Superconducting Gravimeter Observations Show That a Satellite-Derived Snow Depth Image Improves the Simulation of the Snow Water Equivalent Evolution in a High Alpine Site

The lack of accurate information on the spatiotemporal variations of snow water equivalent (SWE) in mountain catchments remains a key problem in snow hydrology and water resources management. This is partly because there is no sensor to measure SWE beyond local scale. At Mt. Zugspitze, Germany, a su...

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Autores: Koch, Franziska, Gascoin, Simon, Achmüller, K., Schattan, Paul, Wetzel, Karl-Friedrich, Deschamps-Berger, César, Lehning, Michael, Rehm, Till, Schulz, Karsten, Voigt, Christian
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2024
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositório:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/378911
Acesso em linha:http://hdl.handle.net/10261/378911
https://api.elsevier.com/content/abstract/scopus_id/85212586039
Access Level:Acceso aberto
Palavra-chave:Mountain hydrology
Satellite-derived snow depth image
Snow water equivalent
Snowpack modeling
Cryo-hydro-gravimetry
Snow cover distribution
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spelling Superconducting Gravimeter Observations Show That a Satellite-Derived Snow Depth Image Improves the Simulation of the Snow Water Equivalent Evolution in a High Alpine SiteKoch, FranziskaGascoin, SimonAchmüller, K.Schattan, PaulWetzel, Karl-FriedrichDeschamps-Berger, CésarLehning, MichaelRehm, TillSchulz, KarstenVoigt, ChristianMountain hydrologySatellite-derived snow depth imageSnow water equivalentSnowpack modelingCryo-hydro-gravimetrySnow cover distributionThe lack of accurate information on the spatiotemporal variations of snow water equivalent (SWE) in mountain catchments remains a key problem in snow hydrology and water resources management. This is partly because there is no sensor to measure SWE beyond local scale. At Mt. Zugspitze, Germany, a superconducting gravimeter senses the gravity effect of the seasonal snow, reflecting the temporal evolution of SWE in a few kilometers scale radius. We used this new observation to evaluate two configurations of the Alpine3D distributed snow model. In the default run, the model was forced with meteorological station data. In the second run, we applied precipitation correction based on an 8 m resolution snow depth image derived from satellite observations (Pléiades). The snow depth image strongly improved the simulation of the snowpack gravity effect during the melt season. This result suggests that satellite observations can enhance SWE analyses in mountains with limited infrastructure.This research was funded by the Austrian Science Fund (FWF) [Grant 10.55776/I6489, FWF-DFG Weave project G-MONARCH, project start in 2023]. For open access purposes, the authors have applied a CC BY public copyright license to any author accepted manuscript version arising from this submission. We are grateful for the funding received under the French-Austrian Campus France Amadeus/OeAD WTZ project FRAU SNOW (FR 07/2023) allowing travels for the authors F. Koch and S. Gascoin to Toulouse, France and Vienna, Austria for joint research stays.Peer reviewedJohn Wiley & SonsAmerican Geophysical UnionAustrian Science FundKoch, Franziska [0000-0001-5826-295X]Gascoin, Simon [0000-0002-4996-6768]Wetzel, Karl-Friedrich [0000-0001-8914-3654]Deschamps-Berger, César [0000-0003-3017-5250]Lehning, Michael [0000-0002-8442-0875]Rehm, Till [0000-0002-5906-6181]Schulz, Karsten [0000-0002-6616-2876]Voigt, Christian [0000-0002-9988-965X]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/378911https://api.elsevier.com/content/abstract/scopus_id/85212586039reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésThe underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1029/2024GL112483https://doi.org/10.1029/2024GL112483Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3789112026-05-22T06:33:51Z
dc.title.none.fl_str_mv Superconducting Gravimeter Observations Show That a Satellite-Derived Snow Depth Image Improves the Simulation of the Snow Water Equivalent Evolution in a High Alpine Site
