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Background Wildfires are important global disturbances influencing ecosystem structure and composition. The moisture content of living and senescent plant components are key determinants of wildfire activity, yet our understanding of how seasonal fluctuations in water availability and biomass control live foliar moisture content (LFMC) across co-occurring plant functional types is limited in diverse forested landscapes. Results We recorded root-zone volumetric water content (VWC) and sampled leaf mass area (LMA) and LFMC of three co-occurring plant functional types across six field sites. We used a linear mixed effects model to quantify the drivers of LFMC and understand whether LFMC dynamics were coupled or decoupled from site conditions. Both LMA and VWC were significant predictors of LFMC variability (p Conclusions LFMC of understorey plants responded to changes in soil water availability (VWC), while overstorey trees responded to biomass fluctuations (LMA). We present a conceptual model describing the influence of these factors on LFMC, which aligns with our findings and draws on the broader literature. This knowledge and conceptual approach can be used to improve our ability to characterize seasonal LFMC variation across different plant functional types, in turn improving our capacity to predict wildfire risk.

Land managers use models to understand potential fire behaviour, which provide insight into the social, economic and environmental implications of fire. Although combustion is a fundamental process, numerous fire behaviour models exist across the world, each with model-specific inputs and outputs. While these are useful for local-level fire management, vegetation- or country-specific fire behaviour models limits knowledge-sharing between jurisdictions – largely because model inputs (particularly fuel arguments) and outputs differ, meaning they cannot be readily compared. At the same time, advancements in remote sensing techniques have resulting in accurate methods to estimate fuel for current fire behaviour models that have not been fully integrated. This project has two key aims: a) to utilise remote sensing and field-based measurement approaches to develop a comprehensive set of model parameters that can be used to universally characterise fuel attributes fundamental to fire behaviour, and b) based on knowledge gaps identified, undertake expert elicitation to identify fuel attributes for fire behaviour not currently utilised in models, and propose methods to estimate these values using remote sensing. To achieve this, we reviewed 25 fire behaviour models to identify similarities and differences in model inputs and outputs. We then subset model inputs to the fuel parameters, and linked each to current remote sensing methods. Following the review, and in conjunction with an expert elicitation process, we developed a list of fundamental fuel attributes missing from current fire behaviour models. From this list, we developed novel fuel parameters that fully utilise information contained within remote sending datasets. The final step in this process is to validate the importance of each fuel argument using further expert elicitation and field burning experiments*. We found many common parameters, including a fuel value, short- and long-term fuel moisture, temperature and wind variables, however, the physical fuel parameters are typically where models diverge. Models across the world use fuel inputs that describe the amount, horizontal and vertical arrangement of fuel, bark hazard and the importance of crown fuels. Preliminary expert elicitation identified shared and divergent opinions related to fuel characteristics that drive fire behaviour. While common parameters related to horizontal and vertical arrangement of fuel were important, gaps exist in our capacity to describe ladder fuels. From these results, we have developed novel remote sensing metrics to describe vertical and horizontal connectivity. Throughout 2022, we aim to validate these preliminary results through further expert elicitation and field observations of fire behaviour. Through this work, we have developed a complete list of fuel parameters for fire behaviour and related these to remote sensing methods. This provides a framework for current models to shift to remotely sensed inputs, and a basis for future fire behaviour models. Overall, we argue that this work provides the foundation for a universal fuel assessment methodology to be developed, that is scalable and captures change over time, that can link physical fuel structure and remote sending techniques to fire behaviour models of the future. * Note that this work is planned for late 2022.

Wildland fires are a common global disturbance and many of these fires burn through mixtures of living and dead vegetation. Live fuels are unique because they regulate biomass and water content actively through processes such as photosynthesis or transpiration. The main goal of these processes is to maintain the growth and maintenance demands of the plants while minimising water loss. Historically, live fuel dynamics were assumed to be only driven by evaporative or drying processes and little attention was paid to the interplay between carbon and water dynamics. Here we present a mechanistic model of live fuel moisture (LFM) which is a critical component of live fuel flammability. The model decouples LFM into metrics that are easy to measure such as particle density, surface-area-to-volume ratio, leaf mass area and relative water content. Each metric serves as a proxy for important components of the seasonal water and carbon cycle or to capture inter-species variations in physical properties.We evaluate this model using field measurements of physical and chemical characteristics for a Douglas Fir (Pseudotsuga menziesii), a common intermountain US tree species that commonly burns in crown fires. This simple, mechanistic model was effective at characterising the seasonal variations in LFM across both new and old foliage as a simple function of foliar density and relative water content (r2=0.984,p < 0.05) . Finally, we show how this decoupled model can be used to more appropriately parameterize a 3-dimensional computational fluid dynamics based fire behaviour model to represent an appropriate live fuel moisture as the combined effects of biomass and moisture variations on canopy flammability.

