10

Long-haul fiber routes 1,321 13

Class 1 petroleum pipelines 1,096 12

Transmission collocations

Secondary roads 2,541 10

Highways 1,354 12

Electric transmission lines 1,186 8

Class 1 natural gas pipelines 1,174 8

Heavy railways 1,026 12

Lifeline infrastructure type

Number of high 

or very high 

hazard

 collocations

Percentage of 

high or very 

high hazard 

collocations

Transmission collocations—Continued

Class 2 or 3 natural gas pipelines 830 11

Long-haul fiber routes 810 12

Class 1 petroleum pipelines 730 12

Class 2 or 3 petroleum pipelines 444 16

Caltrans highway bridges 325 9

Societally critical collocations

Electric transmission lines 431 9

Highways 420 10

Heavy railways 374 11

Long-haul fiber routes 304 12

Class 2 or 3 natural gas pipelines 214 15

Caltrans highway bridges 165 9

Class 2 or 3 petroleum pipelines 143 20

Electric substations 40 5

Water transmission conveyance 

systems

38 7

Macro cellular sites 32 7
42 The HayWired Earthquake Scenario—Societal Consequences

Lifeline Infrastructure Interactions

Up to this point, we have illustrated the complexity 

of multiple lifeline infrastructure systems and collocated 

infrastructure exposed to multiple HayWired scenario 

mainshock hazards. The challenges are further exacerbated 

by other interdependencies and interactions within and 

between lifeline infrastructure systems. Functional 

interactions occur when performance failures of one type of 

lifeline infrastructure may disrupt the function of another that 

depends on it or performs a similar function. For example, 

electric power and telecommunication infrastructure are 

prime examples of interconnected infrastructure (Applied 

Technology Council, 2016): telecommunications depend on 

a power source, fiber optic cables are strung on power poles, 

and electric power depends on communications to monitor 

systems. Also, a performance failure of one infrastructure 

system may transfer demand to a substitutable infrastructure 

(for example, roads could substitute for railways or vice 

versa and more fuel is needed during electric power outages 

to run generators).

Business-as-usual operations do not force coordination 

between different lifeline infrastructure agencies in ways 

that are needed after an earthquake or other disaster. 

Before an event, lifeline infrastructure emergency planners 

make assumptions about the function of other lifeline 

infrastructure systems for their respective response, recovery, 

and mitigation decisions (for example, Bay Area Urban 

Areas Security Initiative, 2013; The Lifelines Council, 

2014; Applied Technology Council, 2016). After an event, 

interactions and dependencies require coordination among 

lifeline infrastructure systems to prioritize the allocation 

of scarce resources and perform restorations and repairs. 

For example, after the 2010–11 Christchurch earthquakes, 

the Stronger Christchurch Infrastructure Repair Team 

(SCIRT) developed a software tool called the Forward 

Works Viewer to share restoration information and data 

among horizontal infrastructure (for example, roads and 

pipelines) organizations. By this means, SCIRT could 

identify opportunities for efficient restoration and issues, 

and notify others (for example, traffic circulation) about 

disruptions at particular locations (Stronger Christchurch 

Infrastructure Rebuild Team, 2017). In the San Francisco Bay 

region, workshops and exercises are currently being used to 

model a disaster environment and practice before disasters 

occur, including exercises with the California Earthquake 

Clearinghouse (http://californiaeqclearinghouse.org/). Tools 

to securely share information are also under development 

(for example, Bay Area Center for Regional Disaster 

Resiliency, n.d.).

A workshop after the Mw 6.0 South Napa earthquake 

in 2014 concluded that current communication constraints 

limit understanding beyond first-level interactions and 

there is a lack of available tools and risk-analysis methods 

that can aid decision making and planning around 

interdependent restoration challenges (Scalingi, 2015). 

Similarly, the Applied Technology Council (2016) highlights 

data and methodological challenges with visualization of 

interdependencies. These conclusions provided the impetus to 

further examine interaction among lifeline infrastructure with 

respect to the multiple HayWired scenario hazards.

Our approach was to interactively zoom in on areas 

characterized by collocated and dense infrastructure in 

high or very high multi-hazard intensity areas and interpret 

them for possible lifeline infrastructure system interactions. 

We performed the analyses within The Lifelines Council 

(2014) framework for different types of interdependencies 

and interactions. We also expanded on the interaction table 

in that report, using what we learned from other regional 

studies including Germeraad and others (2014), HayWired 

workshops, and other HayWired scenario analyses.

Interaction and Interdependencies Among 

Lifeline Infrastructure Systems

The San Francisco Bay region is noted for forwardthinking resilience planning and earthquake preparation and 

mitigation activities, which includes foundational thinking 

about interactions and interdependencies between lifeline 

infrastructure systems (for example, The Lifelines Council, 

2014). We adopt the five categories of lifeline infrastructure 

interdependencies identified in The Lifelines Council (2014). 

What follows are definitions of the categories and comparisons 

with examples offered in Germeraad and others (2014), 

during the Bay Area Center for Regional Disaster Resiliency 

(BACRDR) August 30, 2017, workshop on power and water 

lifelines (which used the HayWired scenario), conversations 

with transportation agencies (see appendixes 4 and 5), and 

HayWired scenario water supply and telecommunications 

analyses (Porter, 2018; Wein, Witkowski, and others, in prep. 

[planned to be published as part of this volume]).

1. General interaction between components of the same 

system.—All systems would have general interaction 

issues but some are more crucial issues for the system’s 

potential disruption and restoration (The Lifelines 

Council, 2014). For example:

•	 Among water systems, owing to the estimated 

8,000 water pipeline breaks or leaks in East Bay 

Municipal Utility District (EBMUD) and San 

Jose Water Company (SJWC) utilities in the 

HayWired scenario (Porter, 2018), there may not 

be enough materials for repairs. Water utilities 

are evaluating inventories and would work with 

the California Office of Emergency Services (Cal 

OES) to prioritize repairs (BACRDR power and 

water lifelines workshop, oral commun., 2017).

•	 The telecommunication industry expressed less 

concern about the availability of materials and 

more concern for shortfalls of trained labor to 

respond, restore, and repair lifelines after a large 

San Francisco Bay region earthquake (Wein, 
Chapter T. Lifeline Infrastructure and Collocation Exposure 43

Witkowski, and others, in prep.; planned to be 

published as part of this volume).

•	 In some instances, refineries rely on one kind 

of fuel to prepare others. For example, natural 

gas may be needed to enhance still gas fuel 

(gas used for power in the refinery) production 

(Schremp, 2015).

2. Functional interaction is disaster propagation and 

cascading interactions from one system to another owing 

to interdependence (The Lifelines Council, 2014).—For 

example:

•	 The operation of roads in daily operations 

requires electric power for traffic signals. 

The short-term and limited solution of traffic 

direction by local officials can help to mitigate 

this type of failure (Jones and others, 2008). 

Similarly, electric power is needed for lighting 

and environmental monitoring systems in some 

tunnels (for example, the Caldecott Tunnel in 

Oakland) (Diablo Magazine, 2013).

•	 BART needs running water for fire safety in 

tunnels (appendix 4).

•	 EBMUD depends on the Pacific Gas and Electric 

Company (PG&E) for power to pump water up 

into their gravity-fed systems. Communications 

between PG&E and EBMUD are ongoing 

to develop methods for understanding 

interdependencies and impact assessments 

between them (BACRDR power and water 

lifelines workshop, oral commun., 2017).

•	 Electric power plants in Santa Clara use recycled 

water from wastewater treatment plants (that 

depend on power to operate) (J. Wollbrinck, San 

Jose Water Company, oral commun., 2018).

•	 Water distribution pipeline leaks and breaks and 

power outages could impact otherwise functional 

hospitals (J. Wollbrinck, San Jose Water 

Company, oral commun., 2017).

•	 Water leaks near San Jose International Airport 

could affect the airport, even though it is 

expected to be the most operational of the three 

regional international airports immediately after 

the HayWired scenario (J. Wollbrinck, San Jose 

Water Company, oral commun., 2017).

•	 Telecommunications infrastructure depends 

strongly on electric power; dependence on water 

for cooling remains to be assessed for the region 

(Wein, Witkowski, and others, in prep.; planned 

to be published as part of this volume).

•	 Electric power is needed to pump fuel to the 

airplanes at San Francisco International Airport 

(BACRDR power and water lifelines workshop, 

oral commun., 2017).

•	 Whereas most refineries in California have 

cogeneration capability, other outside services 

are essential to sustain operations (for example, 

water is needed in the refineries for generating 

steam used during the oil-refining process) 

(Schremp, 2015).

3. Geographic proximity (or collocation) interaction 

represents physical disaster propagation among lifeline 

systems (The Lifelines Council, 2014).—For example:

•	 Damage to highway bridges can disrupt the use 

of underlying surface streets (appendix 5).

•	 Damage to highway bridges can cause collateral 

damage to collocated infrastructure, such as 

petroleum and natural gas pipelines and fiber 

optic cables (BACRDR power and water 

lifelines workshop, oral commun., 2017; Wein, 

Witkowski, and others, in prep. [planned to be 

published as part of this volume]).

•	 Water and (or) wastewater pipeline breaks can 

scour bridge abutments (appendix 5).

•	 Water and (or) wastewater pipeline breaks can 

cause sink holes that are capable of damaging 

collocated fuel pipelines and fiber optic cables 

(S. Terentieff, EBMUD, oral commun., 2017).

•	 Water and (or) wastewater pipeline breaks can 

cause collateral damage to collocated buried 

distribution infrastructure along surface streets 

(for example, pipelines for natural gas, copper 

wire, fiber optic cable, and electric power 

distribution lines) (The Lifelines Council, 2014).

4. Restoration interaction refers to various hindrances in 

the restoration and recovery stages.—For example:

•	 For the HayWired analysis of water supply 

restoration, Porter (2018) factored in 

dependencies on other infrastructure, such as 

electric power and resources, including labor, 

materials, and fuel.

•	 EBMUD learned the importance of sharing 

infrastructure information when the repair of a 

broken water line took a month because a fuel 

pipeline had to be repaired first (S. Terentieff, 

EBMUD, oral commun., 2017).

•	 If water intrudes into the Transbay Tube, 

BART needs electric power to pump water out 

(appendix 4).
44 The HayWired Earthquake Scenario—Societal Consequences

•	 Damages and repair of surface streets and (or) 

collocated distribution infrastructure disrupts 

traffic circulation (Stronger Christchurch 

Infrastructure Rebuild Team, 2017; The 

Lifelines Council, 2014).

•	 The newest bore of the Caldecott Tunnel is 

designated a “lifeline” structure and as such is 

designed to reopen to emergency responders 

within 72 hours of an event such as a major 

earthquake. Until the tunnel is reopened, 

however, alternate routes between Oakland and 

Orinda (in Contra Costa County) must be used 

(California Department of Transportation, 2013).

•	 Water supply (quality and quantity) and other 

resources are needed to accommodate emergency 

operations centers, responders, and restoration 

labor for all infrastructure (J. Wollbrinck, San 

Jose Water Company, oral commun., 2017; Wein, 

Witkowski, and others, in prep. [planned to be 

published as part of this volume]).

•	 Fire following an earthquake could warp 

railroad tracks, and flammable materials (for 

example, trees) could fall into roadways, 

hindering fuel transportation efforts (Radke and 

others, 2018).

5. Substitute interaction occurs when one system’s 

disruption influences dependencies on alternative 

systems.—For example:

•	 Inoperable BART transit rail stations and lines 

would cause commuting traffic to be rerouted 

onto highway and other roadway infrastructure, 

increasing congestion on roadways (M. Salmon, 

BART, oral commun., 2016).

•	 BART transit disruptions could be counteracted 

by increasing usage of telecommunications 

systems, but these systems may also be impacted 

(Wein, Witkowski, and others, in prep.; planned 

to be published as part of this volume).

•	 Within the telecommunication system, 

traditional landline, voice over Internet protocol 

(VoIP), and cellular voice are substitute 

technologies for voice calls. Other means of 

communication (for example, texting or social 

media) substitute for voice calls. Similar to 

transportation systems, loss of one system 

increases the potential to overload another 

(Wein, Witkowski, and others, in prep.; planned 

to be published as part of this volume).

•	 Fuels transportation, which generally relies on 

pipelines to move products, could be moved 

using alternate methods (road, rail, or marine 

systems) in cases where pipelines are damaged 

in areas with high demand for fuels (Schremp, 

2015). This assumes that alternate methods 

are themselves undamaged (or repaired) and 

repurposed quickly.

Cascading failures are a series of lifeline infrastructure 

interactions. For example:

•	 Power outages can cause communication outages 

that reduce the ability to report fires; transportation 

disruptions can affect fire truck access to fires; 

and water supply outages can reduce firefighting 

capability and result in fires following the earthquake 

to spread into conflagrations (Scawthorn, 2018).

•	 Electric power outages affect the pumping of water; 

water is needed by refineries at a time when more fuel 

is needed owing to power outages (BACRDR power 

and water lifelines workshop, oral commun., 2017).

•	 Dam and levee failures from earthquake hazards pose 

a flood threat to populated areas and can cause further 

damage to infrastructure (BACRDR power and water 

lifelines workshop, oral commun., 2017).

Table 15 adapts The Lifelines Council’s (2014) 

interaction table that is constructed to show lifeline 

interactions within the five categories of general, functional, 

collocation/geographic, substitution, and restoration 

interactions. With respect to functional dependencies, 

the table shows upstream and downstream dependencies 

(National Protection and Programs Directorate, 2017) 

by reading the table across by row and down by column, 

respectively. Our additions to The Lifelines Council (2014) 

interdependency table represent the above listed examples. 

These additional interactions pertain mostly to water and 

telecommunications infrastructure systems.

Illustration of Interactions Among Lifeline Infrastructure Systems in the HayWired Scenario

Our collocation analyses processed hundreds of 

thousands of infrastructure assets in a 51,000 km2 geographic 

area exposed to multiple hazards in the HayWired scenario. 

We zoomed in on some areas with collocated infrastructure 

in very high multi-hazard areas and (or) high single hazard 

areas where infrastructure is most dense. We interactively 

examined the layers of lifeline infrastructure and hazard 

data in GIS. We interpreted these areas for possible lifeline 

interactions within the framework of table 15. Table 16 

summarizes four example areas that illustrate lifeline 

interactions and describes how we found them, what 

HayWired hazards are present, what infrastructure is present, 

and what potential interactions are indicated.

Without an understanding of what the service areas and 

system redundancies in the San Francisco Bay region are, 

we cannot clearly see potential functional dependencies that 

extend beyond the immediate environs. In table 16, we note 

some potential functional interactions where lifelines are in 

close proximity, such as electric power for fiber-lit buildings in 

North Richmond and electric power for wastewater in the Port 
Chapter T. Lifeline Infrastructure and Collocation Exposure 45

forts: light blue represents limited interaction and dependency for 

, which is unique to 

ifelines Council, 2014).

. Red text is material added in this study

The table is designed to be read by row—for each lifeline infrastructure type, reading across a row indicates the dependency (if any) on the lifeline 

, San Francisco]

Matrix of potential lifeline infrastructure interdependencies for the San Francisco Bay region, California (modified from The L

s interaction and dependency for service delivery and restoration ef

, and dark blue represents significant interaction and dependency

ransit; SCADA, supervisory control and data acquisition; telecom, telecommunications; SF Area Rapid T T, Bay 

able 15.

[Colors shown in each cell represent the strength of the lifeline system’the lifeline system, medium blue represents moderate interaction and dependency

ired scenario and the San Francisco Bay region. 

Tthe HayWinfrastructure type indicated in that column. BAR

Regional 

roads City streets Electric power Natural gas and oil Telecom Dams and water Auxiliary water (SF only) Wastewater Transit/rail Port Airport Fuel

Regional 

roads General Restoration, Substitute Restoration Restoration Restoration Restoration Restoration Substitute Restoration Restoration

City 

streets

Collocation 

(high and med-high bridge damage closes underlying road), 

Substitute, Restoration

General

Functional 

(traffic lights), 

Collocation, Restoration

Collocation, Restoration

Collocation, Restoration

Collocation, Restoration

Collocation, Restoration

Collocation, Restoration

Collocation, Substitute, Restoration

Collocation, Restoration Restoration

Electric 

power

Restoration Collocation, Restoration General Collocation, Restoration Restoration Collocation, Restoration Collocation, Restoration Collocation, Restoration Collocation Restoration Restoration

Natural 

gas and 

oil

Collocation, Restoration

Functional, Collocation, Restoration

Collocation 

(with transmission), 

Substitute

General Restoration Collocation, Restoration Collocation, Restoration Collocation, Restoration Collocation Collocation Restoration Restoration

Telecom Collocation, Restoration Collocation, Restoration

Collocation 

(on power poles), 

Functional, Restoration

Collocation,Restoration

Substitution, General

Functional 

(cooling), 

Collocation, Restoration

Collocation, Restoration

Collocation, Restoration Collocation Restoration Restoration

Dams and

water

Collocation 

(pipeline break scours bridge foundations), 

Restoration

Collocation 

(distribution system), 

Restoration 

Functional 

(pumps), 

Restoration

Collocation, Restoration

Functional 

(for example, SCADA), 

Restoration

General Collocation Restoration

Auxiliary 

water 

(SF only) 

Restoration Functional, Restoration Restoration Restoration Functional, Restoration General Collocation, Restoration Restoration

Wastewater Restoration Collocation, Restoration Functional, Restoration Restoration Functional, Restoration General Collocation, Restoration Restoration
46 The HayWired Earthquake Scenario—Societal Consequences Table 15.—Continued Regional roads City streets Electric power Natural gas and oil Telecom Dams and water Auxiliary water (SF only) Wastewater Transit/rail Port Airport Fuel Transit/ rail Substitute, Restoration Functional, Substitute, Collocation, Restoration Functional, Restoration Restoration Functional (fire safety in BART tunnels), Collocation, Restoration Collocation, Restoration Collocation, Restoration Collocation, General Collocation, Restoration Functional, Restoration Port Restoration Collocation, Restoration Collocation, Restoration Collocation, Restoration Collocation, Restoration Collocation Collocation Collocation General Restoration Airport Restoration Functional, Restoration Restoration Functional, Restoration Restoration Collocation, Restoration General Functional, Restoration Fuel Restoration Restoration Functional, Restoration Restoration Restoration Restoration Restoration General Table 16. Summary of four representative examples of lifeline system interdependencies in the San Francisco Bay region, California. [The four areas in these examples are outlined in figure 14. BART, Bay Area Rapid Transit; m, meters; I, Interstate; CA, California Route]

Location method High-intensity hazards Infrastructure Interdependencies

North Richmond area, Contra Costa County

Collocated infrastructure (as 

many as 3 different types), including transmission in high and very high multihazard intensity area

High shaking intensity 

and high fire density with as much as 2 m surface rupture and potential afterslip

Petroleum and natural 

gas pipelines, railways, transmission lines, fiber optic cables, and surface streets

Collocation (parallel): (1) railway corridor with petroleum pipeline; (2) railway corridor 

with fiber optic cable and transmission line.

Collocation (intersection): transmission line crossing a natural gas pipeline.Collocation (hub): multiple (4–5) fiber optic cables, 5 petroleum pipelines, and 2 

natural gas pipelines.

Collocation (hub on fault): surface street crosses parallel fiber optic cable and railway.

Collocation (parallel across fault): (1) 3 petroleum pipelines and 2 natural gas pipelines; 

(2) railway and fiber optic cable.

Functional: power substations in the vicinity of a cluster of fiber-lit buildings and some 

cellular sites connected by fiber optic cables.

Restoration: response and restoration complicated and delayed by fire; 

telecommunications dependencies on electric power service possible if substation is affected by fire and backup power discharges; repair of possibly unknown collocated (parallel and intersecting) lifeline infrastructure will require coordination between systems.
Chapter T. Lifeline Infrastructure and Collocation Exposure 47

Table 16.—Continued

Location method High-intensity hazards Infrastructure Interdependencies

Hayward area, Alameda County

Collocated infrastructure (as 

many as 4 different types) including transmission infrastructure in very highintensity multi-hazard area

High shaking intensity, 

high fire density, moderate liquefaction probability, low landslide probability

Fiber optic cables, surface 

streets, highways (I–880, CA–92), and transmission lines

Collocation (intersection): fiber optic cable crossing surface streets and distribution 

infrastructure along these streets.

Collocation (parallel): several fiber optic cables and transmission lines.Substitution: limited by hazards affecting substitutes among roadways and possibly 

among different types of telecommunication (copper and fiber optic) cables.

Restoration: response and restoration complicated and delayed by fire; roadway access 

affected by multiple hazards including high potential impact to bridges and debris on streets; repairs of possibly unknown collocated lifeline infrastructure will require coordination between systems.

Port of Oakland area, Alameda County

High density of collocated 

infrastructure in highintensity multi-hazard area including transmission and potentially societally critical infrastructure

High liquefaction 

probability; moderate shaking intensity

Electric power substation, 

electric power generation plant, and 

wastewater treatment plant

Functional: Electric power for wastewater treatment plant.

Railways, roadways, fiber optic 

cables, and petroleum and natural gas pipelines

General: A hub of fiber optic cable infrastructure.Collocation (intersection): natural gas pipelines crossed by railways, fiber optic cables, 

and petroleum pipelines; petroleum pipelines with fiber optic cables.

