Predicting the Geothermal Heat Flux in Greenland: A Machine Learning Approach
Geothermal heat flux (GHF) is a crucial boundary condition for making accurate predictions of ice sheet mass loss, yet it is poorly known in Greenland due to inaccessibility of the bedrock. Here we use a machine learning algorithm on a large collection of relevant geologic features and global GHF me...
Published in: | Geophysical Research Letters |
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American Geophysical Union
2018
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Online Access: | http://hdl.handle.net/1808/27385 https://doi.org/10.1002/2017GL075661 |
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ftunivkansas:oai:kuscholarworks.ku.edu:1808/27385 2023-05-15T16:03:45+02:00 Predicting the Geothermal Heat Flux in Greenland: A Machine Learning Approach Rezvanbehbahani, Soroush Stearns, Leigh A. Kadivar, Amir Walker, J. Douglas van der Veen, Cornelis J. 2018-11-16T19:17:25Z http://hdl.handle.net/1808/27385 https://doi.org/10.1002/2017GL075661 unknown American Geophysical Union Rezvanbehbahani, S., Stearns, L. A., Kadivar, A., Walker, J. D., and van der Veen, C. J. (2017). Predicting the geothermal heat flux in Greenland: A machine learning approach. Geophysical Research Letters, 44, 12,271–12,279. https://doi.org/10.1002/2017GL075661 http://hdl.handle.net/1808/27385 doi:10.1002/2017GL075661 orcid:0000-0002-3480-6139 orcid:0000-0001-7358-7015 orcid:0000-0002-2469-2633 orcid:0000-0002-3706-2729 orcid:0000-0003-0086-491X © 2017. American Geophysical Union. All Rights Reserved. openAccess Greenland ice sheet Geothermal heat flux Machine learning Article 2018 ftunivkansas https://doi.org/10.1002/2017GL075661 2022-08-26T13:23:49Z Geothermal heat flux (GHF) is a crucial boundary condition for making accurate predictions of ice sheet mass loss, yet it is poorly known in Greenland due to inaccessibility of the bedrock. Here we use a machine learning algorithm on a large collection of relevant geologic features and global GHF measurements and produce a GHF map of Greenland that we argue is within ∼15% accuracy. The main features of our predicted GHF map include a large region with high GHF in central‐north Greenland surrounding the NorthGRIP ice core site, and hot spots in the Jakobshavn Isbræ catchment, upstream of Petermann Gletscher, and near the terminus of Nioghalvfjerdsfjorden glacier. Our model also captures the trajectory of Greenland movement over the Icelandic plume by predicting a stripe of elevated GHF in central‐east Greenland. Finally, we show that our model can produce substantially more accurate predictions if additional measurements of GHF in Greenland are provided. Article in Journal/Newspaper East Greenland glacier Greenland ice core Ice Sheet Jakobshavn Jakobshavn isbræ Nioghalvfjerdsfjorden North Greenland Petermann gletscher The University of Kansas: KU ScholarWorks Greenland Jakobshavn Isbræ ENVELOPE(-49.917,-49.917,69.167,69.167) Nioghalvfjerdsfjorden ENVELOPE(-21.500,-21.500,79.500,79.500) Petermann Gletscher ENVELOPE(-59.500,-59.500,80.500,80.500) Stripe ENVELOPE(9.914,9.914,63.019,63.019) Geophysical Research Letters 44 24 |
institution |
Open Polar |
collection |
The University of Kansas: KU ScholarWorks |
op_collection_id |
ftunivkansas |
language |
unknown |
topic |
Greenland ice sheet Geothermal heat flux Machine learning |
spellingShingle |
Greenland ice sheet Geothermal heat flux Machine learning Rezvanbehbahani, Soroush Stearns, Leigh A. Kadivar, Amir Walker, J. Douglas van der Veen, Cornelis J. Predicting the Geothermal Heat Flux in Greenland: A Machine Learning Approach |
