1. Biome-BGC and Estimations of Tower Fluxes
Chequamegon Ecosystem-Atmosphere Study
August 16-21, 2002
seawinds – 25 km resolution, short wave-length
Faith Ann Heinsch1, John Kimball2, and Steve Running1
1Numerical Terradynamic Simulation Group
2 Flathead Lake Biological Station
University of Montana

2. MODIS NPP Calculation
seawinds – 25 km resolution, short wave-length

3. NPP (Net Primary Production)
Quantifies vegetation growth
Uses:
component of NEP for terrestrial carbon source/sink analyses [global interest]
practical measure of crop/range/forest growth [local interest]

4. MOD17: The MODIS PSN/NPP Algorithm

5. MODIS Productivity Design Limitations….
• Based on Remotely-Sensed LAI estimate
• 1 km2 resolution (possibly 250 m2)
• Accuracy of supplemental inputs
Vegetation Classification
Weather & Radiation Data
• Lack of growth respiration in weekly productivity

6. MODIS Productivity Design Limitations imply that...
• Can provide accurate large scale depictions
of relative differences in productivity
• Accuracy of estimates at smaller scale will
be subject to the accuracy of the vegetation
classification and weather & radiation data

7. Remote Sensing of Vegetation
Satellite
Photosynthetically
Active Radiation
(PAR=0.45Rnet )
Signal Related to
1. Leaf area
2. Canopy structure
3. Viewing angle
Ground
Leaf

8. Fraction of Absorbed Photosynthetically Active Radiation (FPAR)
NDVI
Leaf Area Index (LAI)
Fraction of Absorbed Photosynthetically Active Radiation (FPAR)

9. MODIS Productivity Data Flow
Instrument
1 km2 Reflectance Data
Canopy
Structure
Vegetation
Cover
Algorithm
Leaf Area Index (LAI)
Fraction of Radiation Absorbed (FPAR)
Temperature
&
Radiation
Vegetation
Type
Vegetation
Productivity
Algorithm
Weekly
&
Annual

10. Vegetation Productivity
Gross
Photosynthesis
Respiration
Net Primary
Productivity
(g C/m2) = -
NPP
GPP R

11. Photosynthetically Active
MODIS Photosynthesis
The Monteith equation….
Absorbed
Photosynthetically Active
Radiation
Radiation
Use
Efficiency
GPP = x 
APAR
(FPAR) (0.45Rnet )

12. Radiation Use Efficiency
Maximum
Radiation Use Efficiency
under ideal conditions
for each biome
Vapor Pressure Deficit
Coefficient
 = max [mtmin ][mvpd ]
Temperature Coefficient for each biome
The coefficients are calculated from (DAO 1° resolution) daily minimum and maximum air temperature inputs

13. max (mtmin)(mvpd) (FPAR)(0.45Rnet )
MODIS Productivity
Annual =  (Weekly) - R m_live wood - R g
Weekly = GPP - R m_leaf - R m_fine root
max (mtmin)(mvpd) (FPAR)(0.45Rnet )
GPP =

14. Algorithm Implementation
USE OF BIOME-BGC IN DERIVING THE MOD17
BIOME PROPERTIES LOOK-UP TABLE

15. Entries in the Biome Look-Up Table

16. BIOME LOOK-UP TABLE FOR MOD17 ALGORITHM
UMD vegetation class
Parameter name
epsilon_max
tmin_max
tmin_min
vpd_max
vpd_min
sla
q10_mr
froot_leaf_ratio
livewood_leaf_rat
leaf_mr_base
froot_mr_base
livewood_mr_base
leaf_gr_base
froot_leaf_gr_rati
livewood_leaf_gr_r
deadwood_leaf_gr_r

17. Estimation of Daily NPP in the MOD17 Algorithm

18. Annual Net Primary Production Estimation

20. Biome-BGC
seawinds – 25 km resolution, short wave-length

21. The BIOME-BGC Terrestrial Ecosystem Process Model
BIOME-BGC estimates fluxes and storage of energy, water, carbon, and nitrogen for the vegetation and soil components of terrestrial ecosystems.
Model algorithms represent physical and biological processes that control fluxes of energy and mass:
New leaf growth and old leaf litterfall
Sunlight interception by leaves, and penetration to the ground
Precipitation routing to leaves and soil
Snow (SWE) accumulation and melting
Drainage and runoff of soil water
Evaporation of water from soil and wet leaves
Transpiration of soil water through leaf stomata
Photosynthetic fixation of carbon from CO2 in the air
N uptake from the soil
Distribution of C and N to growing plant parts
Decomposition of fresh plant litter and old soil organic matter
Plant mortality
Plant phenology
Fire/disturbance
The model uses a daily time-step with daily updating of vegetation, litter, and soil components.