Collocated (parallel): railway, petroleum pipelines, fiber optic cables, highways, BART, 

and natural gas pipelines.

Functional: multiple infrastructure systems serving the ports.Substitution: limited by parallel collocation of railway and highway.Restoration: impacts to multiple and substitute transportation routes could restrict 

access; repairs of possibly unknown collocated lifeline infrastructure will require coordination between systems.

San Francisco International Airport area, San Mateo County

High density of collocated 

transmission infrastructure in high-intensity multihazard area, including transmission and potentially societally critical infrastructure 

High liquefaction 

probability, moderate shaking intensity 

Roadways, fiber optic cables, 

transmission lines, and natural gas pipelines

Collocation (parallel and hub): parallel highway and fiber optic cables crossed by 

natural gas and petroleum pipelines; transmission lines running parallel to and crossing natural gas pipelines.

Collocation (parallel): Fiber optic cable running on a bridge.Substitution: less impacted surface streets could substitute for impacted highway.Restoration: repairs of possibly unknown collocated lifeline infrastructure will require 

coordination between systems.
48 The HayWired Earthquake Scenario—Societal Consequences

of Oakland area. These functionally dependent facilities and 

their service providers are affected by the same combination 

and intensity of hazards.

When describing collocation, we differentiate between 

parallel (for example, shared right of ways), intersection 

(for example, one infrastructure crossing another), and hub 

(where there is a convergence of multiple intersections or 

infrastructure intersecting with parallel infrastructure). We find 

collocated infrastructure in high shaking and (or) liquefaction 

hazard intensity areas and on top of the fault rupture. In the 

event of an earthquake, collocated infrastructure is vulnerable 

to collateral damages, cascading failures, and restoration 

repair delays (for example, when one has to be repaired before 

another). In addition to potential collocation impacts on 

repairs, the North Richmond and Hayward examples pose the 

situation of fire delaying response and restoration. Also, the 

Hayward and Port of Oakland examples highlight potential 

access issues.

With respect to substitution between infrastructure 

systems, we find examples where one lifeline may not be 

able to substitute for another. Whereas BART did serve as a 

substitute for road-based transportation when the Bay Bridge 

was damaged by the 1989 Loma Prieta earthquake (Schiff 

and others, 1990), places where BART and the road network 

are collocated or exposed to the same hazards may not have a 

simple solution for substitution. On the other hand, roadways 

to the west of the San Francisco International Airport could 

substitute for any transportation disruptions in those areas.

We cannot conclude that any of the examples in table 

16 are priority areas. We do not know infrastructure design 

regarding potential damages or what coordination has already 

been established between lifeline infrastructure systems. This 

approach simply offers a window into cross-examining lifeline 

interactions for a hypothetical earthquake scenario.

Lifeline Infrastructure System Damage 

and Downtime Assessments

We refer to the lifeline infrastructure damage assessments 

that were obtained for the HayWired scenario: water supply 

(Porter, 2018), telecommunications (Wein, Witkowski, 

and others, in prep.; planned to be published as part of this 

volume), electric power (appendix 3), BART (appendix 4), 

and the Caltrans Division of Research, Innovation, and System 

Information for the Division of Maintenance (appendix 5). 

PG&E shared a written statement about some of their results 

for electric power and natural gas. To fill the gap in damage 

assessments, we display them in the multi-hazard exposure 

map. We also compile the downtime and restoration time 

estimates for these infrastructure systems for the HayWired 

scenario. Lacking specific damage and restoration time 

estimates for fuel infrastructure, refineries, and oil and gas 

pipelines for the HayWired scenario, we refer to the Bay 

Area Urban Areas Security Initiative plan (Bay Area Urban 

Areas Security Initiative, 2014) and the California Energy 

Commission for relevant information in the wake of a large 

earthquake in the San Francisco Bay region. We remark on 

the extent that collocation and restoration dependencies were 

factored into damage and restoration time assessments.

Damage Impact Assessments

Porter (2018) offers the most comprehensive damage 

assessment for lifeline infrastructure in his analysis of 

water distribution pipelines. The analysis factors in all of 

the HayWired scenario earthquake hazards except fire. 

Consequently, although the fire following earthquake hazard 

depends on water supply, the effect of fire on delaying 

water infrastructure restoration was not considered. Wein, 

Witkowski, and others (in prep.; planned to be published 

as part of this volume) map out telecommunications 

infrastructure against all the HayWired mainshock hazards 

(including fire) and performed (1) a damage analysis for 

central offices, data centers, and cellular sites and (2) an 

exposure analysis for fiber optic cables. BART and Caltrans 

used only ground shaking data in their damage assessments 

(appendixes 4 and 5). PG&E conducted their own damage 

assessment of electric power and natural gas systems 

using the HayWired mainshock ground motions, their own 

proprietary system damage models and inventories, and their 

own methods for liquefaction and landslide hazards. The 

baseline Hazus analysis for electric power completed by the 

Federal Emergency Management Agency only uses ground 

shaking data, but estimated the liquefaction hazard and related 

damages in the process (appendix 3). These are examples of 

inconsistent damage assessments and illustrate why a multihazard exposure analysis can help to create a more complete 

picture with respect to all hazards. We compare the multihazard exposure and damage assessments for BART, electric 

power, and transportation infrastructure systems.

Comparing BART ground-shaking hazard damage results 

with exposure to all hazards, we find many of the BART 

facilities that experience the highest impact from ground 

shaking (see appendix 4 for details) are also exposed to 

moderate liquefaction probability, but BART reports that they 

have investigated these sites for liquefaction potential. There 

was general alignment between BART-estimated damages and 

the exposure analysis. Fire following earthquake is the main 

difference between BART’s damage assessment and the multihazard exposure. For example, the Fremont station (classified 

green by BART but in an area of high multi-hazard intensity) 

is the only exception owing to its exposure to moderate ground 

shaking intensity, moderate liquefaction probability, and high 

fire-following-earthquake intensity. Please refer to appendix 4 

for more details on the BART damage assessment and how it 

compares to hazard exposure estimated in this chapter.

The differences between the Caltrans ground-shaking 

damage assessment and multi-hazard intensity was most 

commonly high liquefaction hazard intensities, followed by 

high fire or high landslide hazard intensities (especially for 

bridges Caltrans identified as having low impact potential). 

Consequently, the distribution of highway bridges across the 
Chapter T. Lifeline Infrastructure and Collocation Exposure 49

multi-hazard intensity landscape spans all Caltrans damage 

impact classes and hazard intensity classes—no particular 

hazard explains the differences between exposure and damage 

assessments. Please refer to appendix 5 for more details on the 

Caltrans damage assessment and how it compares to hazard 

exposure estimated in this chapter.

The Hazus results for electric power roughly correspond 

with our multi-hazard exposure results, such that a lower 

percentage of subcomponent damage at substations more 

commonly occurs in lower multi-hazard intensity areas and 

a higher percentage of subcomponent damage at substations 

more commonly occurs in higher multi-hazard intensity areas. 

After shaking, liquefaction affects the most substations and 

is the only other hazard to affect more than ten substations 

with moderate or high-intensity (though the numbers are still 

relatively low compared to the total number of substations in 

the area). One substation is modeled to have more than 

75 percent subcomponent damage; this substation is also in an 

area with high-intensity for fire following earthquake. More 

detailed discussions of substation exposure according to Hazus 

and the analysis in this study are in appendix 3.

Damage to marine terminals, oil refineries, fuel storage 

tanks, fuel transmission lines, and fuel dispensaries is likely 

in a large San Francisco Bay region earthquake (G. Schremp, 

California Energy Commission, written commun., 2018). 

As a result, there will likely not be enough transportation 

fuel supplies available after a large earthquake. This affects 

fuel pipelines as well as end-users—pipelines require 

transportation fuel to push liquid through the system 

(Schremp, 2015). Our multi-hazard exposure analysis shows 

potential shaking, liquefaction, and (or) fire damage to fuel 

facilities, as well as all forms of ground failure (liquefaction, 

landslides, and fault offset) affecting natural gas and 

petroleum pipelines. It should be noted that, at the Lyttelton 

Harbour during the Canterbury, New Zealand, earthquake 

sequence, less damage was observed for fuel storage tanks 

that were less than half full (A. Tang, L&T Consultant, 

written commun., 2018), meaning that fuel inventory levels at 

the time of the earthquake could be a variable to consider in 

the hazard analysis.

HayWired Lifeline Infrastructure Restoration 

Times

For the HayWired scenario, Porter (2018) developed 

a method to factor in the effects of other lifeline services 

(electric power and transportation), materials, and labor on the 

restoration times of water supply. Wein, Witkowski, and others 

(in prep.; planned to be published as part of this volume) 

related the restoration of voice and data services to electric 

power restoration (owing to the heavy dependence on power) 

and assumptions about the arrival of trucks carrying fuel and 

equipment. However, electric power, BART, and Caltrans 

restoration times (appendixes 3–5) were derived independently 

of other lifeline infrastructure restoration times.

The restoration of electric power (according to Hazus), 

voice and data services, water distribution, BART facilities, 

and Caltrans bridges are compiled and graphed by county 

(figs. 15–19). The repair times for BART facilities are used to 

construct a restoration curve for BART facilities (as opposed 

to passengers served) in figure 18. Bridge functionality 

(fig. 19) was calculated as a percentage of bridges that are 

open assuming uniform repair within ranges of repair times. 

Partly opened bridges (1 lane) were assumed to be operating 

at 30 percent functionality. As an example, the five restoration 

curves for Alameda County are combined to illustrate the 

relative restoration times for those services (fig. 20). The 

economic impacts of these utility and transportation service 

outages are investigated by Sue Wing and others (in prep.; 

planned to be published as part of this volume) and Kroll 

and others (in prep.; planned to be published as part of this 

volume), respectively.

Although no restoration curve for natural gas 

was obtained, PG&E provided a written statement that 

their gas transmission model for the HayWired scenario 

estimates approximately 40 locations in need of repair, 

whereas their gas distribution model estimates at least 

2,000 gas leaks. However, PG&E also noted that actual 

damage from a similar event will likely be different from 

what the model estimates (E. Hickey and G. Molnar, PG&E, 

written commun., 2019). PG&E surmises that the majority of 

its gas restoration work would be dedicated to leak surveys 

because the location of a repair or a leak cannot be found 

without extensive leak surveys or patrols. The amount of time 

to assess, repair, and restore gas service to customers would 

be significantly longer than that of electric power restoration. 

In addition, because there would be preemptive gas shut-offs 

and subsequent loss of gas service to many PG&E customers, 

the relight effort would take many months to complete. PG&E 

estimates that more than 200,000 customers could require 

relights after the event, not including those who proactively 

shut off gas to their homes and businesses and the gas safety 

shut-ins performed by PG&E (E. Hickey and G. Molnar, 

PG&E, oral commun., 2017). For comparison, 156,355 

service relights after the 1989 Loma Prieta earthquake were 

accomplished within ten days and involved more than 1,000 

servicemen, many of whom were from out-of-state utility 

companies (Eguchi and Seligson, 1994).

Impacts on the fuel supply after a large earthquake in 

the San Francisco Bay region (not specific to the HayWired 

scenario) are examined by the Bay Area Urban Areas Security 

Initiative plan (Bay Area Urban Areas Security Initiative, 

2014) and the California Energy Commission. After a large 

earthquake, refineries will likely go offline immediately and 

remain offline for at least three days after the event, even 

given limited damage to the physical plant. The refineries 

will likely take some time to return to normal operating 

rates once they are back online. In a recent case, following 

the passage of Hurricane Harvey through Texas, a refining 

complex required six weeks to return to normal operating 

rates after just a brief period of high winds and extraordinary 

rainfall totals (G. Schremp, California Energy Commission, 

written commun., 2018). Refinery restart critically depends 

on natural gas and water supplies, crude oil delivery (that 

may be inhibited by damaged pipelines or marine terminals), 
50 The HayWired Earthquake Scenario—Societal Consequences

men19-7341_fig15

0

25

50

75

100

0

Service available, in percent

Days after mainshock event

5 10 15 20 25 30

Electric power restoration curve

Alameda County

Contra Costa County

Marin County

Napa County

EXPLANATION

San Francisco County

San Mateo County

Santa Clara County

Solano County

Sonoma County

Figure 15. Line graph showing the electric power restoration curve by county for the HayWired earthquake 

scenario mainshock in the nine counties adjacent to San Francisco Bay, California.

0

25

50

75

100

0

Service available, in percent

Days after mainshock event

5 10 15 20 25 30

Voice and data telecommunications restoration curve

Alameda County

Contra Costa County

Marin County

Napa County

EXPLANATION

San Francisco County

San Mateo County

Santa Clara County

Solano County

Sonoma County

Figure 16. Line graph showing the voice and data telecommunications restoration curve by county for the 

HayWired earthquake scenario mainshock in the nine counties adjacent to San Francisco Bay, California.
Chapter T. Lifeline Infrastructure and Collocation Exposure 51

men19-7341_fig17

0

25

50

75

100

0

Service available, in percent

Days after mainshock event

10 20 30 40 50 60 70 80 90

Alameda and Contra Costa Counties

Marin County

Napa and Sonoma Counties

EXPLANATION

San Francisco County

San Mateo County

Santa Clara County

Solano County

Water distribution restoration curve

Figure 17. Line graph showing the water distribution restoration curve by county for the HayWired earthquake 

scenario mainshock in the nine counties adjacent to San Francisco Bay, California. Data originally from Hazus-MH 2.1 

(Federal Emergency Management Agency, 2012) and modified for Alameda, Contra Costa, and Santa Clara Counties by 

Porter (2018).

0

25

50

75

100

Service available, in percent

0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450

Days after mainshock event

BART facility restoration curve

Alameda County

Contra Costa County

San Francisco and San Mateo Counties

EXPLANATION

Figure 18. Line graph showing the Bay Area Rapid Transit (BART) facility restoration curve by county for the 

HayWired earthquake scenario mainshock in the nine counties adjacent to San Francisco Bay, California. Damage 

estimates only apply to stations in the original BART system (built between 1972 and 1976). The Warm Springs/

South Fremont station (opened in 2017), Milpitas station (currently scheduled to open in 2019), and Berryessa 

station (currently scheduled to open in 2019) are not included in the base number of stations used to estimate 

station service fraction (stations built between 1996 and 2016 are included in the base number). 
52 The HayWired Earthquake Scenario—Societal Consequences

men19-7341_fig19

0

25

50

75

100

0

Service available, in percent

Days after mainshock event

10 20 30 40 50 60 70 80 90

Highway bridge restoration curve

Alameda County

Contra Costa County

Marin County

Santa Clara County

EXPLANATION

Sonoma County

Figure 19. Line graph showing the California Department of Transportation highway bridge restoration curve 

by county for the HayWired earthquake scenario mainshock in the nine counties adjacent to San Francisco 

Bay, California.

0

25

50

75

100

0

Service available, in percent

Days after mainshock event

10 20 30 40 50 60 70 80 90

Lifeline restoration curves for Alameda County

Voice and data communications

Electric power

Water distribution

BART system

EXPLANATION

Highway bridges

Figure 20. Line graph showing the lifeline restoration curves (electric power, voice and data, water distribution, 

Bay Area Rapid Transit [BART] service, and California Department of Transportation bridges) for Alameda County 

for the HayWired earthquake scenario mainshock in the nine counties adjacent to San Francisco Bay, California.
Chapter T. Lifeline Infrastructure and Collocation Exposure 53

and operational petroleum pipelines (G. Schremp, California 

Energy Commission, written commun., 2018). Refineries rely 

on “just-in-time” shipments, so fuel supplies for as long as 

72 hours may be available.

Furthermore, fuel distribution would be impeded by 

damages to transportation systems, and power outages would 

make it difficult to pump gas out of the ground at most gas 

stations in the region (placing further demands on fuel for 

generated power). Transporting fuel via rail on land or via 

tankers or barges on water (with a waiver) are suggested 

as potential solutions to consider in anticipation of a major 

earthquake. For example, one train car can transport as much 

fuel as three tanker trucks, and one full train can transport as 

much fuel as nearly 300 tanker trucks (Schremp, 2015). In 

addition, California’s unique gasoline blend—intended to help 

reduce vehicle emissions—prevents the State from bringing in 

fuel from other States unless a waiver is granted by the State 

government. Waivers could also be granted to permit use of 

nontaxable diesel fuel (Schremp, 2015).

The working assumption is that fuel will be limited for 

the initial response phase, the movement of evacuees and 

resources, and power generation. The quantity of fuel needed 

to support all response operations and other critical functions 

is expected to be inadequate; prioritization and coordination 

of fuel allocations will be necessary. The California Energy 

Commission advises locals to plan on being self-sufficient for 

a minimum of 7 to 10 days (G. Schremp, California Energy 

Commission, written commun., 2018). An analysis of a M7.0 

earthquake on the Hayward Fault (with a Berkeley epicenter) 

by Wilson and others (2015) assumes that all refineries in the 

San Francisco Bay region would lose power for 3 days and 

be shut down for 14 days before restarting. They also assume 

that damage to transmission pipelines crossing the fault would 

halt transmission to the west side of San Francisco Bay for 28 

days. Wilson and others (2015) estimate that in the absence 

of temporary fuel shipments by other modes, San Francisco 

(and the rest of the bay region) consumption would be reduced 

to zero for 3 days and then increased to 20 percent of their 

normal fuel load until the pipelines are repaired.

Knowledge Gaps and Modeling 

Limitations

This work advances the lifeline exposure analyses 

performed for prior scenarios. It extends the purview to 

all earthquake hazards, fire following an earthquake, and 

multiple hazards and delves into interactions among lifeline 

infrastructure. We experiment with spatial tools to find, 

visualize, and interpret collocated infrastructure, other 

interactions, and potential cascading failures. A comprehensive 

hazard exposure analysis complements available damage 

analyses for some of the hazards. We also compile lifeline 

infrastructure restoration time estimates. In this section, we 

acknowledge the shortcomings of our analyses.

Uncertainties and limitations of the specific hazard data 

for the HayWired scenario hazards are thoroughly covered 

in the first volume of the HayWired scenario (Detweiler and 

Wein, 2017) and Scawthorn (2018). The lack of site-specific 

information for liquefaction mapping also applies to our 

more detailed liquefaction map. Also, by using only one 

measurement of shaking in the exposure analysis (in this case, 

0.3 second peak spectral acceleration), it is not necessarily 

the most relevant measure for each infrastructure type. For 

example, BART and Caltrans damage assessments use 

1.0 second peak spectral acceleration. As such, only so much 

is captured by the standardized exposure analysis.

We integrated the multiple hazard assessments using 

relative low-, moderate-, or high-intensity classifications to 

manage the complexity of collocated infrastructure exposed 

to multiple hazards. The relative hazard intensity thresholds 

were selected as follows: ground shaking thresholds were 

informed by expert engineering opinion, the high liquefaction 

intensity threshold was limited by a 25 percent probability cap 

(used in Hazus), landslide intensity thresholds were aligned 

with liquefaction intensity thresholds for consistency, and fire 

intensity was classified using natural breaks in the data using 

GIS. The resulting multi-hazard intensity was created from the 

profile of intensity classifications. Others may choose to set 

the hazard intensity thresholds and the multi-hazard intensity 

descriptions differently.

There were issues with the lifeline infrastructure 

dataset. First, some of the data are misrepresented (such 

as a pathway through a park attributed as a surface street). 

Second, though a large amount of lifeline infrastructure 

was assessed, the available data were incomplete. This is 

particularly true for “last-mile” distribution infrastructure 

which generally includes household-level access to water, 

wastewater, electricity, and other infrastructure. In many 

cases, these types of infrastructure can be approximated by 

surface streets, which are included in this analysis. Other 

lifeline infrastructure data (for example, city gates for natural 

gas transmission and distribution, booster stations for water 

transmission and distribution, and mobile telephone switching 

offices for communications) would be useful inputs but were 

not publicly available. A third data limitation was duplication, 

particularly in the telecommunications data. We tried 

wherever possible to remove data points that were duplicated, 

but some double-counting of data owing to these duplicates 

is still possible. A fourth limitation is that many infrastructure 

collocations are unknown; for example, fiber optic cables are 

known to be collocated on Caltrans bridges, yet they are not 

documented (L. Turner, Caltrans, oral commun., 2016), which 

required us to develop GIS methods to identify potential 

collocations that would have to be manually verified. 

Although California utilities use Underground Service Alert 

to identify other infrastructure that may be present before 

digging to make repairs, the data are not kept current (Wein, 

Witkowski, and others, in prep.; planned to be published as 

part of this volume).
54 The HayWired Earthquake Scenario—Societal Consequences

Hazard exposure assessments pick up the presence but 

not performance of lifeline infrastructure. Different types 

of facilities (for example, above and belowground) have 

different responses to the various hazards and have different 

design standards (Applied Technology Council, 2016). 

A limitation of lifeline infrastructure data is that it is not 

attributed with design standards, and fragility functions for 

different hazards may be approximated or unavailable. In 

other cases, information about the physical location of the 

lifeline may be missing (for example, fiber optic cable data 

do not specify whether the cables are above or belowground). 

System retrofitting and enhanced design standard information 

for lifeline infrastructure was not fully accounted for by the 

transportation agencies and Hazus. Comprehensive fragility 

information would be required to gain more insight into 

damages among collocated infrastructure.

