Floodplain mapping based on derived synthetic rating curves linked to simulated streamflows.
Bibliographic record
Abstract
Rating curves are one of the most applied tools in hydrology for first instance flood analysis. However, there are often few of them in a watershed. One way to fill this gap is to derive synthetic rating curves using the conceptual model HAND (Height Above the Nearest Drainage). Indeed, this model computes the height in the nearest water course required for any location to be flooded using a Digital Elevation Model (DEM). To assess the sensitivity of the computed synthetic rating curves to its forcing parameters, a global sensitivity analysis (GSA) was performed using the VARS (Variogram Analysis of Response Surfaces) framework. Then, an a priori criteria of uniform flow such as Froude number and river reach slopes were added to identify the reaches where the model should be valid. HAND was implemented within PHYSITEL, a specialized GIS for distributed hydrological models. Synthetic rating curves were constructed using the geometric properties of a river segment and the Manning equation, providing a posteriori a mean of linking simulated stream flows to potential inundated areas. As part of the calibration process of the model, the GSA was conducted for four parameters (length of the river segment, Manning coefficients for water, forest and other), the variation of the vertical spatial resolution of the LiDAR data was considered as well for this study. This methodology was tested in two different watersheds in Quebec, Canada. Seven hydrometric stations were used as controls. The results showed that accurate synthetic rating curves can be derived with performance index such as PBIAS less than 20% and RMSE between (0.79m³/s and 10 m³/s) during the calibration. Therefore, for first instance, there is a potential to develop flood maps in areas without any hydrometric station or with a lack of high-quality bathymetric data as required by computationally intensive hydraulic models.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".