Energy Release Component Research Articles

Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method

AbstractMore frequent and widespread large fires are occurring in the western United States (US), yet reliable methods for predicting these fires, particularly with extended lead times and a high spatial resolution, remain challenging. In this study, we proposed an interpretable and accurate hybrid machine learning (ML) model, that explicitly represented the controls of fuel flammability, fuel availability, and human suppression effects on fires. The model demonstrated notable accuracy with a F1‐score of 0.846 ± 0.012, surpassing process‐driven fire danger indices and four commonly used ML models by up to 40% and 9%, respectively. More importantly, the ML model showed remarkably higher interpretability relative to other ML models. Specifically, by demystifying the “black box” of each ML model using the explainable AI techniques, we identified substantial structural differences across ML fire models, even among those with similar accuracy. The relationships between fires and their drivers, identified by our model, were aligned closer with established fire physical principles. The ML structural discrepancy led to diverse fire predictions and our model predictions exhibited greater consistency with actual fire occurrence. With the highly interpretable and accurate model, we revealed the strong compound effects from multiple climate variables related to evaporative demand, energy release component, temperature, and wind speed, on the dynamics of large fires and megafires in the western US. Our findings highlight the importance of assessing the structural integrity of models in addition to their accuracy. They also underscore the critical need to address the rise in compound climate extremes linked to large wildfires.

Simulated Future Shifts in Wildfire Regimes in Moist Forests of Pacific Northwest, USA

AbstractFire is an integral natural disturbance in the moist temperate forests of the Pacific Northwest of the United States, but future changes remain uncertain. Fire regimes in this climatically and biophysically diverse region are complex, but typically climate limited. One challenge for interpreting potential changes is conveying projection uncertainty. Using projections of Energy Release Component (ERC) derived from 12 global climate models (GCM) that vary in performance relative to the region's contemporary climate, we simulated thousands of plausible fire seasons with the stochastic spatial fire spread model FSim for mid‐21st century (2035–2064) under RCP8.5 emissions scenario for five northwestern pyromes. The magnitude of projected changes to burn probability, fire size, and number of fires varied among pyromes and GCMs. We projected the largest increases in burn probability and fire size in the cooler and wetter northern parts of the region (North Cascades, Olympics & Puget Lowlands) and Oregon West Cascades, with more moderate changes projected for the Washington West Cascades and Oregon Coast Range. We provide new insights into changing fire regimes characterized by the possibility of shifts toward more frequent and large fires (especially >40,000 ha), as well as shifts in seasonality, including more fires burning at the beginning of fall when extreme synoptic weather events have the potential to increase fire spread. Our work highlights the potential geographic variability in climate change effects in some of the most productive moist temperate forests of the world and points to a rapid acceleration of fire in the coming decades.

Mapping South Florida Daily Fire Risk for Decision Support Using Fuel Type, Water Levels, and Burn History

Mapping fire risk in South Florida depends on spatially varying water levels, fuel characteristics, and topography. When surface water levels recede below the lowest topographic features (cypress strands, marshes, etc.), the ecosystem loses its natural, wetted fire breaks, and landscape-level fire risk increases. We developed a geospatial method to generate daily, categorical fire risk maps; the maps visualize low-to-high risk areas using a newly developed 100 m DEM, modeled water levels, fuel types, and fire management units. We assigned fire risk by creating a water level distribution for each unique combination of fuel type and fire management unit; fire risk was then assigned for each pixel based on risk percentiles commonly used by fire management agencies. Assigning risk based on unique fuel types and management units helped avoid over- or under-assigning fire risk that may occur when applying landscape-level “average” risk relationships. Daily maps also incorporated (1) energy release component data to better estimate fuel moisture and (2) historical burn footprints to reduce risk in recently burned areas. Our data-driven approach generated at management-relevant spatial scales may enable more informed prescribed burn planning and may increase the efficiency of staff and resource allocation across the landscape on high-wildfire-risk days.

