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Assessing Amazon regional energy, water andcarbon exchange using SiB3/LIS
NASA/LBA-ECO (CD36)
Rafael Rosolem (University of Arizona)
Luis Gustavo Goncalves de Goncalves (NASA GSFC)
William James Shuttleworth (University of Arizona)
July, 2007
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Who am I?Where do I come from?
Piracicaba, SP, Brazil
BSc in Meteorology (2002) at Univ. of So
Paulo
- Undergrad Research with soilrespiration controlled by soil temperature
and moisture
MSc in Agricultural Systems Ecology (2005) at Univ. of So Paulo- Impact of regional Amazon deforestation on water-energy fluxes caused by
road paving (BR-163 highway)
Visiting Research Collaborator (2003/2004) at UofA- Automatic parameter estimation techniques applied to SiB2 using Amazon
site dataset
PhD candidate at UofA (2006-?)- Energy, water, and carbon exchange over the Amazon basin, LSM,
O timization, Data Assimilation ???
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What are the scientific questions?1. How are the sources and sinks of CO2 located over the Amazon?
2. What is the seasonality of these sources and sinks along the year?
3. Can we identify and quantify the relationships between the carbon
dynamics and energy-water cycles?
Saleska et al. 2003 (Science) Huete et al. 2006 (GRL)
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Why the Amazon rainforest?
STRONG CARBON
DYNAMICS gives us the
opportunity to better identify
and analyze the carbon
exchange over the rainforest
The LBA project has helpedthe scientific community to
better understand the way the
Amazon rainforest function
with respect to the energy,water and carbon exchange
between the biosphere and
atmosphere.
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Why the Amazon rainforest?We have been studying the region for the last couple of years:
- South American LDAS (de Goncalves et al. 2006 - JGR)
-The impact of selective logging on SiB2
parameters using multicriteriaoptimization algorithms (Rosolem et al.
2005 IAHS Assembly)
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Why the Amazon rainforest?
Available eddy flux
measurements for modeling
(calibration/validation)
Source: Dr. Scott Saleska
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Why the Simple Biosphere Model?
SiB type models have been widely used in climate studies worldwide,
including the Amazon region
e.g. Nobre et al. 1991 (J.
Climate) impact of
large-scale Amazon
deforestation on climate
using SiB
e.g. Randal et al. 1996 (J.Climate) SiB2 and
remote sensing data
coupled to CSU GCM
Temp Prec
NEP
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New Features:
- Prognostic T, e, CO2,
other tracers in canopyair space;
- 10-layer soil (T and w),with adjustable water
extraction profile (roots);- Snowpack of 0-5 layers;
- Mixed canopy physiology
(e.g., savanna); and- Stable isotopefractionation of CO2.
UmP
G
S LRn
Cm
(CO2)
EmHm
S L
ra
rb
rd
rsoil
Ci3
Ci4
TcSPAR
Wg
T
T
ec*
eg*
ec*
Cs3
Cs4
rc3
rc4
rc
Iws
Erc
Ewc
EcHc
Ac
A c3
A c4es
Tc
(Vc fcG LT)
EgRg Hg
Hg
Ewg
Rg Esoil
Rroots
Rmicrobes
Wrun
Esoil
Wthru
Wleaf
Wdrain
Wdrip
Canopy
Air Space
Leaf Surface
Ewg
Ewc
Hc
Ac
-
-
-
-
-
-
+
+
h NADPH
ATP
3-PGA
1,3-diPGA
3PGAld
DHAP
F1,6diP
F-6P
R-5-P
RuBP
Ru-5-P
G-6-P
G-1-P
ADPG
Starch
ATP
NADPHATP
CO2(or O2)
fc4
fc3
Z1
Z2
Zm
Erc
eg*
Tg
emTm
rG
Wc
W
W
W
W
Am
(H2O)
Atmosphere
Canopy
Soil
Win
Ta Ca ea
1
2
T...
T...
T10W10
9W
W
W
8
7
W1 ...
...
...
...
...
