Bayesian integration of flux tower data into process-based simulator for quantifying uncertainty in simulated output

This research implemented a Bayesian statistical method to calibrate a widely used process-based simulator BIOME-BGC against estimates of gross primary production (GPP) data. Six parameters of BIOME-BGC were calibrated, which were also allowed to vary month-by-month to investigate the hypothesis that the phenology exhibited a seasonal cycle that was not accurately reproduced by the simulator. Time varying parameters substantially improved the simulated GPP as compared to GPP obtained with constant parameters.

Identifier
DOI https://doi.org/10.17026/dans-zc7-7549
PID https://nbn-resolving.org/urn:nbn:nl:ui:13-dae0-ke
Metadata Access https://easy.dans.knaw.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:easy.dans.knaw.nl:easy-dataset:67599
Provenance
Creator Raj, R.
Publisher Data Archiving and Networked Services (DANS)
Contributor Hamm, N.A.S.; Tol, C.  van der; Stein, A.; Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands
Publication Year 2016
Rights info:eu-repo/semantics/openAccess; License: http://creativecommons.org/publicdomain/zero/1.0; http://creativecommons.org/publicdomain/zero/1.0
OpenAccess true
Representation
Language English
Resource Type Dataset
Format Text files (*.mtc41, *.txt, *.epc, *.ini, *.m, *.csv); PDF
Discipline Agriculture, Forestry, Horticulture, Aquaculture; Agriculture, Forestry, Horticulture, Aquaculture and Veterinary Medicine; Forestry; Life Sciences; Mathematics; Natural Sciences; Numerical Analysis
Spatial Coverage Speulderbos forest site; The Netherlands