Development of a Decision Matrix for National Weather Service Red Flag Warnings

The National Weather Service is responsible for alerting wildland fire management of meteorological conditions that create an environment conducive for extreme fire behavior. This is communicated via Red Flag Warnings (RFWs), which presently lack a national standardized methodology and rarely are explicitly linked to fuel conditions such those as provided by National Fire-Danger Rating System (NFDRS) indicators. The need for a revamped RFW has been expressed recently by both fire management and fire weather meteorologists. A decision matrix approach was developed to determine criteria that consistently and explicitly associates meteorological and fuels information to extreme fire behavior. Extreme fire behavior is defined here as maximum rates of spread (area per day) observed on documented large fires from 1999–2014 utilizing the ICS209 all-hazard dataset. Meteorological conditions occurring with these rates of spread were compared to historical percentiles of relative humidity, wind speed, and the NFDRS Energy Release Component. These percentiles were assigned a numerical score from one through five based on percentile rank. The additive result of all three scores was plotted against rates of spread yielding a two-step decision matrix of RFW categories where, for example, the highest score is the most extreme RFW case. Actual RFW issuances were compared to this matrix method.

High-resolution wildfire simulations reveal complexity of climate change impacts on projected burn probability for Southern California

BackgroundWildfire is a major contemporary socio-ecological issue facing the people and natural resources of Southern California, and the prospect that a warming climate could lead to a higher probability of fire in the future is cause for concern. However, connecting climate change to projected burn probability is complex. While most models generally show temperature increasing in the future, changes in humidity and precipitation are less certain, and these changes interact to generate projections of future climates that are sometimes, but not always, more conducive to wildfire. We ran FSim, a stochastic, high-resolution spatial (270 m) and temporal (daily) fire spread model, with projected Energy Release Component (ERC) derived from multiple global climate models (GCMs) under RCP8.5 climate change scenario to explore the impact of a range of future climate trajectories on simulated burn probability and to quantify the uncertainty arising from multiple GCMs.ResultsWe observed considerable uncertainty in the future direction of change for burn probability. Future changes were more certain in the Southern Coast region of California, where 75% of simulations projected an increase in burn probability. In the Central Coast region, five out of eight GCM-based simulations projected increased burn probability. Less than 1% of the total burnable study area had unanimous agreement on the projected direction of change. Simulated changes in burn probability were directly correlated to annual projections of changes in ERC, but were also affected by the seasonality of ERC change, as well as interactions between humidity, precipitation, and temperature.ConclusionsThe observed variability offers insights into why, and under what climate conditions, burn probability may increase or decrease in the future. Our study is novel in its examination of a wide range of potential future burn probability projections for Southern California using a regional application of a high-resolution stochastic fire spread model, and the complexity that we demonstrated for Southern California suggests that simple correlations of increasing fire with increasing temperature are likely underestimating the range of plausible future fire scenarios.

Downscaled subseasonal fire danger forecast skill across the contiguous United States

AbstractThe increasing complexity and impacts of fire seasons in the United States have prompted efforts to improve early warning systems for wildland fire management. Outlooks of potential fire activity at lead‐times of several weeks can help in wildland fire resource allocation as well as complement short‐term meteorological forecasts for ongoing fire events. Here, we describe an experimental system for developing downscaled ensemble‐based subseasonal forecasts for the contiguous US using NCEP's operational Climate Forecast System version 2 model. These forecasts are used to calculate forecasted fire danger indices from the United States (US) National Fire Danger Rating System in addition to forecasts of evaporative demand. We further illustrate the skill of subseasonal forecasts on weekly timescales using hindcasts from 2011 to 2021. Results show that while forecast skill degrades with time, statistically significant week 3 correlative skill was found for 76% and 30% of the contiguous US for Energy Release Component and evaporative demand, respectively. These results highlight the potential value of experimental subseasonal forecasts in complementing existing information streams in weekly‐to‐monthly fire business decision making for suppression‐based decisions and geographic reallocation of resources during the fire season, as well for proactive fire management actions outside of the core fire season.

