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Beyond the Fire: The Science of Bushfire Risk Modelling in Australia
Australia's bushfire landscape is as dynamic as it is consequential.
Under many (often typical) conditions, embers can travel several kilometres from their sources, crossing postcode boundaries, jumping firebreaks, and landing on the roofs of structures considered "low risk" in portfolios built on “proximity-to-bushland” logic.
This creates an important challenge for insurers and reinsurers, as risk cannot be assessed solely by asking, "How close is this property to the bush?"
Rather, the more encompassing questions include:
- How do climate patterns impact ignitions?
- How far can embers travel under both normal and extreme wind?
- How does urban density amplify or dampen fire spread?
- When might suppression capability collapse?
- How do building codes affect vulnerability at scale?
One of the latest bushfire models was developed in collaboration with bushfire scientists and engineers, using Australian government data.
It simulates the physical processes that turn vegetation into loss, incorporating fire spread and intensity, probabilistic suppression, and structure-level vulnerability and mitigation.
Why climate-driven modelling changes everything
Understanding bushfire risk begins with understanding when and where fires ignite.
An advanced model's event catalogue incorporates more than 20 of the most recent years of historical data, including weather, claims, and fuel layers from multiple government agencies.
This allows users to identify patterns and trends, stress-test portfolios under characteristic scenarios, support actuarial analysis, and inform risk management strategies.
Climate variability
Australian bushfire activity is often strongly modulated by climatic variability, such as the El Niño–Southern Oscillation (ENSO) phases.
During positive El Niño phases, southeastern Australia experiences below-average rainfall and above-average temperatures; conditions that reduce fuel moisture and extend fire danger periods. [1]
Verisk's latest bushfire model integrates near-present climate assessments by bioregion, explicitly capturing how ENSO phases influence bushfire timing, number, location, intensity, and areal extent.
This climate-informed approach produces a stochastic event set that reflects not just average (or recently observed) outcomes, but the full range of variability in the and risk.
Hazard simulation: Advanced fire clustering and intensity parameters
The best models explicitly simulate the geographic variability in seasonal timing of bushfires across Australia.
To account for reinsurance hours clauses, individual fires occurring within seven days of one another are grouped into events that define single loss “occurrences”.
Bushfires need not be isolated events. In fact, depending on geographic location and time of the year, anywhere from 10% to 50% of bushfires ignite within 24 hours of one or more other bushfires in the vicinity.
This has several important implications, including those associated with reinsurance hours clauses and the deployment of firefighting resources; several concurrent bushfires of significant size and intensity might spread thin the resources available to effectively fight the fires.
Extremes in fire weather, in conjunction with fire ignitions, often enhance the number and intensity of multiple fires co-occurring in space and time.
The advanced bushfire model tracks each individual simulated fire from its time and place of ignition through its eventual areal extent and loss-causing potential.
Local vegetation type, topography, and winds dictate rates and directions of modelled fire spread along with physical intensity (flame length) of the leading edge of the bushfire, called the fire front.
High winds associated with summer cold-front passages in southeastern Australia can result in especially intense fires and large losses in the model, reflecting reality on the ground.
Ember generation and downwind spread can initiate secondary ignitions (spotting) considerably ahead of the fire front and may allow a bushfire to jump natural and human-constructed firebreaks.
In particular, simulated fires that penetrate the wildland-urban interface (WUI) surrounding cities can initiate urban conflagrations, leading to substantial insured loss.
Together, the features that capture these phenomena enable the advanced model to represent all kinds of fire behaviour.
This spectrum ranges from isolated grass fires contained within hours to catastrophic multi-ignition events with compounding regional impacts that stress emergency services, overwhelm suppression capacity and generate correlated losses across portfolios.
Urban conflagration: A driver of extreme loss
Recent fires have shown that the boundary between bushland and developed areas is not a fixed or clearly defined line.
