Duration: 1 hour

This informative webinar, in collaboration with Utility Dive, explores the tactics utilized by leading electric utilities to forecast, mitigate, and respond to wildfire risks and the associated liability.

As wildfires continue to increase in frequency and severity, they present a significant threat to electric utilities infrastructure and communities. Electric utilities face a risk stemming from their infrastructure to trigger wildfires and the liabilities that come with that.

Electric utilities can adopt proactive measures, such as preemptive power shutdowns to minimize the risk of wildfires and safeguard the areas in their service territory as well as using solutions that can help assess assets for mitigation purposes.

During the session, you will learn from Technosylva:

• Insights into the latest trends and patterns in wildfire behavior, and their implications for risk and liability exposure
• Methodologies for assessing wildfire risk and strategies for implementing effective mitigation measures
• Tools and techniques for real-time monitoring and response to wildfire threats

Speakers

The regulator’s question cut straight to the heart of the matter: “So you’re asking us to approve a multimillion-dollar program designed to turn off our customers’ power? How exactly do we explain that to our stakeholders?” The CEO of ElectriCo knew the follow-up questions were coming: What about the customer complaints? How do we handle the public backlash when people lose power during heat waves?

The CEO had anticipated this moment. For months, her team had been developing their Public Safety Power Shutoff program, driven by regulatory pressure and mounting wildfire liability. But although the fire safety logic was sound, PSPS events also brought negative customer response: angry residents, frustrated businesses, and heated community meetings. The technical planning was solid, the legal framework was clear, but sitting in that boardroom, she realized the hardest challenge wasn’t operational: it was cultural.

At the same time, her veteran operations supervisor was grappling with the same challenge from a different angle. After 25 years of perfecting one skill above all others (getting the lights back on as fast as possible), he was now being asked to lead the new “proactive outage team.” Outage duration had been his scorecard, system reliability his measure of success. The irony wasn’t lost on him.

“Help me understand this,” he told his manager during their planning session. “We’re building teams to deliberately cause outages, and we’re calling this progress?”

For electric utility leaders facing growing wildfire threats, these parallel conversations in boardrooms and operations centers capture the essential challenge: transforming an organization built on reliable power delivery into one that embraces proactive power removal for community safety.

When Split-Second Decisions Meet Century-Old Culture

The first reality electric utilities face is that wildfire response operates on an entirely different timeline than traditional utility operations. When meteorological conditions shift rapidly, teams have 48 to 72 hours to analyze vast service territories, assess thousands of assets, and notify potentially tens of thousands of customers.

This creates an immediate tension. Electric utilities must maintain their core operational excellence while building entirely new capabilities that operate under completely different rules. Start here: give your wildfire response teams clear decision-making authority and direct executive reporting lines, separate from traditional operations approval processes.

Beyond 50/50 Calls: Understanding Your Risk Appetite

Perhaps the most challenging cultural shift involves how electric utilities approach uncertainty. Traditional utility planning often seeks to eliminate uncertainty through comprehensive analysis and conservative safety margins. Wildfire response requires utilities to make consequential decisions based on probabilistic forecasts and incomplete information.

This forces a critical organizational conversation: what is your utility’s risk tolerance? A conservative approach might cast a wider net during PSPS events, potentially impacting more customers to ensure comprehensive safety coverage. A higher risk tolerance might focus more narrowly on high-confidence risk zones, trading some uncertainty for reduced customer impacts.

The essential first step: document your risk tolerance explicitly and train teams to apply it consistently under pressure.

Building the Teams That Make It Work

The operational reality of wildfire response demands teams that bridge meteorology, operations, customer communications, and emergency management. These teams must be available 24/7 during high-risk periods and capable of making consequential decisions in compressed timeframes.

For all utilities, staff training becomes critical, focusing on interpreting meteorological data, understanding the fundamentals of fire behavior, and executing protocols under pressure. Most importantly: identify the specific wildfire expertise your teams lack, then find external partners who can provide not just technology, but cultural wisdom from utilities who’ve already made this transition. Executive engagement proves essential, signaling that this represents a fundamental evolution in how the utility serves its communities.

The Path Forward

The electric utilities successfully navigating this transition share common characteristics: they’ve built specialized teams with clear decision-making authority, invested in comprehensive staff training, and secured executive leadership that champions proactive risk management.