Wildfires are a common global disturbance. Many of these fires fill important ecosystem roles but others must be suppressed to prevent loss of life or property. Decision support tools can provide critical information to support effective wildfire management but the many components of these tools lack a supporting framework to integrate risk components to produce effective and useful wildfire risk forecasts. Here we present a framework to support wildfire preparedness and response decision making that incorporates both static and dynamic components of wildfire hazard to produce risk forecasting. Static model components provide a time-invariant ignition probability for both human and natural caused ignitions and are developed using topographic and fuel quantity metrics. Dynamic model components are evaluated using a combination of fire danger rating indices from the new US National Fire Danger Rating System Version 4.0 released in 2016 and the newly derived Severe Fire Danger Index. When combined together, we demonstrate the effectiveness of this model forecast system to predict the spatial and temporal locations of new wildfire ignitions across large areas using a blend of high, moderate and low resolution spatial terrain, fuels and weather forecast data. We demonstrate components of this framework across the United States and Northern South America specifically across Colombia, Ecuador and Peru. This work will improve our ability to leverage modern fire danger rating systems with over conventional wildfire risk assessments to provide better decision support products throughout the world and these products have the potential to reduce wildfire impacts to firefighters and communities.

Background Weather plays an integral role in fire management due to the direct and indirect effects it has on fire behavior. However, fire managers may not use all information available to them during the decision-making process, instead utilizing mental shortcuts that can bias decision-making. Thus, it is important to evaluate if (and how) fire managers use information like weather forecasts when making tactical decisions. We explore USDA Forest Service fire manager confidence in relative humidity, precipitation, and wind models. We then use a choice experiment where key weather attributes were varied to explore how sensitive fire managers were to changes in specific weather variables when choosing to directly or indirectly attack a fire that is transitioning to extended attack. Results Respondents were less confident in the accuracy of wind and precipitation forecasts than relative humidity or weather forecasts more generally. The influence of weather information on the decision depended on the framing used in the choice experiment; specifically, whether respondents were told the initial strategy had been to directly or indirectly attack the fire. Across conditions, fire managers generally preferred to indirectly attack the fire. Decisions about the tactics to apply going forward were more sensitive to time in season when the fire was occurring and wind and precipitation forecasts than to other attributes. Conclusions The results have implications for the design of decision support tools developed to support fire management. Results suggest how fire managers’ use of fire weather information to evaluate forecast conditions and adjust future management decisions may vary depending on the management decision already in place. If fire weather-based decision support tools are to support the use of the best available information to make fire management decisions, careful attention may be needed to debias any effect of prior decisions. For example, decision support tools may encourage users to “consider the opposite,” i.e., consider if they would react differently if different initial decision with similar conditions were in place. The results also highlight the potential importance of either improving wind and precipitation forecast models or improving confidence in existing models.

Patterns of energy and available moisture can vary over small (

Despite the ecological and socioeconomic impacts of wildfires, little attention has been paid to the spatiotemporal patterns of nighttime fire activity across the conterminous United States (CONUS). Daytime fire radiative power (FRP) detected by the Moderate Resolution Imaging Spectroradiometer (MODIS) was nearly evenly split (54% vs. 46%) between inside and outside wildfires from 2003 to 2020. In contrast, 94% of nighttime FRP was detected within wildfires, of which 95% was detected within large wildfires (> 2023 ha). Nighttime proportions (i.e., the proportion of total summed FRP detected by MODIS at night) were lowest (3%) outside wildfires when coincident 1000-hr fuel moistures were highest and vegetation fires were smaller and less intense. As 1000-hr fuel moistures decreased, MODIS active fire pixels shifted out of agricultural and prescribed fires and into wildfires with higher nighttime per-pixel values of FRP such that nighttime proportions peaked at 29% for the largest wildfires. Increases in nighttime proportions within larger wildfires were attributed to increases in nighttime persistence whereby under the driest conditions, daytime fire activity detected by MODIS was more likely to continue burning with sufficient vigour to be detected again at night. From 2003–2020, MODIS detected significant (p < 0.01) increasing trends in nighttime wildfire fire activity, with a +54%, +42% and +21% increase in the annual nighttime sum of FRP, annual nighttime active fire pixel counts and annual mean nighttime per-pixel values of FRP, respectively, detected in the latter half of the study period. Nighttime trends were corroborated using observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) as well annual wildfire statistics reported by U.S. federal, state and local agencies. Moreover, MODIS detected a significant positive trend in the nighttime proportion of FRP emitted from wildfires, indicating that in the absence of diurnal differences in detection biases, increases in nighttime fire activity since 2003 have outpaced daytime increases. However an analysis of MODIS omission rates revealed that increasing nighttime proportions were at least partially attributed to a relatively greater improvement in nighttime detection performance compared to the daytime for larger wildfires burning during drier conditions. Nighttime fire activity already poses additional risks to firefighters and communities, and this work suggests that projected increases in the frequency of large wildfires will be accompanied by increases in the extent and intensity of nighttime fire activity.

