Pre-fire and post-fire surface fuel and cover measurementscollected in the southeastern United States for modelevaluation and development – RxCADRE 2008,2011 and 2012

RogerD.OttmarA,D,AndrewT.HudakB, Susan J. PrichardC,Clinton S.WrightA,Joseph C. RestainoC,Maureen C. KennedyC and Robert E. VihnanekA

AUSDA Forest Service, Pacific Northwest Research Station, Pacific Wildland Fire Sciences

Laboratory, 400 North 34th Street, Suite 201, Seattle, WA 98103, USA.BUSDA Forest Service, Rocky Mountain Research Station, 1221 South Main Street, Moscow,

ID 83843, USA.CSchool of Environmental and Forest Sciences, University of Washington, Box 352100,

Seattle, WA 98195, USA.DCorresponding author. Email: rottmar@fs.fed.us

Abstract. A lack of independent, quality-assured data prevents scientists from effectively evaluating predictions

and uncertainties in fire models used by land managers. This paper presents a summary of pre-fire and post-fire fuel, fuelmoisture and surface cover fraction data that can be used for fire model evaluation and development. The data werecollected in the southeastern United States on 14 forest and 14 non-forest sample units associated with 6 small replicate

and 10 large operational prescribed fires conducted during 2008, 2011, and 2012 as part of the Prescribed Fire Combustionand Atmospheric Dynamics Research Experiment (RxCADRE). Fuel loading and fuel consumption averaged 6.8 and4.1 Mg ha�1 respectively in the forest units and 3.0 and 2.2 Mg ha�1 in the non-forest units. Post-fire white ash cover

ranged from 1 to 28%. Data were used to evaluate two fuel consumption models, CONSUME and FOFEM, and todevelop regression equations for predicting fuel consumption from ash cover. CONSUME and FOFEM produced similarpredictions of total fuel consumption and were comparable with measured values. Simple linear models to predict pre-fire

fuel loading and fuel consumption from post-fire white ash cover explained 46 and 59% of variation respectively.

Additional keywords: ash, fire effects, fuel consumption, fuel loading, longleaf pine, prescribed fire.

Received 13 September 2014, accepted 11 August 2015, published online 13 October 2015

Introduction

Fuel consumption is defined as the amount of biomass that isfully combusted during a wildland fire. It is one of the criticalcomponents for estimating fire behaviour, amount of heatreleased and smoke produced, effectiveness of fire at reducing

fuel or exposing mineral soil, and many other fire effects suchas carbon reallocation, tree mortality and soil heating (Agee1993; Hardy et al. 2001; Agee and Skinner 2005; Peterson

et al. 2005; Urbanski et al. 2011; Ottmar 2013; Wright2013a, 2013b; Parsons et al. in press). For example, millions ofhectares are prescribed-burned each year in the southern

United States and these projects require estimates of fuelconsumption to meet smoke management, fuel treatment andecological restoration guidelines (Waldrop and Goodrick

2012; Ryan et al. 2013).To assist managers in planning for wildland fire, consump-

tion studies of shrubs, forbs, grasses, woody fuel, litter and duffin forests and rangelands have been conducted in temperate,

tropical and boreal regions of the world and offer datasets that

include fuel characteristics, fuel moisture, fuel consumption andenvironmental variables from bothwildfires and prescribed fires(Sandberg 1980; Brown et al. 1991; Scholl and Waldrop 1999;Ottmar and Sandberg 2003; Sullivan et al. 2003; Hollis et al.

2010; Ottmar et al. 2013; Wright 2013a). In most cases, thesedatasets have been used to develop fuel consumption modelsfound in software systems in use today such as CONSUME

(Prichard et al. 2007), FOFEM (Reinhardt et al. 1997), CanFIREand BORFIRE (de Groot et al. 2007, 2009) or in the earlydevelopment of new techniques to retrospectively predict sur-

face fuel loadings and consumption based on residual white ashcover, the first-order product of complete combustion (Stronachand McNaughton 1989; Hudak et al. 2013a, 2013b). Although

many of these models are mainstays of fire effects modelling,most of the systems and processes have not been thoroughlyquantitatively evaluated for uncertainties and potential errorbecause independent, fully documented, quality-assured fuel

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International Journal of Wildland Fire 2016, 25, 10–24

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consumption data are lacking (Cruz and Alexander 2010;Alexander and Cruz 2013). The one exception is the evaluationof CONSUME and the First Order Fire EffectsModel (FOFEM)provided by Prichard et al. (2014) using data collected in the

eastern and southeastern United States (Ottmar et al. 2012;Reid et al. 2012).

The current paper presents ground-based measurements of

fuel loading, fuel moisture content, fuel consumption and post-fire cover fractions of white ash and other surfacematerials. Thedata were collected as part of the Prescribed Fire Combustion

and Atmospheric Dynamics Research Experiment (RxCADRE)(Joint Fire Science Program 2014) to provide novel and criticalobservational data within six discipline areas necessary forbuilding and validating fire models (Fig. 1). The comprehensive

fuels dataset presented here will be useful for future fire and fuelconsumption model development and evaluation (Ottmar andRestaino 2014) and will support the fuel information needs of

other scientists participating in the project (Butler et al. 2015;Clements et al. 2015; Hudak et al. 2015; O’Brien et al. 2015;Rowell et al. 2015; Strand et al. 2015). To demonstrate the

usefulness of these datasets, we compared fuel consumptionmeasurements with predicted consumption from CONSUMEand FOFEM, two models that are commonly used by fire and

fuel managers in the United States. We also assessed whetherground cover fractions of white ash and other surface materialscorrelate significantly with pre-fire fuel loading and fuel con-sumption, thereby providing an alternative approach to predict-

ing fuel loading and consumption in the absence of pre-firefuels data.

Methods

Study areas

Twenty-eight sample units were established within 6 smallreplicate and 10 large operational prescribed fires at the JosephW. Jones Ecological Research Center (JJERC) and Eglin AirForce Base (Eglin AFB) in the southeastern USA in 2008, 2011

and 2012 (Table 1, Fig. 2). The 16 prescribed fires ranged in sizefrom 2 to 828 ha and the sample units ranged in size from 0.04 to19 ha.

The JJERC is located in the Lower Coastal Plain and Flat-wood Province of southwestern Georgia, with elevations rang-ing from 35 to 45 m above sea level (McNab and Avers 1994).

