Developing the next generation of fuel loading data useful for emissions modeling

Nancy French (PI), Susan Prichard (Co-I)Anne Andreu, Michael Billmire, Paige Eagle, Eric Kasischke, Maureen Kennedy, Sim Larkin, Don McKenzie, Roger Ottmar, and Kjell Swedin

JFSP project # 15-1-01-01

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Outline

• Background• Emissions modeling &

uncertainty• Wildland fuel

characterization• Project objectives

• Project status • Database development• Data visualization &

distribution• Sensitivity analysis

• Expected applications• Other Considerations

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Estimating Emissions

Et = A·βB ·Efg

Et is the total EmissionsA is the total Area burned (ha)β is the fraction of biomass/fuel consumed during fireB is the Biomass/fuel loading (t ha-1)Efg is the Emission Factor for each gas species

(g gas/kg fuel) [e.g. CO2, CO, CH4, NMHC]

Total Emissions:

CombustionFactors - ß

Area Burned - A

Fuel Consumption

Emission FactorsEmissions

Fuel Loading - B

Et = A·(βaBa + βbBb …)·EfgPartition fuels into strata

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FCCS Fuelbed Strata

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Estimating Emission Source

Fuel loading and the proportion of the fuel that is combusted (consumption) have highest uncertainty.

Fuel Loading

Fuel Consumption

Emission Factor

Emission Produced

Largest Error (CV= 83)

Second Largest Error (CV=30)

Smallest Error (CV=16)

Area Burned

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Predicted ECO2

Predicted ECH4

Predicted ECO

Emissions Modeling Uncertainty

French, N.H.F., P. Goovaerts and E.S. Kasischke (2004). Uncertainty in estimating carbon emissions from boreal forest fires. Journal of Geophysical Research 109: D14S08 doi: 10.1029/2003JD003635.

Using an Uncertainty Model for carbon emissions (next slide) we computed the distributions of predicted mean annual emissions of (a) CO2, (b) CO, and (3) CH4 for Alaska.

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Emissions Modeling Uncertainty

Uncertainty Model Method• Used a stratified random sampling of probability distributions for

each input parameter;• Monte Carlo simulation;• Each combination of sampled values was combined to retrieve

the corresponding simulated emission value.• Monte Carlo simulation provides a range of possible

outcomes and the probabilities they will occur for all combination of variables

PROBLEM: Implementation of the Monte Carlo simulation required

information regarding the characteristics of the probability distributions (shape, spread) of each fuelbedand strata.

Probability distributions of input parameters were not available, so we assumed Guassian distributions with spread based on published CVs

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Fuelbed Map•Fuelbeds are mapped via crosswalks to satellite-derived vegetation and land cover, at scales from < 25 m (landscape applications) to >36 km (continental and global applications).

•USGS Landfire 30-m scale existing vegetation maps were used for mapping FCCS

•A 1-km scale version is available for regional-scale modeling

http://www.fs.fed.us/pnw/fera/fccs/index.shtml

includes fuel loadings by type

Fuels Mapping: Mapping standard FCCS fuelbeds

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Standard Fuelbeds1-km resolution

234 fuelbeds (5 lost in aggregation)

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Emissions modeling

FCCS-based WFEIS/Consume (French et al.) BlueSky Framework (Larkin et al.)

Others: CanFIRE (de Groot et al.)

GFED (van der Werf et al.)

FINN (Wiednmyer et al.)

French, N.H.F., D. McKenzie, T. Erickson, B. Koziol, M. Billmire, K.A. Endsley, N.K.Y. Scheinerman, L. Jenkins, M.E. Miller, R. Ottmarand S. Prichard (2014). “Modeling regional-scale fire emissions with the Wildland Fire Emissions Information System”. Earth Interactions 18: 1-26 doi: 10.1175/EI-D-14-0002.1.

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Variability of Fuels

Forest/vegetation typeDuff depthConifer vs. deciduousForest structure & density

Lower Duff

Upper Duff

Live MossDead Moss

Mineral Soil

Boreal black spruce sites, for example, have varying amounts of duff.

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Existing Vegetation Type – FCCS Fuelbed Crosswalk

Southeast conifer sites can have sparse or dense understory shrubs.