22. BIOME-BGC Major Features:
Daily time step (day/night partitioning based on daily information);
Single, uniform soil layer hydrology (bucket model);
1 uniform snow layer of SWE (no canopy snow interception/losses);
1 canopy layer (sunlit/shaded leaf partitioning);
Dynamic phenology and C/N allocation (e.g. LAI, biomass, soil and litter)
Disturbance (fire) and mortality functions
Variable litter and soil C decomposition rates (3 litter and 4 soil C pools)
Major Features:

23. Meteorological Parameters Required by Biome-BGC
Daily maximum temperature (°C)
Daily minimum temperature (°C)
Daylight average temperature (°C)
Daily total precipitation (cm)
Daylight average partial pressure of water vapor (Pa)
Daylight average shortwave radiant flux density (W/m2)
Daylength (s)

24. What if Some Met Data is Missing?
Use a nearby weather station
Use MT-CLIM to estimate radiation and humidity measurements from Tmax, Tmin
designed to handle complex terrain
uses a base station to calculate “site” data
Use DAYMET (conterminous U.S. only)
uses several met stations surrounding site
data available from
takes into account complex terrain

29. BIOME-BGC Eco-physiological Parameters
Biome-BGC uses a list of 43 parameters to differentiate biomes. These parameters define the general eco-physiological characteristics of the dominant vegetation type and must be specified prior to each model simulation. These parameters can be measured in the field, obtained from the literature or derived from other measurements.
Default Biome types with defined parameters
Deciduous Broadleaf Forest (temperate)
Deciduous Needleleaf forest (larch)
Evergreen Broadleaf Forest (subtropical/tropical)
Evergreen Needleleaf Forest
Evergreen Shrubland
C3 Grassland
C4 Grassland

30. Biome-BGC Default Eco-physiological Parameters: Evergreen Needleleaf Forest

31. BIOME-BGC Environmental Controls on Canopy Conductance (Walker Branch Site)
M_total,sun,shade = (MPPFD,sun,shade * MTmin * MVPD * MPSI)
where multipliers range from 0 (full Gs reduction) to 1 (no effect)
Gs, sun,shade = Gs,max * M_total, sun,shade

32. Example Initialization File
MET_INPUT (keyword) start of meteorology file control block
BIOME-BGC
Example Initialization
File
metdata/TDE.mtc41 meteorology input filename
(int) header lines in met file
RESTART (keyword) start of restart control block
(flag) 1 = read r
estart file = don't read
restart file
(flag) 1 = write restart file 0 = don't write
restart file
(flag) 1 = use restart metyear 0 = reset metyear
restart/TDE_n.endpoint input restart filename
restart/TDE.
endpoint output restart filename
TIME_DEFINE (keyword -
do not remove)
(int) number of meteorological data years
(int) number of simulation years
(int) first simulation year
(flag) = spinup simulation 0 = normal
simulation
(int) maximum number of spinup years (if spinup
simulation)
CLIM_CHANGE (keyword -
do not remove)
(deg C) offset for Tmax
(deg C) off
set for Tmin
(DIM) multiplier for Prcp
(DIM) multiplier for VPD
(DIM) multiplier for shortwave radiation
CO2_CONTROL (keyword -
do not remove)
(flag) 0=constant 1=vary with fil
e 2=constant, file
for Ndep
(ppm) constant atmospheric CO2 concentration
TDE_co2.txt (file) annual variable CO2 filename
SITE (keyword) start of site physical constants block
(m) effective soil dept
h (corrected for rock
fraction)
(%) sand percentage by volume in rock -
free soil
(%) silt percentage by volume in rock -
free soil
(%) clay percentage by volume in rock -
free soil
(m)
site elevation
(degrees) site latitude ( -
for S.Hem.)
(DIM) site shortwave albedo
(kgN/m2/yr) wet+dry atmospheric deposition of N
(kgN/m2/yr) symbiotic+asymbiotic fixation of N

