Forest Growth and Fire Fuel Forest Growth and Fire Fuel

Predictions for Air Quality ModelingPredictions for Air Quality Modeling

Limei Ran, Uma Shankar, Aijun Xiu, B.H. Baek, Zac AdelmanLimei Ran, Uma Shankar, Aijun Xiu, B.H. Baek, Zac AdelmanInstitute for the Environment, UNCInstitute for the Environment, UNC

Don McKenzieDon McKenziePacific Wildland Fire Sciences Laboratory, USDA FSPacific Wildland Fire Sciences Laboratory, USDA FS

Steve McNulty, Jennifer Moore MyersSteve McNulty, Jennifer Moore Myers

Southern Global Change Program, USDA FSSouthern Global Change Program, USDA FS

Outline of the PresentationOutline of the Presentation

• 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 studyBackground of the study

• 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 SystemIntegrated Modeling System

PnET-IIPnET-II

CCSMCCSM

METCHEMMETCHEM(MM5-MCPL /(MM5-MCPL /

MAQSIPMAQSIP)

BlueSky/EPMMEGANSMOKE

Monthly met.

Base & Base & future yearfuture yearfuel datafuel data

Fire Scenario Builder

Hourly Hourly metmet

Fireactivity data

Anthropogenicinventoriedemissions

Gridded &SpeciatedEmissions

Initial &Initial &boundaryboundary

met.met.

FCCSFCCSFIA FIA

PnET-II Forest Growth ModelPnET-II Forest Growth Model

• 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

Climate Spatial

FIA

FIA Plot

PnET-II

PnET-CN

Volume1

Volume2

Volume3

Inventoryand

Harvest

SRTS

Update Acres

Calculate Acres

Harvested

Allocate Harvest

Calculate Growth

Update Inventory

Update Equilibrium

Flow Chart of PEcon

Vegetation parameters

Model Modifications and Input Data Model Modifications and Input Data PreparationPreparation

• 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 Fuel Load Estimation and BlueSky/SMOKE ModelingBlueSky/SMOKE Modeling

• BlueSky takes three input files to model fire emission 1. 1-km grids with FCCS fuelbed IDs (114 unique IDs)2. Fuel load lookup table (DWM, grass, shrub, and canopy)3. 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

1. Base Year 2002 BlueSky/SMOKE run:1. 2002 FIA DWM and biomass obtained from FS SRS at Knoxville2. VISTAS fire information

54 FCCS Fuelbeds in SE54 FCCS Fuelbeds in SE

0 Urban - agriculture - barren27 Ponderosa pine - Two-needle pine - Juniper forest30 Turbinella oak - Ceanothus - Mountain mahogany

shrubland43 Arizona white oak - Silverleaf oak - Emory oak woodland49 Creosote bush shrubland55 Western juniper / Sagebrush savanna56 Sagebrush shrubland57 Wheatgrass - Cheatgrass grassland66 Bluebunch wheatgrass - Bluegrass grassland90 White oak - Northern red oak forest107 Pitch pine / Scrub oak forest109 Eastern white pine - Northern red oak - Red maple forest110 American beech - Yellow birch - Sugar maple forest114 Virginia pine - Pitch pine - Shortleaf pine forest123 White oak - Northern red oak - Black oak - Hickory

forest131 Bluestem - Indian grass - Switchgrass grassland133 Tall fescue - Foxtail - Purple bluestem grassland135 Eastern redcedar - Oak / Bluestem savanna154 Bur oak savanna157 Loblolly pine - Shortleaf pine - Mixed hardwoods forest164 Sand pine forest165 Longleaf pine / Three-awned grass - Pitcher plant

grassland166 Longleaf pine / Three-awned grass - Pitcher plant

savanna168 Little gallberry - Fetterbush shrubland173 Live oak / Sea oats savanna174 Live oak - Sabal palm forest175 Smooth cordgrass - Black needlerush grassland

180 Red maple - Oak - Hickory - Sweetgum forest181 Pond pine forest182 Longleaf pine - Slash pine / Saw palmetto -

Gallberry forest184 Longleaf pine / Turkey oak forest186 Turkey oak - Bluejack oak forest187 Longleaf pine / Yaupon forest189 Sand pine - Oak forest203 Sawgrass - Muhlenbergia grassland210 Pinyon - Juniper forest232 Mesquite savanna236 Tobosa - Grama grassland240 Saw palmetto / Three-awned grass shrubland264 Post oak - Blackjack oak forest267 American beech - Yellow birch - Sugar maple - Red

spruce forest269 Sugar maple - Yellow poplar - American beech -

Oak forest270 Red spruce - Fraser fir / Rhododendron forest272 Red mangrove - Black mangrove forest274 American beech - Sugar maple forest275 Chestnut oak - White oak - Red oak forest276 Oak - Pine - Magnolia forest280 Bluestem - Gulf cordgrass grassland281 Shortleaf pine - Post oak - Black oak forest282 Loblolly pine forest283 Willow oak - Laurel oak - Water oak forest284 Green ash - American elm - Silver maple -

