From Awareness to Action Accounting for InfrastructureInter

From Awareness to Action: Accounting for Infrastructure
Interdependencies in Disaster Response and
Recovery Planning
Anu Narayanan1 , Melissa Finucane1, Joie Acosta1, and
Amanda Wicker1
1RAND Corporation, Santa Monica, CA, USA
Abstract This paper highlights challenges and open questions
pertaining to physical and social
infrastructure system interdependencies and their implications
for disaster response, recovery, and
resilience planning efforts. We describe the importance of
understanding interdependencies in disaster
contexts and highlight limitations to existing approaches.
Suggestions for understanding and addressing
interdependencies focus on increasing availability of tools for
assessing interdependencies and increasing
stakeholder and decisionmaker uptake of infrastructure
interdependency‐ related information in planning
efforts.
Plain Language Summary Interdependent physical and social
systems offer enormous benefits
for daily life because they produce and distribute essential
goods and services that are necessary for health,
safety, and economic well‐ being. For instance, the power grid
is required for effective functioning of
information systems and cell phones, which underpin effective
functioning of hospitals, water and sewer
systems, traffic lights, and home appliances. In return,
communications and information technology is

required for effective functioning of the power grid, especially
to meet the concurrent demands for reliable
energy supply, protection, and automation. In this paper, w e
describe how failure in interdependent
systems can be catastrophic and lead to death and prolonged
human suffering. We examine difficulties in
linking failures in interdependent systems to measurable social
impacts including: limited availability of
data and models, disciplinary silos that might stand in the way
of different stakeholders, practitioners, and
experts working together on this inherently cross‐ disciplinary
problem, and diversity in infrastructure
systems, disruptive events, and communities. We suggest that
awareness of the vulnerabilities in
interdependent infrastructure systems needs to be coupled with
coordinated action and collaboration among
government agencies, communities, and industries.
1. Introduction
Some of the worst disasters in recent memory are the outcomes
of low‐ probability, high‐ consequence events
that have brought with them failures of interdependent
infrastructure systems (Alexander, 2018). By “infra-
structure,” we mean not just physical assets (e.g., the power
grid, water and wastewater systems, and telecom-
munications networks) but also social systems that play a key
role in human health, safety, and well‐ being
(e.g., government functions, educational programs, parks, and
recreation systems). Interdependent infra-
structure systems are susceptible to a wide array of shocks
(typically abrupt) and stressors (typically slow,
with cumulative effects). In addition to natural disasters, shocks
can also include premeditated attacks on
interdependent infrastructure systems. Stressors can include
gradual aging of assets, lack of maintenance

due to scarce funds or outmigration of skilled labor, overuse
due to population movement or urban expansion
that increases demand, a decline in the global or local economy,
and a range of environmental changes
(e.g., rising sea levels, increasing air temperatures, and greater
intensity or frequency of storms). As described
in the recent report of the Fourth National Climate Assessment
(U.S. Global Change Research Program
[USGCRP], 2018), climate‐ related shocks and stressors can
have a direct impact on multiple infrastructure
systems (e.g., energy, water, communications, and transport)
that interact with and depend on each other
and with other systems (e.g., finance and education) that are
less directly exposed to climate factors.
This paper aims to highlight and offer recommendations for
addressing current knowledge gaps. Specifically,
we take stock of approaches, methods, and tools that are
currently available to (1) better understand and
characterize interdependencies among critical infrastructures
and (2) suggest effective ways for
©2020. The Authors.
This is an open access article under the
terms of the Creative Commons
Attribution‐ NonCommercial‐ NoDerivs
License, which permits use and distri-
bution in any medium, provided the
original work is properly cited, the use
is non‐ commercial and no modifica-
tions or adaptations are made.
COMMENTARY
10.1029/2020GH000251
Key Points:

• Limited data and models and
disciplinary silos make it difficult to
link failures in interdependent
infrastructures to social outcomes
• Awareness of infrastructure
vulnerabilities needs to be coupled
with coordinated action among
government, communities, and
industries
• We suggest ways to improve
understanding of interdependencies
and increase stakeholder uptake of
relevant information in planning
efforts
Correspondence to:
A. Narayanan,
[email protected]
Citation:
Narayanan, A., Finucane, M., Acosta,
J., & Wicker, A. (2020). From awareness
to action: Accounting for infrastructure
interdependencies in disaster response
and recovery planning. GeoHealth, 2,
e2020GH000251. https://doi.org/
10.1029/2020GH000251
Received 18 MAR 2020
Accepted 20 JUN 2020
Accepted article online 26 JUN 2020
NARAYANAN ET AL. 1 of 7

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incorporating an understanding of infrastructure
interdependencies into disaster response and recovery
planning. This paper is not intended to serve as a
comprehensive literature review or a systematic assessment
of existing tools. Rather, by drawing on the authors' experience
in Puerto Rico and other disaster‐ impacted
areas and building on theoretical frameworks and empirical
results reported by experts in the field, we seek
to highlight leading challenges and identify open questions
about how to manage risk in complex, interde-
pendent systems.
2. Example Failures in Interdependent Systems
The consequences of failures that propagate through
interdependent systems can be catastrophic (Buldyrev
et al., 2010; Vespignani, 2010). Many examples demonstrate
how an instigating natural hazard can cause rip-
pling effects across connected infrastructure systems whose
failure then results in significant, adverse social
impacts. In 2011, for instance, the Tōhoku triple disaster was
initiated by an earthquake, which triggered a
tsunami, and contributed to the nuclear meltdown at Fukushima,
ultimately resulting in 19,000 fatalities
(Latcharote et al., 2018). The 1998 Canadian ice storm left 16%

of Canadian population without power,
caused 45 deaths, and cost the Canadian government $1.7
billion (Lecomte et al., 1998). The 2010 eruption
of Eyjafjallajökull Volcano in Iceland projected so much ash
into the atmosphere that flights were grounded
across Europe, stranding 8.5 million people and severely
affecting commerce (Alexander, 2013). During
Hurricane Katrina, initial levee failure, coupled with failure to
evacuate and inadequate shelter, contributed
to an estimated 1,570 deaths of Louisiana residents and a $40–
50 billion economic loss (Kates et al., 2006).
The recovery period after Hurricane Katrina surpassed a decade.
More recently, the severe impacts of serial hurricanes Irma and
Maria on Puerto Rico and its 3.4 million
inhabitants in 2017 clearly demonstrate challenges of
compromised interdependent infrastructure systems.
Puerto Rico suffered a complete and extended loss of the power
grid, a near‐ complete loss of cellular com-
munication, a scarcity of drinking water, and impassable roads,
resulting in nearly 3,000 deaths and pro-
longed human suffering (Santos‐ Burgoa et al., 2018). The poor
and vulnerable were disproportionately
affected (Zorrilla, 2017). Figure 1 provides a simplified view of
how four key infrastructure systems—some-
times called lifeline functions or lifeline systems (Department
of Homeland Security (DHS) Office of
Infrastructure Protection; undated)—interacted during and after
the 2017 hurricanes in Puerto Rico and
the impact that the cascading failures of these lifeline systems
had on the population's health and the
economy.
Lack of power in Puerto Rico had cascading effects on
communications, water, and transportation systems;
the resulting effects impacted every sector of the economy and

