Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/2778
Title: Estimating daily forest carbon fluxes using a combination of ground and remotely sensed data
Authors: Chirici, Gherardo
Chiesi, Marta
Corona, Piermaria
Salvati, Riccardo
Papale, Dario
Fibbi, Luca
Sirca, Costantino
Spano, Donatella
Duce, Pierpaolo
Marras, Serena
Matteucci, Giorgio
Cescatti, Alessandro
Maselli, Fabio
Keywords: GPP and NEP simulations;Modified C-Fix;BIOME-BGC;Mediterranean forest ecosystems
Issue Date: 2016
Publisher: Agu Publications
Source: Chirigi, G. et al. 2016. Estimating daily forest carbon fluxes using a combination of ground and remotely sensed data. "Journal of Geophysical Research: Biogeosciences" 121: 266-279
Abstract: 
Several studies have demonstrated that Monteith’s approach can efficiently predict forest gross primary production (GPP), while the modeling of net ecosystem production (NEP) is more critical, requiring
the additional simulation of forest respirations. The NEP of different forest ecosystems in Italy was currently
simulated by the use of a remote sensing driven parametric model (modified C-Fix) and a biogeochemical
model (BIOME-BGC). The outputs of the two models, which simulate forests in quasi-equilibrium conditions, are combined to estimate the carbon fluxes of actual conditions using information regarding the existing woody biomass. The estimates derived from the methodology have been tested against daily reference GPP
and NEP data collected through the eddy correlation technique at five study sites in Italy. The first test
concerned the theoretical validity of the simulation approach at both annual and daily time scales and was
performed using optimal model drivers (i.e., collected or calibrated over the site measurements). Next, the test was repeated to assess the operational applicability of the methodology, which was driven by spatially
extended data sets (i.e., data derived from existing wall-to-wall digital maps). A good estimation accuracy was
generally obtained for GPP and NEP when using optimal model drivers. The use of spatially extended data
sets worsens the accuracy to a varying degree,which is properly characterized. Themodel drivers with themost
influence on the flux modeling strategy are, in increasing order of importance, forest type, soil features, meteorology, and forest woody biomass (growing stock volume).
URI: http://hdl.handle.net/2067/2778
ISSN: 2169-8953
DOI: 10.1002/2015JG003019
Appears in Collections:DIBAF - Archivio della produzione scientifica

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