Improving calibration of two key parameters in Hydrologic Engineering Center hydrologic modelling system, and analysing the influence of initial loss on flood peak flows
Bibliographic record
Abstract
Parameter calibration is a key and difficult issue for a hydrological model. Taking the Jinjiang Xixi watershed of south-east China as the study area, we proposed methods to improve the calibration of two very sensitive parameters, Muskingum K and initial loss, in the Hydrologic Engineering Center hydrologic modelling system (HEC-HMS) model. Twenty-three rainstorm flood events occurring from 1972 to 1977 were used to calibrate the model using a trial-and-error approach, and a relationship between initial loss and initial discharge for these flood events was established; seven rainstorm events occurring from 1978 to 1979 were used to validate the two parameters. The influence of initial loss change on different return-period floods was evaluated. A fixed Muskingum K value, which was calibrated by assuming a flow wave velocity at 3 m/s, could be used to simulate a flood hydrograph, and the empirical power-function relationship between initial loss and initial discharge made the model more applicable for flood forecasting. The influence of initial loss on peak floods was significant but not identical for different flood levels, and the change rate of peak floods caused by the same initial loss change was more remarkable when the return period increased.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".