As wildfires grow more frequent and devastating, utilities face an urgent challenge: protecting infrastructure, economies, and communities from an escalating threat. The good news is that existing risk management frameworks, used for everything from network reliability to public safety, can be leveraged. Wildfire-related data and strategies should be integrated into your current asset management processes. The same applies to other disaster decision analytics, including atmospheric rivers, floods, heat domes, and polar vortexes, all of which are occurring more often.
Let's take a closer look at how core risk management principles apply directly to wildfire analytics. By leveraging these principles, utilities can improve consistency, enhance efficiency, and respond more effectively to unpredictable events. We'll also explore strategies for handling rare, high-impact wildfires that don’t follow typical patterns, helping utilities better prepare for the unexpected.
Applying the Same Core Principles
Traditional asset management methodologies rely on a fundamental risk equation:
Risk = Likelihood of Event × Consequence of Event
For disasters like wildfires, likelihood refers to the conditional probability of asset failure and ignition, based on fire weather indices. Consequence covers potential infrastructure damage, financial loss, safety risks, and environmental impact. This framework, widely used for other hazards like equipment failures leading to oil/SF6 leaks or customer outages—is just as effective for wildfire risk assessment.
Identifying Likelihood
- Historical Data: Past wildfire events, seasonal trends, and burn frequencies.
- Environmental Indicators: Weather forecasts, drought indices, wind patterns, and vegetation density.
- Infrastructure Vulnerability: Maintenance logs and known weak points in utility lines and assets, identified through corrective work order data.
Gauging Consequence
- Asset Criticality: Which facilities, equipment, or networks are most essential to operations and public safety?
- Population Exposure: How many people or communities would be at risk if a fire spreads from a specific structure?
- Environmental Impact: Potential damage to ecosystems, water quality, or protected habitats.
- Financial & Regulatory Repercussions: Repair/replacement costs, insurance rates, and—most importantly for utilities—liability exposure.
By integrating these factors into the risk algorithms you’re already using (and if you’re not, you’re leaving yourself exposed), you can generate meaningful wildfire insights—without the need for a separate assessment framework.
Recognizing Long-Tail Risk
Unlike many routine operational risks, wildfires have a “long-tail” distribution. In simple terms, this means that while most events tend to be smaller in scale, there’s always a non-negligible chance of an extreme, devastating fire that breaks historical patterns. Relying solely on expected values (averages) can downplay the true threat of these rare, high-impact events.
Distribution vs. Point Estimates
- Beyond the Mean: A typical expected value approach might suggest moderate annual losses. However, a single catastrophic wildfire can dwarf those averages, leading to disproportionate damage.
- Fat Tails: The probability of a large wildfire may appear small on paper, but if it occurs, the consequences can be catastrophic—ranging from widespread infrastructure loss to significant human and environmental harm.
Tail Mitigation
- Scenario Planning: Include worst-case scenarios (extreme drought, high wind conditions) in the decision model to capture the risk of outlier events.
- Buffer and Resilience Investments: Building redundancies and fire-resistant infrastructure helps absorb extreme shocks, rather than optimizing only for the most likely outcomes.
- Ongoing Monitoring: Regularly update probability distributions with new data (e.g., recent climate trends, burn patterns) so that your models reflect the ever-changing nature of wildfire risk.
Incorporating these insights into a standard asset management framework ensures your organization accounts for both routine incidents and highly extreme events.
Expanding Your Data Toolbox
While the risk equation remains the same, wildfire modeling does require specific data inputs. Utilities that have historically focused on asset deterioration or operational hazards may need to incorporate additional datasets:
- Vegetation and Fuel Mapping: High-resolution satellite imagery or LiDAR data to identify areas with dense undergrowth or tree cover.
- Real-Time Weather Monitoring: Automated weather stations tracking temperature, humidity, wind speed, and wind direction.
- Fire Behavior Models: Software that simulates how a wildfire might spread across different terrains and fuel types—especially under extreme conditions.
- Emergency Response Capability: Availability and proximity of firefighting resources, evacuation routes, and water sources.
These inputs integrate seamlessly into existing platforms for data collection and analytics.
