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Record W4392598237 · doi:10.5194/egusphere-egu24-3133

Cryosphere-fed rivers in a warming climate, 1950-205

2024· preprint· en· W4392598237 on OpenAlexaff
Dongfeng Li, Ting Zhang, Irina Overeem, Albert J. Kettner, D. E. Walling, Bodo Bookhagen, Amy E. East, Jim Best, Achim A. Beylich, Michèle Koppes, Jinren Ni, Stuart N. Lane

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCryosphereEnvironmental scienceClimatologyClimate changePhysical geographyAtmospheric sciencesGeologyGeographyOceanographySea ice

Abstract

fetched live from OpenAlex

Cryosphere-fed rivers drain glacier, snow, and permafrost landscapes and are characterized by glacial, nival, pluvial and mixed hydrological regimes. Such river systems originate from high-mountain areas and transport water, sediment, nutrients, and organic carbon downstream, underpinning the freshwater and coastal ecosystems and supporting the lives of more than one-third of the world’s population.In response to the amplified climate change, accelerating glacier-snow melt and permafrost thaw, the cryosphere-fed rivers are overall becoming warmer, wider and muddier associated with markedly increasing river turbidity and suspended sediment concentration. For instance, observational data from 28 headwater rivers in High Mountain Asia reveal that the river suspended sediment loads have been increasing at a rate of ~13% per decade since the 1950s, much faster than rate of increase of river water discharge (~5% per decade). Leveraging over 120 in-field observations and a sediment-climate elasticity model, we estimate that the present-day river suspended sediment load in High Mountain Asia is nearly two billion metric tons per year, and could more than double by 2050 under an extreme climate change scenario. Beyond High Mountain Asia, such warming-driven increases in river turbidity and suspended sediment concentrations have also widely featured in other cryospheric basins such as the Arctic, European mountains, and Andes.The muddier rivers carry pollutants, nutrients, and organic carbon, thus affecting water quality and aquatic ecosystems in the cold regions and beyond. Increases in sediment-driven river turbidity can threaten river biotic conditions by blocking sunlight from reaching the streambed, limiting respiration, and deteriorating feeding conditions of benthic macroinvertebrates and fishes, thereby affecting habitat availability. Elevated turbidity can disturb habitats of macroinvertebrates and fishes by filling interstitial spaces between pebble and cobbles on the riverbed, thereby reducing the flow of oxygenated water through bed sediment that is essential to the survival of their eggs. The increased sediment supply especially the coarse sediment further magnifies river channel instability and migration, affecting fish habitats and carbon storage and release.To better assess the impacts of changing climate on the functions and services of river ecosystems in strategically important cold regions, we highlight the pressing need to integrate multiple-sourced river observations, to develop empirical, physics-based, and AI-based river flux models, and to promote interdisciplinary scientific collaboration. The innovative system approach would best come from the creation of an interdisciplinary collaborative initiative, where climatologists, ecologists, glaciologists, permafrost scientists, hydrologists, civil engineers, and geomorphologists work together to establish an integrated cryosphere–water–sediment–carbon-ecology observation platform that facilitates the mechanism understanding and development of novel and powerful models. Furthermore, dialogues and collaboration between international scientists, stakeholders, local communities, and policymakers would help to bridge the gaps between state-of-the-art scientific findings and practicable adaptation strategies.

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 score0.043
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.025
GPT teacher head0.239
Teacher spread0.214 · 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
Published2024
Admission routes1
Has abstractyes

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