A particular challenge in the assessment of collocation 

and other lifeline infrastructure interactions is the sheer 

volume of infrastructure data that needs to be processed. 

We developed GIS methods to paint a regional picture of 

collocated and dense infrastructure in hazardous areas. The 

collocation algorithm is not perfect and identified facilities 

near surface streets as collocated, but not facilities that were 

set back more than 5 m, which could also be affected by 

damage to surface streets. This is an area where the spatial 

methods could be further developed. In addition, we make 

assumptions about which lifeline infrastructure are more and 

less societally critical in our collocation analysis. Different 

agencies would surely have different opinions on what 

qualifies lifeline infrastructure as more or less critical to 

society and, as a result, could identify different collocations 

of interest. For example, Radke and others (2018) used a 

betweenness centrality measure, which identifies nodes of 

importance along a network based on the number of routes 

needing to pass through a particular node, instead of using 

classifications based on the number of structures within a 

distance of the pipeline (as was done for this chapter).

Quantitative assessment of interactions among lifeline 

infrastructure is still a relatively nascent field with limited 

examples available for anticipating how complex systems 

interact, particularly during hazardous events. Applied 

Technology Council (2016) provides a review of gaps in 

literature and practice. Our approach to lifeline infrastructure 

interactions is limited because not all interactions within and 

between systems occur within a confined area. For example, 

the 2010 gas line explosion in San Bruno, California, began 

with an error in electrical maintenance work 50 miles away, 

which resulted in a natural gas pressure surge and explosion 

of a weak part of pipe in San Bruno (Germeraad and others, 

2014). We approached regional lifeline infrastructure systems 

using the location of physical infrastructure (what is at this 

spot?) rather than an operational or horizontal approach 

(what is downstream from this spot?), and, as such, miss the 

effect of how damage in one place may affect another in the 

network. A recent infrastructure interdependency assessment 

regarding Puerto Rico’s recovery after Hurricane Maria 

(U.S. Department of Homeland Security, 2018) demonstrates 

a comprehensive assessment methodology that combines 

a top-down approach to system operations and dynamics 

with a bottom-up approach that extracts characteristics and 

performance of critical infrastructure and economic activities 

through structured interviews, facilitated discussions, and 

standardized dependency-related question sets. We also 

tackled this analysis from both sides.

Some organizations used their own tools and (or) 

collaborated with the HayWired scenario team to conduct 

damage and restoration assessments. Future lifeline 

infrastructure assessments could attend more closely to 

assessing (1) damage for multiple earthquake hazards 

(including fires following an earthquake), (2) additional 

damage caused by damage to collocated infrastructure, 

(3) restoration delays owing to collocated infrastructure, 

and (4) restoration delays from fire following an earthquake. 

These additions would make damage and restoration 

assessments more robust and more representative of what 

could happen during a major disaster, such as the HayWired 

earthquake scenario.

Summary and Conclusion

This study applied scientific and comprehensive hazard 

data from the HayWired scenario to anticipate impacts on 

lifeline infrastructure and illustrate potential interactions 

(or interdependencies) among them. We created a multihazard map to integrate five hazards: surface rupture, 

mainshock ground shaking, liquefaction, landslides, and 

fire following the earthquake. We performed standardized 

hazard and multi-hazard exposure analyses for various 

lifeline infrastructure systems, including transportation, 

water supply and wastewater, oil and gas, electric power, 

and telecommunications. The hazard exposure analyses 

complement available regional lifeline infrastructure damage 

assessments. We visualized collocated lifeline infrastructure 

systems in the San Francisco Bay region and their exposure 

to multiple hazards. Zooming in on collocated lifeline 

infrastructure in high or very high hazard areas, we illustrate 

potential lifeline infrastructure interactions (that are mostly 

geographical, but also potentially functional, substitutional, 

and restorative). Finally, we compiled utility and 

transportation service restoration timeline estimates and noted 

whether lifeline infrastructure interactions were considered. 

These analyses can be used by lifeline infrastructure 

organizations, communities and government officials, 

businesses, and public safety agencies to develop emergency 

management exercises and support business continuity 

planning. They also provide materials for information-sharing 

tool development and coordinated planning among the 

various organizations and communities in anticipation of a 

large earthquake.

The material in this chapter reinforces prior efforts and 

findings. Applied Technology Council (2016) promotes the 

assessment of lifeline infrastructure and interactions to improve 

the ability of lifeline operators to “set priorities and define 

strategies to reduce risks and improve lifeline resilience.” 
Chapter T. Lifeline Infrastructure and Collocation Exposure 55

Within the San Francisco Bay region, The Lifelines Council 

(2014) undertook a collaborative and qualitative effort to 

offer recommendations to improve the performance of lifeline 

infrastructure and interactions in the event of a large earthquake 

in San Francisco on the San Andreas Fault. The study 

considered port, electricity, transportation, water, wastewater, 

telecommunications, and fuel infrastructure systems. Perkins 

and Hutchings (2010) laid out numerous strategies and funding 

statuses for lifeline infrastructure and interactions for the San 

Francisco Bay region. Subsequently, Germeraad and others 

(2014) used publicly available data to investigate potential 

vulnerabilities in transportation and utility systems in the region. 

The HayWired analysis builds on these studies by expanding 

the extent of the San Francisco study to the entire San Francisco 

Bay region, broadening the infrastructure included (for example, 

adding telecommunications infrastructure), extending analyses 

to identify collocated and interacting lifeline infrastructure, and 

placing these analyses in a comprehensive multi-hazard (and 

non-San Andreas-specific) environment.

Hazard exposures vary by lifeline infrastructure. Shaking 

is the most widespread hazard and affects all lifeline 

infrastructure, but, in general, less than 15 percent of facilities 

and conveyances in each lifeline infrastructure system are 

subjected to high-intensity shaking (as defined for this study); 

cellular sites and fiber-lit building are at the upper end of this 

exposure range. Internet exchange points and oil refineries 

exceed the range with 27 and 20 percent, respectively, in 

high-intensity shaking areas, but the total number of exposed 

facilities in the study area is quite small (11 and 5, respectively).

Building of structures in the fault zone is constrained 

by the Alquist-Priolo Act of 1972 (Perkins and Hutchings, 

2010; California Department of Conservation, 2017): only 

1 microwave tower, 6 bridges, 6 cellular sites, and 8 fiberlit buildings are in the fault zone. In contrast, there are no 

limitations on conveyances in the fault zone: hundreds of 

roadways and fiber optic cables and a few pipelines and water 

conveyances cross the surface rupture and (or) are further 

exposed to afterslip. We know that the East Bay Municipal 

Utility District (EBMUD) has built for and planned for 

damages resulting from offset on the Hayward Fault for the 

few water transmission crossings it manages.

In two places along the fault, surface rupture coincides 

with landslide and fire hazards and, in both cases, surface 

streets are the most exposed infrastructure, in agreement with 

the urban nature of the fires modeled. There is infrastructure 

in the fault zone that is exposed to all five hazards at the 

same site, including 3 bridges, around 4 km of roadways, 

3km of fiber optic cable (1 km of long-haul fiber and 2 km of 

interoffice fiber), less than 1 km of natural gas and petroleum 

pipelines, and 2 cellular sites. Zooming in, we found a few 

instances of collocated infrastructure on the fault: parallel 

natural gas and petroleum pipelines, and a parallel railway 

and fiber optic cable that are crossed by a surface street (and 

presumably other distribution infrastructure).

We generated a more expansive liquefaction probability 

map than provided (by Jones and others, 2017) using 

liquefaction susceptibility data in conjunction with multiple 

Hazus runs (areas outside of the modeled part of Santa Clara 

and Alameda Counties) to improve the resolution within 

census tracts to better match the spatial scale of lifeline 

infrastructure data. Liquefaction is the primary hazard at the 

three international airports and seaports in terms of affected 

area and severity during the HayWired scenario mainshock 

(and likely for some nearby aftershocks). Prior recognition 

of liquefaction as a problem has resulted in some mitigation 

(Germeraad and others, 2014). Wastewater treatment plants 

are also located in flat areas around and near the bay and are 

mostly exposed to liquefaction hazards. We also found nearby 

power plants and substations serving wastewater treatment 

plants simultaneously exposed to the liquefaction hazard.

We show the potential for ground failure (liquefaction 

and landslides) to impact roadways, railways, and fiber optic 

cables. Where respective facilities (bridges, stations, and fiberlit buildings) are impacted by heavy shaking, the impact on the 

system is likely dominated by restoration of facilities rather 

than conveyances. Therefore, the impact of ground failure on 

these systems could extend recovery times where shaking is 

less intense and facility shaking damage is minor. Considering 

the potential bridge damages and exposure of roads to 

landslide hazards, safe egress from the east bay (especially 

away from ensuing fires) might well be west across the transbay bridges rather than to the east.

Bay area refineries are generally out of the way of 

the largest impacts of the HayWired scenario earthquake 

hazards, except for one exposed to high liquefaction hazard 

intensity. PG&E confirmed that ground failure resulting from 

the HayWired scenario mainshock would likely cause more 

damage to gas pipelines than shaking (E. Hickey and 

G. Molnar, PG&E, written commun., 2017). We found oil and 

gas pipelines collocated on the fault. Also, we found pipelines 

running parallel to and crossing various transportation 

infrastructure and fiber optic cables in areas that have high 

liquefaction hazard intensity. Therefore, collateral damage 

and restoration interactions could arise. The proximity 

and potential impacts of hazards on gas and liquid fuel 

pipeline facilities in urban areas is accounted for in current 

construction codes, but is an important consideration in the 

context of larger scale, infrequent events that may exceed 

normal circumstances (Applied Technology Council, 2016).

Fire was not considered in any of the HayWired scenario 

lifeline infrastructure damage assessments or restoration 

estimates. Owing to the urban nature of fire following an 

earthquake in the HayWired scenario, Bay Area Rapid Transit 

(BART) facilities are relatively more exposed to higher fire 

density areas than other infrastructure. Aerial power lines and 

fiber optic cable would be vulnerable to fire, but the data did 

not always distinguish between aerial and buried systems.

Compounding hazards can be problematic for lifeline 

infrastructure, even if one or more of the hazards poses 

relatively minor damage potential—damage from one hazard 

could increase the fragility of infrastructure to a second or 

third hazard, making a facility unsafe. Considering lifeline 

infrastructure exposure to three or more hazards at the same 

site, we found that the most likely combination is shaking, 
56 The HayWired Earthquake Scenario—Societal Consequences

liquefaction, and fire hazards. This is not surprising given 

that (1) liquefaction affects the largest area after shaking, and 

(2) damage to vulnerable water distribution pipelines from

liquefaction and shaking hazards could inhibit firefighting. For

this hazard combination, in the majority of cases, at least one

of the hazards affecting a lifeline is of high-intensity. We find

14 percent of terminals, storage facilities, and tank farms for

petroleum, oil, and lubricants; 9 percent of internet exchange

points; 8 percent of fiber-lit buildings; 6 percent of cellular

sites, natural gas compressor stations, and microwave towers;

5 percent of AM radio antennas; and 3–4 percent of roadways,

electric power generation plants, substations, petroleum

pipelines, and fiber optic cables (both long-haul and interoffice

fiber) exposed to the combination of shaking, liquefaction, and

fire hazards.

Surface streets (and unaccounted collocated distribution 

[“last-mile”] lifeline infrastructure) are involved in 

infrastructure collocations and most commonly collocated 

with other transportation infrastructure (roadways or 

railways) and (or) fiber optic cables, but also with oil and 

gas pipelines and electric transmission lines. More than 40 

percent of the 9,700 surface street collocations in high and 

very high-intensity areas involved three or four types of 

infrastructure. Although fewer in number, interactions from 

collocated transmission or potentially more societally critical 

infrastructure would have a greater impact (for example, the 

Port of Oakland and San Francisco International Airport).

Explorations of the infrastructure that are collocated in 

hazardous areas identify potentially complex situations that 

could arise from a large earthquake in the San Francisco Bay 

region. Such an approach could help multiple lifeline 

infrastructure systems, communities, government officials, and 

emergency managers locate and discuss interactions that may 

be otherwise unknown (for example, collocated water and gas 

pipelines, as discussed by EBMUD).

Our hazard exposure findings precede damage 

assessment. We find that exposure analyses complement 

damage assessments that are limited to a subset of the hazards 

(most commonly, shaking only) and draw attention to ground 

failure and fire hazards. Consideration of fire following an 

earthquake is the main difference between BART’s damage 

assessment and the multi-hazard exposure assessment 

presented here. No one hazard explained the differences 

between highway system exposure and California Department 

of Transportation’s damage assessments.

Service restoration times were derived from damage 

analyses conducted for water distribution (Porter, 2018), 

electric power (appendix 3), BART facilities (appendix 4), 

and highway bridges (appendix 5). Also, Wein, Witkowski, 

and others (in prep.; planned to be published as part of 

this volume) posited restoration curves for voice and data 

services. In general, the indications are that BART stations 

and train yards will take longer (years) than highway bridges 

(months) to restore in the most heavily impacted areas, and 

water supply restoration times (months) extend beyond 

power (weeks to a month) and telecommunications (days to 

weeks) restoration times. The water supply analyses assumed 

road access, but showed that other lifeline infrastructure 

interactions have a greater effect on extending restoration 

times of less impacted systems. Although telecommunications 

infrastructure performance depends heavily on electric power, 

back-up power and portable equipment can help to decouple 

that dependence at the cost of increasing dependence on fuel 

sources and transportation of fuel. Future lifeline infrastructure 

assessments could more closely assess (1) damages from 

multiple earthquake hazards (including fire following the 

earthquake), (2) additional damages caused by damages 

to collocated infrastructure, (3) restoration delays due to 

collocated infrastructure and other types of interactions, and 

(4) restoration delays from fire following the earthquake.

We presented results in maps of hazards, infrastructure

systems, and collocation; counts of facilities and collocations; 

lengths of conveyances; percentages of system assets exposed 

to hazards; and estimations of restoration times. Counts and 

lengths are a basic step to understanding the cost and effort 

required to restore a system and percentages provide a relative 

measure of potential impact to a system. As we developed 

material in parallel with emergency managers for exercises, 

they responded to combining the hazards into a multi-hazard 

landscape as an indicator of problem areas as well as the 

focused examples of complex situations that could arise. This 

is consistent with the need to anticipate, plan, and prepare for 

less predictable and less visible, potentially disastrous, lifeline 

infrastructure system interactions. According to Grain (2014), 

there is great value in well-designed, informative exercises 

that can test the efficacy of contingencies and preparedness 

plans; more sophisticated and challenging scenario-based 

training will be vital in validating and revising these plans.

When lifeline infrastructure information is not available 

or shared among lifeline infrastructure operators, it is difficult 

to anticipate and plan for any interactions or coordinate system 

restoration. At the most fundamental level, data collection 

across multiple systems can improve communication 

across lifeline service providers and support analyses of 

geographically collocated infrastructure, which are currently 

largely unknown (Applied Technology Council, 2016; 

Scalingi, 2015). To our knowledge, our analysis is the first 

attempt to process vast quantities of data that may or may not 

be known between infrastructure asset owners and managers, 

map collocation across a region, and visualize collocation 

within a multi-hazard landscape. Utilities and transportation 

agencies could interact with lifeline infrastructure layers 

from the Department of Homeland Security (P. Beilin, City 

of Walnut Creek, oral commun., 2017) and dive deeper into 

potential interactions pertaining to mitigating damage and 

planning for response and recovery.

The San Francisco Bay region has been receptive to 

earthquake information and has proactively reduced risk of 

damage and disruption in lifeline infrastructure. Since the 1989 

Loma Prieta earthquake, Brocher and others (2018) document 

seismic retrofits of $20 billion spent on transportation, about 

$8 billion on water supply and sewers, $3 billion on electric 
Chapter T. Lifeline Infrastructure and Collocation Exposure 57

power, and $1 billion on telecommunications. As in this study, 

information is lacking on energy infrastructure. Looking 

ahead, the Loma Prieta 25th Anniversary Symposium (LP25) 

policy partners and the Association of Bay Area Governments 

Executive Board recommended the establishment of a Regional 

Lifelines Council (modeled on the San Francisco Lifelines 

Council) to address vulnerabilities in the San Francisco Bay 

region’s infrastructure systems. Our analyses align with the 

proposed goals of the Regional Lifeline Council: sharing risk 

assessments, understanding lifeline infrastructure interactions, 

communicating restoration times, and correcting for serious 

risks, such as those inherent in cascading failures (Association 

of Bay Area Governments, 2015).

Acknowledgments

We could not have compiled lifeline infrastructure damage 

and restoration information without the valuable contributions 

from many organizations. We thank the following people 

for participating: Mark Salmon, Tom Horton, and Maria 

Blagg (Bay Area Rapid Transit); Loren Turner, Brian Chiou, 

Herby Lissade, and Dana Hendrix (California Department 

of Transportation); Greg Molnar, Evermary Hickey, and 

Stuart Nishenko (Pacific Gas and Electric Company); Gordon 

Schremp (California Energy Commission); and Keith Porter 

(University of Colorado at Boulder), who worked with water 

utilities. We appreciate the opportunity to test our analyses in 

a HayWired scenario exercise designed with Paula Scalingi 

(Bay Area Center for Regional Disaster Resiliency) for lifeline 

interdependency workshops. Monika Stoeffl (California 

Resilience Alliance) linked us to useful resources and relevant 

studies. Our work was improved by suggestions from several 

reviewers: Michael Germeraad (Metropolitan Transportation 

Commission and Association of Bay Area Governments); and 

Cherrie Black, Ryan Hruska, Shiloh Elliot, and Stefan Haus 

(Idaho National Laboratory).

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Chapter T. Lifeline Infrastructure and Collocation Exposure 61

Appendix 1. HayWired Scenario Liquefaction Modeling Expansion Methodology

By Jamie L. Jones,1 Anne M. Wein,1 and Hope A. Seligson2

The liquefaction probability estimation for the HayWired 

mainshock uses two methods to cover the San Francisco Bay 

region. First, a detailed estimate of liquefaction probability 

was performed in most of Alameda and Santa Clara Counties 

for the HayWired scenario mainshock by Jones and others 

(2017). Using methods originally developed by Holzer and 

others (2008, 2010), they estimate liquefaction probabilities 

at a spatial resolution of 50 meters (m) ranging from zero to 

as high as 78 percent (fig. 1.1). Second, for the rest of the 

bay area, the Hazus-MH 2.1 (hereafter referred to as Hazus) 

census-tract-scale liquefaction analysis (shown in Jones and 

others, 2017) was refined to make a map that better aligns the 

liquefaction risk with the location of lifeline infrastructure. 

This was accomplished using San Francisco Bay region 

liquefaction susceptibility data (Knudsen and others, 2000) 

and running Hazus liquefaction probability estimation 

multiple times, once for each susceptibility class within census 

tracts. This revised analysis, though limited to a maximum 

liquefaction probability of 25 percent by Hazus methodology, 

provides sub-census-tract level liquefaction probability results 

for a much wider area than originally modeled by Jones and 

others (2017) (fig. 1.2).

Specifically, the liquefaction probability estimation was 

conducted by running the Hazus earthquake module with 

HayWired mainshock ground motions for five liquefaction 

1

 U.S. Geological Survey.

2

 Seligson Consulting.

susceptibility classes (very low, low, moderate, high, and 

very high), as defined by Knudsen and others (2000). For 

each of the five Hazus runs, census tracts were assigned the 

same susceptibility class. For example, the first run forced all 

census tract centroids to have the same very low liquefaction 

susceptibility class. Each run yielded liquefaction probability 

results for the liquefaction susceptibility class in the census 

tract. These results were mapped on to the liquefaction 

susceptibility polygons in the geographic information system 

(GIS) data from Knudsen and others (2000). The census tract 

map used on top of the liquefaction susceptibility map was 

of 2000 vintage because the version of Hazus used for the 

HayWired scenario was compatible with 2000 census data and 

not 2010 census data. We then mapped liquefaction probability 

within a census tract by assigning the susceptibility class 

specific probabilities for the tract to the corresponding area of 

the tract. For example, the liquefaction probability from the 

high liquefaction susceptibility class Hazus run for a census 

tract was mapped on to the high-liquefaction susceptibility 

area within the census tract. Figure 1.3 illustrates the above 

steps for census tract 6051 in the City of Burlingame in San 

Mateo County.

In Alameda and Santa Clara Counties, where liquefaction 

probability data from Jones and others (2017) were available, 

the results from the Hazus runs were replaced, creating a 

final composite liquefaction probability map using Jones and 

others (2017), where available, and application of Hazus data 

elsewhere in the region (fig. 1.4).
62 The HayWired Earthquake Scenario—Societal Consequences

SONOMA

NAPA

SOLANO

MARIN

ALAMEDA

SANTA

CLARA

SANTA

CRUZ

SAN

MATEO

SAN

JOAQUIN

STANISLAUS

SAN

FRANCISCO

CONTRA COSTA

SAN

BENITO

P A C I F I C O C E A N

San Francisco Bay

San Pablo

Bay

-122°

38°

37°

0 7 14 KILOMETERS

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016 0 7 14 MILES

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

Map

area

CALIF.