left the island facing profound challenges.
Without power, the other lifeline sectors (communications,
transportation, and water) and the population's
health and economy were negatively impacted. Specifically,
people were unable to get health care due to
transportation challenges or lack of available facilities.
Inadequate sanitation and poor hygiene further
resulted in increased risk of illness. Many businesses remained
closed due to lack of power, and those that
did open suffered from a loss of revenue because transportation
challenges prevented customers from acces-
sing the businesses.
Following the hurricanes in Puerto Rico, the DHS National
Protection and Programs Directorate, Office of
Infrastructure Protection (DHS‐ IP) conducted regionally
focused assessments that highlighted the most dif-
ficult interdependencies to manage and efforts taken by industry
stakeholders to mitigate potential conse-
quences of their failure (DHS‐ IP, 2018). Retrospective
assessments like this one raise awareness of how
critical infrastructure systems depend on each other and help
improve planning for future disasters.
However, work remains to be done to proactively link failures
in interdependent systems to measurable
social impacts. This is especially important given the increasing
array of social and physical stresses that peo-
ple and communities experience, from disasters to economic
difficulties to environmental stresses (DHS,
2016; U.S. Army Corps of Engineers, Main Report, 2015).
3. Limitations of Existing Methods for Assessing Physical and
Social
Infrastructure Interdependencies
Challenges to assessing the social impacts of failures in

infrastructure stem from several factors including:
limited availability of data and models; disciplinary silos that
might stand in the way of different
10.1029/2020GH000251GeoHealth
stakeholders, practitioners, and experts working together on this
inherently cross‐ disciplinary problem; and
the undeniable heterogeneity of infrastructure systems,
disruptive events, and communities.
Several analytical or computational approaches have focused on
interactions among physical infrastructure
systems, particularly the “lifeline” sectors described above but
have not typically incorporated social sys-
tems. These methodologies include agent‐ based approaches,
system dynamics‐ based approaches, economic
theory‐ based approaches (input–output‐ based approaches and
computable‐ general‐ equilibrium based
methods), and network‐ based approaches (topology‐ based and
flow‐ based methods), among others
(Ouyang, 2014). While some broader attempts at modeling
include effects on other critical infrastructure sec-
tors, like finance (Barton et al., 2004), health care (Arboleda et
al., 2006; DHS‐ IP, 2018), and food distribution
(DHS‐ IP, 2018), these are less common.
A second limitation in analytic approaches is a lack of attention
to the human impacts of failures within and
across physical and social systems. Modeling these impacts
would require not just a complete characteriza-
tion of infrastructure interdepende ncies (spanning both physical

and social systems) but also a translation of
system failures into measurable effects on health, safety, and
well‐ being (e.g., in terms of injuries and lives
lost or extent of population displacement)—a task that requires
engineers, emergency responders, urban
planners, sociologists, and others to work together, crossing
disciplinary lines (Davidson, 2015; Hamburg,
2019; Meng et al., 2019). These social impacts are often the
result of multiple factors (e.g., interaction of pre-
existing vulnerabilities with the stress associated with
experiencing the disaster), making it difficult to isolate
the disaster‐ specific impacts above and beyond preexisting and
changing community‐ wide conditions. There
Figure 1. Lack of power had direct implications for
communications, water, and transportation systems, and indirect
effects on the population's health and
economy. Source: RAND created this figure for the government
of Puerto Rico under contract to the U.S. Federal Emergency
Management Agency. The figure
was included in transformation and innovation in the Wake of
Devastation: An economic and disaster recovery plan for Puerto
Rico, which entered the
public domain when published by the government of Puerto
Rico 8 August 2018 (Government of Puerto Rico, Central Office
for Recovery, 2018).
10.1029/2020GH000251GeoHealth
is an evolving evidence base that documents the human impacts
of disaster (e.g., declining mental health,
increasing substance abuse, and worsening chronic conditions)

