Forest simulation models in Switzerland: main developments and challenges

WG1

COST ACTION FP0603: Forest models for research and decision support in sustainable forest management

1st Workshop and Management Committee Meeting.Institute of Silviculture, BOKU.

8-9 of May 2008Vienna, Austria

Main features of Swiss forests (BAFU, Steckbrief Schweizer Wald, 2nd Swiss National Forest Inventory, 1999)

Forest cover (total, share): 1.2 Mio ha, 30% Timber

growing stock: 418 Mio m3 annual growth: 7.4 Mio m3/year

cuts: 5.7 Mio. m3/year

Main species: Norway spruce (48%), Beech (17%), Fir (15%), Larch (5%), Pine (3.5%)

Main non-wood products and services: Protection against rockfall and avalanches CO2 sequestration Biodiversity

• the herb layer and of the landscape Recreation and scenic beauty

• (walking, nordic ski)• (typical image of wooded pastures in Swiss mountains)

In wooded pastures: forage, milk, meat Regulation of water-household

Main features of Swiss forests

Main risks… … against which the forests protect

• avalanche formation• rockfall

…to forests• Reduced protection function due to too old stands• Climate change

• Inadequate species in already dry regions due to climate change• Increased pest abundance (e.g. bark beetle)

• Changes in landscape structure due to segregation of land use and climate change: separation of closed forests and open grasslands, increased aggregation of land cover, decline of dominant species (Norway spruce and larch)

Management and silvicultural characteristics: Small clear cuts Single tree felling (Plenterwirtschaft)

Static

PNV

Selected tree species

Treeline

Mountland

Ecophys

.

BIOME-BGC, CLM,

LPJ-(GUESS)

SEIB DGVM

Plant hydraulic model

Landsc

ape

TreeMig

WoodPam

MEPHYSTO

LandClim

ForLand

MASSIMOimproved

Gro

wth

/yie

ld

MASSIMO

EFISCEN

Inte

grate

d

Cost/benefit protection forest m.

ForClim (gap m.)

DisCForM

Forece (gap.m.)

Populatio

n

dynam

ics

Markov chain m.

Forest modelling approaches and trends

ForClim improved

Treeline dynamics/ land use

Forest modelling approaches and trends

Empirical models

Approaches Several static models for distribution of

• Potential natural vegetation• Tree species• Timberline position (Gehrig-Fasel, 2005)

Application of EFISCEN MASSIMO (Kaufmann, 2001)

• Individual based, stochastic growth model • NFI derived

Markov-Chain models Recent research is concentrating in:

Recalibration of MASSIMO with latest NFI data (2004-2007) Growth function, harvesting probabilities, regeneration, mortality

Trends in modelling Impact of climate change in MASSIMO on

• growth function, • tree species composition • and mortality

Long-term harvesting potential (30-100 years)

Mechanistic models Approaches

• Population dynamical models• Gap, distribution based models

• Ecophysiological models• Plant water household model • Applications of biogeochemical models and DGVMSs

• BIOME-BGC, LPJ, CLM• Various landscape models• Integrated models

• With disturbances• Cost/Benefit• Starting: with socioeconomy

Trends in modelling• Integrated models• Merging of approaches

Forest modelling approaches and trends

Modelling non-timber products and services

Static models, ForClim, TreeMig Species distributions after climate change Species suitabilities

WoodPaM Forage production available for livestock Diversity indexes at patch and landscape scales Landscape aggregation index

Planned models within MOUNTLAND Diversity indexes at patch and landscape scales Landscape aggregation index

Models for predicting risk of hazards

Protection forest model LANDCLIM

Fire-forest dyn. interaction Mountland model (Davos), starting

Interaction between forest dynamics and avalanche (risk)

Future challenges

Describe the main challenges modelers and modelling face in your country so that can respond effectively to management or scientific questions/problems in your country

Management issues: Prediction of tree species composition and stand structure in forested areas

under various scenarios of management (including silvopastoral management) and climate change (warming, episodic events)

Scientific issues: Heterogeneity due to topography Shifting mosaics in natural and silvopastoral systems (grazing ecology and

forest dynamics) Consequences of the hierarchical organization of ecosystems

Innovative references

Bugmann, H.K.M., 1996. A simplified forest model to study species composition along climate gradients. Ecology, 77: 2055-2074.

Gillet F. (in press). Modelling vegetation dynamics in heterogeneous pasture-woodland landscapes. Ecological Modelling.

Kaufmann, E., 2001. Prognosis and management scenarios. In: P. Brassel and H. Lischke (Editors), Swiss National Forest Inventory: Methods and Models of the Second Assessment. Swiss Federal Research Institute WSL, Birmensdorf, pp. 336.

Lischke, H., Löffler, T.J. and Fischlin, A., 1998. Aggregation of individual trees and patches in forest succession models - Capturing variability with height structured random dispersions. Theor. Popul. Biol., 54: 213-226.