Although most phenology models can analyze and predict future trends in response to climate change, these models often perform poorly in semi-arid regions where precipitation is limited. In this study, we modified an existing phenology model, the Growing Season Index (GSI), to better quantify relationships between weather and vegetation canopy dynamics across various semi-arid regions of Iraq. A modified GSI was created by adding a cumulative precipitation control to the existing GSI framework. Both unmodified and modified GSI values were calculated for three locations in Western Iraq: Sulaymaniyah in the north, Wasit in the centre and Basrah in the south as well as a country-wide mean and the running mean daily unmodified and modified GSI values for these study areas were calculated from 2001-2010 and compared to the Normalized Difference Vegetation Index (NDVI) from MODerate-resolution Imaging Spectroradiometer (MODIS) for the same time period. Country-wide median inter-annual correlations between GSI and NDVI more than doubled with the addition of the precipitation control and within-site correlations also show substantial improvements. The modified model has a huge potential be used to predict future phenological responses to changing climatic conditions, as well as to reconstruct historical vegetation conditions. This study is important to understanding not only the Iraqi region as it considers the results of climatic and environmental changes that have taken place in recent decades, but it should improve vegetation phenological predictions across Iraq and other semiarid regions of the world, particularly in the face of rapid climate change and environmental deterioration.

Despite major advances in numerical weather prediction, few resources exist to forecast wildland fire danger conditions to support operational fire management decisions and community early-warning systems. Here we present the development and evaluation of a spatial fire danger index that can be used to assess historical events, forecast extreme fire danger, and communicate those conditions to both firefighters and the public. It uses two United States National Fire Danger Rating System indices that are related to fire intensity and spread potential. These indices are normalized, combined, and categorized based on a 39-yr climatology (1979&ndash, 2017) to produce a single, categorical metric called the Severe Fire Danger Index (SFDI) that has five classes, Low, Moderate, High, Very High, and Severe. We evaluate the SFDI against the number of newly reported wildfires and total area burned from agency fire reports (1992&ndash, 2017) as well as daily remotely sensed numbers of active fire pixels and total daily fire radiative power for large fires (2003&ndash, 2016) from the Moderate-Resolution Imaging Spectroradiometer (MODIS) across the conterminous United States. We show that the SFDI adequately captures geographic and seasonal variations of fire activity and intensity, where 58% of the eventual area burned reported by agency fire records, 75.2% of all MODIS active large fire pixels, and 81.2% of all fire radiative power occurred when the SFDI was either Very High or Severe (above the 90th percentile). We further show that SFDI is a strong predictor of firefighter fatalities, where 97 of 129 (75.2%) burnover deaths from 1979 to 2017 occurred when SFDI was either Very High or Severe. Finally, we present an operational system that uses short-term, numerical weather predictions to produce daily SFDI forecasts and show that 76.2% of all satellite active fire detections during the first 48 h following the ignition of nine high-profile case study fires in 2017 and 2018 occurred under Very High or Severe SFDI conditions. The case studies indicate that the extreme weather events that caused tremendous damage and loss of life could be mapped ahead of time, which would allow both wildland fire managers and vulnerable communities additional time to prepare for potentially dangerous conditions. Ultimately, this simple metric can provide critical decision support information to wildland firefighters and fire-prone communities and could form the basis of an early-warning system that can improve situational awareness and potentially save lives.

Aims To (1) identify the trade-offs among flammability attributes within grass and litter fuel types; (2) determine how flammability attributes of grass and litter fuels vary across macro-ecological gradients; and (3) test our hypothesis that inherent flammability attributes of grass and litter fuels scale to satellite-derived proxies for fire frequency and intensity. Location Continent of Australia. Methods Samples of litter and grass fuels collected from 133 sites across Australia were oven dried, then burnt under controlled conditions. Measurements of ignitability, combustibility and sustainability were made. Estimates of fire frequency and fire radiative power (a proxy for intensity) were derived from satellite imagery. Multivariate analyses were used to identify inter-relationships among variables and trends across macro-ecological gradients. Results Flammability was best described by two axes: high rate of combustion versus long duration of burning, and fast rate of spread versus high maximum temperature. As expected, our study confirmed that grass and litter fuel types have inherently differently flammability attributes whereby grass samples burn more quickly, with a higher rate of spread, than litter samples. However, there were also smaller differences in flammability attributes within fuel types, which scaled to rainfall, temperature and soil phosphorus concentrations. In keeping with our hypothesis, we found correlations between inherent fuel flammability attributes and landscape fire activity across the Australian continent. Fire frequency and rate of combustion of grass fuels were both highest in the tropics, and fire intensity and maximum temperature during combustion of litter fuels were highest in temperate areas. Main conclusions At a continental scale, we found landscape fire activity was correlated with inherent flammability of grass and litter fuels. This inherent flammability contributes to observed pyrogeographical patterns that are shaped by climate through its known effects on plant productivity, the abundance of cured grass biomass and fire weather.

