Fire Danger Rating System Research Articles

Cold Climate Structural Fire Danger Rating System?

Worldwide, fires kill 300,000 people every year. The fire season is usually recognized to be in the warmer periods of the year. Recent research has, however, demonstrated that the colder season also has major challenges regarding severe fires, especially in inhabited (heated) wood-based structures in cold-climate areas. Knowledge about the effect of dry cellulose-based materials on fire development, indoor and outdoor, is a motivation for monitoring possible changes in potential fire behavior and associated fire risk. The effect of wind in spreading fires to neighboring structures points towards using weather forecasts as information on potential fire spread behavior. As modern weather forecasts include temperature and relative humidity predictions, there may already be sufficient information available to develop a structural fire danger rating system. Such a system may include the following steps: (1) Record weather forecasts and actual temperature and relative humidity inside and outside selected structures; (2) Develop a meteorology-data-based model to predict indoor relative humidity levels; (3) Perform controlled drying chamber experiments involving typical hygroscopic fire fuel; (4) Compare the results to the recorded values in selected structures; and (5) Develop the risk model involving the results from drying chamber experiments, weather forecasts, and separation between structures. Knowledge about the structures at risk and their use is also important. The benefits of an automated fire danger rating system would be that the society can better plan for potentially severe cold-climate fires and thereby limit the negative impacts of such fires.

A comparison of the US National Fire Danger Rating System (NFDRS) with recorded fire occurrence and final fire size

Most previous research has assessed the ability of the National Fire Danger Rating System (NFDRS) to portray fire activity at either single sites or on small spatial scales, despite it being a nation-wide system. This study seeks to examine the relationships between a set of NFDRS fire danger indices (Fire Danger Ratings, Staffing Level and the Ignition Component) and measures of fire activity (fire occurrence and final fire size) across the entire conterminous US over an 8-year period. We reveal that different regions of the US display different levels of correspondence between each of the fire danger indices and recorded fire activity. Areas in the Southern and Eastern Geographic Area Coordination Centers (GACCs) exhibit weaker correlations than those in the Northwest, Northern Rockies, Great Basin and Northern California GACCs. Peaks in fire occurrence are shown to occur at mid–low values of fire danger whereas final fire sizes increase monotonically with each fire danger index. Our findings appear to align with perceived shifts in management practices currently employed across the US and indicate that the ability of the NFDRS to apportion the resources required to combat large fires is in general well developed.

Long‐Lead Prediction of the 2015 Fire and Haze Episode in Indonesia

We conducted a case study of NCEP CFSv2 seasonal model forecast performance over Indonesia in predicting the dry conditions in 2015 that led to severe fire, in comparison to the non-El Niño dry season conditions of 2016. Forecasts of the Drought Code (DC) component of Indonesia's Fire Danger Rating System were examined across the entire Equatorial Asia region and for the primary burning regions within it. Our results show that early warning lead times of high observed DC in September and October 2015 varied considerably for different regions. High DC over Southern Kalimantan and Southern New Guinea were predicted with 180-day lead times, whereas Southern Sumatra had lead times of up to only 60 days, which we attribute to the absence in the forecasts of an eastward decrease in Indian Ocean SSTs. This case study provides the starting point for longer-term evaluation of seasonal fire danger rating forecasts over Indonesia.

Attributing extreme fire risk in Western Canada to human emissions

Canada is expected to see an increase in fire risk under future climate projections. Large fires, such as that near Fort McMurray, Alberta in 2016, can be devastating to the communities affected. Understanding the role of human emissions in the occurrence of such extreme fire events can lend insight into how these events might change in the future. An event attribution framework is used to quantify the influence of anthropogenic forcings on extreme fire risk in the current climate of a western Canada region. Fourteen metrics from the Canadian Forest Fire Danger Rating System are used to define the extreme fire seasons. For the majority of these metrics and during the current decade, the combined effect of anthropogenic and natural forcing is estimated to have made extreme fire risk events in the region 1.5 to 6 times as likely compared to a climate that would have been with natural forcings alone.

