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Record W7043342921

Sediment yield and transport: estimation and climate influence

2019· other· en· W7043342921 on OpenAlexaboutno aff

Bibliographic record

VenueInstitutional Research Information System (Università degli Studi di Brescia) · 2019
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsHydrology (agriculture)SedimentDrainage basinDeposition (geology)Sediment transportSTREAMSLand useClimate changeSurface runoff
DOInot available

Abstract

fetched live from OpenAlex

Erosion, transfer and deposition of soil particles due to water and the impact of climate change on these physical processes have acquired a great importance during the last decades.Indeed, it is the subject of focused research in several fields of the earth sciences (such as hydrology, hydraulics, ecology, agriculture, geology, civil and environmental engineering, etc.), as the result of the continuous increase in hydrogeological risk in different geographical areas around the world, including Italy.Knowledge of the sediment volumes generated and transported by streams is useful, for example, for the successful design of hydraulic infrastructures, dams or reservoirs, for changes to forest waterways and terrain, for the land management and environment, etc. Sediment production at the catchment scale and solid transport in rivers can be assessed and quantified through mathematical and empirical models.Unfortunately, there is a shortage of gauged data regarding sediment fluxes and production at the catchment scale, because of topographical and pedological complexity and high spatial variability of all the hydrological characteristics.Another point that should be taken into account is the scarce availability of the appropriate equipment to make survey measurements.For these reasons, it is hard to implement monitoring techniques.However, in the absence of field data, major elements for land management, planning and protection include an estimate of soil loss and sediment yield and their comparison with other case studies.In the first part of this thesis project, a detailed sediment transport analysis in a reach of the Mimico Creek, which is located in Southern Ontario (Canada), was conducted by making use of the sediment transport investigations.A hydraulic model was developed and calibrated through the HEC-RAS software, by using the Wilcock and Crowe transport function, to a series of discharge events where in-situ bedload sampling occurred.Bedload samplings used in calibrating the transport model were limited by the orifice of the Helley-Smith bedload sampler (ranging between 0.5 mm and 32 mm).Calibration curves, that determine bed material transport rates as a function of the dimensionless reference shear stress, were created considering both step-wise discharge and unsteady flow simulations.The results of the calibrated model were used to calculate the mean travel distance of bed material: the goal was to compare the simulation results achieved to field observations, derived through the bed material particle tracking (RFID technique).The values achieved showed that the Wilcock and Crowe equation under-predicts the transport of the coarsest fractions in the bed load and that the travel distances calculated considering the BSTEM option (which also considers the presence of fine material from bank erosion) are longer.Finally, the travel distances of different grain sizes were estimated using the calibrated model results.The obtained values showed that particles have a steep reduction in travel distance with an increase in their size.Furthermore, transport distance values are higher for the flood events with higher peak discharges, generally in accordance with field measurements.The fractional transport distances of different grain classes estimated by using simulation results are lower than tracer surveys because in the field only the mobile particles were considered, Sediment yield and transport: estimation and climate influence

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.938
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0000.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.005

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 head0.047
GPT teacher head0.301
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 designNot applicable
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

Citations1
Published2019
Admission routes1
Has abstractyes

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