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Record W7037824109

Einfluss des Klimas auf das Auftauen einer rückschreitenden Erosionsfront und die daraus resultierende Sediment- und Kohlenstofffreigabe in den Küstenbereich von Herschel Island, Kanada

2017· dissertation· en· W7037824109 on OpenAlexaboutno aff

Bibliographic record

Venuepublish.UP (University of Potsdam) · 2017
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsPermafrostSlumpThermokarstTundraSnowmeltHydrology (agriculture)Arctic
DOInot available

Abstract

fetched live from OpenAlex

The Yukon Coast in Canada is an ice-rich permafrost coast and highly sensitive to changing environmental conditions. Retrogressive thaw slumps are a common thermoerosion feature along this coast, and develop through the thawing of exposed ice-rich permafrost on slopes and removal of accumulating debris. They contribute large amounts of sediment, including organic carbon and nitrogen, to the nearshore zone. The objective of this study was to 1) identify the climatic and geomorphological drivers of sediment-meltwater release, 2) quantify the amount of released meltwater, sediment, organic carbon and nitrogen, and 3) project the evolution of sediment-meltwater release of retrogressive thaw slumps in a changing future climate. The analysis is based on data collected over 18 days in July 2013 and 18 days in August 2012. A cut-throat flume was set up in the main sediment-meltwater channel of the largest retrogressive thaw slump on Herschel Island. In addition, two weather stations, one on top of the undisturbed tundra and one on the slump floor, measured incoming solar radiation, air temperature, wind speed and precipitation. The discharge volume eroding from the ice-rich permafrost and retreating snowbanks was measured and compared to the meteorological data collected in real time with a resolution of one minute. The results show that the release of sediment-meltwater from thawing of the ice-rich permafrost headwall is strongly related to snowmelt, incoming solar radiation and air temperature. Snowmelt led to seasonal differences, especially due to the additional contribution of water to the eroding sediment-meltwater from headwall ablation, lead to dilution of the sediment-meltwater composition. Incoming solar radiation and air temperature were the main drivers for diurnal and inter-diurnal fluctuations. In July (2013), the retrogressive thaw slump released about 25 000 m? of sediment-meltwater, containing 225 kg dissolved organic carbon and 2050 t of sediment, which in turn included 33 t organic carbon, and 4 t total nitrogen. In August (2012), just 15 600 m? of sediment-meltwater was released, since there was no additional contribution from snowmelt. However, even without the additional dilution, 281 kg dissolved organic carbon was released. The sediment concentration was twice as high as in July, with sediment contents of up to 457 g l-1 and 3058 t of sediment, including 53 t organic carbon and 5 t nitrogen, being released. In addition, the data from the 36 days of observations from Slump D were upscaled to cover the main summer season of 1 July to 31 August (62 days) and to include all 229 active retrogressive thaw slumps along the Yukon Coast. In total, all retrogressive thaw slumps along the Yukon Coast contribute a minimum of 1.4 Mio. m? sediment-meltwater each thawing season, containing a minimum of 172 000 t sediment with 3119 t organic carbon, 327 t nitrogen and 17 t dissolved organic carbon. Therefore, in addition to the coastal erosion input to the Beaufort Sea, retrogressive thaw slumps additionally release 3 % of sediment and 8 % of organic carbon into the ocean. Finally, the future evolution of retrogressive thaw slumps under a warming scenario with summer air temperatures increasing by 2-3 ?C by 2081-2100, would lead to an increase of 109-114% in release of sediment-meltwater. It can be concluded that retrogressive thaw slumps are sensitive to climatic conditions and under projected future Arctic warming will contribute larger amounts of thawed permafrost material (including organic carbon and nitrogen) into the environment.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.035
GPT teacher head0.265
Teacher spread0.230 · 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 teacher head, not a consensus.

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
Published2017
Admission routes1
Has abstractyes

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