The cultural shift isn’t just about accepting PSPS or other necessary tools. It’s about building organizations capable of protecting communities through decisive action, even when that action challenges traditional utility instincts. For electric utilities still building this culture, the communities you serve are counting on your ability to evolve quickly and completely.

Electric utilities across the nation are under immense pressure to demonstrate a comprehensive understanding of their wildfire exposure to stakeholders—communities, creditors, regulators, insurers, and boards. However, a fundamental problem lies in the reliance on traditional, static risk assessments. These assessments, while earnestly detailed, create an illusion of preparedness that leaves electric utilities vulnerable to a fundamental truth: fire is dynamic.

The Disconnect Between Static Data and Analyzing Dynamic Risk

Traditional risk assessments provide a fixed snapshot, relying on historical data, terrain analysis, fuel assessments, and extreme scenario modeling, such as the ‘1-in-100-year event.’ While these elements are valuable for establishing a baseline understanding, they are literally obsolete for decision making purposes before they are completed.

Wildfire risk changes from season to season, month to month, day to day and even hour to hour with the weather and its ongoing impact on the terrain and local vegetation.

For electric utilities facing any ignition risk, relying on static assessments is akin to hiking in the woods with an outdated map. The illusion of control offered by a fixed assessment can lead to catastrophic consequences when faced with the daily fluctuations and unpredictable nature of actual wildfire risk.

If you’re reading this blog, the data suggests that you’re right in believing that your risk may be more than you think.

Dynamic Risk Analysis Provides Clarity on Real Risk

Dynamic risk analysis helps to keep the “map” of fire risk up to date at all times, both for planning and operational decisions.

Dynamic risk analysis: builds upon the foundation of a risk assessment by incorporating real-time and forecasted weather data, including granular, commercial-grade weather information. This approach:

• Reduces Wildfire Ignitions: Proactive mitigation based on dynamic risk analysis can significantly reduce the likelihood of electric utility-caused ignitions.
• Creates More Surgical PSPS: By forecasting high-risk periods and locations, electric utilities can better anticipate the need for Public Safety Power Shutoffs (PSPS), reducing the time and areas of customer disruptions and associated safety risks.
• Improves Decision-Making: Real-time and forecasted risk data empowers electric utilities to make more informed decisions about resource allocation, emergency preparedness, and public communication.
• Enhances Stakeholder Communication: Dynamic risk analysis provides concrete data that electric utilities can use to demonstrate their commitment to wildfire safety to regulators, insurers, credit agencies, investors, and community stakeholders.
• Increases the Cost Effectiveness of Vegetation Management and Asset Hardening: With real-time and forecasted risk information, electric utilities can implement more effective mitigation strategies such as targeted vegetation management based on changing conditions, prioritized inspections and equipment maintenance, and ongoing updates of the most critical assets for hardening and capital improvements.

What’s in Dynamic Risk Analysis?

To be effective in providing these benefits, Dynamic Risk Analysis must include:

• Simulation of Wildfire Ignition and Spread: These models not only predict the likelihood of ignition but also simulate how a fire might spread under specific conditions, giving utilities a better understanding of potential impacts.
• A Real-Time View: By integrating current weather conditions and fuel moisture levels, dynamic analysis offers a constantly updated picture of wildfire risk across a utility’s service area.
• Forecasting: Coupled with weather forecasts, dynamic analysis predicts future wildfire risk, allowing utilities to anticipate high-risk periods and take proactive measures.

The Bottom Line

Many electric utilities today make the dangerous assumption that a static, historical snapshot of wildfire risk is sufficient for managing a dynamic, ever-evolving threat. Relying on these outdated assessments leaves electric utilities blind to the real-time fluctuations and forecasted dangers that directly impact ignition and spread potential.

The strongest utilities embrace dynamic risk analysis, integrating real-time and forecasted data, to demonstrate to themselves, their customers, and their financial stakeholders that they are proactively mitigating the escalating threat of wildfire.

Electric utility risk managers nationwide are confronting an escalating challenge: the low probability, high consequence wildfire event.

While predicting ignition points is a crucial first step, there is a dangerous misconception that preventing ignitions equates to mitigating overall wildfire risk.