title Superconducting Gravimeter Observations Show That a Satellite-Derived Snow Depth Image Improves the Simulation of the Snow Water Equivalent Evolution in a High Alpine Site
spellingShingle Superconducting Gravimeter Observations Show That a Satellite-Derived Snow Depth Image Improves the Simulation of the Snow Water Equivalent Evolution in a High Alpine Site
Koch, Franziska
Mountain hydrology
Satellite-derived snow depth image
Snow water equivalent
Snowpack modeling
Cryo-hydro-gravimetry
Snow cover distribution
title_short Superconducting Gravimeter Observations Show That a Satellite-Derived Snow Depth Image Improves the Simulation of the Snow Water Equivalent Evolution in a High Alpine Site
title_full Superconducting Gravimeter Observations Show That a Satellite-Derived Snow Depth Image Improves the Simulation of the Snow Water Equivalent Evolution in a High Alpine Site
title_fullStr Superconducting Gravimeter Observations Show That a Satellite-Derived Snow Depth Image Improves the Simulation of the Snow Water Equivalent Evolution in a High Alpine Site
title_full_unstemmed Superconducting Gravimeter Observations Show That a Satellite-Derived Snow Depth Image Improves the Simulation of the Snow Water Equivalent Evolution in a High Alpine Site
title_sort Superconducting Gravimeter Observations Show That a Satellite-Derived Snow Depth Image Improves the Simulation of the Snow Water Equivalent Evolution in a High Alpine Site
dc.creator.none.fl_str_mv Koch, Franziska
Gascoin, Simon
Achmüller, K.
Schattan, Paul
Wetzel, Karl-Friedrich
Deschamps-Berger, César
Lehning, Michael
Rehm, Till
Schulz, Karsten
Voigt, Christian
author Koch, Franziska
author_facet Koch, Franziska
Gascoin, Simon
Achmüller, K.
Schattan, Paul
Wetzel, Karl-Friedrich
Deschamps-Berger, César
Lehning, Michael
Rehm, Till
Schulz, Karsten
Voigt, Christian
author_role author
author2 Gascoin, Simon
Achmüller, K.
Schattan, Paul
Wetzel, Karl-Friedrich
Deschamps-Berger, César
Lehning, Michael
Rehm, Till
Schulz, Karsten
Voigt, Christian
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Austrian Science Fund
Koch, Franziska [0000-0001-5826-295X]
Gascoin, Simon [0000-0002-4996-6768]
Wetzel, Karl-Friedrich [0000-0001-8914-3654]
Deschamps-Berger, César [0000-0003-3017-5250]
Lehning, Michael [0000-0002-8442-0875]
Rehm, Till [0000-0002-5906-6181]
Schulz, Karsten [0000-0002-6616-2876]
Voigt, Christian [0000-0002-9988-965X]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Mountain hydrology
Satellite-derived snow depth image
Snow water equivalent
Snowpack modeling
Cryo-hydro-gravimetry
Snow cover distribution
topic Mountain hydrology
Satellite-derived snow depth image
Snow water equivalent
Snowpack modeling
Cryo-hydro-gravimetry
Snow cover distribution
description The lack of accurate information on the spatiotemporal variations of snow water equivalent (SWE) in mountain catchments remains a key problem in snow hydrology and water resources management. This is partly because there is no sensor to measure SWE beyond local scale. At Mt. Zugspitze, Germany, a superconducting gravimeter senses the gravity effect of the seasonal snow, reflecting the temporal evolution of SWE in a few kilometers scale radius. We used this new observation to evaluate two configurations of the Alpine3D distributed snow model. In the default run, the model was forced with meteorological station data. In the second run, we applied precipitation correction based on an 8 m resolution snow depth image derived from satellite observations (Pléiades). The snow depth image strongly improved the simulation of the snowpack gravity effect during the melt season. This result suggests that satellite observations can enhance SWE analyses in mountains with limited infrastructure.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/378911
https://api.elsevier.com/content/abstract/scopus_id/85212586039
url http://hdl.handle.net/10261/378911
https://api.elsevier.com/content/abstract/scopus_id/85212586039
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1029/2024GL112483
https://doi.org/10.1029/2024GL112483
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv John Wiley & Sons
American Geophysical Union
publisher.none.fl_str_mv John Wiley & Sons
American Geophysical Union
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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