Infrastructure Interdependencies Research Approach Dana Brechwald, Earthquake and Hazards Specialist Lifeline Committee Meeting July 26, 2012

Types of Interdependencies (Chang et al 2006) Physical – one system depends on another for operation (ex, wastewater depends on power) Geographic – co-located systems Cyber – linked electronically or through information-sharing Logical – other, such as shared financial market Part of beginning to understand the complex interactions between infrastructure systems is establishing a common language and framework for describing and characterizing interdependencies. Existing academic literature has identified various types of interdependencies, including: Physical, as when one system depends on another to operate, such as a wastewater treatment plant depending on power to run their system Geographic, as when two systems are co-located, so the event impacting one system also impacts the other Cyber, as when one system is linked to another electronically or through information-sharing, such as a server being located at one location Logical – any other way of dependencies, such as a linkage through a shared financial market

Types of Interdependent Failures (Chang et al 2006) Cascading – direct disruption Escalating – exacerbates already-existing disruption, increasing severity or prolonging Restoration – impacts the restoration of another system Compound damage propagation – leads to disruption that causes serious damage Substitutive – disruption due to excessive demands placed on a system to substitute for failed system There are also a variety of descriptive terms used to identify the type of failure caused by interdependencies. These can be described imagining a power outage as the initial system failure. Cascading – disruption of power directly causes the disruption of the impacted system Escalating – disruption of power exacerbates an already-existing disruption in the impacted system, increasing its severity or prolonging the outage time Restoration – disruption of power hampers the restoration of an impacted system Compound damage propagation – disruption of power leads to a disruption that then causes serious damage/accidents/problems in impacted system Substitutive – system is disrupted due to demands placed on it to sub for the power system Direct – power outage is direct cause of a system disruption Second-order – power outage is once removed as the cause of a system disruption Higher-order – power outage is twice removed or more as the cause of a system disruption

Upstream and Downstream Interdependencies Example: Upstream and Downstream Interdependencies Interdependency failures often cause a chain of subsequent impacts, and you can trace upstream and downstream impacts. Major systems such as power tend to be farthest upstream, and are therefore most important to identify and understand.

Describing Characteristics of Interdependent Failures (Chang et al 2006) What is the initiating event? What is the spatial extent? What is the duration of the failure? What are the impacted systems and subsystems? Is the interdependency expected (linear) or unplanned/unexpected (complex)? Is there feedback from the secondary system back to the initial system? What was the operational state of the system before it failed? What is the adaptive capacity of the system(s)? What is the restoration time? Literature has also begun to characterize interdependent failures to better understand their scope and characteristics. For example: What is the initiating event and what was the original infrastructure failure? What is the spatial extent of interdpenednt failures? What is the duration of the failure, including the original failure and subsequent failures due to interdependencies? Is the interdependency linear, meaning was it expected and predictable, or was it complex and unanticipated? Is there feedback? Do impacted systems then impact the original power system? Was the failed system operating at capacity, near capacity, or below capacity when it failed, and did that influence its failure? Is the system able to respond quickly to failure and adapt, or is it inflexible and unable to respond quickly? What is the anticipated restart time? Minutes, days, hours, weeks, months?

Characteristics of Interdependencies Consequences (Chang et al 2006) What is the severity of the consequence? What type of consequence is it? Economic, public health and safety, social, environmental, etc What is the spatial extent of the consequence? How many people are impacted? What is the duration of the consequences? Lastly we can characterize the consequences of interdependencies and begin to describe how they impact society. Is the consequence minor, moderate, or major? What is the extent of motifications in daily routine or plans that causes hardship to people or entities? What sectors do the consequences impact? Economic, public health and safety, social, environmental? Multiple? Are the consequences localized, regional, national, international? Do the consequences impact few, many, or most people within the spatial extent of the consequence? How long will the consequences last? Minutes, days, weeks, months? How is this different from restart time?

Additional terms that can be used to characterize and describe interdependencies, failures, and their consequences. (Rinaldi et al 2001)

Objectives for Our Study Goal: Understand performance of infrastructure systems serving airports to better understand performance of airports Objectives Identify interdependencies Qualify interdependencies Understand vulnerabilities due to interdependencies Find “choke points” Find redundancies and “islands” Project consequences Identify and prioritize key mitigation strategies Facilitate conversation among key stakeholders Better understand how consequences will affect restoration and recovery Are these the right objectives? Should anything be added/changed/removed? I’ve begun to brainstorm some objectives for our study, but I’d like your input to prioritize and identify additional objectives. First, the main objective will be to identify interdependencies, qualify those interdependencies using the language we’ve discussed, and understand the key vulnerabilities of our major systems due to interdependencies. This will allow us to identify key “choke points” with multiple interdependencies and high vulnerability as well as identify system redundancies and “islands” – or areas of self-sufficiency – that inherently make a system less vulnerable. We can take this knowledge to begin to project consequences of likely failures on the operations of Bay Area airports as well as the wider regional economy. All of this will allow us to begin to identify and prioritize key mitigation impacts to reduce the consequences of interependencies. Additionally, we’d like to help to facilitate conversation about interdependencies among key infrastructure providers as well as better understand how consequences of interdependencies will affect restoration and recovery processes.

Scope of Our Study Systems Size Power Water and Wastewater Communications Transportation Jet Fuel Size Region – high level Airport-specific – more detail Is this the right scope? What level of detail should we aim for at each level? Are there any other systems we should look at? While the focus of this study is on interdependencies’ impacts on airports, the suggested geographical scope includes a high-level analysis of the entire 9-county bay area. This is suggested because if we limit our scope too closely without a better understanding of the nature of interdependencies, we may overlook higher-order interdependencies that we didn’t anticipate. We can then perform a more detailed analysis specifically around each airport.