Focusing solely on where and if a fire might start ignores the critical question of what happens when it does. This gap leaves electric utilities vulnerable to catastrophic outcomes, even with robust ignition prevention efforts.

It only takes one bad wildfire to change the entire future of a community and the utility that serves it.

As climate change fuels drought and increases energy demand, electric utilities in every state – this is no longer a problem of the West alone – face mounting pressure to explain to their communities, creditors and boards how they are mitigating wildfire risk and strengthening their reliability.

The Problem: Ignition Probability isn’t Actual Risk

The critical error many electric utilities make is equating ignition prediction with comprehensive risk assessment.

Ignition prediction is essentially the probability that an ignition will occur at a point, but risk is typically measured as probability of an event multiplied with the consequences of that event.

Wildfire risk is not merely about the likelihood of a fire starting; it’s about the magnitude of the potential consequences if one does.

A small fire in a remote, sparsely populated area poses a drastically different risk than a faster spreading fire near a densely populated community or critical infrastructure.

Focusing solely on ignition prediction fails to account for the potential for widespread damage, loss of life, and economic disruption.

This approach leads to a dangerous blind spot, where utilities may believe they have adequately mitigated risk by focusing on ignition prevention, while remaining dangerously exposed to the devastating consequences of a large-scale wildfire. Without understanding the potential consequence of a fire, prioritizing mitigation efforts becomes guesswork rather than a data-driven strategy.

The Challenge to Address

For this critical decision-making, electric utilities need to combine ignition probability with consequence analysis. This means:

• Quantifying Impact: Consequence modeling quantifies the potential damage of a fire, including impacts on human life, property, and infrastructure. This data is essential for prioritizing mitigation efforts and targeting asset-hardening under limited budgets and rate increase abilities.
• Forecasting Fire Spread: Advanced fire spread modeling, integrated with weather forecasts, can predict the path and impact of a fire originating from a specific asset. This allows utilities to identify the most dangerous potential ignitions.
• Understanding Asset-Specific Risk: Every asset has a unique ignition probability based on its condition, age, surrounding environment, and other factors. Electric utilities can analyze historical ignition data alongside potential fire spread models to understand the impact of a fire (ignition probability and consequence) originating from each asset.

Prioritizing Hardening with Risk Spend Efficiency (RSE)

With limited resources, electric utilities need to maximize the impact of their mitigation investments.

Consequence-based risk modeling allows for the calculation of improved Risk Spend Efficiency (RSE). RSE measures the risk reduction achieved per dollar invested in hardening. By prioritizing assets with the highest RSE, utilities can achieve the greatest risk reduction for their budget.

The Bigger Picture: Moving from Planning to Operations

Safety and risk management are driving the adoption of consequence-based modeling, but the benefits extend beyond planning.

Understanding wildfire risk improves operational efficiency and informs critical decisions like Public Safety Power Shutoffs (PSPS) during an extreme weather event. As wildfire severity and frequency increase, this data-driven approach has become essential for all electric utilities.

Looking Ahead

The future of wildfire risk management for electric utilities depends on moving beyond the limited scope of ignition prediction.

By embracing consequence-based risk modeling, electric utilities can gain the critical insights needed to prioritize asset hardening, optimize mitigation strategies, and ultimately, protect communities and infrastructure from the devastating impacts of wildfire. The widening of risk management from solely preventing fires to understanding and mitigating their potential consequences is no longer optional, it is an imperative.

The Big Picture

Electric utility risk managers face a daunting challenge: accurately predicting and mitigating wildfire risk in an increasingly volatile environment.

Red Flag Warnings, issued by the National Weather Service, are often used as a critical tool in this effort. They provide a seemingly clear indication of high-risk fire weather conditions. However, the reality is far more complex.

The gap between the broad warnings and the specific needs of utilities is the core problem that must be addressed.

While Red Flag Warnings are essential for general public awareness, they fall short of providing the precise, actionable intelligence electric utilities need to protect their infrastructure and communities.

The Catch

Spread vs. Start: Why That Difference Matters

There is a critical difference between fire spread and fire starts. The primary focus of Red Flag Warnings is on the spread of existing fires. This is crucial for public safety, but it doesn’t directly translate to the risk of ignition from electric utility infrastructure or other sources.

