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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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