The ‘Spring Dip’ in conifer live foliar moisture content (LFMC) has been well documented but the actual drivers of these variations have not been fully investigated. Here we span this knowledge gap by measuring LFMC, foliar chemistry, foliar density and foliar flammability on new and old foliage for an entire year from both Pinus resinosa (red pine) and Pinus banksiana (jack pine) at a site in Central Wisconsin. We found that needle dry mass increased by up to 70% in just three weeks and these increases were manifested as strong seasonal variations in foliar moisture content and foliar density. These needle dry mass changes were driven by an accumulation of starch in old foliage, likely resulting from springtime photosynthesis onset, and also by accumulations of sugar and crude fat in new needles as they fully matured. Foliar starch, sugar and crude fat content accounted for 84% of the variation in foliar density across both species. Flammability differences were also strongly related to changes in foliar density, where density accounted for 39% and 25% of the variations in foliar time-to-ignition of jack pine and red pine respectively. Finally, we use the computational fluid dynamics-based wildland fire model FIRETEC to examine how these foliar physio-chemical changes may influence wildland fire behavior. Under the lowest canopy density and windspeed, simulated fires in dormant condition stands did not propagate as crown fires while spring dip stands successfully spread as crown fires as a result of the higher potential energy content of the canopy. Simulated wildland fire spread rates increased by as much as 63%, nominal fireline width increased by as much as 89% and active fire area more than doubled relative to dormant season fuel conditions and the most significant changes occurred in areas with low canopy cover and low within-tree bulk density. Our results challenge the assumption that live conifer foliage flammability is limited only by its water content; this study suggests a new theory and an expanded view of the factors that dominate live fuel flammability and that subsequently influence larger scale wildland fire behavior.

In this paper we investigate spatial-temporal associations of fire weather danger and fire regime features from 1979 to 2013. We analyze monthly time series of fire activity (number of fires and burned area) and fire weather danger rating indices (Fire Weather Index, Burning Index and Forest Fire Danger Index) at two spatial scales: (i) regionally, splitting the Spanish mainland into Northwest, Hinterland and Mediterranean regions; and (ii) locally, using the EMCWF grid. All analyses are based on decomposing time series to retrieve differential indicators of seasonal cycles, temporal evolution and anomalies. At regional scale we apply lagged cross-correlation analysis (4 lags or months before fire) to explore seasonal associations; and trend detection tests on the temporal evolution component. At the local scale, we calculate Pearson correlation coefficients between each individual index and the 18 possible fire-activity subsets according to fire size (all sizes, >1 ha and >100 ha) and source of ignition (natural, unintended and arson); this analysis is applied to both cycles, temporal and anomalies series. Results suggest that weather controls seasonal fire activity although it has limited influence on temporal evolution, i.e. trends. Stronger associations are detected in the number of fires in the Northwest and Hinterland regions compared to the Mediterranean, which has desynchronized from weather since 1994. Cross-correlation analysis revealed significant fire-weather associations in the Hinterland and Mediterranean, extending up to two months prior fire ignition. On the other hand, the association between temporal trends and weather is weaker, being negative along the Mediterranean and even significant in the case of burned area. The spatial disaggregation into grid cells reveals different spatial patterns across fire-activity subsets. Again, the connection at seasonal level is noticeable, especially in natural-caused fires. In turn, human-related wildfires are occasionally found independent from weather in some areas along the northern coast or the Ebro basin. In any case, this effect diminishes as the size of the fire increases. Our work suggests that for some regions of mainland Spain, these fire danger indices could provide useful information about upcoming fire activity up to two months ahead of time and this information could be used to better inform wildland fire prevention and suppression activities.

International audience; Correction to: Annals of Forest Science https://doi.org/10.1007/s13595-017-0686-2 In Table 2 in the original article, torching index (TI) and crowning index (CI) values were incorrectly expressed in km/s. The correct units are km/h. The original article has been corrected. The online version of the original article can be found at https://doi.org/10.1007/s13595-017-0686-2

Key message We describe a modeling system that enables detailed, 3D fire simulations in forest fuels. Using data from three sites, we analyze thinning fuel treatments on fire behavior and fire effects and compare outputs with a more commonly used model. Context Thinning is considered useful in altering fire behavior, reducing fire severity, and restoring resilient ecosystems. Yet, few tools currently exist that enable detailed analysis of such efforts. Aims The study aims to describe and demonstrate a new modeling system. A second goal is to put its capabilities in context of previous work through comparisons with established models. Methods The modeling system, built in Python and Java, uses data from a widely used forest model to develop spatially explicit fuel inputs to two 3D physics-based fire models. Using forest data from three sites in Montana, USA, we explore effects of thinning on fire behavior and fire effects and compare model outputs. Results The study demonstrates new capabilities in assessing fire behavior and fire effects changes from thinning. While both models showed some increases in fire behavior relating to higher winds within the stand following thinning, results were quite different in terms of tree mortality. These different outcomes illustrate the need for continuing refinement of decision support tools for forest management. Conclusion This system enables researchers and managers to use measured forest fuel data in dynamic, 3D fire simulations, improving capabilities for quantitative assessment of fuel treatments, and facilitating further refinement in physics-based fire modeling.