An electric utility’s greatest concern, and ability to mitigate a fire, is often the initial spark, which can be triggered by seemingly less severe conditions than those just examined for rapid fire spread. Therefore, basing operational decisions solely on Red Flag Warnings can lead to either over or under-reaction.

Red Flag Warnings cover broad geographic areas, often spanning entire counties or regions. Electric utilities, however, need to pinpoint risks at the circuit level, so they can act to mitigate the threat.

The Red Flag Warning’s lack of granularity can lead to inefficient resource allocation and unnecessary operational disruptions, or worse yet, inaction.

Dry Lightning Adds Hidden Complexity

In addition, when electric utilities base their situational awareness on Red Flag Warnings, they can misinterpret the consequences of “dry lightning”, which are included under the Red Flag scope but represent a fundamentally different risk profile.

Dry lightning is lightning that strikes the ground absent of significant rainfall. This makes it particularly dangerous because it can easily ignite dry vegetation, leading to wildfires without the natural suppression of accompanying rain.

Why This Can Quickly Spiral

This can lead to the potential for numerous, simultaneous ignitions; a scenario that can quickly overwhelm resources regardless of wind speed or humidity.

This requires a completely different operational response than warnings driven by wind and ignoring the unique challenges of dry lightning can leave electric utilities vulnerable to widespread, uncontrollable fires.

Relying solely on Red Flag Warnings can lead to:

• Overreaction: Implementing costly and disruptive measures, like widespread PSPS, when the actual risk to infrastructure is localized or less severe and a surgical approach would have the same efficacy against the fire.
• Underreaction too: Failing to take necessary precautions when localized ignition risks are high, even if the overall Red Flag Warning doesn’t seem dire.
• Inefficient Resource: Allocation: Deploying resources across a broad area when the true risk is concentrated in specific locations ignitions.
• Liability Exposure: Making operational decisions based on incomplete data, potentially leading to preventable ignitions and subsequent legal repercussions.

How Utilities Can Respond—and Improve

Red Flag Warnings are a valuable piece of the puzzle, but not the whole picture. To change, electric utility risk managers can:

• Understand the Nuances: Recognize that not all Red Flag Warnings are created equal. Train teams to ask deeper questions about what’s happening in their environment, and how they should react to different conditions. Dry lightning warnings, for example, require a different response than warnings based on wind and low humidity.
• Operationalize Granular Data: Don’t rely solely on the broad geographic scope of Red Flag Warnings. Supplement them with more precise data that pinpoints specific areas of risk within their service territory, and operationalizes the response to the granular data in resource deployment, asset hardening, vegetation management and PSPS plans.
• Integrate with Broader Information and Risk Assessment: Use Red Flag Warnings as one input among many in a comprehensive wildfire risk assessment. Consider factors like fuel conditions, topography, and proximity to utility infrastructure.

Red Flag Warnings are a valuable starting point, but they represent only a fraction of the information electric utility risk managers need.

The core problem is the need for precise, localized, and ignition-focused risk assessments.

By recognizing the limitations of broad public warnings and actively seeking more granular data, utilities can move beyond reactive responses and develop proactive strategies that safeguard their infrastructure, communities, and financial stability.

Electric utility risk managers are tasked with an increasingly complex challenge: predicting and mitigating wildfire risk in a dynamic and unpredictable environment. While “Red Flag Warnings” from the National Weather Service have traditionally served as a key indicator of potential fire danger, they fall short of providing the specific, actionable insights needed for effective decision-making.

There is a significant gap between the broad, meteorological focus of Red Flag Warnings and the granular, consequence-driven risk assessments required by electric utilities to protect critical infrastructure and communities. This blind spot leaves utilities exposed to potentially devastating consequences.

The Problem: The Limitations of Weather-Centric Warnings

Primarily, Red Flag Warnings focus on meteorological conditions, such as wind and dryness, but fail to account for the complex interplay of factors that drive actual fire behavior. This leaves electric utilities with a limited understanding of how and where a fire might start and truly impact their service territory.

The core issue is that weather forecasts, while essential, provide conditions, whereas electric utilities require an understanding of consequence. Simply knowing it’s windy and dry doesn’t translate to knowing where assets are most at risk of starting a fire if they come in contact with vegetation or how rapidly that fire may spread.

