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Sensitivity and predictive uncertainty of the ACASA model at a spruce forest site

Katharina Staudt1, Eva Falge2, R. David Pyles3, Thomas Foken1
1 Abteilung Mikrometeorologie, Universit├Ąt Bayreuth
2 Biogeochemistry Department, Max Planck Institute for Chemistry, Mainz
3 Department of Land, Air and Water Resources, University of California, Davis

P 1.2 in Ecosystem Function

The Advanced Canopy-Atmosphere-Soil Algorithm (ACASA), developed at the University of California, Davis, was used to model the turbulent fluxes of heat, water vapor and momentum as well as the CO2 exchange within and above a spruce canopy at the FLUXNET-station Waldstein-Weidenbrunnen in the Fichtelgebirge mountains in northern Bavaria, Germany. This multilayer canopy-surface-layer model incorporates a diabatic, third-order closure method to calculate turbulent transfer within and above the canopy.
The present work focusses on the evaluation of the sensitivity and uncertainty of the ACASA model by employing the Generalized Likelihood Uncertainty Estimation (GLUE) method. Flux data above the canopy for five days from each of the intensive observation periods carried out within the EGER (ExchanGE processes in mountainous Regions) project in autumn 2007 and summer 2008 were considered. This sensitivity analysis allowed the identification of the most influential parameters of the ACASA model. However, the sensitivity analysis also revealed the equifinality of many parameters in the ACASA model, similarly to other complex process-based models. The analysis of two time periods, each representing different meteorological conditions (relatively wet and cool in autumn 2007, hot and dry in summer 2008), provides an insight into the seasonal variation of parameter sensitivity. Furthermore, weaknesses of the representation of some processes within the model were detected.

last modified 2009-03-06