Carlos A. Gonzalez-Benecke School of Forest Resources and Conservation University of Florida

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:

Acknowledgements