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Slide 19 -
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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 2008 Vienna, 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 Landscape TreeMig WoodPam MEPHYSTO LandClim ForLand MASSIMO improved Growth/yield MASSIMO EFISCEN Integrated Cost/benefit protection forest m. ForClim (gap m.) DisCForM Forece (gap.m.) Population dynamics 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)
Simulators and information systems List existing forest simulators or decision support systems
Stand level simulators
Forest level decision support systems
Include name and reference or web page
Process based simulators
TreeMig (Lischke et al.
ForClim
MASSIMO
WOODPAM
See specific slides 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
Thürig et al. (2005) Accuracy: -5.44% 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 ranges
Geographic 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 Costs Risk reduction = benefits Gap model ForClim (Bugmann, 1996) Concept of individualistic, cyclical succession on small patches (H. Gleason) 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 tree number per cohort
Modeling of ‘disturbances’
Fire
Windthrow
Management
Modeled processes sensitive to climate 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 models
Dynamic, spatio-temporal, process based
Focus on natural processes
Management included via scenarios Improved landscape model MEPHYSTO: Merging empirical, ecophysiological and spatio-temporal population dynamics forest models
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