The climate of this region is characterised as humid subtropicalwith a mean annual precipitation of 131 cm distributed evenlythroughout the year, and mean daily temperatures ranging from

21 to 348C in summer and from 5 to 178C inwinter (Goebel et al.1997). The province is characterised by flat, weakly dissectedalluvial deposits over Ocala Limestone (Florea and Vacher2009). Parent materials consist of marine and continental sand

and clay deposits formed during the Mesozoic and Cenozoiceras (Wilson et al. 1999). The longleaf pine (Pinus palustris)–wiregrass (Aristida stricta) ecosystems have been maintained

using regular understorey prescribed burning (average returninterval of 2 or 3 years) since the 1930s (Hendricks et al. 2002).The woodlands are characterised by an overstorey of longleaf

pine and an understorey dominated by wiregrass, but with manyother species of perennial grasses, forbs and hardwood sprouts(Goebel et al. 1997, Drew et al. 1998).

Host landmanager

Fuel Meteorology Energy

Study plan andincident management

plan

Data collection,reduction, and analysis

Final report, datarepository, and papers

Emissions Fire effectsFire behaviour

Leadcoordinator

Observational data collection disciplines

Data manager

Fig. 1. Organisational diagram of the Prescribed Fire Combustion Atmospheric Dynamics Experiment

(RxCADRE) and the six discipline teams.

RxCADRE fuel and ash measurements Int. J. Wildland Fire 11

Eglin AFB, in the panhandle of western Florida, is charac-terised by large areas of xeric longleaf pine sandhill forest and

grass, and grass- and shrub-dominated military training areaskept in a tree-free state. Elevation ranges from 52 to 85 m abovesea level, the land is flat, and soils are characterised by

unconsolidated, well-drained sand deposits of Quartzipsam-ments of the Lakeland series (Overing et al. 1995).The climateis subtropical, with warm, humid summers and mild winters.

Mean annual temperature is 19.88C, with a mean annual precip-itation of 158 cm, most of which falls from June throughSeptember (Overing et al. 1995). Experimental prescribed fireswere conducted in both forest and non-forest areas at EglinAFB.

Forest burn units were characterised by an overstorey dominatedby longleaf pine mixed with various deciduous oaks (turkey oak(Quercus laevis), sand post oak (Q. margarettaAshe), blue jack

oak (Q. incanaBartram), sand live oak (Q. germinateSmall) andlaurel oak (Q. laurifolia)) (Hiers et al. 2007). Non-forest burnunits were selected for homogeneity in the coverage and density

of their herbaceous fuel although various shrubs were alsopresent (e.g. woody goldenrod (Chrysoma pauciflosculosa),lowbush huckleberry (Gaylussacia dumosa), gopher apple

(Licania michauxii), saw palmetto (Serenoa repens), persim-mon (Diospyros virginiana) and hawthorne (Crataegus spp.)).

All of the 2008 and 2011 prescribed burn sites at bothlocations were forested. Longleaf pine dominated the over-

storey, with turkey oak or saw palmetto (Serenoa repens)occurring in an understorey matrix of wiregrass and other

grasses (Fig. 3a). Of the nine prescribed burns conducted in2012, one was in a longleaf pine forest (Fig. 3b) and the eight

remaining burns were non-forest with a mix of grass and shrubs,predominantly turkey oak (Fig. 3c). All prescribed burnshad been treated with fire every 2 to 4 years to meet several

management objectives including fuel reduction to mitigate firehazard, longleaf pine ecosystem and associated wildlife habitatmaintenance, and for maintaining sites for military training

operations. One unit (L1G) also had been treated with a herbi-cide (hexazinone) to reduce hardwood recruitment. The 10operational burns were either ignited with all-terrain vehiclescarrying drip torches or helicopter-ignited with incendiary

spheres and typically burned using a strip heading or flankingfire. The six replicate burns were hand-ignited with a single stripof fire using a drip torch producing a single head fire. All burns

followed established prescription criteria for meeting land-management objectives; no burning occurred under extremelydry or wet conditions. Prescription weather parameters included

10-h fuel moisture between 4 and 20%, 1000-h fuel moisturebetween 15 and 40% and wind ,8.9 m s�1.

Field measurements

Pre-fire and post-fire fuel loading and consumption

Fuel sampling protocols were developed from earlier fuel

studies in the southeastern United States (Scholl and Waldrop1999; Ottmar et al. 2012; Wright 2013a) and were modified

Table 1. Sample unit information including location (Eglin Air Force Base (EAFB) or Joseph W. Jones Ecological Research Center (JJERC)),

burn date, cover type (forest or non-forest), size and associated prescribed burn unit

Sample unit ID Location Burn date Cover type Size (ha) Associated prescribed burn

2008_DubignonEast JJERC 06-Mar-08 Forest 1.52 Dubignon East

2008_NorthBoundary JJERC 05-Mar-08 Forest 1.52 North Boundary

2008_TurkeyWoods JJERC 03-Mar-08 Forest 1.52 Turkey Woods

2008_307B EAFB 02-Mar-08 Forest 1.52 307B

2008_608A EAFB 01-Mar-08 Forest 1.52 608A

2011_608A_NW EAFB 09-Feb-11 Forest 0.16 608A

2011_608A_SW EAFB 09-Feb-11 Forest 0.16 608A

2011_608A_SE EAFB 09-Feb-11 Forest 0.29 608A

2011_703C_W EAFB 06-Feb-11 Forest 0.16 703C

2011_703C_E EAFB 06-Feb-11 Forest 0.16 703C

2012_L2F EAFB 11-Nov-12 Forest 19.00 L2F

2012_L2F_HIP1 EAFB 11-Nov-12 Forest 0.04 L2F

2012_L2F_HIP2 EAFB 11-Nov-12 Forest 0.04 L2F

2012_L2F_HIP3 EAFB 11-Nov-12 Forest 0.04 L2F

2012_L1G EAFB 4-Nov-12 Non-forest 19.00 L1G

2012_L1G_HIP1 EAFB 4-Nov-12 Non-forest 0.04 L1G

2012_L1G_HIP2 EAFB 4-Nov-12 Non-forest 0.04 L1G

2012_L1G_HIP3 EAFB 4-Nov-12 Non-forest 0.04 L1G

2012_L2G EAFB 10-Nov-12 Non-forest 19.00 L2G

2012_L2G_HIP1 EAFB 10-Nov-12 Non-forest 0.04 L2G

2012_L2G_HIP2 EAFB 10-Nov-12 Non-forest 0.04 L2G

2012_L2G_HIP3 EAFB 10-Nov-12 Non-forest 0.04 L2G

2012_S3 EAFB 1-Nov-12 Non-forest 2.00 S3

2012_S4 EAFB 1-Nov-12 Non-forest 2.00 S4

2012_S5 EAFB 1-Nov-12 Non-forest 2.00 S5

2012_S7 EAFB 7-Nov-12 Non-forest 2.00 S7

2012_S8 EAFB 7-Nov-12 Non-forest 2.00 S8

2012_S9 EAFB 7-Nov-12 Non-forest 2.00 S9

12 Int. J. Wildland Fire R. D. Ottmar et al.

throughout 2012 after reviewing previous datasets to moreaccurately capture fuel characteristics and consumption and to

accommodate requirements of RxCADRE scientists. In 2008,destructive-sample fuel plots were established in one 5-hasystematic grid pattern in each of the five operational prescribedburn sites at Eglin AFB (.125 ha each) and JJERC (.40 ha

each). A total of 20 pre-fire and 20 post-fire fuel plots (1� 1 m)were alternately located at 20-m intervals along two paralleltransects 40 m apart. In February 2011, the two operational

prescribed burns were conducted at Eglin AFB (.125 ha each),with two widely separated 5-ha sampling sites in one burn andthree widely separated sampling sites in the other. One sampling

site in the latter case had 20 pre-fire and 20 post-fire fuel plots(1� 1 m) alternately situated at 5-m intervals along two paralleltransects 30 m apart (similar to the 2008 sampling design). The

other four sampling sites consisted of 20 pre-fire and 20 post-firefuel plots (1� 1 m) distributed at 5-m intervals around theperiphery of a 40� 40-m highly instrumented plot (HIP) tobetter characterise the fuel and consumption near the fire

behaviour and fire effects instrumentation. In November 2012,six small replicate prescribed fires (2 ha each) and three larger

operational prescribed fires (.125 ha each, comparable in sizewith the 2008 and 2011 prescribed burns) were conducted at

Eglin AFB. The small replicate prescribed burns were sur-rounded by 25 pre-fire and 25 post-fire fuel plots alternatelysituated at 10-m intervals. In each of the large operationalprescribed burns, 30 pre-fire and 30 post-fire fuel plots were

alternately located at 50-m intervals along three approximatelyparallel transects ,100 m apart (similar to the 2008 samplingdesign, but covering a much larger portion of the burn unit).

Three HIPs were located within each large operational pre-scribed burn. Each HIP consisted of 9 (L1G and L2G) or 12(L2F) pre-fire and 9 (L1G and L2G) or 12 (L2F) post-fire plots

alternately situated at 2.5-m intervals around the periphery of a20� 20-m area (similar to the 2011 sampling design). Clip plotsin the 2012 non-forest sample units were 1� 1 m as in 2008 and

2011. Clip plots in the 2012 forest sample units were reduced to0.5� 0.5 m to speed up sampling. Fuel fromwithin all clip plotswas collected and consolidated into four general categories:shrub, herbaceous (grasses and forbs), down-and-dead fine wood

(#7.6 cm in diameter) and litter. Samples were oven-dried at708C for 48 h, then weighed to determine loading.

(a)

Prescribed fire year Administrative boundary2008 Eglin Air Force Base

Joseph W. JonesEcological Research Center

608A (2008 and 2011)

N

0 20 40 80km

F l o r i d a

TurkeyWoods

NorthBoundary

DubignonEast

G e o r g i a

2011

2012

(b)

Fig. 2. (a) Location of the 16 RxCADRE experimental prescribed fires conducted in 2008, 2011 and 2012.

(b) Small replicate (S) and large operational (L) prescribed buns were selected for the 2012 RxCADRE

research project located on the B70 bombing range at Eglin Air Force Base, Florida. Only large operational

prescribed burns were selected for the RxCADRE research burns in 2008 and 2011.

RxCADRE fuel and ash measurements Int. J. Wildland Fire 13

Measureable large woody fuels (.7.6 cm) were onlyencountered at two of the 28 sample units (307B and L2F);therefore, large woody fuel was not included in the loading andconsumption totals considered for FOFEM and CONSUME

evaluation. However, woody fuel is the primary contributor topost-fire white ash cover (Hudak et al. 2013a). Consequently,the large-woody-fuel component was included in the surface

fuel loading and consumption totals at the 307B and L2F sampleunits for predicting total fuel loading and consumption

retrospectively by using post-fire white ash cover across all 28sample units. Planar intersect transects 22 m long (Brown 1974)originating at each fuel sample plot were used tomeasurewoody

fuels .7.6 cm in diameter in these two sample units.Fuel moisture sampling for each fuel bed category (herb,

shrub, fine wood, litter) was conducted 30 min before ignition

for each burn unit. Samples (5# n# 10) of each fuel bedcategory were collected inside the perimeter of each prescribedburn unit near sample units and sealed in airtight 6-L plastic

bags. All moisture samples were weighed as soon as possible inthe field, generally within 6 h of ignition. Samples were oven-dried at 708C for 48 h and then weighed to determine moisturecontent as a fraction of dry weight.

Post-fire surface cover fractions

Post-fire surface cover fractions at all sample units werevisually estimated by the same observer. Estimates were made

from fuels within a square quadrat equal in size to those used forclipping pre-fire fuels, and before they were disturbed duringpost-fire fuel collection. The four cover fractions of green

vegetation, litter (including dead vegetation and woody debris),white ash and mineral soil were estimated under the constraintthat they must sum to one (unity). Char cover was estimated

outside the sum-to-unity constraint and primarily represents thecombined percentage of litter but also some soil that wasblackened by the fire.

Fuel consumption model parameterisation

To represent fuel and environmental conditions for each sampleunit, we used sample data to run CONSUME version 4.2 andFOFEM version 6.0. Inputs included herbaceous, shrub, 1-, 10-,

100- and 1000-h downed wood biomass, and 10-h, 1000-h andduff fuel moisture (FM) content. Additional CONSUME inputsinclude an estimate of the percentage of the area burned, litter

depth (cm), coverage of litter (percentage), litter arrangement,duff derivation (from needles and leaves) and coverage of duff(percentage). We used CONSUME’s southeastern natural fuel

consumption equations. Additional FOFEM inputs includeforest cover type, season of burn (winter or spring), duff depth,duff biomass, litter biomass and percentage of logs that arerotten. Default FOFEM settings include region (southeast), fire

type (moderate) and consumption (natural fuel).There were missing input data for both models, which

required use of representative inputs for some model runs.

Measurements of 1000-h FM were not collected for the non-forest sites because there was no downed woody material.7.6 cm in diameter; moisture content of 41% collected from

the nearest forest site (L2F) was used. As little or no duff wasevident at any prescribed burn sites except L2F, a moderatemoisture content of 70% was assigned (Prichard et al. 2007).Post-burn photos were reviewed to estimate percentage area

burned and a median value of 80% assigned for all sites.

Data analysis

Fuel loading and fuel consumption

Fuel loading by fuel category was measured and calculatedas the mean component loading of all pre-fire or post-fire clipplots. Fuel consumption was calculated for each fuel bed

(a)

(b)

(c)

Fig. 3. (a) Typical forest prescribed burn area at JJERS in 2008, and (b, c)

forest and non-forest prescribed burn areas at Eglin Air Force Base in 2012.

14 Int. J. Wildland Fire R. D. Ottmar et al.

category by subtracting the mean post-fire loading from themean pre-fire loading for each set of plots. Because pre-fire andpost-fire fuel plot locations must always differ when sampling

methods are destructive, absolute consumption (Mg ha�1) andrelative consumption (%) could be calculated only after aggre-gating plot measures within each sample unit.

To assess the distribution of fuel loading data, we used anormal quantile–quantile plot (Q-Q) to visually test the normal-ity of the data. We then used a Shapiro–Wilk test to confirm

the significance of the non-normality of the data distribution.Levene’s test was used to examine within-site and across-sitevariance due to the non-normal distribution of the data. Toidentify potential outliers and protect against the misinterpreta-

tion of fuel loading data, sample variance was calculated foreach vegetation type and burn unit.

Fuel model comparison

We compared measured and predicted consumption of thefollowing fuel categories: herbaceous vegetation, shrubs, finedowned wood (1-, 10- and 100-h size classes) and litter. We did

not compare large woody fuel or duff fuel consumption pre-dicted by CONSUME or FOFEM because of insufficient obser-vations for validation; measureable amounts of large woody

fuels only existed at two sample units (307B and L2F), and ofduff at only one sample unit (L2F). For each of the four fuel bedcategories considered, we plotted predicted consumptionagainst measured consumption and conducted ordinary least-

squares regression to evaluate goodness of fit and trends inmodel residuals. Model evaluation is based on model residuals,which we express as predicted values minus measured values.

We characterised model uncertainty using the paired t-test forequivalence (Robinson and Froese 2004; Robinson 2013) on themodel residuals to estimate a ‘region of indifference’ for the

predicted consumption relative to measured consumption(Prichard et al. 2014). The calculated regions of indifference(Mg ha�1) represent the range in which predicted values fail to

reject the null hypothesis that predicted andmeasured values arenot equivalent. In other words, the regions of indifferencerepresent the range in which predicted and measured valuescan be considered statistically equivalent.

Retrospective fuel load and consumption predictions

Plot-level measurements (loading, consumption and cover)were aggregated to the sample unit level for analysis. Owing

to typically log-normal data distributions, Spearman correla-tions were used to test the strength of relationships betweenpost-fire surface cover fractions and surface fuel loading or

consumption measurements and across all sample units. Naturallog-transformations were used to normalise log-normal distri-butions before developing simple linear regression models topredict fuel load and consumption from highly correlated post-

fire cover fractions. Back-transformation of the model predic-tions from the geometric scale to the arithmetic scale introducesbias, but a bias correction factor (cb) can be calculated based on

the mean square error (MSE) of the model residuals as perBaskerville (1972):

cb ¼ expð0:5MSEÞ ð1Þ

where the back-transformed predictions are multiplied by cb.

Data were analysed using R statistical software (R DevelopmentCore Team 2012).

Results

Fuel loading and fuel consumption

The 28 sample units were divided into two groups (‘forest’and ‘non-forest’) because of differences in fuel categories andpre-fire fuel loads (Table S1 in the Supplementary material,available online only). Pre-fire surface fuels in the forest

units (mean � standard deviation) were often dominated bylitter (3.5� 1.7 Mg ha�1). Fine wood (1.6� 0.8 Mg ha�1),shrubs (1.1� 1.3 Mg ha�1) and herbaceous biomass

(0.6� 0.4Mg ha�1) were also present (Table 2). Pre-fire surfacefuels in the non-forest units were dominated by herbaceous

Table 2. Forest sample unit (n 5 14) and non-forest sample units

(n 5 14) mean and standard deviation (s.d.) of preburn fuel loading,

measured and predicted consumption and relative consumption by

fuel bed category including total, herb, shrub, fine wood (combined

1-, 10-, 100-h time-lag classes) and litter

Fuel bed

category

Method Preburn

loading

Consumption

Mean s.d. Mean s.d. Mean s.d.

(Mg ha�1) (Mg ha�1) (%)

Forest units

Total Measured 6.8 2.4 4.1 1.7 60.8 18.8

CONSUME 5.1 2.0 73.4 12.2

FOFEM 4.6 2.1 65.0 16.6

Herb Measured 0.6 0.4 0.6 0.4 92.2 15.3

CONSUME 0.5 0.4 87.6 11.9

FOFEM 0.6 0.4 93.7 12.4

Shrub Measured 1.1 1.3 0.5 0.6 40.9 24.3

CONSUME 0.8 0.9 77.9 4.0

FOFEM 0.5 0.8 32.0 36.4

Fine wood Measured 1.6 0.8 0.5 0.6 29.4 35.3

CONSUME 1.1 0.6 71.5 18.5

FOFEM 0.3 0.6 17.6 24.2

Litter Measured 3.5 1.7 2.7 1.7 76.5 22.5

CONSUME 2.3 1.0 69.4 18.8

FOFEM 3.2 1.7 92.6 19.4

Non-forest units

Total Measured 3.0 1.0 2.2 1.0 75.3 15.9

CONSUME 2.4 0.7 81.2 5.7

FOFEM 2.9 1.0 97.1 2.7

Herb Measured 1.7 0.3 1.4 0.3 86.4 8.5

CONSUME 1.5 0.2 93.0 0.6

FOFEM 1.7 0.2 99.9 0.3

Shrub Measured 0.7 0.8 0.4 0.7 48.2 35.2

CONSUME 0.5 0.6 68.0 20.0

FOFEM 0.7 0.7 88.9 26.5

Fine wood Measured 0.1 0.2 0.1 0.1 41.1 37.1

CONSUME 0.1 0.1 77.2 29.4

FOFEM 0.1 0.1 28.7 24.9

Litter Measured 0.5 0.3 0.4 0.3 81.4 26.6

CONSUME 0.2 0.1 31.5 2.6

FOFEM 0.5 0.3 99.6 0.9

RxCADRE fuel and ash measurements Int. J. Wildland Fire 15

biomass (1.7� 0.3 Mg ha�1), whereas shrub loading displayedthe greatest pre-fire variance (0.7� 0.8Mg ha�1) (Table 2). Pre-fire loading for all fuel categories in both groups was universally

more variable than post-fire fuel loading. Pre-fire variance insurface fuel loading was greatest in the fine wood and shrubcategories. Pre-fire loading and variance of surface fuels in

forest units were both generally two to three times greater thansurface fuels measured in non-forest units.

Examination of the data distribution for pre-fire surface fuel

loading revealed a potentially non-normal pattern in the forestand non-forest units (Fig. S1). The null hypothesis that pre-firesurface fuel loading is normally distributed in non-forest unitswas rejected (Shapiro–WilkW¼ 0.8219,P¼ 0.0094). The same

null hypothesis for pre-fire loading in forest units was alsorejected (W¼ 0.8375, P¼ 0.0151).

Due to the non-normality of the data, Levene’s tests were

used to explore patterns of variance in the pre-fire surfaceloading data. In both forest (P¼ 0.4734) and non-forest(P¼ 0.0735) units, pre-fire variance in surface fuel loading

was not significantly different (a¼ 0.05) for burn units withdifferent sampling intensity (i.e. n¼ 9–30). This suggests thatthe evolution of the sampling procedure did not have a signi-

ficant effect on inferences from the data. Pre-fire variance ofsurface fuel loading measured in units at JJERC relative to unitsin Eglin AFB was also not significantly different (a¼ 0.05)(P¼ 0.4493), suggesting that inferences may be adequately

made across both study areas.There were large differences in the total mass of fuel

consumed between the forest and non-forest units, ranging from

1.7 to 8.9 Mg ha�1 in forest units and from 1.3 to 5.3 Mg ha�1 innon-forest units (Table S1). Most of the fuel consumed in theforest units was in the litter category, whereas consumption in

the non-forest units was dominated by the herbaceous category,followed by litter and shrubs (Table 2). Higher pre-fire fuelloading in the forest units generally led to greater absolute butlower relative fuel consumption than in the non-forest units. In

forest units, percentage consumption by fuel bed category wasgreatest in herbaceous fuels (92%), followed by litter (77%),shrubs (41%) and fine wood (29%). Percentage consumption in

non-forest units followed a similar pattern and was greatest inherbaceous fuels (86%), followed by litter (81%), shrubs (48%)and fine wood (41%) (Table 2).

Day-of-burn FM content ranged widely across all fuel bedcategories (Table 3). Relative to other fuel bed categories,variance was smallest for litter fuels in both forest and non-

forest units. Shrubs had the largest variance in forest units,whereas herbs had the largest variance in non-forest units.

Fuel model comparison

In the forest sample units, total consumption averaged61� 19% as compared with the 73� 12% predicted by

CONSUME and 65� 17% predicted by FOFEM (Table 2). Inthe non-forest sample units, total consumption averaged75� 16% compared with the 81� 6% predicted by CONSUME

and 97% � 3% predicted by FOFEM (Table 2). Overall,CONSUME and FOFEM made similar predictions of total fuelconsumption (Fig. 4) with no significant bias in model residuals(Table 4). Based on calculated regions of indifference, predicted

values can be considered to be statistically equivalentwithin �1 Mg ha�1.

CONSUME and FOFEM accurately predicted herbaceousfuel consumption in the combined forest and non-forest sampleunits with R2 values of 0.92 (P, 0.01) in each simple linear

regression model. CONSUME predictions had no significantbias whereas FOFEM predictions had a significant positive bias(Table 4, Fig. 5). Regions of indifferencewere narrow; predictedvalues can be considered equivalent to measured values within

�0.10 Mg ha�1 in CONSUME and �0.20 Mg ha�1 in FOFEM(Fig. 4).

Predicted shrub consumption in both CONSUME and

FOFEM was significantly related to measured consumption(R2¼ 0.68 and 0.71 respectively). However, models signifi-cantly overpredicted shrub consumption and had a significant

positive bias (Fig. 5). Regions of indifference were wide;predicted and measured shrub consumption can be consideredstatistically equivalent within �0.45 Mg ha�1 for CONSUMEand �0.35 Mg ha�1 for FOFEM predictions (Fig. 4).

CONSUME predictions of fine wood consumption wereweakly related to measured consumption, with R2 values of0.27 (P, 0.01) and with a significant positive bias in model

residuals (Figs. 4, 5). Even though FOFEM predictions were notcorrelated with measured consumption, they provided a closer

Table 3. Fuel moisture content (% of dry weight) and standard

deviation (s.d.) of pre-fire fuel bed categories including herb, shrub,

fine wood (combined 1-, 10-, 100-h time-lag classes) and litter

One set of fuel moisture samples was associated with more than one sample

unit if time to collect samples was limited

Sample unit ID Fuel moisture (%)

Herb Shrub Fine wood Litter

Mean s.d. Mean s.d. Mean s.d. Mean s.d.

Forest units

2008_Dubignon East 39.4 8.5 12.2 1.0 44.2 6.7 18.8 2.3

2008_North Boundary 54.7 9.6 57.2 25.9 19.8 4.3

2008_Turkey Woods 48.9 18.5 – 22.5 10.4 11.7 1.3

2008_307B 31.1 9.0 64.1 63.3 23.0 9.8 9.5 4.2

2008_608A 18.6 4.6 32.0 19.8 22.6 8.9 16.4 4.7

2011_608A_NW 15.3 5.5 12.6 0.9 50.2 13.1 12.6 5.0

2011_608A_SW – 12.6 0.9 56.4 23.0 15.1 3.9

2011_608A_SE 39.3 11.1 79.0 66.6 78.3 22.4 22.7 5.6

2011_703CA 30.7 6.2 83.2 59.0 61.4 25.2 27.7 13.3

2012_L2FB 92.7 58.5 118.6 43.2 18.5 2.9 11.4 4.1

Non-forest units

2012_L1GC 93.4 54.8 131.6 35.0 10.7 5.2

2012_L2GD 82.2 54.7 131.0 22.9 8.5 2.5

2012_S3 108.8 61.3 142.8 22.1 6.4 1.0

2012_S4 109.7 61.3 144.7 31.3 8.2 6.4

2012_S5 105.1 57.2 167.7 55.6 8.6 5.6

2012_S7 100.7 55.7 124.6 46.1 13.3 3.2

2012_S8 102.7 65.4 124.3 27.4 9.8 1.2

2012_S9 106.7 71.7 123.0 34.3 7.7 0.2

AIncludes 2011_703C_E and 2011_703C_W.BIncludes 2012_L2F_HIP1, HIP2, and HIP3.CIncludes 2012_L1G_HIP1, HIP2, and HIP3.DIncludes 2012_L2G_HIP1, HIP2, and HIP3.

16 Int. J. Wildland Fire R. D. Ottmar et al.

estimate to measured values than CONSUME based on calcu-lated regions of indifference (Table 4).