Probability of liquefaction

<0.05

>0.5

0.4 to 0.5

0.3 to 0.4

0.2 to 0.3

0.1 to 0.2

0.05 to 0.1

Outline—Marks the area where 

Holzer and others' (2008, 2010) 

methods are used to map 

liquefaction probability

Not assessed

EXPLANATION

Figure 1.1. Map of the San Francisco Bay region, California, showing liquefaction probability results modeled by Jones 

and others (2017).
Chapter T. Lifeline Infrastructure and Collocation Exposure 63

SONOMA

NAPA

SOLANO

MARIN

ALAMEDA

SANTA

CLARA

SANTA

CRUZ

SAN

MATEO

SAN

JOAQUIN

STANISLAUS

SAN

FRANCISCO

CONTRA

COSTA

San Pablo

Bay

SAN BENITO

-122°

38°

37°

0 7 14 KILOMETERS

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016 0 7 14 MILES

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

Map

area

CALIF.

Probability of liquefaction

<0.05

>0.5

0.4 to 0.5

0.3 to 0.4

0.2 to 0.3

0.1 to 0.2

0.05 to 0.1

Not assessed

Susceptible soils with 

zero probability of 

HayWired scenario 

mainshock-induced 

liquefaction

EXPLANATION

Outline—Marks the area 

where Holzer and 

others' (2008, 2010) 

methods are used to 

map liquefaction 

probability

P A C I F I C O C E A N

San Francisco Bay

Figure 1.2. Map of the San Francisco Bay region, California, showing liquefaction probability results modeled using 

Hazus-MH 2.1 (Federal Emergency Management Agency, 2012); modified using methods described in this appendix.
64 The HayWired Earthquake Scenario—Societal Consequences

Boundary data from U.S. Census Bureau TIGER data, 2016, and

Federal Emergency Management Agency Hazus-MH 2.1, 2014.

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

0 0.25 0.5 MILES

0 0.25 0.5 KILOMETERS

A. Liquefaction probability from Hazus B. Liquefaction susceptibility

C. Liquefaction probability for each susceptibility class

N

San Francisco Bay

San Francisco Bay

San Francisco Bay

Liquefaction probability Liquefaction susceptibility

EXPLANATION

Very high

High

Moderate

Low

Very low

< 0.05

0.05 to 0.09

0.10 to 0.19

= 0.20

0

Census tract boundary

Figure 1.3. Series of simplified maps showing how census tract-level liquefaction probability data (from Hazus-MH 2.1 

[Federal Emergency Management Agency, 2012]) were downscaled to sub-census-tract level susceptibility class areas 

within a single census tract. A, Original census-tract liquefaction probability from Hazus. B, Liquefaction susceptibility 

classes from Knudsen and others (2000) for the census tract and surrounding area. C, Final census-tract liquefaction 

probabilities from Hazus runs for each susceptibility class.
Chapter T. Lifeline Infrastructure and Collocation Exposure 65

SONOMA

NAPA

SOLANO

MARIN

ALAMEDA

SANTA

CLARA

SANTA

CRUZ

SAN

MATEO

SAN

JOAQUIN

STANISLAUS

SAN

FRANCISCO

CONTRA

COSTA

SAN

BENITO

San Pablo

Bay

-122°

38°

37°

0 7 14 KILOMETERS

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016 0 7 14 MILES

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

Map

area

CALIF.

Probability of liquefaction

<0.05

>0.5

0.4 to 0.5

0.3 to 0.4

0.2 to 0.3

0.1 to 0.2

0.05 to 0.1

Not assessed

Susceptible soils with 

zero probability of 

HayWired scenario 

mainshock-induced 

liquefaction

EXPLANATION

Outline—Marks the area 

where Holzer and 

others' (2008, 2010) 

methods are used to 

map liquefaction 

probability

P A C I F I C O C E A N

San Francisco Bay

Figure 1.4. Map of the San Francisco Bay region, California, showing the final liquefaction probability data used for the 

lifeline exposure analysis.
66 The HayWired Earthquake Scenario—Societal Consequences

Appendix 2. Individual Lifeline Exposure to Individual HayWired Scenario 

Hazards

By Jamie L. Jones,1 Laurel R. Ballanti,1 Amy E. Schweikert,1 and Anne M. Wein1

Each lifeline infrastructure system (transportation, 

water supply and wastewater, oil and gas, electric power, and 

telecommunications infrastructure) was analyzed for exposure 

to a series of hazards caused by the HayWired earthquake 

scenario (surface rupture, mainshock and aftershock 

shaking, liquefaction probability, landslide probability, 

and fire following earthquake). The descriptions for each 

hazard intensity class are described in the HayWired hazard 

descriptions and classifications section of this chapter. Table 

2.1 summarizes lifeline crossings with measurable coseismic 

slip and potential total slip (owing to afterslip), as well as 

coseismic slip ranges. Figure 2.1 provides a visual summary 

of infrastructure intersections with the Hayward Fault, 

aggregating infrastructure crossings at 250-meter intervals. 

Tables 2.2–2.6 show the amounts and percentages of lifeline 

infrastructure exposed to each remaining individual hazard 

by hazard intensity class in the study area. Finally, table 2.7 

shows the amount of lifeline infrastructure exposed to two 

different hazards in the study area (including the amount 

where at least one of the two hazards is high-intensity). 1

 U.S. Geological Survey.

Table 2.1. Surficial coseismic slip and afterslip effects on lifeline infrastructure as a result of the HayWired earthquake scenario 

mainshock in the San Francisco Bay region, California.

[Afterslip includes all segments of the Hayward Fault within the HayWired scenario, including those where surface coseismic slip is zero. “Exposure in zone of 

uncertainty” column presents results in kilometers (km) or number of facilities. m, meters; km, kilometers; BART, Bay Area Rapid Transit; POL, petroleum, oil, 

and lubricants; AM amplitude modulation; FM, frequency modulation; NTSC, National Television System Committee]

Lifelines

Coseismic slip Afterslip

Surface slip 

range (m)

Fault trace 

crossings

Exposure in zone of 

uncertainty

Fault trace 

crossings

Exposure in zone of 

uncertainty

Transportation

Roadways, highway (km) 0.00–2.0 27 3.49 37 5.19

Roadways, secondary (km) 0.00–2.1 80 10.14 127 13.20

Roadways, surface (km) 0.00–2.1 270 30.09 424 37.41

Railways (km) 0.00–1.6 4 0.34 8 0.66

BART facilities 0 0 0 0 0

Highway bridges 0.00–1.0 0 3 0 6

Ports 0 0 0 0 0

Water supply and wastewater

Transmission conveyance systems (km) 0.00–0.9 3 0.18 13 0.82

Dams 0 0 0 0 0

Drinking-water sources 0 0 0 0 0

Drinking-water treatment plants 0 0 0 0 0

Wastewater treatment plants 0 0 0 0 0

Oil and gas

Pipelines, natural gas (km) 0.00–1.9 7 0.50 14 0.93

Pipelines, petroleum (km) 0.00–2.1 8 0.74 11 0.90

Natural gas compressor stations 0 0 0 0 0

Natural gas processing plants 0 0 0 0 0

Natural gas receipt and delivery points 0 0 0 0 0
Chapter T. Lifeline Infrastructure and Collocation Exposure 67

Table 2.1.—Continued

Lifelines

Coseismic slip Afterslip

Surface slip 

range (m)

Fault trace 

crossings

Exposure in zone of 

uncertainty

Fault trace 

crossings

Exposure in zone of 

uncertainty

Oil and gas—Continued

Natural gas storage facilities 0 0 0 0 0

POL terminals, storage facilities, tank farms 0 0 0 0 0

Oil refineries 0 0 0 0 0

Electric power

Transmission lines (km) 0.00–1.7 8 0.65 37 1.69

Electric power generation plants 0 0 0 0 0

Energy distribution control facilities 0 0 0 0 0

Substations 0 0 0 0 0

Telecommunications

Fiber routes, long-haul (km) 0.00–1.6 21 1.69 36 3.50

Fiber routes, interoffice (km) 0.00–2.1 102 8.46 191 16.90

Cellular sites 0.00–1.9 0 5 0 6

Fiber-lit buildings, data centers 0 0 0 0 0

Fiber-lit buildings, central offices 0 0 0 0 0

Fiber-lit buildings, point of presence 0.15–1.0 0 8 0 8

Wireless switch offices 0 0 0 0 0

Internet exchange points 0 0 0 0 0

Microwave towers 1.0–1.0 0 1 0 1

AM radio antennas 0 0 0 0 0

FM radio antennas 0 0 0 0 0

NTSC television transmitters 0 0 0 0 0

Digital television transmitters 0 0 0 0 0
68 The HayWired Earthquake Scenario—Societal Consequences

men19-XXXX_figXX Number of lifelines crossing fault rupture

0

5

10

15

20

25

Richmond

San Pablo

East Richmond Heights

El Cerrito

Kensington

Berkeley

Oakland

San Leandro

Castro Valley

Ashland

Cherryland

Hayward

Union City

Fremont

Cities along Hayward Fault rupture, in 250-meter steps

EXPLANATION

Transmission; coseismic slip and afterslip

Transmission; afterslip only

Distribution; coseismic slip and afterslip

Distribution; afterslip only

Distribution and transmission lifeline infrastructure 

crossings that have measurable surface slip

"

" "

"

"

"

"

Map

area CALIF.

San Francisco Oakland

Hayward

Alameda

Fremont

Livermore

Hayward Fault rupture

San Pablo Bay

Figure 2.1. Schematic illustration showing the number of lifelines that cross the ruptured length of the Hayward Fault within the 

HayWired scenario in the San Francisco Bay region, California. Lifelines are shown in 250-meter segments. The chart reads from San 

Pablo Bay at the northwestern edge of the fault rupture (left) to Fremont at the southeastern edge of the fault rupture (right). Segments 

colored blue are potentially only affected by surface slip as a result of afterslip in the days, weeks, and months following the HayWired 

scenario mainshock; segments colored orange are affected by surface slip as a result of coseismic slip as well as afterslip.
Chapter T. Lifeline Infrastructure and Collocation Exposure 69

Table 2.2. Exposure of transportation infrastructure to HayWired earthquake scenario hazards in the San Francisco Bay 

region, California.

[Exposure is detailed by amount (kilometers or number of facilities) and percentage (relative to the entire study region) of affected infrastructure. 

Owing to rounding, amounts and percentages within exposure classes for an individual hazard may not add up to the total exposed. BART, Bay Area 

Rapid Transit; km, kilometers; %, percent]

Total in study area Roadways, 

highway (km)

Roadways, 

secondary 

(km)

Roadways, 

surface (km)

Railways 

(km)

BART 

facilities

Highway 

bridges Ports

Mainshock ground shaking

Low exposure 1,989 7,595 30,699 916 8 1,361 90

Percentage exposed 43% 46% 44% 34% 17% 39% 13%

Moderate exposure 2,267 7,621 34,233 1,607 33 1,867 586

Percentage exposed 49% 47% 49% 59% 70% 53% 84%

High exposure 368 1,138 4,967 203 6 292 20

Percentage exposed 8% 7% 7% 7% 13% 8% 3%

Total exposure 4,624 16,353 69,899 2,726 47 3,520 696

Percentage exposed 100% 100% 100% 100% 100% 100% 100%

Liquefaction

Low exposure 522 2,095 8,189 291 12 549 10

Percentage exposed 11% 13% 12% 11% 26% 16% 1%

Moderate exposure 633 2,102 7,836 400 21 550 14

Percentage exposed 14% 13% 11% 15% 45% 16% 2%

High exposure 179 314 1,523 90 1 171 45

Percentage exposed 4% 2% 2% 3% 2% 5% 6%

Total exposure 1,334 4,511 17,548 781 34 1,270 69

Percentage exposed 29% 28% 25% 29% 72% 36% 10%

Landslide

Low exposure 306 1,022 4,418 180 12 558 2

Percentage exposed 7% 6% 6% 7% 26% 16% <1%

Moderate exposure 14 54 233 8 0 82 0

Percentage exposed <1% <1% <1% <1% 0% 2% 0%

High exposure 7 42 276 5 0 20 1

Percentage exposed <1% <1% <1% <1% 0% 1% <1%

Total exposure 327 1,118 4,927 193 12 660 3

Percentage exposed 7% 7% 7% 7% 26% 19% <1%

Fire following earthquake

Percentage developed1 78% 67% 65% 56% 100% 87% 17%

Low exposure 737 2,747 10,847 328 14 712 5

Percentage exposed 16% 17% 16% 12% 30% 20% 1%

Moderate exposure 144 541 1,883 82 12 194 3

Percentage exposed 3% 3% 3% 3% 26% 6% <1%

High exposure 47 151 777 40 3 31 2

Percentage exposed 1% 1% 1% 1% 6% 1% <1%

Total exposure 928 3,439 13,507 450 29 937 10

Percentage exposed 20% 21% 19% 17% 62% 27% 1%
70 The HayWired Earthquake Scenario—Societal Consequences

Table 2.2.—Continued

Total in study area Roadways, 

highway (km)

Roadways, 

secondary 

(km)

Roadways, 

surface (km)

Railways 

(km)

BART 

facilities

Highway 

bridges Ports

Aftershocks

3 months2 150 405 2,097 54 0 104 3

1 year3 392 1,271 4,966 150 0 362 2

Total exposed4 303 1,025 4,363 105 0 294 3

Percentage exposed 7% 6% 6% 4% 0% 8% <1%

1

Percentage developed refers to the percentage of an individual lifeline infrastructure system found in areas classified as low-, moderate-, or high-intensity 

developed land by the 2011 National Land Cover Database.

2

This count includes the lifeline infrastructure length or facility count exposed to all aftershocks of magnitude (M) 5 or greater during the first three months 

after the HayWired scenario mainshock. If a lifeline is exposed to multiple aftershocks, it is counted each time it is exposed. Aftershocks included in this 

category are a M6.2 event with a hypocenter in Palo Alto on May 28, 2018, at 4:47 a.m., a M5.7 event with a hypocenter in Palo Alto on May 28, 2018, at 11:53 

p.m., a M5.2 event with a hypocenter in Palo Alto on June 23, 2018, at 8:27 p.m., and a M5.3 event with a hypocenter in Palo Alto on July 1, 2018, at 11:19 a.m.

3

This count includes the lifeline infrastructure length or facility count exposed to all aftershocks M5 or greater between 3 and 12 months after the HayWired 

scenario mainshock. If a lifeline is exposed to multiple aftershocks, it is counted each time it is exposed. Aftershocks included in this category are a M6.0 event 

with a hypocenter in Mountain View on September 30, 2018, at 8:16 p.m., a M6.4 event with a hypocenter in Cupertino on October 1, 2018, at 12:33 a.m., and a 

M5.4 event with a hypocenter in Sunnyvale on October 1, 2018, at 2:24 a.m.

4

Total exposed to aftershocks is the count of exposed lifeline infrastructure length or facilities affected by at least moderate shaking from one or more aftershocks (regardless of the number of aftershocks to which the lifeline infrastructure are exposed). 

Table 2.3. Exposure of water supply and wastewater infrastructure to HayWired earthquake scenario hazards in the San Francisco 

Bay region, California.

[Exposure is detailed by amount (kilometers or number of facilities) and percentage (relative to the entire study region) of affected infrastructure. Owing to 

rounding, amounts and percentages within exposure classes for an individual hazard may not add up to the total exposed. km, kilometers; %, percent]

Total in study area Transmission conveyance 

systems (km) Dams Drinking-water sources

Drinking-water 

treatment plants

Wastewater 

treatment plants

Mainshock ground shaking

Low exposure 305 75 2,115 1,205 33

Percentage exposed 35% 55% 58% 65% 36%

Moderate exposure 488 48 1,459 608 54

Percentage exposed 56% 35% 40% 33% 59%

High exposure 85 14 74 44 5

Percentage exposed 10% 10% 2% 2% 5%

Total exposure 879 137 3,648 1,857 92

Percentage exposed 100% 100% 100% 100% 100%

Liquefaction

Low exposure 78 9 242 65 9

Percentage exposed 9% 7% 7% 4% 10%

Moderate exposure 87 2 203 43 15

Percentage exposed 10% 1% 6% 2% 16%

High exposure 8 7 8 0 8

Percentage exposed 1% 5% <1% 0% 9%

Total exposure 173 18 453 108 32

Percentage exposed 20% 13% 12% 6% 35%
Chapter T. Lifeline Infrastructure and Collocation Exposure 71

Table 2.3.—Continued

Total in study area Transmission conveyance 

systems (km) Dams Drinking-water sources

Drinking-water 

treatment plants

Wastewater 

treatment plants

Landslide

Low exposure 76 12 57 36 2

Percentage exposed 9% 9% 2% 2% 2%

Moderate exposure 13 5 4 15 0

Percentage exposed 1% 4% <1% 1% 0%

High exposure 21 2 7 26 0

Percentage exposed 2% 1% <1% 1% 0%

Total exposure 110 19 68 77 2

Percentage exposed 13% 14% 2% 4% 2%

Fire following earthquake

Percentage developed1 28% 40% 54% 40% 76%

Low exposure 63 8 195 44 19

Percentage exposed 7% 6% 5% 2% 21%

Moderate exposure 3 4 19 11 1

Percentage exposed <1% 3% 1% 1% 1%

High exposure 2 3 1 0 0

Percentage exposed <1% 2% <1% 0% 0%

Total exposure 68 15 215 55 20

Percentage exposed 8% 11% 6% 3% 22%

Aftershocks

3 months2 44 1 37 17 4

1 year3 54 2 160 42 5

Total exposed4 39 3 140 32 2

Percentage exposed 4% 2% 4% 2% 2%

1

Percentage developed refers to the percentage of an individual lifeline infrastructure system found in areas classified as low-, moderate-, or high-intensity 

developed land by the 2011 National Land Cover Database.

2

This count includes the lifeline infrastructure length or facility count exposed to all aftershocks of magnitude (M) 5 or greater during the first three months 

after the HayWired scenario mainshock. If a lifeline is exposed to multiple aftershocks, it is counted each time it is exposed. Aftershocks included in this 

category are a M6.2 event with a hypocenter in Palo Alto on May 28, 2018, at 4:47 a.m., a M5.7 event with a hypocenter in Palo Alto on May 28, 2018, at 11:53 

p.m., a M5.2 event with a hypocenter in Palo Alto on June 23, 2018, at 8:27 p.m., and a M5.3 event with a hypocenter in Palo Alto on July 1, 2018, at 11:19 a.m.

3

This count includes the lifeline infrastructure length or facility count exposed to all aftershocks M5 or greater between 3 and 12 months after the HayWired 

scenario mainshock. If a lifeline is exposed to multiple aftershocks, it is counted each time it is exposed. Aftershocks included in this category are a M6.0 event 

with a hypocenter in Mountain View on September 30, 2018, at 8:16 p.m., a M6.4 event with a hypocenter in Cupertino on October 1, 2018, at 12:33 a.m., and a 

M5.4 event with a hypocenter in Sunnyvale on October 1, 2018, at 2:24 a.m.

4

Total exposed to aftershocks is the count of exposed lifeline infrastructure length or facilities affected by at least moderate shaking from one or more aftershocks (regardless of the number of aftershocks to which the lifeline infrastructure are exposed). 
72 The HayWired Earthquake Scenario—Societal Consequences fected infrastructure. Owing to rounding, amounts and percentages within exposure Exposure of oil and gas infrastructure to HayWired earthquake scenario hazards in the San Francisco Bay region, California. Table 2.4. [Exposure is detailed by amount (kilometers or number of facilities) and percentage (relative to the entire study region) of af classes for an individual hazard may not add up to the total exposed. km, kilometers; POL, petroleum, oil, and lubricants; %, percent] Total in study area Pipelines, natural gas (km) Pipelines, petroleum (km) Natural gas compressor stations Natural gas processing plants Natural gas receipt and delivery points Natural gas storage facilities POL terminals, storage facilities, tank farms Oil refineries Mainshock ground shaking Low exposure 3,237 598 7 0 0 2 3 0 Percentage exposed 42% 26% 21% 0% 0% 33% 8% 0% Moderate exposure 4,021 1,532 22 0 2 4 32 4 Percentage exposed 52% 66% 65% 0% 100% 67% 89% 80% High exposure 416 205 5 0 0 0 1 1 Percentage exposed 5% 9% 15% 0% 0% 0% 3% 20% Total exposure 7,674 2,336 34 0 2 6 36 5 Percentage exposed 100% 100% 100% 100% 100% 100% 100% 100% Liquefaction Low exposure 685 137 3 0 0 0 1 0 Percentage exposed 9% 6% 9% 0% 0% 0% 3% 0%

Moderate exposure 781 398 2 0 0 0 5 0Percentage exposed 10% 17% 6% 0% 0% 0% 14% 0%High exposure 92 81 3 0 0 0 12 1

Percentage exposed 1% 3% 9% 0% 0% 0% 33% 20%Total exposure 1,558 616 8 0 0 0 18 1Percentage exposed 20% 26% 24% 0% 0% 0% 50% 20%

Landslide

Low exposure 263 118 1 0 0 0 0 0Percentage exposed 3% 5% 3% 0% 0% 0% 0% 0%Moderate exposure 14 8 0 0 0 0 0 0Percentage exposed <1% <1% 0% 0% 0% 0% 0% 0%High exposure 16 8 0 0 0 0 0 0Percentage exposed <1% <1% 0% 0% 0% 0% 0% 0%Total exposure 293 134 1 0 0 0 0 0Percentage exposed 4% 6% 3% 0% 0% 0% 0% 0%
Chapter T. Lifeline Infrastructure and Collocation Exposure 73

Table 2.4.—Continued

Total in study area Pipelines, natural gas (km) Pipelines, petroleum (km) Natural gas compressor stations Natural gas processing plants Natural gas receipt and delivery points Natural gas storage facilities POL terminals, storage facilities, tank farms Oil refineries

Fire following earthquake

Percentage developed1 42% 46% 74% 0% 100% 33% 94% 100%

Low exposure 753 313 6 0 0 0 9 2

Percentage exposed 10% 13% 18% 0% 0% 0% 25% 40%

Moderate exposure 75 27 0 0 0 0 0 0

Percentage exposed 1% 1% 0% 0% 0% 0% 0% 0%

High exposure 30 22 1 0 0 0 0 0

Percentage exposed <1% 1% 3% 0% 0% 0% 0% 0%

Total exposure 858 362 7 0 0 0 9 2

Percentage exposed 11% 15% 21% 0% 0% 0% 25% 40%

Aftershocks

3 months2 145 0 0 0 0 0 0 0

1 year3 331 0 0 0 0 0 1 0Total exposed4 244 0 0 0 0 0 1 0

Percentage exposed 3% 0% 0% 0% 0% 0% 3% 0%

1 National Land 

ired scenario mainshock. If a lifeline 

5.7 event with a 

1:19 a.m.

