a training course on co 2 eddy flux data analysis and modeling parameter estimation: practice
DESCRIPTION
A Training Course on CO 2 Eddy Flux Data Analysis and Modeling Parameter Estimation: Practice Katherine Owen John Tenhunen Xiangming Xiao Institute of Geography and Natural Resources, Chinese Academy of Sciences, Beijing, China - PowerPoint PPT PresentationTRANSCRIPT
A Training Course on CO2 Eddy Flux Data Analysis and Modeling
Parameter Estimation: Practice
Katherine OwenJohn TenhunenXiangming Xiao
Institute of Geography and Natural Resources, Chinese Academy of Sciences, Beijing, China
Institute for the Study of Earth, Oceans and Space, University of New Hampshire, USADepartment of Plant Ecology, University of Bayreuth, Germany
The Institute of Geography and Natural Resources, CAS, Beijing, China
July 25, 2006
Practice: Parameter EstimationMany available methods. I will show:
•Hyperbolic Light Response Model
•Physiological Carboxylase-based Process Model
both from Owen et al. 2006, Global Change Biology, submitted
Outline
1. Inputs: data preparation
2. Running the program and potential problems
3. Outputs and potential problems
4. Examples
Practice: Parameter EstimationInputs: Data preparation
•Input files for parameter estimation with the Hyperbolic Light Response Model (1):
1. Half-hourly meteorological and gas flux data (output file from flux partitioning and gap filling - “HE2001Processed.txt”)
Practice: Parameter EstimationInputs: Data preparation
•Input files for parameter estimation with the Physiological Carboxylase-based Process Model (2):
1. Half-hourly meteorological and gas flux data (output file from flux partitioning and gap filling - “HE2001Processed.txt”)
2. Leaf Area Index (LAI) - either constant value or seasonally changing file (“HE2001.lai”)
3. Latitude & Longitude- to calculate sun angle
4. Physiological parameters - previously published values (eg. Leaf angle, Michaelis-Menton constant for oxygenation, Maximum rate of electron transport, etc.) for different vegetation types (“coni.gfx”)
Practice: Flux Partitioning & Gap FillingInputs: Data preparation
•Review daily outputs from flux partitioning and gap filling - Are there problems? Do the results make sense?
•LAI file
•gfx file
Practice: Parameter EstimationPotential problems in running the program
The Hyperbolic Light Response Model stops running:
•Fitter gets “stuck in a local minima” or can not converge on a solution due to high scatter in data (typical for winter or in periods with cut or harvests) - skip parameter estimation for the period
The Physiological Carboxylase-based Process Model stops:
•Latitude & longitude were not defined
•LAI data file has a different number of days than meteorological and gas flux input file
•Fitter gets “stuck in a local minima” - skip parameter estimation for the period
Practice: Parameter EstimationHow the Hyperbolic Light Response Model (1) works
Use PPFD & un-gap filled NEE and non-linear least trimmed squares regression technique to iteratively calculate the , , and for 10 day periods
Set initial random values of , , and
Read in half- hourly meteorological & flux input file
Output: optimal , , and parameters for 10 day periods
Practice: Parameter EstimationHyperbolic Light Response Model (1) Outputs
•Parameters:
•Standard error of and
•Slope, intercept & r2 of observed NEE vs. calculated NEE
Practice: Parameter Estimation: Outputs & Potential Problems: Hyperbolic Light Response Model (1)
“abnormal” results can be due to:
•Winter periods with little light response
•Strong scatter in NEE & PPFD relationship (due to cut or harvest)
•Poor starting values of - results stuck in local minima
We chose to eliminate “abnormal” results with:relative standard error > 0.6, > 0.17, > 100, > 15
Hesse, JD 345-354, =-0.0308 =-109652 =1.2597