Live fuel moisture content (LFMC), the ratio of water mass to dry mass contained in live plant material, is an important fuel property for determining fire danger and for modeling fire behavior. Remote sensing estimation of LFMC often relies on an assumption of changing water and stable dry mass over time. Fundamental understanding of seasonal variation in plant water and dry mass is needed to explain the spectral expression of LFMC changes over time. We conducted a five-month experiment to continuously measure field LFMC samples, biochemical components of dry matter, and leaf spectroscopic data for two species common in the western U.S., lodgepole pine ( Pinus contorta Douglas ex Loudon) and big sagebrush ( Artemisia tridentata Nutt). Our results showed that new lodgepole pine needles initially had higher LFMC and a smaller proportion of dry mass, but differences between new and old needles converged as the new needles matured. New needle dry mass had strong temporal trends, and dry mass explained more variation in LFMC than water in both new and old needles. Sagebrush leaves exhibited decreasing trends in LFMC, but water and dry mass comparably contributed to LFMC seasonal variation. Spectroscopic analysis using partial least squares regression (PLSR) showed good modeling accuracy for LFMC temporal variation in new needles (R 2 = 0.94, RMSE = 5.84%), old needles (R 2 = 0.72, RMSE = 3.51%), and sagebrush (R 2 = 0.91, RMSE = 21.03%). Spectral variation in response to changing LFMC and dry mass was difficult to isolate from broader spectral trends due to chlorophyll absorption, leaf structure, water absorption, and co-varied biochemical components. Our results stress cautious spectral interpretation and wavelength selection for LFMC estimation in some species (e.g. lodgepole pine), since temporal changes in spectra may dominantly reflect temporal variation in dry mass, pigments, and/or structure rather than water content. Since new needles should have stronger spectral expression at the canopy scale, differing temporal trends in new and old lodgepole pine needles provides an additional complicating factor for remote monitoring of LFMC.

A relationship between the likelihood of wildfires and various drought metrics (soil moisture-based fire potential indices) were examined over the southern part of Mississippi. The following three indices were tested and used to simulate spatial and temporal wildfire probability changes: (1) the accumulated difference between daily precipitation and potential evapotranspiration (P - E); (2) simulated moisture content of the top 10 cm of soil; and (3) the Keetch-Byram Drought Index (KBDI). These indices were estimated from gridded meterological data and Mosaic-simulated soil moisture data available from the North American Land Data Assimilation System (NLDAS-2). The relationships between normalized fire potential index deviations and the probability of at least one fire occurring during the following five consecutive days were evaluated using a 23-year (1986-2008) forest fire record for an evenly spaced grid (0.25° x 0.25°) across the state of Mississippi's coastal plain. Two periods were selected and exam...

Fire danger rating systems commonly ignore fine scale, topographically-induced weather variations. These variations will likely create heterogeneous, landscape-scale fire danger conditions that have never been examined in detail. We modeled the evolution of fuel moistures and the Energy Release Component (ERC) from the US National Fire Danger Rating System across the 2009 fire season using very high resolution (30 m) surface air temperature, humidity and snow ablation date models developed from a network of inexpensive weather sensors. Snow ablation date occurred as much as 28 days later on North-facing slopes than on South-facing slopes at upper elevations. South-facing slopes were hotter and drier than North-facing slopes but slope position, in addition to aspect, was also important because nocturnal air temperatures were coolest and humidity was highest in valley bottoms. These factors created heterogeneous fuel moistures and fire danger across the study area. In the late season (August and September), nocturnal cold air drainage and high relative humidity fostered fuel moisture recovery in valley bottoms, where fuel moistures and ERC values were 30% and 45% higher and lower, respectively at peak fire danger (September 29th). Dry fuel moistures and relatively high ERC values persisted on low elevation, South-facing slopes. The driest conditions were observed 100–200 m above the valley floor where mid-slope thermal belts frequently developed above areas of cold air pooling. We suggest that a complete understanding of these variations may help improve fire management decision making.

Wildland firefighters in the United States are exposed to a variety of hazards while performing their jobs. Although vehicle accidents and aircraft mishaps claim the most lives, situations where firefighters are caught in a life-threatening, fire behaviour-related event (i.e. an entrapment) constitute a considerable danger because each instance can affect many individuals. In an attempt to advance our understanding of the causes of firefighter entrapments, a review of the pertinent literature and a synthesis of existing data were undertaken. Examination of the historical literature indicated that entrapment potential peaks when fire behaviour rapidly deviates from an assumed trajectory, becomes extreme and compromises the use of escape routes, safety zones or both. Additionally, despite the numerous safety guidelines that have been developed as a result of analysing past entrapments, we found issues with the way factual information from these incidents is reported, recorded and stored that make quantitative investigations difficult. To address this, a fire entrapment database was assembled that revealed when details about the location and time of entrapments are included in analyses, it becomes possible to ascertain trends in space and time and assess the relative influence of various environmental variables on the likelihood of an entrapment. Several research needs were also identified, which highlight the necessity for improvements in both fundamental knowledge and the tools used to disseminate that knowledge.

