Deriving synthetic rating curves from a digital elevation model to delineate the inundated areas of small watersheds
Bibliographic record
Abstract
Two southern Quebec (Canada) watersheds were used to validate the proposed method for delineating inundated areas of small watersheds: the 554-km2 St.Charles and the 133 km2 À la Raquette watersheds. Observed data from six-gauge stations were used to validate the Synthetic Rating Curves (SRC) developed in the study. This research focuses on the application of the Height Above the Nearest Drainage (HAND) method to derive SRCs and support floodplain mapping in small watersheds. Accurate floodplain delineation is crucial of flood management, particularly in datascarce regions. This study presents a novel approach using (HAND) approach to precisely identify flood-prone areas. It involves generating (SRCs) connecting discharge and terrain derived hydraulic characteristics, integrated into PHYSITEL; a Geographic Information System (GIS) for distributed hydrological modeling. The Froude number established a consistent relationship between mean discharge and water depth across various discharge levels, defining unique hydraulic regimes. A Global Sensitivity Analysis quantified the uncertainty associated with SRC parameters, guiding calibration efforts to achieve biases below 20%. For both watersheds, postcalibration results showed SRCs with NRMSE values between 0.03 and 0.62. HANDSRC-based inundated areas corroborated the Quebec City flood risk zones well, with over 70% recall and 90% precision, validating its efficacy. These results contribute significantly to the region by providing SRCs for ungauged river sections and delineating first-hand floodplain maps.
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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 | 0.002 |
| 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) | 0.001 | 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".