Arriving in Kathmandu for the 2018 RMS Impact Trek, I was already aware of the many years that RMS has provided support for Build Change and its work in areas worst hit by catastrophic disasters. Our first day in the Build Change office was a crash course in their local objectives and challenges. Day Two saw us on a field trip to nearby Kirtipur to survey common building practices. It was a lot of information to process and it was not immediately clear to me what “impact” we could make during our short visit.
But it was later in the week — when, admittedly, the jet lag finally wore off — that I finally caught on.
It became clear that Build Change faces a number of challenges, both logistical and societal, in promoting long-term resilience across Nepal. One of the largest challenges is more fundamental than you’d think: very few people have ever heard of retrofitting. The average Nepalese homeowner does not know its benefits and government officials do not know that it is a cost-effective alternative to rebuilding. So, in this respect, Build Change are essentially starting from square one.
To complicate matters further, government recovery aid provided to homeowners only covers costs related to completely replacing damaged homes; retrofitting costs are not reimbursed. Thus, homeowners are not incentivized at all to retrofit.
In Nepal, like many countries I’ve visited for RMS model research, there is great pride associated in owning a home; many homes are passed down through the generations. Our trip to Dhunkharka showed us that rural homes are typically three stories tall; the cattle reside on the ground floor, the family lives on the middle floor, and grain sits on the top floor.
For those homes damaged, the Nepalese government is quickly closing the window to claim government assistance. The resulting pressure typically leads homeowners to take the first aid installment and build an earthquake-resistant, one-story home. However, not wanting to appear to downsize, families move their cattle into the new home and continue to live in the compromised, three-story home.
Of course, in many matters that concern international governments, the most persuasive arguments get right down to dollars and cents. Thus, during our first brainstorming session, late in the week, Build Change asked, “How can we quantify the impact and cost savings of their retrofitting work in Nepal?”
And that’s when it clicked.
Most commercially-available catastrophe models base their vulnerability relationships on key, “primary” building characteristics, such as a building’s construction material and height. But beyond the commonly-captured primary characteristics lie an extra layer of “secondary” characteristics, meant to highlight unique features that set a structure apart from the average building stock.
In many cases, these mitigative features are designed to protect the most vulnerable features of a structure, such as the roof or windows. Catastrophe model users use secondary characteristics to adjust a building’s vulnerability and commonly translate this into rate discounts to incentivize policyholders. Among the secondary characteristics most frequently used in earthquake modeling is retrofitting.
In giving the Build Change staff a crash course in catastrophe modeling, it became clear to all of us that they were already beginning to capture the data necessary to inform a catastrophe model. Our trip to Kirtipur showed us that. So, why not build an earthquake model that can calculate the damage expected for retrofitted and non-retrofitted buildings — and then calculate the difference?
RMS does not have a Nepal earthquake model, but we had the building blocks all around us to stitch together a rough solution that would perfectly fit the bill for Build Change. They had the local knowledge and we knew how to build and use catastrophe models. By dinnertime, we ended up with a thorough framework which could be used as a basis to build an introductory earthquake model.
In addition to bringing in experts from all over the world into Nepal, Build Change hires brilliant local talent to support their efforts, including structural engineers, project managers, and computer technicians. By the end of our week in Kathmandu, we handed this model framework off to their technical staff, who can build a software platform around it.
Build Change has operations ongoing all over the world and Nepal is one of the few countries they work in where an RMS catastrophe model does not exist. In the future, this approach could be extended to other areas and the pre-packaged solutions RMS offers can help Build Change communicate the key benefits of retrofitting to international governments in a quantitative manner.
You May Also Like
March 31, 2021
RMS 2020 Catastrophe Review: The Year of the COVID-19 Pandemic
Impact of the 2017 North Atlantic Hurricane Season on the RMS Medium-Term Rate
In my years of contributing to this blog, I have written extensively about the long-standing debate about the current state of hurricane activity in the North Atlantic Basin. This debate has become no clearer following the 2017 hurricane season; one of the busiest and costliest seasons on record. 2017 followed a stretch of below- to near-average seasons that began in 2012 and it is unclear whether future seasons will remain active or return the recent level of relative quiet.
Figure 1: Comparison of North Atlantic Basin major hurricane count and its five-year average with climatology, 1970-presentLast year, I wrote about the Version 17 Medium-Term Rate (MTR) update, in which the MTR registered below the long-term rate for the first time in the history of the forecast. You will recall that the MTR analyzes climate models reflecting three main theories of Atlantic hurricane variability and produces a five-year outlook of hurricane landfall frequency. In some cases, the output of these models shows conflicting signals, much like the ongoing scientific debate.
The most statistically skillful of these models in version 17 of the RMS® North Atlantic Hurricane Models identified a shift to a future below-average period of hurricane activity, in part based on the 2012-16 decrease in Atlantic Basin major hurricanes. However, uncertainty exists in identifying phase shifts near the end of a data record and it may be shortsighted to update the MTR forecast on the back of potentially anomalous season.
Indeed, scenario analyses performed by RMS modelers show that an inactive 2018 would produce an MTR forecast that remains below the long-term average.As a result, RMS will not be issuing an MTR forecast update in 2018.
What Factors Drove the Version 17 Update?
As a reminder, it was the “shift” climate models, which view changes in basin phase as natural oscillations, that produced below-average landfall forecasts in version 17. Conversely, the sea surface temperature and “active baseline” climate models did not identify a transition to a less active phase, in part based on Atlantic sea surface temperatures (SSTs) that have remained warmer than average since the mid-1990s.
We take a weighted average across all 13 models, based on tests made of each model’s skill in predicting hurricane activity in sample periods from the past. In Version 17, the shift models demonstrated greater skill than their counterparts and the higher weight allocated to these models decreased the MTR below the long-term rate.
Regionalization Accounts for Above-average Tail Risk
However, understanding the story on this headline alone ignores an extra layer of intelligence built into the RMS forecast. The MTR relies on regionalization — that is, the impact of projected SSTs in determining where along the coastline, and at what strength, hurricanes are likely to make landfall.
Atlantic SSTs measured during the peak of hurricane season have remained warmer than average for many years. Thus, while the Version 17 MTR calls for fewer than average hurricanes, the energy provided by warm sea surface temperatures may lead to hurricanes that are stronger than average.
Comparisons of industry exceedance probability (EP) curves in the U.S. and the Caribbean demonstrate this unique severity distribution. In the U.S., the likelihood of exceeding industry losses beyond the 150-year return period is higher in the MTR than in the long-term rate (LTR). In the Caribbean, this threshold is even lower: the five-year return period. This point corresponds to a US$4 billion industry loss, an amount well exceeded in 2017 by Hurricanes Irma and Maria.
Figure 2: Ratio of RMS MTR and LTR 2017 Industry Occurrence Exceedance Probability Curves for the U.S. (left) and the Caribbean (right)This analysis demonstrates that the MTR still considers the possibility of highly damaging events and seasons, such as 2017, to be greater than the long-term average. As a result, RMS believes the Version 17 MTR forecast to remain a valid view of hurricane risk.
The Future of the Medium-term Rate
RMS will update the MTR forecast next year in Version 19 of the RMS North Atlantic Hurricane Models. This forecast will consider the same three groups of climate models mentioned previously.
We expect the sea surface temperature and active baseline models to produce forecasts above the long-term average, based in part on warmer than average sea surface temperatures. Thus, the Version 19 MTR, and its position relative to the long-term rate, will heavily depend upon the output of the shift models. Will they still project the recent transition to a less active phase?
Anticipating activity in future seasons allows us to add data points to a statistically limited dataset. To do this, RMS modelers ran our systematic and objective forecast methodology with several possible outcomes for the upcoming 2018 season. Three of these scenarios include:
A 2018 season with fewer than three major hurricanes in the basin, with none making landfall in the U.S., maintains an MTR forecast below the long-term average
A 2018 U.S. major hurricane landfall removes the phase change in the shift models, producing a slightly above-average forecast
A repeat of the hyperactive 2017 season in 2018 produces an above-average forecast, but below the version 15 forecast
Figure 3: The outcome of several Version 19 MTR scenario analyses performed by RMS modelersThese scenario analyses prove beneficial in a number of ways. First, they allow RMS modelers to test the sensitivity of the MTR climate models during an uncertain time of vigorous scientific debate. Second, and more importantly, RMS clients can follow the progress of the next hurricane season in real time with an advance understanding of how each major hurricane formed will impact the next update to the RMS model.…
Tom is a Director - Event Response at RMS, and leads the Event Response services operation. He joined RMS in 2009 and spent several years in the Client Support Services organization, primarily providing specialist peril model support. Tom joined RMS upon completion of his bachelor's and master's degrees in meteorology from Pennsylvania State University, where he studied the statistical influence of climate state variables on tropical cyclone frequency. He is a member of the American Meteorological Society (AMS).