Disaster Resilience

Measuring and modeling the recovery potential of critical infrastructure

Image of Chris Zobel in Risk Management Lab
Chris Zobel has studied disaster resilience in New
Orleans and Gulfport, Mississippi, in the context of
Hurricane Katrina. He has also studied the recovery
efforts of the Appalachian Power Company following
the derecho windstorm that struck the mid-Atlantic
states in the summer of 2012.

Chris Zobel’s daily work is far removed from the pressing drama of an ongoing disaster response effort.

Yet the nature of this work puts him front and center of any initiative to improve society’s capacity for withstanding, coping, and recovering from a catastrophe.

Zobel, a professor of business information technology who specializes in decision support systems, is among those who study disaster resilience — the ability to bounce back from a calamity that is typically associated with making a quick recovery but also depends on the capability to resist the disaster’s initial impact.

Keeping the global economy working

Disaster resilience is especially important for critical infrastructure systems that support the daily operations of the global economy, Zobel says — such as those in the energy, finance, transportation, healthcare, and telecommunications sectors.

Protecting such systems and helping them to recover from the effects of disasters is essential to the long-term sustainability of the societies they support.

Winner of a recent Fulbright Scholar Award, Zobel plans to use it to develop a new, more operational approach to measuring and monitoring the resilience and sustainability of critical infrastructure.

Measuring resilience

Operational, he says, means that the approach actually can be applied to help improve an organization’s ability to operate effectively.

In other words, if we can use an approach for measuring resilience to tell us not only how quickly but also how effectively we are currently recovering from a disaster, then that measure of resilience is more than just a theoretical tool — it’s actually useful to support the organization’s operations and its actions during that recovery process.

Reducing the effects of disaster

The Fulbright will give Zobel a valuable opportunity to collaborate directly with researchers at Germany’s Center for Disaster Management and Risk Reduction Technology at the Karlsruhe Institute of Technology.

The center is internationally known for its focus on trying to understand the underlying causes of disasters and the actions that can be taken to reduce their effects.

The ability to identify such actions is crucial to improving the resilience of a real system, says Zobel. If we are going to actively use the measures to help with the organization’s operations, then we need to know what actions or behaviors lead to different levels of resilience. We can then judge which actions would be expected to work best in the future, in terms of the amount of resilience that they might provide.

Adaptive resilience

Zobel says the study of disaster resilience is characterized by two main approaches.

Social scientists have tended to view resilience as a static concept that represents a general capacity for withstanding and/or recovering from a disaster.

In contrast, engineers have tended to focus on more dynamic or adaptive resilience, which is represented by the time-varying amount of loss experienced by a system as it resists and then recovers from a particular disaster event.

An example of static resilience, he says, would be the overall capacity for resilience of a coastal community, with no specific disaster event in mind.

The many dimensions of resilience

Graph of disaster resiliance after Hurricaine Sandy.
A good illustration of dynamic resiliance, Zobel says, is the
performance of the electric power system in New York City
during Hurricane Sandy. "We can track over time the
percentage of households that had power, starting from before
the storm struck and continuing until normal operations were
restored several weeks later. The maximum percentage of
households who were without power, the length of time it took
to completely restore that power, and how long the majority of
households were without power—these are all important
characteristics of how resilient the overall system is."
Graph information from the Department of Energy,
Office of Electricty Delivery and Energy Reliability

This type of resilience is static in that it is measured at a specific point in time, rather than over a given time period. It typically can be measured again a number of years later to see how, and if, things have changed, but the actual measure itself does not really take time into consideration.

It is important to remember that communities are made up of many interrelated components, Zobel says, including people, buildings, organizations, an economy, and the environment — and that each of these items can have its own type and level of resilience.

Resilience has many different dimensions, and it is therefore common to use a large number of different variables to capture the various characteristics that contribute to a community’s resilience.

For example, the number of acres of viable wetlands, the number of citizens over age 80, the employment rate, and the number of homeowners in a community all provide different, complementary, views of how resilient that community is likely to be.

By combining the information provided by each of these variables, we can create a measure of resilience that can be compared across different communities.

Measuring actual behavior

As for dynamic resilience, Zobel says that rather than being a measure of the inherent capacity to withstand and recover from disasters, it is more of a measure of the actual behavior in response to a particular event — i.e., to what extent did the community or organization or person actually resist damage and how quickly was it able to recover.

A good illustration of dynamic resilience, he says, is the performance of the electric power system in New York City during Hurricane Sandy. We can track over time the percentage of households that had power, starting from before the storm struck and continuing until normal operations were restored several weeks later.

If you look at a graph of this data over time, you’ll see a sudden dip in the graph — when a lot of folks lost power because of the storm’s impact — and then a gradual rise back to its original level — as the electric companies were able to restore power.

The maximum percentage of households who were without power, the length of time it took to completely restore that power, and how long the majority of households were without power — these are all important characteristics of how resilient the overall system is.

Obviously, time is a very important factor in this situation, and so are the characteristics of the actual disaster event. If there had been less flooding in Hurricane Sandy, for example, then some of the power substations wouldn’t have shut down, and the shape of the graph would have been very different.

Understanding the overall picture

The static and dynamic concepts represent very different views of resilience, Zobel says, but we need to consider them both because they each describe a very important part of the overall picture.

One significant constraint to developing good resilience measures, he says, is the difficulty of collecting appropriate data. For example, many of the variables used in calculating static resilience are based on sources such as the U.S. Census, which is only updated every 10 years.

Indeed, Zobel adds, only certain types of data lend themselves to measurement over short time intervals, and only data that can readily be collected during a disaster can be used to operationalize an integrated measure of resilience.

It’s very difficult — and not a reasonable use of time and money — to collect data on how many individuals over 80, for example, are still occupying their homes at different times during an actual hurricane.

In contrast, data such as the number of households without power is typically published at least daily by the power companies.

Assessing a community's recovery

Following a disaster, economic variables such as unemployment rates and the output of goods and services in the construction, manufacturing, and leisure and hospitality industries can be used to assess the relative rate and extent of community recovery at regular time intervals, Zobel says.

If you’re looking at longer-term behaviors, the measurements don’t need to be quite as frequent, as long as they can give a good ongoing picture of how the situation is changing and hopefully improving.

By quantitatively analyzing the relative amount of resilience exhibited by a community, we may gain better insight into its ability to recover and thus develop a better understanding of the factors that allow it to return to normal, or even better–than–normal, levels of activity.


Virginia Tech Pamplin College of Business Virginia Tech Pamplin College of Business Magazine Fall 2014

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