Fire Perimeter Research Articles

Techniques for evaluating wildfire simulators via the simulation of historical fires using the AUSTRALIS simulator

A methodology for validating fire spread simulation systems using historical fire data is presented. The key features of this methodology are (a) quantitative comparison between simulator-generated fire perimeters and fire perimeters from an independently produced fire reconstruction at multiple time points during the fire, and (b) extensive sensitivity analyses on simulation variables including simulation spatial resolution, weather, vegetation coverage and fire behaviour model selection to determine the effect of each simulation input on the simulation output. The methodology is demonstrated in a case study in which the ability of the Australis high-performance wildfire simulator to replicate a large wildfire in Western Australia was examined. Simulation accuracy was found to be lower in extreme fire danger conditions and exhibited under-prediction of the head fire rate of spread. This was caused by inaccuracies in at least one of wind speed data, vegetation data or the fire behaviour model applied; however, the source of the inaccuracy could not be further diagnosed with the available data. The gathering of accurate data during and after active wildfires would facilitate more rigorous simulator and fire behaviour model validation studies as well as more accurate prediction of ‘live’ wildfires.

Rebuilding and new housing development after wildfire

The number of wildland–urban interface communities affected by wildfire is increasing, and both wildfire suppression and losses are costly. However, little is known about post-wildfire response by homeowners and communities after buildings are lost. Our goal was to characterise rebuilding and new development after wildfires across the conterminous United States. We analysed all wildfires in the conterminous USA from 2000 to 2005. We mapped 42 724 buildings, of which 34 836 were present before the fire and survived, 3604 were burned, 2403 were post-fire new development, and 1881 were burned and rebuilt. Before the fires, 38 440 buildings were present within fire perimeters (surviving plus burned). Within 5 years post-fire, there were 39 120 buildings (surviving, rebuilt and new development). Nationally, only 25% of burned homes were rebuilt within 5 years, though rates were higher in the west, the south and Kansas. New development rates inside versus outside fire perimeters were similar. That the number of buildings inside fire perimeters within 5 years post-fire was greater than pre-fire indicated that homeowners are either willing to face wildfire risks or are unaware of them; or that economic incentives to rebuild in the same place outweigh perceived risks.

An accuracy assessment of the MTBS burned area product for shrub–steppe fires in the northern Great Basin, United States

Although fire is a common disturbance in shrub–steppe, few studies have specifically tested burned area mapping accuracy in these semiarid to arid environments. We conducted a preliminary assessment of the accuracy of the Monitoring Trends in Burn Severity (MTBS) burned area product on four shrub–steppe fires that exhibited varying degrees of within-fire patch heterogeneity. Independent burned area perimeters were derived through visual interpretation and were used to cross-compare the MTBS burned area perimeters with classifications produced using set thresholds on the Relativised differenced Normalised Burn Index (RdNBR), Mid-infrared Burn Index (MIRBI) and Char Soil Index (CSI). Overall, CSI provided the most consistent accuracies (96.3–98.6%), with only small commission errors (1.5–4.4%). MIRBI also had relatively high accuracies (92.2–97.9%) and small commission errors (2.1–10.8%). The MTBS burned area product had higher commission errors (4.3–15.5%), primarily due to inclusion of unburned islands and fingers within the fire perimeter. The RdNBR burned area maps exhibited lower accuracies (92.9–96.0%). However, the different indices when constrained by the MTBS perimeter provided variable results, with CSI providing the highest and least variable accuracies (97.4–99.1%). Studies seeking to use MTBS perimeters to analyse trends in burned area should apply spectral indices to constrain the final burned area maps. The present paper replaces a former paper of the same title (http://dx.doi.org/10.1071/WF13206), which was withdrawn owing to errors discovered in data analysis after the paper was accepted for publication.

