Wildfire Burn Research Articles

The 10% wind speed rule of thumb for estimating a wildfire’s forward rate of spread in forests and shrublands

The collective analysis of a relatively large number of wildfire observations documented in conifer forests, dry eucalypt forests and temperate shrublands revealed that the forward rate of fire spread is roughly 10% of the average 10-m open wind speed, provided both are expressed in the same units (e.g. km h −1 ). Knowledge of a wildfire’s forward spread rate is a prerequisite for defining adequate fire suppression strategies and to ensure timely public warnings. We wanted to investigate the possibility that a simple relationship exists that could be used as a first approximation for quickly estimating a wildfire’s spread rate simply from the open wind speed alone. We analysed data from a number of high-intensity wildfire observations (n = 118) documented in temperate shrublands, Australian dry eucalypt forests and North American conifer forests to examine the suitability and soundness of a relationship between wind speed and rate of fire spread. We also contrasted the performance of the best function against established fire spread rate models for the three fuel types. The resulting rule of thumb is that the forward rate of spread of wildfires burning in forests and shrublands in relatively dry conditions is approximately equal to 10% of the average 10-m open wind speed, where both values are expressed in the same units. The rule of thumb gives the most accurate results for dry fuel and high wind speed conditions with reduced bias and mean relative errors lower than 50%. Under these conditions, the error statistics are comparable to those obtained by the established fire spread rate models. The rule is not applicable to grasslands.

Forest fires in Canadian permafrost region: the combined effects of fire and permafrost dynamics on soil organic matter quality

Wildfires burn approximately 1% of boreal forest yearly, being one of the most significant factors affecting soil organic matter (SOM) pools. Boreal forests are largely situated in the permafrost zone, which contains half of global soil carbon (C). Wildfires advance thawing of permafrost by burning the insulating organic layer and decreasing surface albedo, thus increasing soil temperatures. Fires also affect SOM quality through chemical and physical changes, such as the formation of resistant C compounds. The long-term post-fire effects on SOM quality, degradability and isotopic composition are not well known in permafrost forests. We studied the effect of forest fires on the proportional sizes of SOM pools with chemical fractionation (extracting with water, ethanol and acid) of soil samples (5, 30 and 50 cm depths) collected from a fire chronosequence in the upland mineral soils of the Canadian permafrost zone. We also determined the 13C and 15N isotopic composition of soil after fire. In the topsoil horizon (5 cm) recent fire areas contained a smaller fraction of labile SOM and were slightly more enriched with 15N and 13C than older fire areas. The SOM fraction ratios reverted towards pre-fire status with succession. Changes in SOM were less apparent deeper in the soil. Best predictors for the size of recalcitrant SOM fraction were active layer depth, vegetation biomass and soil C/N ratio, whereas microbial biomass was best predicted by the size of the recalcitrant SOM fraction. Results indicated that SOM in upland mineral soils at the permafrost surface could be mainly recalcitrant and its decomposition not particularly sensitive to changes resulting from fire.

Controls on Land Surface Temperature in Deserts of Southern California Derived from MODIS Satellite Time Series Analysis, 2000 to 2018

The land surface temperature (LST) in arid regions is a primary controller of many ecological processes. Consequently, we have developed a framework for detection of LST change on a regional scale using data sets covering all deserts of southern California from the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite sensor. The Breaks for Additive Season and Trend (BFAST) methodology was applied to MODIS 1-km monthly LST data from the years 2000 to 2018 to estimate significant time series shifts (breakpoints) and gradual trends. Area-wide results showed five times more positive LST breakpoints (abrupt temperature warming events) than negative (surface cooling) breakpoints. Cross-correlations with high rainfall periods around Mojave dry lake playas, and comparison with timing of wildfire burns for breakpoint patterns, showed that abrupt shifts in LST had the strongest response to these controllers. We detected negative LST (abrupt cooling) breakpoints as consistently associated with the construction of new solar energy facilities. Over the majority of the study area, BFAST results showed warming LST trends between the years 2000 and 2018. The western-most margins of the study area showed consistent widespread warming trends, whereas the eastern portions of the Mojave and Lower Colorado Deserts showed a mix of positive and neutral LST trends. Long-term cooling LST trends were detected only in some of the largest dry lake formations in the Antelope Valley, Death Valley, and Bristol, Cadiz, and Danby playas.

