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 journalArticlepeer-review

15 Citations (Scopus)


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
Early online date22 Mar 2016
Publication statusPublished - Jun 2016


  • 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


Dive into the research topics of 'Modelling heat, water and carbon fluxes in mown grassland under multi-objective and multi-criteria constraints'. Together they form a unique fingerprint.

Cite this