Recent advances and applications of WRF–SFIRE

Abstract. Coupled atmosphere–fire models can now generate forecasts in real time, owing to recent advances in computational capabilities. WRF–SFIRE consists of the Weather Research and Forecasting (WRF) model coupled with the fire-spread model SFIRE. This paper presents new developments, which were introduced as a response to the needs of the community interested in operational testing of WRF–SFIRE. These developments include a fuel-moisture model and a fuel-moisture-data-assimilation system based on the Remote Automated Weather Stations (RAWS) observations, allowing for fire simulations across landscapes and time scales of varying fuel-moisture conditions. The paper also describes the implementation of a coupling with the atmospheric chemistry and aerosol schemes in WRF–Chem, which allows for a simulation of smoke dispersion and effects of fires on air quality. There is also a data-assimilation method, which provides the capability of starting the fire simulations from an observed fire perimeter, instead of an ignition point. Finally, an example of operational deployment in Israel, utilizing some of the new visualization and data-management tools, is presented.

Analyzing the transmission of wildfire exposure on a fire-prone landscape in Oregon, USA

We develop the idea of risk transmission from large wildfires and apply network analyses to understand its importance on a 0.75 million ha US national forest. Wildfires in the western US frequently burn over long distances (e.g., 20–50km) through highly fragmented landscapes with respect to ownership, fuels, management intensity, population density, and ecological conditions. The collective arrangement of fuel loadings in concert with weather and suppression efforts ultimately determines containment and the resulting fire perimeter. While spatial interactions among land parcels in terms of fire spread and intensity have been frequently noted by fire managers, quantifying risk and exposure transmission has not been attempted. In this paper we used simulation modeling to quantify wildfire transmission and built a transmission network consisting of land designations defined by national forest management designations and ownership. We then examined how a forest-wide fuel management program might change the transmission network and associated metrics. The results indicated that the size, shape, and fuel loading of management designations affected their exposure to wildfire from other designations and ownerships. Manipulating the fuel loadings via simulated forest fuel treatments reduced the wildfire transmitted among the land designations, and changed the network density as well. We discuss how wildfire transmission has implications for creating fire adapted communities, conserving biodiversity, and resolving competing demands for fire-prone ecosystem services.

Historical, observed, and modeled wildfire severity in montane forests of the Colorado Front Range.

Large recent fires in the western U.S. have contributed to a perception that fire exclusion has caused an unprecedented occurrence of uncharacteristically severe fires, particularly in lower elevation dry pine forests. In the absence of long-term fire severity records, it is unknown how short-term trends compare to fire severity prior to 20th century fire exclusion. This study compares historical (i.e. pre-1920) fire severity with observed modern fire severity and modeled potential fire behavior across 564,413 ha of montane forests of the Colorado Front Range. We used forest structure and tree-ring fire history to characterize fire severity at 232 sites and then modeled historical fire-severity across the entire study area using biophysical variables. Eighteen (7.8%) sites were characterized by low-severity fires and 214 (92.2%) by mixed-severity fires (i.e. including moderate- or high-severity fires). Difference in area of historical versus observed low-severity fire within nine recent (post-1999) large fire perimeters was greatest in lower montane forests. Only 16% of the study area recorded a shift from historical low severity to a higher potential for crown fire today. An historical fire regime of more frequent and low-severity fires at low elevations (<2260 m) supports a convergence of management goals of ecological restoration and fire hazard mitigation in those habitats. In contrast, at higher elevations mixed-severity fires were predominant historically and continue to be so today. Thinning treatments at higher elevations of the montane zone will not return the fire regime to an historic low-severity regime, and are of questionable effectiveness in preventing severe wildfires. Based on present-day fuels, predicted fire behavior under extreme fire weather continues to indicate a mixed-severity fire regime throughout most of the montane forest zone. Recent large wildfires in the Front Range are not fundamentally different from similar events that occurred historically under extreme weather conditions.