-5
0
5
10
15
20
25
30
35
0 200 400 600 800 1000 1200
PPFD (umol m-2 s-1)
NEE
(um
ol m
-2 s
-1)
Practice: Parameter Estimation: How the Physiological Carboxylase-based Process Model (2) works
Define LAI: constant or seasonally changing from file
Calculate static geometric attributes of the canopy (diffuse & direct radiation on leaf surfaces-sunlit & shaded)
Iteratively calculate energy balance throughout canopy (leaf temperature, incoming and outgoing shortwave & longwave radiation, estimated GPP)
Define latitude, longitude, vegetation type gfx input file
Read in half- hourly meteo & flux input file
Output: (Vcuptake2* and alpha) or (Vcuptake1*) parameters for 10 day periods
Practice: Parameter EstimationCarboxylase-based Process Model (2) Outputs
•Parameters: Vcuptake & alpha
•Standard error of Vcuptake & alpha
•Slope, intercept & r2 of observed GPP vs. calculated GPP
Practice: Parameter EstimationOutputs & Potential Problems
Carboxylase-based Process Model (2)“Abnormal” Vcuptake & alpha results can be due to:
•LAI of 0
•Poor estimates of seasonal LAI
•harvests or cuts
•scatter or errors in data
We chose to eliminate “abnormal” results with:relative standard error > 0.6, Vcuptake > 350, alpha > 0.17
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
0 61 122 183 244 305 366
JD
Para
met
er g
uess
Vcmax
EB2004
-5
0
5
10
15
20
0 50 100 150 200 250 300 350 400
GPP_f
LAIS
Easter Bush, UK, 2005, LAI too low
Hesse, France•Deciduous Beech Forest•Fagus sylvatica•experienced drought in 2003
Practice: Parameter EstimationExamples: Hesse, France
HE2001
-15
-10
-5
0
5
10
15
20
0 100 200 300 400
Julain Day
Flux
g_m
-2_d
ay-1
GPP_f
Reco
NEE_f
HE2001
0
5
10
15
20
25
30
35
40
1 25 49 73 97 121
145
169
193
217
241
265
289
313
337
361
Julain DayTa
ir_f,
P, R
g_f
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
VPD_
f P
Tair_f
Rg_f
VPD_f
HE2002
0
5
10
15
20
25
30
35
40
1 25 49 73 97 121
145
169
193
217
241
265
289
313
337
361
Julain Day
Tair_
f, P,
Rg_
f
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
VPD_
f P
Tair_f
Rg_f
VPD_f
HE2002
-15
-10
-5
0
5
10
15
20
0 100 200 300 400
Julain Day
Flux
g_m
-2_d
ay-1
GPP_f
Reco
NEE_f
HE2001
-5
0
5
10
15
20
0 50 100 150 200 250 300 350 400
GPP_f
LAI7.5
LAI-S_HE
HE2002
-5
0
5
10
15
20
0 50 100 150 200 250 300 350 400
GPP_f
LAI8.4
LAI-S
Practice: Parameter EstimationExamples: Hesse, France
HE2001
0
1
2
3
4
5
6
7
0 100 200 300 400Julain Day
m
ol C
O2
m-2
s-1
g
HE2002
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0 100 200 300 400Julain Day
m
ol C
O2/
m
ol p
hoto
n
a
alpha LAIS
alpha LAI6.6
HE2002
0
20
40
60
80
100
120
140
160
0 100 200 300 400Julain Day
m
ol C
O2
m-2
s-1
b
Vc2 LAIS
Vc2 LAI6.6
Vc1 LAI6.6
HE2002
0
1
2
3
4
5
6
7
0 100 200 300 400Julain Day
m
ol C
O2
m-2
s-1
g
HE2001
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0 100 200 300 400Julain Day
m
ol C
O2/
m
ol p
hoto
n
a
alpha LAIS
alpha LAI5.9
HE2001
0
20
40
60
80
100
120
140
160
0 100 200 300 400Julain Day
m
ol C
O2
m-2
s-1
b
Vc2 LAIS
Vc2 LAI5.9
Vc1 LAI5.9
Takayama, Japan
•Mountain Deciduous Forest
•Quercus crispula Blume, Betula ermanii Cham., Betula platyphylla Sukatchev var. japonica Hara
•Storm damage in 2004
Practice: Parameter EstimationExamples: Takayama, Japan
TK2002
-10
-5
0
5
10
15
0 100 200 300 400
Julain Day
Flux
g_m
-2_d
ay-1
GPP_f
Reco_2rob
NEE_f
TK2002
-10
-5
0
5
10
15
20
25
30
35
40
0 100 200 300 400
Julain Day
Tair_
f, P,
Rg_
f
-1
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
4
VPD_
f Tair_f
Rg_f
VPD_f
TK2003
-10
-5
0
5
10
15
0 100 200 300 400
Julain Day
Flux
g_m
-2_d
ay-1
GPP_f
Reco_2rob
NEE_f
TK2003
-10
-5
0
5
10
15
20
25
30
35
40
0 100 200 300 400
Julain Day
Tair_
f, P,
Rg_
f
-1
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
4
VPD_
f Tair_f
Rg_f
VPD_f
TK2004
-10
-5
0
5
10
15
20
25
30
35
40
0 100 200 300 400
Julain Day
Tair_
f, P,
Rg_
f
-1
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
4
VPD_
f Tair_f
Rg_f
VPD_f
TK2004
-10
-5
0
5
10
15
0 100 200 300 400
Julain Day
Flux
g_m
-2_d
ay-1
GPP_f
Reco
NEE_f
TK2002
-3
-1
1
3
5
7
9
11
13
15
0 50 100 150 200 250 300 350 400
GPP_fLAI4.74
LAIS
TK2003
-3
-1
1
3
5
7
9
11
13
15
0 50 100 150 200 250 300 350 400
GPP_fLAI4.08
LAIS
TK2004
-2
0
2
4
6
8
10
12
0 50 100 150 200 250 300 350 400
GPP_fLAI3.26
LAIS
Practice: Parameter EstimationExamples: Takayama, Japan
TK2004
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0 100 200 300 400Julain Day
m
ol C
O2/
mol
pho
ton
a
alpha LAIS
TK2004
0
10
20
30
40
50
60
70
80
0 100 200 300 400Julain Day
m
ol C
O2
m-2
s-1
b
Vc2 LAIS
Vc1 LAI3.26
TK2003
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0 100 200 300 400Julain Day
m
ol C
O2/
mol
pho
ton
a
alpha LAIS
TK2003
0
20
40
60
80
100
120
140
0 100 200 300 400Julain Day
m
ol C
O2
m-2
s-1
b
Vc2 LAIS
Vc1 LAI4.08
TK2002
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0 100 200 300 400Julain Day
m
ol C
O2/
mol
pho
ton
a
alpha LAIS
TK2002
0
20
40
60
80
100
120
140
0 100 200 300 400Julain Day
m
ol C
O2
m-2
s-1
b
Vc2 LAIS
Vc1 LAI4.74
TK2002
0
1
2
3
4
5
6
7
8
9
0 100 200 300 400Julain Day
m
ol C
O2
m-2
s-1
g
TK2003
0
1
2
3
4
5
6
7
0 100 200 300 400Julain Day
m
ol C
O2
m-2
s-1
g
TK2004
0
0.5
1
1.5
2
2.5
3
3.5
0 100 200 300 400Julain Day
m
ol C
O2
m-2
s-1
g
Barrow, Alaska, USA•Tundra
•Carex aquatilis spp. Stans, Eriophorum angustifolium, Dupontia fisheri, Poa artica
Practice: Parameter EstimationExamples: Barrow, Alaska, USA
BA1998
-20
-10
0
10
20
30
40
0 50 100 150 200 250 300 350
Julain DayTa
ir_f,
P, R
g_f
-0.5
-0.25
0
0.25
0.5
0.75
1
Tair_f
P
Rg_f
VPD_f
BA1999
-20
-10
0
10
20
30
40
0 50 100 150 200 250 300 350
Julain Day
Tair_
f, P,
Rg_
f
-0.5
-0.25
0
0.25
0.5
0.75
1
Tair_f
P
Rg_f
VPD_f
BA1999
-15
-10
-5
0
5
10
15
0 50 100 150 200 250 300 350
Julain Day
Flux
g_m
-2_d
ay-1
GPP_f
Reco
NEE_f
BA1998
-15
-10
-5
0
5
10
15
0 50 100 150 200 250 300 350
Julain Day
Flux
g_m
-2_d
ay-1
GPP_f
Reco
NEE_f
BA1998
-2
-1
0
1
2
3
4
5
6
0 50 100 150 200 250 300 350
GPP_f
LAI1.5
BA1999
-4
-2
0
2
4
6
8
10
0 50 100 150 200 250 300 350
GPP_fLAI1.5
Practice: Parameter EstimationExamples: Barrow, Alaska, USA
BA1998
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0 100 200 300 400Julain Day
m
ol C
O2/
m
ol p
hoto
n
a
BA1998
0
5
10
15
20
25
30
35
0 100 200 300 400Julain Day
mol
CO
2 m
-2 s
-1
b
Vc1 LAI1.5
BA1999
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0 100 200 300 400Julain Day
m
ol C
O2/
m
ol p
hoto
n
a
BA1999
0
5
10
15
20
25
30
35
40
45
0 100 200 300 400Julain Day
m
ol C
O2
m-2
s-1
b
Vc1 LAI1.5
BA1998
0
0.5
1
1.5
2
2.5
0 100 200 300 400Julain Day
m
ol C
O2
m-2
s-1
g
BA1999
0
0.5
1
1.5
2
2.5
3
0 100 200 300 400Julain Day
m
ol C
O2
m-2
s-1
g
Grillenburg, Germany•Grassland•Festuca pratensis, Alopecurus pratensis, Phleum pratensis•Cut 2 or 3 times per year•No grazing•experienced drought in 2003
Practice: Parameter EstimationExamples: Grillenburg, Germany
GR2004
-20
-10
0
10
20
30
40
1 25 49 73 97 121
145
169
193
217
241
265
289
313
337
361
Julain Day
Tair_
f, P,
Rg_
f
-2
-1
0
1
2
3
4
VPD_
f P
Tair_f
Rg_f
VPD_f
GR2004
-10
-5
0
5
10
15
20
0 100 200 300 400
Julain Day
Flux
g_m
-2_d
ay-1
GPP_f
RecoNEE_f
GR2004
-4
-2
0
2
4
6
8
10
12
14
16
0 50 100 150 200 250 300 350 400
GPP_fLAI4.4
LAIGreen
Practice: Parameter EstimationExamples: Grillenburg, Germany
GR2004
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0 100 200 300 400Julain Day
m
ol C
O2/
mol
pho
ton
a
alpha LAIS
alpha LAI4.4
GR2004
0
20
40
60
80
100
120
140
0 100 200 300 400Julain Day
m
ol C
O2
m-2
s-1
b
Vc2 LAIS
Vc2 LAI4.4
Vc1 LAI4.4
GR2004
0
1
2
3
4
5
6
7
8
0 100 200 300 400Julain Day
m
ol C
O2
m-2
s-1
g