Proving WATFLOOD: modelling the nonlinearities of hydrologic response to storm intensities
Bibliographic record
Abstract
This paper examines the effects of modelling the nonlinearities of hydrologic response to various storm intensities. Radar rainfall data, remotely sensed land use and land cover data, measured streamflows, and meteorological data were incorporated into the distributed flood forecasting model WATFLOOD to synthesize runoff hydrographs for three significant warm weather rainfall events occurring in 1995. The watershed selected for study was the 288 km2Duffins Creek drainage basin in southern Ontario. The effects of scaling radar rainfall amounts to match regional storm intensities on the synthesized streamflow hydrographs were examined. Computations and analysis were performed in agreement with widely accepted hydrologic principles and assumptions. The observed and synthesized hydrographs were compared using the unit hydrograph method. The observed and composite unit hydrographs matched extremely well in terms of shape, timing, and peak flow magnitude. These results indicated that WATFLOOD is capable of accurately modelling the nonlinear rainfallrunoff processes for increasing rainfall intensities with respect to peak flow, basin lag, and time to peak flow. However, the arbitrariness of assessing the effective rainfall and base-flow separation for the unit hydrograph method can lead to uncertainties in computing peak flow magnitudes. The grid element size and number and the drainage areas above streamflow gauges are of critical importance to the accuracy of the model.Key words: hydrology, watershed model, flood forecasting, hydrological modelling, model validation, unit hydrograph, nonlinear response.
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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 | 0.000 | 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 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".