Testing the applicability of BIOME-BGC to simulate beech gross primary production in Europe using a new continental weather dataset

• Published in: Annals of forest science. Vol. 73; no. 3; pp. 713 - 727
• Main Authors: Chiesi, Marta, Chirici, Gherardo, Marchetti, Marco, Hasenauer, Hubert, Moreno, Adam, Knohl, Alexander, Matteucci, Giorgio, Pilegaard, Kim, Granier, André, Longdoz, Bernard, Maselli, Fabio
• Format: Journal Article Web Resource
• Language: English
• Published: Paris Springer Paris 2016; Springer Nature B.V; Springer Nature (since 2011)/EDP Science (until 2010); Springer-Verlag France
• Subjects: Beech; Beech forest; BIOME-BGC; Biomedical and Life Sciences; Carbon; Climate; Climate change; Climate models; Climatic conditions; Climatology; Computer simulation; Covariance; Datasets; Eddy covariance; Environment; Environmental sciences & ecology; Evolution; Fagus; Fluctuations; Fluxes; Forest ecosystems; Forestry; Forestry Management; Forests; GPP; Life Sciences; Monitoring; Original Paper; Primary production; Sciences de l’environnement & écologie; Sciences du vivant; Spatial analysis; Spring (season); Statistics; Summer; Temperature effects; Tree Biology; Vortices; Water stress; Weather; Weather dataset; Wood Science & Technology; Europe; Weather dataset; Eddy covariance; BIOME-BGC; GPP; Beech forest
• ISSN: 1286-4560; 1297-966X; 1297-966X
• DOI: 10.1007/s13595-016-0560-7

Key message
A daily 1-km Pan-European weather dataset can drive the BIOME-BGC model for the estimation of current and future beech gross primary production (GPP). Annual beech GPP is affected primarily by spring temperature and more irregularly by summer water stress.
Context
The spread of beech forests in Europe enhances the importance of modelling and monitoring their growth in view of ongoing climate changes.
Aims
The current paper assesses the capability of a biogeochemical model to simulate beech gross primary production (GPP) using a Pan-European 1-km weather dataset.
Methods
The model BIOME-BGC is applied in four European forest ecosystems having different climatic conditions where the eddy covariance technique is used to measure water and carbon fluxes. The experiment is in three main steps. First, the accuracy of BIOME-BGC GPP simulations is assessed through comparison with flux observations. Second, the influence of two major meteorological drivers (spring minimum temperature and growing season dryness) on observed and simulated inter-annual GPP variations is analysed. Lastly, the impacts of two climate change scenarios on beech GPP are evaluated through statistical analyses of the ground data and model simulations.
Results
The weather dataset can drive BIOME-BGC to simulate most of the beech GPP evolution in all four test areas. Both observed and simulated inter-annual GPP variations are mainly dependent on minimum temperature around the beginning of the growing season, while spring/summer dryness exerts a secondary role. BIOME-BGC can also reasonably predict the impacts of the examined climate change scenarios.
Conclusion
The proposed modelling approach is capable of approximately reproducing spatial and temporal beech GPP variations and impacts of expected climate changes in the examined European sites.