Cffdrs: an R package for the Canadian Forest Fire Danger Rating System

IntroductionThe Canadian Forest Fire Danger Rating System (CFFDRS) is a globally known wildland fire risk assessment system, and two major components, the fire weather index system and the fire behavior prediction system, have been extensively used both nationally and internationally to aid operational wildland fire decision making.MethodsIn this paper, we present an overview of an R package cffdrs, which is developed to calculate components of the CFFDRS, and highlight some of its functionality. In particular, we demonstrate how these functions could be used for large data analysis.Results and DiscussionWith this cffdrs package, we provide a portal for not only a collection of R functions dealing with all available components in CFFDRS but also a platform for various additional developments that are useful for the understanding of fire occurrence and behavior. This is the first time that all relevant CFFDRS methods are incorporated into the same platform, which can be accessed by both the management and research communities.

Improved fuel moisture prediction in non-native tropical Megathyrsus maximus grasslands using Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived vegetation indices

The synergistic impacts of non-native grass invasion and frequent human-derived wildfires threaten endangered species, native ecosystems and developed land throughout the tropics. Fire behaviour models assist in fire prevention and management, but current models do not accurately predict fire in tropical ecosystems. Specifically, current models poorly predict fuel moisture, a key driver of fire behaviour. To address this limitation, we developed empirical models to predict fuel moisture in non-native tropical grasslands dominated by Megathyrsus maximus in Hawaii from Terra Moderate-Resolution Imaging Spectroradiometer (MODIS)-based vegetation indices. Best-performing MODIS-based predictive models for live fuel moisture included the two-band Enhanced Vegetation Index (EVI2) and Normalized Difference Vegetation Index (NDVI). Live fuel moisture models had modest (R2=0.46) predictive relationships, and outperformed the commonly used National Fire Danger Rating System (R2=0.37) and the Keetch–Byram Drought Index (R2=0.06). Dead fuel moisture was also best predicted by a model including EVI2 and NDVI, but predictive capacity was low (R2=0.19). Site-specific models improved model fit for live fuel moisture (R2=0.61), but limited extrapolation. Better predictions of fuel moisture will improve fire management in tropical ecosystems dominated by this widespread and problematic non-native grass.

Regional variation in fire weather controls the reported occurrence of Scottish wildfires.

Fire is widely used as a traditional habitat management tool in Scotland, but wildfires pose a significant and growing threat. The financial costs of fighting wildfires are significant and severe wildfires can have substantial environmental impacts. Due to the intermittent occurrence of severe fire seasons, Scotland, and the UK as a whole, remain somewhat unprepared. Scotland currently lacks any form of Fire Danger Rating system that could inform managers and the Fire and Rescue Services (FRS) of periods when there is a risk of increased of fire activity. We aimed evaluate the potential to use outputs from the Canadian Fire Weather Index system (FWI system) to forecast periods of increased fire risk and the potential for ignitions to turn into large wildfires. We collated four and a half years of wildfire data from the Scottish FRS and examined patterns in wildfire occurrence within different regions, seasons, between urban and rural locations and according to FWI system outputs. We used a variety of techniques, including Mahalanobis distances, percentile analysis and Thiel-Sen regression, to scope the best performing FWI system codes and indices. Logistic regression showed significant differences in fire activity between regions, seasons and between urban and rural locations. The Fine Fuel Moisture Code and the Initial Spread Index did a tolerable job of modelling the probability of fire occurrence but further research on fuel moisture dynamics may provide substantial improvements. Overall our results suggest it would be prudent to ready resources and avoid managed burning when FFMC > 75 and/or ISI > 2.

The Potential Predictability of Fire Danger Provided by Numerical Weather Prediction

AbstractA global fire danger rating system driven by atmospheric model forcing has been developed with the aim of providing early warning information to civil protection authorities. The daily predictions of fire danger conditions are based on the U.S. Forest Service National Fire-Danger Rating System (NFDRS), the Canadian Forest Service Fire Weather Index Rating System (FWI), and the Australian McArthur (Mark 5) rating systems. Weather forcings are provided in real time by the European Centre for Medium-Range Weather Forecasts forecasting system at 25-km resolution. The global system’s potential predictability is assessed using reanalysis fields as weather forcings. The Global Fire Emissions Database (GFED4) provides 11 yr of observed burned areas from satellite measurements and is used as a validation dataset. The fire indices implemented are good predictors to highlight dangerous conditions. High values are correlated with observed fire, and low values correspond to nonobserved events. A more quantitative skill evaluation was performed using the extremal dependency index, which is a skill score specifically designed for rare events. It revealed that the three indices were more skillful than the random forecast to detect large fires on a global scale. The performance peaks in the boreal forests, the Mediterranean region, the Amazon rain forests, and Southeast Asia. The skill scores were then aggregated at the country level to reveal which nations could potentially benefit from the system information to aid decision-making and fire control support. Overall it was found that fire danger modeling based on weather forecasts can provide reasonable predictability over large parts of the global landmass.