M

6.4 event with a 

Alto on July 1, 2018, at 1

M

oped land by the 201 Percentage developed refers to the percentage of an individual lifeline infrastructure system found in areas classified as low-, moderate-, or high-intensity devel

 the HayW

Alto on May 28, 2018, at 4:47 a.m., a 

ario mainshock. If a lifeline is exposed to 

s (regardless of the number of aftershocks to 

5.3 event with a hypocenter in Palo 

) 5 or greater during the first three months after

ired scen

6.2 event with a hypocenter in Palo 

5 or greater between 3 and 12 months after the HayW

View on September 30, 2018, at 8:16 p.m., a 

M

M

M

Alto on June 23, 2018, at 8:27 p.m., and a 

6.0 event with a hypocenter in Mountain M

M

fected by at least moderate shaking from one or more aftershock

Aftershocks included in this category are a 

5.2 event with a hypocenter in Palo M

Aftershocks included in this category are a 

5.4 event with a hypocenter in Sunnyvale on October 1, 2018, at 2:24 a.m. M

1:53 p.m., a Alto on May 28, 2018, at 1

otal exposed to aftershocks is the count of exposed lifeline infrastructure length or facilities af

1

T

Cover Database.

This count includes the lifeline infrastructure length or facility count exposed to all aftershocks of magnitude ( 2

is exposed to multiple aftershocks, it is counted each time it is exposed. hypocenter in Palo 

This count includes the lifeline infrastructure length or facility count exposed to all aftershocks 3

multiple aftershocks, it is counted each time it is exposed. hypocenter in Cupertino on October 1, 2018, at 12:33 a.m., and a 

4

which the lifeline infrastructure are exposed). 
74 The HayWired Earthquake Scenario—Societal Consequences

Table 2.5. Exposure of electric power infrastructure to HayWired earthquake scenario hazards in the San Francisco Bay region, 

California.

[Exposure is detailed by amount (kilometers or number of facilities) and percentage (relative to the entire study region) of affected infrastructure. Owing to 

rounding, amounts and percentages within exposure classes for an individual hazard may not add up to the total exposed. km, kilometers; %, percent]

Total in study area Transmission lines (km) Electric power generation 

plants

Energy distribution control 

facilities Substations

Mainshock ground shaking

Low exposure 6,474 99 0 407

Percentage exposed 48% 38% 0% 40%

Moderate exposure 6,169 141 1 537

Percentage exposed 46% 55% 100% 53%

High exposure 769 18 0 63

Percentage exposed 6% 7% 0% 6%

Total exposure 13,413 258 1 1,007

Percentage exposed 100% 100% 100% 100%

Liquefaction

Low exposure 671 25 0 100

Percentage exposed 5% 10% 0% 10%

Moderate exposure 783 42 0 107

Percentage exposed 6% 16% 0% 11%

High exposure 144 12 0 33

Percentage exposed 1% 5% 0% 3%

Total exposure 1,598 79 0 240

Percentage exposed 12% 31% 0% 24%

Landslide

Low exposure 611 6 0 25

Percentage exposed 5% 2% 0% 2%

Moderate exposure 91 0 0 0

Percentage exposed 1% 0% 0% 0%

High exposure 123 0 0 1

Percentage exposed 1% 0% 0% <1%

Total exposure 825 6 0 26

Percentage exposed 6% 2% 0% 3%

Fire following earthquake

Percentage developed1 24% 70% 0% 73%

Low exposure 601 44 0 132

Percentage exposed 4% 17% 0% 13%

Moderate exposure 28 2 0 10

Percentage exposed <1% 1% 0% 1%

High exposure 23 3 0 7

Percentage exposed <1% 1% 0% 1%

Total exposure 652 49 0 149

Percentage exposed 5% 19% 0% 15%
Chapter T. Lifeline Infrastructure and Collocation Exposure 75

Table 2.5.—Continued

Total in study area Transmission lines (km) Electric power generation 

plants

Energy distribution control 

facilities Substations

Aftershocks

3 months2 200 12 0 39

1 year3 502 19 0 76

Total exposed4 358 18 0 59

Percentage exposed 3% 7% 0% 6%

1

Percentage developed refers to the percentage of an individual lifeline infrastructure system found in areas classified as low-, moderate-, or high-intensity 

developed land by the 2011 National Land Cover Database.

2

This count includes the lifeline infrastructure length or facility count exposed to all aftershocks of magnitude (M) 5 or greater during the first three months 

after the HayWired scenario mainshock. If a lifeline is exposed to multiple aftershocks, it is counted each time it is exposed. Aftershocks included in this 

category are a M6.2 event with a hypocenter in Palo Alto on May 28, 2018, at 4:47 a.m., a M5.7 event with a hypocenter in Palo Alto on May 28, 2018, at 11:53 

p.m., a M5.2 event with a hypocenter in Palo Alto on June 23, 2018, at 8:27 p.m., and a M5.3 event with a hypocenter in Palo Alto on July 1, 2018, at 11:19 a.m.

3

This count includes the lifeline infrastructure length or facility count exposed to all aftershocks M5 or greater between 3 and 12 months after the HayWired 

scenario mainshock. If a lifeline is exposed to multiple aftershocks, it is counted each time it is exposed. Aftershocks included in this category are a M6.0 event 

with a hypocenter in Mountain View on September 30, 2018, at 8:16 p.m., a M6.4 event with a hypocenter in Cupertino on October 1, 2018, at 12:33 a.m., and a 

M5.4 event with a hypocenter in Sunnyvale on October 1, 2018, at 2:24 a.m.

4

Total exposed to aftershocks is the count of exposed lifeline infrastructure length or facilities affected by at least moderate shaking from one or more 

aftershocks (regardless of the number of aftershocks to which the lifeline infrastructure are exposed).
76 The HayWired Earthquake Scenario—Societal Consequences fected infrastructure. Owing to rounding, amounts and percentages within exposure 

Exposure of telecommunications infrastructure to HayWired earthquake scenario hazards in the San Francisco Bay region, California.

Television System Committee] AM, amplitude modulation; FM, frequency modulation; NTSC, National 

Table 2.6.

[Exposure is detailed by amount (kilometers or number of facilities) and percentage (relative to the entire study region) of afclasses for an individual hazard may not add up to the total exposed. km, kilometers; 

Total in study 

area

Fiber 

routes, 

long-haul 

(km)

Fiber 

routes, 

interoffice 

(km)

Cellular 

sites

Fiber-lit 

buildings, 

data 

centers

Fiber-lit 

buildings, 

central 

offices

Fiber-lit 

buildings, 

point of 

presence

Wireless 

switch offices

Internet 

exchange 

points

Microwave 

towers

AM radio 

antennas

FM radio 

antennas

NTSC 

television 

transmitters

Digital 

television 

transmitters

Mainshock ground shaking

Low exposure 2,270 12,649 2,744 17 204 8,902 11 0 1,297 27 218 31 20

Percentage 

exposed 

38% 27% 33% 4% 30% 22% 21% 0% 34% 35% 67% 84% 39%

Moderate 

exposure 

3,025 27,943 4,471 345 436 26,677 39 8 2,192 45 96 6 31

Percentage 

exposed

50% 60% 54% 81% 65% 66% 74% 73% 58% 58% 29% 16% 61%

High exposure 754 6,200 1,096 66 33 5,149 3 3 318 6 12 0 0Percentage 

exposed

12% 13% 13% 15% 5% 13% 6% 27% 8% 8% 4% 0% 0%

Total exposure 6,049 46,793 8,311 428 673 40,728 53 11 3,807 78 326 37 51

Percentage 

exposed 

100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Liquefaction

Low exposure 680 5,934 1,392 205 135 10,144 9 9 612 11 2 0 0

Percentage 

exposed 

11% 13% 17% 48% 20% 25% 17% 82% 16% 14% 1% 0% 0%

Moderate 

exposure 

1,495 7,134 1,581 173 151 12,064 11 2 609 5 6 0 0

Percentage 

exposed

25% 15% 19% 40% 22% 30% 21% 18% 16% 6% 2% 0% 0%

High exposure 260 1,064 389 11 5 2,516 0 0 220 11 0 0 0

Percentage 

exposed

4% 2% 5% 3% 1% 6% 0% 0% 6% 14% 0% 0% 0%

Total exposure 2,435 14,132 3,362 389 291 24,724 20 11 1,441 27 8 0 0

Percentage 

exposed 

40% 30% 40% 91% 43% 61% 38% 100% 38% 35% 2% 0% 0%
Chapter T. Lifeline Infrastructure and Collocation Exposure 77

Table 2.6.—Continued

Total in study 

area

Fiber 

routes, 

long-haul 

(km)

Fiber 

routes, 

interoffice 

(km)

Cellular 

sites

Fiber-lit 

buildings, 

data 

centers

Fiber-lit 

buildings, 

central 

offices

Fiber-lit 

buildings, 

point of 

presence

Wireless 

switch offices

Internet 

exchange 

points

Microwave 

towers

AM radio 

antennas

FM radio 

antennas

NTSC 

television 

transmitters

Digital 

television 

transmitters

Landslide

Low exposure 889 5,356 690 1 41 3,078 1 0 261 2 17 2 3Percentage 

exposed 

15% 11% 8% <1% 6% 8% 2% 0% 7% 3% 5% 5% 6%

Moderate 

exposure 

22 549 33 0 0 37 0 0 21 0 2 0 0

Percentage 

exposed

<1% 1% <1% 0% 0% <1% 0% 0% 1% 0% 1% 0% 0%

High exposure 14 816 13 0 0 32 0 0 18 0 0 0 0Percentage 

exposed

<1% 2% <1% 0% 0% <1% 0% 0% <1% 0% 0% 0% 0%

Total exposure 925 6,721 736 1 41 3,147 1 0 300 2 19 2 3Percentage 

exposed 

15% 14% 9% <1% 6% 8% 2% 0% 8% 3% 6% 5% 6%

Fire following earthquake

Percentage 

developed1

63% 60% 73% 98% 95% 95% 96% 100% 66% 22% 18% 11% 27%

Low exposure 1,029 7,882 2,054 287 158 14,650 11 8 905 9 7 0 0Percentage 

exposed 

17% 17% 25% 67% 23% 36% 21% 73% 24% 12% 2% 0% 0%

Moderate 

exposure 

287 1,284 251 3 38 2,264 1 0 111 0 2 0 0

Percentage 

exposed

5% 2% 3% 1% 6% 6% 2% 0% 3% 0% 1% 0% 0%

High exposure 254 409 119 0 9 540 0 0 40 0 2 0 0Percentage 

exposed

4% 1% 1% 0% 1% 1% 0% 0% 1% 0% 1% 0% 0%

Total exposure 1,570 9,575 2,424 290 205 17,454 12 8 1,056 9 11 0 0Percentage 

exposed 

26% 20% 29% 68% 30% 43% 23% 73% 28% 12% 3% 0% 0%
78 The HayWired Earthquake Scenario—Societal Consequences Table 2.6.—Continued Total in study area Fiber routes, long-haul (km) Fiber routes, interoffice (km) Cellular sites Fiber-lit buildings, data centers Fiber-lit buildings, central offices Fiber-lit buildings, point of presence Wireless switch offices Internet exchange points Microwave towers AM radio antennas FM radio antennas NTSC television transmitters Digital television transmitters Aftershocks 3 months2 92 1,866 461 108 27 3,785 1 4 203 3 2 0 0 1 year3 221 4,353 1,322 226 71 10,363 7 8 596 2 5 0 0 Total exposed4 144 3,108 917 236 59 7,000 5 5 439 1 6 0 0 Percentage exposed 2% 7% 11% 55% 9% 17% 9% 45% 12% 1% 2% 0% 0% 1 National Land ired scenario mainshock. If a 5.7 M Alto on May 28, 2018, at 4:47 a.m., a Alto on July 1, 6.4 event with a M oped land by the 201 Percentage developed refers to the percentage of an individual lifeline infrastructure system found in areas classified as low-, moderate-, or high-intensity devel the HayW 5.3 event with a hypocenter in Palo ) 5 or greater during the first three months after 6.2 event with a hypocenter in Palo ired scenario mainshock. If a lifeline is exposed to s (regardless of the number of aftershocks to 

M

iew on September 30, 2018, at 8:16 p.m., a 

M

M

Alto on June 23, 2018, at 8:27 p.m., and a 

5 or greater between 3 and 12 months after the HayW

6.0 event with a hypocenter in Mountain V

5.2 event with a hypocenter in Palo 

M

M

fected by at least moderate shaking from one or more aftershock

Aftershocks included in this category are a 

M 1:53 p.m., a 

Aftershocks included in this category are a 

5.4 event with a hypocenter in Sunnyvale on October 1, 2018, at 2:24 a.m. M

Alto on May 28, 2018, at 1

1:19 a.m.

otal exposed to aftershocks is the count of exposed lifeline infrastructure length or facilities af

1

T

Cover Database.

This count includes the lifeline infrastructure length or facility count exposed to all aftershocks of magnitude (

2

lifeline is exposed to multiple aftershocks, it is counted each time it is exposed. event with a hypocenter in Palo 2018, at 1

This count includes the lifeline infrastructure length or facility count exposed to all aftershocks 

3

multiple aftershocks, it is counted each time it is exposed. hypocenter in Cupertino on October 1, 2018, at 12:33 a.m., and a 

4

which the lifeline infrastructure are exposed).
Chapter T. Lifeline Infrastructure and Collocation Exposure 79

Table 2.7. Amount of lifeline infrastructure exposed to only two hazards for the HayWired earthquake scenario mainshock in the San 

Francisco Bay region, California.

[In all areas of the study region, ground shaking is present, therefore all results include some level of ground shaking. In each column, the total number of 

kilometers (km) or facilities is shown. The “1+ high” column indicates the total amount of infrastructure from the “Total” column exposed to at least one hazard 

with a high classification. The results in this table are exclusive and can be considered with the numbers in table 9 of the main text, which summarizes the 

exposure of lifeline infrastructure to shaking and coincidence with two or more additional hazards. km, kilometers; BART, Bay Area Rapid Transit; 

POL, petroleum, oil, and lubricants; AM, amplitude modulation; FM, frequency modulation; NTSC, National Television System Committee]

Lifeline Study area 

total

Ground shaking 

and landslide

Ground shaking 

and liquefaction

Ground shaking 

and fire

Total 1+ high Total 1+ high Total 1+ high

Transportation

Roadways, highway (km) 4,624 47 19 557 105 158 19

Roadways, secondary (km) 16,353 262 120 1,645 253 602 42

Roadways, surface (km) 69,899 1,594 767 7,066 1,250 2,936 289

Railways (km) 2,726 25 12 372 84 51 6

BART facilities 47 0 0 9 1 5 0

Highway bridges 3,520 53 25 399 65 143 5

Ports 696 1 0 61 41 2 1

Water supply and wastewater

Transmission conveyance systems (km) 879 82 46 104 24 20 0

Dams 137 10 6 10 2 12 2

Drinking-water sources 3,648 29 10 241 21 9 0

Drinking-water treatment plants 1,857 64 45 81 7 23 0

Wastewater treatment plants 92 1 0 23 8 11 1

Oil and gas

Pipelines, natural gas (km) 7,674 103 58 828 108 154 10

Pipelines, petroleum (km) 2,336 61 33 345 75 102 8

Natural gas compressor stations 34 0 0 4 3 2 0

Natural gas processing plants 0 0 0 0 0 0 0

Natural gas receipt and delivery points 2 0 0 0 0 0 0

Natural gas storage facilities 6 0 0 0 0 0 0

POL terminals, storage facilities, tank farms 36 0 0 10 7 1 0

Oil refineries 5 0 0 0 0 1 0

Electric power

Transmission lines (km) 13,413 623 339 970 223 130 14

Electric power generation plants 258 0 0 39 10 8 2

Energy distribution control facilities 1 0 0 0 0 0 0

Substations 1,007 4 4 128 30 34 7
80 The HayWired Earthquake Scenario—Societal Consequences

Table 2.7.—Continued

Lifeline Study area 

total

Ground shaking 

and landslide

Ground shaking 

and liquefaction

Ground shaking 

and fire

Total 1+ high Total 1+ high Total 1+ high

Telecommunications

Fiber routes, long-haul (km) 6,049 96 33 929 168 100 11

Fiber routes, interoffice (km) 46,793 4,186 2,260 5,554 1,379 1,347 98

Cellular sites 8,311 205 124 1,216 402 316 24

Fiber-lit buildings, data centers 428 0 0 0 102 1 27

Fiber-lit buildings, central offices 673 2 0 0 121 19 11

Fiber-lit buildings, point of presence 40,728 281 10 156 9,276 474 2,101

Wireless switch offices 53 0 0 8 2 0 0

Internet exchange points 11 0 0 3 2 0 0

Microwave towers 3,807 150 66 513 119 134 8

AM radio antennas 78 0 0 16 8 0 0

FM radio antennas 326 16 7 2 0 4 0

NTSC television transmitters 37 2 0 0 0 0 0

Digital television transmitters 51 3 0 0 0 0 0
Chapter T. Lifeline Infrastructure and Collocation Exposure 81

Appendix 3. Electric Power Assessment using Hazus-MH

By Jamie L. Jones,1 Anne M. Wein,1 and David T. Witkowski2 

Loss of electrical power can be caused by failure of 

central generation facilities, like substations, and by physical 

failure of transmission lines and service delivery lines 

(through toppling, shear breaks in conduits, stretch breaks 

in conduits or aerial lines, or physical impacts to aerial lines 

from trees or other objects). Electrical power in California 

is primarily generated by burning natural gas (49.9 percent) 

and from renewables (27.9 percent: biomass, geothermal, 

small hydroelectric [local hydroelectric systems that generally 

produce less than 30 megawatts of power], solar, and wind) 

and the remainder is from nuclear, coal, and hydroelectric 

sources (California Energy Commission, 2017). These sources 

are subject to failure either through direct damage or from 

impacts to the fuel delivery systems. Power is distributed 

from generation sources via aerial transmission lines to 

substations, which monitor, route, and control the flow of 

electricity to voltage transformers (either pole-mounted or 

located in underground vaults), and ultimately to homes and 

businesses. Loss of electrical power in distribution can be 

caused by direct damage to substations, toppling of poles, 

flooding of underground vaults, breakage of distribution lines, 

or disconnection at the customer site. For example, during 

the 2014 South Napa earthquake in California, damage to 

buildings cut off mainline power to a telephone central office 

building, requiring the facility to utilize backup generators and 

batteries to stay online (Noyes, 2014).

Conservatively, no individual electric power generation 

facility in the San Francisco Bay region produces more than 

10 percent of the overall energy use share (M. Germeraad, 

Metropolitan Transportation Commission and Association of 

Bay Area Governments, written commun., 2018). However, 

the northern waterfront in Contra Costa County has a high 

density of larger power generation facilities.

Ground failure (liquefaction, landslide, and so on) 

affecting buried systems, rather than shaking damage to 

aerial poles, is the most likely cause of damage to electric 

transmission lines. Overhead electric power systems are 

easier to access and repair than belowground systems; 

whereas power outages may be more extensive, the time 

necessary to restore service is typically shorter (Applied 

Technology Council, 2016). Pacific Gas and Electric 

Company (PG&E) shared that, for the HayWired scenario as 

run through their electric distribution damage model, they 

estimate the work to be evenly distributed between overhead, 

underground, and services.

Facilities associated with the electric system, however, 

are susceptible to all earthquake hazards. In addition, 

facilities using nonrenewable energy sources are more likely 

to have stronger functional interactions with infrastructure 

that supplies fuels (for example, natural gas pipelines that 

feed a natural gas power plant) (M. Germeraad, Metropolitan 

Transportation Commission and Association of Bay Area 

Governments, written commun., 2018). Overall, PG&E 

reported that, in the distribution system, slightly less than 

half of the damage will result from shaking, followed by 

damage resulting from ground deformation. Electric power 

interruption, especially in areas with little to no extensive 

building damage, has historically been caused by damage 

to substations (M. Germeraad, Metropolitan Transportation 

Commission and Association of Bay Area Governments, 

written commun., 2018).