Sustainable fire management has eluded all industrial societies. Given the growing number and magnitude of wildfire events, prescribed fire is being increasingly promoted as the key to reducing wildfire risk. However, smoke from prescribed fires can adversely affect public health. We propose that the application of air quality standards can lead to the development and adoption of sustainable fire management approaches that lower the risk of economically and ecologically damaging wildfires while improving air quality and reducing climate-forcing emissions. For example, green fire breaks at the wildland–urban interface (WUI) can resist the spread of wildfires into urban areas. These could be created through mechanical thinning of trees, and then maintained by targeted prescribed fire to create biodiverse and aesthetically pleasing landscapes. The harvested woody debris could be used for pellets and other forms of bioenergy in residential space heating and electricity generation. Collectively, such an approach would reduce the negative health impacts of smoke pollution from wildfires, prescribed fires, and combustion of wood for domestic heating. We illustrate such possibilities by comparing current and potential fire management approaches in the temperate and environmentally similar landscapes of Vancouver Island in British Columbia, Canada and the island state of Tasmania in Australia.

Wildland fire is a critical process in forests of the western United States (US). Variation in fire behavior, which is heavily influenced by fuel loading, terrain, weather, and vegetation type, leads to heterogeneity in fire severity across landscapes. The relative influence of these factors in driving fire severity, however, is poorly understood. Here, we explore the drivers of high-severity fire for forested ecoregions in the western US over the period 2002–2015. Fire severity was quantified using a satellite-inferred index of severity, the relativized burn ratio. For each ecoregion, we used boosted regression trees to model high-severity fire as a function of live fuel, topography, climate, and fire weather. We found that live fuel, on average, was the most important factor driving high-severity fire among ecoregions (average relative influence = 53.1%) and was the most important factor in 14 of 19 ecoregions. Fire weather was the second most important factor among ecoregions (average relative influence = 22.9%) and was the most important factor in five ecoregions. Climate (13.7%) and topography (10.3%) were less influential. We also predicted the probability of high-severity fire, were a fire to occur, using recent (2016) satellite imagery to characterize live fuel for a subset of ecoregions in which the model skill was deemed acceptable (n = 13). These 'wall-to-wall' gridded ecoregional maps provide relevant and up-to-date information for scientists and managers who are tasked with managing fuel and wildland fire. Lastly, we provide an example of the predicted likelihood of high-severity fire under moderate and extreme fire weather before and after fuel reduction treatments, thereby demonstrating how our framework and model predictions can potentially serve as a performance metric for land management agencies tasked with reducing hazardous fuel across large landscapes.

The most destructive wildland fires occur in mixtures of living and dead vegetation, yet very little attention has been given to the fundamental differences between factors that control their flammability. Historically, moisture content has been used to evaluate the relative flammability of live and dead fuels without considering major, unreported differences in the factors that control their variations across seasons and years. Physiological changes at both the leaf and whole plant level have the potential to explain ignition and fire behavior phenomena in live fuels that have been poorly explained for decades. Here, we explore how these physiological changes violate long-held assumptions about live fuel dynamics and we present a conceptual model that describes how plant carbon and water cycles independently and interactively influence plant flammability characteristics at both the leaf and whole plant scale. This new ecophysiology-based approach can help us expand our understanding of potential plant responses to environmental change and how those physiological changes may impact plant flammability. Furthermore, it may ultimately help us better manage wildland fires in an uncertain future.

The authors examine the effects of latitude and life history on the timing of relationships between satellite measures of normalized difference vegetation index (NDVI) and ground-based measures of tree-ring width in forests at mid- and high latitudes in the Northern Hemisphere. Results indicate a correlation between NDVI and tree rings over the entire growing season for all areas analyzed. For sites south of 40°N, a correlation appears in early spring and late fall while a correlation appears during summer months north of 40°N. For conifers, the correlation appears in summer while deciduous trees show the relationship during early spring and late fall. Of these two correlations, the effect of life history seems to dominate the effect of latitude. The timing of these correlations may help clarify the relationship between climate and tree rings and the effect of canopy duration on carbon uptake by trees.

The National Wildfire Coordinating Group’s definition of extreme fire behavior indicates a level of fire behavior characteristics that ordinarily precludes methods of direct control action. One or more of the following is usually involved: high rate of spread, prolific crowning/ spotting, presence of fire whirls, and strong convection column. Predictability is difficult as such fires often influence their environment to some degree and behave erratically, sometimes dangerously. Alternate terms include “blow up” and “fire storm.” Fire managers examining fires over the last 100 years have come to understand many of the factors necessary for extreme fire behavior development. This effort produced guidelines included in current firefighter training, which presents the current methods of predicting extreme fire behavior by using the crown fire model, which is based on the environmental influences of weather, fuels, and topography. Current training does not include the full extent of scientific understanding nor does it include the most recent scientific knowledge. National Fire Plan funds and the Joint Fire Science Program have sponsored newer research related to wind profiles’ influence on fire behavior, plume growth, crown fires, fire dynamics in live fuels, and conditions associated with vortex development. Of significant concern is that characteristic features of extreme fire behavior depend on conditions undetectable on the ground, namely invisible properties such as wind shear or atmospheric stability. No one completely understands all the factors contributing to extreme fire behavior because of gaps in our knowledge. These gaps, as well as the limitations as to when various models or indices apply should be noted to avoid application where they are not appropriate or warranted. This synthesis summarizes existing extreme fire behavior knowledge. It consists of two volumes. Volume 1 is for fire managers, firefighters, and others in the fire community who are not experts or specialists in fire behavior but need to understand the basics of extreme fire behavior. Volume 2 is more technical and is intended for fire behaviorists and fire researchers. The objective of this project is to synthesize existing extreme fire behavior knowledge in a way that connects the weather, fuel, and topographic factors that contribute to development of extreme fire behavior. This synthesis focuses on the state of the science but also considers how that science is currently presented to the fire management community, including incident commanders, fire behavior analysts, incident meteorologists, National Weather Service office forecasters, and firefighters. The synthesis seeks to delineate the known, the unknown, and areas of research with the greatest potential impact on firefighter protection.

Climate strongly influences global wildfire activity, and recent wildfire surges may signal fire weather-induced pyrogeographic shifts. Here we use three daily global climate data sets and three fire danger indices to develop a simple annual metric of fire weather season length, and map spatio-temporal trends from 1979 to 2013. We show that fire weather seasons have lengthened across 29.6 million km2 (25.3%) of the Earth's vegetated surface, resulting in an 18.7% increase in global mean fire weather season length. We also show a doubling (108.1% increase) of global burnable area affected by long fire weather seasons (>1.0 σ above the historical mean) and an increased global frequency of long fire weather seasons across 62.4 million km2 (53.4%) during the second half of the study period. If these fire weather changes are coupled with ignition sources and available fuel, they could markedly impact global ecosystems, societies, economies and climate., Global wildfires can have severe societal implications and economic cost and have been strongly linked to climate. Here, the authors analyse daily global wildfire trends and show that, during the past 35 years, wildfire season length has increased by 18.7% over more than a quarter of the Earth's surface.

Live foliar moisture content (LFMC) significantly influences wildland fire behaviour. However, characterising variations in LFMC is difficult because both foliar mass and dry mass can change throughout the season. Here we quantify the seasonal changes in both plant water status and dry matter partitioning. We collected new and old foliar samples from Pinus contorta for two growing seasons and quantified their LFMC, relative water content (RWC) and dry matter chemistry. LFMC quantifies the amount of water per unit fuel dry weight whereas RWC quantifies the amount of water in the fuel relative to how much water the fuel can hold at saturation. RWC is generally a better indicator of water stress than is LFMC. We separated water mass from dry mass for each sample and we attempted to best explain the seasonal variations in each using our measured physiochemical variables. We found that RWC explained 59% of variation in foliar water mass. Additionally, foliar starch, sugar and crude fat content explained 87% of the variation in seasonal dry mass changes. These two models combined explained 85% of the seasonal variations in LFMC. These results demonstrate that changes to dry matter exert a stronger control on seasonal LFMC dynamics than actual changes in water content, and they challenge the assumption that LFMC variations are strongly related to water stress. This methodology could be applied across a range of plant functional types to better understand the factors that drive seasonal changes in LFMC and live fuel flammability.

Wildland firefighters must assess potential fire behaviour in order to develop appropriate strategies and tactics that will safely meet objectives. Fire danger indices integrate surface weather conditions to quantify potential variations in fire spread rates and intensities and therefore should closely relate to observed fire behaviour. These indices could better inform fire management decisions if they were linked directly to observed fire behaviour. Here, we present a simple framework for relating fire danger indices to observed categorical wildland fire behaviour. Ordinal logistic regressions are used to model the probabilities of five distinct fire behaviour categories that are then combined with a safety-based weight function to calculate a Fire Behaviour Risk rating that can plotted over time and spatially mapped. We demonstrate its development and use across three adjacent US National Forests. Finally, we compare predicted fire behaviour risk ratings with observed variations in satellite-measured fire radiative power and we link these models with spatial fire danger maps to demonstrate the utility of this approach for landscape-scale fire behaviour risk assessment. This approach transforms fire weather conditions into simple and actionable fire behaviour risk metrics that wildland firefighters can use to support decisions that meet required objectives and keep people safe.

The US national fire danger rating system (NFDRS) generates daily estimates of fire potential throughout the United States. A key component of this system is the condition of live vegetation. Currently, there are no objective methods for determining vegetation condition. Inter-annual climatic variability causes the onset of spring green-up and fall leaf senescence to vary substantially from year-to-year. Therefore, methods used to assess live vegetation condition must be robust to these climatic changes. We present a system designed to integrate both remote sensing and surface weather-derived metrics of foliar greenness. This system provides two independent metrics that are meaningful representations of landscape level greenness responses and are suitable for use in verifying NFDRS greenup dates and greenness factors.