Furthermore, Red Flag Warnings do not quantify the impact of a potential fire. Adding to the problem is the broad geographic scope of weather-centric warnings. This lack of precision can lead to inefficient resource deployment and missed opportunities for targeted mitigation.

Electric utilities need to understand the potential for property damage, population at risk, and infrastructure loss to make informed decisions about resource allocation and mitigation strategies from one possible start to another.

In addition, models that can predict fire risk days in advance afford leading electric utilities the ability to implement proactive mitigation strategies, a capability that Red Flag Warnings alone do not provide.

How Utilities Can Respond…and Improve:

Leading electric utilities are addressing this problem by integrating weather forecast data with sophisticated, granular fire behavior modeling. They are creating their own granular and days-in-advance view of where fires are likely, where they will spread, and which assets and communities could be involved. This approach:

• Captures Complex Relationships: Instead of just looking at the weather, these models incorporate fuel moisture, fuel type, topography, wind speed, wind direction, and other crucial factors to create a more accurate, more granular view for decision-making. They then simulate how these elements interact to influence fire spread across their service territory and assets.
• Quantifies Risk: By simulating fire spread under forecasted conditions, electric utilities can quantify the potential impacts of a fire. This includes estimating the population at risk, the number of buildings affected, and the acreage potentially burned.
• Identifies High-Risk Areas: Running simulations across a grid allows electric utilities to identify high-risk areas where the combination of fuels, topography, and forecasted weather creates the highest potential for large, damaging fires. Many electric utilities are using this information to do fast-cycle asset hardening or vegetation management days before a potential event. This data also drives longer-term asset-hardening and vegetation management decisions under limited budgets and rate increase abilities.
• Streamlining Data into a Singular View: Electric utilities need to aggregate the results of these simulations into a single, easily digestible product, such as a “fire size potential” map, to have actionable information and quicker decision-making for proactive measures.

What’s Next: The Future of Wildfire Management

Electric utilities require granular data that pinpoints specific areas of elevated risk within their service territory, rather than a general regional warning. Integrating weather forecasting with fire behavior modeling is a critical step towards more effective action by electric utilities. It provides the granular, actionable insights that emergency managers at electric utilities need to protect communities and infrastructure in an increasingly wildfire-prone environment.

Technosylva’s Vice President for Weather & Risk Solutions, Steve Vanderburg, often explains that building a wildfire-resilient grid must be appreciated as an ongoing process.

As extreme weather events intensify, electric utilities must continuously adapt their strategies to mitigate wildfire risks. In this effort, data-driven insights, advanced modeling, and a flexible approach are key to success.

Risk managers at electric utilities need to integrate fire spread prediction and weather data to prioritize mitigation efforts. Additionally, engineers should analyze circuits to identify vulnerabilities and plan hardening projects effectively. Together, developing a wildfire-focused workforce at their electric utility is essential. By learning from past experiences and embracing innovation, electric utilities can ensure their grids remain safe and reliable in the face of increasing wildfire and extreme weather threats.

5 Key Takeaways:

• Grid hardening is a continuous process: It requires ongoing investment, innovation, and adaptation.
• Data-driven insights are crucial: Leveraging analytics helps utilities understand vulnerabilities and prioritize mitigation efforts.
• Advanced modeling is essential: Integrating fire spread prediction and weather data provides comprehensive situational awareness
• Flexibility and adaptability are key: Electric utilities must be prepared to adjust their strategies as wildfire threats evolve.
• A wildfire-focused workforce is essential: Developing expertise in extreme weather-related resilience planning is crucial for long-term success.

Read our full article in Utility Dive and see how leading electric utilities are advancing their outage analytics to help prioritize asset hardening and vegetation management decisions.

The term “unprecedented” is often used in the media to describe seemingly new wildfire impacts. Yet, we have seen numerous “unprecedented” wildfire events in the past five years stemming from asset-caused ignitions. For a risk manager at an electric utility, it’s crucial to recognize that the threat of catastrophic wildfires is pervasive and exists almost everywhere.

Technosylva’s Senior Data Scientist, Pavel Grechanuk, explains that the wildfire mitigation strategies of electric utilities demand more than a simple designation of risk levels. Electric utilities need a comprehensive, nuanced approach that recognizes the complexities of fuels, climate, and people. More importantly, by combining climatological analysis with operational risk modeling, electric utilities can forecast catastrophic events and take action ahead of time to minimize impacts.

