Numerical modelling of dynamic flood topographies in the Terai region, Nepal.
Bibliographic record
Abstract
Rivers sourced from the Himalayas support ~10% of the global population living on the Indo-Gangetic Plain. These rivers can be a source of devastating floods. Flood hazard maps used to inform early warnings systems in the Terai region in southern Nepal are based on static, outdated DEMs, which may not reflect the current river and floodplain topography. Sediment dynamics can change the river course and the distribution of flow down large bifurcation nodes, affecting flood inundation extent. These processes are rarely considered in flood prediction models for this region. In this study, using a 2D depth-averaged hydrodynamic model, several flood scenarios for the Karnali River are investigated, including different DEMs, variable bed elevations, and a scenario with bed levels modified at an important bifurcation node to reflect field observations. Inundation extent varied by upto 14% between scenarios for a 1-in-20 year flood discharge. Our results suggest that combining regular field measurements of bed elevation, with updated DEMs, could help to improve future flood prediction maps. Updating model input parameters is particularly important following large flood events and/or large landslides in the upstream catchment, which could increase bed aggradation and provoke channel switching in highly mobile, alluvial river systems.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".