Development of a Multimode Field Deployable Lidar Instrument for Topographic Measurements of Unsaturated Soil Properties: Instrument Description

The hydrological and mechanical behavior of soil is determined by the moisture content, soil water (matric) potential, fines content, and plasticity. However, these parameters are often difficult or impractical to determine in the field. Remote characterization of soil parameters is a non-destructive data collection process well suited to large or otherwise inaccessible areas. A ground-based, field-deployable remote sensor, called the soil observation laser absorption spectrometer (SOLAS), was developed to collect measurements from the surface of bare soils and to assess the in-situ condition and essential parameters of the soil. The SOLAS instrument transmits coherent light at two wavelengths using two, continuous-wave, near-infrared diode lasers and the instrument receives backscattered light through a co-axial 203-mm diameter telescope aperture. The received light is split into a hyperspectral sensing channel and a laser absorption spectrometry (LAS) channel via a multi-channel optical receiver. The hyperspectral channel detects light in the visible to shortwave infrared wavelengths, while the LAS channel filters and directs near-infrared light into a pair of photodetectors. Atmospheric water vapor is inferred using the differential absorption of the on- and off-line laser wavelengths (823.20 nm and 847.00 nm, respectively). Range measurement is determined using a frequency-modulated, self-chirped, coherent, homodyne detection scheme. The development of the instrument (transmitter, receiver, data acquisition components) is described herein. The potential for rapid characterization of physical and hydro-mechanical soil properties, including volumetric water content, matric potential, fines content, and plasticity, using the SOLAS remote sensor is discussed. The envisioned applications for the instrument include assessing soils on unstable slopes, such as wildfire burn sites, or stacked mine tailings. Through the combination of spectroradiometry, differential absorption, and range altimetry methodologies, the SOLAS instrument is a novel approach to ground-based remote sensing of the natural environment.

Soil organic matter and texture estimation from visible–near infrared–shortwave infrared spectra in areas of land cover changes using correlated component regression

AbstractLand use changes due to natural and human‐related factors, which include wildfires and crop abandonment, are among the most important drivers of soil degradation and demand regular monitoring. Proximal soil sensing in visible–near infrared–shortwave infrared spectral regions could offer a solution. However, to become operational, optimal combination of data and technique has to be defined. Thus, the purpose of this study was (a) to predict the soil organic matter (SOM) content and soil texture in areas of wildfire burns and crop abandonment in Aragón Province, Northern Spain, from their laboratory reflectance spectra using novel correlated components regression with a step‐down variable selection algorithm (CCR‐SD) and (b) to compare the CCR‐SD and the partial least squares regression (PLSR) methods. The results obtained by the tested methods were similar. CCR‐SD models showed high predictive capacity with coefficients of determination (R2) in the range of 0.80–0.86 and 0.70–0.87 for calibration and validation data sets, respectively, and the highest R2 value was attained in the SOM estimation. Moreover, the CCR‐SD models stand out for the superior accuracy–parsimony relationship: the number of predictors varied from 16 (silt models) to 49 (SOM models). On average, the CCR‐SD calibrations needed less than a half of the predictors employed in PLSR models. This research confirmed that CCR‐SD can be used for monitoring SOM content and texture of soils from visible–near infrared–shortwave infrared spectra in the study area and, probably, in other areas of land use/land cover change and that CCR‐SD can create highly parsimonious models that achieve results comparable with the commonly used PLSR method.

