Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
 DetailsÜbersicht
  Bayesian calibration, comparison and averaging of six forest models, using data from Scots pine stands across Europe

van Oijen, M., Reyer, C., Bohn, F., Cameron, D., Deckmyn, G., Flechsig, M., et al. (2013). Bayesian calibration, comparison and averaging of six forest models, using data from Scots pine stands across Europe. Forest Ecology and Management, 289, 255-268. doi:10.1016/j.foreco.2012.09.043.

Item is

 

einblenden: alle ausblenden: alle

Basisdaten

einblenden: ausblenden:
Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0014-77AA-3 Versions-Permalink: https://hdl.handle.net/11858/00-001M-0000-0014-79B6-6
Genre: Zeitschriftenartikel

Dateien

einblenden: Dateien
ausblenden: Dateien
:
BPR048.pdf (Verlagsversion), 1017KB
 
Datei-Permalink:
-
Name:
BPR048.pdf
Beschreibung:
-
OA-Status:
Sichtbarkeit:
Eingeschränkt (Max Planck Institute for Biogeochemistry, MJBK; )
MIME-Typ / Prüfsumme:
application/pdf
Technische Metadaten:
Copyright Datum:
-
Copyright Info:
-
Lizenz:
-

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
van Oijen, M.1, Autor
Reyer, C., Autor
Bohn, F.J., Autor
Cameron, D.R., Autor
Deckmyn, G., Autor
Flechsig, M., Autor
Härkönen, S., Autor
Hartig, F., Autor
Huth, A., Autor
Kiviste, A., Autor
Lasch, P., Autor
Mäkelä, A., Autor
Mette, T., Autor
Rammer, F. Minunno W., Autor
Affiliations:
1External Organizations, ou_persistent22              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: Forest management requires prediction of forest growth, but there is no general agreement about which models best predict growth, how to quantify model parameters, and how to assess the uncertainty of model predictions. In this paper, we show how Bayesian calibration (BC), Bayesian model comparison (BMC) and Bayesian model averaging (BMA) can help address these issues. We used six models, ranging from simple parameter-sparse models to complex process-based models: 3PG, 4C, ANAFORE, BASFOR, BRIDGING and FORMIND. For each model, the initial degree of uncertainty about parameter values was expressed in a prior probability distribution. Inventory data for Scots pine on tree height and diameter, with estimates of measurement uncertainty, were assembled for twelve sites, from four countries: Austria, Belgium, Estonia and Finland. From each country, we used data from two sites of the National Forest Inventories (NFIs), and one Permanent Sample Plot (PSP). The models were calibrated using the NFI-data and tested against the PSP-data. Calibration was done both per country and for all countries simultaneously, thus yielding country-specific and generic parameter distributions. We assessed model performance by sampling from prior and posterior distributions and comparing the growth predictions of these samples to the observations at the PSPs. We found that BC reduced uncertainties strongly in all but the most complex model. Surprisingly, country-specific BC did not lead to clearly better within-country predictions than generic BC. BMC identified the BRIDGING model, which is of intermediate complexity, as the most plausible model before calibration, with 4C taking its place after calibration. In this BMC, model plausibility was quantified as the relative probability of a model being correct given the information in the PSP-data. We discuss how the method of model initialisation affects model performance. Finally, we show how BMA affords a robust way of predicting forest growth that accounts for both parametric and model structural uncertainty.

Details

einblenden:
ausblenden:
Sprache(n):
 Datum: 2013
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: Anderer: BPR048
DOI: 10.1016/j.foreco.2012.09.043
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: Forest Ecology and Management
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Amsterdam : Elsevier
Seiten: - Band / Heft: 289 Artikelnummer: - Start- / Endseite: 255 - 268 Identifikator: ISSN: 0378-1127
CoNE: https://pure.mpg.de/cone/journals/resource/954925526822