Forest simulation models in Belgium: main developments and challenges WG1 COST ACTION FP0603: Forest models for research and decision support in sustainable.

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

Main features of Belgian forests Forest cover (total/share): ha (23%) Growing stock, annual growth and cuts: m³ wood used from own forests, In addition 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 Empirical models No own developments Recent research is concentrating on mechanistic modelling and decision support

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 +, Ca 2+,Mg 2+ ), anions (Cl-, SO4-, HCO 3 - ) 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 approach. Output (GPP, NPP, NEP) available on the WWWeb. Forest modelling approaches and trends

Simulators and information systems Stand level simulators ANAFORE, can be downloaded at 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 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 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): 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: Gilliams, S., J. Van Orshoven, B. Muys, H. Kros, G.W. Heil and W. Van Deursen, AFFOREST sDSS: a metamodel based spatial decision support system for afforestation of agricultural land. New Forests 30: 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, , 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 references Van Orshoven, J., Gilliams, S., Muys, B., Stendahl, J., Skov-Petersen, H., Van Deursen, W., 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, Steppe, K., D.J.W. De Pauw, R. Lemeur and P.A. Vanrolleghem A mathematical model linking tree sap flow dynamics to daily stem diameter fluctuations and radial stem growth. Tree Physiology 26: Verstraeten W. W., Veroustraete F., Feyen J, 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 Parameter sensitivity and uncertainty of the forest carbon flux model FORUG: a Monte Carlo analysis. Tree Physiology 26: Verbeeck H, Samson R, Granier A, Montpied P, Lemeur R. Multi-year model analysis of GPP in a temperate beech forest in France Ecological Modelling 210: