Forest simulation models in Germany: 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 Thomas Rötzer Chair of Forest Yield Science TU Muenchen

Main features of German forests Forest cover (total/share): 11 * 10 6 ha (~32 %) Growing stock, annual growth and cuts: growing stock: 3,4 * 10 9 m³ = 309 m³/ha (data: BWI 2 ) annual growth: 134 * 10 6 m³ timber annual cuts: 89 * 10 6 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 namecontactinstitutionremarks SILVAPretzschTU Münchenmore or less a hybrid model BWIN-PRONagelNW-FVA WEHAMBöschFVA BWextrapolating 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 namecontactinstitutionremarks BALANCERötzerTU München 4CLasch, SuckowPIK Potsdam FORMIND/FORMIX Huth UFZ Leipzigrain forest TREEDYN3 BosselUni Kassel TRAGIC++ HauhsBITÖK Bayreuth …

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 eco- physiological 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 (  Todays forests for tomorrows enviroment (

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) 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 b) 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 , ISBN 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: Pretzsch, H., Biber, P., Dursky, J. (2002): The single tree-based stand simulator SILVA: construction, application and evaluation. For. Ecol. Manage. 162: 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 : /s 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 ,ISBN