CONSUME and FOFEM predictions of litter consumptionare both significantly related to measured litter consumption

(R2¼ 0.71 and 0.80 respectively; P, 0.01). Neither modelprediction was significantly biased, but CONSUME tended tounderpredict consumption whereas FOFEM tended to overpre-dict consumption (Fig. 5).Models were comparable in estimated

8

Total Herb Shrub

LitterFine wood

Mea

sure

d (M

g ha

�1 )

6

4

2

8

6

4

2

0

2 4 6 8 0 0.5 1.0 1.5 2.0 0 0.5 1.0

CONSUMEFOFEM

1.5 2.0 2.5 3.0 3.5

0 0.5 1.0 1.5 2.0 2.5 3.0 0 2

Predicted (Mg ha�1)

4 6 8

2.0

1.5

1.0

0.5

0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0

3.0

2.5

2.0

1.5

1.0

0.5

0

Fig. 4. Predicted versusmeasured fuel consumption (Mgha�1) for total, herb, shrub, finewood (,7.6 cm in diameter) and litter categories.Open

symbols represent CONSUME predictions, and closed symbols represent FOFEM predictions. Regions of indifference, for which predicted and

measured consumption can be considered statistically equivalent, are represented by solid lines for CONSUME and hatched lines for FOFEM.

Table 4. Model fit, bias and equivalence of comparisons between measured consumption values and predicted values from

CONSUME and FOFEM from the combined forest and non-forest units

P-values for all model fits were ,0.01

Fuel bed category Model fit (R2) Bias Regions of indifference (Mg ha�1)

CONSUME FOFEM CONSUME FOFEM CONSUME FOFEM

Total 0.5118 0.5133 No bias P¼ 0.0794 No bias P¼ 0.0776 �1.00 �0.95

Herb 0.9213 0.9211 No bias P¼ 0.4702 Positive P¼ 0.0089 �0.10 �0.20

Shrub 0.6762 0.7100 Positive P¼ 0.0056 Positive P¼ 0.0370 �0.45 �0.35

Fine wood 0.2665 0.0000 Positive P¼ 0.0071 No bias P¼ 0.4430 �0.60 �0.30

Litter 0.7084 0.8010 No bias P¼ 0.0653 No bias P¼ 0.0614 �0.65 �0.60

RxCADRE fuel and ash measurements Int. J. Wildland Fire 17

equivalence with measured values; regions of indifferencewere 0.65 and 0.60 Mg ha�1 for CONSUME and FOFEMrespectively (Table 4).

Post-fire surface cover fractions

Post-fire surface cover was primarily composed of litter

(53� 17%) and mineral soil (mean 39� 18%) with minorcontributions of green vegetation (mean 3� 4%) and white ash(4� 3%). Variability between sample units was high for all

cover fractions: mineral soil cover ranged from 4 to 81%, whiteash cover from 1 to 28%, litter cover from 14 to 93%, greenvegetation cover from 0 to 10%, and the black char fraction in

the post-fire plots ranged from 13 to 90%. Litter was the domi-nant post-fire cover fraction in the forest units, given the higherfuel loads than in the non-forest units, whereas mineral soil wasmore prevalent in the non-forest units (Fig. 6). Although a

median of 80% of the plot area burned, slightly less than half(48� 19%) of the post-fire litter and soil components werecharred because most exposed mineral soil remained uncharred.

White ash cover was greater in the forest than in the non-forestunits (Fig. 6) owing to the greater availability of mostly finewoody fuels to consume.

All post-fire surface cover fractions were significantly cor-related with pre-fire and post-fire fuel loadings, except greenvegetation (Table 5). All cover fractions were significantlycorrelated with absolute consumption, but only soil cover was

significantly correlated with relative consumption. Exposedmineral soil correlated highly with fuel loading measured pre-fire (r¼ 0.79, P, 0.001) and post-fire (r¼ 0.77, P, 0.001)

(Table 5). However, unlike white ash, mineral soil cover variedhighly before and after the fire (Fig. 6). If the pre-fire soil cover

fraction is subtracted from the post-fire cover fraction to calcu-late fractional cover change, then the Spearman (r ) correlationswith pre-fire and post-fire fuel loadings decrease to slightly

less-than-significant correlations of �0.69 (P¼ 0.07) and�0.71 (P¼ 0.06) respectively. The increase in mineral soilcover caused by the fire correlates highly with relative con-

sumption (r 5 0.79, P¼ 0.03), particularly relative consump-tion of the woody (r 5 0.90, P¼ 0.005) and litter (r 5 0.92,P¼ 0.001) fuel bed categories. Measured pre-fire and post-firefuel loadings were indicators of consumption: absolute fuel

consumption was significantly correlated with pre-fire fuelloads (r¼ 0.83, P, 0.001), especially in the litter category(r¼�0.86, P, 0.001), whereas relative fuel consumption was

significantly correlated with post-fire fuel loading (r¼�0.87,P, 0.001) but not to any particular fuel bed category. Fromamong the surface cover materials, white ash cover was most

significantly correlated with absolute consumption (Spearman’sr¼ 0.76, P, 0.001) and was nearly as highly correlated withpre-fire fuel loading (r ¼ 0.73, P, 0.001) (Table 5). White ashcover was most strongly correlated with the herbaceous, woody

and litter categories of pre-fire fuel loads, and the herb and littercategories of consumption (Table 6).

Retrospective fuel load and consumption predictions

Given these high correlations and the fact that white ash is thefirst-order product of complete combustion, we selected white

ash as the surface cover fraction from which to retrospectivelypredict fuel loading and consumption. Because the variableswere not normal according to the Shapiro–Wilk normality test

(ash:W¼ 0.6081, P, 0.001; fuel load:W¼ 0.8709, P¼ 0.003;consumption: W¼ 0.8913, P¼ 0.007), we applied natural

CONSUME

Total

4

2

0

�2

�4

Herb Shrub Fine wood Litter Total Herb Shrub Fine wood Litter

FOFEM

Pre

dict

ed –

Mea

sure

d (M

g ha

�1 )

Fig. 5. Boxplots of residuals (predicted – measured) in Mg ha�1 for comparisons with CONSUME and FOFEM.

18 Int. J. Wildland Fire R. D. Ottmar et al.

logarithm (ln) transformations to normalise the data distribu-tions (ash: W¼ 0.9364, P¼ 0.09; fuel load: W¼ 0.9554,P¼ 0.27; consumption: W¼ 0.9876, P¼ 0.98). Simple linear

regression models to predict pre-fire fuel loading and con-sumption from percentage white ash cover were both highlysignificant (Fig. 7a, b). To adjust for logarithmic bias, correction

factors of 1.07 and 1.06 calculated by Eqn 1 were applied whileback-transforming the fuel load and consumption predictionsrespectively from geometric to arithmetic scales. The resultingpredictions were within 2 Mg ha�1 of observed fuel loadings

and 1 Mg ha�1 of observed consumption and highly significantfor both response variables (Fig. 7c, d ).

Soil

Dubignon East (F)North Boundary (F)

Turkey Woods (F)307B (F)608A (F)

608A – NW (F)608A – SW (F)608A – SE (F)703C – W (F)703C – E (F)

L2F – HIP 1 (F)L2F – HIP 2 (F)L2F – HIP 3 (F)

L1G (N)L1G – HIP 1 (N)L1G – HIP 2 (N)L1G – HIP 3 (N)

L2G – HIP 1 (N)L2G – HIP 2 (N)L2G – HIP 3 (N)

S3 (N)S4 (N)S5 (N)S7 (N)S8 (N)S9 (N)

0 100 10050050

Surface material (%) Charred material (%)

L2G (N)

L2F (F)

Ash Litter Vegetation Char

Fig. 6. Mean (s.e. bars) percentage cover of surfacematerials ocularly estimated post-fire in 28 sample units. Surface cover fractions ofmineral

soil, white ash, litter and green vegetationmaterials (left) were constrained to sum to 100%,whereas the cover fraction of charredmaterial (right)

is the percentage of the surface that was charred.

Table 5. Spearman (r) correlations between surface fuel loading or consumption and post-fire surface cover fractions

Significant correlations are indicated as follows: ***, P, 0.001; **, P, 0.01; *, P, 0.05

Green vegetation (%) Litter (%) Black char (%) White ash (%) Mineral soil (%)

Pre-fire loading (Mg ha�1) �0.39* 0.64*** 0.68*** 0.73*** �0.79***

Post-fire loading (Mg ha�1) �0.04 0.61*** 0.42* 0.53** �0.77***

Consumption (Mg ha�1) �0.46* 0.42* 0.58** 0.76*** �0.52**

Consumption (%) �0.27 �0.37 �0.06 �0.15 0.48*

Table 6. Spearman (r) correlations between post-fire white ash frac-

tion (%) and herb, shrub, woody and litter categories of pre-fire fuel

loadings and consumption

Significant correlations are indicated as: ***, P, 0.001

Herb

(Mg ha�1)

Shrub

(Mg ha�1)

Woody

(Mg ha�1)

Litter

(Mg ha�1)

Pre-fire loading

(Mg ha�1)

�0.71*** 0.27 0.72*** 0.80***

Consumption

(Mg ha�1)

�0.66*** 0.29 0.08 0.86***

RxCADRE fuel and ash measurements Int. J. Wildland Fire 19

Discussion

Ground measurements of fuel and fuel consumed supported

other science disciplines participating in the RxCADRE study(Butler et al. 2015; Clements et al. 2015; Hudak et al. 2015;O’Brien et al. 2015; Rowell et al. 2015; Strand et al. 2015) and

providing a valuable dataset for fuel consumption model eval-uation and advancement. The relative percentage consumptionwas similar to values reported by Prichard et al. (2014) andsuggests that this validation set will be a good representation of

southern pine sites. We observed the expected variation of fueland fuel consumption between the forest and non-forest sites.Fuel loading and fuel consumption on forest sites were twice

that of non-forest sites because the forest litter loading waslarger and mostly consumed during the experimental fires(Table 2).

Variability in measured moisture content was observed andexpected because the research burns ranged from November to

March in three different years. This variability provides a rangeof moisture content inputs for evaluating fuel consumption andother fire models.

To demonstrate the use of these datasets in model validation,pre-fire loading and fuel consumption datawere used to evaluatethe fuel consumptionmodel results in CONSUME and FOFEM.

Although a similar evaluation of CONSUME and FOFEM fuelconsumption predictions was conducted by Prichard et al.

(2014) and Wright (2013a) on southeastern pine sites, datacollected for the RxCADRE provide additional independent

points of comparison with which to evaluate the same models.Overall, CONSUME and FOFEM offer similar predictions oftotal fuel consumption. Both models tended to overpredict total

consumption (Fig. 5), but this biaswas non-significant (Table 4).Assessing individual fuel bed components, CONSUME gener-ally overpredicted herb, shrub and fine wood consumption

whereas FOFEM generally overpredicted herb, shrub and litter

1 : 1

2.5

2.0

1.5

1.0

0.5

0

0 0.5 1.0 1.5 2.0

y � 0.44103x � 1.00786

P � 0.0001Adj. R 2 � 0.52

RMSE � 1.97

P � 0.0001Adj. R 2 � 0.46

RMSE � 1.02

P � 0.0001Adj. R 2 � 0.59

y � 0.40432x � 0.61661

P � 0.0001Adj. R 2 � 0.51

2.5

0

1 : 11 : 1

(c)

(a) (b)

(d )

1 : 1

8

6

4

2

0

12

8

10

4

6

2

0

0

Obs

erve

d fu

el lo

adin

g (M

g ha

�1 )

2 4 6

Predicted fuel loading (Mg ha�1) Predicted consumption (Mg ha�1)

Obs

erve

d co

nsum

ptio

n (M

g ha

�1 )

In(C

onsu

mpt

ion

(Mg

ha�

1 ))

In(P

re-f

ire lo

adin

g (M

g ha

�1 )

)

8 10 12 2 4 6 8

In(Ash (%))In(Ash (%))

0 0.5 1.0 1.5 2.0 2.5

2.5

2.0

1.5

1.0

0.5

0

Fig. 7. Simple linear regression models predicting (a) pre-fire fuel loading, and (b) fuel consumption from post-fire white ash cover (%).

Natural log-transformswere used to normalise the log-normal distributions. Back-transformation with bias correction yielded the predicted vs

observed relationships for (c) pre-fire fuel loading, and (d ) fuel consumption.

20 Int. J. Wildland Fire R. D. Ottmar et al.

consumption. The overprediction may be partially explained byCONSUME and FOFEM assuming homogeneous fire spreadacross a continuous fuelbed. Furthermore, fuel consumption

models within CONSUME and FOFEM were derived usingmore data collected from the western United States whereburning conditions, fuel loading and fuel arrangement can be

much different than in the southernUnited States (Ottmar 2013).The overprediction may lead to overestimation of smoke forsmoke management planning and other potential fire effects

such as tree mortality and mineral soil exposure.We found strong correlations between predicted and mea-

sured herb, shrub and litter consumption, and weak correlationsbetween predicted andmeasured fine wood consumption in both

models (Fig. 4). Because prescribed burns generally consume ahigh proportion of herbaceous biomass, measured and predictedherb consumption correlations are both generally high across

sites. FOFEM assumes 100% consumption (Reinhardt et al.1997), and CONSUME uses a value of 92.7% derived fromprescribed burning experiments in grasslands (Prichard et al.

