Download Multi-objective forest planning risk management

COST ACTION FP0603: Forest models for research and decision
support in sustainable forest management
Forest simulation models in Germany:
main developments and challenges
WG1
Thomas Rötzer
Chair of Forest Yield Science TU Muenchen
1st Workshop and Management Committee Meeting.
Institute of Silviculture, BOKU.
8-9 of May 2008
Vienna, Austria
Main features of German forests

Forest cover (total/share): 11 * 106 ha (~32 %)

Growing stock, annual growth and cuts:
growing stock: 3,4 * 109 m³ = 309 m³/ha (data: BWI2)
annual growth: 134 * 106 m³ timber
annual cuts: 89 * 106 m³ timber harvested

Main species: spruce (Picea abies) 35 % (forest area)
pine (Pinus silvestris) 31 % (forest area)
beech (Fagus silvatica) 25 % (forest area)
oak (Quercus petraea) 9 % (forest area)

Main non-wood products and services: recreation, water reserve, other environmental
servcices (e.g. ersoion preotection), hunting

Main risks: wind damages, insects (e.g. bark beetle), droughts and fires (particularly in
NE Germany)

Management and silvicultural characteristics: commercial forests (liability to
manage), multiple use, sustainable management
Forest modelling approaches and trends
Empirical models
Model name
contact
institution
remarks
SILVA
Pretzsch
TU München
more or less a
hybrid model
BWIN-PRO
Nagel
NW-FVA
WEHAM
Bösch
FVA BW
extrapolating
forest inventory
data
Hybrid models are a between pure empirical models (e.g. WEHAM
or BWIN-PRO) and pure mechanistic (better physiological) models.
A type of such a hybrid model is SILVA, in which also mechanistic
approaches are included (type ‚efficiency‘).
Forest modelling approaches and trends
Empirical models
Trends in modelling
Upscaling from tree to stand to enterprise (Landscape) level
Flexible technical frameworks (interfaces to modern forest inventories)
Advanced statistical methods
Introduce lessons learnt from advances in biology/ecology
Recent research is concentrating in
- linking management oriented models with physiologically based models
- climate change and climate adaptation studies
- carbon sequestration
- management scenarios under multi-criterial objectives
- climate change and sustainability
Forest modelling approaches and trends
Mechanistic models
Model name
contact
institution
BALANCE
Rötzer
TU München
4C
Lasch, Suckow PIK Potsdam
FORMIND/FORMIX
Huth
UFZ Leipzig
TREEDYN3
Bossel
Uni Kassel
TRAGIC++
Hauhs
BITÖK Bayreuth
…
remarks
rain forest
Forest modelling approaches and trends
Mechanistic models: BALANCE
Main features:

single tree based model

simulation of physiological processes on compartment (roots, stems, brach,
leaves), tree and stand level

for pure and mixed stands

simulation of water-, carbon- and nitrogen cycle

calculation of the micro-climate (temperatue, radiation) for every single tree

management tool for thinning

influence of competiton, stand structure and species mixture is regarded

phenology module to simualte annual development

species: beech (Fagus sylvatica L.), Norway spruce (Picea abies L. Karst.),
Scots pine (Pinus sylvestris L.), oaks (Quercus robur L., and Quercus petraea
Liebl.)
Forest modelling approaches and trends
Mechanistic models: 4C (‘FORESEE’ Forest Ecosystems in a Changing Environment)
Main features:

has been developed to describe long-term forest behaviour under changing
environmental conditions (Lasch et al., 2005)

describes processes on tree and stand level basing on findings from ecophysiological experiments, long term observations and physiological modelling.

includes descriptions of tree species composition, forest structure, total
ecosystem carbon content as well as leaf area index
 establishment, growth and mortality of tree cohorts are explicitly modelled on a
patch on which horizontal homogeneity is assumed

management of mono- and mixed species forests and short rotation coppice can
be simulated

calculates the water, carbon and nitrogen budget of the soil

coupled with a wood product model and socio-economic analysis tool
 species: beech (Fagus sylvatica L.), Norway spruce (Picea abies L. Karst.), Scots
pine (Pinus sylvestris L.), oaks (Quercus robur L., and Quercus petraea Liebl.),
birch (Betula pendula Roth), aspen (Populus tremula (L.), P. tremuloides
(Michx.)), Aleppo pine (Pinus halepensis Mill.), Ponderosa pine (Pinus ponderosa
Dougl.).
Modelling non-timber products and services
Scenic beauty and recreation
L-Vis (S. Seifert, TUM), Silvisio (ZALF) and Lenne 3D (lenne.de) for the
visualisation of (forest) landscapes
Water balance
BALANCE, 4C
Nitrogen leaching
4C
Biodiversity and Habitat Assessment
SILVA (Silva provides many indices for stand structure and diversity (as well
as for monetary yield)
Models for predicting risk of hazards
CAfSD (TUM)
Cellular automaton for simulating storm damages after
disturbation (e.g. construction of highway tracks through a
forest).
BALANCE (TUM)
droughts, mechanistic disturbances (insect damages),
ozone stress
4C (PIK)
simulates the climatic fire risk according to the fire risk
index of the German Weather Service (DWD)
(FVA BW)
Schmidt, M.; Bayer, J.; Kändler, G. (2005) storm "Lothar" – Approach
for a inventory based risik analysis. FVA-Einblick 2/2005
Research highlight

National Research Program “Sustainable Forestry”

Consequently managed long-term research plot network
(since mid/end of the 18ties) as data source for the model
SILVA (TUM)

Long-term experience in constructing forest growth
models AND transfer to management practice (TUM)

Overall study (SILVAKLIM) of the German forest growth
sector under climate change

Potential and dynamic of carbon sequestration in forests
and timber (www.cswh.worldforestry.de)

Todays forests for tomorrows enviroment
(www.enforchange.de)
Future challenges
GENERAL
a)
Developing concepts for embedding models in the decision flow of
forest management
b)
Link management issues with C-sequestration and climate change
c)
Including hazards
SILVA
a)
Linking a process based model with a management oriented model
(SILVA) and a soil model (mCentury)
b)
Including wood quality
c)
Estimation of carbon storage
d)
Including nutrient storage and export
FVA-BW
a)
Development and implementation of efficient approaches for
prognosis and imputation in forest inventory software applications
Future challenges
BALANCE
a)
Linking a process based model with a management oriented model
(SILVA) and a soil model (mCentury)
b)
Simulations regarding adaptation strategies as a response to
climate change
c)
Influence of extreme events (e.g. droughts) on forest growth
4C
a)
b)
A model of root growth dynamics, as a part of the forest growth
model to improve the simulation of the water balance in the soil
and stand
Modelling the competition in the rooting zone
Innovative references
Nagel, J.; Schmidt, M. (2006):The Silvicultural Decision Support System BWINPro. In
Hasenauer, H. (Ed.) Sustainable Forest Management, Growth Models For Europe,
Springer, Berlin, Heidelberg. 59-63. , ISBN-10 3-540-26098-6
Nothdurft, A. and Kublin, E. and Lappi, J. (2006) A non-linear hierarchical mixed model to
describe tree height growth. European Journal of Forest Research 125/3: 281—289.
Pretzsch, H., Grote, R., Reineking, B., Rötzer, T., Seifert, S.(2007): Models for Forest
Ecosystem Management: A European Perspective. Annals of Botany 101: 1065-1087.
Pretzsch, H., Biber, P., Dursky, J. (2002): The single tree-based stand simulator SILVA:
construction, application and evaluation. For. Ecol. Manage. 162: 3-21.
Rötzer, T., Seifert, T., Pretzsch, H. (2008): Modelling above and below ground carbon
dynamics in a mixed beech and spruce stand influenced by climate. European Journal
of Forest Research DOI : 10.1007/s10342-008-0213-y.
Schmidt, M.; Nagel, J.; Skovsgaard, J.-P. (2006):Evaluating Individual Tree Models. In
Hasenauer, H. (Ed.) Sustainable Forest Management, Growth Models For Europe,
Springer, Berlin, Heidelberg. 151-163. ,ISBN-10 3-540-26098-6