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Predicting climate change impacts on southern pines productivity in SE
United States using physiological process based model 3-PG
Carlos A. Gonzalez-BeneckeSchool 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 StudyClimate 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
Tree growth model based on :
Physiological Principles that
Predict Growth
• Light Interception• Carbon Acquisition• Carbon Allocation
Forest Production :
3-PG (Landsberg and Waring, 1997)
State v ar iables
Subs id iary v ar iab les
Climate & s ite Inputs
Los s es
Mater ia l f low s
Inf luenc es
Carbon
W ater
Trees
Ke y to colours & sha pe s
Subs id iary v ar iab les
+
H20 R ain
g C
Soil H20
ET
+
+
+
_
_
+
wS x
Deadtrees
Stocking+
+
_
wS +w S >w S x
_ _
N+
+
__
S tres s
VPD
T
FR
f
+
_
+
_
+
++
+
+
D BH
F /SR
LAILUE
SLA
+
+
_
NPP
Stem
Foliage
Roots
GPP
CO2
C ,N
Litter
+
3-PG Model
BA
Landsberg and Waring 1997
All modifiers affect canopy production:
TemperatureTemperature FrostFrost NutritionNutrition VPDVPD ASWASW AgeAge Max Canopy Quantum Efficiency
Max Canopy Quantum Efficiency
(0 fi 1)
NPP = Q0* *C * R
C = fT fF fN min{fD , fq} fage fCa Cx
CO2CO2
3-PG Model
Parameterization for Slash Pine
Canopy Quantum Yield = 0.056 mol CO2 / mol PAR
3-PG Model
C = fT fF fN min{fD , fq} fage fCa Cx
where
D = current VPDkD = strength of VPD
response
( ) Dk DDf D e
Gonzalez-Benecke et al. 2014
[CO2] ppm
300 400 500 600 700 800 900 1000
Eff
ect
of C
O2
in
Can
opy
Qua
ntum
Yie
ld(f
Cal
pha)
1.00
1.02
1.04
1.06
1.08
1.10
C = fT fF fN min{fD , fq} fage fCa Cx
3-PG Model
Temperature (C)
0 10 20 30 40 50
Eff
ect
of T
empe
ratu
re in
C
anop
y Q
uant
um Y
ield
(fT
emp)
0.0
0.2
0.4
0.6
0.8
1.0
Parameterization for Slash Pine
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.2 0.4 0.6 0.8 1.0
Relative available soil water
So
il w
ater
gro
wth
mo
dif
ier
(fS
W)
Sand
Sandy-loam
Clay-loam
Clay
Teskey et al. 1994
Teskey et al. (in preparation)
Results Validation Sites
14 sites in US
7 sites in Uruguay
118 permanent plots
686 year x plot observations
Abov
e G
roun
d Bi
omas
s
(Mg
ha-1
)
Basa
l Are
a (m
2 ha
-1)
Volu
me
(m3
ha-1
)
Hei
ght
(m)
Tr
ees
per h
ecta
re
Above Ground Biomass (Mg ha-1)
Basal Area (m2 ha-1)
Volume (m3 ha-1)
Trees per hectare
Height (m)
Results Validation
X=observed
Y=predicted
Gonzalez-Benecke et al. 2014
VariableBias
(%)R2
AGB (Mg/ha)
-5.3 0.89
BA (m2/ha)
-6.9 0.93
Height (m)
0.4 0.96
Nha (ha-1)
0.8 0.98
VOB (m3/ha)
4.1 0.97
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(Representative Concentration Pathways)
Scenario Climate Data CO2
- Historical 1950 – 2010 400 ppm- RCP 4.5 2070 – 2100 550 ppm- RCP 8.5 2070 – 2100 850 ppm
• Based on Site Quality(site index)
Productivity Site Index- Low 19 m- Medium 23 m- High 28 m
19.8+2.1+2.8
19.6+2.0+2.8
18.8+2.8+4.8
18.3+2.1+3.0
21.1+2.0+2.8
22.9+1.8+2.6
20.1+2.0+2.8
19.1+2.9+4.8
18.0+2.1+3.0
18.3+2.1+2.9
19.4+2.0+2.7
Historical Mean Annual Temperature (°C)and Mean Increment in Temperature due toClimate Change (RCP 4.5 and 8.5)
Sites location
11 sites in SE US4 sites in Northern Limit
Case Study Climate Change Scenarios
Summary
Variable RCP4.5 RCP8.5Tmax (C) + 1.8 to +2.9 + 2.6 to +4.8Tmin (C) + 1.8 to +3.0 + 2.7 to +4.8Rain (mm) - 49 to + 67 - 66 to 45Radiation (%) +2% to + 6% +1% to + 6%
L: 18 - 29M: 8 - 12H: 4 - 8
L: 18 - 28M: 8 - 12H: 4 - 10
L: 24 - 41M: 17 - 34H: 16 - 28
L: 26 - 39M: 29 - 37H: 14 - 20
L: 18 - 29M: 8 - 12H: 5 - 9
L: 10 - 20M: 8 - 10H: 3 - 6
L: 22 - 37M: 12 - 19H: 7 - 13
L: 18 - 29M: 8 - 12H: 5 - 8
L: 32 - 46M: 32 - 42H: 16 - 25
L: 25 - 41M: 10 - 22H: 6 - 23
L: 26 - 42M: 16 - 22H: 12 - 15
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: Low ProductivityM: Medium ProductivityH: High Productivity
Climate Change Effect on Slash Pine Productivity
Change in Above Ground Biomass (Mg/ha) at age=25 yearsRCP's v/s Historical Scenarios
Site Index (m)
19 23 28C
hang
e in
Abo
ve G
roun
d B
iom
ass
(Mg
ha-1
)
0
10
20
30
40
50
Tmnean > 19 CTmnean < 19 C
Site Index (m)
19 23 28
Cha
nge
in A
bove
Gro
und
Bio
mas
s(M
g ha
-1)
0
10
20
30
40
50
Tmnean > 19 CTmnean < 19 C
RCP 4.5 RCP 8.5
Site QualityLow Medium High
Site QualityLow Medium High
Conclusions:
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%).
Climate Change Effect on Slash Pine Productivity
Under Future Climate Scenarios Used:
Conclusions:
• Responses to Climate Change should be larger in colder range of distribution.
• Responses to Climate Change should be larger in low productivity sites.
Climate Change Effect on Slash Pine Productivity
Under Future Climate Scenarios Used: