Bayesian optimization of a light use efficiency model for the estimation of daily gross primary productivity in a range of Italian forest ecosystems

Bagnara, Maurizio and Sottocornola, Matteo and Cescatti, Alessandro and Minerbi, Stefano and Montagnani, Leonardo and Gianelle, Damiano and Magnani, Federico (2015) Bayesian optimization of a light use efficiency model for the estimation of daily gross primary productivity in a range of Italian forest ecosystems. Ecological Modelling, 306. pp. 57-66. ISSN 0304-3800

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In this study we applied a modified version of Prelued, a simple semi-empirical light use efficiency (LUE) model, to eight eddy-covariance Italian sites. Since this model has been successfully applied mainly to coniferous forests located at northern latitudes, in our study we aimed to test its generality, by comparing Prelued's outputs in coniferous, broadleaf forests and in a Mediterranean macchia, at different climatic and environmental conditions. The model was calibrated for daily gross primary production (GPP) observed over one year in each flux site and validated for another year. The model uncertainties on both GPP and model parameters were estimated, applying a Bayesian calibration based on a multiple chains Markov Chain Monte Carlo sampling.The accuracy of the model estimates of daily GPP over the entire period of simulation differed widely depending on the site considered, with generally good model performance when applied to evergreen and broadleaf forests and poor performances in the Mediterranean macchia. The values of the modifiers accounting for the response to climatic variables suggested the soil water content to be non-limiting in temperate mountain evergreen but limiting in Mediterranean forests. Model uncertainties were always smaller than data uncertainties, with variable magnitude depending on the site considered. Both modeled GPP and uncertainties were largely dependent also on uncertainties on the data, which made their calculation a key process in this modelling exercise.In conclusion, this semi-empirical model appears to be suitable for estimating daily and annual forest GPP in most of the considered sites, with the exception of Mediterranean macchias, and for supporting its application to a large range of ecosystems provided a site-specific calibration. The Bayesian calibration did not confer a clear advantage in terms of model performances in respect to other methods used in previous studies, but allowed us to estimate uncertainties on both parameter values and model estimates, which were useful to analyse more in detail the ecosystem response to environmental drivers of GPP.

Item Type: Article
Additional Information: Funding Information: Maurizio Bagnara's PhD fellowship “AM07 – Forest Modelling” was funded by the FIRS>T FEM International PhD School Trentino . We thank Francesco Minunno, University of Lisbon, for providing the R code for DEMC algorithm. Matteo Sottocornola acknowledges funding by the Marie-Curie FP7 – PCOFUND-GA - 2008-226070 , “Progetto Trentino”, CfPAT project. This work used eddy-covariance data acquired by the FLUXNET community, in particular we acknowledge the financial support obtained by the CarboItaly project funded by the Italian Government. Publisher Copyright: © 2014 Elsevier B.V..
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/2300/2302
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Date Deposited: 19 Oct 2022 23:03
Last Modified: 15 Aug 2023 16:05

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