Cottonwood forest288 Bald-cypress - Water tupelo forest289 Pond-cypress / Muhlenbergia - Sawgrass savanna

29 FIA Species Groups to FCCS Fuelbeds29 FIA Species Groups to FCCS FuelbedsSPGRP Species Group FCCS1 WEIGHT1 FCCS2 WEIGHT2 FCCS3 WEIGHT3

1 Longleaf and slash pine 182 12 Loblolly and shortleaf pine 157 0.5 282 0.53 Other yellow pines 157 0.5 114 0.54 Eastern white and red pine 109 16 Spruce and balsam fir 265 17 Eastern hemlock 287 18 Cypress 135 0.5 288 0.59 Other eastern softwoods 107 1/3 181 1/3 164 1/3

22 Western redcedar 135? 125 Select white oaks 275 1/3 264 1/3 186 1/326 Select red oaks 283 127 Other white oaks 275 1/3 264 1/3 186 1/328 Other red oaks 283 129 Hickory 180 130 Yellow birch 110 131 Hard maple 269 1/2 266 1/232 Soft maple 180 2/3 284 1/333 Beech 274 134 Sweetgum 180 135 Tupelo and blackgum 288 136 Ash 284 137 Cottonwood and aspen 142 138 Basswood 266 139 Yellow-poplar 269 140 Black walnut 180 1/2 288 1/241 Other eastern soft hardwoods 276 1/3 284 1/3 110 1/342 Other eastern hard hardwoods 154 1/3 283 1/3 173 1/343 Eastern noncommercial hardwoods 272 1/3 284 1/3 107 1/348 Western woodland hardwoods 283 1

Durham County

FCCS FIA Data (Tons)FCCS Beds 3 Total FIA Biomass 5511166.69Total Forest Cells 484 Total FIA DWM 1086267.56

FCCS Fuel IDNumber of CELLS

Xwalk Biomass Allocation (32%)

Residual Allocation (68%)

123 - Oak+Hickory 211 0.00 1642254.20281 Shortleaf Pines+Oak 72 0.00 560390.06282 Loblolly Pines 201 1744100.20 1564422.24

FCCS1_BIOM = C1W1 * FIA_SPGRP_BIOM / (C1W1 + C2W2 + C3W3)FCCS1_DWM =DWM * FCCS1_BIOM / TOTAL_BIOM

37063 - Durham County NC

Original FCCS Fuel LoadsmapID VEG 1HR 10HR 100HR 1kHR 10kHR 10k+HR DUFF GRASS SHRUB CANOPY

123

White oak - Northern red oak - Black oak - Hickory forest 0.6 1.2 1.6 1.5 1.8 0 1.92 0.04 0.93 18.84

281Shortleaf pine - Post oak - Black oak forest 0.6 0.9 2.3 3.8 6.1 0 0.96 0 0.29 9.54

282 Loblolly pine forest 0.6 1.7 2 3 2 0 1.92 0.01 0.34 9.78

Modified FCCS Fuel Loads for Base Year 2002mapID VEG 1HR 10HR 100HR 1kHR 10kHR 10k+HR DUFF GRASS SHRUB CANOPY

123

White oak - Northern red oak - Black oak - Hickory forest 0.072 0.326 2.991 0.883 0.883 0 0.068 0.04 0.93 3.15

281Shortleaf pine - Post oak - Black oak forest 0.072 0.326 2.991 0.883 0.883 0 0.068 0 0.29 3.15

282 Loblolly pine forest 0.152 0.690 6.326 1.868 1.868 0 0.144 0.01 0.34 6.661

PEC Emission from Revised PEC Emission from Revised

and Original FCCS Fuel Loadsand Original FCCS Fuel Loads

Original FCCSOriginal FCCS

Revised FCCSRevised FCCS

Future Year Fuel Load EstimationFuture Year Fuel Load Estimation

• 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 Fire Scenario Builder and Biogenic Emission EstimationEmission Estimation

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 ModelIssues with PnET II Model

• 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 EstimationIssues with Fuel Estimation

• 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...

AcknowledgmentsAcknowledgments

• 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.

Ignition Avail

Fire Scenario BuilderFire Scenario Builder

Flammability

Fire frequency & fuel maps

Management RxFire/suppression

MM5 (mesoscale model)

AtmosphericInstability- CAPE

MapTypes-500mb-700mb

Fire Generator

Fire Starts

Fire Sizes

Equations predict fuel moisture in fuel size classes that carry fire.

NFDRSHuman ignitions

(East)