Modelling heat, water and carbon fluxes in mown grassland under multi-objective and multi-criteria constraints. (June 2016)

• Record Type: Journal Article
• Authors: Senapati, Nimai; Jansson, Per-Erik; Smith, Pete; Chabbi, Abad
• Is Part Of: Environmental modelling & software. Volume 80(2016:Jun.)
• Page Start: 201
• Page End: 224
• Publication Date: 2016-06
• Subjects: Modelling heat, water, carbon flux -- Multi-objective and multi-criteria constraints -- Model performance -- Parameter uncertainty; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.02.025
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Highlights: Uncertainty-based modelling was done for heat, water and carbon flux in grassland. Multi-objective and multi-criteria were enforced to constrain model simulations. Multi-objective and multi-criteria constraints were effective in model calibration. Different multi-constraints conditioned model parameters differently. Different multi-constraints improved the model performance in different way.