Case Studies Don’t Wing It case studies (Perkins, 2000) 1989 Loma Prieta 1994 Northridge 1995 Kobe 1999 Turkey 1999 Taiwan Should we do case studies? Should we add/change/remove from this list? Case studies can provide useful insights into real-life interdependencies based on actual disasters. Many approaches to interdependencies analysis utilize case studies to better understand the nature of interdependencies. ABAG’s 2000 study, Don’t Wing It, included five case studies of earthquakes, which we can build upon with an emphasis on infrastructure failures. Do we think that case studies are worthwhile for this study? Should we do the case studies on this list, or different case studies? Maybe more recent earthquakes like Haiti?

Quantitative Approaches Many models are being developed to anticipate potential interdependencies Use algorithms to examine: Given a set of initiating events, what is the cascading impact on a subset of nodes? Given a set of nodes and a desired end state, what is a set of events that would cause this effect? Given a set of events and a set of observed outcomes on nodes, is it possible to determine derived interdependencies? Given a set of infrastructure networks and a critical function, what is the subset of critical nodes that will adversely impact a specific functionality due to a dependency? Require high level of specific data, expertise Not clear how outputs relate to multiple sector consequences

Modeling Interdependent Systems Output of a quantitative model simulation showing key “nodes” within infrastructure systems and the interdependencies both within the system and with other systems. (Pederson et al, 2006)

Qualitative Approaches Rely on empirical observation, expert interviews Typically use a damage scenario Examine whole systems, not just nodes Look at multi-sectoral societal impacts Wide variety of outputs Qualitative approaches rely more on assumptions using a potential damage scenario, and can help to paint a broad situational picture based on expert knowledge and observation of past performance of systems. This approach integrates social science and planning to better understand a wider variety of social, economic, health, and environmental impacts, and focus more on decision-making and prioritization of mitigation and risk management.

Example of a qualitative chart identifying high, medium, and low interdependencies on various infrastructure systems and servies (Pederson et al, 2006)

AIDRC Here’s an example of a study done by University of British Columbia to examine existing studies in order to craft their own. The JIIRP study is quantitative, using modelling software, while the others are largely qualitative. JELC – Joint Emergency Liaison Committee – Metro Vancouver JIIRP – Joint Infrastructure Interdependencies Research Project Emerald Links – Public safety Canada Initiative (AIDRC, UBC)

Analyzing Infrastructures for Disaster Resilient Communities (AIDRC) Bases study in earthquake damage scenario Conducts interviews Verify scenario Characterize vulnerabilities and interdependencies Estimate the ability of infrastructures to withstand and recover from extreme events Create diagrams based on data gathered from interviews Conducts Workshop Review interdependencies and vulnerabilities identified in interviews Ranked and prioritized mitigation strategies Based out of University of British Columbia, Analyzing Infrastructure for Disaster Resilient Communities utilizes a qualitative approach. Their overall research approach is fourfold: Situating their work in the larger research context Outlines key research questions, including what sectors are most vulnerable to interdependency failures, what types cause the most severe consequences, what patterns occur in all disasters and what are specific to certain types, and how can communities plan to reduce consequences Identifies key characteristics in the framework they created, which we looked at before Collects data and case studies. Their approach for earthquakes is fairly specific – they developed a vancouver-specific earthquake scenario, then conducted interviews with experts to verify the validity of the scenario, characterize and identify vulnerabilities and interdependencies, and estimate the adaptability of systems. They then created diagrams to “map” the interdependencies, and conducted a workshop to validate their findings and rank and prioritize mitigation strategies.

AIDRC This is one of the tables that resulted from their research, identifying anticipated service disruption levels from their earthquake scenario.

AIDRC This then led to identifying anticipated disruptions of other sectors due to initial disruptions.

The final diagram illustrates anticipated service disruptions in all of the major sectors, then the upstream and downstream impacts and their severity.

San Francisco Lifelines Council Scenario-based study – repeat of 1906 (consistent with other studies done in SF) Emphasis on response and restoration preparedness and coordination and development of performance standards Working groups develop questions for infrastructure operators on system performance, upstream and downstream interdependencies, and preparedness and coordination strategies/issues Synthesize data and conduct interviews/workshop to evaluate responses Prepare more detailed scenario based on responses Draft performance standards and preparedness and coordination strategies The San Francisco Lifelines Council is currently conducting an interdependencies study based around infrastructure serving San Francisco. They are using a scenario that’s essentially a repeat of the 1906 earthquake, which has been used for other studies in the City, for consistency. Their study’s emphasis is on response and restoration preparedness and coordination, and developing performance standards for infrastructure, based on SPUR’s Resilient City standards. They formed working groups, who developed questions for each infrastructure operator on system performance, upstream and downstream interdependencies, and preparedness and coordination. The expectation is that each operator will perform its own internal analysis on their system performance and respond to the questions. This data will then be synthesized, and the council will either conduct targeted interviews or a workshop to evaluate and validate the data. The goal is to enrich the scenario using interdependency information and draft performance standards and preparedness and coordination strategies.

Our Potential Approach Take cues from AIDRC and SF Lifelines Council Add high-level mapping of systems Craft interview questions and conduct interviews Synthesize data Confirm data Any other thoughts and ideas?

Questions What do we like about these approaches, and what should we use from them? Should we use a scenario, and if so, what should it be? How can we improve upon these? To what extent to we align with SF Lifelines Council approach? What is the right approach for us?

Potential Issues and Needs May have difficulty gaining access to data Need to know the right people to contact Don’t want to duplicate efforts of SF How can we find the right people to access?