Weather-based estimation of wildfire risk
Catastrophic wildfires in California have become more frequent in past decades, while insured losses per event have been rising substantially. On average, California ranks the highest among states in the U.S. in the number of fires as well as the number of acres burned each year. The study of catastrophic wildfire models plays an important role in the prevention and mitigation of such disasters. Accurate forecasts of potential large fires assist fire managers in preparing resources and strategic planning for fire suppression. Furthermore, fire forecasting can a priori inform insurers on potential financial losses due to large fires. This paper describes a probabilistic model for predicting wildland fire risks using the two-stage Heckman procedure. Using 37 years of spatial and temporal information on weather and fire records in Southern California, this model measures the probability of a fire occurring and estimates the expected size of the fire on a given day and location, offering a technique to predict and forecast wildfire occurrences based on weather information that is readily available at low cost.
|Date of creation:||Jun 2009|
|Contact details of provider:|| Postal: Spandauer Str. 1,10178 Berlin|
Web page: http://sfb649.wiwi.hu-berlin.de
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- Heckman, James, 2013.
"Sample selection bias as a specification error,"
Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
- Wolfgang Karl Härdle & Brenda López Cabrera, 2010.
"Calibrating CAT Bonds for Mexican Earthquakes,"
Journal of Risk & Insurance,
The American Risk and Insurance Association, vol. 77(3), pages 625-650.
- Wolfgang Härdle & Brenda López Cabrera, 2007. "Calibrating CAT bonds for Mexican earthquakes," SFB 649 Discussion Papers SFB649DP2007-037, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- Haerdle, Wolfgang & Cabrera, Brenda Lopez, 2007. "Calibrating CAT bonds for Mexican earthquakes," 101st Seminar, July 5-6, 2007, Berlin Germany 9265, European Association of Agricultural Economists.
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