Forest growth and fire fuel predictions for air quality modeling
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Forest Growth and Fire Fuel Predictions for Air Quality Modeling. Limei Ran, Uma Shankar, Aijun Xiu, B.H. Baek, Zac Adelman Institute for the Environment, UNC Don McKenzie Pacific Wildland Fire Sciences Laboratory, USDA FS Steve McNulty, Jennifer Moore Myers

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Forest growth and fire fuel predictions for air quality modeling

Forest Growth and Fire Fuel Predictions for Air Quality Modeling

Limei Ran, Uma Shankar, Aijun Xiu, B.H. Baek, Zac Adelman

Institute for the Environment, UNC

Don McKenzie

Pacific Wildland Fire Sciences Laboratory, USDA FS

Steve McNulty, Jennifer Moore Myers

Southern Global Change Program, USDA FS


Outline of the presentation
Outline of the Presentation Modeling

  • Background of the Study.

  • PnET-II Forest Growth Model (2000-2050)

  • Base Year and Future Year Fuel Estimation and BlueSky/SMOKE Modeling

  • Fire Scenario Builder and Biogenic Emission Estimation

  • Issues with PnET II Model and Fuel Estimation

  • Acknowledgements


Background of the study
Background of the study Modeling

  • Part of research under project -- Integrated Modeling of Forest Growth, Fire Emissions, and Air Quality in Future Climate (EPA STAR)

  • Purpose of the project is to:

    • Study the effects of climate change on forest growth and fire frequency and intensity

    • Investigate methods to model fire and biogenic emissions from future forest.

    • Examine impact of climate change from wild fire on U.S. air quality


Integrated Modeling System Modeling

Monthly met.

Fire Scenario

Builder

CCSM

FCCS

Base &

future year

fuel data

Initial &

boundary

met.

PnET-II

Hourly

met

METCHEM

(MM5-MCPL /

MAQSIP)

FIA

BlueSky/EPM

MEGAN

SMOKE

Fire

activity data

Anthropogenic

inventoried

emissions

Gridded &

Speciated

Emissions


Pnet ii forest growth model
PnET-II Forest Growth Model Modeling

  • Use PnET-II model within the PEcon Model developed at SGCP.

  • PEcon is a coupled modeling system with:

    • PnET model and SRTS (Sub-regional Timber Supply Model )

  • PnET model developed to predict forest productivity based on climate, site information, and vegetation parameters.

  • SGCP provided the model and data bases to run the model in SE 11 states from 1990-2198


Flow Chart of PEcon Modeling

Climate

Spatial

Update Equilibrium

PnET-II

PnET-CN

Volume1

UpdateInventory

Allocate Harvest

FIA

SRTS

Volume2

Calculate Acres Harvested

Calculate Growth

Volume3

Update Acres

Inventory

and

Harvest

FIA Plot

Vegetation

parameters


Model modifications and input data preparation
Model Modifications and Input Data Preparation Modeling

  • Modified PEcon codes to fit our project

  • Created new input data in SE 13 states at county-level

    • New site data (WHC, DEM, lat-long, land area)

    • Monthly met data (9 parameters) from CCSM (2000-2050) and NARR (2000-2006) historic data

    • Spatial table to relate counties to CCSM and NARR grids

  • Computed yearly biomass from plot/species to county/species groups from 2000 FIA biomass and PnET output.

  • For the presentation, used PnET output from SGCP database due to extreme temp. problem in CCSM


Fuel load estimation and bluesky smoke modeling
Fuel Load Estimation and BlueSky/SMOKE Modeling Modeling

  • BlueSky takes three input files to model fire emission

    • 1-km grids with FCCS fuelbed IDs (114 unique IDs)

    • Fuel load lookup table (DWM, grass, shrub, and canopy)

    • Fire information data (location and area burned)

  • Created FIA species group to FCCS fuel bed cross walking table to consistently revise FCCS DWM and canopy

  • Created a Perl program to revise fuel loads based on:

    • 1. DWM_Biomass 2. FCCS fuel cell with FIPS

    • 3. FCCS fuel table 4. FIA2FCCS table

  • Base Year 2002 BlueSky/SMOKE run:

    • 2002 FIA DWM and biomass obtained from FS SRS at Knoxville

    • VISTAS fire information


54 fccs fuelbeds in se
54 FCCS Fuelbeds in SE Modeling

180 Red maple - Oak - Hickory - Sweetgum forest

181 Pond pine forest

182 Longleaf pine - Slash pine / Saw palmetto - Gallberry forest

184 Longleaf pine / Turkey oak forest

186 Turkey oak - Bluejack oak forest

187 Longleaf pine / Yaupon forest

189 Sand pine - Oak forest

203 Sawgrass - Muhlenbergia grassland

210 Pinyon - Juniper forest

232 Mesquite savanna

236 Tobosa - Grama grassland

240 Saw palmetto / Three-awned grass shrubland

264 Post oak - Blackjack oak forest

267 American beech - Yellow birch - Sugar maple - Red spruce forest

269 Sugar maple - Yellow poplar - American beech - Oak forest

270 Red spruce - Fraser fir / Rhododendron forest

272 Red mangrove - Black mangrove forest

274 American beech - Sugar maple forest

275 Chestnut oak - White oak - Red oak forest

276 Oak - Pine - Magnolia forest

280 Bluestem - Gulf cordgrass grassland

281 Shortleaf pine - Post oak - Black oak forest

282 Loblolly pine forest

283 Willow oak - Laurel oak - Water oak forest

284 Green ash - American elm - Silver maple - Cottonwood forest

  • Bald-cypress - Water tupelo forest

    289 Pond-cypress / Muhlenbergia - Sawgrass savanna

0 Urban - agriculture - barren

27 Ponderosa pine - Two-needle pine - Juniper forest

30 Turbinella oak - Ceanothus - Mountain mahogany shrubland

43 Arizona white oak - Silverleaf oak - Emory oak woodland

49 Creosote bush shrubland

55 Western juniper / Sagebrush savanna

56 Sagebrush shrubland

57 Wheatgrass - Cheatgrass grassland

66 Bluebunch wheatgrass - Bluegrass grassland

90 White oak - Northern red oak forest

107 Pitch pine / Scrub oak forest

109 Eastern white pine - Northern red oak - Red maple forest

110 American beech - Yellow birch - Sugar maple forest

114 Virginia pine - Pitch pine - Shortleaf pine forest

123 White oak - Northern red oak - Black oak - Hickory forest

131 Bluestem - Indian grass - Switchgrass grassland

133 Tall fescue - Foxtail - Purple bluestem grassland

135 Eastern redcedar - Oak / Bluestem savanna

154 Bur oak savanna

157 Loblolly pine - Shortleaf pine - Mixed hardwoods forest

164 Sand pine forest

165 Longleaf pine / Three-awned grass - Pitcher plant grassland

166 Longleaf pine / Three-awned grass - Pitcher plant savanna

168 Little gallberry - Fetterbush shrubland

173 Live oak / Sea oats savanna

174 Live oak - Sabal palm forest

175 Smooth cordgrass - Black needlerush grassland



Pec emission from revised and original fccs fuel loads

Revised FCCS Modeling

PEC Emission from Revised and Original FCCS Fuel Loads

Original FCCS


Future year fuel load estimation
Future Year Fuel Load Estimation Modeling

  • Preliminary statistical analysis to build predictive models (in SAS E. Miner) for DWM from biomass and related data did not show good results.

  • DWM variability is more associated with stand disturbances and climate than directly with biomass.

  • Use base year DWM data

  • Update canopy fuels as a fixed proportion of total simulated biomass


Fire scenario builder and biogenic emission estimation
Fire Scenario Builder and Biogenic Emission Estimation Modeling

Fire Prediction

  • Dr. McKenzie will provide us base and future fire information from FSB in Western US for better boundary conditions.

  • Working on creating input data sets to run FSB in eastern US (including human-caused ignitions with FS SRS in RTP).

    Biogenic Emission

  • Will Use MEGAN to generate biogenic emissions.

  • MEGAN takes gridded monthly LAI, climate data, plant function type and emission factor files.

  • Compute LAI for species group at county level from PnET.

  • Update MEGAN LAI grid file by allocating county-level LAI of species groups with matching MEGAN plant function types.

  • Update monthly met data using CCSM.


Issues with pnet ii model
Issues with PnET II Model Modeling

  • PnET does not model spatial changes in vegetation species

  • Constant expansion factor for species biomass from plot to county

  • Assumed that mortality and removal rates remain the same

  • Used 10% of biomass for canopy fuel for all forest types.

  • CCSM: extreme monthly min and max temperature during summer months.


Issues with fuel estimation
Issues with Fuel Estimation Modeling

  • Potential biases in crosswalk from FIA to FCCS

  • FCCS fuelbeds have default values with no spatial variability across landscapes and it is difficult to verify current FCCS fuels

  • Impossible to verify future prediction

  • Assume all other emissions remain the same

  • Despite many uncertainties, we can still predict changing fire and biogenic emissions from changing canopies.

  • It does provide new ways of integrating forest growth and fuel changes for future air quality modeling.

Nevertheless...


Acknowledgments
Acknowledgments Modeling

  • We gratefully acknowledge the support of the USDA Forest Service Southern Research Station at Knoxville, TN. We thank Jeffery Turner, Samuel Lambert, and Sonja N. Oswalt for processing FIA P2 and P3 data in southeast US for us and advising us how to use the data properly. The work benefited a lot from the SQL support provided by Darin Del Vecchio on processing current and future biomass data into counties.

  • This project is fully funded by US EPA under the STAR Grant RD 83227701.


Atmospheric Modeling

Instability

- CAPE

  • Map

  • Types

  • 500mb

  • 700mb

Flammability

Ignition Avail

Fire Generator

Fire Scenario Builder

MM5

(mesoscale model)

Fire frequency

& fuel maps

Management

RxFire/suppression

NFDRS

Equations predict fuel

moisture in fuel size

classes that carry fire.

Fire Starts

Fire Sizes

Human ignitions

(East)


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