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Assessing model performance via the most limiting environmental driver in two differently stressed pine stands

Nadal-Sala, Daniel 1; Grote, Rüdiger ORCID iD icon 1; Birami, Benjamin 1; Lintunen, Anna; Mammarella, Ivan; Preisler, Yakir; Rotenberg, Eyal; Salmon, Yann; Tatarinov, Fedor; Yakir, Dan; Ruehr, Nadine K. 1
1 Institut für Meteorologie und Klimaforschung – Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):
Climate change will have a considerable impact on forest productivity worldwide. Forecasting the magnitude of such impact, with multiple environmental stressors changing simultaneously, is only possible with the help of process-based models. In order to assess their performance, such models require careful evaluation against measurements. However, direct comparison of model outputs against observational data is often not reliable, as models may provide the right answers due to the wrong reasons. This would severely hinder forecasting abilities under unprecedented climate conditions. Here, we present a methodology for model assessment, which supplements the traditional output-to-observation model validation. It evaluates model performance through its ability to reproduce observed seasonal changes of the most limiting environmental driver (MLED) for a given process, here daily gross primary productivity (GPP).
We analyzed seasonal changes of the MLED for GPP in two contrasting pine forests, the Mediterranean Pinus halepensis Mill. Yatir (Israel) and the boreal Pinus sylvestris L. Hyytiälä (Finland) from three years of eddy-covariance flux data. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000133601
Veröffentlicht am 02.06.2021
DOI: 10.1002/eap.2312
Zitationen: 2
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung – Atmosphärische Umweltforschung (IMK-IFU)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 06.2021
Sprache Englisch
Identifikator ISSN: 1051-0761, 1939-5582
KITopen-ID: 1000133601
HGF-Programm 12.11.24 (POF IV, LK 01) Forest resilience to climate change
Weitere HGF-Programme 12.11.22 (POF IV, LK 01) Biogeochemical modeling of ecosystem-climate interactions
Erschienen in Ecological applications
Verlag Ecological Society of America
Band 31
Heft 4
Projektinformation DFG, DFG EIN, RU 1657/2-1
Vorab online veröffentlicht am 25.02.2021
Schlagwörter Aleppo pine; classification and regression trees; gross primary productivity; model evaluation; most limiting environmental driver; productivity seasonality; Scots pine
Nachgewiesen in Scopus
Web of Science
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