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Most land surface models can, depending on the simulation experiment, calculate the vegetation distribution and dynamics internally by making use of biogeographical principles or use vegetation maps to prescribe spatial and temporal changes in vegetation distribution. Irrespective of whether vegetation dynamics are simulated or prescribed, it is not practical to represent vegetation across the globe at the species level because of its daunting diversity. This issue can be circumvented by making use of 5 to 20 plant functional types (PFTs) by assuming that all species within a single functional type show identical land–atmosphere interactions irrespective of their geographical location. In this study, we hypothesize that remote-sensing-based assessments of aboveground biomass can be used to constrain the process in which real-world vegetation is discretized in PFT maps. Remotely sensed biomass estimates for Africa were used in a Bayesian framework to estimate the probability density distributions of woody, herbaceous and bare soil fractions for the 15 land cover classes, according to the United Nations Land Cover Classification System (UN-LCCS) typology, present in Africa. Subsequently, the 2.5th and 97.5th percentiles of the probability density distributions were used to create 2.5 % and 97.5 % credible interval PFT maps. Finally, the original and constrained PFT maps were used to drive biomass and albedo simulations with the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) model. This study demonstrates that remotely sensed biomass data can be used to better constrain the share of dense forest PFTs but that additional information on bare soil fraction is required to constrain the share of herbaceous PFTs. Even though considerable uncertainties remain, using remotely sensed biomass data enhances the objectivity and reproducibility of the process by reducing the dependency on expert knowledge and allows assessing and reporting the credible interval of the PFT maps which could be used to benchmark future developments. [ABSTRACT FROM AUTHOR]

Most land surface models can either calculate the vegetation distribution and dynamics internally by making use of biogeographical principles or use vegetation maps to prescribe spatial and temporal changes in vegetation distribution. Irrespective of whether vegetation dynamics are simulated or prescribed, it is not practical to represent vegetation across the globe at the species level because of its daunting diversity. This issue can be circumvented by making use of 5 to 20 plant functional types (PFT) by assuming that all species within a single functional type show identical land-atmosphere interactions irrespective of their geographical location. In this study, we hypothesize that remote-sensing based assessments of above-ground biomass can be used to refine discretizing real-world vegetation in PFT maps. Remotely sensed biomass estimates for Africa were used in a Bayesian framework to estimate the probability density distributions of woody, herbaceous, and bare soil fractions for the 15 land cover classes, according to the UN- LCCS typology, present in Africa. Subsequently, the 2.5 and 97.5 percentile of the probability density distributions were used to create 2.5 % and 97.5 % confidence interval PFT maps. Finally the original and refined PFT maps were used to drive biomass and albedo simulations with the ORCHIDEE model. This study demonstrates that remotely sensed biomass data can be used to better constrain PFT maps. Among the advantages of using remotely sensed biomass data were the reduced dependency on expert knowledge and the ability to report the confident interval of the PFT maps. Applying this approach at the global scale, would increase confidence in the PFT maps underlying assessments of present day biomass stocks. [ABSTRACT FROM AUTHOR]

We aimed to evaluate the importance of modulations of within-tree carbon (C) allocation by water and low-temperature stress for the prediction of annual forest growth with a process-based model., A new C allocation scheme was implemented in the CASTANEA model that accounts for lagged and direct environmental controls of C allocation. Different approaches (static vs dynamic) to modelling C allocation were then compared in a model-data fusion procedure, using satellite-derived leaf production estimates and biometric measurements at c. 104 sites., The modelling of the environmental control of C allocation significantly improved the ability of CASTANEA to predict the spatial and year-to-year variability of aboveground forest growth along regional gradients. A significant effect of the previous year's water stress on the C allocation to leaves and wood was reported. Our results also are consistent with a prominent role of the environmental modulation of sink demand in the wood growth of the studied species., Data available at large scales can inform forest models about the processes driving annual and seasonal C allocation. Our results call for a greater consideration of C allocation drivers, especially sink-demand fluctuations, for the simulations of current and future forest productivity with process-based models. [ABSTRACT FROM AUTHOR]