On the influence of extreme rainfall and antecedent soil moisture on flood hazards in Africa
Bibliographic record
Abstract
The African continent is severely impacted by floods, with an increasing vulnerability to these events in the most recent decades. Our improved preparation against and response to this hazard would benefit from an enhanced understanding of the physical processes at play. A database recently compiled on Africa allows to conduct studies at the continental scale: the African Database of Hydrometric Indices (ADHI: https://doi.org/10.5194/essd-2020-281). Daily river discharge data have been extracted for 399 African rivers to analyze the seasonality of observed annual maximum discharge. In addition, extreme precipitation from CHIRPS and ERA5, and soil moisture from ERA5-Land between 1981 and 2018 have been also considered as potential flood drivers. The database includes a total of 11,302 flood events, covering most African regions. The analysis is based on directional statistics to compare the annual maximum river discharge with annual maximum rainfall and soil moisture. Results show that the annual peak flow in most areas is more strongly associated with the annual peak of soil moisture than of extreme precipitation. In addition, the interannual variability of flood magnitudes is better explained by the variability of annual maximum soil moisture or the precipitation summed over 5 days prior to an event, than by changes in the annual maximum daily precipitation. These results have important implications for the design of efficient flood forecasting systems or the investigation of the long-term evolution of these hydrological hazards.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".