(Cutter et al., 2008; Hobfoll, 1991; Kwok
et al., 2017; McFarlane & Williams, 2012; Norris et al., 2002;
Tierney, 2006). However, the specific link
between disaster‐ related failures in infrastructure systems and
resulting social challenges is less well studied.
Complex systems theory has provided a useful lens to study the
links between infrastructure systems and
social challenges because it acknowledges the interdependencies
within a system and how those interdepen-
dencies influence how a system interacts with its broader
environment (Coetzee & Van Niekerk, 2016;
Fraccascia et al., 2018; Quail et al., 2018). While researchers
have discussed the interlocking systems that
influence a community's resilience and argued that an integrated
resilience approach is needed to enhance
resilience, the field of resilience lacks a shared framework or
set of metrics to bridge the research across dis-
ciplines (e.g., social science, natural science, and economics)
and units of analysis (e.g., individuals,
families/households, organizations, and systems)
(Gillespie‐ Marthaler et al., 2019; Liu et al., 2017; Long &
Bonanno, 2018). This is in part because metrics that capture
these complex and dynamic relationships
and system adaptations (not just system performance) require
new data capture and storage (e.g., machine
learning algorithms to capture patterns in “big data” from social
media and linked data sets from across mul-
tiple systems that require public‐ private partnerships)
(Freeman et al., 2019). Although there are examples of
communities moving toward integrated data systems (e.g., NYC
Data Integration initiative) and of research
institutions mapping a complex adaptive system of systems
(e.g., Sandia National Laboratories CASoS
Initiative), these examples are more the exception than the norm
(Acosta et al., 2017).

A third limitation in analytic approaches is their inability to
address the considerable variation in human
impacts that depend critically on the scale and scope of the
disaster, the heterogeneity of infrastructure sys-
tems, and the characteristics of the affected communities
(Lindell & Prater, 2003). Additionally, social
impacts can take years or decades to materialize requiring
models that take into account the lengthy and
complex nature of disaster recovery (Gill et al., 2016). These
considerations make it difficult (if not impossi-
ble) to produce a model that predicts outcomes across contexts
and over the lengthy recovery period. For this
reason, outcome‐ oriented modeling efforts and tools tend to
focus more broadly on the economic impacts of
disasters (Hasan & Foliente, 2015). Common methods for
assessing economic impacts include input–output
modeling and computable general equilibrium models (Rose,
2004). Some models and tools (e.g., Federal
Emergency Management Agency [FEMA's] HAZUS tool) do
provide a way to assess or account for noneco-
nomic human impacts resulting specifically from infrastructure
failures triggered by disasters but do not
account for infrastructure interdependencies in their
assessments (Chang, 2003; Hasan & Foliente, 2015).
In contrast to analytical efforts, which are geared toward
describing, diagnosing, or predicting problems and
prescribing solutions where possible, empirical approaches
review historical events and so reveal interdepen-
dencies as they actually occurred, including frequent or
common failure patterns (Ouyang, 2014). Since the
empirical approach centers on events that have already
occurred, it can offer valuable insight into the rela-
tionships between the failures of networked infrastructure
systems and the social impacts that result from

these failures. These insights could then inform future modeling
efforts. But this value is only realized if
efforts are taken to carefully trace and document the root causes
of adverse social impacts. Further, empirical
reviews are often conducted through analysis of documents such
as media reports, government assessments,
and reports from utilities—such reports are inherently not free
of biases (Ouyang, 2014). For example, media
reporting biases tend to favor news of interest to their audiences
and may not include instances where rou-
tine operation was successfully maintained (Van Eeten et al.,
2011). Additionally, media reports do not con-
tain proprietary or sensitive information that may be relevant to
the cause of the failure. Despite these
shortcomings, systematic cataloguing and analysis of the
aftermath of disasters can provide useful insights
for model development.
4. Limitations of Existing Methods for Incorporating
Interdependency
Assessments Into Disaster Response and Recovery Planning
Existing methods fall short when it comes to incorporating
interdependency assessments into disaster
response and recovery planning. Utilizing knowledge of
interdependencies in planning is not straightfor-
ward for emergency planners and responders; local, state, and
federal government officials; or NGOs, all
of whom play key roles in the disaster cycle.
10.1029/2020GH000251GeoHealth

