Effect of the importance of tree demography and flexible root water uptake for modelling the carbon and water cycles of Amazonia

Amazonian forest plays a crucial role in regulating the carbon and water cycles in the global climate system. 20 However, the representation of biogeochemical fluxes and forest structure in dynamic global vegetation models (DGVMs) remains challenging. This situation has considerable implications for modelling the state and dynamics of Amazonian forest. To address these limitations, we present an adaptation of the ORCHIDEE-CAN DGVM, a second-generation DGVM that explicitly models tree demography and canopy structure with an allometry-based carbon allocation scheme and accounts for hydraulic architecture in the soil-stem-leaf continuum. We use two versions of this DGVM: the first one (CAN) includes a 25 new parameterization for Amazonian forest; the second one (CAN-RS) additionally includes a mechanistic root water uptake module, which models the hydraulic resistance of the water transfer from soil pores to roots. We compared the results with the simulation output of the “big-leaf” standard version of the ORCHIDEE DGVM (TRUNK) and with observations of turbulent energy and CO2 fluxes at flux tower locations, of carbon stocks and stand density at inventory plots and observation-based models of photosynthesis (GPP) and evapotranspiration (LE) across the Amazon basin. CAN-RS 30 reproduced observed carbon and water fluxes and carbon stocks as well as TRUNK across Amazonia, both at local and at regional scales. In CAN-RS, water uptake by tree roots in the deepest soil layers during the dry season significantly improved the modelling of GPP and LE seasonal cycles, especially over the Guianan and Brazilian Shields. While CAN-RS correctly captures forest structure, CAN-RS does not outperform TRUNK to reproduced observed carbon and water fluxes and carbon stocks across Amazonia, both at local and at regional scales. These results imply that explicit coupling of the 35 water and carbon cycles improves the representation of biogeochemical cycles in Amazonia and their spatial variability. Representing the variation in the ecological functioning of Amazonia should be the next step to improve the performance and predictive ability of new generation DGVMs.