In terms of restoration, PG&E identified the limitations 

owing, in part, to access issues and transportation challenges 

that will occur after the earthquake. In addition, as with all 

major or catastrophic incidents, restoration timing is driven 

by resources, materials, and equipment with varying lead 

times, ability to secure external assistance, and weather 

impacts. Any restoration estimates provided would likely be 

affected by any number of factors that are outside of PG&E’s 

control (E. Hickey and G. Molnar, PG&E, written commun., 

2019). Aftershocks would be a major concern for employees 

involved in the restoration effort, and whereas most of the 

damage would be created by the mainshock scenario, PG&E 

expects aftershocks and repeated damage assessments would 

also impact the restoration effort. The overall restoration and 

assessment would likely be completed within the peripheral 

areas, as the effort converges towards the most heavily 

damaged areas (E. Hickey and G. Molnar, PG&E, written 

commun., 2019).

Service restoration would also be dictated by priorities 

that were determined by the affected counties to be critical 

and essential to the local communities. Even though emphasis 

would be placed on restoring service to the majority of 

customers as quickly as possible, PG&E would focus its 

efforts on providing power to critical facilities such as 

hospitals, water treatment and pumping plants, fire stations, 

and others. This prioritization would minimize further 

impact to the affected areas and enable a foundation for first 

responders to attend to the needs of its affected citizens

(E. Hickey and G. Molnar, PG&E, written commun., 2019).

1

 U.S. Geological Survey.

2

 Joint Venture Silicon Valley.
82 The HayWired Earthquake Scenario—Societal Consequences

To evaluate interdependencies, substation damage and 

power outages were estimated by the Federal Emergency 

Management Agency using its Hazus-MH 2.1 (hereafter 

referred to as Hazus) loss-estimation application (Federal 

Emergency Management Agency, 2012). The Hazus results 

estimate damages to substations resulting from ground 

shaking and Hazus assessment of the liquefaction hazard 

(using Knudsen and others, 2000). County power restoration 

estimates were also estimated using Hazus. Some notes on 

these results from PG&E are included for comparison to the 

Hazus analysis.

Results

Hazus results for shaking and liquefaction damages to 

electric power substations are expressed as the percentage 

of electric subcomponent failures (table 3.1). Of nearly 

1,800 substations analyzed by Hazus, 44 were estimated to 

have higher than 50 percent subcomponent failure. Most of 

these substations are located in Alameda and Contra Costa 

Counties within 5 kilometers of the Hayward Fault, though a 

small number of substations on one trunk line near the City 

Table 3.1. Electric substation analysis groupings and exposure 

determined using Hazus-MH 2.1.

[Functionality percentages are only estimated for 6 time steps in Hazus: 1-day, 

3-day, 7-day, 14-day, 30-day, and 90-day]

Damage 

class Description

Number of 

substations 

in class

Repair 

duration, 

in months1

<25 percent Likely damage restricted 

to less than 25 

percent of substation 

subcomponents

1,4022 0–1.5

25–50 percent Likely damage between 

25 and 50 percent 

of substation 

subcomponents

190 0.5–??3

50–75 percent Likely damage between 

50 and 75 percent 

of substation 

subcomponents

43 1.5–??3

>75 percent Likely damage affects 

at least 75 percent 

of substation 

subcomponents

1 1.5

1

Repair durations are estimated by referring to the estimated percentage of 

functionality restored to a substation calculated by Hazus. If the functionality 

percentage was 99 or higher, the substation was considered repaired.

2

The number of substations listed here only include those within the hazard 

extent used for the analyses completed in the body of the lifelines chapter. The 

total including substations outside this hazard extent is 1,773.

3

If the estimated functionality percentage was not 99 at the 90-day mark, 

then no further assumptions could be made about the repair duration and 

the high range value was listed as “??”. This affects 37 substations in the 

25–50 percent class (19 percent) and 5 substations in the 50–75 percent class 

(12 percent).

of Livermore in Alameda County (running north into Contra 

Costa County) also have estimated subcomponent failure 

percentages above 50 percent.

Table 3.1 also provides an estimate of the repair duration 

for ranges of subcomponent failure. Functionality percentages 

are estimated at 6 time steps for each facility: 1 day, 3 days, 

7 days, 14 days, 30 days, and 90 days. In addition, Hazus 

does not assign a full functionality percentage of 100 to any 

facility. In order to assign repair durations to the substations, 

the time step at which the substation reached 99 or greater was 

selected as the duration. For a small number of substations, the 

functionality percentage was still less than 99 at 90 days (the 

percentage was between 96 and 98 for all substations in this 

situation). The unknown restoration times are reflected in table 

3.1 using the notation “??”. All substations where less than 

25 percent of subcomponents were estimated to be damaged 

would likely be repaired within 90 days of the HayWired 

scenario mainshock, as would around 80 percent of substations 

that have between 25 and 50 percent of subcomponents 

damaged, and around 90 percent of substations that have 

between 50 and 75 percent of subcomponents damaged. 

Interestingly, the one substation with greater than 75 percent 

subcomponent damage is estimated to be repaired within 

90 days, whereas some substations with lower subcomponent 

damage are estimated to take longer than 90 days to be 

repaired. Though the exact reason for this in the Hazus 

model is not investigated, it does reflect the fact that different 

facilities built at different times to different standards would 

likely fare differently in a major earthquake event.

Table 3.2 shows estimates for the restoration of electric 

power service to households by county for five time steps: 

after 1 day, 3 days, 1 week, 1 month, and 3 months. After 

day 1, Hazus estimates that 1.4 million households (2000 

census data) will lose power. According to Hazus, the 

largest numbers (350,000–360,000) and percentages (25–26 

percent) of households are without power on the first day in 

Alameda and Santa Clara County. Alameda County takes 

the longest to restore power to 90 percent of its households, 

with 16.7 percent of households (around 87,400 households) 

still without power after one week (followed by Santa Clara 

County at 8.7 percent, or 49,230 households). Santa Clara 

County has the highest proportion of power restored to 

households in the first three days, at 33.5 percent (189,564 

households). The largest improvements in household power 

restoration occur in the first three days, but three counties 

(Alameda, Contra Costa, and Santa Clara) have power 

restoration percentages between 17 and 23 percent between 

day 3 and day 7. By the end of the first week after the 

HayWired scenario mainshock, only 6 percent of households 

are without power, and by the end of the first month less than 

2 percent of households are without power. However, 

0.1 percent of households (3,393 households) will potentially 

still be without power after three months.

Note that the electric power restoration estimates from 

Hazus are calculated at the census-tract level based on the 

tract’s total population and each substation’s power outage 

probability. Power service outages are assumed to be dependent 
Chapter T. Lifeline Infrastructure and Collocation Exposure 83

Table 3.2. Electric power restoration estimated using Hazus-MH 2.1 for 15 counties for select time steps after the HayWired 

earthquake scenario mainshock in the San Francisco Bay region, California.

[Numbers in this table represent the percentage of households with power restored for a county]

County Total number of households Time since HayWired mainshock

1 day 3 days 1 week 1 month 3 months

Alameda 523,366 32 61 83 96 100

Contra Costa 344,129 43 74 91 98 100

Marin 100,650 73 93 99 100 100

Merced 63,815 89 98 100 100 100

Monterey 121,236 94 99 100 100 100

Napa 45,402 89 98 100 100 100

Sacramento 453,602 98 100 100 100 100

San Benito 15,885 63 89 98 100 100

San Francisco 329,700 53 84 97 100 100

San Joaquin 181,629 78 96 100 100 100

San Mateo 254,103 47 79 95 99 100

Santa Clara 565,863 37 70 91 98 100

Santa Cruz 91,139 83 97 100 100 100

Solano 130,403 79 95 99 100 100

Sonoma 172,403 96 100 100 100 100

Region total 3,393,325 60 82 94 99 100

on the functionality (or lack thereof) of the region’s substations. 

Hazus uses a weighted combination to determine the estimated 

power restoration/outage for each substation using the 

probability of subcomponent restoration at each Hazus damage 

state. The average for each county is established based on 

the outage percentages for each substation relative to each 

substation’s service area (Federal Emergency Management 

Agency, 2012). The combination of methods here leads to the 

possibility that power could be essentially completely restored 

even though not all substations have been completely restored 

to full functionality.

Unrelated to the provided Hazus results, PG&E (using 

their proprietary model) estimate independently that around 

700,000 customers would lose power across their customer 

base as a result of the HayWired scenario mainshock. 

PG&E’s estimates for overall restoration times are consistent 

with the estimates provided by Hazus, but prior restoration 

events within PG&E service territories suggest that the 

majority of restoration effort would come from distribution 

system damage instead of substation damage. So, despite the 

fact that substations may be restored in significantly less time 

than what was stipulated by Hazus, the damage within the 

distribution system would prevent customers from receiving 

service until available distribution crews have sufficient time 

to assess and restore service. It should also be noted that 

PG&E has 959 substations in its service territory, but Hazus 

includes substations from other utilities (for example, those 

owned by local water utility and energy companies) that 

would also be impacted by the HayWired scenario mainshock 

(E. Hickey and G. Molnar, PG&E, written commun., 2019).

Comparing Hazus Electric Power Assessment 

with Chapter Exposure Results

Hazus does not factor in the potential damages resulting 

from landslide and fire hazards. Figure 3.1 maps Hazusestimated electric substation subcomponent failure in relation 

to the ground failure hazard datasets generated in this chapter. 

Figures 3.2 and 3.3 map estimated substation failure and 

substation repair time relative to multi-hazard exposure. 

Around one quarter of the substations analyzed by Hazus 

are also potentially impacted by landslide or fire following 

earthquake (refer to table 3.3 for specifics) in addition to 

shaking and liquefaction. In the HayWired scenario analysis, 

fire in urban areas is more of a concern than landslides. Fire 

can affect substations by causing equipment shorts as a result 

of ash and soot deposition on bushings and buses; however, 

the standoff distance from the perimeter fence is generally 

sufficient for protecting substations from radiant heat

(C. Scawthorn, SPA Risk LLC, written commun., 2018). 

Several substations in Berkeley (between 25 and 50 percent 

subcomponent damage) and Richmond (more than 50 percent 

subcomponent damage) are also exposed to high firefollowing-earthquake intensity. One substation (between 50 

and 75 percent subcomponent damage) on the Oakland-Orinda 

border is exposed to high landslide intensity.

Less than 15 percent of substations that are assigned less 

than 25 percent subcomponent damage may be affected by 

hazards other than ground shaking, with liquefaction being 

the most expansive (approximately 11 percent), followed by 

fire following earthquake (approximately 5 percent). Over 
84 The HayWired Earthquake Scenario—Societal Consequences

90 percent of substations that are assigned between 25 and 

50 percent subcomponent damage and between 50 and 75 

percent subcomponent damage are affected by liquefaction, 

landslide, or fire following earthquake, with the damages 

following a similar pattern of hazard exposure as the group 

with less than 25 percent estimated subcomponent damage. 

The substation that is assigned more than 75 percent 

subcomponent damage may potentially be affected by 

landslides and fire following earthquake.

In general, the pattern of substation damages is 

consistent with the multi-hazard dataset (see table 3.4 for 

details). The lowest damage class (substations with less than 

25 percent subcomponent damage) has the greatest amount 

of exposure to low and moderate multi-hazard intensity, 

and the highest damage class is also in the very high hazard 

intensity class. The 25 to 50 percent class and the 50 to 

75 percent class are mostly exposed to moderate-high or 

high multi-hazard intensity, where the 25 to 50 percent 

Map

area CALIF.

Fremont

Hayward

Union City

Newark

-122°

Richmond

Pinole Hercules

El Cerrito

San Pablo

HAYWARD FAULT

HAYWARD FAULT

CONTRA COSTA

ALAMEDA

SAN

MATEO

SANTA

CRUZ

SANTA

CLARA

SAN

FRANCISCO

SOLANO

San Francisco Bay

San Pablo

Bay

122°30’ 122°

38°

37°

30’

MARIN

SONOMA NAPA

A

A

B B

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

San Pablo Bay

Coseismic slip along Hayward Fault, 

in meters

<0.5

0.5–1.0

1.0–1.5

1.5–2.0

>2.0

No surface offset

Electric transmission substation 

subcomponent damage level, 

in percent

<25

25 to 50

50 to 75

>75

Liquefaction/landslide 

probability, in percent

>25

5–25

<5

EXPLANATION

Electric transmission line—

Lines appear thicker 

where separate lines are 

parallel

0 6 12 KILOMETERS

0 6 12 MILES

0 1 2 KILOMETERS

0 1 2 MILES

Figure 3.1. Map of the San Francisco Bay region, California, showing the ground failure (coseismic slip, 

landslide, and liquefaction) exposure of the electric transmission system (electric transmission lines and 

substations) relative to the electric transmission substation subcomponent damage level estimated by 

Hazus-MH 2.1 (Federal Emergency Management Agency, 2012) for the HayWired earthquake scenario.
Chapter T. Lifeline Infrastructure and Collocation Exposure 85

MARIN

CONTRA

COSTA

ALAMEDA

SAN

MATEO

San

Joaqu

SANTA

CLARA

SOLANO

SONOMA

NAPA

S a n F r a n c i s c o B a y

San Pablo

Bay

P A C I F I C O C E A N

SACRAMENTO

SANTA

CRUZ

STANISLAUS

SAN

JOAQUIN

San Jose

SAN FRANCISCO

Oakland

Fremont

Hayward

Alameda

Livermore

Richmond

Foster City

Palo

Alto

122°30’ 122° 121°30’

38°

37°

30’

0 4 8 KILOMETERS

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016 0 4 8 MILES

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

Map

area CALIF.

Electric transmission line

multi-hazard exposure

Low

Moderate

Moderate-high

High

Very high

EXPLANATION

Electric transmission 

substation subcomponent 

damage level, in percent

25–50

<25

50–75

>75

Figure 3.2. Map of the San Francisco Bay region, California, showing the multi-hazard exposure of the electric transmission system 

(electric transmission lines and substations) relative to the electric transmission substation subcomponent damage level estimated by 

Hazus-MH 2.1 (Federal Emergency Management Agency, 2012) for the HayWired earthquake scenario.
86 The HayWired Earthquake Scenario—Societal Consequences

class is proportionally more exposed to the moderatehigh multi-hazard class and the 50 to 75 percent class is 

proportionally more exposed to the high multi-hazard class. 

A few substations do not fit this pattern, however. More 

than 5 substations estimated to experience between 25 and 

50 percent subcomponent damage are in areas with very 

high multi-hazard intensity resulting from high shaking and 

high liquefaction intensity—several of these are in the cities 

of Menlo Park and Palo Alto in San Mateo and Santa Clara 

Counties, respectively, and others are in the cities of Hercules 

MARIN

CONTRA

COSTA

ALAMEDA

SAN

MATEO

San

Joaqu

SANTA

CLARA

SOLANO

SANTA

CRUZ

STANISLAUS

SAN

JOAQUIN

SACRAMENTO

SONOMA

NAPA

PACIFIC OCEAN

S a n F r a n c i s c o B a y

San Pablo

Bay

P A C I F I C O C E A N

San Jose

SAN FRANCISCO

Oakland

Fremont

Hayward

Alameda

Livermore

Richmond

Foster City

Palo

Alto

122°30’ 122° 121°30’

38°

37°

30’

Map

area CALIF.

0 4 8 KILOMETERS

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016 0 4 8 MILES

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

Electric transmission line

multi-hazard exposure

Low

Moderate

Moderate-high

High

Very high

EXPLANATION

Electric transmission 

substation subcomponent 

repair time

1 to 3 months

More than 3 months

2 to 4 weeks

1 to 2 weeks

3 to 7 days

1 to 3 days

Less than 1 day

Figure 3.3. Map of the San Francisco Bay region, California, showing the multi-hazard exposure of the electric transmission system 

(electric transmission lines and substations) relative to the transmission substation repair time estimated by Hazus-MH 2.1 (Federal 

Emergency Management Agency, 2012) for the HayWired earthquake scenario.
Chapter T. Lifeline Infrastructure and Collocation Exposure 87

Table 3.3. Summary of electric substation shaking damages estimated using Hazus-MH 2.1 relative to exposure of ground-movement 

and fire-following-earthquake hazards for the HayWired earthquake scenario mainshock in the San Francisco Bay region, California.

[Liquefaction exposure is included because the analysis in this chapter uses an improved liquefaction probability dataset rather than the default Hazus 

liquefaction analysis, which is reliant on liquefaction susceptibility and tract centroid locations]

Damage 

class Description Coseismic 

slip

Landslide intensity Liquefaction intensity Fire following earthquake 

intensity

Low Moderate High Low Moderate High Low Moderate High

<25 percent Likely damage restricted to less 

than 25 percent of substation 

subcomponents

0 4 0 0 75 64 12 76 0 0

25–50 percent Likely damage between 25 

and 50 percent of substation 

subcomponents

0 18 0 0 41 80 37 80 5 3

50–75 percent Likely damage between 50 

and 75 percent of substation 

subcomponents

0 21 1 1 3 21 1 16 3 3

>75 percent Likely damage affects at least 

75 percent of substation 

subcomponents

0 1 0 0 0 0 0 0 0 1

Total 0 44 1 1 119 165 50 172 8 7

and Richmond in Contra Costa County. A couple of substations 

with estimated subcomponent damages between 50 and 75 

percent are also located in areas with very high multi-hazard 

intensity, one caused by high shaking and high fire hazards and 

the other caused by high shaking and high liquefaction hazards; 

these substations are located in or near the cities of Richmond 

and Hayward.

Electric Power Interdependencies

PG&E notes dependencies on equipment and material 

issues, continued aftershocks, and safety concerns. According 

to Yao and others (2004), several potential additional 

interdependencies could affect electric power systems. For 

example, lost power can cause transportation delays and 

impact fuel and materials distribution, leading to increased 

difficulty for electric utility repair vehicles to get themselves 

and their equipment to locations that need restoration. 

Karagiannis and others (2017) point out that most critical 

infrastructure rely on electric power to operate, but, in a 

disaster, some of these infrastructure systems may also be 

supporting electric power system repairs. As such, electric 

power recovery is adversely affected by other systems 

lacking electric power. For example, electric utilities may 

rely on two-way radios or cellular phones to communicate 

during a disaster; these telecommunication systems may be 

significantly less effective when a large-scale power outage 

occurs, making communication between electric utility 

workers more difficult (Karagiannis and others, 2017).

Table 3.4. Summary of electric substation shaking damages estimated using Hazus-MH 2.1 relative to multi-hazard exposure intensity 

classes for the HayWired earthquake scenario mainshock in the San Francisco Bay region, California.

Damage class Description

Multi-hazard intensity

Low Moderate Moderate-high High Very high

<25 percent Likely damage restricted to less than 25 percent of 

substation subcomponents

664 667 60 11 0

25–50 percent Likely damage between 25 and 50 percent of 

substation subcomponents

1 45 91 46 7

50–75 percent Likely damage between 50 and 75 percent of 

substation subcomponents

0 1 27 12 3

>75 percent Likely damage affects at least 75 percent of 

substation subcomponents

0 0 0 0 1

Total 665 713 178 69 11
88 The HayWired Earthquake Scenario—Societal Consequences

Appendix 4. Bay Area Rapid Transit Damage Assessment to HayWired Scenario 

Ground Shaking

By Mark Salmon,1 Jamie L. Jones,2 Anne M. Wein,2 Laurel R. Ballanti,2 and Thomas Horton1

1

 Bay Area Rapid Transit.

2

 U.S. Geological Survey.

The Bay Area Rapid Transit (BART) system in the San 

Francisco Bay region is a major commuter rail system that 

services areas throughout the east bay and the peninsula, 

with future expansions planned in the south bay. With 

annual ridership of around 130 million people in 2016 

(Bay Area Rapid Transit, 2017a), BART is a popular transit 

method for commuters who work on one side of the bay area 

and live on the other side (for example, live in Oakland and 

work in San Francisco).

BART ran HayWired ground shaking data through 

ShakeCast for the original BART system (built between 1966 

and 1976), incorporating the seismic retrofits constructed 

since 2008 as developed by the BART Earthquake Safety 

Program to enhance the seismic performance of BART. 

BART extensions (stations and tracks) built in the 1990s or 

later have been built to a higher seismic design standard (Bay 

Area Rapid Transit, 2017b), and were thus not included in 

the BART Earthquake Safety Program. Therefore, analytical 

fragilities were not developed to analyze facilities in the 

BART extensions. The ShakeCast results provided damage 

estimates for 34 stations and 4 train yards run by BART (nine 

stations were built after 1996 and are not counted in the BART 

analysis; the new Warm Springs station is not included in 

this list, nor are the Milpitas and Berryessa stations currently 

scheduled to open in 2019), as well as the interconnecting 

tunnels and aerial guideways. The BART results classified the 

34 stations, 4 train yards, tunnels, and aerial guideways into 

one of five damage states: green, yellow, orange, hi-orange, 

and red (descriptions of the damage states are in table 4.1). 

Though BART developed a custom analytical fragility 

curve for each of the stations analyzed, the fragilities for 

the buildings and facilities at the train yards were based on 

the default fragility curves from Hazus-MH 2.1 (Federal 

Emergency Management Agency, 2012).

