Predicting climate change impacts
on southern pines productivity in
SE United States using
physiological process based model
3-PG
Carlos A. Gonzalez-Benecke
School of Forest Resources and Conservation
University of Florida
Outline
1. Southern forests in SE United States
2. 3-PG Model
3. Model Calibration for Pinus elliottii (slash pine)
4. Model Validation
5. Case Study
Climate Change Impacts on Productivity of Slash Pine Stands
Background
• Forests have multiple goods and services: wild-life, water, soil, C
seq,… wood.
• In SE United States : 60% of landscape if forested including 28 million
ha of southern pines.
• SE U.S. produces 58% of the total U.S. timber harvest and 18% of the
global supply of roundwood (more than any other country).
• SE pine forests contain 1/3 of the contiguous U.S. forest C and can
sequester 23% of regional GHG emissions.
• Most important southern pine species: Pinus taeda (loblolly pine),
Pinus elliottii (slash pine) and Pinus palustris (longleaf pine).
Background
•
•
•
•
•
Slash Pine (Pinus elliottii Engelm.)
Medium-Long-Lived.
Fast-growing
Important commercial species in SE United States
Objectives: Pulpwood and sawtimber production
Area of timberland: 4.2 million ha
http://www.forestryimages.org
3-PG
(Landsberg and Waring, 1997)
Tree growth model based on :
Physiological Principles that
Predict Growth
Forest Production :
• Light Interception
• Carbon Acquisition
• Carbon Allocation
3-PG Model
Key to colours & shapes
Carbon
Water
Trees
State variables
T

+
ET
VPD
gC
f
Losses
+
Subsidiary variables
SLA
Subsidiary variables

Material flow s
Influences
CO2
_
_
R
+
Soil H20
+
Climate & site Inputs
+
+
H20
LAI
GPP
_
+
+
+
FR
+
+
LUE
F/S
NPP
Stocking
_
+
Rain
_
DBH
BA
+
Roots
_
wSx
_
+
Stem
+ w
S
Foliage
+
C,N
+
_
Dead
trees
_
+
w S >w Sx
+ 
N
+
Stress
_
Litter
Landsberg and Waring 1997
3-PG Model
NPP = Q0* 𝟏 − 𝒆−k∗LAI *C * R
All modifiers affect canopy production:
C = fT fF fN min{fD , f} fage fC Cx
Temperature
Frost
Nutrition
VPD
ASW
(0  fi  1)
Age
CO2
Max Canopy
Quantum
Efficiency
3-PG Model
Parameterization for Slash Pine
C = fT fF fN min{fD , f} fage fC Cx
f D ( D)  e  k D D
Canopy Quantum Yield = 0.056 mol CO2 / mol PAR
where
D = current VPD
kD = strength of VPD response
Gonzalez-Benecke et al. 2014
Parameterization for Slash Pine
3-PG Model
C = fT fF fN min{fD , f} fage fC Cx
1.0
SW )
Soil water growth modifier (f
Effect of Temperature in
Canopy Quantum Yield
(fTemp)
1.0
0.8
0.6
0.4
0.2
0.8
0.6
0.4
Sand
Sandy-loam
0.2
Clay-loam
Clay
0.0
0.0
0.2
0.4
0.6
0.8
1.0
Relative available soil water
0.0
0
10
20
30
40
50
1.10
Teskey et al. 1994
Effect of CO2 in
Canopy Quantum Yield
(fCalpha)
Temperature (C)
1.08
1.06
1.04
1.02
1.00
Teskey et al. (in preparation)
300
400
500
600
700
[CO2] ppm
800
900
1000
Results
Validation Sites
14 sites in US
118 permanent plots
7 sites in Uruguay
686 year x plot observations
Results
-5.3
0.89
-6.9
0.93
0.4
0.96
Height (m)
Above Ground Biomass
(Mg ha-1)
Basal Area (m2 ha-1)
0.98
4.1
0.97
Trees per hectare
Volume
0.8
Height (m)
ha-1)
