The Source Model
The 2010 M7.1 Darfield earthquake in
New Zealand started a sequence of events that propagated eastward in the
Canterbury region over several years, collectively causing upward of 15
individual loss-causing events for the insurance industry. The Insurance
Council of New Zealand state that the total insured loss was more than NZ$31
billion (US$19.4 billion).
With such a significant sequence of events, a lot had to be learned and reflected into earthquake risk modeling, both to be scientifically robust and to answer the new regulatory needs. GNS Science – the New Zealand Crown Research Institute, had issued its National Seismic Hazard Model (NSHM) in 2010, before the Canterbury Earthquake Sequence (CES) and before Tōhoku. The model release was a major project, and at the time, in response to the CES, GNS only had the bandwidth for a mini-update to the 2010 models, to allow M9 events on the Hikurangi Subduction Interface, New Zealand’s largest plate boundary fault, and to get a working group started on Canterbury earthquake rates.
But given the high penetration rate of earthquake insurance in New Zealand and the magnitude of the damage in the Canterbury region, the (re)insurance and regulatory position was in transition. Rather than wait for a new National Seismic Hazard Map (NSHMP) update (which is still in not available), RMS joined the national effort and started a collaboration with GNS Science as well as our own research, to build a model that would help during this difficult time, when many rebuild decisions had to be made. The RMS® New Zealand Earthquake High Definition (HD) model was released in mid-2016.
There used to be several ways to ensure risk diversification in a California earthquake insurance portfolio. You could select risks on the Peninsula and risks in the East Bay; or select risks in Ventura and Orange counties; or risks in Santa Barbara and Los Angeles counties. Better yet, it was considered that selecting risks in the San Francisco Bay Area and in the Los Angeles region was a perfectly good way of achieving risk diversification. This practice was largely based on an understanding of the spatial correlation of expected loss between counties in California and selecting risks for counties which decreased loss correlations in the insured portfolio.
While California and the large-scale plate motions that it is subjected to have not changed in recent years, the way earthquake sources are modeled has. The two main areas scientists are trying to explore are: first, whether there are preferential locations in a fault network where ruptures are likely to start or stop. The second area examines what the relationship is, if any, between the timing of the latest events on a fault network and the timing of the next event that will overlap with those events. A third avenue of research that is relevant for California is the behavior of aseismic faults — faults that deform without making felt earthquakes, and what happens to them when large ruptures propagate in their direction.
RMS led a study to quantify the impact of these three major modeling assumptions on spatial loss correlations. The study used sixteen county portfolios made using the RMS Industry Exposure Database (2017), and two vintages of source model: the Uniform California Earthquake Rupture Forecast 2 and 3 (UCERF2 and UCERF3). One major conclusion was that new and different risk selection strategies would be required by the spatial loss correlation study to ensure portfolio diversification with the most recent United States Geological Survey (USGS) model (UCERF3) as compared to the previous versions of the model (i.e. UCERF1 or 2).