Quantifying fire‐wide carbon emissions in interior Alaska using field measurements and Landsat imagery

Carbon emissions from boreal forest fires are projected to increase with continued warming and constitute a potentially significant positive feedback to climate change. The highest consistent combustion levels are reported in interior Alaska and can be highly variable depending on the consumption of soil organic matter. Here we present an approach for quantifying emissions within a fire perimeter using remote sensing of fire severity. Combustion from belowground and aboveground pools was quantified at 22 sites (17 black spruce and five white spruce-aspen) within the 2010 Gilles Creek burn in interior Alaska, constrained by data from eight unburned sites. We applied allometric equations and estimates of consumption to calculate carbon losses from aboveground vegetation. The position of adventitious spruce roots within the soil column, together with estimated prefire bulk density and carbon concentrations, was used to quantify belowground combustion. The differenced Normalized Burn Ratio (dNBR) exhibited a clear but nonlinear relationship with combustion that differed by forest type. We used a multiple regression model based on transformed dNBR and deciduous fraction to scale carbon emissions to the fire perimeter, and a Monte Carlo framework to assess uncertainty. Because of low-severity and unburned patches, mean combustion across the fire perimeter (1.98 ± 0.34 kg C m−2) was considerably less than within a defined core burn area (2.67 ± 0.40 kg C m−2) and the mean at field sites (2.88 ± 0.23 kg C m−2). These areas constitute a significant fraction of burn perimeters in Alaska but are generally not accounted for in regional-scale estimates. Although total combustion in black spruce was slightly lower than in white spruce-aspen forests, black spruce covered most of the fire perimeter (62%) and contributed the majority (67 ± 16%) of total emissions. Increases in spring albedo were found to be a viable alternative to dNBR for modeling emissions.

Multi-scale influence of vapor pressure deficit on fire ignition and spread in boreal forest ecosystems

Abstract. Climate-driven changes in the fire regime within boreal forest ecosystems are likely to have important effects on carbon cycling and species composition. In the context of improving fire management options and developing more realistic scenarios of future change, it is important to understand how meteorology regulates different aspects of fire dynamics, including ignition, daily fire spread, and cumulative annual burned area. Here we combined Moderate-Resolution Imaging Spectroradiometer (MODIS) active fires (MCD14ML), MODIS imagery (MOD13A1) and ancillary historic fire perimeter information to produce a data set of daily fire spread maps for Alaska during 2002–2011. This approach provided a spatial and temporally continuous representation of fire progression and a precise identification of ignition and extinction locations and dates for each wildfire. The fire-spread maps were analyzed with daily vapor pressure deficit (VPD) observations from the North American Regional Reanalysis (NARR) and lightning strikes from the Alaska Lightning Detection Network (ALDN). We found a significant relationship between daily VPD and likelihood that a lightning strike would develop into a fire ignition. In the first week after ignition, above average VPD increased the probability that fires would grow to large or very large sizes. Strong relationships also were identified between VPD and burned area at several levels of temporal and spatial aggregation. As a consequence of regional coherence in meteorology, ignition, daily fire spread, and fire extinction events were often synchronized across different fires in interior Alaska. At a regional scale, the sum of positive VPD anomalies during the fire season was positively correlated with annual burned area during the NARR era (1979–2011; R2 = 0.45). Some of the largest fires we mapped had slow initial growth, indicating opportunities may exist for suppression efforts to adaptively manage these forests for climate change. The results of our spatiotemporal analysis provide new information about temporal and spatial dynamics of wildfires and have implications for modeling the terrestrial carbon cycle.