5 Key Takeaways:

• Unprecedented Wildfires Are Increasingly Common: Climate change is making severe wildfires more frequent and unpredictable, even in previously low-risk areas.
• Comprehensive Risk Assessment is Essential: Utilities need to go beyond simple risk designations and consider factors like fuels, climate, and people to accurately assess their wildfire risk.
• Climatological Analysis and Operational Risk Modeling Are Crucial: Combining these two approaches helps utilities understand what is normal and what is coming in the near term, enabling better forecasting of unprecedented events.
• Advanced Fire Spread Modeling is Necessary: This technology can predict where a fire will go and its potential impacts, aiding in decision-making.
• Proactive Management is Key: By implementing a comprehensive modeling framework, utilities can better prepare for and respond to severe weather events, even those that were previously considered unprecedented.

Read our full article in Utility Dive and see how leading electric utilities are advancing their outage analytics to help prioritize asset hardening and vegetation management decisions.

Electric companies need to leverage a comprehensive understanding of their asset wildfire risk to plan for a safer future. With applied technology, they can reconstruct past fire seasons to truly define outlier events and model the consequences of asset-caused ignitions to identify trends and patterns that better prepare them for future wildfire risks. A shifting regulatory and legal landscape around wildfire liability is requiring electric companies to think differently about risk.

Furthermore, by contextualizing future events with a robust database of historical risks, electric companies can effectively monitor the frequency and intensity of weather events and identify specific assets along their lines that will be most impacted by climate change. This proactive approach to risk management not only ensures the safety of assets and communities, but also helps in minimizing the potential consequences of asset-caused wildfires.

Learn how you can predict, mitigate, and prevent your evolving wildfire risk and additionally, how Technosylva solutions provide leading electric utilities with increased risk management, operations, asset mitigation, emergency planning, regulatory compliance, and improved public safety.

You can read the full article in Electric Perspectives Magazine here.

Energy companies in the United States are increasingly relying on wildfire spread prediction models to assess asset-caused ignition risk. Yet, these models are conditional on the occurrence of an ignition and do not represent the expected risk of an asset. Knowing the possibility of wildfire risk conditions in your service area is important-making comes from knowing the probability that one of your assets could start a wildfire.

Here, fire spread simulations must consider the potential consequences of a wildfire, while addressing the probability of ignition and failure based on various asset attributes and environmental factors. By understanding the expected risk from their assets, utilities can efficiently prioritize grid-hardening and mitigation efforts, making the best use of their limited budget resources to minimize the impact of wildfires on the communities they serve and the environment.

It is crucial for utilities to continuously assess and update their wildfire risk assessments to stay ahead of changing conditions and ensure the most effective risk management strategies are in place. Just knowing your service area’s wildfire risk isn’t enough anymore. Electric companies must invest in advanced wildfire spread prediction technologies and integrate them into their risk management strategies for a safer and more resilient energy system. The future of energy utility operations depends on proactive and data-driven approaches to mitigate the risks posed by wildfires, ensuring safety and reliability for all stakeholders involved.

Learn how you can predict, mitigate, and prevent your evolving wildfire risk and additionally, how Technosylva solutions provide leading electric utilities with increased risk management, operations, asset mitigation, emergency planning, regulatory compliance, and improved public safety.

You can read the full article on Utility Dive here.

Understanding potential consequence from asset ignited wildfires: it’s more than just ignition modeling.

Simply knowing the probability of a wildfire across an electric company’s service area does not explain what the possible consequences are – including the possible liability – from an asset-caused ignition. Energy companies in the United States are facing a growing challenge of reducing wildfire risk while ensuring reliable operations. To achieve this, their managers need sophisticated risk modeling that can help them quantify both the probability and consequence of asset-caused ignitions to ensure they are providing the best operations and mitigation decision-making.

Learn how you can predict, mitigate, and prevent your evolving wildfire risk and additionally, how Technosylva solutions provide leading electric utilities with increased risk management, operations, asset mitigation, emergency planning, regulatory compliance, and improved public safety.

You can read the full article on Utility Dive here.

