I N T E G R A T E D S I N K E N H A N C E M E N T A S S E S S M E N T INSEA PARTNERS Forest production and carbon storage -potentials of European forestry.

I N T E G R A T E D S I N K E N H A N C E M E N T A S S E S S M E N T

INSEA PARTNERS

Forest production and carbon storage-potentials of European forestry

FORESTRYMODELLING

Oskar Franklin

Elena Moltchanova

Michael Obersteiner

Florian Kraxner

Rupert Seidl (BOKU, Wien)

Manfred J Lexer (BOKU, Wien)

Dimitry Rokityanskiy

Kentaro Aoki

Ian McCallum

Dagmar Schwab

Glen Armstrong

Forestry Program

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Overview•Aims and framework•Forest model (the OSKAR model)•Results for different scenarios•Conclusions and implications

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Forests and management

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Output data:year biomass dead wood harvests costs2000 x x x x2001

.

.

.2100

age cohort0-10 y10-20y...>150y

scenario12...

speciessprucebeech...

Region

NPPpotential (climate)

species growth response

management effects

biomass/density effects

Output data:year biomass dead wood harvests costs2000 x x x x2001

.

.

.2100

age cohort0-10 y10-20y...>150y

scenario12...

speciessprucebeech...

Region

NPPpotential (climate)

species growth response

management effects

biomass/density effects

FASOM

OSKAR forestry model

the OSKAR Forestry model and data

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Forestry modeling framework

Forestry output:•C storage (soil, biomass)•Wood •Energy biom.•Forested area

Potentials:•C storage •Wood •Energy biom.

•managementscenarios: (harvest, thinning, species)

•climate change effect

•Initial state forest and soil

climate

FASOM model-economic optimization of land use

OSKAR model-forestry scenarios NPP model

•management costs

•prices

•alternative land uses

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Forest growth modeling

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Tree growth

•Productivity of the site (NPP) controls growth rate and equilibrium biomass

0 50 1000

1

22

0

GN b 1 1( )

GN b 1 0.1( )

GN b 2 0.01( )

GN b 2 1( )

1000 b

biomass

biom

ass

incr

emen

t

0 50 1000

1

22

0

GN b 1 1( )

GN b 1 0.1( )

GN b 2 0.01( )

GN b 2 1( )

1000 b

biomass

biom

ass

incr

emen

t

0 100 2000

50

10099.989

0

B 0.1 1 t 0.01( )

B 0.1 2 t 0.01( )

B 0.1 1 t 1( )

B 0.1 2 t 1( )

2000 ttimest

andi

ng b

iom

ass

0 100 2000

50

10099.989

0

B 0.1 1 t 0.01( )

B 0.1 2 t 0.01( )

B 0.1 1 t 1( )

B 0.1 2 t 1( )

2000 ttimest

andi

ng b

iom

ass

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Self-thinning and mortality

•Growth and competition causes self-thinning •The number of trees per are is limited by the self thinning line. This number decreases with increasing tree size

N tr

ees/

area

stand biomass

self-thin

nin

g lim

it

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Forest management

“And see this ring right here, Jimmy? . . . That’s another time the old fellow miraculously survived some big forest fire.”

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Thinning management

•Thinning purposes: •get larger trees (but fewer)•harvest more•take out bad trees •facilitate regeneration

•Growth effect: reduced density but more resources available per tree

•Mortality effect: reduced self-thinning mortality

relative density

self-

thin

ning

stan

d gr

owth

relative density

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Thinning scenarios

•Thinning at an early stage have a small effect on final biomass

•After thinning at a late stage, the old trees does fill up the space

•Large thinnings leaves space and resources (light) for new generation

•Thinnings can result in larger total harvests (thinnings + final harvest)

ste

m b

iom

ass 20%

70%

0%

time

biom

ass

+ a

cc.

thin

nin

gs

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Other management options

•Rotation length•Species selection•Fertilization

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Validation of the model

•Production estimate agrees well with a detailed tree level physiological model PICUS (hybrid –patch model)

