National Fire Danger Rating System Research Articles

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

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

Downscaled subseasonal fire danger forecast skill across the contiguous United States

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

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

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

Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States

Wildfire danger assessment is essential for operational allocation of fire management resources; with longer lead prediction, the more efficiently can resources be allocated regionally. Traditional studies focus on meteorological forecasts and fire danger index models (e.g., National Fire Danger Rating System—NFDRS) for predicting fire danger. Meteorological forecasts, however, lose accuracy beyond ~10 days; as such, there is no quantifiable method for predicting fire danger beyond 10 days. While some recent studies have statistically related hydrologic parameters and past wildfire area burned or occurrence to fire, no study has used these parameters to develop a monthly spatially distributed predictive model in the contiguous United States. Thus, the objective of this study is to introduce Fire Danger from Earth Observations (FDEO), which uses satellite data over the contiguous United States (CONUS) to enable two-month lead time prediction of wildfire danger, a sufficient lead time for planning purposes and relocating resources. In this study, we use satellite observations of land cover type, vapor pressure deficit, surface soil moisture, and the enhanced vegetation index, together with the United States Forest Service (USFS) verified and validated fire database (FPA) to develop spatially gridded probabilistic predictions of fire danger, defined as expected area burned as a deviation from “normal”. The results show that the model predicts spatial patterns of fire danger with 52% overall accuracy over the 2004–2013 record, and up to 75% overall accuracy during the fire season. Overall accuracy is defined as number of pixels with correctly predicted fire probability classes divided by the total number of the studied pixels. This overall accuracy is the first quantified result of two-month lead prediction of fire danger and demonstrates the potential utility of using diverse observational data sets for use in operational fire management resource allocation in the CONUS.

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

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

Severe Fire Danger Index: A Forecastable Metric to Inform Firefighter and Community Wildfire Risk Management

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

A Review on Forest Fire Detection Techniques: A Decadal Perspective

Forest fire disasters have always been mankind’s constant and inconvenient companion since time immemorial. In the recent past years, managing crisis for example a large scale fire has become a very difficult and challenging task. Things that are common in most of the forest fire that occur at large scale are loss of life (human or animal), loss of vegetation, loss of flora and fauna, and communication failure (if any). Apart from causing a great loss to valuable natural resources of nature forest fire pose a greater risk not only to life of human being but also to the inhabitant’s such as wild life living in the forest. As per National Fire Danger Rating System (NFDRS), if a fire is detected within 6 minutes of its occurrence then it can be easily disposed-off before it turns into a large scale fire. For this a network that can detect fire at a very early stage is required. There are numerous techniques to detect the occurrence of forest fire and this article is dedicated towards reviewing detection techniques present in the literature. This work will give a bird’s eye view of the technologies used in automatic detection of forest fires and reviews almost all the detection techniques available in the literature. To the best of our knowledge this is the first time that almost all the techniques available in the literature are reviewed and considering almost all the parameters.

A Probabilistic Method Predicting Forest Fire Occurrence Combining Firebrands and the Weather-Fuel Complex in the Northern Part of the Daxinganling Region, China

The fire danger rating method currently used in the northern part of the Daxinganling Region with the most severe forest fires in China only uses weather variables without considering firebrands. The discrepancy between fire occurrence and fire risk by FFDWR (Forest Fire-Danger Weather Rating, a method issued by the National Meteorological Bureau, that is used to predict forest fire probability through links between forest fire occurrence and weather variables) in the northern part is more obvious than that in the southern part. Great discrepancy has emerged between fire danger predicted by the method and actual fire occurrence in recent years since a strict firebrand prohibition policy has significantly reduced firebrands in the region. A probabilistic method predicting fire probability by introducing an Ignition Component (IC) in the National Fire Danger Rating System (NFDRS) adopted in the United States to depict effects of both firebrand and weather-fuel complex on fire occurrence is developed to solve the problem. The suitability and accuracy of the new method in the region were assessed. Results show that the method is suitable in the region. IC or the modified IC can be adopted to depict the effect of the weather-fuel complex on fire occurrence and to rate fire danger for periods with fewer firebrands. Fire risk classes and corresponding preparedness level can be determined from IC in the region. Methods of the same principle could be established to diminish similar discrepancy between actual fire occurrence and fire danger in other countries.

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.

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.

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.

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.

Environmental circumstances surrounding bushfire fatalities in Australia 1901–2011

Abstract This paper describes the development and analysis of a dataset covering bushfire related life loss in Australia over the past 110 years (1901–2011). Over this time period 260 bushfires have been associated with a total of 825 known civilian and firefighter fatalities. This database was developed to provide an evidence base from which an Australian national fire danger rating system can be developed and has benefits in formalising our understanding of community exposure to bushfire. The database includes detail of the spatial, temporal and localised context in which the fatalities occurred. This paper presents the analysis of 674 civilian fatalities. The analysis has focused on characterising the relationship between fatal exposure location, weather conditions (wind speed, temperature, relative humidity and drought indices), proximity to fuel, activities and decision making leading up to the death. The analysis demonstrates that civilian fatalities were dominated by several iconic bushfires that have occurred under very severe weather conditions. The fatalities from Australia's 10 worst bushfire days accounted for 64% of all civilian fatalities. Over 50% of all fatalities occurred on days where the McArthur Forest Fire Danger Index (FFDI) exceeded 100 (the current threshold for declaring a day as ‘catastrophic’) proximal to the fatality. The dominant location category was open air representing 58% of all fatalities followed by 28% in structures, and 8% in vehicles (6% are unknown). For bushfires occurring under weather conditions exceeding an FFDI value of 100, fatalities within structures represented over 60% of all fatalities. These were associated with people dying while attempting to shelter mainly in their place of residence. Of the fatalities that occurred inside a structure in a location that was specifically known, 41% occurred in rooms with reduced visibility to the outside conditions. Over 78% of all fatalities occurred within 30 m of the forest.

