Modelling heat, water and carbon fluxes in mown grassland under multi-objective and multi-criteria constraints

Nimai Senapati*, Per-Erik Jansson, Pete Smith, Abad Chabbi

*Corresponding author for this work

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

A Monte Carlo-based calibration and uncertainty assessment was performed for heat, water and carbon (C) fluxes, simulated by a soil-plant-atmosphere system model (CoupModel), in mown grassland. Impact of different multi-objective and multi-criteria constraints was investigated on model performance and parameter behaviour. Good agreements between hourly modelled and measurement data were obtained for latent and sensible heat fluxes (R-2 = 0.61, ME = 0.48 MJ m(-2) day(-1)), soil water contents (R-2 = 0.68, ME = 0.34%) and carbon-dioxide flux (R-2 = 0.60, ME = -0.18 g C m(-2) day(-1)). Multi-objective and multi criteria constraints were efficient in parameter conditioning, reducing simulation uncertainty and identifying critical parameters. Enforcing multi-constraints separately on heat, water and C processes resulted in the highest model improvement for that specific process, including some improvement too for other processes. Imposing multi-constraints on all groups of variables, associated with heat, water and C fluxes together, resulted in general effective parameters conditioning and model improvement. (C) 2016 Elsevier Ltd. All rights reserved.

Original languageEnglish
Pages (from-to)201-224
Number of pages24
JournalEnvironmental Modelling and Software
Volume80
Early online date22 Mar 2016
DOIs
Publication statusPublished - Jun 2016

Keywords

  • Modelling heat, water, carbon flux
  • Multi-objective and multi-criteria constraints
  • Model performance
  • Parameter uncertainty
  • Pasture simulation-model
  • Scots Pine ecosystem
  • Soil carbon
  • Terrestrial carbon
  • Porous-media
  • NET carbon
  • Bare soil
  • Uncertainty
  • Calibration
  • Temperature

Cite this

Modelling heat, water and carbon fluxes in mown grassland under multi-objective and multi-criteria constraints. / Senapati, Nimai; Jansson, Per-Erik; Smith, Pete; Chabbi, Abad.

In: Environmental Modelling and Software, Vol. 80, 06.2016, p. 201-224.

Research output: Contribution to journalArticle

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abstract = "A Monte Carlo-based calibration and uncertainty assessment was performed for heat, water and carbon (C) fluxes, simulated by a soil-plant-atmosphere system model (CoupModel), in mown grassland. Impact of different multi-objective and multi-criteria constraints was investigated on model performance and parameter behaviour. Good agreements between hourly modelled and measurement data were obtained for latent and sensible heat fluxes (R-2 = 0.61, ME = 0.48 MJ m(-2) day(-1)), soil water contents (R-2 = 0.68, ME = 0.34{\%}) and carbon-dioxide flux (R-2 = 0.60, ME = -0.18 g C m(-2) day(-1)). Multi-objective and multi criteria constraints were efficient in parameter conditioning, reducing simulation uncertainty and identifying critical parameters. Enforcing multi-constraints separately on heat, water and C processes resulted in the highest model improvement for that specific process, including some improvement too for other processes. Imposing multi-constraints on all groups of variables, associated with heat, water and C fluxes together, resulted in general effective parameters conditioning and model improvement. (C) 2016 Elsevier Ltd. All rights reserved.",
keywords = "Modelling heat, water, carbon flux, Multi-objective and multi-criteria constraints, Model performance, Parameter uncertainty, Pasture simulation-model, Scots Pine ecosystem, Soil carbon, Terrestrial carbon, Porous-media, NET carbon, Bare soil, Uncertainty, Calibration, Temperature",
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note = "Acknowledgements This work was supported financially by the European community's Seventh Framework Programme (FP2007-2013) under grant agreement n° 262060 (ExpeER). The research leading to these results has received funding principally from the National Research Infrastructure “Agro-{\'e}cosyst{\`e}mes, Cycles Biog{\'e}ochimique et Biodiversit{\'e}” (ANR-11-INBS-0001) (SOERE-ACBB http://www.soere-acbb.com/fr/), Allenvi and ICOS France. We are grateful to Christophe de-Berranger, C{\'e}dric Perrot and Xavier Charrier for technical and field work assistance. We thank the anonymous Reviewers and Associate Editor for their valuable comments and suggestions that help us to improve the manuscript.",
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AU - Chabbi, Abad

N1 - Acknowledgements This work was supported financially by the European community's Seventh Framework Programme (FP2007-2013) under grant agreement n° 262060 (ExpeER). The research leading to these results has received funding principally from the National Research Infrastructure “Agro-écosystèmes, Cycles Biogéochimique et Biodiversité” (ANR-11-INBS-0001) (SOERE-ACBB http://www.soere-acbb.com/fr/), Allenvi and ICOS France. We are grateful to Christophe de-Berranger, Cédric Perrot and Xavier Charrier for technical and field work assistance. We thank the anonymous Reviewers and Associate Editor for their valuable comments and suggestions that help us to improve the manuscript.

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N2 - A Monte Carlo-based calibration and uncertainty assessment was performed for heat, water and carbon (C) fluxes, simulated by a soil-plant-atmosphere system model (CoupModel), in mown grassland. Impact of different multi-objective and multi-criteria constraints was investigated on model performance and parameter behaviour. Good agreements between hourly modelled and measurement data were obtained for latent and sensible heat fluxes (R-2 = 0.61, ME = 0.48 MJ m(-2) day(-1)), soil water contents (R-2 = 0.68, ME = 0.34%) and carbon-dioxide flux (R-2 = 0.60, ME = -0.18 g C m(-2) day(-1)). Multi-objective and multi criteria constraints were efficient in parameter conditioning, reducing simulation uncertainty and identifying critical parameters. Enforcing multi-constraints separately on heat, water and C processes resulted in the highest model improvement for that specific process, including some improvement too for other processes. Imposing multi-constraints on all groups of variables, associated with heat, water and C fluxes together, resulted in general effective parameters conditioning and model improvement. (C) 2016 Elsevier Ltd. All rights reserved.

AB - A Monte Carlo-based calibration and uncertainty assessment was performed for heat, water and carbon (C) fluxes, simulated by a soil-plant-atmosphere system model (CoupModel), in mown grassland. Impact of different multi-objective and multi-criteria constraints was investigated on model performance and parameter behaviour. Good agreements between hourly modelled and measurement data were obtained for latent and sensible heat fluxes (R-2 = 0.61, ME = 0.48 MJ m(-2) day(-1)), soil water contents (R-2 = 0.68, ME = 0.34%) and carbon-dioxide flux (R-2 = 0.60, ME = -0.18 g C m(-2) day(-1)). Multi-objective and multi criteria constraints were efficient in parameter conditioning, reducing simulation uncertainty and identifying critical parameters. Enforcing multi-constraints separately on heat, water and C processes resulted in the highest model improvement for that specific process, including some improvement too for other processes. Imposing multi-constraints on all groups of variables, associated with heat, water and C fluxes together, resulted in general effective parameters conditioning and model improvement. (C) 2016 Elsevier Ltd. All rights reserved.

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KW - Terrestrial carbon

KW - Porous-media

KW - NET carbon

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