Evaluation of six process‐based forest growth models using eddy‐covariance measurements of CO2 and H2O fluxes at six forest sites in Europe

Reliable models are required to assess the impacts of climate change on forest ecosystems. Precise and independent data are essential to assess this accuracy. The flux measurements collected by the EUROFLUX project over a wide range of forest types and climatic regions in Europe allow a critical testing of the process-based models which were developed in the LTEEF project. The ECOCRAFT project complements this with a wealth of independent plant physiological measurements. Thus, it was aimed in this study to test six process-based forest growth models against the flux measurements of six European forest types, taking advantage of a large database with plant physiological parameters. The reliability of both the flux data and parameter values itself was not under discussion in this study. The data provided by the researchers of the EUROFLUX sites, possibly with local corrections, were used with a minor gap-filling procedure to avoid the loss of many days with observations. The model performance is discussed based on their accuracy, generality and realism. Accuracy was evaluated based on the goodness-of-fit with observed values of daily net ecosystem exchange, gross primary production and ecosystem respiration (gC m2 d1), and transpiration (kg H2O m2 d1). Moreover, accuracy was also evaluated based on systematic and unsystematic errors. Generality was characterized by the applicability of the models to different European forest ecosystems. Reality was evaluated by comparing the modelled and observed responses of gross primary production, ecosystem respiration to radiation and temperature. The results indicated that: Accuracy. All models showed similar high correlation with the measured carbon flux data, and also low systematic and unsystematic prediction errors at one or more sites of flux measurements. The results were similar in the case of several models when the water fluxes were considered. Most models fulfilled the criteria of sufficient accuracy for the ability to predict the carbon and water exchange between forests and the atmosphere. Generality. Three models of six could be applied for both deciduous and coniferous forests. Furthermore, four models were applied both for boreal and temperate conditions. However, no severe water-limited conditions were encountered, and no year-to-year variability could be tested. Realism. Most models fulfil the criterion of realism that the relationships between the modelled phenomena (carbon and water exchange) and environment are described causally. Again several of the models were able to reproduce the responses of measurable variables such as gross primary production (GPP), ecosystem respiration and transpiration to environmental driving factors such as radiation and temperature. Stomatal conductance appears to be the most critical process causing differences in predicted fluxes of carbon and water between those models that accurately describe the annual totals of GPP, ecosystem respiration and transpiration. As a conclusion, several process-based models are available that produce accurate estimates of carbon and water fluxes at several forest sites of Europe. This considerable accuracy fulfils one requirement of models to be able to predict the impacts of climate change on the carbon balance of European forests. However, the generality of the models should be further evaluated by expanding the range of testing over both time and space. In addition, differences in behaviour between models at the process level indicate requirement of further model testing, with special emphasis on modelling stomatal conductance realistically

[1]  On the surface layer similarity at a complex forest site , 1998 .

[2]  Ü. Rannik Turbulent atmosphere: Vertical fluxes above a forest and particle growth , 1998 .

[3]  Giorgio Matteucci,et al.  Seasonal net carbon dioxide exchange of a beech forest with the atmosphere , 1996 .

[4]  Ü. Rannik,et al.  Respiration as the main determinant of carbon balance in European forests , 2000, Nature.

[5]  M. G. Ryan,et al.  Comparing nocturnal eddy covariance measurements to estimates of ecosystem respiration made by scaling chamber measurements at six coniferous boreal sites , 1997 .

[6]  B. Law,et al.  Carbon and water vapor exchange of an open-canopied ponderosa pine ecosystem , 1999 .

[7]  R. Leuning A critical appraisal of a combined stomatal‐photosynthesis model for C3 plants , 1995 .

[8]  A. Granier,et al.  Water balance, transpiration and canopy conductance in two beech stands , 2000 .

[9]  Ü. Rannik,et al.  Gap filling strategies for defensible annual sums of net ecosystem exchange , 2001 .

[10]  Effects of Elevated CO2 and Decreased Water Availability on Holm-Oak Seedlings in Controlled Environment Chambers , 1997 .

