1Institute of Marine and Atmospheric research Utrecht (IMAU), Utrecht University, Utrecht, the Netherlands
2SRON Netherlands Institute for Space Research, Utrecht, the Netherlands
3Department of Systems Ecology, Vrije Universiteit, Amsterdam, the Netherlands
4INRA, UMR 1220 TCEM, 33883 Villenave d'Ornon, France
5Climate and Environmental Physics, Physics Institute, and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
6Department of Physical Geography, Utrecht University, Utrecht, the Netherlands
Abstract. Tropical wetlands are estimated to represent about 50% of the natural wetland emissions and explain a large fraction of the observed CH4 variability on time scales ranging from glacial-interglacial cycles to the currently observed year-to-year variability. Despite their importance, however, tropical wetlands are poorly represented in global models aiming to predict global CH4 emissions. This study documents the first regional-scale, process-based model of CH4 emissions from tropical floodplains. The LPX-Bern Dynamic Global Vegetation Model (LPX hereafter) was modified to represent floodplain hydrology, vegetation and associated CH4 emissions. The extent of tropical floodplains was prescribed using output from the spatially-explicit hydrology model PCR-GLOBWB. We introduced new Plant Functional Types (PFTs) that explicitly represent floodplain vegetation. The PFT parameterizations were evaluated against available remote sensing datasets (GLC2000 land cover and MODIS Net Primary Productivity). Simulated CH4 flux densities were evaluated against field observations and regional flux inventories. Simulated CH4 emissions at Amazon Basin scale were compared to model simulations performed in the WETCHIMP intercomparison project. We found that LPX simulated CH4 flux densities are in reasonable agreement with observations at the field scale but with a~tendency to overestimate the flux observed at specific sites. In addition, the model did not reproduce between-site variations or between-year variations within a site. Unfortunately, site informations are too limited to attest or disprove some model features. At the Amazon Basin scale, our results underline the large uncertainty in the magnitude of wetland CH4 emissions. In particular, uncertainties in floodplain extent (i.e., difference between GLC2000 and PCR-GLOBWB output) modulate the simulated emissions by a factor of about 2. Our best estimates, using PCR-GLOBWB in combination with GLC2000, lead to simulated Amazon-integrated emissions of 44.4 ± 4.8 Tg yr−1. Additionally, the LPX emissions are highly sensitive to vegetation distribution. Two simulations with the same mean PFT cover, but different spatial distributions of grasslands within the basin modulated emissions by about 20%. Correcting the LPX simulated NPP using MODIS reduces the Amazon emissions by 11.3%. Finally, due to an intrinsic limitation of LPX to account for seasonality in floodplain extent, the model failed to reproduce the seasonality in CH4 emissions. The Inter Annual Variability (IAV) of the emissions increases by 90% if the IAV in floodplain extent is account for, but still remains lower than in most of WETCHIMP models. While our model includes more mechanisms specific to tropical floodplains, we were unable to reduce the uncertainty in the magnitude of wetland CH4 emissions of the Amazon Basin. Our results stress the need for more research to constrain floodplain CH4 emissions and their temporal variability.