Despite California being a major fire hotspot in the Americas, there is no extensive scientific analysis of operational fire spread models allowing analysis of their performance and drivers leading to model inaccuracies.  Recent advances in technology have allowed monitoring the fire progression of most wildfires every 15 min in the United States through the National Fireguard Detections platform. This data, when available for use on a fire, provides unprecedented capabilities to analyze factors influencing fire behavior and compare the observed and predicted wildfire rate-of-spread (ROS) modeling in fires distributed across different and complex landscapes.

Building on other studies that analyzed these modeling techniques, Technosylva joined with CALFIRE and led a 2023 peer-reviewed study, published in the International Journal of Wildland Fire that assesses the performance of fire spread models used in California by comparing observed fire growth data with simulated data. The analysis reviewed operational settings under different environmental conditions using 1853 California wildfires from 2019 to 2021 to determine what conditions the current models may over, or underestimate ROS and subsequently, the burned area and associated fire impacts on buildings and other assets.

Insight from the Research

Wildfire spread models play a crucial role in predicting how fires propagate, but their accuracy is influenced by various factors, including fuel availability, topography, and weather. Among these models, Rothermel’s semi-empirical model has been widely used for its simplicity and computational efficiency. However, the inherent limitations and assumptions of these models, along with input data quality, can impact their reliability.

This study, conducted in California, aimed to assess the predictive accuracy of wildfire spread models under different environmental conditions. It utilized high-resolution data from the National Fireguard Detections product to compare observed and predicted Rates of Spread (ROS) for 1853 wildfires occurring from 2019 to 2021. The analysis sought to identify conditions under which the models overestimate or underestimate ROS, ultimately affecting the burned area and fire impacts on buildings and assets.

Key observations and findings from the Research

• Fire Progression Data: The study used the National Fireguard Detections product data, offering high temporal resolution to monitor fire progression every 15 minutes. A grid-growing clustering algorithm was employed to classify polygons into individual fire incidents, enabling a quantitative analysis of fire behavior.
• Fire Modeling with WFA-e: Fire simulations were conducted using WFA-e, incorporating various fire spread models, including Rothermel’s surface and crown fire spread models. Fuel type, topography, and weather data were integrated to run simulations.
• Statistical Analysis: The accuracy of the fire spread models was assessed using error metrics such as ROS residuals, mean absolute error (MAE), mean bias error (MBE), and mean absolute percentage error (MAPE).
• Environmental Factors: The study revealed that the accuracy of fire spread predictions was influenced by environmental variables such as wind speed and fuel moisture content (both live and dead). Low wind speeds and high fuel moisture levels tended to lead to underestimations of ROS, while high wind speeds resulted in overestimations.
• Fuel Types: Different fuel types played a significant role in the accuracy of predictions. Models performed relatively well for shrub, grass, and grass-shrub fuel types, while they consistently underpredicted ROS for timber fuel types.
• Overall Model Accuracy: The models had an average MAPE of 47% for automatic fire simulations, with better performance in shrub, grass, and grass-shrub fuel types. Timber fuel types exhibited the highest MAPE (approximately 67%).

The study found that the model errors and biases were reasonable for simulations performed automatically. It identified environmental variables that might bias ROS predictions, particularly in timber areas where some fuel models might underestimate ROS. Overall, the performance of fire spread models for California aligns with studies developed in other regions, and the models are deemed accurate enough to be used in real-time to assess initial attack fires.

Next Steps from the Research

The study highlighted challenges related to pyroconvection, local wind fields, and the estimation of ROS in timber areas. It recommended the development of improved fire spread models to address these challenges and enhance prediction accuracy.

The study found that while current fire spread models have limitations and biases, they are accurate enough to be used in real-time operational settings, particularly with the capability for manual adjustments and calibration. However, there is a need for ongoing improvements, especially for modeling fire spread in timber areas, predicting crown fire behavior, and considering the effects of pyroconvection. This research contributes valuable insights to wildfire prediction and management, emphasizing the importance of continuously refining and enhancing predictive models in the face of growing wildfire threats.

The research underscores the importance of wildfire simulators in supporting planning and incident analysis in real-time, despite the potential uncertainties derived from input data quality and model inaccuracies. The study additionally provides insights into the performance of fire spread models in California, offering a foundation for understanding and potentially improving upon current operational models in the future.

Learn more about how this science is put into practice.