Multi-objective conditioning of a simple SVAT model

It has previously been argued that current Soil Vegetation atmosphere Trans fer (SVAT) models are over-parameterised given the calibration data typical ly available. Using the Generalised Likelihood Uncertainty Estimation (GLUE ) methodology, multiple feasible model parameter sets are here conditioned on latent heat fluxes and then additionally on the sensible and ground heat fluxes at a single site in Amazonia. The model conditioning schemes were t hen evaluated with a further data set collected at the same site according to their ability to reproduce the latent, sensible and ground heat fluxes. The results indicate that conditioning the model on only the latent heat fl ux component of the energy balance does not constrain satisfactorily the pr edictions of the other components of the energy balance. When conditioning on all heat flux objectives, significant additional constraint of the feasi ble parameter space is achieved with a consequent reduction in the predicti ve uncertainty. There are still, however, many parameter sets that adequate ly reproduce the calibration/validation data, leading to significant predic tive uncertainty. Surface temperature measurements, whilst also subject to uncertainty, may be employed usefully in a multi-objective calibration of S VAT models.