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Record W7011519272

Mathematical modelling of supraglacial meltwater production and drainage

2021· dissertation· en· W7011519272 on OpenAlexaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2021
Typedissertation
Languageen
FieldMedicine
TopicMedical and Biological Sciences
Canadian institutionsnot available
Fundersnot available
KeywordsMeltwaterSurface runoffGlacierGreenland ice sheetCryosphereIce sheetHydrology (agriculture)Sea iceDrainage
DOInot available

Abstract

fetched live from OpenAlex

Mountain glaciers and the polar ice sheets exert a critical control on water resource availability, drive sea level change, and impact global ocean circulation. These and other impacts are controlled by surface meltwater that flows through the glacier hydrologic system to the base of the ice and drives seasonal and long-term changes in ice flow velocity. This thesis presents numerical models for the production and transport of meltwater runoff across the surface of melting glaciers and ice sheet. \n \nFirst, a surface energy balance model is developed that improves on existing models by utilizing high resolution satellite data to capture spatial variations in surface melt. The model is applied to Kaskawulsh Glacier and Nàłùdäy (Lowell) Glacier in the St. Elias Mountains, Yukon, Canada using six years of in-situ meteorological data. By validating model outputs against in-situ measurements, it is shown that modelled seasonal melt agrees with observations within 9% across a range of elevations. \n \nIn order to determine how surface meltwater is transported through moulins, we develop the Subaerial Drainage System (SaDS) model. SaDS is a physics-based, finite-volume numerical model that calculates supraglacial runoff in both a distributed sheet and through supraglacial channels. The benefit of this approach is that a connected network of supraglacial channels and lakes naturally emerges without using prior information about the channel network, for example from satellite-derived maps. In synthetic settings and when applied to the Greenland Ice Sheet, model outputs show realistic and varied moulin flux rates, and modelled supraglacial lake and channel locations match those mapped from satellite images. These results demonstrate that SaDS is a promising tool to provide moulin inputs for subglacial and ice dynamic studies. \n \nThese models represent significant steps forward in their respective domains. Together, these tools will be valuable components of future modelling work, including for studies that aim to constrain how climatic variables control sea level contributions from glaciers and ice sheets.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.223
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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