Modeling topographic influences on fuel moisture and fire danger in complex terrain to improve wildland fire management decision support

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

Point process modeling of wildfire hazard in Los Angeles County, California

The Burning Index (BI) produced daily by the United States government's National Fire Danger Rating System is commonly used in forecasting the hazard of wildfire activity in the United States. However, recent evaluations have shown the BI to be less effective at predicting wildfires in Los Angeles County, compared to simple point process models incorporating similar meteorological information. Here, we explore the forecasting power of a suite of more complex point process models that use seasonal wildfire trends, daily and lagged weather variables, and historical spatial burn patterns as covariates, and that interpolate the records from different weather stations. Results are compared with models using only the BI. The performance of each model is compared by Akaike Information Criterion (AIC), as well as by the power in predicting wildfires in the historical data set and residual analysis. We find that multiplicative models that directly use weather variables offer substantial improvement in fit compared to models using only the BI, and, in particular, models where a distinct spatial bandwidth parameter is estimated for each weather station appear to offer substantially improved fit.

Resource Location and Relocation Models with Rolling Horizon Forecasting for Wildland Fire Planning

A location and relocation model are proposed for air tanker initial attack basing in California for regional wildland fires that require multiple air tankers that may be co-located at the same air base. The Burning Index from the National Fire Danger Rating System is modeled as a discrete mean-reverting process and estimated from 2001–2006 data for select weather stations at each of 12 California Department of Forestry's units being studied. The standard p-median formulation is changed into a k-server p-median problem to assign multiple servers to a node. Furthermore, this static problem is extended into the time dimension to obtain a chance-constrained dynamic relocation problem. Both problems are solved using branch and bound in the numerical example. The relocation model is shown to perform better than the static location model by as much as 20–30% when using fire weather data to forecast short term future demand for severe fires, whereas relocating without rolling horizon forecasting can be less cost-effective than a static location model. The results suggest that state fire agencies should identify the threshold beyond which it would be more cost-effective to adopt a regional relocation model with forecasting from fire weather data, especially in a global warming environment.

Estimation of Fire Danger in Hawai'i Using Limited Weather Data and Simulation

The presence of fire in Hawai'i has increased with introduction of nonnative grasses. Fire danger estimation using the National Fire Danger Rating System (NFDRS) typically requires 5 to 10 yr of data to determine percentile weather values and fire activity. The U.S. Army Pōhakuloa Training Area in Hawai'i is located in the interface zone between windward and leeward weather conditions and needed to develop fire danger values but did not have sufficient weather or fire occurrence data. Use of simulation to estimate fire danger (expressed as fire risk) for areas with limited weather data was investigated. Influence of spatial resolution of weather information on fire risk was examined by comparing fire risk calculated using one or three weather stations and gridded weather predictions from the Mesoscale Spectral Model. Predicted gridded temperature was positively correlated with observed temperature; predicted and observed relative humidity were not significantly correlated. Simulated fire risk differed between weather data percentiles and between weather data resolutions. Predicted risk estimated from gridded weather data agreed more closely with observed risk estimated from weather data observed at all three remote automated weather stations. Correlation between simulated fire risk and the NFDRS Ignition Component was statistically significant for the single weather station simulations. Correlations between risk and the Ignition Component were not statistically significant for the three station and gridded weather data scenarios, which illustrates the difference between fire danger determined at broad spatial scales and fire risk resolved at finer spatial scales. Fire spread simulation modeling to estimate fire risk in areas with limited historical weather and fire occurrence data can provide finer-scale information than the NFDRS, which is better suited to larger, homogeneous areas with more complete fire and weather data. Values for the NFDRS Burning Index were determined and incorporated into the wildland fire management plan for Pōhakuloa Training Area.

Estimating Fuel Consumption for the Upper Coastal Plain of South Carolina

Abstract Recent changes in air quality regulations present a potential obstacle to continued use of prescribed fire as a land management tool. Lowering of the acceptable daily concentration of particulate matter from 65 to 35 μg/m3 will bring much closer scrutiny of prescribed burning practices from the air quality community. To work within this narrow window, land managers need simple tools to allow them to estimate their potential emissions and examine trade-offs between continued use of prescribed fire and other means of fuels management. A critical part of the emissions estimation process is determining the amount of fuel consumed during the burn. This study combines results from a number of studies along the Upper Coastal Plain of South Carolina to arrive at a simple means of estimating total fuel consumption on prescribed fires. The result is a simple linear relationship that determines the total fuel consumed as a function of the product of the preburn fuel load and the burning index of the National Fire Danger Rating System.