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Record W4396567948 · doi:10.24124/2024/59480

Estimation of snow water equivalent in mountainous terrain using airborne laser altimetry and empirical modelling, Coast Mountains, B.C., Canada

2024· dissertation· en· W4396567948 on OpenAlexaboutno aff
Sergio Vazquez Tagle Gallegos

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsSnowTerrainAltimeterWater equivalentEstimationRemote sensingPhysical geographyGeologyEnvironmental scienceClimatologyGeographyMeteorologyCartographyEngineering

Abstract

fetched live from OpenAlex

This research aims to enhance scholarly understanding of snow dynamics, the remotely sensed snowpacks, and the calculation of basin-wide snow water equivalent in mountainous terrain. Mountain snow is a critical source of meltwater. However, forecasting snow distributions and total water equivalence in mountain basins is limited due to complex terrain, challenging environmental conditions, and lack of observations. Laser altimetry can provide detailed observations of snow depth, but an estimate of snow density is required to evaluate the total basin water equivalence. This study uses laser altimetry surveys and empirically modelled snow densities to estimate mountainous basin-wide snow-water equivalent (SWE). Between 2017 and 2020, seven laser altimeter surveys during late winter and spring were conducted in the LaJoie Basin, Coast Mountains, British Columbia (B.C.), a strategic hydroelectric power reservoir. The laser-derived snow depths averaged between 1.4 and 2.1 m for non-glacierized terrain, while glacierized terrain weighted averages ranged between 2.2 and 5.4 m. The laser-derived depths were combined with empirical snow density models to derive distributed SWE for the Lajoie Basin. Ten linear and three non-linear snow density empirical models were tested and developed, from which (a) a snow course multi-parameter, non-linear relation and (b) snow pillow robust (Huber loss) linear regressions yielded this study’s lowest root mean squared errors (51.65 and 74.12 kg m-3, respectively). For non-glacierized terrain, the multi-parameter, non-linear model produced basin-wide SWE averages between 0.56 and 1.06 m.w.e. and propagated uncertainties from ± 0.1 to ± 0.14 m.w.e. Conversely, glacierized terrain exhibited weighted SWE averages between 0.82 and 2.93 m.w.e., with estimates of uncertainty ranging from ± 0.31 to ± 0.52 m.w.e. The robust linear regressions yielded non-glacierized SWE averages from 0.49 to 0.92 m.w.e., with uncertainties between ± 0.14 and ± 0.2 m.w.e. The weighted SWE averages in glacierized terrain ranged between 0.77 and 1.92 m.w.e., with estimates of uncertainty between ± 0.29 and ± 0.45 m.w.e. The SWE estimates from lidar and modelled density are comparable to snow pillow observations at the watershed, which demonstrates the effectiveness of these coupled techniques and improves our forecasting capabilities.,

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.268
Teacher spread0.239 · 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
Published2024
Admission routes1
Has abstractyes

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