Several efforts have aimed to raise awareness of
interdependencies. For instance, the Critical Infrastructure
Modeling System, from Idaho National Laboratory, was
designed as a data visualization system for decision
makers to aid in their understanding of interdependencies
(Dudenhoeffer et al., 2007). Hasan and Foliente,
in their review of ongoing modeling efforts, provided
recommended actions for various types of decision
makers and suggested the most appropriate method to begin
undertaking actions. University College
London's Institute for Risk and Disaster Reduction and London
Resilience published a guide on the cascad-
ing effects of power outages to serve as a reference for
emergency planners (Pescaroli et al., 2017).
Unfortunately, simply raising awareness of the vulnerabilities in
interdependent critical infrastructure sys-
tems and in the communities they serve—and even
recommendations for how to remedy these vulnerabil-
ities—is ultimately insufficient to prevent failures and other
negative impacts when not coupled with
coordinated action. This was vividly demonstrated in the case of
Hurricane Katrina, where tabletop exercises
held before the storm raised concerns about flooding due to
levee failure and evacuating a large population
without personal transportation (Leavitt & Kiefer, 2006).
Inadequate funding, failed policies, and ineffective
intergovernmental communication prevented the problems
identified in the exercises from being addressed
prior to Katrina. In Puerto Rico, absent or ineffective
collaboration among government agencies, commu-
nities, industries, and utilities led to unmet needs in the wake of
Hurricane Maria. For instance, insufficient
coordination between FEMA and local food suppliers may have
caused delays in food supply and delivery
(DHS‐ IP, 2018).

The National Institute of Standards and Technology (NIST)
Community Resilience Planning Guide for
Buildings and Infrastructure Systems is a useful resource that
communities can use to set and incorporate
resilience goals into various planning activities, including those
associated with infrastructure systems
(Coetzee & Van Niekerk, 2016). While this NIST guide is not
particularly focused on infrastructure interde-
pendencies (though Volume II does touch on the topic), the
objective and structure of the NIST guide can
serve as a template for designing guidance that is more geared
toward capturing the specific challenges of
accounting for infrastructure interdependencies in disaster
response and recovery.
5. Discussion and Open Questions
As discussed in sections 3 and 4, understanding and
incorporating knowledge of infrastructure interdepen-
dencies are difficult but critical elements of effective disaster
response and recovery planning.
Comprehensive characterization of how systems and assets
depend on each other is necessary to help predict
when and why failures might occur so that resources can be
distributed appropriately. While sophisticated
modeling tools and analytic efforts have elucidated interactions
among components of physical infrastruc-
ture (especially among lifeline systems), assessing
interdependencies among social systems and the human
impacts of failures poses a challenge.
No less important than improving our understanding of
infrastructure interdependencies is improving the
rapid uptake of the knowledge and tools by real‐ world decision
makers who work with communities to pre-

pare for and respond to disasters. Incorporating physical and
social components into planning efforts can be
difficult because key factors governing resilience outcomes and
options for improving them vary across con-
texts that span socio‐ economic, demographic, cultural,
historical, and geographical realms. That said,
regardless of context, first responders, planners, and community
leaders need to be able to share informa-
tion, in real time, at little cost. One suggestion for improving
uptake of data, analytic methods, and knowl-
edge in recovery planning is to employ a collaborative model
that promotes information sharing structures
among organizations, between organizations and individuals,
across multiple levels and branches of govern-
ment, and the private sector (e.g., through coalitions)
(Madrigano et al., 2017). Specifically, the approach
encourages building community outreach and engagement skills
and develops expertise in translational
science among the emergency management and public health
workforce responsible for planning
(Satterfield et al., 2009; Wells et al., 2013). The Community
Tool Box (Center for Community Health and
Development), a toolkit utilized by coalition and advocacy
groups in the United States, is a good resource
in this regard. However, given the increasingly open and
exponentially growing data sources for understand-
ing communities, planners may need to also possess the ability
to understand “big data” and better reap the
benefits of databases containing decision‐ relevant information
(Arribas‐ Bel, 2014; Miller, 2010).
10.1029/2020GH000251GeoHealth

Several open questions need to be addressed to continue
improving awareness of infrastructure interdepen-
dencies and increasing uptake of knowledge as it relates to
recovering from and rebuilding after disaster
events:
1. To what extent (and how) do contextual variables (e.g.,
preexisting vulnerabilities, legal frameworks, and
institutional relationships) affect the value and use of
information about infrastructure interdependen-
cies and how they are represented and analyzed?
2. What would motivate or incentivize disaster planners and
policy makers to more systematically assess
and identify ways to address system interdependencies and
prevent future cascading failures from occur-
ring during a disaster?
3. How can system‐ level dynamics and interactions be
modified as the nature of risk exposures, sensitiv-
ities, impacts, and resilience changes due to global
environmental and demographic trends?
4. What strategic institution building is needed to enhance
longitudinal and multifaceted data collection,
sharing and modeling tools for the field of hazards and di saster
recovery?
The last question highlights a challenge that applies more
broadly to disaster response and recovery plan-
ning—a lack of appropriate social science research
infrastructure for examining trajectories of disasters
across time. Predisaster data are no less important than
postimpact data for social scientists; without robust
baseline data, it is impossible to assess changes in situation,

behavior, and beliefs that result from infrastruc-
ture failures, regardless of whether they are induced or
exacerbated by interdependencies (Parker et al.,
2019). Sharing and integrating diverse types of data and
modeling tools requires interdisciplinary efforts that
are complex and resource intensive. As a result, gaps in
knowledge about the cause and consequences of
infrastructure interdependencies remain.
The challenges and questions described in this paper need
immediate and substantial attention if the disas-
ter research and practice community is to adequately address the
changing risks faced by U.S. communities.
Mobilizing the necessary resources is a complex undertaking,
but nonetheless critical for building resilience.
Conflict of Interest
The authors declare no conflicts of interest relevant to this
study.
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/PTB
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/ENG (Modified PDFX1a settings for Blackwell publications)
/ENU (Use these settings to create Adobe PDF documents
that are to be checked or must conform to PDF/X-1a:2001, an
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please refer to the Acrobat User Guide. Created PDF
documents can be opened with Acrobat and Adobe Reader 4.0
and later.)
>>
/Namespace [
(Adobe)
(Common)
(1.0)
]
/OtherNamespaces [
<<
/AsReaderSpreads false
/CropImagesToFrames true
/ErrorControl /WarnAndContinue
/FlattenerIgnoreSpreadOverrides false
/IncludeGuidesGrids false
/IncludeNonPrinting false
/IncludeSlug false
/Namespace [
(Adobe)
(InDesign)
(4.0)
]

/OmitPlacedBitmaps false
/OmitPlacedEPS false
/OmitPlacedPDF false
/SimulateOverprint /Legacy
>>
<<
/AddBleedMarks false
/AddColorBars false
/AddCropMarks false
/AddPageInfo false
/AddRegMarks false
/ConvertColors /ConvertToCMYK
/DestinationProfileName ()
/DestinationProfileSelector /DocumentCMYK
/Downsample16BitImages true
/FlattenerPreset <<
/PresetSelector /HighResolution
>>
/FormElements false
/GenerateStructure false
/IncludeBookmarks false
/IncludeHyperlinks false
/IncludeInteractive false
/IncludeLayers false
/IncludeProfiles false
/MultimediaHandling /UseObjectSettings
/Namespace [
(Adobe)
(CreativeSuite)
(2.0)
]
/PDFXOutputIntentProfileSelector /DocumentCMYK
/PreserveEditing true
/UntaggedCMYKHandling /LeaveUntagged
/UntaggedRGBHandling /UseDocumentProfile
/UseDocumentBleed false

>>
]
>> setdistillerparams
<<
/HWResolution [2400 2400]
/PageSize [612.000 792.000]
>> setpagedevice

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