Modelling heat, water and carbon fluxes in mown grassland under multi-objective and multi-criteria constraints. (June 2016) - Senapati, Nimai; Jansson, Per-Erik; Smith, Pete; Chabbi, Abad
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.