Schmidt, James (2024): County Wildfire Risk Ratings in Northern California: FAIR Plan Insurance Policies and Simulation Models vs. Red Flag Warnings and Diablo Winds.
Preview |
PDF
MPRA_paper_120195.pdf Download (2MB) | Preview |
Abstract
Because of increasing wildfire risk and associated losses, fire insurance has become more difficult to obtain in Northern California. The only insurance alternative for homeowners who are unable to find conventional home insurance is limited and costly coverage available through the California FAIR Plan. Counties located in the Central Sierras have been particularly hard hit with insurance cancellations. FAIR Plan policies in several of those counties exceeded 20% of all policies in 2021.
Results from three recent assessments, based on wildfire simulation models, agree that counties in the Central Sierras are among the most at-risk for wildfire-caused structure loss. Most housing losses in the 2013-2022 decade, however, were the result of wind-driven fires in the Northern Sierras and in the Northern San Francisco Bay Area. 85% of all losses occurred in fires where a Red Flag Warning (RFW) for high winds had been issued by the National Weather Service. The Northern Sierras and the North Bay Area averaged 60% more RFW days during the fall fire season compared to the Central Sierras.
Strong downslope “Diablo” winds from the Great Basin deserts were involved in seven of the most destructive fires, accounting for 65% of the total housing losses. Based on records from 109 weather stations throughout the Sierras and the Bay Area, these wind events occur primarily in the Northern Sierras and the Bay Area. Climate models have predicted that Diablo-type winds should decrease as the interior deserts warm, but weather stations in both the Bay Area and the Sierras recorded a large increase in the number of strong DiabIo wind days in the 2017 through 2021 years. All seven of the Diablo wind fires occurred during that time span.
Fires driven by strong Diablo winds fit into a category of disasters referred to as “black swan” events – rare occurrences that have very large effects. Because these fires occur so infrequently, they have minimal effect on risk estimates produced by averaging together the outcomes of thousands of simulations. Exceedance probability analysis (Ager et al., 2021) can help to identify the communities most at risk from such high-loss, low-probability events. Combining exceedance probability analysis with simulation models that capture the frequency and location of extreme wind events should cause county risk rankings to more closely match actual losses. As a result, the relative risk ratings (and FAIR Plan policies) assigned to the Central Sierras should be reduced.
Item Type: | MPRA Paper |
---|---|
Original Title: | County Wildfire Risk Ratings in Northern California: FAIR Plan Insurance Policies and Simulation Models vs. Red Flag Warnings and Diablo Winds |
English Title: | County Wildfire Risk Ratings in Northern California: FAIR Plan Insurance Policies and Simulation Models vs. Red Flag Warnings and Diablo Winds |
Language: | English |
Keywords: | Wildfire; Fire Insurance; FAIR Plan; Diablo Wind; Red Flag Warnings; Exceedance Probability; Black Swan; Simulation; FSIM; ELMFIRE; Exposure; Ignition Density; Risk; California; Downslope Winds; Climate models; RAWS; weather stations; Wildland Urban Interface; WUI; Camp Fire; Tubbs Fire; Central Sierras; San Francisco Bay Area; Northern Sierras; |
Subjects: | G - Financial Economics > G2 - Financial Institutions and Services > G22 - Insurance ; Insurance Companies ; Actuarial Studies Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q0 - General Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q54 - Climate ; Natural Disasters and Their Management ; Global Warming Y - Miscellaneous Categories > Y1 - Data: Tables and Charts Y - Miscellaneous Categories > Y9 - Other > Y91 - Pictures and Maps |
Item ID: | 120195 |
Depositing User: | James Schmidt |
Date Deposited: | 21 Feb 2024 10:25 |
Last Modified: | 21 Feb 2024 10:25 |
References: | Abatzoglou, JT., Hatchett, BJ., Fox‐Hughes, P., Gershunov, A., & Nauslar, NJ. (2021). Global climatology of synoptically‐forced downslope winds. International Journal of Climatology, 41, 31 - 50. https://doi.org/10.1002/joc.6607 Abatzoglou, JT., Kolden, CA., Williams, AP., Sadegh, M., Balch, JK., & Hall, A. (2023). Downslope wind-driven fires in the western United States. Earth's Future, 11, e2022EF003471. https://doi.org/10.1029/2022EF003471 Ager, AA., Day, MA., Alcasena, FJ., Evers, CR., Short, KC., & Grenfell, I. (2021). Predicting Paradise: Modeling future wildfire disasters in the western US. Science of the Total Environment, 784, 147057. https://doi.org/10.1016/j.scitotenv.2021.147057 Behnke, R., Vavrus, S., Allstadt, A., Albright, T., Thogmartin, WE., & Radeloff, VC. (2016). Evaluation of downscaled, gridded climate data for the conterminous United States. Ecological Applications, 26(5), 1338-1351. https://doi.org/10.1002/15-1061 CALFIRE Forest and Resource Assessment Program (FRAP) fire history data: https://www.fire.ca.gov/what-we-do/fire-resource-assessment-program California Department of Insurance, FAIR Plan Statistics, (https://www.insurance.ca.gov/01-consumers/200-wrr/DataAnalysisOnWildfiresAndInsurance.cfm) First Street Foundation (2022). The 5th National Risk Assessment, Fueling the Flames. https://report.firststreet.org/fire Guzman‐Morales, J., & Gershunov, A. (2019). Climate change suppresses Santa Ana winds of Southern California and sharpens their seasonality. Geophysical Research Letters, 46(5), 2772-2780. (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018GL080261) Iowa State Mesonet (Red Flag Warning Data) (https:/mesonetagron.iastate.edu/request/gis/watchwarn.phtml) Jakober, S., Brown, T., & Wall, T. (2023). Development of a Decision Matrix for National Weather Service Red Flag Warnings. Fire, 6(4), 168. (https://doi.org/10.3390/fire6040168) Kearns, EJ., Saah, D., Levine, CR., Lautenberger, C., Doherty, OM., Porter, JR., Amodeo, M., Rudeen, C., Woodward, KD., Johnson, GW. and Markert, K., 2022. The construction of probabilistic wildfire risk estimates for individual real estate parcels for the contiguous United States. Fire, 5(4), p.117. (https://doi.org/10.3390/fire5040117) Keeley, JE., and Syphard, AD. (2019). Twenty-first century California, USA, wildfires: fuel-dominated vs. wind-dominated fires. Fire Ecology, 15(1), 1-15. (https://doi.org/10.1186/s42408-019-0041-0.) Knapp, EE., Valachovic, YS., Quarles, SL., and Johnson, NG. (2021) Housing arrangement and vegetation factors associated with single-family home survival in the 2018 Camp Fire, California. Fire Ecology 17, 25 (https://doi.org/10.1186/s42408-021-00117-0) Kovner, Guy (September 1, 2013). "Redwood Empire fire history remains visible in wild spots". The Press Democrat. Santa Rosa, California Maranghides, A., Link, ED., Nazare, S., Hawks, S., McDougald, J., Quarles, S., & Gorham, D. (2022). WUI Structure/Parcel/Community Fire Hazard Mitigation Methodology. NIST Technical Note, 2205. 2022 (https://doi.org/10.6028/NIST.TN.2205 ) Mass, Clifford F., and David Ovens. (2019). "The Northern California wildfires of 8–9 October 2017: The role of a major downslope wind event." Bulletin of the American Meteorological Society 100.2 (2019): 235-256. (https://doi.org/10.1175/BAMS-D-18-0037.1) McClung, B. and Mass, CF. (2020). The strong, dry winds of central and northern California: Climatology and synoptic evolution. Weather and Forecasting, 35(5), 2163-2178. (https://doi.org/10.1175/WAF-D-19-0221.1) McDonald, C. (2019). September 17, 1923: The Day that Berkeley Burned. California Magazine, Spring, 2019. (https://alumni.berkeley.edu/california-magazine/spring 2019/september-17-1923-day-berkeley-burned) Microsoft Building Footprints (2018). (https://www.microsoft.com/en-us/maps/building-footprints) National Weather Service Assessment, November 2018 Camp Fire, January 2020. (https://www.weather.gov/media/publications/assessments/sa1162SignedReport.pdf) Parker, DR. (1992) The Oakland-Berkely Hills Fire: An Overview. (http://www.sfmuseum.org/oakfire/overview.html) Pera, M. (2023). Exploring the Conditions that Led to the Camp Fire, Five Years Later The Lookout (https://the-lookout.org/2023/11/09/exploring-the-conditions-that-led-to-the-camp-fire-five-years-later/) Prein, AF., Coen, J., & Jaye, A. (2022). The character and changing frequency of extreme California fire weather. Journal of Geophysical Research: Atmospheres, 127(9), e2021JD035350. RAWS USA Climate Archive, Western Regional Climate Center (2023). (https://raws.dri.edu/) Schmidt, J. (2023). Defensible Space, Housing Density, and Diablo-North Wind Events: Impacts on Loss Rates for Homes in Northern California Wildfires, Munich Personal RePEc Archive, (https://mpra.ub.uni-muenchen.de/116166/) Scott, JH., Short, KC, Finney, M. FSIM Best Practices version 0.3.1 (2018). (http://pyrologix.com/downloads/) Scott, JH.; Gilbertson-Day, JW.; Moran, C.; Dillon, GK.; Short, KC.; Vogler, K. C. (2020). Wildfire Risk to Communities: Spatial datasets of landscape-wide wildfire risk components for the United States. Fort Collins, CO: Forest Service Research Data Archive. Updated 25 November 2020. (https://doi.org/10.2737/RDS-2020-0016) Smith, C., Hatchett, BJ., Kaplan, M. (2018). A Surface Observation Based Climatology of Diablo-Like Winds in California’s Wine Country and Western Sierra Nevada Fire, 1, 25. (https://doi.org/10.3390/fire1020025) Short, KC.; Grenfell, I.C.; Riley, KL.; Vogler, KC. (2020). Pyromes of the conterminous United States. Forest Service Research Data Archive. Fort Collins, CO: (https://doi.org/10.2737/RDS-2020-0020) Short, KC. (2022). Spatial wildfire occurrence data for the United States, 1992-2020 [FPA_FOD_20221014]. 6th Edition. Fort Collins, CO: Forest Service Research Data Archive. (https://doi.org/10.2737/RDS-2013-0009.6) Silvis Lab, University of Wisconsin (2020). Wildland-Urban Interface (WUI) Change 1990-2020. (https://silvis.forest.wisc.edu/data/wui-change/) Synoptic Weather Station Data Archive: (https://synopticdata.com/) Tukman, M. New Statewide Wildfire Data from Pyrologix (2022). (https://storymaps.arcgis.com/stories/32de73f1cfb040c79f80c189ccefe061) Thompson, CF., Jones, C., Carvalho, LM., Trugman, AT., Lucas, D., Seto, D., & Varga, K. (2023). Autumn Surface Wind Trends over California during 1979-2020. Climate. (https://doi.org/10.3390/cli11100207) Vogler, KC., Brough, A., Moran, CJ., Scott, JH., Gilberson-Day, JW. (2021). Contemporary Wildfire Hazard Across California, Pyrologix LLC, (http://pyrologix.com/reports/Contemporary-Wildfire-Hazard-Across-California.pdf) Note: Basemap for all maps from ESRI. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/120195 |