Mapping a burned forest area from Landsat TM data by multiple methods

Forest fire is one of the dominant disturbances in boreal forests. It is the primary process responsible for organizing the physical and biological attributes of the boreal biome, shaping landscape diversity and influencing biogeochemical cycles. The Greater Hinggan Mountain of China is rich in forest resources while suffers from a high incidence of forest fires simultaneously. In this study, focusing on the most serious forest fire in the history of P. R. China which occurred in this region, we made use of two Landsat-5 TM (Thematic Mapper) images, and proposed to map the overall burned area and burned forest area by multiple methods. During the mapping, the fire perimeter, as well as rivers, roads and urban areas were first extracted and masked visually, and then four indices of Normalized Difference Vegetation Index, Enhanced Vegetation Index, Vegetation Fractional Cover and Disturbance Index were calculated. For each index, the optimal threshold for separating burned from unburned forest area was determined using their histograms. For comparison, threshold segmentation using single-band reflectance was performed, in addition to a Maximum Likelihood Classifier (MLC) based supervised classification of all features and forest area alone; their accuracies were also evaluated and analysed. Among all the methods compared here, mapping by EVI threshold segmentation proved to be optimal by the comparisons of overall accuracy (99.78%) and the kappa coefficient (0.9946). Finally, the calculated burned area and burned forest area were compared with the values from official statistics. Compared with the classical methods used to report official statistics on burned areas, the remote sensing-based mapping is more objective and efficient, less labour- and time-consuming, and more repeatable.

Wildfire hazard in the home ignition zone: An object-oriented analysis integrating LiDAR and VHR satellite imagery

Many spatially explicit studies of wildfire hazard focus on the wildland–urban interface (WUI), the area where natural vegetation intersects or mixes with structures. However, research suggests that the characteristics of a small portion of the WUI, the home ignition zone, largely determine potential for ignition from wildfire. The home ignition zone (HIZ) is the area that includes a structure and its surroundings out to 30–60 m. The primary goal of this study is to develop metrics to characterize land cover, burned area, and topography in the HIZ. Pre-fire metrics (i.e. related to land cover and topography) help identify relatively hazardous individual HIZes or neighborhoods of HIZes. Post-fire metrics can be used to assess the burned area across land cover types, inside and outside the HIZ. To calculate the HIZ metrics, multiple data sources (e.g. high resolution 8-band multispectral imagery and LiDAR point clouds) were integrated using an object-oriented image analysis. The setting for the study is the Fourmile Canyon area west of Boulder, Colorado, a data-rich area which experienced a large, destructive wildfire in September 2010. The land cover, burn area, and topography metrics were successfully and accurately calculated and then pre-fire metrics were combined into a simple HIZ hazard index. HIZ characteristics broadly mirror the characteristics of the WUI within the fire perimeter as a whole, though the HIZ on average contains more bare and less forest land, has more widely spaced canopies, and experienced less burning during the fire. The HIZ hazard index values were spatially heterogeneous, but with several distinct high and low hazard clusters. The methods described in this study, paired with in situ data collection, can be applied to other areas to inform hazard mitigation plans.

Secure Data Aggregation in Wireless Sensor Networks: Filtering out the Attacker's Impact

Wireless sensor networks (WSNs) are increasingly used in many applications, such as volcano and fire monitoring, urban sensing, and perimeter surveillance. In a large WSN, in-network data aggregation (i.e., combining partial results at intermediate nodes during message routing) significantly reduces the amount of communication overhead and energy consumption. The research community proposed a loss-resilient aggregation framework called synopsis diffusion, which uses duplicate-insensitive algorithms on top of multipath routing schemes to accurately compute aggregates (e.g., predicate count or sum). However, this aggregation framework does not address the problem of false subaggregate values contributed by compromised nodes. This attack may cause large errors in the aggregate computed at the base station, which is the root node in the aggregation hierarchy. In this paper, we make the synopsis diffusion approach secure against the above attack launched by compromised nodes. In particular, we present an algorithm to enable the base station to securely compute predicate count or sum even in the presence of such an attack. Our attack-resilient computation algorithm computes the true aggregate by filtering out the contributions of compromised nodes in the aggregation hierarchy. Extensive analysis and simulation study show that our algorithm outperforms other existing approaches.

