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Record W4408431463 · doi:10.5194/egusphere-egu25-13257

Challenges in Alpine Snow and Ice Hydrology

2025· preprint· en· W4408431463 on OpenAlexaff
John W. Pomeroy

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSnowHydrology (agriculture)Environmental sciencePhysical geographyCryosphereClimatologyGeographyGeologyMeteorologySea iceGeotechnical engineering

Abstract

fetched live from OpenAlex

Advances in alpine snow and ice hydrology have occurred due to the relentless efforts of field researchers to study snow processes in remote research sites, improvements in automated instrumentation, advances in remote sensing, and improvements in numerical modelling. Crucial has been the joint consideration of the mass and energy conservation equations and phase change in various calculation procedures. For instance, energy budget snowmelt and icemelt methods have replaced calibrated, anti-physical and highly uncertain temperature index melt models. Slope, aspect, remote shading, katabatic flow and wind flow over complex terrain are considered in energy and mass balance calculations. Albedo decay considers changes in grain size and increasingly addresses deposition of impurities such as soot. Snow redistribution by wind and by gravity have been recognized as important processes controlling snow accumulation. Blowing snow redistribution has advanced from flat-earth physics to 3-D complex terrain representations of saltation and suspension transport and sublimation due to turbulent transfer with blowing snow particles. Snow redistribution by forest canopies considers the role of canopy structure in interception and of the competing processes of unloading, sublimation and melt in ablating canopy snow. Snow-soil interactions consider the role of freezing soils on heat flow to snow and infiltration of snowmelt. Snow depth can be measured by LiDAR from planes and drones and snow-covered area and albedo estimated by satellite.However, several challenges remain unsolved or very uncertain. Advection of latent and sensible heat from bare ground or open water to snow or ice is not fully understood in complex terrain. Ice ablation from glaciers terminating in proglacial lakes is uncertain. Alpine blowing snow calculations do not fully consider the role of terrain roughness and sparse vegetation on transport fluxes and atmospheric exchanges. Wind flow calculations in steep alpine terrain are still problematic and incapable of reliable estimation of wind speed and direction. Intercepted snow calculations lack an understanding of wind erosion and redistribution from forest canopies. Snow avalanche calculations used in hydrology are highly empirical and tuned to regional observations, so lack the flexibility and global robustness of physically based methods. Snow water equivalent observations still depend on gravimetric methods and lack reliable high resolution remote sensing approaches. Snowfall measurements are too sparse and in wind swept terrain are still highly uncertain due to wind-induced undercatch and other gauge errors. Albedo impacts from atmospheric deposition on snow and ice and biological magnifiers such as snow and ice algae are understood but not incorporated in calculations. The role of edge effects such as treelines, glacier edges, canopy gaps and ridges on upscaled hydrological responses are incompletely understood. And the full understanding of what fine-scale processes are emergent or are compensated for in larger scale energy and water budget calculations is still being developed.This talk considers the advances in and the prospects for improving snow and ice process understanding, parameterisation and prediction in alpine catchments and calls for new research to solve the remaining uncertainties.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0060.006
Open science0.0020.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0080.002

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 head0.065
GPT teacher head0.257
Teacher spread0.193 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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
Published2025
Admission routes1
Has abstractyes

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