Percent Fire Return Interval Departure (pFRID)

from Vibrant Planet

Overview

How Wildfires Are Changing

Understanding how wildfire patterns change over time is crucial for effective wildfire management. Percent Fire Return Interval Departure (pFRID) is a valuable tool that helps land managers identify shifts in these patterns. This information supports the development of more informed planning and response strategies.

Photo by Andrii Chagovets / Adobe Stock

Photo by Nicole Geri on Unsplash

Percent Fire Return Interval Departure (pFRID) helps us understand the changes in wildfire occurrences over time. Simply put, it compares how often wildfires happen now versus how often they happened in the past.

what is pFrid data?

Traditional pFRID measures how much current wildfire frequencies differ from historical norms in a given area. It compares the average time between fires now to the average time between fires before pre-European settlement. A positive pFRID represents a fire deficit— fires are burning less frequently than in the past, which can be associated with uncharacteristically extreme fire behavior and ecosystem loss when a fire does occur. A negative pFRID indicates a fire surplus, which results from increased ignition pressure from humans and may hinder an ecosystem’s ability to naturally regenerate.
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Vibrant Planet's pFRID dataset takes this analysis a step further. It recognizes that wildfire patterns are naturally variable, especially in areas with historically long intervals between fires. Their approach accounts for this variability, providing a more accurate and nuanced picture of how fire patterns are truly changing. This helps land managers distinguish between natural variations and genuine shifts in wildfire behavior, equipping them to make more informed management decisions.

Geography

Where previous pFRID measurements focused only on California, Vibrant Planet's pFRID analysis now covers seventeen western U.S. states in their entirety, with additional partial coverage in five more states. This expanded scope provides a more comprehensive understanding of fire patterns and environmental conditions across various landscapes.

Square Miles

1.27

Billion Acres

Locator map showing extent of pFRID data across western U.S. States.

Map by VPDC, 2024.

pFRID based on Safford and Van de Water 2014

This map, based on Safford and Van de Water (2014), shows changes in fire frequency since pre-European settlement. Cool colors represent areas where fire intervals are much longer than historical norms, indicating fewer fires in recent decades, often leading to fuel accumulation and potential for uncharacteristic fire behavior. Warm colors show areas where fires are occurring more frequently than in the past, which may lead to shifts in vegetation and increased fire risk.

Fire Return Interval Departure (pFRID) with P-Value

This map incorporates pFRID data with P-values derived from a binomial distribution, reflecting the likelihood that current fire return frequencies differ from historical norms. The analysis assumes the inverse of the historic mean fire return interval is the true fire frequency, with the observed number of fires as the "successes" and the total years between 1950 and 2023 as the "trials." Cool colors show areas with longer-than-expected intervals, suggesting fewer fires and potential fuel buildup. Warm colors highlight areas with more frequent fires, which may increase fire risk and cause vegetation changes.

Significantly Departed Fire Return Interval Departure (pFRID)

This map highlights areas where pFRID values significantly depart from historical fire return intervals. Cool colors (positive values) represent areas with longer fire intervals, indicating potential fuel buildup and uncharacteristic fire behavior. Warm colors (negative values) indicate more frequent fires, which could increase fire risk and drive changes in vegetation. The displayed values represent strong deviations from historical norms, focusing on areas where fire frequency is notably higher or lower than expected.

Are you interested in applying pFRID data to your research or management project?

We want to hear from you! Vibrant Planet and Vibrant Planet Data Commons are tracking the applied use of this dataset so that we can demonstrate the impact of good data and research. Share your thoughts and help us improve our resources to better serve the land management, fire, and research communities.

what sets this dataset apart?

