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Record W3168335259 · doi:10.5194/egusphere-egu21-16594

Sustained Impact of a Glacial Lake Outburst Flood on Winter Turbidity Regimes across the Land-Ocean Aquatic Continuum

2021· article· en· W3168335259 on OpenAlexaffabout
Ian Giesbrecht, Suzanne E. Tank, Justin Del Bel Belluz, Jennifer M. Jackson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of AlbertaSimon Fraser University
Fundersnot available
KeywordsBiogeochemical cycleEnvironmental scienceOceanographyFjordFlood mythWatershedClimate changeEcosystemHydrology (agriculture)GeologyEcologyGeography

Abstract

fetched live from OpenAlex

Rainforest rivers export large quantities of terrestrial materials from watersheds to the coastal ocean, with important implications for local ecosystems and global biogeochemical cycles. However, the impact of episodic disturbance on this process is a critical knowledge gap in our understanding of land-sea connections. Fjords represent a global hotspot for terrestrial carbon burial in marine sediments, yet the relative importance of typical riverine fluxes vs. mass wasting fluxes is uncertain and dynamic. Similarly, mass wasting events can generate both an instantaneous pulse and a sustained shift in the material export regime. Riverine sediment regimes also have important implications for freshwater ecosystems and fisheries resources. A recent mass wasting event in Bute Inlet – Homalco First Nation traditional territory and British Columbia, Canada – presents an important opportunity to quantify the sustained impact of such an infrequent large disturbance on the source-to-sink linkages between glacierized mountains, rivers, and fjords. On November 28, 2020, a landslide in the headwaters of the Elliot Creek watershed (118 km2) triggered a glacial lake outburst flood (GLOF) that eroded 3 km2 of forested land and exported large volumes of water and terrestrial materials to the lower reaches of the Southgate River watershed (1986 km2) and ultimately to the head of Bute Inlet. Here we assess river and ocean surface turbidity over four winter months following the event, in comparison to pre-event measurements taken across all seasons in recent years. River turbidity was measured on the Southgate River above and below the confluence of Elliot Creek, beginning in December 2020, and at the mouth of the Southgate and nearby Homathko Rivers prior to November 2020. Bute Inlet turbidity was measured (every month to two months) starting in May 2017. Prior to the GLOF event, Southgate River turbidity ranged from a low of 3.3 ± 0.4 FNU in the winter to a high of 71.4 FNU in the summer meltwater period. Since the event, Southgate River turbidity has been consistently elevated ≥6 times background levels recorded above Elliot Creek. At the extreme, on January 13, 2021, seven weeks after the GLOF, Southgate River mean turbidity (105.2 ± 3.3 FNU) was 32 times the background (3.3 ± 0.4 FNU), equating to a sustained increase in wintertime turbidity that sometimes exceeds the historical summertime peak. Given the typical coupling of turbidity with discharge, we expect further increases in turbidity with the coming freshet of 2021; the first meltwater season following the GLOF. These results suggest the potential for a sustained shift in the seasonal turbidity regime of the Southgate River and the estuarine waters of Bute Inlet. The elevated turbidity signals broader changes to: sediment export and carbon burial, the depth and seasonality of light penetration, river water quality, and spawning habitat quality for anadromous fish. Ongoing monitoring will be used to characterize the duration, dynamics, and potential recovery of elevated turbidity regimes across the land-to-ocean aquatic continuum in Bute Inlet.

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)0.000
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 score0.086
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.263
Teacher spread0.243 · 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
Published2021
Admission routes2
Has abstractyes

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