A Simplified Conceptual Model Using Global Open-Source Datasets to Provide Continental and Global Scale Fluvial Flood Risk.
Bibliographic record
Abstract
Hydraulic modelling is used to accurately model extreme flood events but comes with high computational costs, significant data requirements, and long simulation times. Increasing computational resources and higher-resolution data with more spatial coverage means that global-scale flood risk modelling capabilities are constantly evolving. Taking a nested approach, we used the HAND-SRC methodology to develop flood risk data at a continent-scale level and identify areas that would benefit from hydraulic modelling at a more granular level.Height Above Nearest Drainage (HAND) is a simplified method used for flood zoning and identifying areas at risk of flooding using a Digital Elevation Model (DEM), and drainage network – which can be derived from the DEM. The HAND-SRC method uses channel geometry estimates, obtained from the DEM, and the Manning’s equation, to develop synthetic rating curves (SRC) which allow the conversion of flood discharges to a water height. The flood height can then be combined with a HAND model to produce a flood map. Existing applications of HAND SRC include Central and Eastern Canada (Scriven et al. 2021), and rivers in Texas and North Carolina (Zheng et al. 2018), using national datasets. We applied the HAND-SRC methodology using Python and open-source global datasets, to create continental-scale flood risk maps for Europe and the US. The use of open-source global datasets and Python means the method has the potential to be applied anywhere globally.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".