Relationships between Wildfire Burn Severity, Cavity-Nesting Bird Assemblages, and Habitat in an Eastern Ponderosa Pine Forest

Historically, eastern ponderosa pine (Pinus ponderosa) forests were described as sparse patches of old-growth trees maintained by frequent, low-severity fires; however, in recent decades, there have been a number of large mixed-severity wildfires throughout the range of these forests. Wildlife responses to severe fire disturbance in eastern ponderosa pine forests are not well understood. Our study investigates how cavity-nesting bird species in an eastern ponderosa pine forest are impacted by burn severity. The objectives of our study were to: (1) identify the community composition of cavity-nesting birds in a 27 y old burn of mixed severity, (2) assess how habitat variables important to cavity-nesting birds differ in the mixed-severity fire, and (3) determine what habitat variables best predict bird occurrence 27 y after mixed-severity fire. We surveyed 56 sites across four burn severity classes, ranging from unburned to severely burned forest, in the Pine Ridge region of Nebraska. We measured multiple habitat characteristics (tree and snag diameter at breast height (DBH), coarse woody debris (CWD), tree and snag density, shrub height, and shrub cover) in May–August 2016 and conducted bird count surveys between 25 May and 8 June 2016. Cavity-nesting bird species' occurrence varied among the burn severity variables. Burn severity class (unburned, low severity, moderate severity, high severity) was a significant predictor of habitat characteristics for cavity-nesting birds, including tree density, snag density, mean snag DBH, variance in DBH, and CWD, which also was the best indicator of cavity-nesting bird community composition. We report evidence that mixed-severity wildfires in eastern ponderosa pine forests create variation in habitat characteristics and cavity-nesting bird occurrence.

Investigation of mercury emissions from burning of Australian eucalypt forest surface fuels using a combustion wind tunnel and field observations

Environmental cycling of the toxic metal mercury (Hg) is ubiquitous, and still not completely understood. Volatilisation and emission of mercury from vegetation, litter and soil during burning represents a significant return pathway for previously-deposited atmospheric mercury. Rates of such emission vary widely across ecosystems as they are dependent on species-specific uptake of atmospheric mercury as well as fire return frequencies. Wildfire burning in Australia is currently thought to contribute between 1 and 5% of the global total of mercury emissions, yet no modelling efforts to date have utilised local mercury emission factors (mass of emitted mercury per mass of dry fuel) or local mercury emission ratios (ratio of emitted mercury to another emitted species, typically carbon monoxide). Here we present laboratory and field investigations into mercury emission from burning of surface fuels in dry sclerophyll forests, native to the temperate south-eastern region of Australia. From laboratory data we found that fire behaviour — in particular combustion phase — has a large influence on mercury emission and hence emission ratios. Further, emission of mercury was predominantly in gaseous form with particulate-bound mercury representing <1% of total mercury emission. Importantly, emission factors and emission ratios with respect to carbon monoxide and carbon dioxide, from both laboratory and field data all show that gaseous mercury emission from biomass burning in Australian dry sclerophyll forests is currently overestimated by around 60%. Based on these results, we recommend a mercury emission factor of 28.7 ± 8.1 μg Hg kg−1 dry fuel, and emission ratio of gaseous elemental mercury relative to carbon monoxide of 0.58 ± 0.01 × 10−7, for estimation of mercury release from the combustion of Australian dry sclerophyll litter.

A disturbance weighting analysis model (DWAM) for mapping wildfire burn severity in the presence of forest disease

Forest ecosystems are subject to recurring fires as one of their most significant disturbances. Accurate mapping of burn severity is crucial for post-fire land management and vegetation regeneration monitoring. Remote-sensing-based monitoring of burn severity faces new challenges when forests experience both fire and non-fire disturbances, which may change the biophysical and biochemical properties of trees in similar ways. In this study, we develop a Disturbance Weighting Analysis Model (DWAM) for accurately mapping burn severity in a forest landscape that is jointly affected by wildfire and an emerging infectious disease – sudden oak death. Our approach treats burn severity in each basic mapping unit (e.g., 30 m grid from a post-fire Landsat image) as a linear combination of burn severity of trees affected (diseased) and not affected by the disease (healthy), weighted by their areal fractions in the unit. DWAM is calibrated using two types of inputs: i) look-up tables (LUTs) linking burn severity and post-fire spectra for diseased and healthy trees, derived from field observations, hyperspectral sensors [e.g., Airborne Visible InfraRed Imaging Spectrometer (AVIRIS)], and radiative transfer models; and ii) pre-fire fractional maps of diseased and healthy trees, derived by decomposing a pre-fire Landsat image using Multiple Endmember Spectral Mixture Analysis (MESMA). Considering the presence of tree disease in DWAM improved the overall map accuracy by 42%. The superior performance is consistent across all three stages of disease progression. Our approach demonstrates the potential for improved mapping of forest burn severity by reducing the confounding effects of other biotic disturbances.

