Quantifying snow controls on vegetation greenness
Snow is a key driver for biotic processes in Arctic ecosystems. Yet, quantifying relationships between snow metrics and biological components is challenging due to lack of temporally and spatially distributed observations at ecologically relevant scales and resolutions. In this study, we quantified...
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Online Access: | https://pure.au.dk/portal/en/publications/1ba4cf1d-e748-49b2-89bd-3ed9b2aa9f9b https://doi.org/10.1002/ecs2.2309 https://pure.au.dk/ws/files/128589587/Pedersen_et_al_2018_Ecosphere.pdf |
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ftuniaarhuspubl:oai:pure.atira.dk:publications/1ba4cf1d-e748-49b2-89bd-3ed9b2aa9f9b 2024-04-21T08:03:28+00:00 Quantifying snow controls on vegetation greenness Pedersen, Stine Højlund E. Liston, Glen Tamstorf, Mikkel P. Abermann, Jakob Lund, Magnus Schmidt, Niels Martin 2018 application/pdf https://pure.au.dk/portal/en/publications/1ba4cf1d-e748-49b2-89bd-3ed9b2aa9f9b https://doi.org/10.1002/ecs2.2309 https://pure.au.dk/ws/files/128589587/Pedersen_et_al_2018_Ecosphere.pdf eng eng https://pure.au.dk/portal/en/publications/1ba4cf1d-e748-49b2-89bd-3ed9b2aa9f9b info:eu-repo/semantics/openAccess Pedersen , S H , E. Liston , G , Tamstorf , M P , Abermann , J , Lund , M & Schmidt , N M 2018 , ' Quantifying snow controls on vegetation greenness ' , Ecosphere (Washington, D.C.) , vol. 9 , no. 6 , e02309 . https://doi.org/10.1002/ecs2.2309 environmental gradient ground observations modeling moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) snow article 2018 ftuniaarhuspubl https://doi.org/10.1002/ecs2.2309 2024-03-28T00:38:58Z Snow is a key driver for biotic processes in Arctic ecosystems. Yet, quantifying relationships between snow metrics and biological components is challenging due to lack of temporally and spatially distributed observations at ecologically relevant scales and resolutions. In this study, we quantified relationships between snow, air temperature, and vegetation greenness (using annual maximum normalized difference vegetation index [MaxNDVI] and its timing [MaxNDVI_DOY]) from ground-based and remote-sensing observations, in combination with physically based models, across a heterogeneous landscape in a high-Arctic, northeast Greenland region. Across the 98-km distance from the Greenland Ice Sheet (GrIS) to the coast, we quantified significant inland–coast gradients of air temperature, winter precipitation (using pre-melt snow-water-equivalent [SWE]), and snowmelt timing (using snow-free day of year [SnowFree_DOY]). Near the coast, the mean annual air temperature was 4.5°C lower, the mean SWE was 0.3 m greater, and the mean SnowFree_DOY was 37 d later, than near the GrIS. The regional continentality gradient was eight times stronger than the south-to-north air–temperature gradient along the Greenland east coast. Across this strong gradient, the mean vegetation greening-up period (SnowFree_DOY-MaxNDVI_DOY) varied spatially by 24–57 d. We quantified significant non-linear relationships between the vegetation characteristics of MaxNDVI and MaxNDVI_DOY, and SWE, SnowFree_DOY, and growing degree-days-sums during greening-up (Greening_GDD) across the 16-yr study period (2000–2015). These demonstrated that the snow metrics, both SWE and SnowFree_DOY, were more important drivers of MaxNDVI and MaxNDVI_DOY than Greening_GDD within this seasonally snow-covered region. The methodologies that provided temporally and spatially distributed snow, air temperature, and vegetation greenness data are applicable to any snow- and vegetation-covered area on Earth. Article in Journal/Newspaper Greenland Ice Sheet Aarhus University: Research Ecosphere 9 6 |
institution |
Open Polar |
collection |
Aarhus University: Research |
op_collection_id |
ftuniaarhuspubl |
language |
English |
topic |
environmental gradient ground observations modeling moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) snow |