Soil Temperature and Soil
Water are solved after
Canopy Air Space
Iws
ISOFRAX
Improved version: Simple Biosphere Model v.3 CSU
Adapted from Dr. Denning slides
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Photosynthesis, stomatal function and water
Plants need CO2(photosynthesis)
Open their stomata to absorb
CO2
H2O is then released as a
consequence
For every CO2 molecule
absorbed ~400 molecules of
H2O is released
Adapted from Dr. Denning slides
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Stomatal conductance is linearly related to photosynthesis:Semi-empirical model of leaf conductance gs
(Ball-Berry-Collatz)
Photosynthesis is controlled by three limitations
(The Farquhar-Berry model):
Enzyme-limitedrate (RuBisCO)
Light-limitedrate
Sink-limitedrate
n ss
s
A hg m p b
c= +stomatalconductance
Net CO2
assimilation(photosynthesis)
CO2 at leaf surface
RH at leaf surface
min( , , )n C L S d A A A A R=
Photosynthesis and stomatal conductance (leaf scale)
empirical coefficient(C3 ~ 9, C4 ~ 4)
Atmospheric pressuregs_MIN
(C3 ~ 0.01,C4 ~ 0.04)
Darkrespiration
rate
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Photosynthesis and stomatal conductance (canopy scale)
( )ns Afg =
( ) ( ) =2
12
,,,,0maxz
zCOc dzWTepfPARVfg
( )( )c
p
bc
acct W
c
rg
eTeE
+
= 121
*
From previous slide: (leaf scale)Integrate for the entire canopyCanopy top
Canopy bottom Max RuBisCOcapacity at top
canopy
PhotosyntheticallyActive Radiation
CO2 partialpressure
Water vaporpressure deficit
Temperature
Soil moisture
PAR-useparameter
Photosynthesis-conductance model (e.g. SiB) directly linksthe plant transpiration with net assimilation (An)
Canopytranspiration rate
Saturated vaporpressure at canopyAtmosphere vaporpressure
Canopy stomatalconductance
Canopy stomatalconductance
Plant to CASaerodynamic resistance
Density, specificheat of air
Psychrometric constant
Fractional canopywetted area
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Carbon exchange
Usually neglected interrestrialecosystems(may be important inthe oceans)
(NEE)
(GPP)
(Rautotrophic)
(Rheterotrophic)
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Carbon exchange(NEE)
(GPP)
(Rautotrophic)
(Rheterotrophic)
( )
NEPNEE
RGPPNEP
RRGPPNEP
RNPPNEPRGPPNPP
ysteme
hicheterotropcautotrophi
hicheterotrop
cautotrophi
=
=
+=
= =
cos
Carbon
exchange
(Ecolog
y)
Jan Jul Dec
0
Carb
onexchange
(AtmosphericScience
s)
GPP
Recosystem
Carbonuptake
Carbonrelease
NEP
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Feedback mechanisms and limiting factors
Structural Physiological
positive
negative
Adapted from Pitman 2003 Int J Clim.
Nemani et al. 2003 Science
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Why Sib3/LIS?
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Why Sib3/LIS?
-Integrates different sources of data (e.g., remotesensing, ground-based data) with LSM;
-Improves land surface characterization;
-Applications in agriculture, water resource management,and flood, weather, and climate predictions
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Why Sib3/LIS?
Different sources of forcing data in the Amazon (examples)
South American LDASDe Goncalves et al. 2006 - JGR
Adapted from Dr. Goulden slides
h /
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Why Sib3/LIS?
SiB3
An Rc
RrootRg
NEECarbon
Balance
Ti i i
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Time-varying inputs
New phenology module that reads monthly time-varyinginputs from 1o x 1o map (climatology)
-Absorbed fraction of PAR (aparc)
-Leaf area idex (m2 m-2) (zlt)-Greenness fraction (-) (green)-Roughness length (m) (z0d)
-Zero plane displacement height (m) (zp_disp)-Bulk PBL resistance coefficient (rbc)-Ground to CAS resistance coefficient (rdc)-Time-mean leaf projection (gmudmu)
-Canopy gap fraction for thermal IR radiation (-) (thermk)
Better results than look-up table values associated with
vegetation type!!!