In certain conditions, high ember density, aligned wind direction and limited suppression capability, a bushfire can result in urban conflagration, where losses escalate rapidly due to structure-to-structure spread and high density of insured properties. [2]
An advanced model will explicitly capture this phenomenon using high-resolution occupancy data, building density mapping and vulnerability curves calibrated for Australian construction classes.
It will represent how fire can propagate through urbanised zones. Building codes, roof covering, and defensible space regulations are encoded into the model as secondary characteristics.
This is critical for reinsurers and underwriters assessing aggregation risk in peri-urban corridors where one ignition can expose thousands of policies simultaneously.
Suppression and resilience: Quantifying what happens next
Fire suppression isn’t a binary outcome. Its effectiveness can depend on resource availability, access constraints, fire intensity and the number of simultaneous incidents demanding attention.
A defining advancement is the inclusion of fire suppression effectiveness as a probabilistic modifier.
Using population data as a critical proxy for resource availability and response time, and incorporating wind and fuel moisture data, spread dynamics are adjusted dynamically within each simulation to account for firefighting activities.
This matters because suppression capability can dramatically alter outcomes, especially near major population centers with robust emergency infrastructure versus remote areas with limited access.
In practice, this enables insurers to distinguish between regions with identical hazards but vastly different expected losses due to response readiness, infrastructure access and policy density.
Accounting for Australia's bushfire risk diversity
It's important to capture distinct characteristics across Australia. For example, New South Wales and Victoria have tropical and subtropical conditions with fuel types and fire seasonality differing from those in most other regions.
Regional calibration enables location-specific risk assessment rather than applying continental-scale averages that smooth over critical heterogeneity.
Translating insights into portfolio decisions
Australian insurers also need loss estimates aligned with observed performance in marquee events such as the 2009 Black Saturday fires and the 2019-20 Black Summer.
This validation matters because while hazard remains the largest source of uncertainty when assessing bushfire risk, accurate vulnerability assessments for code-compliant versus legacy construction significantly narrow total uncertainty in loss estimation.
For insurance and reinsurance professionals, the value proposition is clear: better models enable better decisions.
A bushfire model that integrates physics-based hazard simulation with engineering-based vulnerability functions, produces realistic risk estimates across residential, commercial, industrial and agricultural property classes.
Enhanced, occupancy-specific loss estimation allows for precise portfolio differentiation across and within Australia's diverse fire-prone regions.
Building resilience through better risk quantification
Advanced bushfire models, such as Verisk's, provide clearer visibility into correlated risks and resilience pathways. This clarity enables more confident capital deployment, more sophisticated risk selection and more sustainable portfolio construction.
For brokers, it means delivering clients differentiated insights that drive competitive advantage, whether identifying underpriced opportunities or flagging over-concentrations before they materialise.
When underwriting is informed by factors in addition to proximity-based approximations such as local fuel load, vegetation type, suppression capability, topography and community preparedness, the industry gains a more accurate view of bushfire risk.
This deeper understanding enables faster recovery, fairer pricing, and improved safety outcomes across both portfolios and communities.
A key question now is how quickly the industry will continue to develop and adopt the tools needed to manage one of Australia's most consequential and rapidly evolving natural hazards.
In an era of lengthening fire seasons, expanding peri-urban exposure, and climate-driven risk volatility, continued innovation and adoption cannot come soon enough.
About the authors
- Andrew O’Donnell works in the Verisk Research and Modeling department as an engineer on the Earthquake Vulnerability team.
- Dr. Jeff Amthor is an assistant vice president in Verisk’s catastrophe and risk solutions team. He develops and supports Verisk wildfire risk models and agricultural risk models used by reinsurers, reinsurance brokers, and insurers across the globe.
- Åsa Bergman is Principal Geospatial Analyst for Wildfire at Verisk Analytics, specialising in modelling fire behaviour and risk using geospatial data and climate-informed approaches.
References
This article first appeared on the Verisk Website and is reproduced here with permission.
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