A new method to restore the impact of land‐use change on flood frequency based on the Hydrologic Engineering Center‐Hydrologic Modelling System model
Bibliographic record
Abstract
Abstract To reveal the impacts of land‐use change on flood frequency distribution, a method to contradiction restore the largest‐gauged annual flood series to current land‐use conditions was proposed, based on the Hydrologic Engineering Center‐Hydrologic Modelling System (HEC‐HMS), with a newly developed iterative asymptotic method to calibrate the model parameters. Using the Xixi Basin on the southeastern coast of China as a case‐study, the HEC‐HMS model was applied to forward restore the largest annual floods between 1956 and 2011 by using the land‐use conditions of 2010. The flood peak flow series derived from forward restoration were used for flood frequency analysis. The results showed that (a) the iterative asymptotic method could calibrate the initial loss ratio and wave velocity relatively well. The physical meaning of the parameter values obtained was clear. The overall model simulation result was satisfactory, with Nash–Sutcliffe efficiency coefficients of 0.827 and 0.843 in the calibration and verification periods, respectively. (b) The calibration method effectively addressed the difficulty in determining the model parameters needed for resolving the restoration of the impacts of land‐use changes on the largest‐gauged annual flood peak flows and provided a newer HEC‐HMS‐based restoration approach for nonstationary flood frequency analysis. (c) Urbanization in the Xixi Basin caused a degradation in forested and arable lands, as well as in grasslands. Its main impact on the flood frequency distribution was that the average flood peak flow increased from 2,633.32 to 2,889.48 m3s−1and the changes in the coefficient of variation and coefficient of skewness were very small.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".