VEGECLIM - Integrating SPOT-VEGETATION 10-yr Time Series and Land-Surface Modelling to Forecast the Terrestrial Carbon Dynamics in a Changing Climate

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Context and objectives

Vegetation is a major carbon sink and is as such a key component of the international response to greenhouse effect," but is a more descriptive term. Climate change refers to the buildup of man-made gases in the atmosphere that trap the suns heat, causing changes in weather patterns."> climate change, caused by the build-up of greenhouse gases in the atmosphere. However, anthropogenic disturbances like deforestation or fires are the primary mechanism that changes ecosystems from carbon sinks to sources, and are hardly included in the current carbon modelling approaches. Moreover, in tropical regions, theseasonal/interannual variability of carbon fluxes is still uncertain. In the context of greenhouse effect," but is a more descriptive term. Climate change refers to the buildup of man-made gases in the atmosphere that trap the suns heat, causing changes in weather patterns."> climate change and  mitigationpolicies like REDD, it is particularly important to be able to quantify and forecast the vegetation dynamics  andcarbon fluxes. In this purpose, the overall objectives of this research is to dynamically assimilate the  land surface characterisation obtained from long SPOT-VEGETATION time series (e.g. plant functional type (PFT), phenology, Leaf Area Index (LAI), land cover change) into the ORCHIDEE global vegetation model, which simulate vegetation dynamics and carbon balance, in order to improve the forecast of the terrestrial Carbon cycle in tropical regions under different anthropogenic forcings. Such approach will allow us to determine whether the African terrestrial carbon balance will remain a net sink or could become a carbon source by the end of the century, according to different climate-change and deforestation scenarios. The challenge of this research is to bridge the gap between the land cover and the land surface model communities.

Project outcome

Expected scientific results

The first products of the research are a 1-km global reference data set characterizing the spectral response to vegetation canopy seasonality of each representative land cover for the main ecoregions throughout the world, and the optimized and validated ORCHIDEE model for Amazon and Central Africa. Then, a Cellular Automat land use land cover change model deforestation and degradation designed for Central Africa will generate land cover simulations based on actual observed conditions and extended in time and space through land cover change drivers. This model, coupled to optimized ORCHIDEE model, will provide quantitative estimates of the carbon stocks and fluxes in Central African forests relevant to scientists and decision-makers, for the current situation and predictions under 9 different scenarios for greenhouse effect," but is a more descriptive term. Climate change refers to the buildup of man-made gases in the atmosphere that trap the suns heat, causing changes in weather patterns."> climate change and deforestation. It will allow us to quantify the potential impact of greenhouse effect," but is a more descriptive term. Climate change refers to the buildup of man-made gases in the atmosphere that trap the suns heat, causing changes in weather patterns."> climate change and anthropogenic disturbances like deforestation on that fragile region as well as the potential effects of mitigation policy like the REDD. Indeed, results will be analysed in the light of the recent REDD effort to tackle deforestation in developing countries.

Moreover, thanks to a great partnership between very complementary teams, the Belgian expertise will be enhanced and its networking reinforced. Finally, this research will contribute to bridge the gap between the Carbon community and Earth Observation community.

Expected products and services

Deliverables concern global land cover map and vegetation seasonality reference database, optimized version ORCHIDEE for Central Africa, a LULCC model for Central Africa, Central African carbon stock estimates.