Wildfire Burn Intensity Affects the Quantity and Speciation of Polycyclic Aromatic Hydrocarbons in Soils

Wildfire-altered soil may be an important source of polycyclic aromatic hydrocarbons (PAHs) in the environment. With projected increase of wildfire frequency and intensity due to changing global climate, understanding the quantity and speciation of PAHs, including halogenated PAHs (XPAHs), resulting from different burn intensities has important ramifications for environmental quality concerns and global soil carbon dynamics. Here, we quantified levels of 16 U.S. Environmental Protection Agency regulated PAHs, 3 chlorinated PAHs, and 6 brominated PAHs in nonburned forest soils and burned ash/soil samples covered with black ash (B-Ash; moderate burn intensity) or white ash (W-Ash; severe burn intensity) from the 2013 Rim Fire (1,041 km2) in California. The ∑16PAH concentrations follow (mean ± standard deviation; μg/kg) B-Ash (893 ± 285) > W-Ash (515 ± 333) ≈ nonburned soils (247 ± 58). Moreover, the ∑16PAH profiles were altered by both moderate and severe burn conditions with the size of aromatic structures...

Determining the use of Sentinel-2A MSI for wildfire burning & severity detection

ABSTRACTAccurate, reliable, and timely burn severity maps are necessary for planning, managing and rehabilitation after wildfires. This study aimed at assessing the ability of the Sentinel-2A satellite to detect burnt areas and separate burning severity levels. It also attempted to measure the spectral separability of the different bands and derived indices commonly used to detect burnt areas. A short investigation into the associated environmental variables present in the burnt landscape was also performed to explore the presence of any correlation. As a case study, a wildfire occurred in the Sierra de Gata region of the province of Caceres in North-Eastern Spain was used. A range of spectral indices was computed, including the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR). The potential added value of the three new Red Edge bands that come with the Sentinel-2A MSI sensor was also used. The slope, aspect, fractional vegetation cover and terrain roughness were all derived to produce environmental variables. The burning severity was tested using the Spectral Angle Mapper (SAM) classifier. European Environment Agency’s CORINE land cover map was also used to produce the land cover types found in the burned area. The Copernicus Emergency Management Service have produced a grading map for the fire using 0.5 m resolution Pleiades imagery, that was used as reference. Results showed a variable degree of correlation between the burning severity and the tested herein spectral indices. The visible part of the electromagnetic spectrum was not well suited to discern burned from unburned land cover. The NBRb12 (short-wave infrared 2 – SWIR2) produced the best results for detecting burnt areas. SAM resulted in a 73% overall accuracy in thematic mapping. None of the environmental variables appeared to have a significant impact on the burning severity. All in all, our study result showed that Sentinel-2 MSI sensor can be used to discern burnt areas and burning severity. However, further studies in different regions using the same dataset types and methods should be implemented before generalizing the results of the current study.

Environmental Conditions, Ignition Type, and Air Quality Impacts of Wildfires in the Southeastern and Western United States.

This research contrasts the environmental conditions, meteorological drivers, and air quality impacts of human‐ and lightning‐ignited wildfires in the southeastern and western United States, the two continental U.S. regions with the most wildfire burn area. We use the Fire Program Analysis Wildfire Occurrence Data (FPA FOD) to determine wildfire abundance and ignition sources between 1992 and 2015. We investigate specific ecoregions within these two U.S. regions and find that in the majority of ecoregions, annual lightning‐ and human‐ignited wildfire burn area have similar relationships with key meteorological parameters. We investigate the fuel moisture values where wildfires occur segregated by ignition type and show that within a given ecoregion, the differences in median fuel moisture between ignition types are generally smaller than the differences between ecoregions. Our results suggest that annual wildfire burn area for human‐ and lightning‐ignited wildfires within a given ecoregion are modulated by environmental conditions, and climate change may similarly impact wildfires of both ignition types. Finally, we estimate fine particulate matter emissions for Fire Program Analysis Wildfire Occurrence Data wildfires using the Fire INventory from NCAR model framework. We show that emissions of fine particulate matter from human‐ignited wildfires is significant and of a similar total magnitude between the west and southeastern United States. Additionally, the west and southeast have a similar number of wildfires associated with National Weather Service air quality smoke forecasts.

