Bayesian calibration of the Pasture Simulation model (PaSim) to simulate European grasslands under water stress
| Autor: | Gianni Bellocchi, H. Ben Touhami |
|---|---|
| Přispěvatelé: | UR 0874 Unité de recherche sur l'Ecosystème Prairial, Institut National de la Recherche Agronomique (INRA)-Unité de recherche sur l'Ecosystème Prairial (UREP)-Ecologie des Forêts, Prairies et milieux Aquatiques (EFPA), Institut National de la Recherche Agronomique (INRA), MACSUR |
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
IMPACTS
Mathematical optimization INTEGRATED ASSESSMENT MODELS Computer science Computation [SDE.MCG]Environmental Sciences/Global Changes Monte Carlo method Bayesian probability FRANCE CHAIN MONTE-CARLO Bayesian calibration framework COMPUTATION Pasture Simulation model (PaSim) Underwater Ecology Evolution Behavior and Systematics Simulation Uncertainty reduction theory VULNERABILITY CLIMATE-CHANGE Ecology Estimation theory Applied Mathematics Ecological Modeling Water stress LIKELIHOODS Computer Science Applications Computational Theory and Mathematics Modeling and Simulation Grasslands Bayesian calibration |
| Zdroj: | Ecological Informatics Ecological Informatics, Elsevier, 2015, 30, pp.356-364. ⟨10.1016/j.ecoinf.2015.09.009⟩ |
| ISSN: | 1574-9541 |
| DOI: | 10.1016/j.ecoinf.2015.09.009⟩ |
| Popis: | As modeling becomes a more widespread practice in the agro-environmental sciences, scientists need reliable tools to calibrate models against ever more complex and detailed data. We present a generic Bayesian computation framework for grassland simulation, which enables parameter estimation in the Bayesian formalism by using Monte Carlo approaches. We outline the underlying rationale, discuss the computational issues, and provide results from an application of the Pasture Simulation model (PaSim) to three European grasslands. The framework was suited to investigate the challenging problem of calibrating complex biophysical models to data from altered scenarios generated by precipitation reduction (water stress conditions). It was used to infer the parameters of manipulated grassland systems and to assess the gain in uncertainty reduction by updating parameter distributions using measurements of the output variables. (C) 2015 Elsevier B.V. All rights reserved. |
| Databáze: | OpenAIRE |
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