Land surface phenology and greenness in Alpine grasslands driven by seasonal snow and meteorological factors.

Alpine grasslands Land surface phenology Meteorological drivers Snow cover melt Snow water equivalent Swiss Alps

Journal

The Science of the total environment
ISSN: 1879-1026
Titre abrégé: Sci Total Environ
Pays: Netherlands
ID NLM: 0330500

Informations de publication

Date de publication:
10 Jul 2020
Historique:
received: 15 10 2019
revised: 06 03 2020
accepted: 30 03 2020
pubmed: 17 4 2020
medline: 17 4 2020
entrez: 17 4 2020
Statut: ppublish

Résumé

Snow accumulation and melt have multiple impacts on Land Surface Phenology (LSP) and greenness in Alpine grasslands. Our understanding of these impacts and their interactions with meteorological factors are still limited. In this study, we investigate this topic by analyzing LSP dynamics together with potential drivers, using satellite imagery and other data sources. LSP (start and end of season) and greenness metrics were extracted from time series of vegetation and leaf area index. As explanatory variables we used snow accumulation, snow cover melt date and meteorological factors. We tested for inter-annual co-variation of LSP and greenness metrics with seasonal snow and meteorological metrics across elevations and for four sub-regions of natural grasslands in the Swiss Alps over the period 2003-2014. We found strong positive correlations of snow cover melt date and snow accumulation with the start of season, especially at higher elevation. Autumn temperature was found to be important at the end of season below 2000 m above sea level (m asl), while autumn precipitation was relevant above 2000 m asl, indicating climatic growth limiting factors to be elevation dependent. The effects of snow and meteorological factors on greenness revealed that this metric tends to be influenced by temperatures at high elevations, and by snow melt date at low elevations. Given the high sensitivity of alpine grassland ecosystems, these results suggest that alpine grasslands may be particularly affected by future changes in seasonal snow, to varying degree depending on elevation.

Identifiants

pubmed: 32298886
pii: S0048-9697(20)31893-3
doi: 10.1016/j.scitotenv.2020.138380
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

138380

Informations de copyright

Copyright © 2020 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Auteurs

Jing Xie (J)

Remote Sensing Laboratories, Department of Geography, University of Zurich, Winterthurerstr. 190, 8057 Zurich, Switzerland. Electronic address: jing.xie@geo.uzh.ch.

Tobias Jonas (T)

WSL Institute for Snow and Avalanche Research, SLF Davos, Flüelastr. 11, 7260 Davos Dorf, Switzerland.

Christian Rixen (C)

WSL Institute for Snow and Avalanche Research, SLF Davos, Flüelastr. 11, 7260 Davos Dorf, Switzerland.

Rogier de Jong (R)

Remote Sensing Laboratories, Department of Geography, University of Zurich, Winterthurerstr. 190, 8057 Zurich, Switzerland.

Irene Garonna (I)

Remote Sensing Laboratories, Department of Geography, University of Zurich, Winterthurerstr. 190, 8057 Zurich, Switzerland.

Claudia Notarnicola (C)

Institute for Earth Observation, EURAC, Viale Druso 1, I-39100 Bolzano, Italy.

Sarah Asam (S)

Institute for Earth Observation, EURAC, Viale Druso 1, I-39100 Bolzano, Italy; German Remote Sensing Data Center, Earth Observation Center, German Aerospace Center, 82234 Wessling, Germany.

Michael E Schaepman (ME)

Remote Sensing Laboratories, Department of Geography, University of Zurich, Winterthurerstr. 190, 8057 Zurich, Switzerland.

Mathias Kneubühler (M)

Remote Sensing Laboratories, Department of Geography, University of Zurich, Winterthurerstr. 190, 8057 Zurich, Switzerland.

Classifications MeSH