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Land surface/ecosystem models (LSEMs) play a key role in understanding the Earth’s climate. They represent ecosystem dynamics by simulating fluxes occurring between the biosphere and atmosphere. However, for a correct flux simulation, it is critical to calibrate the model using robust and state-of-the-art calibration techniques. In this work, we optimize parameters of the Integrated Model of Land Surface Processes (INLAND) using the hierarchical multi-objective calibration method (AMALGAM) to improve the representation of surface processes in a natural ecosystem over the Pampa biome in South America. The calibration was performed using experimental data of energy and CO2 flux collected in a native field located in southern Brazil. We compared simulations using the default and calibrated parameter set. The results show that the calibration of the model significantly improved all fluxes analyzed. The mean errors and bias values were significantly reduced, and the seasonality of fluxes was better represented. This work is one of the first to apply a multi-objective calibration in an LSEM to represent surface fluxes in the Pampa biome, presenting a consistent set of parameters for future applications used in studies of biome land use and land cover.

Energy and CO2 Fluxes over Native Fields of Southern Brazil through Multi-Objective Calibration of INLAND Model