Responses of dead forest fuel moisture to climate change

AbstractForest fuel moisture is an important factor for wildland fire behavior. Predicting future wildfire trends and controlled burned conditions is essential to effective natural resource management, but the associated effects of forest fuel moisture remain uncertain. This study investigates the responses of dead forest fuel moisture to climate change in the continental United States, one of the global regions with frequent wildfire and controlled burning activities. Moisture content was calculated for dead fuels with 1‐ and 1000‐hr lags (MC1 and MC1000) using the algorithms from the U.S. National Fire Danger Rating System. A set of dynamically downscaled regional climate change scenarios provided by the North American Regional Climate Change Assessment Program was used. The present fuel moisture shows large seasonal variations peaked in winter and spatial variability with dominant meridional change in winter and zonal change in summer. Fuel moisture is projected to decrease overwhelmingly across the United States, mainly caused by temperature increases. The largest MC1 decrease of over 1% mainly occurs in the southwestern United States in spring and southeastern United States in summer, while the largest MC1000 decrease of over 1.5% occurs in the southwestern United States in spring and in the southern Plains and eastern United States in summer. The spatial patterns and seasonal variations of future fuel moisture trends, however, vary considerably with regional climate change scenarios. The drying fuel trends suggest that frequency, size, and intensity of wildfires would increase and prescribed burning windows would decrease in the future in the Southwest and the inter‐mountains during spring and the Rocky Mountains during summer if other fuel conditions remain the same. These results highlight the general vulnerability of semiarid forests to drying fuels trends.

Calibration and evaluation of the Canadian Forest Fire Weather Index (FWI) System for improved wildland fire danger rating in the United Kingdom

Abstract. Wildfires in the United Kingdom (UK) pose a threat to people, infrastructure and the natural environment. During periods of particularly fire-prone weather, wildfires can occur simultaneously across large areas, placing considerable stress upon the resources of fire and rescue services. Fire danger rating systems (FDRSs) attempt to anticipate periods of heightened fire risk, primarily for early-warning and preparedness purposes. The UK FDRS, termed the Met Office Fire Severity Index (MOFSI), is based on the Fire Weather Index (FWI) component of the Canadian Forest FWI System. The MOFSI currently provides daily operational mapping of landscape fire danger across England and Wales using a simple thresholding of the final FWI component of the Canadian FWI System. However, it is known that the system has scope for improvement. Here we explore a climatology of the six FWI System components across the UK (i.e. extending to Scotland and Northern Ireland), calculated from daily 2km × 2km gridded numerical weather prediction data and supplemented by long-term meteorological station observations. We used this climatology to develop a percentile-based calibration of the FWI System, optimised for UK conditions. We find this approach to be well justified, as the values of the "raw" uncalibrated FWI components corresponding to a very "extreme" (99th percentile) fire danger situation vary by more than an order of magnitude across the country. Therefore, a simple thresholding of the uncalibrated component values (as is currently applied in the MOFSI) may incur large errors of omission and commission with respect to the identification of periods of significantly elevated fire danger. We evaluate our approach to enhancing UK fire danger rating using records of wildfire occurrence and find that the Fine Fuel Moisture Code (FFMC), Initial Spread Index (ISI) and FWI components of the FWI System generally have the greatest predictive skill for landscape fire activity across Great Britain, with performance varying seasonally and by land cover type. At the height of the most recent severe wildfire period in the UK (2 May 2011), 50 % of all wildfires occurred in areas where the FWI component exceeded the 99th percentile. When all wildfire events during the 2010–2012 period are considered, the 75th, 90th and 99th percentiles of at least one FWI component were exceeded during 85, 61 and 18 % of all wildfires respectively. Overall, we demonstrate the significant advantages of using a percentile-based calibration approach for classifying UK fire danger, and believe that our findings provide useful insights for future development of the current operational MOFSI UK FDRS.