Table 4.1. Damage state definitions and exposure as determined 

by Bay Area Rapid Transit’s ShakeCast analysis in the San 

Francisco Bay region, California.

[BART, Bay Area Rapid Transit]

Damage 

class Description

Number of 

stations/yards 

in class

Repair 

duration, 

in months1

Green Likely minor or no damage 20 0

Yellow Likely operable after short 

term repairs

10 0.5–63

Orange Likely not operable, but 

likely safe

2 3–9

Hi-orange2 Likely not operable, but 

likely safe

5 9–18

Red Likely unsafe or collapsed 1 24–364

1

Repair durations in this table are for the stations and yards only. In many 

cases the interconnecting guideways have longer repair durations, as shown in 

table 4.3.

2

Hi-orange is distinguished from orange to help BART prioritize response 

and repair and indicates a greater degree of damage than orange.

3

The Rockridge station has a repair time of 3–6 months owing to some parts 

of the station receiving orange-class damage. The majority of the station is 

classified as yellow, however, so the station as a whole was classified yellow.

4

The estimated sequential repair time for the buildings at the Hayward 

train yard is 42 months, but BART states that all repairs would be completed 

after no more than 36 months. If repairs are estimated to take longer, multiple 

contracts would be awarded in order to speed up repairs to within the 

36-month time frame.
Chapter T. Lifeline Infrastructure and Collocation Exposure 89

One item to note regarding damage here is that the BART 

Earthquake Safety Program design objective for most of the 

original BART system (excluding the “core BART system”) 

is for safety rather than operability, where either the safety 

design basis earthquake is a probabilistic 500-year return 

period ground motion (or 10 percent probability of ground 

motion in 50 years) or the deterministic median ground motion 

plus one-half standard deviation, whichever is greater. For the 

core BART system, from Daly City to Rockridge Station and 

Downtown Berkeley, the BART Earthquake Safety Program 

design objective also includes operability after short-term 

repairs, where the operability design-basis earthquake is 

typically the deterministic median ground motion (except 

for the Transbay Tube, which is designed for higher ground 

motions). BART identifies the deterministic ground motion for 

the core BART system by using estimates of maximum ground 

motions owing to earthquakes principally on three different 

faults (Concord, Hayward, and San Andreas Faults).

Results

Twenty BART facilities (stations or train yards; 53 

percent) are modeled to experience only minor damage as a 

result of the HayWired scenario mainshock shaking. Another 

10 facilities (26 percent) may experience damage that leaves 

the facilities operable or almost operable. Seven facilities 

(18 percent) are modeled to experience severe enough damage 

to shut the facility down but not be severely enough damaged 

to be considered unsafe (5 facilities are more severely 

impacted than the remaining 2 facilities). Only one facility 

(the Hayward train yard) is expected to experience damage 

severe enough to require a complete rebuild of some of the 

buildings at the facility. A summary of the damage states, 

along with the number of stations and train yards and the 

range of repair times for each class, can be found in table 4.1.

While performing the ShakeCast analysis of the 

BART facilities for the HayWired scenario ground motions, 

BART observed that, in some local areas, the HayWired 

ground motions were considerably stronger than the BART 

Earthquake Safety Program design earthquakes, such as near 

the Hayward train yard, potentially explaining why some 

of the retrofitted facilities at this yard indicate red damage 

potential. Likewise, some stations indicate greater damage 

from the HayWired scenario than would be expected from 

their design earthquakes, owing to locally higher motions 

provided by the HayWired scenario.

Facilities classified as green impact potential would be 

repaired during operation of the facility, whereas facilities 

classified as yellow impact potential would take only a few 

weeks to reopen (with the exception of one station with a 

couple of supports classified as higher damage, which would 

take around 6 months to reopen). Several stations in Alameda 

County that run near the Hayward Fault could take as long as 

3 years to repair; the more extensive repairs to these stations 

would potentially cut off one BART line (the BART extension 

through Castro Valley into Pleasanton) and seriously truncate 

another (the Fremont line, which now extends into the Warm 

Springs neighborhood in Fremont and is currently scheduled 

to extend into Santa Clara County in 2019). Tracks run 

through the same structure as the stations, so it is possible 

that tracks could be repaired a bit earlier and the impaired 

station could be bypassed. System-level damages and repair 

durations by damage state can be found in table 4.1; station 

level damages are provided in table 4.2 and station-level repair 

durations are in table 4.3.

Two of the largest aftershocks in the HayWired scenario 

earthquake sequence were also run through ShakeCast for the 

BART system. BART did not have adjusted fragility curves 

for damaged stations or train yards. Assuming facilities 

were undamaged, BART found that the potential damages 

resulting from the two closest aftershocks—in Union City 

on the same day as the mainshock and in Oakland 33 days 

after the mainshock—are lower than the mainshock, with 

the highest damage potential being orange. The majority of 

potential damages would be along the A-line, which, among 

other stations, includes the Fruitvale, Coliseum/Oakland 

Airport, San Leandro, Bay Fair, and Hayward stations (which 

were classified to have hi-orange mainshock damage) and 

the Hayward train yard (the only facility with some of its 

buildings classified red). BART assumes a maximum repair 

duration of 3 years, with contractors available for mobilization 

within 50 days. Since these two aftershocks occur within about 

one month of the mainshock, any resulting damages would not 

interrupt repairs and any additional damage to stations could 

be incorporated into initial contracts.
90 The HayWired Earthquake Scenario—Societal Consequences

Table 4.2. Results by damage state for Bay Area Rapid Transit stations impacted by ground shaking from the HayWired earthquake 

scenario mainshock in the San Francisco Bay region, California.

Line code Station name Number of principal structural supports or subdivisions by damage state

Green Yellow Orange Hi-orange Red Total1

A-line North Lake Merritt 1 1

Fruitvale 2 9 11

Coliseum 2 2 7 11

San Leandro 11 11

Bay Fair 1 10 11

A-line South Hayward 7 7

South Hayward 1 1

Union City 1 1

Fremont 1 1

C-line Rockridge 8 2 10

Orinda 2 4 6

Lafayette 1 1

Walnut Creek 1 10 11

Pleasant Hill/Contra Costa Centre 7 17 24

Concord 4 4

R-line North El Cerrito Plaza 10 10

El Cerrito del Norte 9 9

Richmond 1 1

R-line South Ashby 1 1

Downtown Berkeley 1 1

North Berkeley 1 1

N. Oak 12th Street/Oakland City Center 1 1

19th Street/Oakland 1 1

MacArthur 1 1

W. Oak West Oakland 11 11

M-Line Embarcadero 1 1

Montgomery Street 1 1

Powell Street 1 1

Civic Center/UN Plaza 1 1

16th Street Mission 1 1

24th Street Mission 1 1

Glen Park 1 1

Balboa Park 1 1

Daly City 9 9

1

At-grade or underground stations were typically considered as a single unit, whereas aerial stations were subdivided into the number of principal 

structural supports at each station.
Chapter T. Lifeline Infrastructure and Collocation Exposure 91

Table 4.3. Repair duration for Bay Area Rapid Transit stations as a result of ground shaking from the HayWired earthquake scenario 

mainshock in the San Francisco Bay region.

Line code Station name

Approximate repair duration, in months

Yellow to 

green1

Orange and hi-orange 

to green

Number of repair 

contracts

Total line segment 

repair time2

A-line North Lake Merritt 2 24–36

Fruitvale 12–18

Coliseum 12–18

San Leandro 12–18

Bay Fair 12–18

Interconnecting guideways 24–36 3

A-line South Hayward 9–12 1 24–36

South Hayward 3–6

Union City 0.5

Fremont

Interconnecting guideways 24–36 1

C-line Rockridge 3–6 1 9–15

Orinda 6–9

Lafayette

Walnut Creek 0.5

Pleasant Hill/Contra Costa Centre 0.5

Concord

Interconnecting guideways 9–12 1

R-line North El Cerrito Plaza 0.5 6–12

El Cerrito del Norte

Richmond 0.5

Interconnecting guideways 6–12 1

R-line South Ashby 0.5 0.5–1

Downtown Berkeley 0.5

North Berkeley

Interconnecting guideways 0.5–1 1

N. Oak 12th Street/Oakland City Center 1–2

19th Street/Oakland

MacArthur 0.5

Interconnecting guideways 1–2 1

W. Oak West Oakland 0.5 0.5–1

Interconnecting guideways 0.5–1 1

M-line Embarcadero 0.5–1

Montgomery Street

Powell Street

Civic Center/UN Plaza

16th Street Mission

24th Street Mission

Glen Park

Balboa Park

Daly City

Interconnecting guideways 0.5

1

Minor repairs, such as inspections and shoring.

2

Assumes multiple concurrent repair contracts are issued such that the maximum repair duration is 3 years, and contractors are available for mobilization within 

50 days. Some of the repairs involve temporary shoring sufficient to restore operations; permanent repairs or reconstruction may take longer.
92 The HayWired Earthquake Scenario—Societal Consequences

Comparing BART Assessment with Chapter 

Exposure Results

The multi-hazard assessment of all shaking, liquefaction, 

landslide, slip, and fire following earthquake hazards is 

described in the body of this chapter. Figure 4.1 shows the 

BART system shaking damage results relative to ground 

failure hazards (coseismic slip, landslide, and liquefaction); 

figure 4.2 presents the multi-hazard intensity for the BART 

rail system with the damage state for each BART facility 

on top for comparison; and figure 4.3 couples the multihazard intensity with estimates of downtime for repairs. 

Tables 4.4 and 4.5 compare BART shaking damage states 

to the single-hazard and multi-hazard intensity classes used 

in this chapter. BART facilities classified as green generally 

are not located in areas where other hazards that result from 

Map

area CALIF.

A

A

B

B

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

#*

#*

#*

#*

#*

#*

Hayward

Union City

San Leandro

Dublin

#*

#*

#*

#*

#*

#*

#*

#*

*

#*#*

San Francisco Bay

Oakland

Alameda

Berkeley Orinda

Piedmont

Emeryville

San Francisco

Moraga

San Francisco Bay

*# *#

*#

*# *#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*# *#

*# *#

*#

*#*#

*#

*#

*#

*#*# *# *#*#

*#

*#

*#

*#

*#

*#

*#

*#

*#*#

*#

*#

*# *#

*#

CONTRA

COSTA

ALAMEDA

SANTA

CLARA

SAN

MATEO

SAN

FRANCISCO

San Francisco Bay

122°30’ 122°

38°

37°

30’

Coseismic slip along Hayward Fault, in meters

<0.5

0.5 to 1.0

1.0 to 1.5

1.5 to 2.0

>2.0

No surface offset

BART station damage potential

#* Likely minor or no damage

#* Likely operable after short-term repairs

#* Likely not operable, but likely safe (less damage)

#* Likely not operable, but likely safe (more damage)

#* Likely unsafe or collapsed

Liquefaction/landslide probability, in percent

>25

5–25

<5

#* New station; not analyzed

EXPLANATION

BART rail line

MW 7.0 epicenter

HAYWARD FAULT

HAYWARD FAULT

0 4 8 KILOMETERS

0 4 8 MILES

0 1 2 KILOMETERS

0 1 2 MILES

A

B

A

B

Figure 4.1. Map of the San Francisco Bay region, California, showing the ground failure (coseismic slip, 

landslide, and liquefaction) exposure of the Bay Area Rapid Transit (BART) system relative to the BART estimated 

shaking damage potential for the HayWired earthquake scenario. Map does not include the 2017 extension to 

Warm Springs or the planned 2019 extensions to Milpitas and Berryessa.
Chapter T. Lifeline Infrastructure and Collocation Exposure 93

#*

#*#*#*#* #*

#*

#* #*

*#

*#

*#

#*

#* *#

#* *#

#*

#* #*

#*

#* #*

#*

#* #*#*

#*

#* #*

#* *# #* #*

#*

#*

*#

#*

#* *#

*#

*#

#*

*#

#*

#*

*#

CONTRA

COSTA

ALAMEDA

SAN

MATEO

SAN

FRANCISCO

S a n F r a n c i s c o B a y

San Pablo Bay

Lake Merritt Embarcadero

Montgomery Street

Powell Street

Civic Center Fruitvale

16th St Mission

Coliseum/

Oakland Airport

24th St Mission

Pittsburg/Bay Point

North Concord/Martinez

Concord

Richmond

Pleasant Hill

El Cerrito

Del Norte

Walnut Creek

El Cerrito Plaza

Lafayette

Orinda

North

Berkeley

Downtown

Berkeley

Ashby

Rockridge

MacArthur

19th Street/Oakland West Oakland

12th Street/Oakland City Center

Glen Park San

Leandro Balboa Park

Daly City Dublin/

Pleasanton

Bay Fair Castro

Valley

Colma

Hayward

South Hayward

San Bruno

Millbrae

Union City

Fremont

Concord Yard Richmond Yard

West Dublin/

Pleasanton

Daly City

Yard

South San

Francisco

Hayward Yard

-122°30' -122°20' -122°10' -122°

38°

37°

50'

37°

40'

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

Map

area CALIF.

BART rail multi-hazard exposure

Very high

High

Low

Moderate-high

Moderate

BART station damage potential

*# New station; not analyzed

*# Likely unsafe or collapsed

*# Likely not operable, but likely safe (more damage)

*# Likely not operable, but likely safe (less damage)

*# Likely operable after short-term repairs

*# Likely minor or no damage

EXPLANATION

0 2 4 KILOMETERS

0 2 4 MILES

Figure 4.2. Map of the San Francisco Bay region, California, showing the multi-hazard exposure of the Bay Area Rapid Transit (BART) 

system relative to the BART-estimated shaking damage potential for the HayWired earthquake scenario. Map does not include the 2017 

extension to Warm Springs or the planned 2019 extensions to Milpitas and Berryessa.
94 The HayWired Earthquake Scenario—Societal Consequences

#

#

#

#

#

#

#

#

#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*# *#

*# *#

*#

*#

*#

*#

*#

*#

*#*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

*#

-122°30' -122°20' -122°10' -122°

38°

37°

50'

37°

40'

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

Map

area CALIF.

BART rail multi-hazard exposure

Very high

High

Low

Moderate-high

Moderate

BART station repair time range

EXPLANATION

#* New station; not analyzed

#* <1 day

#* 1 to 14 days

#* 2 to 36 weeks

#* 9 to 12 months

#* 1 to 3 years

0 2 4 KILOMETERS

0 2 4 MILES

Lake Merritt Embarcadero

Montgomery Street

Powell Street

Civic Center Fruitvale

16th St Mission

Coliseum/

Oakland Airport

24th St Mission

Pittsburg/Bay Point

North Concord/Martinez

Concord

Richmond

Pleasant Hill

El Cerrito

Del Norte

Walnut Creek

El Cerrito Plaza

Lafayette

Orinda

North

Berkeley

Downtown

Berkeley

Ashby

Rockridge

MacArthur

19th Street/Oakland West Oakland

12th Street/Oakland City Center

Glen Park San

Leandro Balboa Park

Daly City Dublin/

Pleasanton

Bay Fair Castro

Valley

Colma

Hayward

South Hayward

San Bruno

Millbrae

Union City

Fremont

Concord Yard Richmond Yard

West Dublin/

Pleasanton

Daly City

Yard

South San

Francisco

Hayward Yard

CONTRA

COSTA

ALAMEDA

SAN

MATEO

SAN

FRANCISCO

S a n F r a n c i s c o B a y

San Pablo Bay

Figure 4.3. Map of the San Francisco Bay region, California, showing the multi-hazard exposure of the Bay Area Rapid Transit (BART) 

system relative to BART-estimated facility repair duration for the HayWired earthquake scenario. Map does not include the 2017 extension 

to Warm Springs or the planned 2019 extensions to Milpitas and Berryessa.
Chapter T. Lifeline Infrastructure and Collocation Exposure 95

Table 4.4. Summary of Bay Area Rapid Transit shaking damages to facilities relative to exposure to ground movement and fire 

following earthquake hazards for the HayWired earthquake scenario mainshock in the San Francisco Bay region, California.

Damage 

class Description Coseismic 

slip

Landslide intensity Liquefaction intensity Fire following earthquake 

intensity

Low Moderate High Low Moderate High Low Moderate High

None New station; not analyzed 0 2 0 0 1 3 0 3 1 0

Green Likely minor or no damage 0 1 0 0 7 7 0 6 3 1

Yellow Likely operable after short-term 

repairs

0 4 0 0 3 6 0 4 4 0

Orange Likely not operable, but likely safe 0 1 0 0 0 1 0 0 0 1

Hi-orange Likely not operable, but likely safe 0 3 0 0 1 3 1 1 3 1

Red Likely unsafe or collapsed 0 1 0 0 0 1 0 0 1 0

Total 0 12 0 0 12 21 1 14 12 3

Table 4.5. Summary of Bay Area Rapid Transit shaking damages to facilities relative to multi-hazard exposure intensity classes for the 

HayWired earthquake scenario mainshock in the San Francisco Bay region, California.

Damage 

class Description

Multi-hazard intensity

Low Moderate Moderate-high High Very high

None New station; not analyzed 3 3 1 2 0

Green Likely minor or no damage 4 6 9 1 0

Yellow Likely operable after short-term repairs 0 4 6 0 0

Orange Likely not operable, but likely safe 0 1 0 0 1

Hi-orange Likely not operable, but likely safe 0 0 3 1 1

Red Likely unsafe or collapsed 0 0 0 1 0

Total 7 14 19 5 2

the HayWired scenario mainshock are high-intensity. The 

Fremont station illustrates an exception owing to high fire 

following earthquake intensity (for the HayWired scenario), 

which was not considered in BART’s ShakeCast analysis. The 

Fremont station is also in an area of moderate liquefaction 

and moderate ground shaking intensity, so the station is 

classified as being in a high multi-hazard intensity area. 

Yellow stations also tend to be in areas with only moderate 

multi-hazard intensity, with no single hazard identified as 

high-intensity. The stations that have very high multi-hazard 

intensity are classified as orange and hi-orange (both are 

considered potentially operable, but hi-orange has longer 

associated repair times) in BART’s ShakeCast analysis. 

Two of these stations, the Hayward and South Hayward 

stations, demonstrate the potential for high ground-shaking 

intensity and high fire following earthquake intensity, along 

with moderate liquefaction intensity. This demonstrates the 

possibility of multiple hazards exacerbating the shaking 

damages such that the potentially inoperable (orange and 

hi-orange) stations may have longer repair times or even 

become unsafe (red). In the HayWired scenario, fire is the 

most likely cause of high-intensity damage beyond shaking 

to BART facilities owing to the urban nature of the firefollowing-earthquake model. Moderate liquefaction (5–25 

percent probability) is near more than half of the facilities. 

It should be noted that BART performed site-specific 

liquefaction vulnerability analysis for each facility and found 

that, in general, the facilities are not vulnerable to seismically 

induced liquefaction, either because liquefaction does not 

actually occur at the facility site (based on local geotechnical 

site investigation data) or the structures were found to be 

essentially undamaged by liquefaction effects.

Please note that the discussion above focuses on core 

BART system facilities rather than rail lines. BART estimates 

that the most impacted rail line would take about 6 months 

to be repaired (T. Horton, BART, written commun., 2018). 

BART assumes that stations will take longer to repair 

than lines, so they focused their more extensive hazard 

impact analysis on the facilities. In 2014, a seismic retrofit 

feasibility study was completed for the Berkeley Hills tunnels 

between the Rockridge and Orinda stations. The study 

considered three different levels of fault offset (not including 

postseismic creep) resulting from the maximum magnitude 

(M) earthquake (M7.25 in this instance): 3.9 feet (ft) for a 
96 The HayWired Earthquake Scenario—Societal Consequences

lower level design-basis earthquake (median displacement), 

7.8 ft for a design-basis earthquake (median plus one sigma 

displacement), and 12.4 ft for an upper level design basis 

earthquake (median plus two-sigma displacement). In the 

HayWired scenario, the Berkeley Hills tunnels would be 

exposed to around 0.9 meters, or 2.9 ft, of offset. This is lower 

than the lowest level of fault offset considered by the Berkeley 

Hills tunnels feasibility study (Jacobs Associates and G&E 

Engineering Systems, 2014). The study looks at repair times 

for several different fault offset amounts; the two closest 

scenarios have 1.0 and 5.1 ft of offset, respectively, resulting 

in potential repair times of 3.5 to 7.4 months for restoring one 

tunnel to half speed and 13.5 to 25.5 months to restore both 

tunnels to full speed. A BART estimate puts the completion 

time for repairs to the Caldecott Tunnel at about 2 years 

(T. Horton, BART, written commun., 2018)—within the range 

established from the seismic feasibility study.

