Learning Soil Freeze Characteristic Curves with Universal Differential Equations

Permafrost thaw is considered one of the major climate feedback processes and is currently a significant source of uncertainty in predicting future climate states. Coverage of in-situ meteorological and land-surface observations is sparse throughout the Arctic, making it difficult to track the large...

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Main Authors: Groenke, Brian, Langer, Moritz, Gallego, Guillermo, Boike, Julia
Format: Conference Object
Language:unknown
Published: 2021
Subjects:
Online Access:https://epic.awi.de/id/eprint/54994/
https://hdl.handle.net/10013/epic.42685fad-fda9-422e-a87c-40c35880c9b1
id ftawi:oai:epic.awi.de:54994
record_format openpolar
spelling ftawi:oai:epic.awi.de:54994 2024-09-15T18:29:45+00:00 Learning Soil Freeze Characteristic Curves with Universal Differential Equations Groenke, Brian Langer, Moritz Gallego, Guillermo Boike, Julia 2021-04-30 https://epic.awi.de/id/eprint/54994/ https://hdl.handle.net/10013/epic.42685fad-fda9-422e-a87c-40c35880c9b1 unknown Groenke, B. orcid:0000-0003-2570-9342 , Langer, M. orcid:0000-0002-2704-3655 , Gallego, G. and Boike, J. orcid:0000-0002-5875-2112 (2021) Learning Soil Freeze Characteristic Curves with Universal Differential Equations , EGU General Assembly 2021, online, 19 April 2021 - 30 April 2021 . doi:10.5194/egusphere-egu21-13409 <https://doi.org/10.5194/egusphere-egu21-13409> , hdl:10013/epic.42685fad-fda9-422e-a87c-40c35880c9b1 EPIC3EGU General Assembly 2021, online, 2021-04-19-2021-04-30 Conference notRev 2021 ftawi https://doi.org/10.5194/egusphere-egu21-13409 2024-06-24T04:27:29Z Permafrost thaw is considered one of the major climate feedback processes and is currently a significant source of uncertainty in predicting future climate states. Coverage of in-situ meteorological and land-surface observations is sparse throughout the Arctic, making it difficult to track the large-scale evolution of the Arctic surface and subsurface energy balance. Furthermore, permafrost thaw is a highly non-linear process with its own feedback mechanisms such as thermokarst and thermo-erosion. Land surface models, therefore, play an important role in our ability to understand how permafrost responds to the changing climate. There is also a need to quantify freeze-thaw cycling and the incomplete freezing of soil at depth (talik formation). One of the key difficulties in modeling the Arctic subsurface is the complexity of the thermal regime during phase change under freezing or thawing conditions. Modeling heat conduction with phase change accurately requires estimation of the soil freeze characteristic curve (SFCC) which governs the change in soil liquid water content with respect to temperature and depends on the soil physical characteristics (texture). In this work, we propose a method for replacing existing brute-force approximations of the SFCC in the CryoGrid 3 permafrost model with universal differential equations, i.e. differential equations that include one or more terms represented by a universal approximator (e.g. a neural network). The approximator is thus tasked with inferring a suitable SFCC from available soil temperature, moisture, and texture data. We also explore how remote sensing data might be used with universal approximators to extrapolate soil freezing characteristics where in-situ observations are not available. Conference Object permafrost Thermokarst Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center)
institution Open Polar
collection Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center)
op_collection_id ftawi
language unknown
description Permafrost thaw is considered one of the major climate feedback processes and is currently a significant source of uncertainty in predicting future climate states. Coverage of in-situ meteorological and land-surface observations is sparse throughout the Arctic, making it difficult to track the large-scale evolution of the Arctic surface and subsurface energy balance. Furthermore, permafrost thaw is a highly non-linear process with its own feedback mechanisms such as thermokarst and thermo-erosion. Land surface models, therefore, play an important role in our ability to understand how permafrost responds to the changing climate. There is also a need to quantify freeze-thaw cycling and the incomplete freezing of soil at depth (talik formation). One of the key difficulties in modeling the Arctic subsurface is the complexity of the thermal regime during phase change under freezing or thawing conditions. Modeling heat conduction with phase change accurately requires estimation of the soil freeze characteristic curve (SFCC) which governs the change in soil liquid water content with respect to temperature and depends on the soil physical characteristics (texture). In this work, we propose a method for replacing existing brute-force approximations of the SFCC in the CryoGrid 3 permafrost model with universal differential equations, i.e. differential equations that include one or more terms represented by a universal approximator (e.g. a neural network). The approximator is thus tasked with inferring a suitable SFCC from available soil temperature, moisture, and texture data. We also explore how remote sensing data might be used with universal approximators to extrapolate soil freezing characteristics where in-situ observations are not available.
format Conference Object
author Groenke, Brian
Langer, Moritz
Gallego, Guillermo
Boike, Julia
spellingShingle Groenke, Brian
Langer, Moritz
Gallego, Guillermo
Boike, Julia
Learning Soil Freeze Characteristic Curves with Universal Differential Equations
author_facet Groenke, Brian
Langer, Moritz
Gallego, Guillermo
Boike, Julia
author_sort Groenke, Brian
title Learning Soil Freeze Characteristic Curves with Universal Differential Equations
title_short Learning Soil Freeze Characteristic Curves with Universal Differential Equations
title_full Learning Soil Freeze Characteristic Curves with Universal Differential Equations
title_fullStr Learning Soil Freeze Characteristic Curves with Universal Differential Equations
title_full_unstemmed Learning Soil Freeze Characteristic Curves with Universal Differential Equations
title_sort learning soil freeze characteristic curves with universal differential equations
publishDate 2021
url https://epic.awi.de/id/eprint/54994/
https://hdl.handle.net/10013/epic.42685fad-fda9-422e-a87c-40c35880c9b1
genre permafrost
Thermokarst
genre_facet permafrost
Thermokarst
op_source EPIC3EGU General Assembly 2021, online, 2021-04-19-2021-04-30
op_relation Groenke, B. orcid:0000-0003-2570-9342 , Langer, M. orcid:0000-0002-2704-3655 , Gallego, G. and Boike, J. orcid:0000-0002-5875-2112 (2021) Learning Soil Freeze Characteristic Curves with Universal Differential Equations , EGU General Assembly 2021, online, 19 April 2021 - 30 April 2021 . doi:10.5194/egusphere-egu21-13409 <https://doi.org/10.5194/egusphere-egu21-13409> , hdl:10013/epic.42685fad-fda9-422e-a87c-40c35880c9b1
op_doi https://doi.org/10.5194/egusphere-egu21-13409
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