Towards Improved Flash Flood Forecasting over Dire Dawa, Ethiopia Using WRF-Hydro
Bibliographic record
Abstract
Flash floods are increasingly frequent worldwide. Recent flooding in eastern Ethiopia resulted in casualties, destruction of property and interruptions of service. National flash flood forecasts made today primarily consider precipitation, putting less emphasis on surface processes. Enhancing accurate flash flood forecasts by accounting for surface processes and hydrological models together with a deeper understanding of heavy precipitation mechanisms is of paramount importance. To this end, an uncoupled WRF-Hydro model was calibrated for eastern Ethiopia to simulate extreme floods. Sensitivity analysis for August 2006 showed that infiltration runoff, hydraulic soil conductivity and saturated volumetric soil moisture with parameter values of 0.1, 1.5 and 1.0 produced realistic streamflow distribution. Extreme floods in March 2005 and April 2007 were further studied. The results showed that WRF-Hydro replicates temporal and spatial patterns well. Analysis using observational/reanalysis data revealed associated physical processes. Precipitation during these events exceeded long-term climatology and spanned wider areas in eastern Ethiopia. These heavy precipitation events are associated with strong upper-level westerly jet streams and rainfall-conducive circulation anomalies at lower levels. Positive outcomes from WRF-Hydro suggest operational implementation for flood monitoring and early warning systems in forecasting centers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".