Modeling soil organic matter and texture from satellite data in areas affected by wildfires and cropland abandonment in Aragón, Northern Spain

Land use/land cover (LULC) changes create the need for regular monitoring of soil properties. Modern technology and data sources offer possibilities to perform it. In this context, the objective of the study is to explore the possibility of predicting soil organic matter (SOM) content and texture from the spectral information of the three last generation satellites [Landsat-8, Sentinel-2, and the Environmental Mapping and Analysis Program (EnMAP)]. Soil samples (113) were collected in areas affected by wildfires and cropland abandonment in Aragon, Northern Spain. Reflectance spectra of soils were obtained in controlled laboratory conditions using the analytical spectral device Fieldspec4 spectroradiometer (spectral range 350 to 2500 nm). Reflectances simulated for Landsat-8, Sentinel-2, and EnMAP bands were used as predictors in multivariate models developed using partial least squares regression (PLSR) and step-down variable selection algorithm (SD). Modeling of all soil variables was performed simultaneously. The EnMAP models employed few predictors (10 to 58 of 244) and demonstrated good fit (Rcal2>0.8 and Rval2∼0.8), especially for SOM (Rcal2 and Rval2=0.9; RMSEP ∼1.6). Landsat models showed the least reliable estimates (Rcal2 0.54 to 0.77 and Rval2 0.42 to 0.80), whereas Sentinel-2 models showed Rcal2 of 0.70 to 0.81 and Rval2 between 0.67 (clay) and 0.79 (SOM). The results confirm high potential of spectral data from multispectral and hyperspectral satellites for soil monitoring. Application of PLSR combined with SD results in sparser and better-fit models. SOM and soil texture can be estimated with an acceptable accuracy from EnMAP and Sentinel-2 data enabling soil status monitoring in areas of wildfire burns and cropland abandonment.

Estimating wildfire growth from noisy and incomplete incident data using a state space model

Wildfire behaviors are complex and are of interest to fire managers and scientists for a variety of reasons. Many of these important behaviors are directly measured from the cumulative burn area time series of individual wildfires; however, estimating cumulative burn area time series is challenging due to the magnitude of measurement errors and missing entries. To resolve this, we introduce two state space models for reconstructing wildfire burn area using repeated observations from multiple data sources that include different levels of measurement error and temporal gaps. The constant growth parameter model uses a few parameters and assumes a burn area time series that follows a logistic growth curve. The non-constant growth parameter model uses a time-varying logistic growth curve to produce detailed estimates of the burn area time series that permit sudden pauses and pulses of growth. We apply both reconstruction models to burn area data from 13 large wildfire incidents to compare the quality of the burn area time series reconstructions and computational requirements. The constant growth parameter model reconstructs burn area time series with minimal computational requirements, but inadequately fits observed data in most cases. The non-constant growth parameter model better describes burn area time series, but can also be highly computationally demanding. Sensitivity analyses suggest that in a typical application, the reconstructed cumulative burn area time series is fairly robust to minor changes in the prior distributions.

Quantifying relations between surface runoff and aridity after wildfire

AbstractPost‐wildfire runoff and erosion are major concerns in fire‐prone landscapes around the world, but these hydro‐geomorphic responses have been found to be highly variable and difficult to predict. Some variations have been observed to be associated with landscape aridity, which in turn can influence soil hydraulic properties. However, to date there has been no attempt to systematically evaluate the apparent relations between aridity and post‐wildfire runoff. In this study, five sites in a wildfire burnt area were instrumented with rainfall‐runoff plots across an aridity index (AI) gradient. Surface runoff and effective rainfall were measured over 10 months to allow investigation of short‐ (peak runoff) and longer‐term (runoff ratio) runoff characteristics over the recovery period. The results show a systematic and strong relation between aridity and post‐wildfire runoff. The average runoff ratio at the driest AI site (33.6%) was two orders of magnitude higher than at the wettest AI site (0.3%). Peak runoff also increased with AI, with up to a thousand‐fold difference observed during one event between the driest and wettest sites. The relation between AI, peak 15‐min runoff (Q15) and peak 15‐min rainfall intensity (I15) (both in mm h‐1) could be quantified by the equation: Q15 = 0.1086I15 × AI 2.691 (0.65&lt;AI&lt;1.80, 0&lt;I15&lt;45) (adjusted r2 = 0.84). The runoff ratios remained higher at drier AI sites (AI 1.24 and 1.80) throughout the monitoring period, suggesting higher AI also lengthens the window of disturbance after wildfire. The strong quantifiable link which this study has determined between AI and post‐wildfire surface runoff could greatly improve our capacity to predict the magnitude and location of hydro‐geomorphic processes such as flash floods and debris flows following wildfire, and may help explain aridity‐related patterns of soil properties in complex upland landscapes. Copyright © 2018 John Wiley &amp; Sons, Ltd.

