Large-scale flood modelling based on LiDAR data: a case study in the Southwest Miramichi watershed, New Brunswick, Canada
Bibliographic record
Abstract
With Canada’s river basins extending over almost 10 million km<sup>2</sup>, producing flood maps sufficiently accurate to be used for planning purposes at a reasonable cost is a challenge. Bathymetric data, in particular, are difficult and expensive to obtain and represent a major challenge for regional or global flood models as it is clearly not realistic to acquire river bathymetry data for large territories. In this study, we present an improved version of a large-scale flood modelling approach developed in Quebec, where bathymetry is estimated from Manning’s equation based on LiDAR data water surface slope, and apply it to network of 512 km of rivers in the Southwest Miramichi watershed in New Brunswick, Canada. Comparison with a HEC-RAS model that did not include bathymetry highlights how crucial it is to correctly assess the bed elevation. When compared to the measured discharge and water level data at a gauging station, the HEC-RAS model systematically overestimates flood levels (bias of 2.36 m) whereas for the large-scale flood model the bias is 0.09 m and the RMSE 0.11 m. The semi-automated large-scale methodology took about 1 hr/km and was able to simulate flood levels for the Southwest Miramichi watershed at an unprecedented accuracy at this 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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".