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  European larch phenology in the Alps: can we grasp the role of ecological factors by combining field observations and inverse modelling?

Migliavacca, M., Cremonese, E., Colombo, R., Busetto, L., Galvagno, M., Ganis, L., et al. (2008). European larch phenology in the Alps: can we grasp the role of ecological factors by combining field observations and inverse modelling? International Journal of Biometeorology, 52(7), 587-605. doi:10.1007/s00484-008-0152-9.

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BEX347.pdf (Verlagsversion), 899KB
 
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Migliavacca, Mirco1, Autor           
Cremonese, E., Autor
Colombo, R., Autor
Busetto, L., Autor
Galvagno, M., Autor
Ganis, L., Autor
Meroni, M., Autor
Pari, E., Autor
Rossini, M., Autor
Siniscalco, C., Autor
di Cella, U. Morra, Autor
Affiliations:
1External Organizations, ou_persistent22              

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Schlagwörter: SPRUCE PICEA-ABIES; BUD-BURST; SPRING PHENOLOGY; TEMPERATE TREES;GROWING-SEASON; CLIMATE-CHANGE; INTERANNUAL VARIABILITY; SOIL-TEMPERATURE; UNITED-STATES; THERMAL TIMEBiophysics; Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences; Physiology; European larch phenology; alpine environment; phenological models; inverse modelling;
 Zusammenfassung: Vegetation phenology is strongly influenced by climatic factors. Climate changes may cause phenological variations, especially in the Alps which are considered to be extremely vulnerable to global warming. The main goal of our study is to analyze European larch (Larix decidua Mill.) phenology in alpine environments and the role of the ecological factors involved, using an integrated approach based on accurate field observations and modelling techniques. We present 2 years of field-collected larch phenological data, obtained following a specifically designed observation protocol. We observed that both spring and autumn larch phenology is strongly influenced by altitude. We propose an approach for the optimization of a spring warming model (SW) and the growing season index model (GSI) consisting of a model inversion technique, based on simulated look-up tables (LUTs), that provides robust parameter estimates. The optimized models showed excellent agreement between modelled and observed data: the SW model predicts the beginning of the growing season (B(GS)) with a mean RMSE of 4 days, while GSI gives a prediction of the growing season length (L(GS)) with a RMSE of 5 days. Moreover, we showed that the original GSI parameters led to consistent errors, while the optimized ones significantly increased model accuracy. Finally, we used GSI to investigate interactions of ecological factors during springtime development and autumn senescence. We found that temperature is the most effective factor during spring recovery while photoperiod plays an important role during autumn senescence: photoperiod shows a contrasting effect with altitude decreasing its influence with increasing altitude.

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Sprache(n): eng - English
 Datum: 2008
 Publikationsstatus: Erschienen
 Seiten: 19
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: Anderer: BEX347
DOI: 10.1007/s00484-008-0152-9
 Art des Abschluß: -

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Titel: International Journal of Biometeorology
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: Berlin, Heidelberg : Springer Science + Business Media
Seiten: - Band / Heft: 52 (7) Artikelnummer: - Start- / Endseite: 587 - 605 Identifikator: ISSN: 0020-7128
CoNE: https://pure.mpg.de/cone/journals/resource/110978977731811