Nested hydrological modeling for flood prediction using CMIP6 inputs around Lake Tana, Ethiopia
Bibliographic record
Abstract
Tana basin is the origin of the Blue Nile Basin located in Ethiopia. The Lake Tana’s mean annual precipitation is approximately 1400 mm/yr and its outflow drains an area of about 15,096 km2. There is limited effort to apply nested hydrological models for flood prediction due to being poorly gauged for validation. The objective of this study is to show how climate simulations can be used to generate reliable local predictions of flood and runoff for the Blue Nile in the Ethiopian highlands. In this study, outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were used to initialize a nested hydrological model to reveal long-term trends of runoff in flood-prone areas of the Lake Tana basin, Ethiopia. Available satellite and reanalysis datasets were used for selection of CMIP6 products and the five models with best validation were used to force a nested hydrological model: Known as Wflow_sbm. A model-independent multi-algorithm optimization estimation tool was implemented for calibration of Wflow with insitu observations. In terms of simulating runoff and flood events, application of Wflow_sbm to the Lake Tana basin gave promising results. This study serves as a major step towards the development and implementation of global model-driven nested hydrological assessments of flood risk in future projections for Lake Tana basin.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".