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Record W4408002773 · doi:10.5194/hess-29-1083-2025

The influence of permafrost and other environmental factors on stream thermal sensitivity across Yukon, Canada

2025· article· en· W4408002773 on OpenAlexafffundabout
András Szeitz, Sean K. Carey

Bibliographic record

VenueHydrology and earth system sciences · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPermafrostEnvironmental scienceSensitivity (control systems)Hydrology (agriculture)Physical geographyAtmospheric sciencesGeologyEarth scienceOceanographyGeographyGeotechnical engineering

Abstract

fetched live from OpenAlex

Thermal sensitivity, defined as the slope of a linear regression between stream and air temperatures, is a useful indicator of the strength of coupling between meteorological forcings and stream temperature or, conversely, of the presence of non-atmospheric thermal influences such as groundwater contributions to streamflow. Furthermore, thermal sensitivity is known to be responsive to environmental change. This study expands the current state of knowledge of stream thermal sensitivity in cold northern regions across catchment scales, investigates the environmental controls of thermal sensitivity across a range of catchment dispositions, and assesses the thermal influence of environmental conditions unique to cold regions, namely permafrost. We conducted a linear regression analysis relating modelled mean daily air and stream temperatures in 57 catchments in Yukon, Canada, with catchment areas ranging from 5.4 to 86 500 km 2 and with catchment mean permafrost probabilities ranging from 0.0 to 0.99. Thermal sensitivities obtained from the linear regressions ranged from 0.14 to 0.84 °C °C −1 , with a median of 0.56 °C °C −1 , and the regression intercepts ranged from −0.07 to 7.60 °C, with the mean regression Nash–Sutcliffe efficiency of 0.81. Thermal sensitivity was positively related to catchment area, land covers representing surface water storage, and streamflow flashiness or a lack of groundwater contributions. The greatest single environmental characteristic explaining the variance in thermal sensitivity was catchment topography (9 % variance explained); however, 39 % of the variance in thermal sensitivity was jointly explained by catchment physiography, land cover, and permafrost presence indicators, suggesting thermal sensitivity is the result of multiple interacting controls. The primary influence of permafrost on thermal sensitivity appeared to be indirect; permafrost controls on catchment properties affecting stream water residence time, subsurface water storage, and subsurface runoff processes provide separate and counteracting effects that are influencing thermal sensitivity.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.012
GPT teacher head0.215
Teacher spread0.203 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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