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Record W4408423428 · doi:10.5194/egusphere-egu25-3731

Intact rock glaciers as weathering reactors: influence on spring water quality

2025· preprint· en· W4408423428 on OpenAlexaff
Nicola Colombo, Stefano Brighenti, Thomas Wagner, Michael Pettauer, Nicolas Guyennon, Karl Krainer, Monica Tolotti, Michela Rogora, Luca Paro, Sandra Steingruber, Chantal Del Siro, Cristian Scapozza, Noelia R. Sileo, Cristian Daniel Villarroel, Masaki Hayashi, Jeffrey S. Munroe, Darío Trombotto Liaudat, Leonardo Cerasino, Werner Tirler, Francesco Comiti, Michele Freppaz, Franco Salerno, M. Iggy Litaor, Edoardo Cremonese, Umberto Morra di Cella, Gerfried Winkler

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsWeatheringSpring (device)GlacierGeologyGeochemistryEarth scienceQuality (philosophy)Environmental scienceHydrology (agriculture)Physical geographyMining engineeringGeomorphologyGeotechnical engineeringGeographyEngineering

Abstract

fetched live from OpenAlex

During the last decades, most glaciers have been retreating and losing mass in all high-mountain regions, where permafrost has also undergone warming, degradation, and ice loss. In this context, rock glaciers, a visual indication of the presence of mountain permafrost, have gained attention because they host shallow groundwater resources. Hence, rock glaciers could represent a contributor for future water supply, especially in arid and semi-arid mountain areas and/or during dry periods. However, a growing body of literature, mostly composed of local scale studies, has reported high concentrations of solutes, including trace elements, in rock glacier-fed waters, with negative implications on water quality. Therefore, the potential for rock glaciers to function as safe sources for drinking water supply may be questioned, although the main drivers of solute export from rock glaciers are still little understood. Here, we investigated how geographical and geological settings, together with cryospheric conditions, influence the water chemistry of intact (containing internal ice) and relict (without internal ice) rock glaciers, and assessed the potential implications for water quality. To do this, we assembled an unprecedented dataset on 201 rock glacier springs from mountain ranges across Europe, North and South America, and we applied a combination of machine learning, multivariate and univariate analyses, as well as geochemical modelling. Several intact rock glacier springs had higher concentrations of sulphate and trace elements (e.g., Ni, Al, U) than relict ones. Accordingly, one third of springs issuing from intact rock glaciers had a water quality that did not meet the requirements of drinking water standards, with respect to only 5 % of relict rock glacier springs. The ice presence combined with specific lithologies (e.g., paragneisses) enhanced solute concentrations in rock glacier springs, due to intense oxidation of sulphide minerals that was also responsible for the elevated trace element concentrations. Since rock glaciers are emerging as key mountain water resources as well as potential threats to water quality, we call for an international effort to investigate the hydrochemistry of rock glacier springs across the globe, especially in understudied mountain ranges (e.g., Himalayas, Caucasus) and where these springs are used for drinking purposes.Brighenti, S., Colombo, N., et al. Factors controlling the water quality of rock glacier springs in European and American mountain ranges. Science of the Total Environment 953, 175706 (2024). https://doi.org/10.1016/j.scitotenv.2024.175706NC and SB equally contributed to this work. NC and MF were supported by the project NODES, which has received funding from the MUR – M4C2 1.5 of PNRR funded by the European Union – NextGenerationEU (Grant agreement no. ECS00000036).

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.050
GPT teacher head0.300
Teacher spread0.250 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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