Modeling Carbon Sequestration
in High Alpine Forest
Ecosystems
Stephan A. Pietsch
Hubert Hasenauer
Institute of Forest Growth Research
University of Agricultural Sciences Vienna
Peter-Jordan-Straße 82
1190 Vienna, AUSTRIA
Introduction
Characteristics of High Alpine Ecosystems
•
•
•
•
•
Day/Night temperature extremes
Cold temperatures → growing season
Low partial pressure of CO2
High radiation loads
Potential water stress in winter
Trade off: Carbon Uptake vs. Water Loss
Carbon Sequestration
High Alpine Forest Ecosystems
•
•
•
•
Uptake and storage of carbon
Photosynthesis reacts more sensitive
Changes in climate and CO2?
Method for assessing carbon cycle:
Biogeochemical-mechanisitc modeling
Methods
The Model: BIOME-BGC (Thornton 1998)
• Simulates the fluxes of water, energy,
carbon and nitrogen on a daily time step
• Original model includes 4 forest biomes
• 46 Parameters: turnover, mortality, C/Nratios, phenology, ecophysiology, etc.
• Species specific parameter settings:
beech, oak, spruce
Objectives
Carbon Sequestration in Alpine Forests
• Parameterisation of the
BGC-model for stone pine
forests ecosystems
• Model validation using an
independent data set
Data
• Parameterisation: literature
• Evaluation: 1 plot with
detailed measurements
• Validation: 23 stone pine
stands with Volume and 10
year volume increment data
Results – Evaluation
Stomatal Conductance Summer 96
Conductance [m s-1]
0.0014
0.0012
0.001
0.0008
0.0006
0.0004
0.0002
Observations
Predictions
0
15.Mai
12.Jun
10.Jul
07.Aug
04.Sep
02.Okt
30.Okt
Results – Evaluation
Stomatal Conductance Winter 94/95
Standard Model
Conductance [m s-1]
0.0014
0.0012
Predictions
Observations
0.001
0.0008
0.0006
0.0004
0.0002
0
01.Nov 29.Nov 27.Dez 24.Jän 21.Feb 21.Mär 18.Apr 16.Mai
Reducing Stomatal Conductance
Due to Low Temperatures:
What options do we have:
• Previous night Tmin (standard model)
• 3d-mean Tmin (Körner 1994)
• Stem temperature (Wieser 2002)
Results – Model Changes
Stomatal Conductance Winter 94/95
3d-mean Tmin
(According to Körner 1994)
Conductance [m s-1]
0.0012
Observations
0.001
0.0008
0.0006
Predictions
0.0004
0.0002
0
01.Nov 29.Nov 27.Dez 24.Jän 21.Feb 21.Mär 18.Apr 16.Mai
Results – Model Changes
Stem Temperature Prediction
• Stem temperature is a predictor for
stomatal conductance (Wieser 2002)
• How to predict stem temperature?
– Tmin, Tavg, 3d-mean Tavg, ....
Results – Model Changes
3d-mean Tnight vs. Stem Temperature
y = 0.89x + 0.06
15
2
R = 0.88
10
5
0
-15
-10
-5
-5 0
-10
-15
5
10
15
Results – Model Changes
Stomatal Conductance Winter 94/95
Conductance [m s-1]
Stem Temperature
0.0012
0.001
Observations
0.0008
0.0006
Predictions
0.0004
0.0002
0
1. Nov. 29. Nov. 27. Dez. 24. Jän. 21. Feb. 21. Mär. 18. Apr. 16. Mai.
Model Validation
Carbon Sequestration in Stone pine Forests
23 stone pine stands
Stand age [yrs]
Elevation [m]
Volume [m3 ha-1]
Site class
SDI
mean
134
1753
355
4.9
626
min
66
1555
112
1.6
83
max
250
2050
553
7.3
4342
Results – Validation
Observed Vol. [m3 ha-1]
Standing tree volume – 23 stands
600
500
400
300
200
y = 0.93x + 13.9
R2 = 0.78
100
0
0
100
200
300
400
500
Predicted Vol. [m3 ha-1]
600
Results – Validation
Observed [m3 ha-1 10yr.-1]
10-year Volume Increment – 23 stands
y = 0.97x + 1.7
50
2
R = 0.84
40
30
20
10
0
0
10
20
30
40
Predicted [m3 ha-1 10yr.-1]
50
Results – Validation
Residual Analysis of the Increment Data
Stand age
Elevation
3
3
2
2
1
1
0
-1 0
50
100
150
200
0
-11500 1600 1700 1800 1900 2000 2100
250
-2
-2
-3
-3
Site class
SDI
3
3
2
2
1
1
0
0
-1 0
1
2
3
4
5
6
7
8
-1 0
-2
-2
-3
-3
200
400
600
800
1000
Results – Validation
Error Analyses
Volume
[% of obs.]
10-yr. Vol. Incr.
[% of obs.]
Confidence Int.
-4.8 to 8.7
-3.5 to 11.4
Prediction Int.
-32.0 to 36.1
-33.5 to 41.5
Tolerance Int.
-41.2 to 45.1
-42.8 to 50.8
Summary of Model Changes
Carbon Sequestration in Stone pine Forests
• 3-d mean night temperature
is a precise predictor for stem
temperature in stone pines
• Stem temperature is good
predictor for reducing
stomatal conductance
Conclusion
Carbon Sequestration in Stone pine Forests
•
•
•
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Model adaptation was important
Improved the accuracy of predictions
Model validation exhibited no bias
BGC-Model for stone pine forest
ecosystems