Flood Inundation Modeling of the Gulin River Using HEC-RAS
Bibliographic record
Abstract
Residents in hilly areas often select sites for housing on floodplains, riverbanks, and similar locations, which makes them highly vulnerable to casualties and property damage during flood events. Flood inundation analysis for mountainous rivers can provide critical data for urban flood prevention and disaster mitigation. This study takes the mountainous Gulin River as a case study, employing the hydrological analogy method and the inference formula method to calculate design peak discharges for recurrence intervals of 2, 5, 10, 20, and 50 years. Comparative analysis demonstrated that the hydrological analogy method yields results that are more representative of the design peak discharges across various river segments. Utilizing the derived design flood data and measured DEM data, a hydrodynamic model was developed using HEC-RAS for the Jinlan and Yongle reaches of Gulin River. The model computed key hydraulic parameters, including the inundation elevation and flow velocity under different recurrence intervals. The flood inundation extents were subsequently delineated using ArcGIS. The simulation results indicate that: (1) the study reaches begin to experience bank inundation under the 10-year flood scenario; (2) the maximum inundation depths for Jinlan reach reach 6.74 m, 7.81 m, and 8.92 m for 10-, 20-, and 50-year floods respectively; (3) the Yongle reach exhibits more severe flooding with maximum depths of 9.63 m, 11.45 m, and 16.77 m for the corresponding scenarios. The inundation depth in residential areas ranges from 0 to 2 m, with Xinhe Village and Yongle Town being the most severely affected; and (4) Building upon the one-dimensional model, two-dimensional hydrodynamic modeling was conducted for the river bend near Xinhe Village and the river reach near Yongle Town. The results indicate that particular attention should be given to floodplain inundation on the inner side of the bend at Xinhe Village, where flow deceleration may lead to extensive overbank flooding. In Yongle Town, riverside buildings and infrastructure are exposed to significant inundation risks under medium- to high-return period flood events. To mitigate these risks, structural flood protection measures such as levee construction along the riverbanks are recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".