Mortality as a key driver of the spatial distribution of aboveground biomass in Amazonian forest: results from a dynamic vegetation model
- 1Laboratoire des Sciences du Climat et de l'Environnement, UMR 8212 (CEA/CNRS/UVSQ), CEA-orme des Merisiers, 91191 Gif-sur-Yvette, France
- 2Laboratoire Evolution et Diversité Biologique, UMR 5174 (CNRS/Universite Paul Sabatier Toulouse 3), 31062 Toulouse, France
- 3Environmental Change Institute, School of Geography and the Environment, Oxford University, South Parks Road, Oxford, UK
- 4Centre d'Etudes Spatiales de la Biosphère, UMR 5126 (CNRS/CNES/IRD/UPS), Toulouse, France
- *now at: Université Paris Diderot-Paris 7, Paris, France
Abstract. Dynamic Vegetation Models (DVMs) simulate energy, water and carbon fluxes between the ecosystem and the atmosphere, between the vegetation and the soil, and between plant organs. They also estimate the potential biomass of a forest in equilibrium having grown under a given climate and atmospheric CO2 level. In this study, we evaluate the Above Ground Woody Biomass (AGWB) and the above ground woody Net Primary Productivity (NPPAGW) simulated by the DVM ORCHIDEE across Amazonian forests, by comparing the simulation results to a large set of ground measurements (220 sites for biomass, 104 sites for NPPAGW). We found that the NPPAGW is on average overestimated by 63%. We also found that the fraction of biomass that is lost through mortality is 85% too high. These model biases nearly compensate each other to give an average simulated AGWB close to the ground measurement average. Nevertheless, the simulated AGWB spatial distribution differs significantly from the observations. Then, we analyse the discrepancies in biomass with regards to discrepancies in NPPAGW and those in the rate of mortality. When we correct for the error in NPPAGW, the errors on the spatial variations in AGWB are exacerbated, showing clearly that a large part of the misrepresentation of biomass comes from a wrong modelling of mortality processes.
Previous studies showed that Amazonian forests with high productivity have a higher mortality rate than forests with lower productivity. We introduce this relationship, which results in strongly improved modelling of biomass and of its spatial variations. We discuss the possibility of modifying the mortality modelling in ORCHIDEE, and the opportunity to improve forest productivity modelling through the integration of biomass measurements, in particular from remote sensing.