spellingShingle |
environmental gradient ground observations modeling moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) snow Pedersen, Stine Højlund E. Liston, Glen Tamstorf, Mikkel P. Abermann, Jakob Lund, Magnus Schmidt, Niels Martin Quantifying snow controls on vegetation greenness |
topic_facet |
environmental gradient ground observations modeling moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) snow |
description |
Snow is a key driver for biotic processes in Arctic ecosystems. Yet, quantifying relationships between snow metrics and biological components is challenging due to lack of temporally and spatially distributed observations at ecologically relevant scales and resolutions. In this study, we quantified relationships between snow, air temperature, and vegetation greenness (using annual maximum normalized difference vegetation index [MaxNDVI] and its timing [MaxNDVI_DOY]) from ground-based and remote-sensing observations, in combination with physically based models, across a heterogeneous landscape in a high-Arctic, northeast Greenland region. Across the 98-km distance from the Greenland Ice Sheet (GrIS) to the coast, we quantified significant inland–coast gradients of air temperature, winter precipitation (using pre-melt snow-water-equivalent [SWE]), and snowmelt timing (using snow-free day of year [SnowFree_DOY]). Near the coast, the mean annual air temperature was 4.5°C lower, the mean SWE was 0.3 m greater, and the mean SnowFree_DOY was 37 d later, than near the GrIS. The regional continentality gradient was eight times stronger than the south-to-north air–temperature gradient along the Greenland east coast. Across this strong gradient, the mean vegetation greening-up period (SnowFree_DOY-MaxNDVI_DOY) varied spatially by 24–57 d. We quantified significant non-linear relationships between the vegetation characteristics of MaxNDVI and MaxNDVI_DOY, and SWE, SnowFree_DOY, and growing degree-days-sums during greening-up (Greening_GDD) across the 16-yr study period (2000–2015). These demonstrated that the snow metrics, both SWE and SnowFree_DOY, were more important drivers of MaxNDVI and MaxNDVI_DOY than Greening_GDD within this seasonally snow-covered region. The methodologies that provided temporally and spatially distributed snow, air temperature, and vegetation greenness data are applicable to any snow- and vegetation-covered area on Earth. |
format |
Article in Journal/Newspaper |
author |
Pedersen, Stine Højlund E. Liston, Glen Tamstorf, Mikkel P. Abermann, Jakob Lund, Magnus Schmidt, Niels Martin |
author_facet |
Pedersen, Stine Højlund E. Liston, Glen Tamstorf, Mikkel P. Abermann, Jakob Lund, Magnus Schmidt, Niels Martin |
author_sort |
Pedersen, Stine Højlund |
title |
Quantifying snow controls on vegetation greenness |
title_short |
Quantifying snow controls on vegetation greenness |
title_full |
Quantifying snow controls on vegetation greenness |
title_fullStr |
Quantifying snow controls on vegetation greenness |
title_full_unstemmed |
Quantifying snow controls on vegetation greenness |
title_sort |
quantifying snow controls on vegetation greenness |
publishDate |
2018 |
url |
https://pure.au.dk/portal/en/publications/1ba4cf1d-e748-49b2-89bd-3ed9b2aa9f9b https://doi.org/10.1002/ecs2.2309 https://pure.au.dk/ws/files/128589587/Pedersen_et_al_2018_Ecosphere.pdf |
genre |
Greenland Ice Sheet |
genre_facet |
Greenland Ice Sheet |
op_source |
Pedersen , S H , E. Liston , G , Tamstorf , M P , Abermann , J , Lund , M & Schmidt , N M 2018 , ' Quantifying snow controls on vegetation greenness ' , Ecosphere (Washington, D.C.) , vol. 9 , no. 6 , e02309 . https://doi.org/10.1002/ecs2.2309 |
op_relation |
https://pure.au.dk/portal/en/publications/1ba4cf1d-e748-49b2-89bd-3ed9b2aa9f9b |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1002/ecs2.2309 |
container_title |
Ecosphere |
container_volume |
9 |
container_issue |
6 |
_version_ |
1796943300918247424 |