AGB
(Mg/ha)
BA
(m2/ha)
Height
(m)
Nha
(ha-1)
VOB
(m3/ha)
(%)
R2
(m3
Variable
Bias
Trees per hectare
Y=predicted
Basal Area (m2 ha-1)
X=observed
Above Ground Biomass
(Mg ha-1)
Validation
Volume (m3 ha-1)
Gonzalez-Benecke et al. 2014
Case Study:
Climate Change Effect on Slash Pine Productivity
Future Climate Data: CanESM2 model
Downscaled using MACA method
(Multivariate Adaptive Constructed Analogs)
http://maca.northwestknowledge.net/
Scenarios (combination of climate and site quality):
• Based on 2 RCPs
• Based on Site Quality
(Representative Concentration Pathways) (site index)
Scenario
- Historical
- RCP 4.5
- RCP 8.5
Climate Data
1950 – 2010
2070 – 2100
2070 – 2100
CO2
400 ppm
550 ppm
850 ppm
Productivity
- Low
- Medium
- High
Site Index
19 m
23 m
28 m
Sites location
18.3
+2.1
+2.9
18.0
+2.1
+3.0
19.8
19.1
+2.9
+4.8
21.1
19.6
+2.0
+2.8
+2.1
+2.8
+2.1
+3.0
+2.8
+4.8
19.4
+2.0
+2.7
18.3
18.8
20.1
+2.0
+2.8
11 sites in SE US
4 sites in Northern Limit
Historical Mean Annual Temperature (°C)
and Mean Increment in Temperature due to
Climate Change (RCP 4.5 and 8.5)
+2.0
+2.8
22.9
+1.8
+2.6
Case Study
Climate Change Scenarios
Summary
Variable
Tmax (C)
Tmin (C)
Rain (mm)
Radiation (%)
RCP4.5
+ 1.8 to +2.9
+ 1.8 to +3.0
- 49 to + 67
+2% to + 6%
RCP8.5
+ 2.6 to +4.8
+ 2.7 to +4.8
- 66 to 45
+1% to + 6%
Climate Change Effect on Slash Pine Productivity
Change in Above Ground Biomass (Mg/ha)
at age=25 years
RCP's v/s Historical Scenarios
L: 25 - 41
M: 10 - 22
H: 6 - 23
L: 18 - 29
M: 8 - 12
H: 4 - 8
L: 26 - 42
M: 16 - 22
H: 12 - 15
L: 32 - 46
M: 32 - 42
H: 16 - 25
L: 18 - 28
M: 8 - 12
H: 4 - 10
L: Low Productivity
M: Medium Productivity
H: High Productivity
L: 24 - 41
M: 17 - 34
H: 16 - 28
L: 26 - 39
M: 29 - 37
H: 14 - 20
L: 18 - 29
M: 8 - 12
H: 5 - 8
L: 22 - 37
M: 12 - 19
H: 7 - 13
L: 18 - 29
M: 8 - 12
H: 5 - 9
L: 10 - 20
M: 8 - 10
H: 3 - 6
Climate Change Effect on Slash Pine Productivity
50
Tmnean > 19 C
Tmnean < 19 C
40
30
20
10
RCP 4.5
0
19
Low
23
Medium
Site Index (m)
Site Quality
28
High
Change in Above Ground Biomass
(Mg ha-1)
Change in Above Ground Biomass
(Mg ha-1)
Change in Above Ground Biomass (Mg/ha) at age=25 years
RCP's v/s Historical Scenarios
50
Tmnean > 19 C
Tmnean < 19 C
40
30
20
10
RCP 8.5
0
19
Low
23
Medium
Site Index (m)
Site Quality
28
High
Conclusions:
Climate Change Effect on Slash Pine Productivity
Under Future Climate Scenarios Used:
For Sites with Mean Annual Temperature > 19 C:
• Under RCP4.5 : AGB can be increased between 2% to 27%
(Mean=8%).
•
Under RCP8.5 : AGB can be increased between 2% to 44%
(Mean=13%).
For Sites with Mean Annual Temperature < 19 C (North Limit):
• Under RCP4.5 : AGB can be increased between 2% to 44%
(Mean=17%).
•
Under RCP8.5 : AGB can be increased between 8% to 63%
(Mean=27%).
Conclusions:
Climate Change Effect on Slash Pine Productivity
Under Future Climate Scenarios Used:
• Responses to Climate Change should be larger in colder
range of distribution.
• Responses to Climate Change should be larger in low
productivity sites.
Acknowledgements