DAS to discharge: using distributed acoustic sensing (DAS) to infer glacier runoff
Observations of glacier melt and runoff are of fundamental interest in the study of glaciers and their interactions with their environment. Considerable recent interest has developed around distributed acoustic sensing (DAS), a sensing technique which utilizes Rayleigh backscatter in fiber optic cab...
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ftdoajarticles:oai:doaj.org/article:1f6e5765c5b6468f808d53d77def2d74 2024-09-15T18:15:41+00:00 DAS to discharge: using distributed acoustic sensing (DAS) to infer glacier runoff John-Morgan Manos Dominik Gräff Eileen Rose Martin Patrick Paitz Fabian Walter Andreas Fichtner Bradley Paul Lipovsky https://doi.org/10.1017/jog.2024.46 https://doaj.org/article/1f6e5765c5b6468f808d53d77def2d74 EN eng Cambridge University Press https://www.cambridge.org/core/product/identifier/S0022143024000467/type/journal_article https://doaj.org/toc/0022-1430 https://doaj.org/toc/1727-5652 doi:10.1017/jog.2024.46 0022-1430 1727-5652 https://doaj.org/article/1f6e5765c5b6468f808d53d77def2d74 Journal of Glaciology, Pp 1-9 glaciological instruments and methods glacier discharge glacier hydrology melt–surface seismology Environmental sciences GE1-350 Meteorology. Climatology QC851-999 article ftdoajarticles https://doi.org/10.1017/jog.2024.46 2024-09-02T15:34:39Z Observations of glacier melt and runoff are of fundamental interest in the study of glaciers and their interactions with their environment. Considerable recent interest has developed around distributed acoustic sensing (DAS), a sensing technique which utilizes Rayleigh backscatter in fiber optic cables to measure the seismo-acoustic wavefield in high spatial and temporal resolution. Here, we present data from a month-long, 9 km DAS deployment extending through the ablation and accumulation zones on Rhonegletscher, Switzerland, during the 2020 melt season. While testing several types of machine learning (ML) models, we establish a regression problem, using the DAS data as the dependent variable, to infer the glacier discharge observed at a proglacial stream gauge. We also compare two predictive models that only depend on meteorological station data. We find that the seismo-acoustic wavefield recorded by DAS can be utilized to infer proglacial discharge. Models using DAS data outperform the two models trained on meteorological data with mean absolute errors of 0.64, 2.25 and 2.72 m3 s−1, respectively. This study demonstrates the ability of in situ glacier DAS to be used for quantifying proglacial discharge and points the way to a new approach to measuring glacier runoff. Article in Journal/Newspaper Journal of Glaciology Directory of Open Access Journals: DOAJ Articles Journal of Glaciology 1 9 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
glaciological instruments and methods glacier discharge glacier hydrology melt–surface seismology Environmental sciences GE1-350 Meteorology. Climatology QC851-999 |
spellingShingle |
glaciological instruments and methods glacier discharge glacier hydrology melt–surface seismology Environmental sciences GE1-350 Meteorology. Climatology QC851-999 John-Morgan Manos Dominik Gräff Eileen Rose Martin Patrick Paitz Fabian Walter Andreas Fichtner Bradley Paul Lipovsky DAS to discharge: using distributed acoustic sensing (DAS) to infer glacier runoff |
topic_facet |
glaciological instruments and methods glacier discharge glacier hydrology melt–surface seismology Environmental sciences GE1-350 Meteorology. Climatology QC851-999 |
description |
Observations of glacier melt and runoff are of fundamental interest in the study of glaciers and their interactions with their environment. Considerable recent interest has developed around distributed acoustic sensing (DAS), a sensing technique which utilizes Rayleigh backscatter in fiber optic cables to measure the seismo-acoustic wavefield in high spatial and temporal resolution. Here, we present data from a month-long, 9 km DAS deployment extending through the ablation and accumulation zones on Rhonegletscher, Switzerland, during the 2020 melt season. While testing several types of machine learning (ML) models, we establish a regression problem, using the DAS data as the dependent variable, to infer the glacier discharge observed at a proglacial stream gauge. We also compare two predictive models that only depend on meteorological station data. We find that the seismo-acoustic wavefield recorded by DAS can be utilized to infer proglacial discharge. Models using DAS data outperform the two models trained on meteorological data with mean absolute errors of 0.64, 2.25 and 2.72 m3 s−1, respectively. This study demonstrates the ability of in situ glacier DAS to be used for quantifying proglacial discharge and points the way to a new approach to measuring glacier runoff. |
format |
Article in Journal/Newspaper |
author |
John-Morgan Manos Dominik Gräff Eileen Rose Martin Patrick Paitz Fabian Walter Andreas Fichtner Bradley Paul Lipovsky |
author_facet |
John-Morgan Manos Dominik Gräff Eileen Rose Martin Patrick Paitz Fabian Walter Andreas Fichtner Bradley Paul Lipovsky |
author_sort |
John-Morgan Manos |
title |
DAS to discharge: using distributed acoustic sensing (DAS) to infer glacier runoff |
title_short |
DAS to discharge: using distributed acoustic sensing (DAS) to infer glacier runoff |
title_full |
DAS to discharge: using distributed acoustic sensing (DAS) to infer glacier runoff |
title_fullStr |
DAS to discharge: using distributed acoustic sensing (DAS) to infer glacier runoff |
title_full_unstemmed |
DAS to discharge: using distributed acoustic sensing (DAS) to infer glacier runoff |
title_sort |
das to discharge: using distributed acoustic sensing (das) to infer glacier runoff |
publisher |
Cambridge University Press |
url |
https://doi.org/10.1017/jog.2024.46 https://doaj.org/article/1f6e5765c5b6468f808d53d77def2d74 |
genre |
Journal of Glaciology |
genre_facet |
Journal of Glaciology |
op_source |
Journal of Glaciology, Pp 1-9 |
op_relation |
https://www.cambridge.org/core/product/identifier/S0022143024000467/type/journal_article https://doaj.org/toc/0022-1430 https://doaj.org/toc/1727-5652 doi:10.1017/jog.2024.46 0022-1430 1727-5652 https://doaj.org/article/1f6e5765c5b6468f808d53d77def2d74 |
op_doi |
https://doi.org/10.1017/jog.2024.46 |
container_title |
Journal of Glaciology |
container_start_page |
1 |
op_container_end_page |
9 |
_version_ |
1810453621358198784 |