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This week, I wanted to put together some thoughts on the National Flood Insurance Program (NFIP) gathered from across the web. Actually, this is the only story I've wanted to write about, but I haven't sat down for my #TWIST note in about a month.

In fact, I'm not sure there has been any story worth looking into except hurricane impacts on communities and infrastructure when we're talking about infrastructure and infrastructure resilience. Hurricanes Harvey and Irma are crucial because, if nothing else, they demonstrate to us that community resilience has everything to do with the communities' and local government leaders' capacity to respond in the moment and adapt to future possibilities. While it is important to build infrastructure that can withstand a variety of challenges, there are two things we must consider when planning for resilience. First, infrastructure is almost impossible to adapt after it is built. One can harden existing infrastructure, but infrastructure is almost, by definition, completely un-adaptive. Second, the radically de-centralized way in which infrastructure is owned and built--especially in the United States (and I include buildings in infrastructure, which many researchers do not)--makes it nearly impossible to forecast the types of loads that individual systems will be called on to respond to.

Well, I don't want to go too far into that direction. However, I did want to share some stories about the NFIP because we are going to need to call on this program more frequently and deeply in the future. What are the major issues? Is the program vulnerable? Are folks who rely on the program vulnerable? What kind of losses will it be called on to insure in the future? Hopefully, a few of the articles/resources below can shed some light on the state of the NFIP as we enter into new climate realities.

  • Irma, Harvey, Jose, Katia: The Costliest Year Ever? Bloomberg asks whether Harvey et al will be among the costliest disasters ever. A snapshot from their article demonstrates that, globally, American hurricanes are responsible for five of the top 10 most costly events--in terms of insured losses. Where will this year's hurricane season rank?

    The ten most costly disasters in terms of insured losses (in Billions).
    The 10 most costly global disasters in terms of insured losses (in Billions). Source: https://www.bloomberg.com/graphics/2017-costliest-insured-losses/
  • Hurricane Sandy Victims: Here’s What ‘Aid’ Irma and Harvey Homeowners Should Expect. While it is critical to re-authorize NFIP and help to ensure that families receive the aid they need, it is unclear whether NFIP in its current form can deliver that assistance. Writing in Fortune Magazine describing the efforts of a group called Stop FEMA Now to promote awareness about some of the major shortcomings (as they see it) of NFIP, Kirsten Korosec writes:

Stop FEMA Now is a non-profit organization that launched after flood insurance premiums spiked as a result of the Biggert-Waters Act of 2012, inaccurate or incomplete FEMA flood maps, and what it describes as questionable insurance risk and premium calculations by actuaries, according to the group.

  • The NAIC has published a very interesting report that shows that, in the average year, NFIP is self-supporting. While in most years it pays out fewer claims that it receives in premiums, catastrophes are well beyond their capability to pay and NFIP must rely on borrowing. Consider Figure 1 from their report:
    Difference between NFIP premiums and claims per year.
    Difference between NFIP premiums and claims per year. Source: <http://www.naic.org/documents/cipr_study_1704_flood_risk.pdf>

    Do you see what they say in those two paragraphs after the figure?! First, note that NFIP must have its borrowing authority reauthorized by Congress before Sep. 30 (it has been extended to Dec. 8), and that it is already $25 billion in debt. Second, note that the NFIP has not priced its policies at "market rates," making NFIP unable to cover losses from major catastrophes. Even with these artificially low rates, vulnerable parties do not purchase the insurance!

  • Finally, J. Robert Hunter writes in the Hill about the fact that NFIP originally contained long-range planning in the legislation. Nonetheless, communities are not enforcing the land-use provisions contained in the law:

When I ran the NFIP in the 1970s, I saw a far-sighted idea that Congress put into action. Congress brilliantly embedded long-range planning into the program: in exchange for subsidies for flood insurance on then existing homes and businesses, communities would enact and enforce land use measures to steer construction away from high-risk areas and elevate all structures above the 100-year flood level. Only pre-1970s structures would be subsidized.

Clearly, from the snippets I've placed here for you, NFIP is in trouble. This is the story. How much longer can we afford to ignore the state of NFIP as a major tool for supporting community resilience?

Today, I'm pleased to present a guest entry from SEED Ph.D. student, Vikram Rao.  This article, an advance from Risk Analysis by Stephanie Chang and colleagues, is an exciting introduction to the use of expert judgment to investigate infrastructure resilience.  Traditionally, expert elicitation is used to evaluate probabilities to assess the vulnerability of a critical system to outages of feeder systems or incidence of extreme exogenous events.  In this article, Chang and colleagues emphasize the use of expert elicitation to assess such resilience quantities as time to recover and disruption to system services over time.  I hope you enjoy this as much as I did, and thank you Vikram for your insights...

This article examines resilience of infrastructure systems using expert judgments. This is of interest since disasters such as earthquakes can cause multiple failures of infrastructure systems since they are interdependent. The approach here is to characterize system resilience, understand the relationships between interdependent systems in the context of resilience, and understand ways to improve resilience, which is of interest to risk managers. Many infrastructure systems are considered here, including water, electricity, and healthcare.

The researchers use expert judgments in a non-probabilistic approach. One goal is to elicit the service disruption levels, given as degree of impact/degree of extent, for numerous sectors. Interdependency diagrams show the dependencies between systems and provide clues as to the cascading nature of disaster events. For example, healthcare is heavily dependent on water, which tells health risk managers that it is advisable to have alternate water sources available in the event of emergency. One thing I find interesting is that there is no agreement on the extent of infrastructure reliance on water. Some studies claim that water is needed for other infrastructures to function, others do not. So the importance of water in infrastructure resilience remains to be seen.

When discussing the results, the authors bring up the fact that the representatives (experts) revise their judgments in the face of new information. Experts realize that the importance of a system is greater than originally believed, or that interdependencies exist that they had not considered. Since infrastructure systems are so interdependent and functional systems are critical for human well-being, the sharing of information between infrastructure systems is needed going forward.

One area I would like to see additional research is to explore resilience in water distribution systems, particularly looking at costs associated with disaster recovery and time to restore water distribution functionality. We could use expert judgments to examine the quantitative nature of water system resilience, for example eliciting the cumulative distribution of water functionality as a function of time (e.g. 25% water functionality restored after 1 week, 75% after 3 weeks). This is of course valuable to risk managers who are seeking to understand the nature of water system functionality in the wake of a disaster.

Today, Dr. Francis is giving a talk titled "Two Studies in Using Graphical Model for Infrastructure Risk Models" discussing some recent peer-reviewed conference papers given at ICVRAM and PSAM11/ESREL12.  The abstract for today's talk is:

In this talk, I will discuss the use of Bayesian Belief Networks (BBNs) and Classification and Regression Trees (CART) for infrastructure risk modeling.  In the first case study, we focus on supporting risk models used to quantify economic risk due to damage to building stock attributable to hurricanes. The increasingly complex interaction between natural hazards and human activities requires more accurate data to describe the regional exposure to potential loss from physical damage to buildings and infrastructure. While databases contain information on the distribution and features of the building stock, infrastructure, transportation, etc., it is not unusual that portions of the information are missing from the available databases. Missing or low quality data compromise the validity of regional loss projections. Consequently, this paper uses Bayesian Belief Networks and Classification and Regression Trees to populate the missing information inside a database based on the structure of the available data. In the second case study, we use Bayesian Belief Networks (BBNs) to construct a knowledge model for pipe breaks in a water zone.  BBN modeling is a critical step towards real-time distribution system management.  Development of expert systems for analyzing real-time data is not only important for pipe break prediction, but is also a first step in preventing water loss and water quality deterioration through the application of machine learning techniques to facilitate real-time distribution system monitoring and management.  Our model is based on pipe breaks and covariate data from a mid-Atlantic United States (U.S.) drinking water distribution system network. The expert model is learned using a conditional independence test method, a score-based method, and a hybrid method, then subjected to 10-fold cross validation based on log-likelihood scores.

This talk is hosted by Ketra Schmitt in the Center for Engineering in Society on the Faculty of Engineering and Computer Science.