Modelling biogeochemical cycles in forest ecosystems: a Bayesian approach

Bagnara, Maurizio (2015) Modelling biogeochemical cycles in forest ecosystems: a Bayesian approach , [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Scienze e tecnologie agrarie, ambientali e alimentari, 27 Ciclo. DOI 10.6092/unibo/amsdottorato/7188.
Documenti full-text disponibili:
Documento PDF (English) - Richiede un lettore di PDF come Xpdf o Adobe Acrobat Reader
Download (4MB) | Anteprima


Forest models are tools for explaining and predicting the dynamics of forest ecosystems. They simulate forest behavior by integrating information on the underlying processes in trees, soil and atmosphere. Bayesian calibration is the application of probability theory to parameter estimation. It is a method, applicable to all models, that quantifies output uncertainty and identifies key parameters and variables. This study aims at testing the Bayesian procedure for calibration to different types of forest models, to evaluate their performances and the uncertainties associated with them. In particular,we aimed at 1) applying a Bayesian framework to calibrate forest models and test their performances in different biomes and different environmental conditions, 2) identifying and solve structure-related issues in simple models, and 3) identifying the advantages of additional information made available when calibrating forest models with a Bayesian approach. We applied the Bayesian framework to calibrate the Prelued model on eight Italian eddy-covariance sites in Chapter 2. The ability of Prelued to reproduce the estimated Gross Primary Productivity (GPP) was tested over contrasting natural vegetation types that represented a wide range of climatic and environmental conditions. The issues related to Prelued's multiplicative structure were the main topic of Chapter 3: several different MCMC-based procedures were applied within a Bayesian framework to calibrate the model, and their performances were compared. A more complex model was applied in Chapter 4, focusing on the application of the physiology-based model HYDRALL to the forest ecosystem of Lavarone (IT) to evaluate the importance of additional information in the calibration procedure and their impact on model performances, model uncertainties, and parameter estimation. Overall, the Bayesian technique proved to be an excellent and versatile tool to successfully calibrate forest models of different structure and complexity, on different kind and number of variables and with a different number of parameters involved.

Tipologia del documento
Tesi di dottorato
Bagnara, Maurizio
Dottorato di ricerca
Scuola di dottorato
Scienze agrarie
Settore disciplinare
Settore concorsuale
Parole chiave
Bayesian calibration, Markov Chain Monte Carlo, Forest model, carbon fluxes, Prelued, HYDRALL, Light-Use Efficiency, Lavarone, FluxNet, Metropolis-Hastings, Adaptive Metropolis, DEMC, MCMC algorithms, model uncertainties, parameter estimation, parameter uncertainties
Data di discussione
18 Maggio 2015

Altri metadati

Statistica sui download

Gestione del documento: Visualizza la tesi