BART Interdependencies

Some interactions between BART and other lifelines 

include water and power. Tunnel sections of the BART 

system require the availability of running water in case of 

fire while trains are operating through the tunnels. In the 

event of a major earthquake, the San Francisco Fire Marshal 

may limit the number of trains running if the water system 

is compromised. The Transbay Tube is a good example of 

where lifeline interdependencies are important. In addition 

to the above-mentioned water dependency, electric power 

is necessary in the Transbay Tube—in the event of water 

leakage into the tunnel, power will be needed to pump out the 

intruding water. If the tube were to become filled with water, 

the restoration time for the tube would be much longer than 

if water intrusion had not occurred. Currently, the Transbay 

Tube has high-priority power from the Pacific Gas and Electric 

Company (PG&E), but that is dependent on the overall power 

situation in the region. BART is also planning to install a large 

emergency generator to provide pumping and lighting power 

for the Transbay Tube as part of retrofits to reduce reliance on 

PG&E; however, this is dependent on adequate fuel supplies 

in storage tanks. Retrofits are scheduled to be completed for 

the Transbay Tube in 2023. Structural repairs in the Berkeley 

Hills tunnels (for a scenario that has between 1.0 and 5.1 ft 

of fault offset) would potentially require removing existing 

utilities along a 250 ft section of one tunnel during initial 

repairs (to restore one track to half speed) and along a 400ft 

section of both tunnels during the remainder of the repairs 

(to restore both tracks to full speed) (Jacobs Associates and 

G&E Engineering Systems, 2014). While plans are being 

made to complete repairs owing to fault creep damage to 

the Berkeley Hills tunnels, there are not any current plans to 

retrofit the tunnels (Bay Area Rapid Transit, 2017c). Finally, 

BART would be able to implement bus bridges to transport 

commuters to operational stations during repairs to damaged 

stations, but this would be contingent on the road network 

being restored to functionality and the availability of fuel for 

buses also.
Chapter T. Lifeline Infrastructure and Collocation Exposure 97

Appendix 5. California Department of Transportation Bridge Damage Assessment 

to HayWired Scenario Ground Shaking

By Loren Turner,1 Jamie L. Jones,2 Anne M. Wein,2 and Amy E. Schweikert2

1

 California Department of Transportation.

2

 U.S. Geological Survey.

Mainshock ground shaking in the HayWired scenario 

was used by the California Department of Transportation 

(Caltrans) in ShakeCast (https://earthquake.usgs.gov/

research/software/shakecast.php) to identify the potential 

effect on highway bridge inventory in the study region. The 

Caltrans ShakeCast analysis uses a 1.0 second peak spectral 

acceleration (PSA) measure of ground shaking to indicate 

bridge fragility—the likelihood of impact to each bridge. 

There are other possible impacts to bridges owing to ground 

failure (for example, landslides, liquefaction, and fault 

crossings). However, those hazards are not considered in the 

ShakeCast analysis. Bridge fragility is determined by a range 

of asset-specific details including year of construction (or 

retrofit), length of span(s), column heights, material types, and 

number of spans. The output is a potential impact rating based 

on a statistical assessment of the operational state probability 

for each bridge. Figure 5.1 illustrates an example of how 

potential impact is determined for a bridge: each bridge has 

multiple potential damage levels but the one with the highest 

likelihood of exceeding the limit of an impact state is reported 

(Division of Research, Innovation, and System Information 

for the Division of Maintenance, California Governor’s Office 

of Emergency Management, 2014). Please note that Caltrans 

uses the term “potential impact class” in lieu of “damage 

state” (this term is used in table 5.1 and in Hazus-MH 2.1 

[hereafter referred to as Hazus]) so that ShakeCast results are 

not incorrectly interpreted as reports of actual damage.

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Probability of

Exceeding

Limit State

Trigger (Floor) for

Bridge Inclusion on List Case A: IM=0.5g

IM (PSA1)

2 Slight

3 Moderate

4 Extensive

5 Complete

Figure 5.1. Chart showing an example of one set of potential impact curves for a highway bridge in the San Francisco Bay region, 

California (reproduced here with slight modification from Division of Research, Innovation, and System Information for the Division of 

Maintenance, California Governor’s Office of Emergency Management, 2014). This graph shows the probability that the bridge will see 

a given impact class (slight, moderate, extensive, or complete) based upon the level of ground shaking experienced (in 1.0-second 

peak spectral acceleration [PSA1

]) and the bridge fragility of the asset. As shown, the designated potential impact is based upon the 

largest (greater than 50 percent) likelihood of impact; likelihood of impact is shown on the x-axis and the colored areas highlight ranges 

of likelihoods (white the lowest and red the highest). In this example, a PSA1

 of 0.5g (0.5 times gravitational acceleration, g) results in 

a 72 percent chance of a “low” classification (corresponding to “slight” damage), a 57 percent chance of a “medium” classification 

(corresponding to “moderate” damage), a 44 percent chance of a “medium-high” classification (corresponding to “extensive” damage), 

and a 22 percent chance of a “high” classification (corresponding to “complete” damage). As a result, the PSA1

 value here would be 

assigned a “medium” classification because this is the highest impact class that is greater than 50 percent.
98 The HayWired Earthquake Scenario—Societal Consequences erner and others, 2000). Default traffic states for bridges damaged by ground shaking (modified slightly from W ransportation] able 5.1. T[N/A, not applicable; Caltrans, California Department of T Damage state Caltrans equivalent Repair procedure Traffic state Description Duration Bridge Underpass None N/A None None Fully open Fully open Slight Low Epoxy injection of cracks 2–3 weeks Fully open Fully open Moderate (repairable structural damage) Medium 1. Close bridge for inspections and shoring 1–3 days Closed Partly closed (one lane each direction) during cleaning, shoring, and column repair operations (2–3 weeks). Open to emergency vehicles. 2. Repair columns, that is, remove damaged concrete, design/implement repair (for example, increase lap splice, add transverse steel, grouting, and jacketing) 3-span bridge: 2–3 weeks 4-span bridge: 2.5–5 weeks 5-span bridge: 4–6 weeks Partly open to reduced traffic during duration of repairs 3. Repair abutments, bearings, and so on. Extensive (severe damage to structural elements but no overall collapse mechanism) Medium-high 1. Close bridge for inspections and shoring 1 week Closed Partly closed (one lane each direction) during cleaning, shoring, and column repair operations (3–4 weeks). Open to emergency vehicles. 2. Repair or replace columns, that is, remove damaged concrete, design/implement repair (for example, increase lap splice, add transverse steel, grouting, and jacketing) 3-span bridge: 4–8 weeks 4-span bridge: 5–10 weeks 5-span bridge: 6–12 weeks Partly open to reduced traffic during duration of repairs 3. Repair abutments, repair or replace bearings, and so on. Collapse

 (severe structural 

damage leading to overall collapse mechanism, or unseating of deck leading to collapse of deck)

High Replace bridge for case of severe structural 

damage. See table 12 of Werner and others (2000) for breakdown of repair procedure.

3-span bridge: 3–6 months4-span bridge: 4–8 months5-span bridge: 5–10 months

Closed during bridge 

reconstruction

Fully closed during demolition and 

cleaning (3–6 days)

Partly closed (one lane each 

direction) during cleaning, shoring, and column repair operations (4–8 weeks) Replace fallen deck span, replace bearings, and repair or replace damaged substructure. 3–6 months Closed during bridge repair and 

replacement.
Chapter T. Lifeline Infrastructure and Collocation Exposure 99

The bridge fragility used in ShakeCast is derived 

from existing engineering research and follows the Hazus 

methodology described by Federal Emergency Management 

Agency (2012). In many cases, the default system data used 

in Hazus is based on studies of bridges on the east coast of 

the United States and are intended to model typical highway 

bridges, likely not accurately capturing the damage potential 

for bridges that are more complex, longer, or otherwise outside 

normal design parameters (for example, bridges that have been 

seismically retrofitted). The ShakeCast software assessment 

also does not account for multiple hazards including 

liquefaction, landslide, coseismic slip, or fire.

Results

In total, 4,216 bridges were assessed by Caltrans using 

ShakeCast for the HayWired scenario. Of these, 2,734 

(65 percent) were at locations where ground shaking was 

determined to exceed 10 percent of gravitational acceleration, 

g, the minimum value observed for damages to bridges in past 

earthquakes. The analysis showed that, of the bridges where 

shaking exceeded 10 percent of g, 51 bridges (2 percent) had 

high potential impact, 60 bridges (2 percent) had medium-high 

potential impact, 59 (2 percent) had medium potential impact, 

and 2,564 (96 percent) had low potential impact.

Using information from Werner and others (2000), 

we applied the range of repair times for each bridge based 

on the potential impact estimated by ShakeCast and the 

number of bridge spans included in the bridge geographic 

information system (GIS) dataset (California Department 

of Transportation, 2015). Table 5.1 summarizes the repair 

procedures and durations for three-, four-, and five-span 

bridges. The repair time is based upon select shaking hazard 

exposure measures (1.0 second PSA and moment magnitude 

intensity [MMI]) as well as a number of bridge-specific 

attributes. However, many bridges in the GIS dataset had 

listed bridge span counts outside of the range used in table 5.1. 

For bridges with fewer than three spans listed, we assigned 

the same time range as a three-span bridge, and for bridges 

with more than five spans, we assigned the same time range 

as a five-span bridge. Bridges with low potential impact have 

estimated repair times under one month and no associated road 

closures; some of the bridges with high potential impact could 

take as long as ten months to be fully repaired and require 

closing roads that pass under the bridges.

Table 5.2 provides descriptions of the potential impact 

classes and summarizes the number of bridges and repair 

duration ranges for each class. The ShakeCast analysis was 

also run for five aftershocks, assuming that all bridges were 

in their initial condition (before mainshock impacts). None of 

the aftershocks identified any bridges with medium, mediumhigh or high impact likelihood. Figure 5.2 shows the location 

of each bridge analyzed using ShakeCast and the resulting 

potential impact relative to the ground failure hazards 

(coseismic slip, landslides, and liquefaction) for the HayWired 

scenario.

In figure 5.2, many bridges on Interstate 880 (I–880) 

between Fremont and Oakland are identified as high potential 

impact, taking at least four months and as many as ten 

months to be repaired. With several bridges also classified 

Table 5.2. Potential impact class definitions and exposure of highway bridges in California Department of Transportation’s ShakeCast 

analysis in the San Francisco Bay region, California.

[PSA, peak spectral acceleration; g, gravitational acceleration; in., inches]

Potential impact 

class Description Number of bridges in class

Repair duration, 

in months

None 1.0-second PSA at location is less than 0.1 g 1,482 0

Low1 Minor cracking and spalling to the abutment; cracks in shear keys at abutments; 

minor spalling and cracks at hinges; minor spalling at the column 

(damage requires no more than cosmetic repair) or minor cracking to the deck

2,564 0.5–0.75

Medium Any column experiencing moderate (shear cracks) cracking and spalling 

(column structurally still sound); moderate movement of the abutment 

(<2 in.); extensive cracking and spalling of shear keys; any connection having 

cracked shear keys or bent bolts; keeper bar failure without unseating; rocker 

bearing failure or moderate settlement of the approach

59 0.5–1.25

Medium-high Any column degrading without collapse owing to shear failure (column 

structurally unsafe); significant residual movement at connections; major 

settlement approach; vertical offset of the abutment; differential settlement at 

connections; shear key failure at abutments

60 1–3

High Any column collapsing and connection losing all bearing support, which may 

lead to imminent deck collapse; tilting of substructure owing to 

 foundation failure

51 3–10

1

Direct calculations were not completed to define this class—1.0 second PSA less than 0.1 g is used as lower limit, so any points not classified in the higher 

impact classes or below the lower threshold are classified as low.
100 The HayWired Earthquake Scenario—Societal Consequences

as high potential impact on I–680 between Fremont and 

Pleasanton, getting through Alameda County will potentially 

be difficult. Bridge impacts on I–580 between Oakland and 

Pleasanton would also disrupt travel from the east into the 

San Francisco Bay area. Contra Costa County could also have 

some connectivity issues owing to bridge impacts, especially 

around the intersection of California Routes (CA) 13 and 24. 

With several major highways that have four or more months 

of repair time in the San Francisco Bay region, emergency 

response, evacuation, and debris removal would be hampered 

and recovery after the HayWired scenario mainshock could 

take longer than anticipated. A small number of bridges in 

Marin and Sonoma Counties also have high potential impact. 

Figures 5.2A and B highlight areas affected by ground failure 

and landslides, which may further exacerbate potential 

impacts to bridges.

Pleasanton

Fremont

Union City

Livermore

Hayward

680

Oakland

^_

Moraga

Orinda

Berkeley

Lafayette

Alameda

Albany

Piedmont

Emeryville

580

880

980

24

13

580 880

280

680

80

101

101

237

24

13

SOLANO

CONTRA

COSTA

ALAMEDA

CLARA

SANTA

SAN

MATEO

NAPA

SONOMA

MARIN

SAN

FRANCISCO San Francisco Bay

PabloSan 

Bay

122°30’ 122°

38°

37°

30’

A

A

B

B

Map

area CALIF.

Highway bridge potential impact

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

None

Low

Medium

Medium-high

High

Fault, in meters

<0.5

EXPLANATION

Coseismic slip along Hayward 

0.5 to 1.0

1.0 to 1.5

1.5 to 2.0

>2.0

No surface offset

Liquefaction/landslide 

probability, in percent

>25

5–25

<5

Highway

Secondary road

0 5 10 MILES

0 5 10 KILOMETERS

0 1 2 MILES

0 1 2 KILOMETERS

Figure 5.2. Map of the San Francisco Bay region, California, showing highway bridge potential impacts 

generated by California Department of Transportation’s ShakeCast assessment relative to the HayWired scenario 

mainshock ground-failure hazards (coseismic slip, landslides, and liquefaction). Most bridges of high potential 

impact are located in Berkeley (A) and Hayward (B); a few bridges east of Hayward near Livermore and Fremont 

have high and medium-high potential impacts.
Chapter T. Lifeline Infrastructure and Collocation Exposure 101

Comparing Caltrans Assessment with Chapter 

Exposure Results

The multi-hazard assessment of all shaking, liquefaction, 

landslide, slip, and fire following earthquake hazards is 

described in the body of this chapter. Figure 5.3 compares the 

potential impacts to bridges against the HayWired mainshock 

multi-hazard analysis for transportation lifelines. Table 

5.3 summarizes the exposure of Caltrans potential impact 

bridges (from shaking) relative to landslide, liquefaction, fire 

following earthquake, and slip. Table 5.4 summarizes Caltrans 

potential impact bridges relative to the multi-hazard intensity. 

Some specific examples from this comparison follow.

Overall, the results for landslide and liquefaction hazards, 

in particular, suggest that using a shaking only assessment may 

underestimate damages to bridges that are assessed as low 

potential impact by ShakeCast for the HayWired scenario. A 

set of bridges in Castro Valley along I–580 have low potential 

impact from ground shaking but high landslide intensity. More 

than 150 low potential impact bridges are located in areas of 

high liquefaction: these are located along U.S. Route 101 in 

the south bay and along the peninsula and along I–580, I–880, 

and I–80 in the east bay. A few low potential impact bridges 

along I–80 in Richmond are located in areas that have high 

fire-following-earthquake intensity, as does one on I–880 in 

Hayward and one on I–680 between Fremont and Sunol.

Medium, medium-high, and high potential impact bridges 

(from shaking) are also exposed to high-intensity landslides 

and liquefaction. For example, along I–580 in Castro Valley 

there are several medium, medium-high, and high potential 

impact bridges that also have high landslide hazard intensity; 

one of the high potential impact bridges in this area is also 

affected by overlapping high liquefaction hazard intensity. A 

medium potential impact bridge on CA–13 in Oakland is in 

an area with high landslide and high fire following earthquake 

hazard intensity, and a medium-high potential impact bridge 

along the same road is also situated in a high landslide hazard 

intensity area. Several medium-high potential impact bridges 

in the Dublin-Pleasanton area along I–680 and I–580, along 

with several high potential impact bridges along I–580 in 

Castro Valley, are located in high liquefaction hazard intensity 

locations. In addition, one medium-high potential impact 

Table 5.3. Summary of California Department of Transportation’s ShakeCast assessment of potential shaking impacts to highway 

bridges relative to single-hazard exposure intensity classes for the HayWired earthquake scenario mainshock in the San Francisco Bay 

region, California.

[Values are number of bridges]

Potential 

impact class

Coseismic 

slip

Landslide intensity Liquefaction intensity Fire following earthquake intensity

Low Moderate High Low Moderate High Low Moderate High

None 0 0 0 0 3 6 0 2 0 0

Low 0 459 37 8 531 459 154 686 133 12

Medium 0 30 15 4 3 35 2 12 25 2

Medium-high 3 41 11 5 10 28 9 9 30 6

High 0 28 19 3 2 22 6 3 6 11

Total 3 558 82 20 549 550 171 712 194 31

Table 5.4. Summary of California Department of Transportation’s ShakeCast assessment of potential shaking impacts to highway 

bridges relative to multi-hazard exposure intensity classes for the HayWired earthquake scenario mainshock in the San Francisco 

Bay region, California.

[Values are number of bridges]

Potential 

impact class

Multi-hazard intensity

Low Moderate Moderate-high High Very high

None 507 332 2 0 0

Low 762 1,010 498 217 22

Medium 2 4 20 28 5

Medium-high 0 10 18 15 17

High 1 4 15 12 19

Total 1,272 1,360 553 272 63
102 The HayWired Earthquake Scenario—Societal Consequences

"

"

"

"

"

"

"

"

"

MARIN

CONTRA

COSTA

ALAMEDA

SAN

MATEO

SANTA

CLARA

SOLANO

SONOMA

NAPA

San Francisco Bay

San Pablo Bay

P A C I F I C O C E A N

580

580

580

680

880

880

280

280

680

680

680

80

80

101

101

101

92

84

237

24

13

San Jose

SAN FRANCISCO

Oakland

Richmond

Fremont

Hayward

Alameda

Livermore

Foster City

Palo Alto

-122°

38°

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

0 3 6 KILOMETERS

0 3 6 MILES Map

area CALIF.

Highway Secondary

street

Surface

street Highway bridge potential impact

Medium

Low

Medium-high

High

None

Multi-hazard

exposure

Low

Moderate

Moderate-high

High

Very high

EXPLANATION

Transportation network

Figure 5.3. Map of the San Francisco Bay region, California, showing the multi-hazard exposure of roadways and potential impacts to 

California Department of Transportation highway bridges in the HayWired earthquake scenario.
Chapter T. Lifeline Infrastructure and Collocation Exposure 103

bridge in Hayward is in an area with high fire-followingearthquake hazard intensity.

These findings suggest that whereas many of the low 

potential impact bridges are not likely to be exposed to 

additive damages from other HayWired scenario hazards, the 

larger liquefaction extent may have an effect on a number of 

bridges otherwise classified as low impact potential. There is 

a higher chance of affecting traffic on underlying roads for 

bridges in medium, medium-high, and high potential impact 

classes because many of the bridges in these classes cross 

over other roads. In general, estimated repair times may end 

up being longer for some of the bridges in order to complete 

repairs not accounted for in the ShakeCast analysis. Caltrans 

bridge repair times in relation to our multi-hazard analysis are 

shown in figure 5.4.

Caltrans Interdependencies

Bridges in the Caltrans system have varying levels of 

importance, and when a major incident occurs, bridges are 

repaired in order of importance (L. Turner, Caltrans, oral 

commun., 2014). Any additional lifelines located on bridges may 

be subject to this order as well. For example, if a bridge is low 

on the priority list, then lifelines on the bridge may be delayed 

in finalizing repairs while they wait for the bridge to be repaired. 

Pipes can be temporarily repaired independently of a bridge, but 

the final repairs cannot be completed until bridge repairs are done. 

Fiber optic cables commonly run along bridges, so damages to 

bridges could have an impact on telecommunications as well. In 

some cases, utilities have retrofitted or removed their systems from 

the bridges, but no information is available on where this may 

have been completed.

With regard to the differential motion between 

superstructure spans and abutment-superstructure, the Caltrans 

design approach since 1990 has provided for protection of 

utilities (for example, water lines) that are carried on their 

structures (California Department of Transportation, 1994). 

We do not know what details were used prior to 1990 (for 

example, if retrofits have been carried out on older bridges 

carrying utilities or even which bridges in the study region 

carry utilities).

Damage to other lifelines near a bridge could also 

potentially delay repairs to the bridge—for example, if a water 

pipe breaks near an abutment, the water leaking out of the pipe 

could scour the abutment (L. Turner, Caltrans, oral commun., 

2014) and weaken it more than it would have been affected 

without the water damage.
104 The HayWired Earthquake Scenario—Societal Consequences

"

"

"

"

"

"

"

"

"

MARIN

CONTRA

COSTA

ALAMEDA

SANTA

CLARA

SOLANO

SONOMA

NAPA

580

580

580

680

880

880

280

280

680

680

680

80

80

101

101

101

92

84

237

24

13

San Jose

SAN FRANCISCO

Oakland

Fremont

Hayward

Alameda

Livermore

Foster City

Richmond

Palo Alto

San Francisco Bay

San Pablo Bay

P A C I F I C O C E A N

SAN

MATEO

-122°

38°

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016

Boundary data from U.S. Census Bureau TIGER data, 2016

Universal Transverse Mercator zone 10N projection

North American Datum of 1983

0 3 6 KILOMETERS

0 3 6 MILES Map

area CALIF.

Highway bridge repair time

3 to 6 months

4 to 10 months

5 to 12 weeks

4 to 8 weeks

2.5 to 6 weeks

2 to 3 weeks

None

Highway Secondary

street

Surface

street

Multi-hazard

exposure

Low

Moderate

Moderate-high

High

Very high

EXPLANATION

Transportation network

Figure 5.4. Map of the San Francisco Bay region, California, showing repair times for highway bridges as determined by the California 

Department of Transportation relative to multi-hazard exposure in the HayWired earthquake scenario.