development under the different forest development under the different forest

ECEM/EAML 2004

ECEM/EAML 2004

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Simulation modelling of forest ecosystem Simulation modelling of forest ecosystem

development under the different forest development under the different forest

management scenarios management scenarios

Alexey Mikhailov Alexey Mikhailov

, ,

Alexander Komarov

Alexander Komarov

and Oleg Chertov and Oleg Chertov

Gennady Andrienko

Gennady Andrienko

, Natalia Andrienko , Natalia Andrienko

, ,

1. 1. Institute of Physicochemical and Biological Problems in Soil Sci Institute of Physicochemical and Biological Problems in Soil Sci ence ence of Russian Academy of Sciences, 142290 Pushchino, Moscow Region, of Russian Academy of Sciences, 142290 Pushchino, Moscow Region, Russia,

Russia, just_send_me_mail@rambler.ru just_send_me_mail@rambler.ru , tel.:+7(0967)73- , tel.:+7(0967)73 -07 07- -55 55

2. 2. Biological Institute of St. Petersburg State University, 198504, Biological Institute of St. Petersburg State University, 198504, St. St.

Petersburg

Petersburg- -Peterhoff 2 Oranienbaum Rd., Russia, Peterhoff 2 Oranienbaum Rd., Russia, oleg_chertov@hotmail.com

oleg_chertov@hotmail.com

3. 3. Fraunhofer Institute of Autonomous Intelligent Systems, Germany Fraunhofer Institute of Autonomous Intelligent Systems, Germany

ECEM/EAML 2004

ECEM/EAML 2004

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Objectives Objectives

 Implementation of the EFIMOD model for local level of forest enterprise at case study in Central European

Russia

 Comparison of 4 different silvicultural regimes at long- term simulation

 Analysis of Carbon budget of forest territory

 Analysis of Biodiversity of forest territory

 Analysis of wood production of forest territory

ECEM/EAML 2004

ECEM/EAML 2004

3 3 Short description of the

Short description of the EFIMOD model

EFIMOD model

 EFIMOD is spatially explicit individual based model of forest ecosystem

 The system consists of 3 main parts: tree sub model, soil sub model (ROMUL) and statistical climate generator (SCLISS)

 Tree sub model is an individual growth simulator in dependence on light and soil nitrogen

 ROMUL is the model of forest soil organic matter dynamics

 SCLISS allows for estimation of soil temperature and moisture using

measured standard long-term meteorological data

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Input Output

Climate data

Air & soil temperature, Precipitation

(Recalculated into forest floor & soil moisture)

Soil temperature and

moisture with monthly step

Soil data

Pools of Soil Organic Matter and Nitrogen in forest floor and mineral soil

Pools of Soil Organic Matter and Nitrogen in organic and mineral soil horizons

Tree Species data

Potential growth, specific nitrogen

consumption, allocation of biomass between tree organs

Forest data Tree species

composition, number of trees, height, diameter with standard deviation

Tree species composition, number of trees, height, diameter, Growing stock, BA, biomass

Silvicultural data

Cutting regimes, type of cutting, rotation length

Harvested wood, removal of carbon and nitrogen from the ecosystem

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5 5 EFIMOD 2: Tree sub model

EFIMOD 2: Tree sub model

Full PAR

Initial tree biomass

Soil available Nitrogen

SOIL

Available PAR after shadowing

Portion of available Nitrogen for the tree

Leaf Biomass

Fine root biomass

αmax

n_T

Tree biomass increment due

to light, Ipe

Tree biomass increment due to soil Nitrogen,Ipn

Annual tree biomass increment Ip = min { Ipe , Ipe }

Tree biomass, D, H, stem

volume at the end of

time step Tree (leaf, branch

and root) litter

Reallocation of increment by tree

organs

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6 6 EFIMOD 2: Soil sub model ROMUL

EFIMOD 2: Soil sub model ROMUL

Lⁱ - Undecomposed

litter on soil surface

F^.i - complex of humus substances with undecomposed debris (humified organic layer)

L^ju - undecomposed

litter in mineral topsoil

F^ju - complex of humus substances with undecomposed debris in mineral topsoil (“labil humus”)

H - humus bonded with clay minerals L^ju0 -

Belowground litter fall

kⁱ_1L kⁱ_2L

kⁱ_3L

K^j4L K^j_5S

K₆ K^j1S

K^j_3S

K^j2S K^j4S

K^j_5S

Soil surface Lⁱ₀ - Aboveground

litter fall

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Model levels Model levels

Modelling of single tree growth

Modelling of stand

development

Modelling at local

scale

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Case study Case study

The State Forest

“Russky Les” is

situated 100 km South of Moscow (Russia) on Central East European Plain with continental climate of the border between coniferous and broad-leaved forest zones

It was selected 104

stands (Total area is

273.4 ha)

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Model V

Model V erification erification

1а

0 5 10 15 20 25 30 35

10 20 30 40 50 60 70 80 1б

0 5 10 15 20 25 30 35

10 20 30 40 50 60 70 80

2а

0 5 10 15 20 25 30 35 40

10 30 50 70 90 110 2б

0 5 10 15 20 25 30 35 40

10 30 50 70 90 110

3а

0 5 10 15 20 25 30 35

10 20 30 40 50 60 70 80

3б

0 5 10 15 20 25 30 35

10 20 30 40 50 60 70 80

Height Diameter

Pine Spruce Birch

T r e e a g e

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Silvicultural regimes selected for 200

Silvicultural regimes selected for 200 -year simulation - year simulation

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 Na tura l

de ve lopme nt S e le ctive Cutting

s ys te m Ille ga l pra ctice

Le ga l pra ctice

a ge of fore s t Oa k

Pine Spruce Birch

- regeneration; - thinning; - Clear cutting NAT

SCU LRU

ILL

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Silvicultural regimes selected for Silvicultural regimes selected for

200 200 - - year simulation year simulation

 Natural development (Nat). This scenario is a full protection of the forest in all forest compartments without cutting

 Russian legal practice (LRU). The scenario describes managed forest with 4 thinnings (at 5, 10, 25 and 50 years), the final clear cutting (90 year age for coniferous and oak, 60 year age for birch and lime), with successful natural regeneration

 Selective cutting system (SCU). Managed forest with 2 thinnings and then selective cuttings each 30 years (30% of basal area from above)

 Illegal practice (ILL). It is a heavy upper thinning and removing of the best trees, clear cutting without conservation of natural

regeneration

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Carbon dynamics Carbon dynamics

Carbon in trees Carbon in Soil

NAT SCU

LRU ILL

NAT SCU

LRU ILL

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Carbon dynamics in stands Carbon dynamics in stands

10 60 110 160 210

0 50 100 150 200

ye a r

t/ha

NAT S CU LRU ILL

NAT SCU

LRU ILL

NAT – natural

development SCU – selective cutting LRU – legal practice ILL – illegal practice

Difference of tree carbon between last and Difference of tree carbon between last and first time step of simulation

first time step of simulation

Mean value of Carbon in Mean value of Carbon in

Trees Trees

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Dynamics of soil carbon Dynamics of soil carbon

Difference of soil carbon between last and first time step of simulation

NAT

SCU

LRU

ILL

30 50 70 90 110

0 50 100 150 200

ye a r

t/ha

NAT SCU LRU ILL

Mean carbon value in soil

NAT – natural

development SCU – selective cutting LRU – legal practice ILL – illegal practice

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Frequency distribution

Frequency distribution dynamic dynamic of of stands with stands with different Carbon stocks

different Carbon stocks

SCU

LRU ILL Nat

Brown: from 0 to 100 t/ha; White from 101 to 200 t/ha; Green from 201

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16 16 Long Long -term simulation of natural development - term simulation of natural development

Total Carbon

0 100 200 300 400 500 600 700

5 105 205 305 405

Years

Ton ha-1

C in Trees

C in Dead Wood C in Soil

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Carbon balance

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00

NAT SCU LRU ILL

Сценарии

Carbon t/ha per year

NPP CO2Cuttings

Burne d of fe lling de bris

-0.30 -0.10 0.10 0.30 0.50 0.70 0.90

NAT SCU LRU ILL

Carbon t/ha per year

Carbon balance Carbon sourses and sinks

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Stand composition Stand composition

NAT

ILL LRU

SCU

12.5 % mixed

23.1 % mixed

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19 19 Conclusions

Conclusions

 Simulated forestry regimes show the different ecological and silvicultural effects

 Carbon sequestration: a clear advantage of selective cutting and natural development over clear-cut systems both in relation to stand biomass and SOM

 Biodiversity: selective cutting and natural development have higher proportion of mixed stands and deadwood. Russian legal forestry has also complicated spatial mosaics of stands of

different age and composition

 Illegal practice leads to absolute domination of deciduous stands of low commercial value

 The silvicultural effectiveness of selective and Russian legal

regimes is approximately the same. Illegal regime demonstrates lager volume of harvested wood due to heavy thinning

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Acknowledgements

This work was supported by

 R&D Russian Federal Program “Global climate changes and its possible consequences”,

contract № 43-016.11.1625

 Russian Academy of Science Program

“Environmental and climatic changes: natural disasters”

 EU-INTAS grants 2001-0633 and 2001-0512

Thank you for your attention!