Using Multitemporal and Multispectral Airborne Lidar to Assess Depth of Peat Loss and Correspondence With a New Active Normalized Burn Ratio for Wildfires

AbstractAccuracy of depth of burn (an indicator of consumption) in peatland soils using prefire and postfire airborne light detection and ranging (lidar) data is determined within a wetland‐upland forest environment near Fort McMurray, Alberta, Canada. The relationship between peat soil burn depth and an “active” normalized burn ratio (ANBR) is also examined beneath partially and fully burned forest and understory canopies using state‐of‐the‐art active reflectance from a multispectral lidar compared with normalized burn ratio (NBR) derived from Landsat 7 ETM+. We find significant correspondence between depth of burn, lidar‐derived ANBR, and difference NBR (dNBR) from Landsat. However, low‐resolution optical imagery excludes peatland burn losses in transition zones, which are highly sensitive to peat loss via combustion. The findings presented here illustrate the utility of this new remote sensing technology for expanding an area of research where it has previously been challenging to spatially detect and quantify such wildfire burn losses.

How wildfire risk is related to urban planning and Fire Weather Index in SE France (1990–2013)

Wildfires burn >450,000ha of forest every year in Euro-Mediterranean countries. Many fires originate in the Wildland Urban Interface (WUI) where housing density and weather conditions affect fire occurrence. Housing density is determined by long term land use policies while weather conditions evolve quickly. The first objective was to quantify the impacts of land use policy on WUI characteristics and fire risk in SE France during 1990–2012. The second objective was to quantify how Fire Weather Index (FWI) is related to fire occurrence. WUI was mapped from 1990, 1999, and 2012 building layers and crossed with a NDVI derived vegetation layer. In all, 12 WUI categories were derived: 4 building density classes and 3 vegetation layers. The I87 FWI was based on daily temperature, wind speed, relative humidity and soil water content. Despite a 30% increase in the number of new buildings, WUI area increased by only 5% as new housing filled in open space in existing WUI area. This trend can be linked to national level urban planning legislation and forest fire protection laws. Major driver variables determining housing location were aspect, slope, and distance to city centers. Fire frequency and burned area were nonlinearly related to FWI: 73% of the 99 fires occurred during weeks with FWI values ≥90 even though these accounted for only 44% of all weeks. Burned area was even more sensitive to FWI since 97% of total burned area occurred during weeks with mean FWI values ≥90. All days with burned areas >100ha had FWI values >150. The study demonstrated that WUI legislation can be an efficient tool to limit WUI fire risk. FWI results suggest the predicted increase in extreme summer heat events with global warming could increase burned area as firefighting resources are stretched beyond capacity.

Occurrence and significance of mono-, di- and anhydrosaccharide biomolecules in Mesozoic and Cenozoic lignites and fossil wood

