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Record W7047024874

Einfluss von Hangbewegungen auf Kohlenstoffspeicher und Küstenerosion in Permafrostgebieten

2015· dissertation· en· W7047024874 on OpenAlexaboutno aff

Bibliographic record

Venuepublish.UP (University of Potsdam) · 2015
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsnot available
Fundersnot available
KeywordsPermafrostErosionCoastal erosionMass wastingArcticThermokarstSoil carbonDeposition (geology)
DOInot available

Abstract

fetched live from OpenAlex

Accelerated permafrost thaw under the warming Arctic climate can have a significant impact on Arctic landscapes. Areas underlain by permafrost store high amounts of soil organic carbon (SOC). Permafrost disturbances may contribute to increased release of carbon dioxide and methane to the atmosphere. Coastal erosion, amplified through a decrease in Arctic sea-ice extent, may also mobilise SOC from permafrost. Large expanses of permafrost affected land are characterised by intense mass-wasting processes such as solifluction, active-layer detachments and retrogressive thaw slumping. Our aim is to assess the influence of mass wasting on SOC storage and coastal erosion. We studied SOC storage on Herschel Island by analysing active-layer and permafrost samples, and compared non-disturbed sites to those characterised by mass wasting. Mass-wasting sites showed decreased SOC storage and material compaction, whereas sites characterised by material accumulation showed increased storage. The SOC storage on Herschel Island is also significantly correlated to catenary position and other slope characteristics. We estimated SOC storage on Herschel Island to be 34.8 kg C m-2. This is comparable to similar environments in northwest Canada and Alaska. Coastal erosion was analysed using high resolution digital elevation models (DEMs). Two LIDAR scanning of the Yukon Coast were done in 2012 and 2013. Two DEMs with 1 m horizontal resolution were generated and used to analyse elevation changes along the coast. The results indicate considerable spatial variability in short-term coastline erosion and progradation. The high variability was related to the presence of mass-wasting processes. Erosion and deposition extremes were recorded where the retrogressive thaw slump (RTS) activity was most pronounced. Released sediment can be transported by longshore drift and affects not only the coastal processes in situ but also along adjacent coasts. We also calculated volumetric coastal erosion for Herschel Island by comparing a stereo-photogrammetrically derived DEM from 2004 with LIDAR DEMs. We compared this volumetric erosion to planimetric erosion, which was based on coastlines digitised from satellite imagery. We found a complex relationship between planimetric and volumetric coastal erosion, which we attribute to frequent occurrence of mass-wasting processes along the coasts. Our results suggest that volumetric erosion corresponds better with environmental forcing and is more suitable for the estimation of organic carbon fluxes than planimetric erosion. Mass wasting can decrease SOC storage by several mechanisms. Increased aeration following disturbance may increase microbial activity, which accelerates organic matter decomposition. New hydrological conditions that follow the mass wasting event can cause leaching of freshly exposed material. Organic rich material can also be directly removed into the sea or into a lake. On the other hand the accumulation of mobilised material can result in increased SOC storage. Mass-wasting related accumulations of mobilised material can significantly impact coastal erosion in situ or along the adjacent coast by longshore drift. Therefore, the coastline movement observations cannot completely resolve the actual sediment loss due to these temporary accumulations. The predicted increase of mass-wasting activity in the course of Arctic warming may increase SOC mobilisation and coastal erosion induced carbon fluxes.

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 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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.016
GPT teacher head0.270
Teacher spread0.254 · 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 designOther design
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
Published2015
Admission routes1
Has abstractyes

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