HR: 0800h
AN: B31A-07 [Abstracts]
TI: Carbon Fluxes Estimates in Grasslands by Mean of Harvest Practices Simulation: improvement of Model Performance
AU: * Tomelleri, E
EM: etomell@bgc-jena.mpg.de
AF: Centro di Ecologia Alpina, Viote del Monte Bondone, Trento, 38100, Italy
AU: * Tomelleri, E
EM: etomell@bgc-jena.mpg.de
AF: Max Planck Institute für Biogeochemie, Hans Knöll Straß e, 10, Jena, 07745, Germany
AU: Churkina, G
EM: churkina@bgc-jena.mpg.de
AF: Max Planck Institute für Biogeochemie, Hans Knöll Straß e, 10, Jena, 07745, Germany
AU: Gianelle, D
EM: gianelle@cealp.it
AF: Centro di Ecologia Alpina, Viote del Monte Bondone, Trento, 38100, Italy
AB:
The present work aims to improve, test, and optimize a model simulating carbon fluxes in managed grasslands.
The process model Biome-BGC (version 4.1.1) was chosen. The harvesting was implemented as a leaf carbon
reduction in a user defined day. The estimated Gross Primary Productivity (GPP) and Total Ecosystem
Respiration (TER) were compared with the partitioned eddy covariance data at a measurement site -- Mt.
Bondone (I) -- for two years: 2003 and 2004. For each year the most sensitive input model parameters were
selected using a parameter fixing method. The top ranked input parameters were optimized using a Bayesian
approach based on the Metropolis algorithm. The sensitivity analysis of the model input parameters gave the
same results for each combination of year/component of carbon cycle. The most sensitive input parameters
resulted to be C:N of leaves, C:N of roots, C:N of litter, specific leaf area and maximum stomatal conductance.
The biggest variation between a priori and a posteriori input parameter mean values was found for maximum
stomatal conductance (49.7%), then C:N of roots follows (15.8%). The remaining input parameters had a mean
value difference of less than 10%. The a posteriori parametrization produced a better agreement between
observed and estimated fluxes (a priori mean RMSE 2.3 g C m-2 day-1, a posteriori mean RMSE 1.7 g
C m-2 day-1).
DE: 0402 Agricultural systems
DE: 0414 Biogeochemical cycles, processes, and modeling (0412, 0793, 1615, 4805, 4912)
DE: 0428 Carbon cycling (4806)
DE: 0434 Data sets
DE: 0466 Modeling
SC: Biogeosciences [B]
MN: 2007 Joint Assembly