Sediment yield and transport: estimation and climate influence
Bibliographic record
Abstract
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 PhD dissertation 1 ).The sediment yield for the rainfall event analysed was also estimated, using the MUSLE equation, for the 7 sub-basins identified in the Guerna catchment.The found values were used as input sediment load in the HEC-RAS model, that was developed for the Guerna Creek in order to simulate sediment transport.Wilcock and Crowe is the transport function that was chosen and the "Time-Area method" was adopted to create runoff hydrographs.The mean value of sediment discharge achieved is 6632 t/d and 66810 t/d, respectively at the upstream cross section and downstream cross section of the study reach.The downstream cross section is the Guerna watershed outlet, with an area of 30.9 km 2 .The upstream cross section is the outlet of its upstream sub-basin, with an area of 3 km 2 .Results are comparable to the measured value in the Rio Cordon catchment, a small mountain basin (5 km 2 ) in the northeastern Italian Alps with similar characteristics to the Guerna catchment: during an intensive flood event the sediment discharge recorded is 8040 t/d.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".