33. Example Initialization File (cont.)
RAMP
_NDEP (keyword -
do not remove)
BIOME-BGC
Example Initialization File (cont.)
(flag) do a ramped N -
deposition run? 0=no, 1=yes
(int) reference year for industrial N deposition
(kgN/m2/yr) industrial N deposition value
EPC_FILE (keyword -
do no
t remove)
dbf.epc (file) TDE DBF ecophysiological constants
W_STATE (keyword) start of water state variable initialization block
(kg/m2) water stored in snowpack
(DIM) initial soil water as a proportion of sa
turation
C_STATE (keyword) start of carbon state variable initialization block
(kgC/m2) first -
year maximum leaf carbon
(kgC/m2) first -
year maximum stem carbon
(kgC/m2) coarse woody debris carbon 0.
(kgC/m2) litter carbon, labile pool
(kgC/m2) litter carbon, unshielded cellulose pool
(kgC/m2) litter carbon, shielded cellulose pool
(kgC/m2) litter carbon, lignin pool
(kgC/m2)
soil carbon, fast microbial recycling pool
(kgC/m2) soil carbon, medium microbial recycling pool
(kgC/m2) soil carbon, slow microbial recycling pool
(kgC/m2) soil carbon, recalcitrant SOM (slowest)
N_STA
TE (keyword) start of nitrogen state variable initialization block
(kgN/m2) litter nitrogen, labile pool
(kgN/m2) soil nitrogen, mineral pool
OUTPUT_CONTROL (keyword -
do not remove)
outputs/TDE_out (text) pr
efix for output files
1 (flag) 1 = write daily output 0 = no daily output
0 (flag) 1 = monthly avg of daily variables 0 = no monthly avg
0 (flag) 1 = annual avg of daily variables 0 = no annual avg
1 (flag) 1 = write annual output 0 =
no annual output
1 (flag) for on -
screen progress indicator
DAILY_OUTPUT (keyword)
3 (int) number of daily variables to output
epv.vwc (%)
wf.soilw_trans (kg m^ - 2)
wf.canopyw_evap (kg m^ - 2)
ANNUAL_OUTPUT
(keyword)
(int) number of annual output variables
annual maximum projected LAI
vegetation C
END_INIT (keyword) indicates the end of the initialization file

34. Soil Water Potential Curves
BIOME-BGC 1Soil Water –
Soil Water Potential Curves
(%)
(MPa)
Soil Class Silt loam Silt Loam
β-value
VWC_sat
PSI_sat
1after Cosby et al., 1984

36. BIOME-BGC Simulated Daily Carbon and Water Exchange
(1Barrow Tussock / Wet Sedge Tundra Site, 2000)
Daily 1Meteorology
Daily C Budget
BGC shows LAI = 0.5-1
MODIS msrs 1.3 as max LAI
good job of biomass, but showing much less C uptake than tower data – must all be going underground (can’t msr well)
doubled precip to prevent dryout.
1 Daily meteorological data obtained from Barrow W Post Station, 71.28N W

37. BIOME-BGC Simulated Cumulative Net Carbon Exchange
(1Barrow Tussock / Wet Sedge Tundra Site)
C sink (+)
C source (-)
run for a couple of years with re-init every yr
resp dominate – source for much of yr
may not be true if sat during winter
really more like what Atqasuk where it is drier (BGC does not do Barrow well).
1 Daily meteorological data obtained from Barrow W Post Station, 71.28N W

38. Biome-BGC runs for 4 areas in Alaska Alaska Study Region C source (+)
C sink (+)
Biome-BGC runs
for 4 areas in Alaska
Alaska Study Region
electra sites
sap flow, etc.
grew forest, compared with Seawinds data and now MODIS
latit variation between sites for C02 exchange -> freeze-thaw variation
Site Name Latitude
Kenai AK N
Bonanza Creek AK 64.7N
Coldfoot AK N
Atigun AK N

40. MODIS vs. Biome-BGC LAI
not arctic, but shows that MODIS sees higher lai – modeling for deciduous stand, but pixel sees mixed forest

41. Biome-BGC Estimates of LAI Park Falls, WI

42. Biome-BGC Estimates of NPP
Park Falls, WI

43. Biome-BGC Estimates of GPP
Park Falls, WI

44. Comparing Biome-BGC with Tower Flux Data

45. Verification of BIOME-BGC Daily and Seasonal Dynamics: Comparisons with Tower Eddy-flux Measurements
Mature Black Spruce Stand (NSA-OBS Ameriflux site)
Mature Aspen Stand (SSA-OA BERMS site)
NEP ET
Kimball et al., 1997a,b
validating bgc with boreal sites