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

Wildfire risk has become a year-round planning consideration for electric utilities across the country. Longer fire seasons, shifting weather patterns, and growing regulatory expectations have made wildfire resilience a core part of long-term grid strategy. For utilities building or refining that strategy, it helps to think across six interconnected areas. Gaps in any one of them can limit the effectiveness of investments made in the others.

1. Identifying and Prioritizing Threats

Effective wildfire mitigation starts with knowing which assets carry the most risk and why. That requires more than a static risk assessment. Ignition probability varies across assets based on equipment age, conductor type, span length, vegetation conditions, terrain, and local weather patterns. Consequence varies too, depending on what lies in a fire’s potential path.

Integrating these factors through risk modeling allows utilities to move from broad hazard zones to asset-level prioritization. That specificity matters when hardening budgets are limited and every dollar needs to be directed where it will produce the most meaningful reduction in expected risk.

2. Maintaining Service During Extreme Events

Operational resilience during a wildfire event depends on having accurate, real-time information about how fire is behaving relative to grid infrastructure. Operators making decisions about de-energizing lines, rerouting power, or deploying crews need more than weather forecasts. They need visibility into how fire spread is likely to interact with specific assets under current and forecasted conditions.

For planners at utilities of any size, the design question is what information and decision-support tools need to be in place before an event occurs. Operational gaps during an active fire are difficult to close in the moment.

3. Measuring the Impact of Investments

Utilities invest in vegetation management, infrastructure hardening, and other mitigation activities at varying scales. Demonstrating the risk reduction those investments produce is increasingly important, both for internal planning and for regulatory and stakeholder accountability.

Analysis that compares pre- and post-mitigation conditions, and models fire behavior in treated versus untreated areas, allows utilities to quantify what their investments have accomplished. That evidence base also informs future prioritization by showing where mitigation has been most effective and where diminishing returns may be setting in.

4. Responding Effectively to Wildfire Events

Emergency response plans are most effective when they are built on realistic modeling of how fires behave and how infrastructure responds under stress. Scenario-based planning, informed by fire spread prediction, helps utilities anticipate where resources will be needed and how response timelines are likely to unfold.

From a planning perspective, emergency response capability is partly a function of decisions made well in advance: where crews are positioned, what mutual aid agreements are in place, and how communication protocols are structured before an event begins.

5. Meeting and Exceeding Evolving Regulatory and Stakeholder Requirements

The regulatory environment around wildfire safety continues to evolve, with utilities in higher-risk areas facing increasingly detailed reporting and documentation requirements. Tracking mitigation activities, documenting risk assessments, and generating compliance reports are resource-intensive tasks regardless of utility size.

Building organized, consistent documentation practices early makes compliance more manageable and ensures that reporting reflects the same risk information driving planning and operational decisions. For utilities entering more regulated environments, getting that foundation in place before requirements intensify reduces the burden of catching up later.

6. Protecting Communities and Workers in the Field

Field crew safety and public protection are the ultimate measure of a wildfire resilience program. Real-time visibility into fire hazards, weather conditions, and infrastructure vulnerabilities supports better decisions about when and where to deploy personnel and when to communicate proactively with the public.

Safety outcomes are shaped by the quality of information available at the moment decisions are made. That makes investment in situational awareness a safety consideration as much as an operational one, and one that scales to the resources available.

Building a Connected Strategy

These six areas are most valuable when they are treated as connected parts of a single strategy rather than separate programs. Risk assessment informs investment prioritization. Investment measurement feeds back into planning. Operational capability depends on the information foundations built during non-event periods. Regulatory compliance is easier to sustain when it is integrated into existing workflows rather than managed separately.

For utilities of any size building long-term wildfire resilience, the goal is a strategy where progress in each area reinforces the others.

Grid hardening is not a project with a finish line. As fire weather patterns shift and service territory conditions change, the work of building a wildfire-resilient grid continues. For electric utilities, that means treating resilience not as a one-time capital program but as an ongoing planning discipline, one that requires regular reassessment, updated data, and a clear method for deciding where investment will produce the most meaningful risk reduction.

Start with a Strong Data Foundation

For utilities earlier in this process, the starting point is data. Understanding wildfire risk at the asset level requires integrating multiple inputs: historical fire weather, fuel conditions, terrain, vegetation, and the physical attributes of the infrastructure itself. Without a consistent data foundation, prioritization decisions rest on incomplete information and risk being driven by the most visible consequences rather than the actual distribution of risk across the system.

Building that foundation does not require solving everything at once. Utilities of any size can begin by identifying what data they have, where the gaps are, and what decisions are currently being made without adequate information. That assessment shapes the path forward.

Use Modeling to Prioritize at the Asset Level

Once a data foundation is in place, the next step is applying it. Engineers analyzing circuits for hardening and rebuilding projects need more than general hazard zone designations. They need to understand which specific assets carry the highest expected risk and what mitigation measures will produce the greatest reduction per dollar invested.

In a Utility Dive article, Vanderburg explained that by combining historical fire weather scenarios with advanced wildfire spread modeling, utilities can calculate potential impacts at the individual asset level. That analysis surfaces which circuits are most exposed, how past fire weather would have interacted with current infrastructure, and where hardening investment is likely to yield the most return. It also reveals where prior investments have already reduced risk substantially, so that new dollars are not directed toward assets where diminishing returns have already set in.

This kind of asset-level modeling is what separates a defensible capital prioritization from one based primarily on consequence footprint or geographic proximity to recent fires.

Integrate Real-Time Conditions with Long-Term Planning

Long-term planning and operational awareness are not separate activities. Risk managers who integrate fire spread prediction with forecasted weather data develop a clearer picture of where ignition risk is highest across their service territory at any given time. That situational awareness informs not just day-to-day operations but longer-term decisions about where to focus vegetation management and where to accelerate hardening timelines.

When planning and operations share the same underlying risk picture, investment decisions are easier to defend and easier to adjust as conditions evolve.

Plan for Adaptation, Not Just Completion

One of the more common planning errors is treating a hardening program as complete once a set of projects is finished. Grid conditions change. Vegetation grows back. Equipment ages. Fire weather shifts. A resilience strategy that does not build in regular reassessment will gradually fall out of alignment with actual risk.

Building adaptation into the planning cycle means scheduling periodic risk reassessments, tracking how mitigation investments have changed the risk profile of the system, and being willing to reprioritize when the data supports it. It also means maintaining the organizational capacity to do that work: staff who understand wildfire risk modeling, can interpret the outputs, and can connect them to capital planning decisions.

A Continuous Investment, Not a One-Time Fix

The utilities making the most progress on wildfire resilience are the ones that have moved past the question of whether to invest and into the harder question of how to invest most effectively. That shift requires reliable data, asset-level modeling, and a planning process that treats risk reassessment as a regular input rather than an occasional project.

The goal is not a perfect grid. It is a planning approach that consistently directs investment toward the assets and strategies most likely to reduce risk for communities, the environment, and the system as a whole.

This article is adapted from a piece originally published in Utility Dive.

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.