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Record W4392654408 · doi:10.5194/egusphere-egu24-17766

LiDAR assessment of sediment transport in a small, highly erosive alpine catchment

2024· preprint· en· W4392654408 on OpenAlexaboutno aff
Yassine Boukhari, Caroline Le Bouteiller, Antoine Łucas, Stéphane Jacquemoud, Sébastien Klotz, Mathilde Griffaton

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicSoil erosion and sediment transport
Canadian institutionsnot available
Fundersnot available
KeywordsHydrology (agriculture)ErosionLandslideDrainage basinDenudationWatershedGeologySediment transportSnowmeltVegetation (pathology)Surface runoffLidarStructural basinSedimentGeomorphologyPhysical geographyRemote sensingGeography

Abstract

fetched live from OpenAlex

With rates locally exceeding one centimeter of denudation per year [1,2], i.e., more than 100 t/ha/year, the Durance basin in the French Alps is one of the world’s most heavily eroding areas [3]. A combination of favorable conditions explains this phenomenon, including a very steep topography, sparse vegetation and a particular susceptibility of the Jurassic black marls, also called Terres Noires, to seasonal climatic forcing [4]. The Draix-Bléone observatory uses hydro-sedimentary stations to instrument several of these small, non-anthropized catchments, where hydrological responses to seasonal storms are rapid and intense [5]. The work presented combines the use of LiDAR time series from airborne, UAV and ground measurements, with sediment flux chronicles recorded at the outlet of the Laval catchment, a small steep watershed (0.86 m2), to address questions of upstream/downstream transport modalities. An assessment of the potential of remote sensing methods for attributing the contributions of each critical zone compartment (channel, gullies, landslides, etc.) to the erosive dynamics of this basin and its connectivity is carried out.References :[1] N. Mathys, S. Brochot, M. Meunier and D. Richard (2003). Erosion quantification in the small marly experimental catchments of Draix (Alpes de Haute Provence, France). Calibration of the ETC rainfall-runoff-erosion model. CATENA. 50(2–4):527–548. DOI : 10.1016/S0341-8162(02)00122-4.[2] A. Carriere, C. Le Bouteiller, G. E. Tucker, S. Klotz, and M. Naaim (2020). Impact of vegetation on erosion: Insights from the calibration and test of a landscape evolution model in alpine badland catchments. Earth Surf. Process. Landf., 45(5):1085–1099. DOI : 10.1002/esp.4741.[3] D. E. Walling (1998). Measuring sediment yield from river basins. in Soil Erosion Research Methods (R. Lal, Ed.). Soil and Water Conservation Society, Iowa, USA, pp 39–73.[4] L. Descroix and N. Mathys (2003). Processes, spatio-temporal factors and measurements of current erosion in the French Southern Alps: A review. Earth Surf. Process. Landf. 28( 9): 993–1011. DOI : 10.1002/esp.514.[5] Draix-Bleone Observatory. (2015). Observatoire hydrosédimentaire de montagne Draix-Bléone [Data set]. Irstea. DOI : 10.17180/obs.draix

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 score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.028
GPT teacher head0.263
Teacher spread0.235 · 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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