Risk Prioritization and Resource Allocation
Once you’ve measured likelihood and consequence, and considered the full range of potential outcomes into account, the next step is to rank the highest-risk assets or regions. In traditional asset management, this might look like a composite numeric score or a traffic-light rating system (red = high, yellow = medium, green = low). In modern asset management, however, you’re looking at high resolution, monetized risk data that can generate net benefit, cost benefit, and Net Present Value metrics for every possible intervention.
This process helps decision-makers focus on the highest priorities. Just like budgeting for aging transformers or high-failure-rate components, your utility can direct capital and operational resources to wildfire mitigation where it’s needed most, with a clear focus on tail risk. And, as with all utility investments, it’s rare that a single driver is all that we should consider, value stacking of extreme risk with the remainder of the existing risk framework provides a stronger basis for decision making.
Mitigation Strategies in a Unified Framework
Once risks are prioritized, the same mitigation strategies used for other hazards can be applied to wildfire prevention and response:
- Vegetation Management: Clear brush and trim trees around critical facilities and transmission lines. Assess risk reduction for each span individually.
- Infrastructure Upgrades: Fire-wrap poles, replace expulsion fuses, underground lines where feasible, and enhance inspection/monitoring in high-risk areas.
Integrating these actions into a single enterprise-wide risk plan ensures alignment with existing risk management processes—while accounting for rare but high-impact events.
Continuous Improvement
The Plan-Do-Check-Act (PDCA) cycle, often the backbone of asset management, applies just as effectively to wildfire risk:
- Plan: Identify wildfire risk objectives, data sources, and mitigation strategies, including extreme event scenarios.
- Do: Execute the proposed actions, such as vegetation clearing or sensor deployment.
- Check: Monitor outcomes, track incidents or near-misses, and evaluate performance metrics.
- Act: Refine processes, allocate resources to where they’ll have the greatest impact, and update policies or protocols based on lessons learned.
By treating wildfire risk as part of a continuous improvement loop, your utility can stay agile in response to shifting climate conditions, evolving regulations, and emerging technologies. This approach also ensures your risk models stay up-to-date with the latest insights into tail events..
Benefits of Integrating Wildfire Risk
- Consistency: A single set of metrics, rating scales, and terminology fosters clearer communication and decision-making.
- Efficiency: Leveraging existing systems reduces duplication of effort, streamlines data management, and speeds up stakeholder engagement.
- Comparability: With all risks measured in a consistent fashion, leadership can more easily see which threats truly demand the largest investments and stack drivers to build stronger cases.
- Regulatory and Stakeholder Confidence: Demonstrating a robust, enterprise-wide risk management approach that includes wildfires can reassure regulators, insurers, and community partners.
- Acknowledgment of Extreme Events: By considering the tail of the risk distribution, organizations ensure preparedness and resource allocation are appropriate for worst-case scenarios—not just the average fire season.
Key Takeaways
Integrating wildfire risk into asset management makes sense because it builds on existing risk frameworks rather than requiring a new approach. A unified strategy ensures wildfire risk is part of a broader asset management or risk assessment program, leading to more efficient operations. Data-driven insights from weather forecasts, vegetation metrics, and fire behavior models enhance risk intelligence, while planning for rare but severe wildfire events strengthens resilience. Aligning preventive measures and response plans within a single framework improves safety, operational readiness, and public trust.
In an era of more frequent and severe wildfires, integrating wildfire analysis into your standard asset management processes isn’t just feasible—it’s essential. By leveraging established methodologies and continuously refining them with relevant data, utilities can stay ahead of the curve and mitigate even the most extreme impacts of wildfires.
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Ted Zalucki is the CEO and co-founder of Engineered Intelligence Inc, an infrastructure analytics technology company. Ted has 10+ years of hands-on experience in the T&D sector working within several utilities and as a consultant across North America. His expertise includes advanced analytics, investment planning, asset management, risk modelling, productivity, process optimization, construction management, design supervision, operations, and regulatory filings and defense. Ted’s background is in Industrial Engineering and Financial Engineering, he holds an ELITE certificate from the University of Toronto and is a practicing Professional Engineer.