Mono-, di- and polysaccharides are common constituents of living organisms, but their occurrence and state of preservation in geological materials have only rarely been considered. Here, we present the monosaccharide, and for the first time the di- and anhydrosaccharide, identifications and distributions in Middle Miocene lignite and Middle Jurassic fossil wood samples. Detritic lignites contain fructose and glucose as dominant monosaccharides, and sucrose and trehalose as important disaccharides. Xylites contain monosaccharides (arabinose, arabinofuranose, glucose, and minor xylose and fructose), saccharols (erythritol, arabitol and mannitol), and also some disaccharides. The Middle Jurassic fossil wood samples contain glucose, glucofuranose and levoglucosan. The high content of holocellulose (up to 55 wt%) and co-occurrence of characteristic monosaccharides as arabinose, xylose and mannose in xylites suggests that not only cellulose, but also hemicellulose was preserved in samples as old as 13 Ma. Compounds like trehalose and mannitol appear to be products of wood-degrading fungi. Surprisingly, glucose, the most stable monosaccharide, and levoglucosan can occur in much older organic matter (ca. 168 Ma) as products from cellulose degradation, and possibly a remnant from wildfire burning of wood, respectively. Our findings confirm that saccharides can be preserved under favorable conditions in sedimentary organic matter of the Mesozoic to the Cenozoic eras, and can be used as specific biomarkers of cellulose and hemicellulose degradation, fungal metabolism, and wildfire events. However, we cannot exclude the possibility that at least part of the saccharides may be preserved in sedimentary rocks as the free compounds, common in plants and microorganisms.

Rare, Intense, Big fires dominate the global tropics under drier conditions

Wildfires burn large parts of the tropics every year, shaping ecosystem structure and functioning. Yet the complex interplay between climate, vegetation and human factors that drives fire dynamics is still poorly understood. Here we show that on all continents, except Australia, tropical fire regimes change drastically as mean annual precipitation falls below 550 mm. While the frequency of fires decreases below this threshold, the size and intensity of wildfires rise sharply. This transition to a regime of Rare-Intense-Big fires (RIB-fires) corresponds to the relative disappearance of trees from the landscape. Most dry regions on the globe are projected to become substantially drier under global warming. Our findings suggest a global zone where this drying may have important implications for fire risks to society and ecosystem functioning.

Automated prediction of extreme fire weather from synoptic patterns in northern Alberta, Canada

Wildfires burn an average of 2 million hectares per year in Canada, most of which can be attributed to only a few days of severe fire weather. These “spread days” are often associated with large-scale weather systems. We used extreme threshold values of three Canadian Fire Weather Index System (CFWIS) variables — the fine fuel moisture code (FFMC), initial spread index (ISI), and fire weather index (FWI) — as a proxy for spread days. Then we used self-organizing maps (SOMs) to predict spread days, with sea-level pressure and 500 hPa geopotential height as predictors. SOMs require many input parameters, and we performed an experiment to optimize six key parameters. For each month of the fire season (May–August), we also tested whether SOMs performed better when trained with only one month or with neighbouring months as well. Good performance (AUC of 0.8) was achieved for FFMC and ISI, while nearly good performance was achieved for FWI. To our knowledge, this is the first study to develop a machine-learning model for extreme fire weather that could be deployed in real time.

Who Among the Elderly Is Most Vulnerable to Exposure to and Health Risks of Fine Particulate Matter From Wildfire Smoke?

Wildfires burn more than 7 million acres in the United States annually, according to the US Forest Service. Little is known about which subpopulations are more vulnerable to health risks from wildfire smoke, including those associated with fine particulate matter. We estimated exposure to fine particles specifically from wildfires, as well as the associations between the presence of wildfire-specific fine particles and the amount of hospital admissions for respiratory causes among subpopulations older than 65 years of age in the western United States (2004-2009). Compared with other populations, higher fractions of persons who were black, lived in urban counties, and lived in California were exposed to more than 1 smoke wave (high-pollution episodes from wildfire smoke). The risks of respiratory admissions on smoke-wave days compared with non-smoke-wave days increased 10.4% (95% confidence interval: 1.9, 19.6) for women and 21.7% (95% confidence interval: 0.4, 47.3) for blacks. Our findings suggest that increased risks of respiratory admissions from wildfire smoke was significantly higher for women than for men (10.4% vs. 3.7%), blacks than whites (21.7% vs. 6.9%), and, although associations were not statistically different, people in lower-education counties than higher-educated counties (12.7% vs. 6.1%). Our study raised important environmental justice issues that can inform public health programs and wildfire management. As climate change increases the frequency and intensity of wildfires, evidence on vulnerable subpopulations can inform disaster preparedness and the understanding of climate change consequences.