Evaluating the Coupled Dynamic Vegetation-Fire-Emissions model, LPJ-GUESS-SPITFIRE, against EO-based Tropical Tree Biomass Allan Spessa 1 , Matthew Forrest 2 , Thomas Hickler 2 1. Dept Environment, Earth & Ecosystems, Open University (Milton Keynes, UK) 2. Biodiversity and Climate Research Centre, Senkenberg Museum & Goethe University (BiK-F) (Frankfurt, Germany)
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Evaluating the Coupled Dynamic Vegetation-Fire-Emissions model,
LPJ-GUESS-SPITFIRE, against EO-based Tropical Tree Biomass
Allan Spessa 1, Matthew Forrest 2, Thomas Hickler 2
1. Dept Environment, Earth & Ecosystems, Open University (Milton Keynes, UK)
2. Biodiversity and Climate Research Centre, Senkenberg Museum & Goethe
University (BiK-F) (Frankfurt, Germany)
Ignitions
Area Burned
Fuel Moisture & Fire Danger
Index
Rate of Spread
& Fire Duration
Fire Intensity
Emissions (trace greenhouse gases + aerosols)
Human-caused
Lightning-caused
Plant Mortality
Fuel Load & Fuel Structure
Wind speed
Temperature
Relative Humidity
Rainfall
LPJ Dynamic Global Vegetation
Model
Population Density & land-use
SPITFIRE- original
Fuel Consumed
Thonicke, Spessa, Prentice et al (2010) Biogeosc.
LPJ-GUESS simulates ecological succession
Smith et al (2001) GEB
LPJ-GUESS-SPITFIRE simulates
differential fire-induced tree
mortality
Lehsten et al (2008) Biogeosciences
LPJ-GUESS LPJ-DGVM
Fuel Consumed
Observed Area Burned
Fuel Moisture & Fire Danger
Index
Rate of Spread
& Fire Duration
Fire Intensity
Plant Mortality
Fuel Load & Fuel Structure
Wind speed
Temperature
Relative Humidity
Rainfall
LPJ-GUESS Dynamic
Vegetation Model
SPITFIRE- driven by observed burnt area
Emissions (trace greenhouse gases + aerosols)
Princeton Climate Reanalysis Data (1948-2008) ( after Sheffield et al 2006 J. Climate)
Burnt Area Data:
• Mouillot , 1901-1996 (Mouillot & Field GCB (2005)
• GFEDv4, 1997-2008 (Giglio et al 2013 JGR)
Two recent EO-based datasets of pan-tropical tree biomass @ 1 sq km
Saatchi et al (2011) PNAS
Baccini et al (2011) Nature Climate Change
ESA GLOBCOVER 2009 @ 1sq km
GUESS-SPITFIRE simulated tree carbon versus two EO-based datasets (Saatchi et al 2011, Baccini et al 2012)
(mean 1997-2008) (GC 2009 land cover corrected) (kgC.m-2)
Five run scenarios:
I. Default model (ca. 2010 version) with no fire.
II. Default model with fire.
III. Default model with fire and new cambial kill as a function of bark thickness (Hoffman et al 2009 Ecology).
IV. Default model with fire and new allometry for tropical trees (Feldpausch et al 2011 Biogeosc.) and tropical
savanna trees (Dantos & Pausas 2012 J. Ecol).
V. Default model with fire and new cambial kill and new tree allometry.
GUESS-SPITFIRE simulated tree carbon versus two EO-based datasets (Saatchi et al 2011, Baccini et al 2012)
(mean 1997-2007) (GC 2009 land cover corrected) (kgC.m-2)
(Spessa , Forrest, Hickler et al)
C[GUESS-SPITFIRE_No_Fire – C[Baccini] C[GUESS-SPITFIRE_No_Fire – C[Saatchi]
C[GUESS-SPITFIRE_Fire] – C[Saatchi] C[GUESS-SPITFIRE_Fire] – C[Baccini]
GUESS-SPITFIRE simulated tree carbon versus two EO-based datasets (Saatchi et al 2011, Baccini et al 2012)
(mean 1997-2007) (GC 2009 land cover corrected) (kgC.m-2)
(Spessa , Forrest, Hickler et al)
C[GUESS-SPITFIRE_Fire] – C[Saatchi] C[GUESS-SPITFIRE_Fire] – C[Baccini]
C[GUESS-SPITFIRE_Fire + new tree allometry + new cambial kill] – C[Saatchi]
C[GUESS-SPITFIRE_Fire + new tree allometry + new cambial kill] – C[Baccini]
Conclusions 1. LPJ-GUESS-SPITFIRE tree biomass simulations improved when observed fire assimilated into vegetation-fire
model.
2. Further improvement evident by new formulations for tree allometry in tropical forest and savanna trees, and
fire-induced mortality due to cambial damage.
3. Focus areas for further work…
• Compare simulated versus observed tree cover (MODIS).
• Simulated residence time of fires in LPJ-GUESS-SPITFIRE- is this realistic?
• Comparison with CASA biomass (CASA driven by EO-based FAPAR, and underpins GFEDv4 emissions
database).
• How does biomass uncertainty affect emissions?
EXTRAS
Global FWI Seasonality, 1980-2012
CPC Drought Code, 1982-83 fires in Indonesia
• Sufficient spatial resolution to capture drought driving transition in burning from southern Sumatra and southern Kalimantan in late 1982 to East Kalimantan in early 1983
Cambial kill and new bark thickness relationships
• Pm: probability of cambial kill based on ratio of i) critical time to
cambial kill to ii) residence time of fire.
• tau_l: residence time of fire (function of total fuel load W and amount
consumed C ).
• tau_c: critical time (mins) to cambial kill as a function of bark thickness
BT (cm)
• Peterson & Ryan 1986; Hoffman et al 2009, 2012; Lawes et al 2013.
New tree allometry relationships
• ↓ tree height ↑ stem diameter ↑ ↑ crown area per tree ↓ density of trees (less biomass per unit area)
• ↓ tree height ↑ stem diameter ↑ ↑ bark thickness ↓ cambial kill (more biomass per unit area)
New bark thickness relationships
• Pm: probability of cambial kill
• tau_l: residence time of fire (function of total fuel load W and amount
consumed C ).
• tau_c: critical time (mins) to cambial kill as a function of bark thickness
BT (cm)
• Peterson & Ryan 1986; Hoffman et al 2009, 2012; Lawes et al 2013.
New tree allometry relationships
• ↓ tree height ↑ stem diameter ↑ ↑ crown area per tree ↓ density of trees (less biomass per unit area)
New tree allometry II
PFT-based vs Patch-based Approaches to Vegetation Modelling
Bare Gd
C4
TrBlRg tree
TrBlEg tree
1 y.o. 5 y.o.
15 y.o.
30 y.o. 60 y.o.
90 y.o.
LPJ-DGVM, TRIFFID, SDGVM etc LPJ-GUESS Patch-based tile structure. PFT-based tile structure.
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