The Synergy of the $0.05^\circ$ ($\sim5\nbsp\hbox{km}$) AVHRR Long-Term Data Record (LTDR) and Landsat TM Archive to Map Large Fires in the North American Boreal Region From 1984 to 1998

A Bayesian network classifier-based algorithm was applied to map the burned area (BA) in the North American boreal region using the 0.05° ( ~ 5\nbspkm) Advanced Very High Resolution Radiometer (AVHRR) Long-Term Data Record (LTDR) data version 3 time series. The results showed an overall good agreement compared to reference maps ( slope = 0.62; R2 = 0.75). The study site was divided into six sub-regions, where south-western Canada performed the worst ( slope = 0.25; R2 = 0.47). The algorithm achieved good results as long as a year with high fire incidence was employed to train the Bayesian network, and the vegetation response to fire remained consistent across the region. Years with higher fire activity and larger fires, which were easier to detect at the LTDR spatial scale, matched the reference maps better. The LTDR postfire signal remained detectable for 6-9 years, extending opportunities to map the full fire extent with Landsat Thematic Mapper (TM). For fires larger than 1000\nbspkm2, Landsat TM mapped 99%, whereas LTDR caught 69% of the reference BA reported. Landsat TM took four satellite overpasses (2 months) to map these large fires, and in some cases even until the following year, but LTDR detected them within days. Thus, results suggest that LTDR could be used to trigger the search for fires and then map their perimeter with Landsat TM. This study demonstrates an LTDR BA algorithm that could be extrapolated to other boreal regions using a similar methodology, although reference fire perimeters would be needed to train the Bayesian classifier and its thresholds.

An automated two-step NDVI-based method for the production of low-cost historical burned area map records over large areas

The recognition and understanding of long-term fire-related processes and patterns, such as the possible connection between the increased frequency of wildfires and global warming, requires the study of historical data records. In this study, a methodology was proposed for the automated production of long historical burned area map records over large-scale regions. The methodology was based on remotely sensed, high temporal resolution, normalized difference vegetation index (NDVI) data that could be easily acquired at medium or low spatial resolution. The proposed methodology was used to map the burned areas of the wildfires that occurred over the Peloponnese peninsula, Greece, during the summer of 2007. The method was built upon the NDVI data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Système Pour l’Observation de la Terre (SPOT)-VEGETATION. The higher spatial resolution data of MODIS resulted in higher burned area user accuracy (91.10%) and kappa (0.85) values than the respective ones for VEGETATION (79.29% and 0.77). The majority of classification errors were located along the perimeter of the burned areas and were mainly attributed to spatial resolution limitations of the remotely sensed data. The commission errors located away from the fire perimeter were primarily attributed to topographically shaded areas and land-cover types spectrally similar to burned areas. The omission errors resulted primarily from the small size and elongated shape of remote burned areas. Owing to their geometry, they have a high proportion of mixed pixels, whose spectral properties failed to meet the strict set of criteria for core fire pixels. The benefits of the proposed methodology are maximized when applied to data of the highest available spatial resolution, such as those collected by MODIS and the Project for On-Board Autonomy – Vegetation (PROBA-V) and when land-cover types spectrally similar to burned areas are masked prior to its application.

The New VIIRS 375 m active fire detection data product: Algorithm description and initial assessment

The first Visible Infrared Imaging Radiometer Suite (VIIRS) was launched in October 2011 aboard the Suomi-National Polar-orbiting Partnership (S-NPP) satellite. The VIIRS instrument carries two separate sets of multi-spectral channels providing full global coverage at both 375m and 750m nominal resolutions every 12h or less depending on the latitude. In this study, we introduce a new VIIRS active fire detection algorithm, which is driven primarily by the 375m middle and thermal infrared imagery data. The algorithm builds on the well-established MODIS Fire and Thermal Anomalies product using a contextual approach to detect both day and nighttime biomass burning and other thermal anomalies. Here we present the fire algorithm's design and implementation, including important information describing the input data characteristics and potential artifacts associated with pixel saturation and the South Atlantic Magnetic Anomaly, both found to affect the middle infrared channel data. Initial assessment using results derived from the global processing of the algorithm indicated small, although variable, commission errors (<1.2%) for nominal confidence fire pixels. We achieved improved performance using the 375m active fire data compared to the VIIRS 750m baseline fire product, resulting in a 3× and 25× factor increase in the absolute number of fire pixels detected using day and nighttime data, respectively. Similarly, VIIRS 375m fire data showed significantly superior mapping capabilities compared to current MODIS fire detection data with improved consistency of fire perimeter delineation for biomass burning lasting multiple days.

