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MODISNet Primary Productivity (NPP)
Theory, algorithm development, and example applications
Peter E. Thornton
Numerical Terradynamic Simulation Group
School of Forestry, University of Montana, Missoula, MT
Outline
• Background and theory (radiation use efficiency)
• Parameterization (global simulations with Biome-
BGC)
• Example applications (regional, continental, and
global)
• Future developments (meteorology, landcover,
complete carbon budget)
MODIS-NPP Objectives
• Global estimate of productivity each week at 1km resolution
• Algorithm driven mainly by remote sensing inputs
• Include biophysical variables that can be produced globally at appropriate resolution
• Biome-specific parameterization
MODIS-NPPProduction Algorithm Summary
• Incident radiation (PAR)...
• Scaled by canopy cover (FPAR)...
• Converted to carbon (radiation use efficiency)...
• Modified by temperature and humidity...
• Different parameters for each landcover
Radiation Productionx =
Radiation Use Efficiency ()
(MJ m-2 day-1) (gC m-2 day-1)(gC MJ-1)
Absorbed Photosynthetically Active Radiation (APAR)
depends on incident PAR and canopy cover...
Incident Photosynthetically Active Radiation
(PAR)
Absorbed Photosynthetically Active Radiation (APAR)
depends on incident PAR and canopy cover...
Absorbed Photosynthetically Active Radiation
(APAR)
Absorbed Photosynthetically Active Radiation (APAR)
depends on incident PAR and canopy cover...
APAR
PAR= FPAR
Fraction of Photosynthetically Active Radiation absorbed by the
canopy
Absorbed Photosynthetically Active Radiation (APAR)
depends on incident PAR and canopy cover...
APAR = PAR FPAR
Depends on weather
Depends on canopy
structure
• Low air temperature
(Tair)
• High vapor pressure
deficit (VPD)
Potential Radiation Use Efficiency (max)
is modified by biophysical environment...
Reductions due to...
max STair SVPD
0
1
1
0
STair
SVPD
T2T1
VPD2VPD1
Gross Primary Production (GPP) Algorithm:
GPP = PAR FPAR max STair SVPD
Depends on…
• MODIS-FPAR
• PAR, air temperature, and VPD (from DAO)
• Parameters defined for each vegetation type
• MODIS Landcover
MODIS-GPPBiome-specific parameterization
• All parameters are derived from global-
scale simulations using the Biome-BGC
terrestrial ecosystem process model
• Detailed landcover information is used to
translate Biome-BGC results to aggregated
MODIS landcover classes
Fractional veg coverSoils
Daily weather data
Biome-BGCGPP, NPP, g s, etc
LAI / fPAR
Translation codeDaily weather
data
Biome PropertiesLook-up Table
(BPLUT)
MOD-17LAI / fPARLandcover
DAO daily surface weather
Daily NPPAnnual NPP
Biome-BGC MOD-17
AlgorithmCalibration
Example of detailed ecophysiological parameterization
10° C
18° C
28° C Rubisco limited
RuBP regenlimited
Global Biome-BGC simulations
1 km Landcover from “continuous fields” AVHRR product:
Ruth DeFries and Matt Hansen, University of Maryland
1 kmspectral data
woody
ENF
DBF
EBF
C3
C4
gra
ss
ENF DBF EBF C3C4
Biome-BGC
outputs(GPP, NPP,
etc.)
inputs(ecophysiology,
climate,etc.)
input remotesensing data
first split betweenwoody/nonwoody
fractional cover forfundamental types
process model
analysisby type weighted average for
gridcellor
Use of fractional vegetation cover in Biome-BGC
ENF DBF EBF C4 C3
Global Biome-BGC simulations
1x1 degree simulations for 14 years driven with daily weather data from
Steve Piper and C.D. Keeling, Scripps Institute of Oceanography
Fractional veg coverSoils
Daily weather data
Biome-BGCGPP, NPP, g s, etc
LAI / fPAR
Translation codeDaily weather
data
Biome PropertiesLook-up Table
(BPLUT)
MOD-17LAI / fPARLandcover
DAO daily surface weather
Daily NPPAnnual NPP
Biome-BGC MOD-17
AlgorithmCalibration
Several more steps to go from GPP to NPP...
• Maintenance respiration costs - depend on
tissue N concentration and temperature
• Growth respiration costs - depend on
amount of new growth
• Allometric relationships relate annual leaf
area growth to stem and root growth
maxTmin , VPD
GPP
LAI SLA
fine rootmass
allometry
leaf mass
Q10, Tavg MR
DailyNPP*
MRindex
MOD-17Daily NPP*
Photosynthesis
Maintenance Respiration
*does not include growth respiration orlive wood maintenance respiration costs
leafmass
Daily Outputs
FPAR Rnet
PAR x
-
Leafmass
MRindex
DailyNPP*
max
Annual sumDaily NPP*
Annual sumMR index
allometryAnnual averagelive wood mass
MR scalarAnnual sum
live wood MR
leaflongevity
Annualleaf growth
allometryAnnual
fine root andwood growth
GR scalarAnnual sum
GR
AnnualNPP
-
-
Annual maxleaf mass
MOD-17Annual NPP
MOD-17 Daily Outputs (Annual Inputs)
Example MODIS-NPP output
Subset of results from first global implementation of the algorithm
Some problems that we know about...
• Coarse resolution surface weather data from DAO leaves a noticeable imprint on weekly output (probably on annual output also)
• Use of discreet landcover makes parameterization from Biome-BGC difficult
• Geographic variation of parameters within biomes
Example application using Daymet surface weather inputs
Western Montana, northern Idaho, eastern Oregon and Washington,
USA