HR: 1340h
AN: B33E-1663    [Abstracts]
TI: Deriving temporal and spatial variation in ecosystem parameters from FLUXNET data
AU: * Groenendijk, M
EM: margriet.groenendijk@falw.vu.nl
AF: Vrije Universiteit Amsterdam, Hydrology and Geo-environmental Sciences, De Boelelaan 1085, Amsterdam, 1081HV, Netherlands
AU: Dolman, H
EM: han.dolman@geo.falw.vu.nl
AF: Vrije Universiteit Amsterdam, Hydrology and Geo-environmental Sciences, De Boelelaan 1085, Amsterdam, 1081HV, Netherlands
AB: To understand the global variation in carbon and water balances and to predict the ecosystem responses to climate changes it is important to identify the processes driving the differences and thus make progress beyond the simple regressions end empirical relationships that have been found. This study presents a method using the FLUXNET data and a simple ecosystem model to obtain five model parameters. Two parameters (reference respiration and activation energy) are related to ecosystem respiration and three parameters (carboxylation capacity, water use efficiency and light use efficiency) to photosynthesis and transpiration. The model is constrained by both the observed carbon and water fluxes, and determines the parameter uncertainty from the uncertainty in the observations. The main question is how the parameters are varying in time and space and if this can be related to environmental variations. The parameters are derived for four European forests. Annual parameter values are significantly different between sites and years, with no overlap in parameter space, taking into account the uncertainty. These site year parameters are very useful for comparing sites. The carboxylation capacity and light use efficiency are lowest for dry Mediterranean sites, while here the water use efficiency is highest. The light use efficiency is higher for the more northern sites. Adaptation to the environment may explain most of this, because sites are generally water limited in the south and light limited in the north of Europe. Whereas the parameter values appear meaningful when the model is applied on an annual basis, the correlation between observations and simulations improves when the model is applied on smaller time scales, but noise increases. The change in performance raises the interesting question how the different time scales are related. Improvement of the model could, for instance, be including this relation between time scales constrained by the environment.
DE: 0414 Biogeochemical cycles, processes, and modeling (0412, 0793, 1615, 4805, 4912)
DE: 0428 Carbon cycling (4806)
DE: 0430 Computational methods and data processing
SC: Biogeosciences [B]
MN: 2007 Fall Meeting