Wildfires are a major threat to communities and their impacts can be devastating. That’s why it’s so important to have accurate information about the time and likelihood of a wildfire reaching a particular area. This is where wildfire decision trigger modeling comes into play. It helps fire agencies assess fire exposure and create evacuation plans with enough warning time.
But there’s one key aspect that hasn’t received much attention: input data uncertainty. To address this, Technosylva led a 2019 peer-reviewed study, published in the academic journal, Science of The Total Environment, showcasing a new stochastic fire simulation decision trigger modeling method that considers potential variations in input data to assess the probability of wildland fire impact.
”This was the first time that we tried to apply fire impacts for building consequence modeling instead of evaluating the impacts. For a fire, we wanted to explore how we can better model the vulnerability that a community has in its critical asset in much the same way others model flood impacts to coastal communities at risk. It means asking what is the exposure that you may have from considering the historical conditions on weather and the immediate conditions at the location.
The analysis allows you to model risk associated with an asset’s exposure variable. In this analysis, we explained the method of that risk associated to balance exposure, which is very powerful. We know where the assets are and their individual risk, but we don't know where the fire is. So, the model can assess asset risk, and with applied weather conditions, we can tell what the exposure of this community would be to a fire and the possible outcome if it was hit. This is a big advantage to fire agency operations for evacuation planning and timing of those decisions,Joaquin Ramirez, Ph.DLead Author - President & CTO
Insight from the Research
Accurately predicting wildfire behavior is challenging due to uncertainties in input data and the inherent limitations of fire models. Wildfire simulations, which use various models and data to predict fire behavior, have been crucial in managing wildfires. These simulations help fire management agencies plan controlled burns, assess fire risks, and develop real-time strategies for suppression.
Several types of fire spread models have been developed, ranging from physical-based models to empirical and mathematical ones. These models consider various factors like vegetation, weather conditions, and fuel moisture to estimate critical fire behavior parameters such as the rate of spread and flame intensity.
Protecting vulnerable areas, such as wildland-urban interfaces (WUIs), is a primary concern for fire agencies. They need accurate predictions of when and where fires might impact these areas to make informed decisions about evacuations or shelter-in-place strategies. However, the accuracy of such predictions depends on the uncertainty associated with input data, including factors like topography, fuel type, and weather conditions.
To address these challenges, the study presents an innovative approach leveraging wildfire decision trigger modeling. This uses well-established semi-empirical fire spread models and incorporates stochastic elements to simulate decision trigger buffers around areas to be protected. These buffers represent the time it would take for a fire to reach the protected area under various input conditions, providing probabilities of impact.
Cite: Joaquin Ramirez
Defining The Modeling in the Research
The model incorporates random elements into a simulation, which better reflects the chaotic and unpredictable nature of wildfires. By using random data inputs, you can produce a range of possible outcomes, rather than just one deterministic prediction. By simulating different scenarios, it produces a probability map of the fire’s arrival to areas that need to be protected.
To demonstrate the decision trigger modeling concept, the study used data from the Tubbs fire, one of California’s most destructive wildfires. The analysis ran fire simulations backward in time from selected ignition points to the edge of the WUI around Santa Rosa. The simulations estimated the time it would take for a fire to reach the WUI, and the results were compared with the actual fire progression to validate the accuracy of the decision trigger modeling method.
The decision trigger modeling method uses an ensemble approach, varying input conditions such as wind speed, wind direction, and fuel moisture content to account for input data uncertainties. It generates multiple fire simulations under different conditions to provide probabilistic estimates of fire arrival times.
The study found that the fire simulations using the decision trigger modeling method had a high degree of accuracy when compared to the actual fire spread during the Tubbs fire. The evacuation trigger buffers estimated by decision trigger modeling correctly predicted the time of arrival of the fire at the WUI edge. The ensemble method used for weather conditions showed that the predicted rate of spread (ROS) varied significantly depending on the input conditions. This variability highlighted the importance of considering weather uncertainty in fire behavior modeling.
The decision trigger modeling method offers a valuable framework for estimating the probability of wildfire impact in areas to be protected in real time, accounting for input data uncertainties. It provides decision-makers with information about potential fire spread and the associated probabilities, aiding in evacuation planning and the development of fire suppression strategies.
The study emphasized the need for considering uncertainties beyond weather conditions, including model limitations, fire-atmosphere interactions, and data quality. While the decision trigger modeling method showed promise, it is essential to combine it with expert knowledge and judgment for more accurate decision-making.
Next Steps from the Research
The wildfire decision trigger modeling approach presented in this study offers a novel way to predict wildfire impacts on vulnerable areas. By incorporating input data uncertainties and providing probabilistic estimates,decision trigger modeling enhances decision support for fire management agencies.
However, the study highlights the importance of considering various sources of uncertainty to improve the accuracy of fire behavior predictions in operational environments. Decision trigger modeling has the potential to become a valuable tool for assessing fire risk and protecting communities in the face of increasing wildfire threats.
Learn more about how this science is put into practice.