Traditional Limitations of pFRID

The traditional pFRID approach often encounters problems when estimating fire return intervals, especially in landscapes with infrequent historical fires. Here's a breakdown of the issues:

Overestimation of Departure – Traditional pFRID calculations can lead to misleading results in areas with long fire return intervals (FRI). For example, if an area historically experienced a wildfire every 150 years but has recorded one or two fires in a shorter contemporary period (such as from 1950 to 2023), traditional methods would calculate a high departure from the norm. This means the area might be incorrectly classified as highly departed from its historical fire regime.
Misleading Management Decisions - Because the departure is exaggerated, land managers may perceive a greater change in fire frequency than actually exists. This could lead to unnecessary alarm and possibly misdirected management actions. In areas where fires are naturally rare, even a single fire can skew the perceived risk and response strategies, diverting resources or prioritizing actions that might not be the most effective under the actual conditions.
Simplistic Historical Comparisons - Traditional methods often don't adequately account for the natural variability and stochastic nature of fire occurrences. They might treat the historical and contemporary data points as more static and reliable than they are, not considering the probability and randomness in fire events. This can lead to a less nuanced understanding of how fire regimes are changing over time.

Photo by Federico Bottos on Unsplash

Photo by Daniel Seßler on Unsplash

Null Hypothesis Significance Testing (NHST) is a statistical method used to determine whether there is enough evidence to reject a null hypothesis in favor of an alternative hypothesis. NHST is widely used in scientific studies, including environmental research, to test hypotheses about patterns, relationships, and effects (e.g., testing whether climate change increases wildfire frequency). However, it does not measure effect size or prove causation—only whether observed data provide strong evidence against H₀.

vibrant planet's approach

Vibrant Planet's approach to creating pFRID data significantly improves upon these traditional limitations by introducing a dual-metric system that pairs the standard pFRID calculation with a robust statistical analysis of fire frequency data.
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VP's new pFRID data tackles these issues in a unique way. Instead of simply focusing on the difference in fire frequency, it introduces a second metric that considers the natural randomness of fire events . This involves:

Understanding Fire Probability: Based on historical fire patterns, VP's approach calculates how likely fires are to occur in a given area.
Statistical Testing: It then tests whether the number of recent fires falls within the expected range. If not, the area is considered significantly different from its historical fire pattern.

This methodology treats each year as an independent trial in which a fire can occur, calculating the likelihood of observing the actual number of fires based on historic fire return intervals.

The core of this new approach lies in its use of null hypothesis significance testing. This statistical method allows for an assessment of whether the observed fire frequencies in a contemporary period align with what would be expected based on historical data. By calculating the probability of a given number of fires occurring if the historical conditions still applied, Vibrant Planet can determine if the observed fire activity is significantly different from the norm. This is currently done using a binomial distribution model, which accounts for the stochastic (random) nature of fire occurrences and provides a more accurate measure of departure from historical fire regimes.

Furthermore, Vibrant Planet enhances this model by incorporating a Beta distribution in the probability assessments. This adjustment allows for a more conservative test of departure, considering additional uncertainties and making the analysis less prone to overreacting to anomalies. This method not only addresses the traditional pFRID's tendency to overestimate departure in cases of rare historical fires but also provides land managers with a more nuanced and reliable tool for making informed decisions about fire management and resource allocation. This sophisticated approach ensures that actions are based on data that more accurately reflects the natural variability and true risk of wildfire in their specific landscapes.

key benefit and value

For instance, the pFRID data can bolster Quantitative Wildfire Risk Assessments (QWRA), offering more precise insights into fire behavior and risk. Another significant value of this dataset is its expanded geographic scope. Previously limited to California, the pFRID data now covers nearly half of the United States, providing land managers with a broader understanding of fire patterns across diverse ecosystems.Finally, the dataset distinguishes between the magnitude of change and the strength of evidence for that change. This differentiation helps land managers make more informed decisions without unnecessary alarm, focusing their efforts on areas where intervention is most critical.

Photo by Nicole Geri on Unsplash

References

Safford HD, Van de Water KM. 2014. Using fire return interval departure (FRID) analysis to map spatial and temporal changes in fire frequency on national forest lands in California. U.S. Forest Service, Pacific Southwest Research Station. Available at: https://research.fs.usda.gov/treesearch/45476.