[11]  Seppo Kellomäki,et al.  Modelling the dynamics of the forest ecosystem for climate change studies in the boreal conditions , 1997 .

[12]  I. Leinonen A Simulation Model for the Annual Frost Hardiness and Freeze Damage of Scots Pine , 1996 .

[13]  G. Mohren,et al.  Modeling daily gas exchange of a Douglas-fir forest: comparison of three stomatal conductance models with and without a soil water stress function. , 2000, Tree physiology.

[14]  K. Kramer A modelling analysis of the effects of climatic warming on the probability of spring frost damage to tree species in the Netherlands and Germany , 1994 .

[15]  A. Mäkelä,et al.  Field evidence for the optimality hypothesis of gas exchange in plants , 1999 .

[16]  Godefridus M. J. Mohren,et al.  Simulation of forest growth, applied to douglas fir stands in the Netherlands , 1987 .

[17]  S. Sabaté,et al.  Modelling the Responses to Climate Change of a Mediterranean Forest Managed at Different Thinning Intensities: Effects on Growth and Water Fluxes , 1997 .

[18]  Maurizio Mencuccini,et al.  Age‐related decline in stand productivity: the role of structural acclimation under hydraulic constraints , 2000 .

[19]  D. Wallach,et al.  Mean squared error of prediction in models for studying ecological and agronomic systems , 1987 .

[20]  G. Mohren,et al.  Simulation of Direct Effects of CO2 and Temperature Increase on Forest Growth: The Lteef Project , 1997 .

[21]  Ü. Rannik,et al.  Productivity overshadows temperature in determining soil and ecosystem respiration across European forests , 2001 .

[22]  C. Bernhofer,et al.  The HartX-synthesis: An experimental approach to water and carbon exchange of a Scots pine plantation , 1996 .

[23]  A. Verhoef,et al.  A system to measure surface fluxes of momentum, sensible heat, water vapour and carbon dioxide , 1997 .

[24]  A. Cutini,et al.  Estimation of leaf area index with the Li-Cor LAI 2000 in deciduous forests , 1998 .

[25]  John Moncrieff,et al.  The propagation of errors in long‐term measurements of land‐atmosphere fluxes of carbon and water , 1996 .

[26]  Sune Linder,et al.  Botany: Constraints to growth of boreal forests , 2000, Nature.

[27]  P. Jarvis,et al.  Integration of Results from Elevated CO2 Experiments on European Forest Species: The Ecocraft Project , 1997 .

[28]  Eric Ceschia,et al.  The carbon balance of a young Beech forest , 2000 .

[29]  P. Berbigier,et al.  Comparison of two methods for estimating the evaporation of a Pinus pinaster (Ait.) stand: sap flow and energy balance with sensible heat flux measurements by an eddy covariance method , 1991 .

[30]  D. Epron,et al.  Soil CO2 efflux in a beech forest: dependence on soil temperature and soil water content , 1999 .

[31]  P. Berbigier,et al.  Radiation and water use efficiencies of two coniferous forest canopies , 1996 .

[32]  D. Paslier,et al.  Net Exchange of CO2 in a Mid-Latitude Forest , 1993, Science.

[33]  P. Berbigier,et al.  CO2 and water vapour fluxes for 2 years above Euroflux forest site , 2001 .

[34]  J. William Munger,et al.  Measurements of carbon sequestration by long‐term eddy covariance: methods and a critical evaluation of accuracy , 1996 .

[35]  Mark G. Tjoelker,et al.  Modelling respiration of vegetation: evidence for a general temperature‐dependent Q10 , 2001 .

[36]  Heikki Hänninen,et al.  Modelling bud dormancy release in trees from cool and temperate regions. , 1990 .

[37]  Hartmut Bossel,et al.  treedyn3 forest simulation model , 1996 .

[38]  Daniel Wallach,et al.  Mean squared error of prediction as a criterion for evaluating and comparing system models , 1989 .

[39]  Belinda E. Medlyn,et al.  Design and use of a database of model parameters from elevated [CO2] experiments , 1999 .