Diasaster Recovery One takeaway from the article. 200 words Article attached From Awareness to Action: Accounting for Infrastructure Interdependencies in

Diasaster Recovery One takeaway from the article. 200 words

Article attached 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, we 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,
anarayan@rand.org

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

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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

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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 interdependencies (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).

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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.

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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).

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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 disaster 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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Acknowledgments
Funding for this work was provided by
the RAND Corporation. This
manuscript does not present new
observational or modeling data.

https://doi.org/10.1007/s13753-013-0003-0

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