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Towards the development of an automated electrical self-potential sensor of melt and rainwater flow in snow

Alex Priestley Orcid Logo, Bernd Kulessa Orcid Logo, Richard Essery, Yves Lejeune, Erwan Le Gac, Jane Blackford

Journal of Glaciology, Volume: 68, Issue: 270, Pages: 720 - 732

Swansea University Author: Bernd Kulessa Orcid Logo

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DOI (Published version): 10.1017/jog.2021.128

Abstract

To understand snow structure and snowmelt timing, information about flows of liquid water within the snowpack is essential. Models can make predictions using explicit representations of physical processes, or through parameterization, but it is difficult to verify simulations. In situ observations g...

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Published in: Journal of Glaciology
ISSN: 0022-1430 1727-5652
Published: Cambridge University Press (CUP) 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa58636
first_indexed 2021-11-12T15:49:05Z
last_indexed 2023-01-11T14:39:22Z
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spelling 2022-09-26T15:36:53.1197222 v2 58636 2021-11-12 Towards the development of an automated electrical self-potential sensor of melt and rainwater flow in snow 52acda616e9f6073cbebf497def874c9 0000-0002-4830-4949 Bernd Kulessa Bernd Kulessa true false 2021-11-12 BGPS To understand snow structure and snowmelt timing, information about flows of liquid water within the snowpack is essential. Models can make predictions using explicit representations of physical processes, or through parameterization, but it is difficult to verify simulations. In situ observations generally measure bulk quantities. Where internal snowpack measurements are made, they tend to be destructive and unsuitable for continuous monitoring. Here, we present a novel method for in situ monitoring of water flow in seasonal snow using the electrical self-potential geophysical method. A prototype geophysical array was installed at Col de Porte (France) in October 2018. Snow hydrological and meteorological observations were also collected. Results for two periods of hydrological interest during winter 2018-19 (a marked period of diurnal melting and refreezing, and a rain-on-snow event) show that the electrical self-potential method is sensitive to internal water flow. Water flow was detected by self-potential signals before it was measured in conventional snowmelt lysimeters at the base of the snowpack. This initial feasibility study shows the utility of the self-potential method as a non-destructive snow sensor. Future development should include combining self-potential measurements with a high-resolution snow physics model to improve prediction of melt timing. Journal Article Journal of Glaciology 68 270 720 732 Cambridge University Press (CUP) 0022-1430 1727-5652 Glacier geophysics; glaciological instruments and methods; snow 1 8 2022 2022-08-01 10.1017/jog.2021.128 COLLEGE NANME Biosciences Geography and Physics School COLLEGE CODE BGPS Swansea University Another institution paid the OA fee NERC E3 Doctoral Training Partnership studentship under grant NE/L002558/1 in partnership with British Geological Survey 2022-09-26T15:36:53.1197222 2021-11-12T15:44:23.5427367 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Geography Alex Priestley 0000-0003-1149-2672 1 Bernd Kulessa 0000-0002-4830-4949 2 Richard Essery 3 Yves Lejeune 4 Erwan Le Gac 5 Jane Blackford 6 58636__22078__e88c953a53194add949d7c971b165bd9.pdf 58636.pdf 2022-01-07T16:09:36.7050175 Output 3650481 application/pdf Version of Record true © The Author(s), 2021. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence true eng https://creativecommons.org/licenses/by/4.0/
title Towards the development of an automated electrical self-potential sensor of melt and rainwater flow in snow
spellingShingle Towards the development of an automated electrical self-potential sensor of melt and rainwater flow in snow
Bernd Kulessa
title_short Towards the development of an automated electrical self-potential sensor of melt and rainwater flow in snow
title_full Towards the development of an automated electrical self-potential sensor of melt and rainwater flow in snow
title_fullStr Towards the development of an automated electrical self-potential sensor of melt and rainwater flow in snow
title_full_unstemmed Towards the development of an automated electrical self-potential sensor of melt and rainwater flow in snow
title_sort Towards the development of an automated electrical self-potential sensor of melt and rainwater flow in snow
author_id_str_mv 52acda616e9f6073cbebf497def874c9
author_id_fullname_str_mv 52acda616e9f6073cbebf497def874c9_***_Bernd Kulessa
author Bernd Kulessa
author2 Alex Priestley
Bernd Kulessa
Richard Essery
Yves Lejeune
Erwan Le Gac
Jane Blackford
format Journal article
container_title Journal of Glaciology
container_volume 68
container_issue 270
container_start_page 720
publishDate 2022
institution Swansea University
issn 0022-1430
1727-5652
doi_str_mv 10.1017/jog.2021.128
publisher Cambridge University Press (CUP)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Geography{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Geography
document_store_str 1
active_str 0
description To understand snow structure and snowmelt timing, information about flows of liquid water within the snowpack is essential. Models can make predictions using explicit representations of physical processes, or through parameterization, but it is difficult to verify simulations. In situ observations generally measure bulk quantities. Where internal snowpack measurements are made, they tend to be destructive and unsuitable for continuous monitoring. Here, we present a novel method for in situ monitoring of water flow in seasonal snow using the electrical self-potential geophysical method. A prototype geophysical array was installed at Col de Porte (France) in October 2018. Snow hydrological and meteorological observations were also collected. Results for two periods of hydrological interest during winter 2018-19 (a marked period of diurnal melting and refreezing, and a rain-on-snow event) show that the electrical self-potential method is sensitive to internal water flow. Water flow was detected by self-potential signals before it was measured in conventional snowmelt lysimeters at the base of the snowpack. This initial feasibility study shows the utility of the self-potential method as a non-destructive snow sensor. Future development should include combining self-potential measurements with a high-resolution snow physics model to improve prediction of melt timing.
published_date 2022-08-01T14:15:30Z
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