Simulating flooding for tropical cyclones in Southern Africa with SWOT
Bibliographic record
Abstract
Southern Africa has recently been impacted by flooding from a series of devastating tropical cyclones, including Idai in 2019 and Freddy in 2023. Estimating the risk of flooding from these extreme weather events has been hampered by a lack of observation data in the region, especially with respect to river flows and water levels. We present research from the REPRESA project, funded by the UK and Canadian governments under their climate adaptation and resilience (CLARE) program, which aims to improve the simulation of present-day and future tropical cyclone flooding in Southern Africa. Specifically, we present a flood inundation model driven by tropical cyclone simulations from kilometre-scale convection-permitting climate models, which has been calibrated to SWOT water height data. To set up the model, weather reanalysis from ERA5 and a hydrological model were used to simulate river discharge in the region. Given these discharges, river bathymetry was estimated from all available SWOT River SP node heights, providing a means to calibrate a large-scale LISFLOOD-FP flood inundation model. To facilitate the simulation of complex river systems with multi-threaded rivers, we link SWOT River SP node data to the recently released Global River Topology (GRIT) river network and Fathom DEM. Compound flooding along the coastline was simulated by linking to an ocean tide and surge model (ADCIRC). The resulting model was validated against flood extents observed by Sentinel 1 from Tropical Cyclone Idai.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".