Wildfire Selectivity for Land Cover Type: Does Size Matter?

Previous research has shown that fires burn certain land cover types disproportionally to their abundance. We used quantile regression to study land cover proneness to fire as a function of fire size, under the hypothesis that they are inversely related, for all land cover types. Using five years of fire perimeters, we estimated conditional quantile functions for lower (avoidance) and upper (preference) quantiles of fire selectivity for five land cover types - annual crops, evergreen oak woodlands, eucalypt forests, pine forests and shrublands. The slope of significant regression quantiles describes the rate of change in fire selectivity (avoidance or preference) as a function of fire size. We used Monte-Carlo methods to randomly permutate fires in order to obtain a distribution of fire selectivity due to chance. This distribution was used to test the null hypotheses that 1) mean fire selectivity does not differ from that obtained by randomly relocating observed fire perimeters; 2) that land cover proneness to fire does not vary with fire size. Our results show that land cover proneness to fire is higher for shrublands and pine forests than for annual crops and evergreen oak woodlands. As fire size increases, selectivity decreases for all land cover types tested. Moreover, the rate of change in selectivity with fire size is higher for preference than for avoidance. Comparison between observed and randomized data led us to reject both null hypotheses tested ( = 0.05) and to conclude it is very unlikely the observed values of fire selectivity and change in selectivity with fire size are due to chance.

Corrigendum to: Wildland fire ash and particulate distribution in adjacent residential areas

In recent decades, the frequency of wildland fire incidents near residential areas has decreased but the number of acres burned has increased, in large part due to changes in forest management methods and further human encroachment in forested regions. There is much debate about whether the wood ash generated by these wildfires can significantly affect residential buildings outside the fire zone perimeter. This study investigates the distribution of ash, soot and char that are generated from wildfires and migrate into adjacent residential regions. For this purpose, a wildland fire in Bastrop, Texas, was studied with samples collected from a variety of locations within the fire site and in adjacent areas. The collected samples were assayed for pH, asbestos, heavy metals and polynuclear aromatic hydrocarbons. The results of this investigation showed that the magnitude of the deposition on residential buildings near wildfires is dependent on a variety of variables, in particular the distance from the centre of the fire.

A comparison of three approaches for simulating fine-scale surface winds in support of wildland fire management. Part II. An exploratory study of the effect of simulated winds on fire growth simulations

The effect of fine-resolution wind simulations on fire growth simulations is explored. The wind models are (1) a wind field consisting of constant speed and direction applied everywhere over the area of interest; (2) a tool based on the solution of the conservation of mass only (termed mass-conserving model) and (3) a tool based on a solution of conservation of mass and momentum (termed momentum-conserving model). Fire simulations use the FARSITE fire simulation system to simulate fire growth for one hypothetical fire and two actual wildfires. The momentum-conserving model produced fire perimeters that most closely matched the observed fire spread, followed by the mass-conserving model and then the uniform winds. The results suggest that momentum-conserving and mass-conserving models can reduce the sensitivity of fire growth simulations to input wind direction, which is advantageous to fire growth modellers. The mass-conserving and momentum-conserving wind models may be useful for operational use as decision support tools in wildland fire management, prescribed fire planning, smoke dispersion modelling, and firefighter and public safety.

The EFFIS forest fire atmospheric emission model: Application to a major fire event in Portugal

