Dynamic Regional Carbon Budget Based on Multi

Dynamic Regional Carbon Budget Based
on Multi-Scale Data-Model Fusion
Mingkui Cao, Jiyuan Liu, Guirui Yu
Institute Of Geographic Science and Natural
Resource Research
Chinese Academy of Sciences
Toward dynamic regional carbon budgets
High uncertainties exist in the magnitude, regional
distribution, and temporal variations of the
terrestrial carbon sink
Dynamic regional carbon budgets will
™ Provide spatially and temporally explicit,
quantitative information on the terrestrial
carbon sink
™ Link the spatio-temporal variations to specific
driving forces and mechanisms
The quantitative and mechanistic information is
fundamentally important to
™ Effective implementation of the
Kyoto Protocol, which request
each signatory country to report
annual GHG inventory
™ Planning and practicing regional
ecosystem carbon management
Various approaches based on different
techniques have been used in regional
carbon budgets
Atmospheric
Atmospheric
measurement
measurement
or modeling
Inverse
modeling
Globe
Region
Landscape
Local
Plot
Remotesensing
sensing
Remote
Mechanistic
Process
-based
ecosystem
Ecosystem
ecosystem
Forest
modeling
modeling
inventory
inventory
Eddy
covariance
Eddy
flux
measurement
flux measurement
Point
sampling
Point
flux
measurement
measureme
Days
Seasons
Years Decades Centuries
The estimates using different approaches vary
widely, for example,
™ For the US, from 0.1 to 0.8 Gt C yr-1, and
atmosphere-based estimate is about twice of
land-based estimate (e.g. Pacala et al. 2001;
Houghton, 2003)
™ For Europe, from 0.1 to 0.5 Gt C yr-1,
and atmosphere-based estimate is about 3
times of land-based estimate (e.g. Jassens
et al. 2003)
The estimates using differ approaches vary
widely, for example,
™ For the tropics, from a substantial source to a
moderate sink (Malhi and Grace 2001). Land
use-induced C release is 2.2 Gt C yr-1 based on
statistical data (Houghton et al. 2003), but
just 0.9 Gt C yr-1 based on remote sensing data
(DeFries et al 2002)
™ For China, from 0.02-0.09 Gt C yr-1 based
on forest inventories (e.g. Fang et al. 2002, Li et
al. 2003) and ecosystem modeling (Cao et al. 2003)
And the studies did not clearly identify the
causal factors or mechanisms, for example,
™ Some studies estimated the “natural” mechanism
play a significant role (e.g. Friedlingstein et al.
1998), but others attributed primarily to land use
mechanism (e.g. Caspersen et al. 2000)
™ Remote sensing-based studies attributed the
increasing carbon sink in the north to warming
(e.g. Myneni et al. 2001), but ecosystem observations
and modeling indicate to increases in precipitation
(e.g. Nemani et al. 2002, Cao et al. 2002)
The high uncertainties arise mainly from incomplete
carbon
accounting
or
using
inappropriate
methodologies
1. Incomplete carbon accounting
Existing regional carbon budgets are mostly based on
measured changes in the carbon stocks or fluxes of single
ecosystems (forest, grass, crop etc ) or single ecosystem
components (standing biomass, soil carbon etc)
2. Lack of quantification of the combined effect of different
driving forces on both ecosystem pattern and process
Land use change
Ecosystem
pattern and
structure
The
terrestrial
carbon sink
Autotrophic
respiration
Photo
synthesis
Climate atmospheric
change
Litter
production
Harvestr and
fires
Heterotrophic
respiration
Ecosystem
carbon
processes
3. Most studies on mechanisms of ecosystem carbon
cycle or on the response of environmental changes
neglect their different effects at different scales
Regional pattern
Community
composition
Canopy
structure
micro- ecophysiology
4. Regional carbon budgets are often based on
Temperature
measurements
of changes in ecosystem carbon
stocks or fluxes for a short time (few years)
Net C flux at global and continental
scales
Precipitation
Net C flux from at ecosystem level
In the past decade, there
have been extensive and
intensive observations at
different
scales
using
various technologies
However, the rich data
have not been exploited in
regional carbon budgets
because of lack of an
approach to assimilate the
data obtained at multiple
scales
Atmospheric data Satellite data
Eddy flux data
Biomass data
FACE data
Soil carbon data
Traditional cross-scaling approaches
used in regional carbon budgets:
The “top-down” approach (based on atmospheric
measurement and inverse inverse modeling, satellite
remote sensing) is difficulty to identify the driving
force and mechanisms of ecosystem changes
The “Bottom-Up” approach directly extrapolates
small-scale results (from controlled experiments and
point observation) or uses mechanistic models based
small-scale studies, neglecting mechanisms that
operate at large scales
A new cross-scaling approach is emerging: datamodel fusion based on multi-scale observation
and cross-scale mechanistic modeling
Land flux measurement
Ecosystem inventory
Cross-scale modeling
Multi-scale observation
Satellite observation
A multi-scale data-model fusion system
Multi-Scale Observations
From site to landscape to regional scales
using techniques, e.g. satellite remote sensing,
eddy covariance, ecosystem inventory
Cross-Scale Data-Model Fusion
Integrate multi-scale data into newgeneration ecosystem models to simulate
cross-scale mechanistic interconnections
Dynamic data assimilation
Continuously
assimilating
multi-scale
observational data into dynamic simulation to
achieve realistic ecological forecasting
Remote Sensing observations
Vegetation
activity
Remote sensingbased Modeling
Process-based
modeling
Climate and soil conditions
Land-based observations
Bottom - up
Top - down
We have
developed
an approach
to combine
satellite
observation
and
mechanistic
modeling
Vegetation
pattern
Satellite remote sensing is currently the only means
available to
‹ observe actual changes in ecosystem pattern and
activity at regional scales and high resolutions
‹ reflect the combined effect of various driving forces
But cannot directly measure carbon fluxes or stocks,
is weak in detecting mechanisms of ecosystem changes
Mechanistic modeling is the best approach to
™ Integrate observational data at different scales,
using different technologies,
™ Build mechanistic, quantitative connections of
ecosystem processes at different scales
™ Conduct diagnostic analysis to understand
ecosystem mechanisms
™ Rebuild and predict ecosystem changes
But it is difficult to validate mechanistic models at large scale,
particularly for modeling regional ecosystem pattern
A remote sensing-based ecosystem model: GLO-PEM
Other
observations
PAR
Biological
variables
FAPAR
LUE
NDVI
Land cover
Environmental
variables
Temperature
GPP
VPD
Rainfall
LAI
NPP
Ra
Prince
& Goward,
1995,
(Cao
et al. 2004)
Cao et al. 2004
Atmospheric
CO2
Photosynthetic
enzyme
efficiency
ε
max
Temperature
Light
use
efficiency
Radiation
Water vapor
pressure deficit
Stomotal
CO2
conductance
ε
σ
Soil moisture
GLO-PEM estimates LUE on a mechanistic basis
(Cao et al. 2004)
GLO-PEM’s calculation of NPP can be represented
NPP = Σt (PAR FPAR) (εmax σ) – (Rg + Rm)
where (PAR FPAR) represents plant light harvesting,
εmax is the maximum light use efficiency in terms of
gross primary production (GPP), σ is the reduction of
εmax by environmental conditions, and Rm and Rg are
the maintenance and growth respiration.
Prince & Goward, 1995, Cao et al. 2004)
GLO-PEM Calculation of Light Using Efficiency
ε *g = 4 .42 Pi − Γ *
Pi + 2 Γ *
2 .76 g / MJ
for C 3
for C 4
Potential
light use
efficiency
σ = δ f (gs )
δ = 1−
1
4
(
)
PAR
−
p
1 + exp( 8 NDVI −
)
p
f ( gs ) = f (T ) f (δ q ) f (δθ ) f ( Pa )
Radiation
saturation
Prince &
Goward, 1995,
Cao et al. 2004)
Effects of temperature, humidity, soil moisture, and Atmospheric CO2
Climate, Atmospheric CO2, Soil, land cover …
Photosynthesis
Canopy
conductance
A processbased
ecosystem
model
CEVSA
Photosynthetic
capacity
Leaves
C&N
allometry
Stems
Roots
Autotrophic
respiration
Net
primary
Productivity
(NPP)
Plant C & N
Radiation,
energy,
and water
balance
N
uptake
Litter
C&N
Heterotrophic
Respiration
(HR)
Net
ecosystem
Productivity
(NEP)
Organic C & N
decomposition
Soil C & N
(Cao & Woodward 1998a, Cao et al. 2002)
CEVSA
integrates
the wholeplant and
ecosystem
processes
Regional pattern
Reference Height
Tm
em
ra
ra
ra
Canopy
Hg
Canopy
e*s (Tc)
Hc
Ta
Cm
λE
H
rb
Tc
rc
es λ Ec
ea
rb
λ Eg
Fca
Ci
Cs
An 1.6rc
1.4rb
Ca
rd
rd
Fcs
Heat
Tg
Surface Layer
Water
rsoil
Ecosystem processes
Root Zone
Whole-plant processes
CO2
It uses
satellite-based
land cover
map as an
input to
account
vegetation
pattern
(Cao & Woodward
1998a, Cao et al.