•Thinning effect is almost identical in both models

0 20 40 60 800

500

10001100

4.510323

S12 1

wd

S102 1

wd

picdata321

picdata32m1

9011 S10 S1

0 picdata32

0 picdata32m

0

0 20 40 60 800

500

10001100

6.652179

S12 1

wd

S102 1

wd

picdata301

picdata30m1

9011 S10 S1

0 picdata30

0 picdata30m

0

0 20 40 60 800

500

1000

1500

2000

25002250

3.115005

S16

S106

picdataN01

picdataN0m1

9011 S10 picdataN0

0 picdataN0m

0

ste

m v

olum

e (m

3/h

a)

red thin line: OSKARblue thick line: PICUS

num

ber

of t

rees

pe

r h

a

yearsyears

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Model summary

•Predicts carbon accumulation, forestry production and management costs in response to management (thinning, species selection, rotation) and climate change

•In contrast to most existing management models, it does not rely on local empirical relations and local site indexes, but is based on globally applicable biophysical principles and species characteristics.

•it can be run for any region and time period and is easily integrated with global models of climate change effects (LPJ) and land use economic optimization models (FASOM model), which is done in the European carbon sink project INSEA.

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Reality and results

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Current and future forests

modeled biomass and estimated by FAO for EU countries, for 2005 and mean of 2005-2100 for two scenarios: 1) all managed, 2) old forest protected

Biomass (MtC)

200

400

600

800

1000

1200

1400

Austria

Czech R

epublic

Denm

ark

Estonia

Finland

France

Germ

any

Hungary

Ireland

Italy

Latvia

Lithuania

Luxembourg

Netherlands

Poland

Portugal

Slovak R

epublic

Slovenia

Sw

eden

United K

ingdom

2005

FAO 2005

2005-2100

2005-2100protect oldforest

*old forests:7.1 % of total forest area on average

GH

G e

mis

sio

n E

U 2

5

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Current and future forestsHarvests (MtC/year)

5

10

15

20

25

30

Austria

Czech R

epublic

Denm

ark

Estonia

Finland

France

Germ

any

Hungary

Ireland

Italy

Latvia

Lithuania

Luxembourg

Netherlands

Poland

Portugal

Slovak R

epublic

Slovenia

Sw

eden

United K

ingdom

2005

FAO 2005

2005-2100

2005-2100protect oldforest

Total maximum sustainable harvests in EU ≈ 200 MtC/year (2005-2100)

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Forest development scenariosBiomass and dead wood for different harvesting scenarios

(MtC)

0

1000

2000

3000

4000

5000

6000

7000

8000

2000 2020 2040 2060 2080 2100 2120 2140

harvest all

harvest stem

protect old forest

Harvest all:harvest of stem + branches

protect old forest:forest older than 1.5 times normal harvesting age are protected dead wood

biomass

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Forest development scenarios

Harvests for different harvesting scenarios (MtC)

0

50

100

150

200

250

300

2000 2020 2040 2060 2080 2100 2120 2140

harvest all

harvest stem

protect old forest

Harvest all:harvest of stem + branches

protect old forest:forest older than 1.5 times normal harvesting age are protected

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Forest development scenarios

Mean age of forests (years)

0

10

20

30

40

50

60

2000 2050 2100 2150

all forest managed

protect old forest

old forests = 7.1% of total forest area

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Forest development scenariosBiomass and dead wood for different rotation lengths (MtC)

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

2000 2020 2040 2060 2080 2100 2120 2140

short rotation

medium rotation

long rotation

dead wood

biomass

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Forest development scenarios

Harvests for different rotation lengths (MtC)

0

50

100

150

200

250

300

350

2000 2020 2040 2060 2080 2100 2120 2140

short rotation

medium rotation

long rotation

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Forest development scenariosBiomass and dead wood for different thinning scenarios (MtC)

0

1000

2000

3000

4000

5000

6000

7000

8000

2000 2050 2100 2150

no thinning

estimated thinning

>25% thinning

dead wood

biomass

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Forest development scenarios

Harvests for different thinning scenarios (MtC)

0

50

100

150

200

250

2000 2020 2040 2060 2080 2100 2120 2140

no thinning

estimated thinning

>25% thinning

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Conclusions•Future forest prediction strongly depends on estimates of current

forests.•There is a potential to increase harvests substantially in about 20

years from now•Increasing the rotation time/age at harvest is a way to increase the

carbon storage in the forest, but initially reduces harvest.•By protecting old forests, carbon storage can be increase about

20% almost without reduction in harvests