Lischke, H., Zimmermann, N.E., Bolliger, J., Rickebusch, S. and Löffler, T.J., 2006. TreeMig: A forest-landscape model for simulating spatio-temporal patterns from stand to landscape scale. Ecol. Model., 199: 409-420.

Rickebusch, S., Lischke, H., Bugmann, H., Guisan, A. and Zimmermann, N.E., 2007. Understanding the low-temperature limitations to forest growth through calibration of a forest dynamics model with tree-ring data. For. Ecol. Manage., 246: 251-263.

Schumacher, S., Bugmann, H. and Mladenoff, D.J., 2004. Improving the formulation of tree growth and succession in a spatially explicit landscape model. Ecol. Model., 180: 175-194.

Zweifel, R., Zimmermann, L. and Newbery, D.M., 2005. Modeling tree water deficit from microclimate: an approach to quantifying drought stress. Tree Physiol., 25: 147-156.

MASSIMO (Kaufmann 2001): (Management Scenario-Simulation Model)

Model type Empirical, stochastic & dynamic,

individual-based, distance independent model

4 Modules: Growth, mortality, harvesting, and regeneration

Calibration data NFI 1 (1983-85) & 2 (1993-95)

Evaluation Growth-function (non-linear

regression function estimating individual basal-area increment)

Validation data Forest Inventory Liechtenstein

Predicted basal area increment minus observed [cm]

Nu

mb

er

of

tre

es

-20 -10 0 10

02

00

40

06

00

Thürig et al. (2005)

Accuracy: -5.44%

Local forest dynamics

TREEMIG: a spatio-temporal forest model (Lischke et al. 2006, www.wsl.ch/projects/TreeMig/treemig.html

Implemented in space

Seed production Seed dispersal

-

Density regulation

WOODPAM: (Gillet, in press)Vegetation dynamics in pasture-woodland landscapes under climate change- towards a modeling tool for active adaptive management of silvopastoral systems

Goal To develop a decision tool for active adaptive

management of silvopastoral systems Spatially explicit dynamic mosaic model

suitable to simulate various scenarios of climate change and land use

Geographic area and scale Jura, Alps Extent: local landscapes (up to several km2) Grain: 625 m2 (25 m x 25 m square cells)

Modeling approach 3 hierarchical levels (cell, management unit,

landscape) and 6 submodels (wood, herb, cattle, soil, management, climate)

Coupling of population, community and ecosystem processes

Focus on vegetation-cattle interactions under climate and management constraints

Warming? Storms?Fires?

Goal: Build upon a climate-sensitive forest

succession model to Increase local precision,

thus bridging the gap between forest growth (local precision) and forest succession (wide range of applicability) models

Approach: Systematic model evaluation against

empirical data (yield trials etc.) and systematic model-model comparisons

Model improvements (growth, regeneration)

Model applications to study climate change impacts on protection forests in the Alps & other European mountain rangesGeographic area and scale:

Alps, other European mountain ranges (TBD)

Stand scale assessments

Left:Simulated (filled bars) vs. measured (semi-transparent bars) stand structure at the site Niederhünigen after 54 simulation years.

Right:Simulated equilibrium species basal area for the Swiss sites Grande Dixence (cold), Adelboden (cool-wet) and the eastern German site Schwerin (dry and warm)

ForClim Improvement :Bridging the gap between forest growth and forest succession models

Protection forest model

The protection forest model

combines a markov chain approach for simulating forest dynamics with risk assess-ment and cost-benefit analysis.

integrates ecological, technical and economic aspects of protection forest management.

can be used to comparatively evaluate the long-term effects of management strategies (e.g., thinning, planting, salvage harvesting, construction of defensive structures). Management

CostsRisk reduction

= benefits

Gap model ForClim (Bugmann, 1996)

Concept of individualistic, cyclical succession on small patches

(H. Gleason)

Jahre 0

100

200

360

0

Vorrat (m3 ha -1) Quantitative description of

tree population dynamics:

“gap“ models

(D. Botkin, H. Shugart)

Landscape model LandClim(Schumacher et al, 2004)

Spatially explicit(grid cells, ca. 30x30 m)

Dynamic Modeling of succession

Dynamics of cohorts of trees: establishment, growth, mortality

based on biomass and treenumber per cohort

Modeling of ‘disturbances’ Fire Windthrow Management

Modeled processes sensitive to climate

N

1km

Schumacher et al. (2004, 2006)

DIVERSITY

Goal: Spatial forest model stand-size grain to be applied on large areas for assessment of, e.g., climate change or management effects on forest functions

Model approach: Combination of

• large scale ecophysiological,

• forest growth, • tree species migration

modelsDynamic, spatio-temporal, process based Focus on natural processesManagement included via scenarios

Improved landscape model MEPHYSTO: Merging empirical, ecophysiological and spatio-temporal population dynamics forest models