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Record W7014615412

From processes to predictions in hydrological modelling of glacierized basins

2022· dissertation· en· W7014615412 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsStreamflowGlacierClimate changeSnowDrainage basinHydrological modellingWater resourcesFlood forecasting
DOInot available

Abstract

fetched live from OpenAlex

Glacierized mountain headwaters act as water towers, providing critical water resources to downstream environments when other sources are unavailable. These headwaters are currently witnessing a shift in their coupled hydrological and glaciological systems. This shift is reducing glacier volume, extent and elevation range, in addition to changing the snow dynamics across both glacierized and non-glacierized areas. These interconnected changes occur simultaneously, driven by complex physical feedbacks, and they impact streamflow generation processes. To properly characterize this transition period and predict future hydrological behaviour in these glacierized basins, physically based glacio-hydrological models representing the full range of both glacier and basin hydrological processes are needed. However, obtaining the data to apply such modelling approaches is complicated by the scarce data availability in mountain regions. New approaches to collect the required data and parametrize these complex processes need to be developed in parallel with increased process representations in glacio-hydrological models. This thesis aims to assess the impact of future climate and glacier change on glacierized basin hydrological processes and streamflow generation. Its specific objectives are to (1) develop and apply innovative approaches to characterize hydro-glaciological processes in glacierized basins, (2) diagnose hydrological and glaciological processes resulting in streamflow generation and variability and (3) assess the coupled impacts of climate and landscape change on the hydrological processes and streamflow generation in a glacierized basin. Field-based investigations of streamflow measurement uncertainty, sub-debris melt and surface energy balance were conducted and used to guide new and revised algorithms for the Cold Region Hydrological Modelling (CRHM) platform. Using CRHM with the newly added process representations for katabatic wind turbulent transfer, hourly energy balance and sub-debris melt, a physically based glacio-hydrological model was developed and tested in the Peyto Glacier Research Basin, a 53% glacierized headwater basin (as of 2013) located in the Canadian Rockies. This glacio-hydrological model was used to investigate the recent past and current (1990-2020) hydrology of the basin using in-situ weather observations. Over the 32 years, strong inter-annual variability in the meteorological forcings caused highly variable streamflow in this cold alpine basin. Snowmelt always provided the largest fraction of annual streamflow (44 to 89%), with lower snowmelt contributions occurring in high streamflow years. Ice melt provided between 10 to 45% of total streamflow, with a higher contribution associated with high flow years. Both rainfall-runoff and firn melt contributed less than 13% of annual streamflow. Years with high streamflow were on average 1.43˚C warmer than low streamflow years, and high streamflow years had lower winter snow accumulation, earlier snowmelt and higher summer rain than years with low streamflow. The glacier hydrology of current (2000-2015) and future periods (2085-2100) was compared, using bias-corrected, dynamically downscaled, convection-permitting high-resolution atmospheric model outputs. The simulations show that the end-of-century increase in precipitation, mainly expressed as an increase in rainfall at the expense of snowfall, will nearly compensate for the decreased ice melt associated with almost complete deglaciation, resulting in a decrease of 7% in annual streamflow. However, the timing of streamflow will advance substantially, with the timing of peak flow shifting from July to June, and August streamflow dropping by 68%. To examine the sensitivity of future hydrology to possible future post-glacial landscapes, the end-of-century simulations were run under a range of boundary conditions and were most sensitive to initial ice volume and surface water storage. This research provides better modelling techniques to represent the complex systems of headwater glacierized basins, as well as robust estimates of future glacier contributions to streamflow in reference basins of the Canadian Rockies and should be useful for water availability studies and water management mitigation strategies.

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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)0.001
metaresearch head score (Gemma)0.003
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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.169
Teacher spread0.154 · 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
Published2022
Admission routes1
Has abstractyes

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