Quantifying the impact of bathymetry changes on flood  events for the Trois-Lacs Basin
Bibliographic record
Abstract
Floods are a major threat to the security of populations worldwide. Their impact is dependent on the flood extension and water levels, topographic factors, and many other variables. Meteorological factors such as precipitation, snow melt and base flow also influence the magnitude of flood events. Factors like river morphology, slopes, presence of flood plains, vegetation and soil types determine the response of the river or lake to meteorological conditions and hydrological events. Meteorological factors tend to be cyclical while topography is generally considered to remain constant over extended periods of time. However, river bathymetry is subject to changes over time due to sediment transport. For example, sediment re-positioning and accumulation can modify the bathymetry of water bodies. Furthermore, lakes or reservoirs receive and accumulate sediments transported from upstream which could influence flood levels. In this study we use a two-dimensional hydraulic model (Telemac2D) to simulate different flood scenarios coupled with several different bathymetries of Trois-Lacs Lake in the province of Quebec, Canada. Four bathymetries were obtained between November 2020 and August 2021 and 3 historical bathymetries were also provided (yrs, 1974, 2004, and 2019). To compare the bathymetries, total ‘available water volume’ is calculated, taking a common and constant reference water surface elevation. Streamflows entering the lake system were estimated using Hydrotel, a physics-based semi distributed hydrological model. These streamflows are used to calibrate the two-dimensional hydraulic model with measured water levels. The project may help to establish a direct relation between sediment shifting and deposition and water distribution for extreme flood events, while also allowing the local community to improve measures for civil security and land-use planning at a regional scale.
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 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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".