Modelling Wildfire Risk
Committed To Securing Livelihoods
Quantifying Wildfires & Bushfires Risk
As more and more people settle in areas where wildfires are likely to burn, highly developed commercial and residential areas are being consumed by wildfires that once raged across large expanse of forests and grasslands. The losses from these wildfires (or bushfires) rival those of other natural catastrophes, such as floods, cyclones or earthquakes.
Every year wildfire and bushfires cause billions of insured losses in the U.S., Southern Europe and Australia, respectively and these numbers will continue to rise as wildland-urban interface is further increases.
Today’s extreme conditions that are a result of a warming planet due to climate change compels the industry to accurately and timely locate and
model wildfires as they start and propagation due to many factors including drive ignition, wind, presence of vegetation, spread, etc. This will enable insurers, reinsurers, governments, and financial institutions to characterise dangerous structures from those that are safe. It will also aid in organising and carrying out risk assessments, estimating potential damages, accurately valuate fire hazard, and optimize risk transfer.
Infinite Observations ZineQx advanced probabilistic Wildfire Catastrophic Model helps to monitor and manage the risk associated with each policy, whether individual or commercial, or entire portfolio’s of properties.
Wildfire Catastrophe Models
Modelled Regions :
Click a region to see countries or islands where courage is available.
Central America & Caribbean
Asia & Pacific
The Greater Antilles
Turks and Caicos Islands
Lesser Antilles (Leeward Islands)
Saint Martin (Fr.)
Sint Maarten (Neth.)
Sint Eustatius (Neth.)
La Désirade (Fr.)
Les Saintes archipelago (Fr.)
St. Thomas (US VI)
St. John (US VI)
St. Croix (US VI)
Water Island (US VI)
Tortola (UK VI)
Virgin Gorda (UK VI)
Anegada (UK VI)
Jost Van Dyke (UK VI)
Los Roques Archipelago
Lesser Antilles (Windward Islands)
Asia & Pacific
Hong Kong (China)
New Caledonia (Fr)
Papua New Guinea
Northern Mariana Islands (US)
Wake Island (US)
American Samoa (US)
Cook Islands (NZ)
Easter Island (Ch)
French Polynesia (Fr)
Norfolk Island (A)
Wallis and Futuna (Fr)
Wildfire Risk Vulnerability, Exposure & Risk Modelling
High-Resolution Wildfire Model
Infinite Observations provides a comprehensive view of wild- and bushfires in U.S., Southern Europe and Australia. Three components are necessary to be able model wildfires adequately: (1) Fuel: Our model leverages cutting-edge technology of remote-sensing to obtain high-resolution spatial layers that consistently identifies fire behaviour fuel model (FBFM’s) classes (based on the nature and type of vegetation) and distribution, analogous to those of Anderson. (2) Weather (or meteorological dataset): Wind direction and velocity, temperature, relative humidity, precipitation, aridity effect the development, intensity and propagation of fires. (3) Topography is relatively static in nature compared to the fire. Topological factors such as slope, elevation and orientation towards the sun dictates fire propagation. These three components serve as input for a mathematical surface fire behaviour and spread algorithm, analogous to that of Rothermel.
Quantifying Wildfire Losses
To be able to assess wildfire losses Infinite Observations has developed a high-resolution exposure database. This database contains a spatial and temporal multi-fire event catalogue and the latest information on risk counts, building characteristics such as construction materials of roof and wall-siding materials, building height, occupancy, inventory and construction costs as well as business disruption from a wide variety of international, regional and local industry sources. Base on all this information (weighted) damage functions for risk exposure are determined and used to estimate the losses. Our wildfire models are validated against results from industry loss data, post-disaster damage surveys, historical events data, scientific and engineering research data and reports. The model can be validated in its entirety or each component can be validated independently to ensure a most robust and scientifically rigorous model as possible.
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