Reformulation of the Drought Code in the Canadian Fire Weather Index System Implemented in New Zealand

AbstractThe fire danger rating system implemented in New Zealand is the Canadian Fire Weather Index (FWI) System developed 40 years ago for Canadian temperate forests. Issues have been raised in relation to this system when applied in other regions with different climate and vegetation environments. For the first time, two methods were proposed for improving the Drought Code (DC) component of the FWI System for New Zealand. The first method (PotE) employs a potential evaporation (PE) scheme that considers wind speed, surface air stability, and water vapor mixing ratio gradient. The second method (soilM) uses soil moisture. For the latter, when soil moisture is derived from observations, the calculated DC represents the actual drought status of the soil. DC and FWI have been calculated with the original and the two new DC methods at 28 climate stations in New Zealand for a pair of 2-yr periods. The Joint U.K. Land Environment Simulator (JULES) was run to provide the PE and soil moisture for the two methods. The original DC method underestimated the drought status in New Zealand, especially in summer, leading to underestimation of FWI. The PotE method significantly overestimated the drought status in summer. The errors in the calculated drought status and FWI were largely reduced by using the soilM method with simulated soil moisture from JULES. In this paper, the reasons for this reduction in error are investigated by testing the sensitivity of DC to surface evaporation and to soil parameters. Potential benefit is found from using the proposed soilM method for monitoring drought status and for FWI calculations.

Development of a Global Fire Weather Database

Abstract. The Canadian Forest Fire Weather Index (FWI) System is the mostly widely used fire danger rating system in the world. We have developed a global database of daily FWI System calculations, beginning in 1980, called the Global Fire WEather Database (GFWED) gridded to a spatial resolution of 0.5° latitude by 2/3° longitude. Input weather data were obtained from the NASA Modern Era Retrospective-Analysis for Research and Applications (MERRA), and two different estimates of daily precipitation from rain gauges over land. FWI System Drought Code calculations from the gridded data sets were compared to calculations from individual weather station data for a representative set of 48 stations in North, Central and South America, Europe, Russia, Southeast Asia and Australia. Agreement between gridded calculations and the station-based calculations tended to be most different at low latitudes for strictly MERRA-based calculations. Strong biases could be seen in either direction: MERRA DC over the Mato Grosso in Brazil reached unrealistically high values exceeding DC = 1500 during the dry season but was too low over Southeast Asia during the dry season. These biases are consistent with those previously identified in MERRA's precipitation, and they reinforce the need to consider alternative sources of precipitation data. GFWED can be used for analyzing historical relationships between fire weather and fire activity at continental and global scales, in identifying large-scale atmosphere–ocean controls on fire weather, and calibration of FWI-based fire prediction models.

Tools for Assessing the Impacts of Climate Variability and Change on Wildfire Regimes in Forests

Fire is an intrinsic element of many forest ecosystems; it shapes their ecological processes, determines species composition and influences landscape structure. However, wildfires may: have undesirable effects on biodiversity and vegetation coverage; produce carbon emissions to the atmosphere; release smoke affecting human health; and cause loss of lives and property. There have been increasing concerns about the potential impacts of climate variability and change on forest fires. Climate change can alter factors that influence the occurrence of fire ignitions, fuel availability and fuel flammability. This review paper aims to identify tools and methods used for gathering information about the impacts of climate variability and change on forest fires, forest fuels and the probability of fires. Tools to assess the impacts of climate variability and change on forest fires include: remote sensing, dynamic global vegetation and landscape models, integrated fire-vegetation models, fire danger rating systems, empirical models and fire behavior models. This review outlines each tool in terms of its characteristics, spatial and temporal resolution, limitations and applicability of the results. To enhance and improve tool performance, each must be continuously tested in all types of forest ecosystems.

Estimating Spatially Varying Severity Thresholds of a Forest Fire Danger Rating System Using Max-Stable Extreme-Event Modeling

AbstractFire danger indices are used in many countries to estimate the potential fire danger and to issue warnings to local regions. The McArthur fire danger rating system is used in Australia. The McArthur forest fire danger index (FFDI) uses only meteorological elements. It combines information on wind speed, temperature, relative humidity, and recent rainfall to produce a weather index of fire potential. This index is converted into fire danger categories to serve as warnings to the local population and to estimate potential fire-suppression difficulty. FFDI values above the threshold of 75 are rated as extreme. The spatial behavior of large values of the FFDI is modeled to investigate whether a varying threshold across space may serve as a better guide for determining the onset of elevated fire danger. The authors modify and apply a statistical method that was recently developed for spatial extreme events, using a “max-stable” process to model FFDI data at approximately 17 000 data sites. The method that is described here produces a quantile map that can be employed as a spatially varying fire danger threshold. It is found that a spatially varying threshold may serve to more accurately represent high fire danger, and an adjustment is proposed that varies by local government area. Temporal change was also investigated, and evidence was found of a recent increase in extreme fire danger in southwestern Australia.

Forecasting Simulation of Smoke Dispersion from Forest and Land Fires in Indonesia

Transboundary haze pollution has been a regular phenomenon since the last decades, particularly in ASEAN region. It was due to forest and land fires, which dominated by peatland fire. In contrast to a great impacts of the smoke haze, study on this area is still limited. Therefore, this study aimed to simulate smoke dispersion forecasting from forest and land fires in Indonesia. The output of the study can be used for supporting Fire Danger Rating System of forest and land fires occurrences. Simulation of smoke dispersion as hindcasting used Weather Research and Forecasting with Chemistry (WRF-Chem) model on forest and land fires cases occurred on 19 and 22 June 2013 for Pekanbaru-Sumatera region. Data used for this study were coming from Global Forecast System (GFS) with 0.5o resolution and Emission Database Global Atmospheric Research (EDGAR).Simulation results for Carbon Monoxide (CO) concentration as output of WRF-Chem indicated an identical pattern with Total Column CO - Atmospheric Infrared Sounders (AIRS-NASA) data with correlation of 0.61 – 0.98. The study revealed that WRF-Chem is able to forecast smoke dispersion from forest and land fires. Thus,it is possibly to include this study as one of early step in Early Warning System for smoke dispersion from forest and land fires in Indonesia.

Assessing the probability of sustained flaming in masticated fuel beds

Mechanical mastication is increasingly used as a fuel management treatment to reduce fire risk at the wildland–urban interface, although ignition and fire behaviour in these novel fuel beds are poorly understood. We investigated the influence of observed fuel moisture content, wind speed, and firebrand size on the probability of sustained flaming of masticated fuel beds under both laboratory and field settings. Logistic regression techniques were applied to assess the probability of sustained flaming in both datasets. Models for the field were also developed using estimated moisture from three sets of weather-based models: (i) the hourly Fine Fuel Moisture Code (FFMC) from the Canadian Forest Fire Weather Index System, (ii) the National Fire Danger Rating System (NFDRS) moisture estimates for 1 h and 10 h fuels, and (iii) a masticated surface fuel moisture model (MAST). In both laboratory and field testing, the likelihood of a successful ignition increased with decreasing moisture content and increasing wind speed; the effect of firebrand size was only apparent in laboratory testing. The FFMC, NFDRS, and MAST predictions had somewhat reduced discriminative power relative to direct moisture in predicting the probability of sustained flaming based on our field observations. Our results speak to the disparity between the fire behaviour modeling that occurs in the laboratory and the fire behavior modeling that occurs in the field, as the methodology permitted comparison of predictions from sustained flaming models that were developed for one experimental setting and applied to the other.

Relationships between fire danger and the daily number and daily growth of active incidents burning in the northern Rocky Mountains, USA

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.

