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Record W4321492139 · doi:10.5194/egusphere-egu23-1498

Warming-driven erosion and sediment transport in the world’s cold regions

2023· preprint· en· W4321492139 on OpenAlexaff
Ting Zhang, Amy E. East, Desmond E. Walling, Stuart N. Lane, Irina Overeem, Michèle Koppes, Xixi Lu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPermafrostEnvironmental scienceGlacierSedimentArcticErosionCryosphereCoastal erosionThermokarstHydrology (agriculture)Physical geographyOceanographyGeologySea iceGeomorphologyGeography

Abstract

fetched live from OpenAlex

The world’s cryospheric regions, ranging from high mountains to polar regions, have experienced unprecedented atmospheric warming, glacier melting and permafrost thawing since the mid-20th century. This rapid cryosphere degradation has dramatically altered terrestrial/coastal landscapes, characterized by creating debuttressed valleys and thermokarst hillslopes, expanding unstable landscapes, and increasing the access to sub-/pro-glacially stored sediment. Such rapid landscape changes have resulted in increases in erosion and sediment loads, posing threats to riverine and near-shore marine environments and triggering cascading impacts on water-food-energy securities which support the livelihoods of a quarter of the global population.Here, we present a global inventory of cryosphere degradation-driven increases in erosion and sediment yield, including 76 locations from the high Arctic, European mountains, High Mountain Asia and Andes, and 18 Arctic permafrost-coastal sites, collected from over 80 publications. This inventory confirms the widespread increase in sediment transport from cold regions in response to modern deglaciation. Moreover, we identify two to eight-fold increases in sediment fluxes and more than doubled coastal erosion rates in many cold regions between the 1950s and 2010s.Such increases in sediment evacuation from deglaciating/thermokarst regions have been blamed for introducing large amounts of carbon, nitrogen, and pollutants into aquatic ecosystems, impacting primary productivity, river biodiversity, and water quality. In high-mountain areas, increased sediment fluxes have also hampered hydropower exploitation through reservoir sedimentation and turbine abrasion. Meanwhile, accelerated erosion along ice-rich Arctic permafrost coasts has caused an irreversible land loss, costing billions of dollars for relocating or protecting coastal infrastructure.With continuous cryosphere degradation, sediment fluxes are likely to increase in the next decades until reaching a maximum (“Peak Sediment”). Theoretically, the timing of peak sediment can lag decades to hundreds of years behind the peak meltwater due to the remobilization of paraglacial and subglacial sediment legacy. Thereafter, sediment fluxes will decline as glacier/permafrost erosion ceases and active sediment contributing area shrinks. We predict that sediment-transport regimes will shift through three stages, from the ongoing temperature-dominated regime to a temperature-precipitation jointly controlled regime, eventually shifting toward a rainfall-dominated regime roughly between 2100-2200.However, the understanding of sediment dynamics in cold regions is still limited by the lack of long-term observations and the inherent complexity of geomorphic processes, such as episodic events, scale/threshold effects in sediment transport, and positive/negative feedbacks of geomorphic responses. To underpin the forward-looking mitigation strategies for climate-sensitive and fragile cold regions, we call for the enhancement of multi-source sediment monitoring programs, fully distributed physics-based sediment-yield models, and interdisciplinary-international scientific collaborations.

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 score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.091
GPT teacher head0.275
Teacher spread0.184 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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