Reference Time of Concentration Estimation for Ungauged Catchments
Bibliographic record
Abstract
Accurate modelling of flood flow hydrographs in ungauged catchments is a challenging task due to large errors in the estimation of its response time using existing empirical equations. The time of concentration (Tc) is a key catchment response time parameter needed for forecasting of the peak discharge rate and the timing of the flood event. At least eight different definitions have been presented in the literature for the time of concentration. In this study, a new definition of “Reference Tc” is presented along with a practical procedure for its estimation using readily available basin catchment characteristic parameters with the aim of standardizing this key parameter for practitioners. Nine different empirical models were calibrated and tested on nine catchments of the Credit River watershed, Ontario, Canada to determine which method would provide the most accurate prediction of the Reference Tc. The NRCS velocity method (1986) proved once again to be the most reliable and an accurate method. This study shows that the main reason for the higher accuracy of the NRCS velocity method predictions compared to the empirical equations is attributed to the inclusion of the Manning's roughness coefficient.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".