Ti i i t ( l )
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Time-varying inputs (examples)aparc (January) aparc (June)
aparc (June minus January)
Q lit ti l i ( J t M )
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Qualitative analysis (average Jan to May)
CO2 assimilation Plant respiration
Net CO2 assimilation
Q lit ti l i ( ti l t l l i )
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Qualitative analysis (spatial-temporal analysis)
CO2 assimilation Plant respiration
Night (no assim.)
Day Day (photo resp)
Night (maintenance resp)Net CO2 assimilation
Units: mol m-2 s-1 Lat = 3oS
S il pi ti
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Soil respiration
Shows also diurnal variation and heterogeneity
But values are TOO HIGH!!!
Units: mol m-2 s-1 Lat = 3oS
Carbon exchange components (L t 3 S L 54 W)
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Carbon exchange components (Lat = 3oS; Lon = 54oW)Units: mol m-2 s-1
Step 1: CO2 assimilation
Carbon exchange components
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Carbon exchange componentsUnits: mol m-2 s-1
Step 2: Plant respiration
Carbon exchange components
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Carbon exchange components
Up to this point, it is OK.
Units: mol m-2 s-1Step 3: Net CO2 assimilation
Carbon exchange components
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Carbon exchange components
TOO HIGH!!!
Units: mol m-2 s-1Step 4: Soil respiration
Carbon exchange components
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Carbon exchange components
As a consequence, values are unrealistically high
Units: mol m-2 s-1Step 5: Net ecosystem exchange (NEE)
Comparison with EC flux tower (Lat=3oS/Lon=54oW)
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Comparison with EC flux tower (Lat=3oS/Lon=54oW)
Off course, this was expected !!!
Units: mol m-2 s-1
Current modifications
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Current modifications
SiB3
An Rc
RrootRg
NEECarbon
Balance
Phenology moduleaparc, zlt, green, z0d,
zp_disprbc, rdc, gmudmu,
and thermk(Monthly)
In summary
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In summary
SiB3 may benefit from LIS framework in terms of:
- Handling several potential input data to the model(remote sensing data, regional forcing data SALDAS,ground-based data);
- Its ability to perform regional simulation with highresolution components (parameters, time-varying inputs);
- Providing a better estimation of the carbon exchangecomponents (land cover and soil texture heterogeneity);
Challenges
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Challenges
-energy and water balance components (not yet tested);
-Model initialization: soil moisture (couple of years);
carbon (couple of centuries) (e.g., soil respirationresults);
-Model calibration and validation within the domain;
Note: The LBA-MiP may provide useful procedures and
techniques related to above challenges that we may applyto our study (e.g., 10 km x 10 km maps of time-varyinginputs are already available for the region and will beimplemented in the future)
10km x 10km time-varying input maps
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10km x 10km time varying input maps-Derived from 20 year long NASA/NOAA AVHRR Pathfinder NDVI
dataset (using empirical relationships);
10km x 10km Leaf area index (Jan)
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10km x 10km Leaf area index (Jan)
Next steps
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p
-SiB3 offline experiments (run in parallel with SiB3/LISframework);
-10km x 10km time-varying input maps (Dr. Stockli from
Dr. Scott Dennings group) already available;-Spin-up experiments (soil moisture and carbon);
-Discuss the results with Scott Dennings group which
provided the model code (not yet released) and support
(Dr. Ian Baker);
-Survey of remote sensing products that may be usefulfor this study (e.g., MODIS);
-Use EC flux towers for validation and calibration
experiments.
Acknowledgements
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Acknowledgements
-Gustavo and David Toll (visiting NASA/GSFC);
-Dr. Jim Shuttleworth (supervisor);
-Dr. Christa (excellent comments and suggestions);
-Regina Izzo (computer and technical support)
Any comments, suggestions, ideas (PLEASE)
LBA site Bananal
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Rplant
-GPP
LBA site Bananal
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Rground
-NPP
LBA site Bananal
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NEE
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