A seasonal algorithm of the snow-covered area fraction for mountainous terrain
The snow cover spatial variability in mountainous terrain changes considerably over the course of a snow season. In this context, fractional snow-covered area (fSCA) is an essential model parameter characterizing how much ground surface in a grid cell is currently covered by snow. We present a seaso...
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ftdoajarticles:oai:doaj.org/article:ba88ce0bac794d97b82d67b7041a9e00 2023-05-15T18:32:27+02:00 A seasonal algorithm of the snow-covered area fraction for mountainous terrain N. Helbig M. Schirmer J. Magnusson F. Mäder A. van Herwijnen L. Quéno Y. Bühler J. S. Deems S. Gascoin 2021-09-01T00:00:00Z https://doi.org/10.5194/tc-15-4607-2021 https://doaj.org/article/ba88ce0bac794d97b82d67b7041a9e00 EN eng Copernicus Publications https://tc.copernicus.org/articles/15/4607/2021/tc-15-4607-2021.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-15-4607-2021 1994-0416 1994-0424 https://doaj.org/article/ba88ce0bac794d97b82d67b7041a9e00 The Cryosphere, Vol 15, Pp 4607-4624 (2021) Environmental sciences GE1-350 Geology QE1-996.5 article 2021 ftdoajarticles https://doi.org/10.5194/tc-15-4607-2021 2022-12-31T13:00:23Z The snow cover spatial variability in mountainous terrain changes considerably over the course of a snow season. In this context, fractional snow-covered area (fSCA) is an essential model parameter characterizing how much ground surface in a grid cell is currently covered by snow. We present a seasonal fSCA algorithm using a recent scale-independent fSCA parameterization. For the seasonal implementation, we track snow depth (HS) and snow water equivalent (SWE) and account for several alternating accumulation–ablation phases. Besides tracking HS and SWE, the seasonal fSCA algorithm only requires subgrid terrain parameters from a fine-scale summer digital elevation model. We implemented the new algorithm in a multilayer energy balance snow cover model. To evaluate the spatiotemporal changes in modeled fSCA, we compiled three independent fSCA data sets derived from airborne-acquired fine-scale HS data and from satellite and terrestrial imagery. Overall, modeled daily 1 km fSCA values had normalized root mean square errors of 7 %, 12 % and 21 % for the three data sets, and some seasonal trends were identified. Comparing our algorithm performances to the performances of the CLM5.0 fSCA algorithm implemented in the multilayer snow cover model demonstrated that our full seasonal fSCA algorithm better represented seasonal trends. Overall, the results suggest that our seasonal fSCA algorithm can be applied in other geographic regions by any snow model application. Article in Journal/Newspaper The Cryosphere Directory of Open Access Journals: DOAJ Articles The Cryosphere 15 9 4607 4624 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Environmental sciences GE1-350 Geology QE1-996.5 |
spellingShingle |
Environmental sciences GE1-350 Geology QE1-996.5 N. Helbig M. Schirmer J. Magnusson F. Mäder A. van Herwijnen L. Quéno Y. Bühler J. S. Deems S. Gascoin A seasonal algorithm of the snow-covered area fraction for mountainous terrain |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
description |
The snow cover spatial variability in mountainous terrain changes considerably over the course of a snow season. In this context, fractional snow-covered area (fSCA) is an essential model parameter characterizing how much ground surface in a grid cell is currently covered by snow. We present a seasonal fSCA algorithm using a recent scale-independent fSCA parameterization. For the seasonal implementation, we track snow depth (HS) and snow water equivalent (SWE) and account for several alternating accumulation–ablation phases. Besides tracking HS and SWE, the seasonal fSCA algorithm only requires subgrid terrain parameters from a fine-scale summer digital elevation model. We implemented the new algorithm in a multilayer energy balance snow cover model. To evaluate the spatiotemporal changes in modeled fSCA, we compiled three independent fSCA data sets derived from airborne-acquired fine-scale HS data and from satellite and terrestrial imagery. Overall, modeled daily 1 km fSCA values had normalized root mean square errors of 7 %, 12 % and 21 % for the three data sets, and some seasonal trends were identified. Comparing our algorithm performances to the performances of the CLM5.0 fSCA algorithm implemented in the multilayer snow cover model demonstrated that our full seasonal fSCA algorithm better represented seasonal trends. Overall, the results suggest that our seasonal fSCA algorithm can be applied in other geographic regions by any snow model application. |
format |
Article in Journal/Newspaper |
author |
N. Helbig M. Schirmer J. Magnusson F. Mäder A. van Herwijnen L. Quéno Y. Bühler J. S. Deems S. Gascoin |
author_facet |
N. Helbig M. Schirmer J. Magnusson F. Mäder A. van Herwijnen L. Quéno Y. Bühler J. S. Deems S. Gascoin |
author_sort |
N. Helbig |
title |
A seasonal algorithm of the snow-covered area fraction for mountainous terrain |
title_short |
A seasonal algorithm of the snow-covered area fraction for mountainous terrain |
title_full |
A seasonal algorithm of the snow-covered area fraction for mountainous terrain |
title_fullStr |
A seasonal algorithm of the snow-covered area fraction for mountainous terrain |
title_full_unstemmed |
A seasonal algorithm of the snow-covered area fraction for mountainous terrain |
title_sort |
seasonal algorithm of the snow-covered area fraction for mountainous terrain |
publisher |
Copernicus Publications |
publishDate |
2021 |
url |
https://doi.org/10.5194/tc-15-4607-2021 https://doaj.org/article/ba88ce0bac794d97b82d67b7041a9e00 |
genre |
The Cryosphere |
genre_facet |
The Cryosphere |
op_source |
The Cryosphere, Vol 15, Pp 4607-4624 (2021) |
op_relation |
https://tc.copernicus.org/articles/15/4607/2021/tc-15-4607-2021.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-15-4607-2021 1994-0416 1994-0424 https://doaj.org/article/ba88ce0bac794d97b82d67b7041a9e00 |
op_doi |
https://doi.org/10.5194/tc-15-4607-2021 |
container_title |
The Cryosphere |
container_volume |
15 |
container_issue |
9 |
container_start_page |
4607 |
op_container_end_page |
4624 |
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1766216575718785024 |