topic_facet |
Greenland ice sheet Geothermal heat flux Machine learning |
description |
Geothermal heat flux (GHF) is a crucial boundary condition for making accurate predictions of ice sheet mass loss, yet it is poorly known in Greenland due to inaccessibility of the bedrock. Here we use a machine learning algorithm on a large collection of relevant geologic features and global GHF measurements and produce a GHF map of Greenland that we argue is within ∼15% accuracy. The main features of our predicted GHF map include a large region with high GHF in central‐north Greenland surrounding the NorthGRIP ice core site, and hot spots in the Jakobshavn Isbræ catchment, upstream of Petermann Gletscher, and near the terminus of Nioghalvfjerdsfjorden glacier. Our model also captures the trajectory of Greenland movement over the Icelandic plume by predicting a stripe of elevated GHF in central‐east Greenland. Finally, we show that our model can produce substantially more accurate predictions if additional measurements of GHF in Greenland are provided. |
format |
Article in Journal/Newspaper |
author |
Rezvanbehbahani, Soroush Stearns, Leigh A. Kadivar, Amir Walker, J. Douglas van der Veen, Cornelis J. |
author_facet |
Rezvanbehbahani, Soroush Stearns, Leigh A. Kadivar, Amir Walker, J. Douglas van der Veen, Cornelis J. |
author_sort |
Rezvanbehbahani, Soroush |
title |
Predicting the Geothermal Heat Flux in Greenland: A Machine Learning Approach |
title_short |
Predicting the Geothermal Heat Flux in Greenland: A Machine Learning Approach |
title_full |
Predicting the Geothermal Heat Flux in Greenland: A Machine Learning Approach |
title_fullStr |
Predicting the Geothermal Heat Flux in Greenland: A Machine Learning Approach |
title_full_unstemmed |
Predicting the Geothermal Heat Flux in Greenland: A Machine Learning Approach |
title_sort |
predicting the geothermal heat flux in greenland: a machine learning approach |
publisher |
American Geophysical Union |
publishDate |
2018 |
url |
http://hdl.handle.net/1808/27385 https://doi.org/10.1002/2017GL075661 |
long_lat |
ENVELOPE(-49.917,-49.917,69.167,69.167) ENVELOPE(-21.500,-21.500,79.500,79.500) ENVELOPE(-59.500,-59.500,80.500,80.500) ENVELOPE(9.914,9.914,63.019,63.019) |
geographic |
Greenland Jakobshavn Isbræ Nioghalvfjerdsfjorden Petermann Gletscher Stripe |
geographic_facet |
Greenland Jakobshavn Isbræ Nioghalvfjerdsfjorden Petermann Gletscher Stripe |
genre |
East Greenland glacier Greenland ice core Ice Sheet Jakobshavn Jakobshavn isbræ Nioghalvfjerdsfjorden North Greenland Petermann gletscher |
genre_facet |
East Greenland glacier Greenland ice core Ice Sheet Jakobshavn Jakobshavn isbræ Nioghalvfjerdsfjorden North Greenland Petermann gletscher |
op_relation |
Rezvanbehbahani, S., Stearns, L. A., Kadivar, A., Walker, J. D., and van der Veen, C. J. (2017). Predicting the geothermal heat flux in Greenland: A machine learning approach. Geophysical Research Letters, 44, 12,271–12,279. https://doi.org/10.1002/2017GL075661 http://hdl.handle.net/1808/27385 doi:10.1002/2017GL075661 orcid:0000-0002-3480-6139 orcid:0000-0001-7358-7015 orcid:0000-0002-2469-2633 orcid:0000-0002-3706-2729 orcid:0000-0003-0086-491X |
op_rights |
© 2017. American Geophysical Union. All Rights Reserved. openAccess |
op_doi |
https://doi.org/10.1002/2017GL075661 |
container_title |
Geophysical Research Letters |
container_volume |
44 |
container_issue |
24 |
_version_ |
1766399447453925376 |