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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| Format: | Article |
| Language: | English |
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Cambridge University Press
2024-01-01
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| Series: | Journal of Glaciology |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S0022143024000467/type/journal_article |
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| 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 |
| collection | DOAJ |
| 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 |
| id | doaj-art-1f6e5765c5b6468f808d53d77def2d74 |
| institution | Kabale University |
| issn | 0022-1430 1727-5652 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Journal of Glaciology |
| spelling | doaj-art-1f6e5765c5b6468f808d53d77def2d742025-01-16T21:52:25ZengCambridge University PressJournal of Glaciology0022-14301727-56522024-01-017010.1017/jog.2024.46DAS to discharge: using distributed acoustic sensing (DAS) to infer glacier runoffJohn-Morgan Manos0https://orcid.org/0000-0001-9378-9271Dominik Gräff1Eileen Rose Martin2Patrick Paitz3Fabian Walter4Andreas Fichtner5Bradley Paul Lipovsky6https://orcid.org/0000-0003-4940-0745Department of Earth and Space Sciences, University of Washington, Seattle, WA, USADepartment of Earth and Space Sciences, University of Washington, Seattle, WA, USADepartment of Geophysics and Department of Applied Math and Statistics, Colorado School of Mines, Golden, CO, USAETH Zurich, Department of Earth Sciences, Institute of Geophysics, Zürich, SwitzerlandSwiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürich, SwitzerlandETH Zurich, Department of Earth Sciences, Institute of Geophysics, Zürich, SwitzerlandDepartment of Earth and Space Sciences, University of Washington, Seattle, WA, USAObservations 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.https://www.cambridge.org/core/product/identifier/S0022143024000467/type/journal_articleglaciological instruments and methodsglacier dischargeglacier hydrologymelt–surfaceseismology |
| spellingShingle | 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 Journal of Glaciology glaciological instruments and methods glacier discharge glacier hydrology melt–surface seismology |
| title | 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_short | 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 |
| topic | glaciological instruments and methods glacier discharge glacier hydrology melt–surface seismology |
| url | https://www.cambridge.org/core/product/identifier/S0022143024000467/type/journal_article |
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