Modeling Wildfire Spread in Mountain Pine Beetle-Affected Forest Stands, British Columbia, Canada

AbstractThe mountain pine beetle (Dendroctonus ponderosaeHopkins; MPB) has killed lodgepole pines (Pinus contortaDougl. ex Loud.) across 20 million hectares of central British Columbia, Canada, since the late 1990s, challenging land managers as well as fire management personnel. Although recent studies have used models to simulate how MPB might affect fire spread, very little fire behaviour has been documented in MPB-affected stands. We documented rate of spread (ROS) in experimental fires and wildfires in recent MPB-killed stands in British Columbia using interpretations of oblique photographs, airborne measurements of wildfire spread, and experimental burns. Fire spread observations were used to develop ROS models following the empirical approach of the Canadian Forest Fire Danger Rating System (CFFDRS). Sixteen fire runs were examined that occurred in mature MPB-affected pine stands from 1 to 5 years since peak attack. Observations of ROS were associated with corresponding weather measurements from nearby weather stations and non-linear regression curves were fit to paired ROS and Initial Spread Index (ISI) data according to CFFDRS convention. Although the dataset is less robust than a strictly experimental approach, fires had faster spread and more crown fire than predicted, with a linear average of 2.7 times higher ROS in best fit models than expected for unaffected pine. The most likely crown fire initiation threshold (P= 0.5) was ISI 5.5. Fire intensity is likely higher in early post-MPB stands due to increased ROS, lower crowning thresholds, and greater consumption of fine dead branches. Further studies on fire behaviour in MPB-affected stands are needed, but the present findings can help reduce uncertainty in fire and land management decisions in the interim.

DROUGHT AND FINE FUEL MOISTURE CODE EVALUATION: AN EARLY WARNING SYSTEM FOR FOREST/LAND FIRE USING REMOTE SENSING APPROACH

This study evaluated two parameters of fire danger rating system (FDRS) using remote sensing data i.e. drought code (DC) and fine fuel moisture code (FFMC) as an early warning program for forest/land fire in Indonesia. Using the reference DC and FFMC from observation data, we calculated the accuracy, bias, and error. The results showed that FFMC from satellite data had a fairly good correlation with FFMC observations (r=0.68, bias=7.6, and RMSE=15.7), while DC from satellite data had a better correlation with FFMC observations (r=0.88, bias=49.91, and RMSE=80.22). Both FFMC and DC from satellite and observation were comparable. Nevertheless, FFMC and DC satellite data showed an overestimation values than that observation data, particularly during dry season. This study also indicated that DC and FFMC could describe fire occurrence within a period of 3 months before fire occur, particularly for DC. These results demonstrated that remote sensing data can be used for monitoring and early warning fire in Indonesia.

Flammability properties of British heathland and moorland vegetation: Models for predicting fire ignition

Temperate ecosystems, for example British heathlands and moorlands, are predicted to experience an increase in severe summer drought and wildfire frequency over the next few decades. The development of fire ignition probability models is fundamental for developing fire-danger rating systems and predicting wildfire outbreaks. This work assessed the flammability properties of the fuel complex of British moorlands as a function of their moisture content under laboratory conditions. Specifically, we aimed to develop: (1) models of the probability of fire ignition in peat/litter fuel-beds (litter of four different plant species, Sphagnum moss and peat); (2) flammability properties in terms of ignitability, sustainability, consumability and combustibility of these peat/litter fuel-beds; (3) the probability of ignition in a canopy-layer of Calluna vulgaris (the most dominant heath/moor species in Britain) as a function of its dead-fuel proportion and moisture content; (4) the efficacy of standardized smouldering and flaming ignition sources in developing sustained ignitions. For this, a series of laboratory experiments simulating the fuel structure of moor vegetation were performed. The flammability properties in peat/litter fuel-beds were influenced strongly by the fuel moisture content. There were small differences in moisture thresholds for experiencing initial flaming ignitions (35–59%), however, the threshold for sustained ignitions (i.e. spreading a fixed distance from the ignition point) varied across a much wider range (19–55%). Litter/peat fuel-beds were classified into three groups: fuel-beds with high ignitability and combustibility, fuel-beds with high levels of sustainability, and fuel-beds with low levels in all flammability descriptors. The probability of ignition in the upper Calluna-vegetation layer was influenced by both the proportion of dead fuels and their moisture content, ranging from 19% to 35% of moisture as dead fuel proportion increased. Smouldering sources were more efficient in igniting peat/litter fuel-beds but in the Calluna-vegetation layer flaming sources performed better. This work can assist in improving the predictions of fire-rating systems implemented in British moorlands, by providing better warnings based on critical moisture thresholds for various fuel types.