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

Marta Chiesi, Gherardo Chirici, Marco Marchetti, Hubert Hasenauer, Adam Moreno, Alexander Knohl, Giorgio Matteucci, Kim Pilegaard, Andre Granier, Bernard Longdoz, Fabio Maselli

Research output: Contribution to journalJournal articleResearchpeer-review

1 Downloads (Pure)

Abstract

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.The spread of beech forests in Europe enhances the importance of modelling and monitoring their growth in view of ongoing climate changes.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.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.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.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.
Original languageEnglish
JournalAnnals of Forest Science
Volume73
Issue number3
Pages (from-to)713-727
Number of pages15
ISSN1286-4560
DOIs
Publication statusPublished - 2016

Keywords

  • Weather dataset
  • Beech forest
  • GPP
  • BIOME-BGC
  • Eddy covariance
  • Mathematical biology and statistical methods
  • Ecology: environmental biology - General and methods
  • Ecology: environmental biology - Bioclimatology and biometeorology
  • Ecology: environmental biology - Plant
  • Biophysics - Biocybernetics
  • Forestry and forest products
  • carbon
  • Angiosperms, Dicots, Plants, Spermatophytes, Vascular Plants
  • climate change
  • climatic condition
  • gross primary production
  • spring minimum temperature
  • continental weather dataset
  • growing season dryness
  • Life Sciences
  • Forestry
  • Wood Science & Technology
  • Forestry Management
  • Tree Biology
  • Environment, general
  • SC3

Cite this

Chiesi, Marta ; Chirici, Gherardo ; Marchetti, Marco ; Hasenauer, Hubert ; Moreno, Adam ; Knohl, Alexander ; Matteucci, Giorgio ; Pilegaard, Kim ; Granier, Andre ; Longdoz, Bernard ; Maselli, Fabio. / Testing the applicability of BIOME-BGC to simulate beech gross primary production in Europe using a new continental weather dataset. In: Annals of Forest Science. 2016 ; Vol. 73, No. 3. pp. 713-727.
@article{8ec67d71e5b04991a31dbcdebbb34333,
title = "Testing the applicability of BIOME-BGC to simulate beech gross primary production in Europe using a new continental weather dataset",
abstract = "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.The spread of beech forests in Europe enhances the importance of modelling and monitoring their growth in view of ongoing climate changes.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.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.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.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.",
keywords = "Weather dataset, Beech forest, GPP, BIOME-BGC, Eddy covariance, Mathematical biology and statistical methods, Ecology: environmental biology - General and methods, Ecology: environmental biology - Bioclimatology and biometeorology, Ecology: environmental biology - Plant, Biophysics - Biocybernetics, Forestry and forest products, carbon, Angiosperms, Dicots, Plants, Spermatophytes, Vascular Plants, climate change, climatic condition, gross primary production, spring minimum temperature, continental weather dataset, growing season dryness, Life Sciences, Forestry, Wood Science & Technology, Forestry Management, Tree Biology, Environment, general, SC3",
author = "Marta Chiesi and Gherardo Chirici and Marco Marchetti and Hubert Hasenauer and Adam Moreno and Alexander Knohl and Giorgio Matteucci and Kim Pilegaard and Andre Granier and Bernard Longdoz and Fabio Maselli",
year = "2016",
doi = "10.1007/s13595-016-0560-7",
language = "English",
volume = "73",
pages = "713--727",
journal = "Annals of Forest Science",
issn = "1286-4560",
publisher = "Springer-Verlag France",
number = "3",

}

Chiesi, M, Chirici, G, Marchetti, M, Hasenauer, H, Moreno, A, Knohl, A, Matteucci, G, Pilegaard, K, Granier, A, Longdoz, B & Maselli, F 2016, 'Testing the applicability of BIOME-BGC to simulate beech gross primary production in Europe using a new continental weather dataset', Annals of Forest Science, vol. 73, no. 3, pp. 713-727. https://doi.org/10.1007/s13595-016-0560-7

Testing the applicability of BIOME-BGC to simulate beech gross primary production in Europe using a new continental weather dataset. / Chiesi, Marta; Chirici, Gherardo; Marchetti, Marco; Hasenauer, Hubert; Moreno, Adam; Knohl, Alexander; Matteucci, Giorgio; Pilegaard, Kim; Granier, Andre; Longdoz, Bernard; Maselli, Fabio.

In: Annals of Forest Science, Vol. 73, No. 3, 2016, p. 713-727.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Testing the applicability of BIOME-BGC to simulate beech gross primary production in Europe using a new continental weather dataset

AU - Chiesi, Marta

AU - Chirici, Gherardo

AU - Marchetti, Marco

AU - Hasenauer, Hubert

AU - Moreno, Adam

AU - Knohl, Alexander

AU - Matteucci, Giorgio

AU - Pilegaard, Kim

AU - Granier, Andre

AU - Longdoz, Bernard

AU - Maselli, Fabio

PY - 2016

Y1 - 2016

N2 - 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.The spread of beech forests in Europe enhances the importance of modelling and monitoring their growth in view of ongoing climate changes.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.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.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.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.

AB - 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.The spread of beech forests in Europe enhances the importance of modelling and monitoring their growth in view of ongoing climate changes.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.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.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.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.

KW - Weather dataset

KW - Beech forest

KW - GPP

KW - BIOME-BGC

KW - Eddy covariance

KW - Mathematical biology and statistical methods

KW - Ecology: environmental biology - General and methods

KW - Ecology: environmental biology - Bioclimatology and biometeorology

KW - Ecology: environmental biology - Plant

KW - Biophysics - Biocybernetics

KW - Forestry and forest products

KW - carbon

KW - Angiosperms, Dicots, Plants, Spermatophytes, Vascular Plants

KW - climate change

KW - climatic condition

KW - gross primary production

KW - spring minimum temperature

KW - continental weather dataset

KW - growing season dryness

KW - Life Sciences

KW - Forestry

KW - Wood Science & Technology

KW - Forestry Management

KW - Tree Biology

KW - Environment, general

KW - SC3

U2 - 10.1007/s13595-016-0560-7

DO - 10.1007/s13595-016-0560-7

M3 - Journal article

VL - 73

SP - 713

EP - 727

JO - Annals of Forest Science

JF - Annals of Forest Science

SN - 1286-4560

IS - 3

ER -