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Project Overview

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Fuel Loadings Database

Data Sources:• FIA plot data• LANDFIRE reference database• Natural fuels photo series data• Continuous Vegetation Survey Plots (USFS)• Source data for FOFEM development

(courtesy of Bob Keane)• Source data for Fuel Loading Model development

(courtesy of D. Lutes)• FCCS fuelbed development referencesCurrently “complete” but considered a “work in progress”

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0.14

0.12

0.10

0.08

0.06

0.04

0.02

0

CANOPY

0 100

0.6

0.5

0.4

0.3

0.2

0.1

0 0 10 20

SHRUB3.5

3.0

2.5

2.0

1.5

1.0

0.5

00 2 4 6

HERB

Mg/ha

dens

ity

Sample distributions

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0.16

0.14

0.12

0.10

0.08

0.06

0.04

0.02

0

COARSE WOOD

0 100

Mg/ha

0.25

0.20

0.15

0.10

0.05

0 0 20

LITTER

3.5

3.0

2.5

2.0

1.5

1.0

0.5

00 100

DUFF

dens

ity

Sample distributions

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Sensitivity Analysis

Example: Emissions (y-axis) and fuel loading (x-axis)

not sensitive to shrub loadings

but are to duff loadings

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Sensitivity Analysis

Explore the sensitivity of emissions estimates to uncertainty in fuel loadingsCandidate distributions:• normal • lognormal • gamma • weibull

weibull

gamma

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Estimate emissions for each sampled

value

Calculate distribution of

emissions

Sample loading values from fit distributions

Sensitivity Analysis

As in French et al. 2004

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Web-based application for visualizing uncertainty and fuel loading distributions by region and fuel category

Data access and visualization

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Two different uncertainties to be represented:1. Uncertainty of fuel loading Represented as probability

distributions

Visualizing uncertainty

2. Uncertainty of emissions resulting from fuelbed variability Represented as distribution of

values resulting from Monte Carlo simulation of fuel loading Probability Density Functions (PDFs)

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Local and global sensitivity analysis ranks fuels categories for their contribution to variability in emissions predictions

Cross-reference important fuels categories with data gaps found in Task 1a

Draw randomly from empirical joint distributions of important fuels categories, predict emissions for each

Prioritize resources for data acquisition

Produce expected distributions and prediction intervals for emissions estimates

Using the Database & Sensitivity Results

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Visualizing uncertainty

Challenges for geospatial display of uncertainty:– Each map pixel is multidimensional, having values for:

• multiple fuel strata• multiple measures of uncertainty for each strata (i.e.

lower bound, upper bound, standard deviation)• representations of uncertainty that are themselves

multidimensional (PDFs for each variable)– For a single map pixel, what is the optimal way to

simultaneously show pixel value + some measure(s) of pixel uncertainty?

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Possible solutions:– HSI model (aka whitening)

• hue represents the value• whiteness/opacity represents pixel uncertainty• Requires two-dimensional legend

Visualizing uncertainty

Hengl, T., 2003. Visualisation of uncertainty using the HSI colour model: computations with colours. In 7th International Conference on GeoComputation.

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Possible solutions (continued):– 3D mapping

• Where “elevation” can be used to represent value or uncertainty

• Example: value=elevation uncertainty=hue

Visualizing uncertainty

Sanyal et al. 2009. A user study to compare four uncertainty visualization methods for 1d and 2d datasets. IEEE transactions on visualization and computer graphics, 15(6) Figure 5c

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Possible solutions (continued):

– Animation• Automatic cycling between lower/mid/upper bounds

– Side-by-side views• Evidence that this is more effective than whitening

(Gerharz and Pebesma 2009) for targeted data analysis (as opposed to broad pattern display)

Visualizing uncertainty

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Applications

Emissions estimation

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Smoke and Air-quality Modelsdraw from probability distributions of mapped fuels rather than single-average values.Community Multiscale Air-quality System (CMAS)

Global Climate Modelsenable coupled models* to incorporate spatial variation in fuels when projecting uncertainty in GHG emissions.(*e.g., GCMs + land-surface models + smoke dispersion models) Note: this methodology can be extended, in theory, to coarse-scale GCMs

Applications

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Other Considerations

Improving methods for characterizing & mapping– Advancing measurement methodologies, such as LiDAR-

based fuel density– Map improvements and validation (a part of this project)

Quantifying consumption & emissions with Thermal IR Fire Radiative Energy (FRE)– Proven satellite-based method operationally used in Europe– Method is reliable independent fuel-loadings

• more energy = more fuel consumption– Fuels are still important to know for

• Emission factors depend on material burning• Flaming vs. smoldering is not well studied• Need for uncertainty (this study)

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Thank-You

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