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COST ACTION FP0603: Forest models for research and decision support in sustainable forest management. Forest simulation models in Belgium: main developments and challenges WG1. 1st Workshop and Management Committee Meeting. Institute of Silviculture, BOKU. 8-9 of May 2008 Vienna, Austria.

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forest simulation models in belgium main developments and challenges wg1

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

Forest simulation models in Belgium: main developments and challenges


1st Workshop and Management Committee Meeting.Institute of Silviculture, BOKU.8-9 of May 2008Vienna, Austria

main features of belgian forests
Main features of Belgian forests
  • Forest cover (total/share): 693.000 ha (23%)
  • Growing stock, annual growth and cuts: 4 106 m³ wood used from own forests,
  • In addition 7 106 m³ wood imported
  • Main species: picea, (scots) pine, beech, birch, oak, poplar
  • Main non-wood products and services: passive recreation, nature protection, biodiversity
  • Main risks: fragmentation, deforestation
  • Management and silvicultural characteristics: small patches, abundant private ownership, high productivity management practices
forest modelling approaches and trends
Forest modelling approaches and trends

Empirical models

No own developments

Recent research is concentrating on mechanistic modelling and decision support

forest modelling approaches and trends4
Forest modelling approaches and trends

Mechanistic models Which exist :

  • ANAFORE (ANAlysing FORest Ecosystems), growth, C and H2O balance, stand scale

Main features: rather complex, will become user friendly, includes effects of changing climate (and ozone) and management

  • FORUG: carbon and water balance model, stand scale
  • SECRETS: growth, C and H2O balance, stand scale
  • ASPECTS-WiTCh : water, carbon, nitrogen cycles in plant and soil, and major cations (Na+, K+, Ca2+,Mg2+), anions (Cl-, SO4-, HCO3-) and pH in soil water, vegetation growth, weathering, etc (stand scale)
  • CARAIB : water and carbon cycles, photosynthesis, growth, competition of plant types, plant type/species potential ranges (regional, continental or global scales)
  • C-Fix:Main features:

Based on Remote Sensing hence Spatially explicit.

Continental to global scale.

Includes effects of changing climate, with a coupled carbon – hydrological


Output (GPP, NPP, NEP) available on the WWWeb.

simulators and information systems
Simulators and information systems

Stand level simulators

  • ANAFORE, can be downloadedat

Forest level decision support systems

  • SimForTree: decision support system under construction, based on ANAFORE
  • running Flemish project,
  • AFFOREST: developed within European project,
  • C-Fix: Operational forest flux production


Research highlight

  • Including mechanistic simulation of wood density in function of growth/envirmonment/management (from pipe theory)
  • Multicriteria decision support (allowing multiple goals in finding the ‘optimal’ management
  • Including bayesian optimisation and uncertainty (underway)
  • Application of remote sensing in area and carbon flux estimations using the VEGETATION instrument
future challenges
Future challenges
  • In Belgium forests are very fragmented
  • A lot of small patches are in private ownership without common management practices.
  • Decision support management is needed at the forest patch level.
  • Multipurpose forestry is required in Belgium (because of limited available area). Recreation, biodiversity and nature protection are important forest functions.
innovative references
Innovative references
  • Deckmyn G., Verbeeck H., Op de Beeck M., Vansteenkiste D., Steppe K., and R. Ceulemans. ANAFORE: a stand-scale mechanistic forest model that includes wood tissue development and labile carbon storage as affected by climate, management and tree dominance.
  • Garcia Quijano J, Deckmyn G, Moons E, Proost S, Ceulemans R, Muys B (2005) An integrated decision support framework for the prediction and evaluation of efficiency, environmental impact and total social cost of domestic and international forestry projects for greenhouse gas mitigation: description and case studies. Forest Ecology and Management, 207(1-2): 245 - 262.
  • Garcia-Quijano, J.F., Deckmyn, G., Ceulemans, R., Van Orshoven, J., Muys, B. (2008). Scaling from Stand to Landscape of Climate Change Mitigation by Afforestation and Forest Management: a Modeling Approach, Climatic Change, 86:397-424.
  • Gilliams, S., J. Van Orshoven, B. Muys, H. Kros, G.W. Heil and W. Van Deursen, 2005. AFFOREST sDSS: a metamodel based spatial decision support system for afforestation of agricultural land. New Forests 30: 33-53.
  • Goddéris Y., L.M. François, A. Probst, J. Schott, D. Moncoulon, D. Labat, D. Viville, Modelling weathering processes at the catchment scale with the WITCH numerical model. Geochim. Cosmochim. Acta 70, 1128-1147, 2006.
  • Laurent J.-M., L. François, A. Bar-Hen, L. Bel, R. Cheddadi, European Bioclimatic Affinity Groups: data-model comparisons. Global Planet. Change, 61, 28-40, 2008.
innovative references9
Innovative references
  • Van Orshoven, J., Gilliams, S., Muys, B., Stendahl, J., Skov-Petersen, H., Van Deursen, W., 2007. Support of decisions on afforestation in North-Western Europe with the Afforest-sDSS. In: Heil, G.W., Muys, B., Hansen, K. (eds.) Environmental Effects of Afforestation in North-Western Europe: From Field Observations to Decision Support. Springer Publ., Series Plant and Vegetation Vol. 1, 227-247.
  • Steppe, K., D.J.W. De Pauw, R. Lemeur and P.A. Vanrolleghem. 2006. A mathematical model linking tree sap flow dynamics to daily stem diameter fluctuations and radial stem growth. Tree Physiology 26: 257-273.
  • Verstraeten W. W. , Veroustraete F., Feyen J, 2006. On temperature and water limitation of net ecosystem productivity: Implementation in the C-Fix model. Ecological Modelling 199: 4–22.
  • Verbeeck, H., R. Samson, F. Verdonck and R. Lemeur. 2006. Parameter sensitivity and uncertainty of the forest carbon flux model FORUG: a Monte Carlo analysis. TreePhysiology 26: 807-817.
  • Verbeeck H, Samson R, Granier A, Montpied P, Lemeur R. Multi-year model analysis of GPP in a temperate beech forest in France. 2008. Ecological Modelling 210:85-103.