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Record W4388496494 · doi:10.5194/egusphere-2023-2391

Overview: Cascading spatial, seasonal, and temporal effects of permafrost thaw on streamflow in changing nested Arctic catchments

2023· preprint· en· W4388496494 on OpenAlexaboutno aff
Alexa Hinzman, Ylva Sjöberg, Steve W. Lyon, Wouter R. Berghuijs, Ype van der Velde

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
FundersNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsBaseflowStreamflowEnvironmental sciencePermafrostHydrology (agriculture)SeasonalityDischargeDrainage basinFlood mythArcticSnowmeltClimatologyGeologySnowOceanographyGeographyEcologyGeomorphology

Abstract

fetched live from OpenAlex

Abstract. In the Arctic, the thawing of permafrost affects how catchments store and release water. However, the effects of thawing on the hydrological response remain poorly documented. In addition, it remains unclear how the effects of a thawing landscape will propagate through nested catchments. Here we investigate 10 nested catchments within the Yukon basin (Alaska and Canada) to study how permafrost thaw impacts catchments’ streamflow seasonality and storage-discharge relationships, and how these effects cascade through the nested catchments, from headwaters to downstream. Our results indicate that upstream catchments, characterized by continuous permafrost, have stronger streamflow seasonality and that these catchments also exhibit the most nonlinear storage-discharge relationships. Larger catchments downstream sustain year-round streamflow with baseflow continuing during winter. Since the 1950s flow regimes have become increasingly seasonal in the upstream catchments, with an earlier and more abrupt freshet, whereas further downstream flow seasonality has remained stable. Across the Yukon, storage-discharge relationships for 9 out of 10 sub-catchments have become increasingly nonlinear over time, with the biggest change occurring in the largest downstream catchments. In smaller catchments, each season has distinct recession characteristics, but those seasonal differences are not apparent further downstream. Upstream catchments are strongly influenced by localized change, whereas downstream catchments receive the effects of many different localized upstream impacts, making it difficult to detect a singular cause of change. Seasonal and long-term shifts in storage-discharge relationships are typically not accounted for by hydrological models and make accurate streamflow predictions more difficult. These shifts highlight how the changing landscape of the Arctic has far-reaching hydrological consequences.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.054
GPT teacher head0.280
Teacher spread0.225 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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