Multi-Indices Diagnosis of the Conditions That Led to the Two 2017 Major Wildfires in Portugal

Forest fires, though part of a natural forest renewal process, when frequent and on a large -scale, have detrimental impacts on biodiversity, agroforestry systems, soil erosion, air, and water quality, infrastructures, and the economy. Portugal endures extreme forest fires, with a record extent of burned areas in 2017. These complexes of extreme wildfire events (CEWEs) concentrated in a few days but with highly burned areas are, among other factors, linked to severe fire weather conditions. In this study, a comparison between several fire danger indices (named ‘multi-indices diagnosis’) is performed for the control period 2001–2021, 2007 and 2017 (May–October) for the Fire Weather Index (FWI), Burning Index (BI), Forest Fire Danger Index (FFDI), Continuous Haines Index (CHI), and the Keetch–Byram Drought Index (KBDI). Daily analysis for the so-called Pedrógão Grande wildfire (17 June) and the October major fires (15 October) included the Spread Component (SC), Ignition Component (IC), Initial Spread Index (ISI), Buildup Index (BUI), and the Energy Release Component (ERC). Results revealed statistically significant high above-average values for most of the indices for 2017 in comparison with 2001–2021, particularly for October. The spatial distribution of BI, IC, ERC, and SC had the best performance in capturing the locations of the two CEWEs that were driven by atmospheric instability along with a dry environment aloft. These results were confirmed by the hotspot analysis that showed statistically significant intense spatial clustering between these indices and the burned areas. The spatial patterns for SC and ISI showed high values associated with high velocities in the spread of these fires. The outcomes allowed us to conclude that since fire danger depends on several factors, a multi-indices diagnosis can be highly relevant. The implementation of a Multi-index Prediction Methodology should be able to further enhance the ability to track and forecast unique CEWEs since the shortcomings of some indices are compensated by the information retrieved by others, as shown in this study. Overall, a new forecast method can help ensure the development of appropriate spatial preparedness plans, proactive responses by civil protection regarding firefighter management, and suppression efforts to minimize the detrimental impacts of wildfires in Portugal.

Historical spatiotemporal changes in fire danger potential across biomes

This study 1) identifies the seasons and biomes that exhibit significant (1980–2019) changes in fire danger potential, as quantified by the Canadian Fire Weather Index (FWI); 2) explores what types of fire behavior potentials may be contributing to changes in fire danger potential, as quantified by the United States Energy Release Component (ERC) and the Ignition Component (IC); 3) provides spatiotemporal insight on how fire danger potential and fire behavior potential are responding in relation to changes in seasonal precipitation totals and seasonal mean air temperature across biomes. Time series of these fire potentials, as well as seasonal mean temperature, and seasonal precipitation totals are generated using data from the 0.25° ECMWF spatial resolution Reanalysis 5th Generation (ERA5) and the Climatic Research Unit gridded Time Series (CRU TS). The Mann-Kendall test is then applied to identify significant spatiotemporal trends across each biome. Results indicate that the September–November season (SON) exhibits the greatest rate of increase in fire danger potential, followed by the June–August season (JJA), December, January–February season (DJF), and March–May season (MAM), and this is predominant over the Tropical and Subtropical Moist Broadleaf Forest Biome, as well as all vegetation types of the temperate biomes. Similarly, the temperate biomes experience the greatest rate of increase in fire intensity potential and ignition potential, but prevalent during the DJF and MAM seasons. Furthermore, there is a significant positive correlation between fire danger potential and seasonal mean air temperature during JJA in the Northern Hemisphere for the temperate biomes in North America and Europe, as well as the Tropical and Subtropical biomes in Africa. Our analysis provides quantitative insight as to how fire danger potential and fire behavior potential have been responding to changes in seasonal mean temperature and seasonal precipitation totals across different ecoregions around the world.