2002)
Couple the Remote Sensing- and Process-Based Model
GPP
Net
primary
production
NDVI
LAI
APAR
Vegetation
type
Leaves
Stems
GLOPEM
Roots
Plant C & N
Radiation
Temperature
VPD
Autotrophic
respiration
CEVSA
LUE
N
uptake
Litter
C&N
Heterotrophic
respiration
Rainfall
Carbon decomposition
Nitrogen mineralization
Evapotranspiration
Soil water, C and N
Net
ecosystem
production
Satellite-based estimate of changes in annual
NPP from the 1980s to the 1990s
(Cao et al. 2004)
Satellite-detected global NPP variability and the
dynamic response to the El Niño/La Niña cycle
NP P Anomaly
NOAA 7
NOAA 9
10-day NP P
NOAA 11
24
NOAA 14
NPP Anomaly and MEI
3
21
2
18
1
0
15
-1
12
-2
9
-3
Pinatubo eruption
-4
1981
NPP (g C m -2 per 10 days)
4
MEI
6
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
(Cao et al. 2004)
The regional pattern of NPP changes in a transition
from an El Niño and to an a La Niña year
1986/1987
El Niño
1989/1990
La Niña
(Cao et al. 2004)
The different
regional
pattern of
NPP changes
in different
El Niño year
NPP
anomaly (g
C m-2 per 10
days)
1982/1983
1993/1994
1997/1998
(Cao et al. 2004)
Seasonal and interannual changes in NPP in China
（Net Primary Productivity, CO2 fixation by plants）
NPP
alyyears
Increase by
2.5% duringAnom
last 20
28
4
10 days NPP(g c/m )
2
2
20
16
0
12
8
4
-2
-4
0
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002
10 days NPP anomalies
24
Interannual Variation in NPP (1981-2000)
NPP changes from the 1980s to the 1990s
NPP
Total NPP (Gt C/yr)
1980s
3.23
1990s
3.31
Precipitation
Seasonal and interannual change in HR
（Soil heterotrophic respiration, carbon release）
24
2
16
3
2
1
0
12
-1
8
4
-2
-3
0
-4
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002
10 days HR anomalies
10 days HR(g c/m )
20
H R 4.1% during
A nlast
o m a20
ly years
Increase by
Interannual Variation in HR (1981-2000)
Soil HR changes from the 1980s to the 1990s
Total HR (Gt C/yr)
1980s
3.14
1990s
3.27
Soil HR
Temperature
Seasonal and interannual changes in NEP
（Net Ecosystem Productivity, net carbon uptake or release）
10
4
3
2
4
1
2
0
0
-1
-2
-2
-4
-3
2
6
-6
-4
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002
10 days NEP anomalies
10 days NEP(g c/m )
8
decreased from 0.09 Gt C/yr in the
N E0.04
P Gt C/yr in
A nthe
o m a1990s
ly
1980s to
Interannual Variation in NEP (1981-2000)
NEP
Changes
from the
1980s to
the 1990s
NPP
HR
Research direction
To conduct
joint
observations
with remote
sensing and
eddy flux
measurement
at ChinaFlux
sites
ChinaFlux Tower
flux sites
Research direction
Assimilate
data from
intensive site
measurement
into remote
sensing-based
and
mechanistic
ecosystem
models
Satellite
remote
sensing
Data-model
fusion and
simulation
ChinaFlux (8 sites)
CERN (36 stations)
Research direction
Human driving force
A comprehensive,
dynamic
national carbon
budget
Quantification
of naturally and
human induced
changes
Dynamic carbon budget
Natural driving force
！！
Thank You !!