Forest fires are a major contributor of gaseous and particulate compounds to the atmosphere, impairing air quality and affecting human health. A new forest fire emissions module was developed and integrated into the European Forest Fire Information System (EFFIS), which systematically compiles, since 2000, series of burnt area statistics mapped from satellite imagery. This new forest fire emission model was built on classical methodologies of fuel-map based emission estimation that were improved, especially on burning efficiency, fuel consumption estimation and emission factors. It makes the best use of EFFIS near-real time and detailed information on forest fires, mainly concerning products with a high temporal resolution, which is needed to simulate smoke dispersion and chemical transformation in the atmosphere.A case study of a forest fire event in the north of Portugal on October 14, 2011, with a total of 4400 ha of burnt area, was selected to test this forest fire emission model. The fine scale information used in this study included: (1) 3-h resolution meteorological fields; (2) daily evolution of the cumulative fire perimeter from the EFFIS rapid damage assessment system; and (3) a fine spatial resolution fuel map, forest type map and topography. The 3-h evolution of pollutant emissions was calculated for gas and particulate species based on the evolution of the burnt area increase and fuel consumption. The estimated forest fire emissions represent more than 90% of the total annual (anthropogenic and natural) emissions over the study region. The impact of these forest fire emissions was analyzed in terms of air quality, using observational data from the nearest air quality monitoring station. High peaks of NO2 and SO2 were registered simultaneously during the period 06–09 a.m. and a later peak of PM from 07 a.m. to 15 p.m.

Wildland fire modeling with an Eulerian level set method and automated calibration

This paper presents the mathematical development of a geospatial model for simulating wildland fire spread. The Eulerian level set method (LSM), a mathematical technique that tracks interfaces between separate regions on a regular grid, is applied here to track the interface between burned (or burning) areas and green areas. Model physics include surface fire spread rate and direction, transition from surface fire to passive or active crown fire, ember lofting, trajectory tracking, and spot fire formation, acceleration from point ignitions, and modifications to fuel strata attributed to suppression activities. A novel aspect of this work involves application of a stochastic optimization algorithm to automatically calibrate baseline model inputs by comparing calculated fire perimeters to observed (target) fire perimeters. The wildland fire model and associated automated calibration technique are assessed by simulating the first 22h of progression of the 2007 Moonlight Fire in Northern California. Fuels and topography inputs are obtained from the Landfire project while wind and weather inputs are obtained from high resolution numerical weather prediction. Fire areas simulated with the calibrated model agree well with target perimeters.

Peat consumption and carbon loss due to smouldering wildfire in a temperate peatland

Temperate peatlands represent a substantial store of carbon and their degradation is a potentially significant positive feedback to climate change. The ignition of peat deposits can cause smouldering wildfires that have the potential to release substantial amounts of carbon and to cause environmental damage from which ecosystem recovery can be slow. Direct estimates of the loss of carbon due to smouldering wildfires are needed to inform global estimates of the effect of wildfire on carbon dynamics and to aid with national emissions accounting. We surveyed the effect of a severe wildfire that burnt within an afforested peatland in the Scottish Highlands during the summer of 2006. The fire ignited layers of peat which continued to burn as a sub-surface smouldering wildfire for more than a month after the initial surface fire and despite several episodes of heavy rain. The smouldering fire perimeter enclosed an area of 4.1ha. Analysis of weather records showed that the fire coincided with unusually warm, dry conditions and a period when the Canadian Fire Weather Index system predicted both generally high danger conditions (high Fire Weather Index) and low fuel moisture content in deep organic soil layers (high Drought Code values). Remaining peat layers in the burn area had comparatively low fuel moisture contents of ca. 250% dry weight. Within the smouldering fire’s perimeter, mean depth of burn was estimated at 17.5±2.0cm but ranged from 1 to 54cm. Based on field measurements, our estimates suggested that, in total, the smouldering wildfire burnt 773±120t of organic matter corresponding to 396±63t of carbon and a carbon loss per unit area burnt of 96±15tha−1 (9.6±1.5kgm−2). This corresponds to between 0.1% and 0.3% of the estimated total amount of carbon sequestered annually by UK peatlands. Our results also provide circumstantial evidence that afforestation of peatland soils, and associated site preparation, may contribute to an increased risk of peat fires. Smouldering fires are difficult to detect using remotely sensing techniques due to their low temperature and low heat release and the fact that the tree canopy remains intact for months afterwards. If similar smouldering fires are underreported in other temperate, boreal and tropical peatland regions then emissions from peatland burning may well be a substantially greater issue than currently assumed.