2007). Based on narrow regions of indifference in statisticalcomparisons of both modelled values, predicted herb consump-tion would closely approximate measured values in similar sites

and burning conditions.Predicted and measured shrub consumption values were

significantly correlated in the present study, but both modelstended to overpredict shrub consumption (Fig. 5). The wide

regions of indifference and overprediction bias in CONSUMEand FOFEM outputs indicate that actual consumption might beconsiderably lower than predicted consumption, which could be

of particular concern in sites with high shrub loading. Bothmodel predictions of litter consumption were highly correlatedwith measured values. Although neither model had significant

bias in comparisonwithmeasured values, CONSUMEgenerallyunderpredicted litter consumption whereas FOFEM’s assump-tion of 100% tended to overpredict consumption. In either case,calculated regions of indifference suggest that predicted values

will be within ,0.6 Mg ha�1 of measured values.Our study highlights a need for better predictive models of

fine-wood consumption in the southeastern United States. There

was little correlation between predicted and measured fuelconsumption of fine wood. Although CONSUME predictionswere statistically correlated with measured consumption, the

model predicted very high consumption (72 and 77%) versusactual consumption (29 and 41%) for the forest and non-forestsample units, respectively. FOFEM predictions (18 and 29%)

have narrower regions of indifference and are probably moresuitable for providing estimates of fine wood consumption.

Post-fire surface cover fraction estimates have utility forretrospective inferences, as we demonstrated by using white ash

fraction, the direct result of complete combustion (Smith andHudak 2005), and therefore the surface material most highlycorrelated with consumption (Table 5), as a predictor of con-

sumption. This result demonstrates the potential utility of whiteash cover for retrospective estimates of fuel loading and con-sumption on which emissions estimates are based (Jenkins

et al. 1998), especially in wildfire situations where pre-firefuel loading is typically unknown. The root-mean-square errorfrom our retrospective prediction of consumption based onwhite ash cover matched that estimated with FOFEM and

CONSUME (1 Mg ha�1) but for much less time and effort.White ash, however, is a minor post-fire cover constituent thatcan quickly dissipate, making it important to quantify immedi-

ately post-fire, or as soon as possible before rain or wind eventsdisturb and redistribute it (Hudak et al. 2013a; Bodı et al. 2014).Litter, soil and black char persist much longer thanwhite ash and

have greater areal coverage, making it more feasible to quantifythem not just on the ground but remotely (Hudak et al. 2007).This idea of scalable variables is supported by Smith et al.

(2007), who found black char fraction to be a good indicator oftree mortality in ponderosa pine forests. Black char is indicativeof incomplete combustion and is more resistant to environmen-tal degradation than the original biomass. This makes char

production an effective means to sequester carbon (Santınet al. 2015), especially in fuel bed types with a sizeable largewoody or duff fuel component. Lewis et al. (2011) found

significant correlations between fuel consumption and post-firecover materials (green vegetation, litter, black char, white ashand exposed mineral soil or rock) estimated on the ground and

by remote sensing at the 2004 Taylor Complex wildfires ininterior Alaska. In that study, the post-fire cover measurestrongly correlated with fuel consumption was green vegetation

(or the lack thereof), rather than black char or white ash. Furtherresearch is needed to improve our understanding of the utility ofpost-fire surface cover fractions for retrospective assessments offire effects, especially for fire severity applications (Morgan

et al. 2014).The present research project also provided an opportunity

to modify and calibrate fuel loading and fuel consumption

inventory techniques on sites with a fairly flat and homogeneousfuel bed. Although we used a standard sampling protocol ofdestructive-sample plots, a higher concentration of plots should

be considered to reduce the error associatedwith fuel variability.The next step will be to extend this research to more complexfuel beds with greater fuel loading and spatial variability and toburn them under more extreme conditions to provide a more

robust dataset for evaluating fire models. The fuel beds of thisstudy were characteristic of longleaf pine understories that areregularly burned and therefore lacked large, dead and down

woody fuels or duff. These fuel bed components leave a thicklayer of white ash if fully combusted, or black char if not. Intypes of fuel beds with large woody fuels or duff components, it

would be advisable to measure the depth of white ash and blackchar, in addition to coverage, to allow the volume of white ashand black char to be estimated. Further, bulk densitymeasures of

white ash and black char are also recommended to convertvolumetric units to mass quantities.

The quality-assured datasets of pre-fire fuel loading, FM,fuel consumption and surface cover fractions collected as part of

the RxCADRE project in the southeastern United States in2008, 2011 and 2012 provide critical observational data withinsix discipline areas necessary for building and evaluating fire

models. These fuel datasets are publicly available from the USForest Service ResearchData Archive (Hudak 2014; Ottmar andRestaino 2014).

Conclusion

These validation datasets of ground-based measurements offuel loading, FM content, fuel consumption and surface cover

RxCADRE fuel and ash measurements Int. J. Wildland Fire 21

fractions provide opportunities to evaluate and advance opera-tional fire models. The RxCADRE fuel datasets are available ona globally accessible repository maintained by the US Forest

Service Research Data Archive (Hudak 2014; Ottmar andRestaino 2014) for use in testing and evaluation of fuel, fire andfire effects models.

Our datasets were used to evaluate how accurately CON-SUME and FOFEM predict fuel consumption in forest andnon-forest types of the southeastern United States. Overall,

CONSUME and FOFEM offer reasonable predictions of totalfuel consumption and, in similar sites and prescription windows,could be expected to be within 1 Mg ha�1 of actual fuel con-sumption. Models differed only slightly in comparison with

measured values. These findings are similar to those in Prichardet al. (2014) and suggest that either CONSUME or FOFEMwould be suitable for predicting total fuel consumption in

southeastern longleaf pine forests with similar fuel characteris-tics. The models are less reliable for estimating fuel consumptionfor individual fuel bed components including fine wood and

shrubs. Relationships between surface fuel loadings and totalconsumption (laborious measurements) and post-fire surfacecover fractions (easy measurements) producedmostly significant

correlations. In particular, we demonstrated that white ash coveris a strong predictor of pre-fire fuel loading and surface fuelconsumption, justifying the quantification of white ash inretrospective assessments of fuel consumption and fire severity.

Although the datasets presented can be used for modelvalidation, the amount of fuel consumption was quite smallfor both the forest and non-forest sites because of limited fuel

loading and the narrow range of environmental conditions.Additional fuel and fuel consumption datasets will be neededthat characterise heavier fuel loads in complex terrain burned

under a range of fuel moisture and environmental conditions inother parts of the country. This will provide a more robustdataset for improved model validation.

Acknowledgements

We acknowledge funding from the Joint Fire Science Program (project no.

11-2-1-11), with additional support from the Pacific Northwest, Rocky

Mountain, Northern and Southern research stations of the US Forest Service

and from the National Fire Plan. We thank James Furman, J. Kevin Hiers,

BrettWilliams and the entire staff at JacksonGuard, EglinAFB, andLindsey

Boring and Mark Melvin at the JJERC for hosting the RxCADRE and

providing the logistical and planning support to complete the research burns.

We also thank Jon Dvorak for assistance with the fuel data collection and

reduction, along with Eva Strand, Donovan Birch, Benjamin Bright and

Ben Hornsby.

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