Wildland fires propagate by liberating energy contained within living and senescent plant biomass. The maximum amount of energy that can be generated by burning a given plant part can be quantified and is generally referred to as its heat content (HC). Many studies have examined heat content of wildland fuels but studies examining the seasonal variation in foliar HC among vegetation types are severely lacking. We collected foliage samples bi-weekly for five months from two common species in the western USA: lodgepole pine (Pinus contorta Douglas ex Loudon) and big sagebrush (Artemisia tridentata Nutt). We measured HC, live fuel moisture content (LFMC) and biochemical components in the leaf dry mass. Our results showed that HC increased for both species, coinciding with LFMC decrease during the growing season. Measured HC values were higher than the constant value in standard fuel models. Lasso regression models identified biochemical components for explaining temporal HC and LFMC variation in lodgepole pine (HC: R2adj = 0.55, root mean square error (RMSE) = 0.35; LFMC: R2adj = 0.84, RMSE = 10.79), sagebrush (HC: R2adj = 0.90, RMSE = 0.13; LFMC: R2adj = 0.96, RMSE = 7.66) and combined data from both species (HC: R2adj = 0.77, RMSE = 0.33; LFMC: R2adj = 0.61, RMSE = 19.75). These results demonstrated the seasonal change in HC and LFMC resulted from temporal biochemical composition variation in dry mass. This new knowledge about HC seasonal change will ultimately lead to improved predictions of wildland fire spread and intensity.

Daily National Fire Danger Rating System (NFDRS) indices are typically associated with the number and final size of newly discovered fires, or averaged over time and associated with the likelihood and total burned area of large fires. Herein we used a decade (2003–12) of NFDRS indices and US Forest Service (USFS) fire reports to examine daily relationships between fire danger and the number and growth rate of wildfires burning within a single predictive service area (PSA) in the Northern Rockies, USA. Results demonstrate that daily associations can be used to: (1) extend the utility of the NFDRS beyond the discovery date of new fires; (2) examine and justify the temporal window within which daily fire danger indices are averaged and related to total burned area; (3) quantify the probability of managing an active incident as a function of fire danger; and (4) quantify the magnitude and variability of daily fire growth as a function of fire danger. The methods herein can be extended to other areas with a daily history of weather and fire records, and can be used to better inform fire management decisions or to compare regional responses of daily fire activity to changes in fire danger.

We explore the basis of understanding wildland fire behaviour with the intention of stimulating curiosity and promoting fundamental investigations of fire spread problems that persist even in the presence of tremendous modelling advances. Internationally, many fire models have been developed based on a variety of assumptions and expressions for the fundamental heat transfer and combustion processes. The diversity of these assumptions raises the question as to whether the absence of a sound and coherent fire spread theory is partly responsible. We explore the thesis that, without a common understanding of what processes occur and how they occur, model reliability cannot be confirmed. A theory is defined as a collection of logically connected hypotheses that provide a coherent explanation of some aspect of reality. Models implement theory for a particular purpose, including hypotheses of phenomena and practical uses, such as prediction. We emphasise the need for theory and demonstrate the difference between theory and modelling. Increasingly sophisticated fire management requires modelling capabilities well beyond the fundamental basis of current models. These capabilities can only be met with fundamental fire behaviour research. Furthermore, possibilities as well as limitations for modelling may not be known or knowable without first having the theory.

Foliar moisture content is an important factor regulating how wildland fires ignite in and spread through live fuels but moisture content determination methods are rarely standardised between studies. One such difference lies between the uses of rapid moisture analysers or drying ovens. Both of these methods are commonly used in live fuel research but they have never been systematically compared to ensure that they yield similar results. Here we compare the foliar moisture content of Pinus contorta (lodgepole pine) at multiple sites for an entire growing season determined using both oven-drying and rapid moisture analyser methods. We found that moisture contents derived from the rapid moisture analysers were nearly identical to oven-dried moisture contents (R2 = 0.99, n = 68) even though the rapid moisture analysers dried samples at 145°C v. oven-drying at 95°C. Mean absolute error between oven-drying and the rapid moisture analysers was low at 2.6% and bias was 0.62%. Mean absolute error was less than the within-sample variation of an individual moisture determination method and error was consistent across the range of moisture contents measured. These results suggest that live fuel moisture values derived from either of these two methods are interchangeable and it also suggests that drying temperatures used in live fuel moisture content determination may be less important than reported by other studies.

Fire behaviour models are used to assess the potential characteristics of wildland fires such as rates of spread, fireline intensity and flame length. These calculations help support fire management strategies while keeping fireline personnel safe. Live fuel moisture is an important component of fire behaviour models but the sensitivity of existing models to live fuel moisture has not been thoroughly evaluated. The Rothermel surface fire spread model was used to estimate key surface fire behaviour values over a range of live fuel moistures for all 53 standard fuel models. Fire behaviour characteristics are shown to be highly sensitive to live fuel moisture but the response is fuel model dependent. In many cases, small changes in live fuel moisture elicit drastic changes in predicted fire behaviour. These large changes are a result of a combination of the model-calculated live fuel moisture of extinction, the effective wind speed limit and the dynamic load transfer function of some of the fuel models tested. Surface fire spread model sensitivity to live fuel moisture changes is discussed in the context of predicted fire fighter safety zone area because the area of a predicted safety zone may increase by an order of magnitude for a 10% decrease in live fuel moisture depending on the fuel model chosen.