THE LARGE-SCALE WILDFIRE SPREAD PREDICTION USING A MULTI-KERNEL CONVOLUTIONAL NEURAL NETWORK

Abstract. In the last twenty years, destructive wildfires have affected the environment to the tune of billions of dollars. An accurate model is crucial for predicting the spreading of wildfires in a variety of conditions. In this study, a multi-kernel convolution neural network (CNN) deep learning model was proposed based on elevation, wind direction, and speed, minimum and maximum temperatures, humidity, precipitation, drought index, normalized difference vegetation index (NDVI), and energy release component to predict wildfire spread across the United States. Using multi-kernel CNN, it is possible to predict whether a pixel will be on fire at a future time. Compared to the model presented by other authors, the multi-kernel CNN model achieved high accuracy and F1 score. In comparison with CNNs without a multi-kernel mechanism and fixed kernel size, the proposed model predicted more accurate results based on the test data set. The multi-kernel CNN model reached an overall accuracy of 98.6 and F1 score of 70.97 on test data.

Reliability of cross-regional applications of global fire danger models: a Peruvian case study

BackgroundFire danger indexes (FDIs) are used as proxies for fire potential and are often developed for specific locations. For practical purposes, the extrapolation of the underlying calculations into novel locations is common, but it is generally uncertain if the relationships between FDIs and fire potential observed in the environment in which the index was developed are equally relevant in others. For example, although a topographically, ecologically, and climatologically complex country, f ire danger forecasts in Peru use a standard set of nationwide thresholds applied to the Fire Weather Index. In this study, we validate the underlying assumption that weather-fire relationships are spatially uniform within Peru by (1) making cross-regional comparisons of the statistical distributions of four FDIs—Burning Index, Energy Release Component, Fire Weather Index, and Keetch-Byram Drought Index, and (2) making cross-regional comparisons of the expected daily MODIS hotspot count percentiles conditioned on FDI values.ResultsSignificant regional differences in the distributions of daily FDI values were observed in every pair of regions within Peru, and with the exception of a pair of regions within the Amazon, little data (<90 days) were necessary to detect these differences. After controlling for FDI values and seasonal and annual effects with regressions, differences in predicted hotspot percentiles were common, differing by as much as 47 percentage points. Across the pairs of regions, the magnitude of these differences tended to decrease as climatic similarity increased, but some counterexamples were also apparent.ConclusionsThe noticeable differences in the distributions of daily FDI values suggest that a standard set of breakpoints may produce unreliable inferences regarding fire potential. We also find that even if the climatic conditions were similar across Peru, the same FDI values in two locations can produce substantially differing predictions of wildfire activity. This suggests that other factors besides FDI values can strongly mediate wildfire activity and that better fire potential predictions could be produced if these factors are accounted for.

Climate‐Driven Tree Mortality and Fuel Aridity Increase Wildfire's Potential Heat Flux

AbstractWildfire is capable of rapidly releasing the energy stored in forests, with the amount of water in live and dead biomass acting as a regulator on the amount and rate of energy release. Here, we used temperature and fuel moisture data to examine climate‐driven changes in fuel moisture content over the past three decades. We then calculated the changes in energy release (energy release component and fire radiant energy) for two forests that experienced drought and bark beetle mortality and were subsequently burned by wildfires. We found that mortality transitioned substantial amounts of biomass from live to dead pools. Coupled with climate‐driven decreases in fuel moisture content, this change in fuel availability increased the amount of energy that could be released as heat during wildfire in these forests. Our results demonstrate that climate‐driven tree mortality and fuel aridity may be increasing the amount of energy that is released during wildfire.

Trends in Fire Danger and Population Exposure along the Wildland–Urban Interface

The increased risk of wildfires and associated smoke exposure in the United States is a growing public health problem, particularly along the Wildland-Urban Interface (WUI). Using the measure of fire danger, the Energy Release Component, we define fire danger as the onset and duration of fire season, in the continental US, between 1979 and 2016. We then combine the measure of fire danger with census data to quantify changes in population fire exposure across the WUI. We determined that the largest increases in fire danger were observed in the Southwest, Intermountain, and Pacific Southwest regions. The increased fire danger, specifically during peak fire season, accounted for 6.1 more fires each year and 78,000 more acres burned each year, underscoring the link between fire danger and the risks of large fire occurrence and burn acreage. Finally, we observed significant population growth (121.2% between 1990 and 2010) within high-danger WUI areas, further implying significant increases in potential fire exposure.

