東華大學圖書館 |

Modeling the Subglacial Environment with Geostatistics.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modeling the Subglacial Environment with Geostatistics./
作者:	MacKie, Emma Johanne.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	110 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-06, Section: B.
Contained By:	Dissertations Abstracts International83-06B.
標題:	Mapping. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28746149
ISBN:	9798494445827

Modeling the Subglacial Environment with Geostatistics.
MacKie, Emma Johanne.

Modeling the Subglacial Environment with Geostatistics. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 110 p.

Source: Dissertations Abstracts International, Volume: 83-06, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

The conditions beneath glaciers and ice-sheets including the topography, hydrology, and geology are essential for understanding ice dynamics, interpreting ice-sheet history, and predicting ice-sheet retreat and sea-level-rise in response to a changing climate. Located in remote regions and overlain by several kilometers of ice, subglacial conditions are difficult to characterize at ice-sheet scales. In this thesis, I explore the use geostatistical simulation and machine learning methods to characterize subglacial hydrologic and geologic conditions and produce the first ensemble realizations of stochastically simulated subglacial topography.In Chapter 2 of this thesis, I consider the physical and geological controls on the sizes and locations of Antarctic subglacial lakes. Subglacial lakes play an important role in ice-sheet dynamics, biology, and geology. Hundreds of these lakes have been observed with ice-penetrating radar or inferred through changes in ice-surface elevation detected by satellite altimetry. However, the difference between radar and altimetry detected lakes is not fully understood, and the sizes, quantity, and locations of subglacial lakes across the continent remains unknown. In this study, I investigate statistical relationships between known subglacial lake locations and physical parameters such as heat flux and ice velocity in order to predict lake locations across the continent. I performed geostatistical simulations of Antarctic topography so that the predicted lakes would have realistic surface areas. My results provide estimates of the number and sizes of Antarctic subglacial lakes and expose major physical differences between radar and altimetry detected lakes.In Chapter 3 of this thesis, I investigate uncertainty in subglacial water routing at Jakobshavn Glacier in Greenland. Water routing is important for understanding ice-sheet dynamics. However, water routing models rely on uncertain and unrealistically smooth bed topography. I conduct geostatistical simulations of subglacial topography and apply a water routing model to each of these realizations. The results demonstrate that subglacial water pathways are highly sensitive to uncertainties in subglacial topography. This shows that geostatistical simulation is a powerful tool for quantifying uncertainty in subglacial drainage and could be used to add context to geophysical observations or investigate hydrologic controls on ice-sheet movement.In Chapter 4 of this thesis, I provide subglacial geological estimates for the Amundsen Sea Embayment in West Antarctica. The Amundsen Sea Embayment, and Thwaites Glacier in particular, are undergoing rapid ice loss that threatens the stability of the West Antarctic Ice Sheet. One of the primary sources of uncertainty in ice-sheet models for this region is the subglacial geology. However, observations of subglacial geology are extremely sparse due to the severely limited spatial extent of seismic surveys. In this chapter, I use a training image-based geostatistical simulation technique to stochastically simulate subglacial topography such that the simulated morphology is allowed to vary locally based on nearby data. I use the simulated topography to predict the geology by training on seafloor topography data. These results provide the first catchment-scale estimates of sediment and bedrock locations and offer vital information for ice-sheet models.

ISBN: 9798494445827Subjects--Topical Terms:

3355992
Mapping.

Modeling the Subglacial Environment with Geostatistics.
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520 $a The conditions beneath glaciers and ice-sheets including the topography, hydrology, and geology are essential for understanding ice dynamics, interpreting ice-sheet history, and predicting ice-sheet retreat and sea-level-rise in response to a changing climate. Located in remote regions and overlain by several kilometers of ice, subglacial conditions are difficult to characterize at ice-sheet scales. In this thesis, I explore the use geostatistical simulation and machine learning methods to characterize subglacial hydrologic and geologic conditions and produce the first ensemble realizations of stochastically simulated subglacial topography.In Chapter 2 of this thesis, I consider the physical and geological controls on the sizes and locations of Antarctic subglacial lakes. Subglacial lakes play an important role in ice-sheet dynamics, biology, and geology. Hundreds of these lakes have been observed with ice-penetrating radar or inferred through changes in ice-surface elevation detected by satellite altimetry. However, the difference between radar and altimetry detected lakes is not fully understood, and the sizes, quantity, and locations of subglacial lakes across the continent remains unknown. In this study, I investigate statistical relationships between known subglacial lake locations and physical parameters such as heat flux and ice velocity in order to predict lake locations across the continent. I performed geostatistical simulations of Antarctic topography so that the predicted lakes would have realistic surface areas. My results provide estimates of the number and sizes of Antarctic subglacial lakes and expose major physical differences between radar and altimetry detected lakes.In Chapter 3 of this thesis, I investigate uncertainty in subglacial water routing at Jakobshavn Glacier in Greenland. Water routing is important for understanding ice-sheet dynamics. However, water routing models rely on uncertain and unrealistically smooth bed topography. I conduct geostatistical simulations of subglacial topography and apply a water routing model to each of these realizations. The results demonstrate that subglacial water pathways are highly sensitive to uncertainties in subglacial topography. This shows that geostatistical simulation is a powerful tool for quantifying uncertainty in subglacial drainage and could be used to add context to geophysical observations or investigate hydrologic controls on ice-sheet movement.In Chapter 4 of this thesis, I provide subglacial geological estimates for the Amundsen Sea Embayment in West Antarctica. The Amundsen Sea Embayment, and Thwaites Glacier in particular, are undergoing rapid ice loss that threatens the stability of the West Antarctic Ice Sheet. One of the primary sources of uncertainty in ice-sheet models for this region is the subglacial geology. However, observations of subglacial geology are extremely sparse due to the severely limited spatial extent of seismic surveys. In this chapter, I use a training image-based geostatistical simulation technique to stochastically simulate subglacial topography such that the simulated morphology is allowed to vary locally based on nearby data. I use the simulated topography to predict the geology by training on seafloor topography data. These results provide the first catchment-scale estimates of sediment and bedrock locations and offer vital information for ice-sheet models.
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