Evaluation of the "Advanced Canopy-Atmosphere Soil Algorithm" (ACASA) model performance using micrometeorological techniques

In recent years, climate change and global warming are important topics. Kyoto Conference participants agreed that the addition of carbon dioxide to the atmosphere by fossil fuel emissions, the removal of carbon dioxide by crops and natural ecosystems, and the effect of deforestation on carbon balance require future monitoring. This research was conducted as part of that effort and the main objective was to understand and quantify ecosystems capacity to absorb atmospheric carbon for planning long-term political action and sustainable development. This research project used Eddy Covariance measurements to monitor mass and energy fluxes in two different ecosystems: a natural ecosystem (Mediterranean maquis) and an agricultural ecosystem (wine grape vineyard). For this research we also used one of the more elaborate higher-order closure models for flux modelling: the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA) model. ACASA model flux outputs were compared with field measurements from three consecutive years (2004-2006) over Mediterranean maquis in North-western Sardinia, and for two different seven-day periods (2005) and about one month (2006) over a wine grape vineyard in Tuscany, near Montalcino, Italy. ACASA simulations were compared with measured fluxes of net radiation, sensible heat, latent heat, soil heat, and CO2 fluxes. Comparisons were evaluated using linear regression, root mean squared error, mean absolute error, and mean bias error. In general, model output matched well the observations. The use of ACASA model to predict energy and mass fluxes between the vegetation and atmosphere is promising and it could greatly improve our ability to estimate fluxes for use in carbon balance studies.