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On the use of the bayesian approach for the calibration, evaluation and comparison of process-based forest models
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Autores
Orientador(es)
Resumo(s)
Forest ecosystems have been experiencing fast and abrupt changes in the environmental
conditions, that can increase their vulnerability to extreme events such as drought, heat waves,
storms, fire. Process-based models can draw inferences about future environmental dynamics, but
the reliability and robustness of vegetation models are conditional on their structure and their
parametrisation.
The main objective of the PhD was to implement and apply modern computational
techniques, mainly based on Bayesian statistics, in the context of forest modelling. A variety of case
studies was presented, spanning from growth predictions models to soil respiration models and
process-based models. The great potential of the Bayesian method for reducing uncertainty in
parameters and outputs and model evaluation was shown.
Furthermore, a new methodology based on a combination of a Bayesian framework and a
global sensitivity analysis was developed, with the aim of identifying strengths and weaknesses of
process-based models and to test modifications in model structure.
Finally, part of the PhD research focused on reducing the computational load to take full
advantage of Bayesian statistics. It was shown how parameter screening impacts model
performances and a new methodology for parameter screening, based on canonical correlation
analysis, was presented
Descrição
Doutoramento em Engenharia Florestal e dos Recursos Naturais - Instituto Superior de Agronomia
Palavras-chave
process-based models Bayesian statistics carbon cycle water cycle uncertainty analysis global sensitivity analysis
Contexto Educativo
Citação
Minunno, F. - On the use of the bayesian approach for the calibration, evaluation and comparison of process-based forest models. Lisboa: ISA, 2014, 104 p.