Southern Great Plains Wildfire Outbreaks

Destructive wildfire outbreaks are a preeminent natural hazard on the grass-dominated landscape of the southern Great Plains. These southern Great Plains wildfire outbreaks (SGPWOs) are characterized by tens of wildfires that evolve on spatial and temporal scales closely tied to the passage of midlatitude cyclones when dormant herbaceous vegetation is particularly dry and abundant. Ten SGPWOs inflicted tragic losses of life and property across eastern New Mexico, west Texas, and Oklahoma between December 2005 and April 2009. This study reviews the conditions that promoted these dangerous phenomena. Texas A&amp;M; Forest Service records reveal that enhanced seasonal wildfire activity and increased potential for SGPWOs typically occurs during El Niño Southern Oscillation cold phases (La Niña), especially when preceded by positive growing-season rainfall anomalies. The antecedent state of predominately fine grassland vegetative fuels associated with SGPWOs is quantified per Energy Release Component (ERC, fuel model G). Average ERC values &gt;50 (&gt;70th percentile) supported the 2005-2009 SGPWOs on the Great Plains of Texas. Meteorological composites that quantify mean synoptic patterns during SGPWOs are generated via Rapid Update Cycle analyses, and averaged vertical temperature, moisture, and wind profiles are presented. Further analyses of subsynoptic low and midlevel tropospheric temperatures and winds illustrate a tendency for wildfires to occur near 2-m and 850-hPa thermal ridges when overspread by 500-hPa wind maxima. The juxtaposition of these atmospheric features appears to be a useful meso-α-scale predictor of heightened wildfire risks. Recognition of the presented seasonal indicators toward a fire-prone regime influenced strategic preparations for the historic 2011 Texas wildfires. Operational use of composite pattern recognition-based forecasts in tactical decision support is demonstrated for the 27 February 2011 “firestorm”, a particularly damaging SGPWO during an unprecedented fire season. Average ERC values &gt;75 (&gt;95th percentile) additionally supported prolonged burn periods with the passage of subsequent fire outbreak-bearing weather systems during the spring of 2011. Lastly, seasonal trends and the chronology of climatic and environmental signals prior to SGPWOs are highlighted, per a summary of conditions that preceded all of the 2005-2011 episodes.

Identifying Key Drivers of Wildfires in the Contiguous US Using Machine Learning and Game Theory Interpretation.

Understanding the complex interrelationships between wildfire and its environmental and anthropogenic controls is crucial for wildfire modeling and management. Although machine learning (ML) models have yielded significant improvements in wildfire predictions, their limited interpretability has been an obstacle for their use in advancing understanding of wildfires. This study builds an ML model incorporating predictors of local meteorology, land‐surface characteristics, and socioeconomic variables to predict monthly burned area at grid cells of 0.25° × 0.25° resolution over the contiguous United States. Besides these predictors, we construct and include predictors representing the large‐scale circulation patterns conducive to wildfires, which largely improves the temporal correlations in several regions by 14%–44%. The Shapley additive explanation is introduced to quantify the contributions of the predictors to burned area. Results show a key role of longitude and latitude in delineating fire regimes with different temporal patterns of burned area. The model captures the physical relationship between burned area and vapor pressure deficit, relative humidity (RH), and energy release component (ERC), in agreement with the prior findings. Aggregating the contribution of predictor variables of all the grids by region, analyses show that ERC is the major contributor accounting for 14%–27% to large burned areas in the western US. In contrast, there is no leading factor contributing to large burned areas in the eastern US, although large‐scale circulation patterns featuring less active upper‐level ridge‐trough and low RH two months earlier in winter contribute relatively more to large burned areas in spring in the southeastern US.

