Bayesian methods in glaciology
Dissertation (Ph.D) University of Alaska Fairbanks, 2017 The problem of inferring the value of unobservable model parameters given a set of observations is ubiquitous in glaciology, as are large measurement errors. Bayes' theorem provides a unified framework for addressing such problems in a ri...
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ftunivalaska:oai:scholarworks.alaska.edu:11122/8113 2023-05-15T16:20:39+02:00 Bayesian methods in glaciology Brinkerhoff, Douglas Truffer, Martin Aschwanden, Andy Tape, Carl Bueler, Ed 2017-12 http://hdl.handle.net/11122/8113 en_US eng http://hdl.handle.net/11122/8113 Department of Geosciences Glaciology Models Hydrologic models Mountain hydrology Bayesian statistical decision theory Dissertation phd 2017 ftunivalaska 2023-02-23T21:36:58Z Dissertation (Ph.D) University of Alaska Fairbanks, 2017 The problem of inferring the value of unobservable model parameters given a set of observations is ubiquitous in glaciology, as are large measurement errors. Bayes' theorem provides a unified framework for addressing such problems in a rigorous and robust way through Monte Carlo sampling of posterior distributions, which provides not only the optimal solution for a given inverse problem, but also the uncertainty. We apply these methods to three glaciological problems. First, we use Markov Chain Monte Carlo sampling to infer the importance of different glacier hydrological processes from observations of terminus water flux and surface speed. We find that the opening of sub-glacial cavities due to sliding over asperities at the glacier bed is of a similar magnitude to the opening of channels due to turbulent melt during periods of large input flux, but also that the processes of turbulent melting is the greatest source of uncertainty in hydrological modelling. Storage of water in both englacial void spaces and exchange of water between the englacial and subglacial systems are both necessary to explain observations. We next use Markov Chain Monte Carlo sampling to determine distributed glacier thickness from dense observations of surface velocity and mass balance coupled with sparse direct observations of thickness. These three variables are related through the principle of mass conservation. We develop a new framework for modelling observational uncertainty, then apply the method to three test cases. We find a strong relationship between measurement uncertainty, measurement spacing, and the resulting uncertainty in thickness estimates. We also find that in order to minimize uncertainty, measurement spacing should be 1-2 times the characteristic length scale of variations in subglacial topography. Finally, we apply the method of particle filtering to compute robust estimates of ice surface velocity and uncertainty from oblique time-lapse photos for the ... Doctoral or Postdoctoral Thesis glacier Alaska University of Alaska: ScholarWorks@UA Fairbanks |
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Open Polar |
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University of Alaska: ScholarWorks@UA |
op_collection_id |
ftunivalaska |
language |
English |
topic |
Glaciology Models Hydrologic models Mountain hydrology Bayesian statistical decision theory |
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Glaciology Models Hydrologic models Mountain hydrology Bayesian statistical decision theory Brinkerhoff, Douglas Bayesian methods in glaciology |
topic_facet |
Glaciology Models Hydrologic models Mountain hydrology Bayesian statistical decision theory |
description |
Dissertation (Ph.D) University of Alaska Fairbanks, 2017 The problem of inferring the value of unobservable model parameters given a set of observations is ubiquitous in glaciology, as are large measurement errors. Bayes' theorem provides a unified framework for addressing such problems in a rigorous and robust way through Monte Carlo sampling of posterior distributions, which provides not only the optimal solution for a given inverse problem, but also the uncertainty. We apply these methods to three glaciological problems. First, we use Markov Chain Monte Carlo sampling to infer the importance of different glacier hydrological processes from observations of terminus water flux and surface speed. We find that the opening of sub-glacial cavities due to sliding over asperities at the glacier bed is of a similar magnitude to the opening of channels due to turbulent melt during periods of large input flux, but also that the processes of turbulent melting is the greatest source of uncertainty in hydrological modelling. Storage of water in both englacial void spaces and exchange of water between the englacial and subglacial systems are both necessary to explain observations. We next use Markov Chain Monte Carlo sampling to determine distributed glacier thickness from dense observations of surface velocity and mass balance coupled with sparse direct observations of thickness. These three variables are related through the principle of mass conservation. We develop a new framework for modelling observational uncertainty, then apply the method to three test cases. We find a strong relationship between measurement uncertainty, measurement spacing, and the resulting uncertainty in thickness estimates. We also find that in order to minimize uncertainty, measurement spacing should be 1-2 times the characteristic length scale of variations in subglacial topography. Finally, we apply the method of particle filtering to compute robust estimates of ice surface velocity and uncertainty from oblique time-lapse photos for the ... |
author2 |
Truffer, Martin Aschwanden, Andy Tape, Carl Bueler, Ed |
format |
Doctoral or Postdoctoral Thesis |
author |
Brinkerhoff, Douglas |
author_facet |
Brinkerhoff, Douglas |
author_sort |
Brinkerhoff, Douglas |
title |
Bayesian methods in glaciology |
title_short |
Bayesian methods in glaciology |
title_full |
Bayesian methods in glaciology |
title_fullStr |
Bayesian methods in glaciology |
title_full_unstemmed |
Bayesian methods in glaciology |
title_sort |
bayesian methods in glaciology |
publishDate |
2017 |
url |
http://hdl.handle.net/11122/8113 |
geographic |
Fairbanks |
geographic_facet |
Fairbanks |
genre |
glacier Alaska |
genre_facet |
glacier Alaska |
op_relation |
http://hdl.handle.net/11122/8113 Department of Geosciences |
_version_ |
1766008589578665984 |