Detecting and Parameterizing Wildfire Induced Land-surface Changes for Earth System Models

Yongqiang Liu USDA Forest Service, Athens, GA

Xianjun Hao, John QuGeorge Mason University, Fairfax, VA

Agroclimatology Project Directors MeetingDecember 16-18, 2016

JW Marriott, San Francisco

(NIFA award #: 2013-35100-20516)

Climate impacts of wildfire smoke

Southern California fires in fall, 2003 (Image from NASA)

Fire

Vegetation coverage

Stomata

Albedo

Radiation

Roughness

Wind

Evapotrans-piration

Sensible heat flux

Climate

- Large vegetation removal Díaz-Delgado et al. 2001, Díaz-Delgado et al. 2003, Li et al. 2008, van Leeuwen2008, 2010.

- Post-fire surface albedo recovers quickly after initial drop, and even exceeds pre-fire values when char materials are removed and vegetation starts to regenerate

Amiro et al. 2006, Randerson et al. 2006, Lyons et al. 2008, Veraverbeke et al. 2012

- Immediate post-fire day-time surface temperature increase

Lyons et al. 2008, Veraverbeke et al. 2012

Fire-climate interactions in Earth System Modeling

(from Spessa et al., 2015)

Issues Complex land-surface property changes due to variations

in fire intensity, region, fuel type, and many other factors.

Quantitative description of instant land cover change and subsequent variations with DGVM modeling

Objectives Obtain quantitative estimation of land-surface property

changes through detecting and analyzing multiple mega-fires in the U.S.

Develop an observation based scheme to parameterize the land-surface property changes to be used in CESM

Remote Sensing

Data

MTBSWildlandFire Data

GHCNWeather

Data

RS Data Processing

Rasterization

Extract Ground

Weather Data

Time Series of

Land Properties

Wildland Fire

Burned Area Map

Time Series of Weather

Data

SurfaceVegetative Changes

Surface RadiativeChanges

SurfaceHydrological

Changes

Statistical Analysis and

Inference

Impacts of Large Fires

Spatio-Temporal Data Integration and

Analysis

Detection ProcedureData collection Data analysis Surface change Statistics

MODIS: Global, 500 m and 1000 m resolutions, 2000-present, 8-day products, Land cover type data (MCD12), Surface Albedo Data (MCD43A3), Surface Reflectance Data (MOD09A1), Leaf Area Index (LAI) data (MOD15A2), Land Surface Temperature Data (MOD11A2)

Monitoring Trends in Burning Severity (MTBS) (USGS and USFS)Burned area and severity map. CONUS, 30 m resolution, 1984-present

Spatial: comparison between burned and control pixels - Select burned pixels and control pixels with same prefire land cover

type and very close surface albedo.- Compare for fire year and postfire years.

Temporal: comparison before and after a fire- Significance test of differences between fire year and prior year

T-1 T T+1Change

DifferenceX X+1

Fire eventsGroup No. Fire Name Time State Lat Long Burned Acres Climate

11 Cedar 10/25-11/5, 2003 CA 33.30 -116.80 280k Warm

2 Zaca 7/4-9/4, 2007 CA 34.60 -119.70 240k Warm

3 Egley 7/6-7/25, 2007 OR 43.49 -119.23 140k Cool

4 Rodeo 6/8-9/1, 2002 AZ 34.11 -110.49 260k Warm

5 Derby 8/21-11/7, 2006 MT 45.54 -109.93 210k Cool

6 Big Turnaround 5/5-10/10, 2007 GA 30.77 -82.27 305k Warm

7 Biscuit 7/12-7/15, 2002 OR 42.27 -123.54 495k Cool

2 8 Crystal 8/15-11/9, 2006 ID 42.96 -113.24 220k Cool

9 Winters 7/25-9/10, 2006 NV 41.36 -116.91 240k Cool10 S Nevada 6/22-7/10, 2005 NV 37.20 -114.38 240k Warm11 Milford Flat 7/6-8/3, 2007 UT 38.58 -112.97 360k Cool12 Cave Creek 6/21-8/1, 2005 AZ 33.98 -111.82 250k Warm

LAI – Group 1(≥ 1.0)

Cedar Fire

Zaca Fire

Egley Fire

LAI – Group 2 (<1.0)

LAI change – Group 1 (LAI≥1.0)Cedar Fire

LAI change – Group 2 (LAI<1.0)

NDVI change – Group 1 (NDVI ≥ 0.5)

Cedar Fire

Day temperature change – Group 1

Albedo change – Group 1

Cedar Fire

Egley Fire

Forest vs. shrub/grass (Egleyfire)

fire period

Scheme development procedureDetected change

Fit with natural

exponential function

TrendRemove

trend from detected change

Fluctuation

Fourier analysis

Periodic variation

Amplitude of the year

Simulated change

Max – Min within a

year

Before and after fire termination

t0

Cedar fire

Cedar fire case

Separate into trend and fluctuation

F(t)=

a0=-0.597a1=-0.0073a2=0.00039

Express fluctuation as multiple of periodic variation and amplitude for fluctuation

b0=-1.851b1=-0.444b2=0.0403

Simulation scheme

F(t)= ＋ x

SummaryLand-surface changes are remarkable only if

land coverage meets certain thresholds.Large changes over 4-10 years, longer in cool

regions, larger for forested lands, and opposite phases for albedo.Post-fire changes are featured by long-term

deceasing trend and fluctuation, which contains periodic variations and decaying amplitude.The scheme developed based on these features

would provide a tool for simulating the regional climate effects of wildfires with CESM.

Plan for next year

• Detect more fires• Detect atmospheric properties for evaluating

simulation of climate impacts• Couple with CESM• Simulate the climate impacts of fires

Thanks!

Funding support from USDA NIFA