Contrasting the role of human- and lightning-caused wildfires on future fire regimes on a Central Oregon landscape

Climate change is expected to increase fire activity in many regions of the globe, but the relative role of human vs. lightning-caused ignitions on future fire regimes is unclear. We developed statistical models that account for the spatiotemporal ignition patterns by cause in the eastern slopes of the Cascades in Oregon, USA. Projected changes in energy release component from a suite of climate models were used with our model to quantify changes in frequency and extent of human and lightning-caused fires and record-breaking events based on sizes of individual fires between contemporary (2006 −2015) and mid-century conditions (2031–2060). No significant change was projected for the number of human-caused fire ignitions, but we projected a 14% reduction in lightning-caused ignitions under future conditions. Mean fire sizes were 31% and 22% larger under future conditions (2031–2060) for human and lightning-caused ignitions, respectively. All but one climate model projected increased frequency of record-breaking events relative to the contemporary period, with the largest future fires being about twice the size of those of the contemporary period. This work contributes to understanding the role of lightning- and human-caused fires on future fire regimes and can help inform successful adaptation strategies in this landscape.

Reaction intensity partitioning: a new perspective of the National Fire Danger Rating System Energy Release Component

The Rothermel fire spread model provides the scientific basis for the US National Fire Danger Rating System (NFDRS) and several other important fire management applications. This study proposes a new perspective of the model that partitions the reaction intensity function and Energy Release Component (ERC) equations as an alternative that simplifies calculations while providing more insight into the temporal variability of the energy release component of fire danger. We compare the theoretical maximum reaction intensities and corresponding ERCs across 1978, 1988 and 2016 NFDRS fuel models as they are currently computed and as they would be computed under the proposed scheme. The advantages and disadvantages of the new approach are discussed. More study is required to determine its operational implications.

Megafires on the Southern Great Plains

A global increase in megafires has occurred since the mid-1990s. Defined as wildfires that burn more than 405 km2 (100 000 ac), megafires are complex phenomena with wide ranging societal impacts. In the United States, scientific literature and wildland fire policy has traditionally focused upon megafires in forests of the American West. However, megafires also pose a significant threat to life and property on the southern Great Plains. The southern Great Plains is characterized by grass-dominated prairie and is climatologically prone to dry and windy weather, which facilitates extreme rates of fire spread leading to some of the largest wildfires in North America. This study documents 16 megafires on the plains of New Mexico, Texas, Oklahoma, and Kansas between 2006 and 2018. Most of these megafires occurred during southern Great Plains wildfire outbreaks, or plains firestorms, characterized by fire-effective low-level thermal ridges. Fuel and weather conditions supporting the 2006–2018 plains megafires are quantified by antecedent precipitation anomalies, fuel moisture, Energy Release Component, relative humidity, sustained wind speed, and temperature percentiles. Three modes of plains megafire evolution are identified by the analyses as short-duration, long-duration, and hybrid. Abrupt wind shifts and carryover fire in heavy dead fuels dictate megafire potential and evolutionary type. The presented analyses define favorable fuel and weather conditions that allow forecasters to discriminate megafire environments from typical plains fire episodes. Further, predictive signals for plains megafire conceptual model types can improve anticipation of southern Great Plains megafire evolution, threats, and management strategies.

A statistical model for predicting PM2.5 for the western United States

ABSTRACTA new statistical model for predicting daily ground level fine scale particulate matter (PM2.5) concentrations at monitoring sites in the western United States was developed and tested operationally during the 2016 and 2017 wildfire seasons. The model is site-specific, using a multiple linear regression schema that relies on the previous day’s PM2.5 value, along with fire and smoke related variables from satellite observations. Fire variables include fire radiative power (FRP) and the National Fire Danger Rating System Energy Release Component index. Smoke variables, in addition to ground monitored PM2.5, include aerosol optical depth (AOD) and smoke plume perimeters from the National Oceanic and Atmospheric Administration’s Hazard Mapping System. The overall statistical model was inspired by a similar system developed for British Columbia (BC) by the BC Center for Disease Control, but it has been heavily modified and adapted to work in the United States. On average, our statistical model was able to explain 78% of the variance in daily ground level PM2.5. A novel method for implementation of this model as an operational forecast system was also developed and was tested and used during the 2016 and 2017 wildfire seasons. This method focused on producing a continuously-updating prediction that incorporated the latest information available throughout the day, including both updated remote sensing data and real-time PM2.5 observations. The diurnal pattern of performance of this model shows that even a few hours of data early in the morning can substantially improve model performance.Implications: Wildfire smoke events produce significant air quality impacts across the western United States each year impacting millions. We present and evaluate a statistical model for making updating predictions of fine particulate (PM2.5) levels during smoke events. These predictions run hourly and are being used by smoke incident specialists assigned to wildfire operations, and may be of interest to public health officials, air quality regulators, and the public. Predictions based on this model will be available on the web for the 2019 western U.S. wildfire season this summer.

Mid‐21st‐century climate changes increase predicted fire occurrence and fire season length, Northern Rocky Mountains, United States

AbstractClimate changes are expected to increase fire frequency, fire season length, and cumulative area burned in the western United States. We focus on the potential impact of mid‐21st‐century climate changes on annual burn probability, fire season length, and large fire characteristics including number and size for a study area in the Northern Rocky Mountains. Although large fires are rare they account for most of the area burned in western North America, burn under extreme weather conditions, and exhibit behaviors that preclude methods of direct control. Allocation of resources, development of management plans, and assessment of fire effects on ecosystems all require an understanding of when and where fires are likely to burn, particularly under altered climate regimes that may increase large fire occurrence. We used the large fire simulation modelFSim to model ignition, growth, and containment of wildfires under two climate scenarios: contemporary (based on instrumental weather) and mid‐century (based on an ensemble average of global climate models driven by the A1BSRESemissions scenario). Modeled changes in fire patterns include increased annual burn probability, particularly in areas of the study region with relatively short contemporary fire return intervals; increased individual fire size and annual area burned; and fewer years without large fires. High fire danger days, represented by threshold values of Energy Release Component (ERC), are projected to increase in number, especially in spring and fall, lengthening the climatic fire season. For fire managers,ERCis an indicator of fire intensity potential and fire economics, with higherERCthresholds often associated with larger, more expensive fires. Longer periods of elevatedERCmay significantly increase the cost and complexity of fire management activities, requiring new strategies to maintain desired ecological conditions and limit fire risk. Increased fire activity (within the historical range of frequency and severity, and depending on the extent to which ecosystems are adapted) may maintain or restore ecosystem functionality; however, in areas that are highly departed from historical fire regimes or where there is disequilibrium between climate and vegetation, ecosystems may be rapidly and persistently altered by wildfires, especially those that burn under extreme conditions.

Near-term probabilistic forecast of significant wildfire events for the Western United States

Fire danger and potential for large fires in the United States (US) is currently indicated via several forecasted qualitative indices. However, landscape-level quantitative forecasts of the probability of a large fire are currently lacking. In this study, we present a framework for forecasting large fire occurrence – an extreme value event – and evaluating measures of uncertainties that do not rely on distributional assumptions. The statistical model presented here incorporates qualitative fire danger indices along with other location and seasonal specific explanatory variables to produce maps of forecasted probability of an ignition becoming a large fire, as well as numbers of large fires with measures of uncertainties. As an example, 6 years of fire occurrence data from the Western US were used to study the utility of two fire danger indices: the 7-Day Significant Fire Potential Outlook issued by Predictive Services in the US and the National Fire Danger Rating’s Energy Release Component. This exercise highlights the potential utility of the quantitative risk index as a real-time decision support tool that can enhance managers’ abilities to discriminate among planning areas in terms of the likelihood and range of expected significant fire events. The approach is applicable wherever there are archived historical data from both observed fires and fire danger indices.