Effect of rating curve hysteresis on flood extent simulation with a 2D hydrodynamic model: A case study of the Inner Niger Delta, Mali, West Africa
Bibliographic record
Abstract
The Inner Niger Delta (IND) is a complex hydraulic system where the flood dynamics and connectivity between water bodies is the main driver for ecosystem services and economic activities. Therefore, it is of pivotal importance that hydraulic models used to assess ecosystem services and socio-economic usages in the IND are capable of capturing both the inundation and connectivity dynamics. A particularity of the IND is that a strong hysteresis effect can be observed in the stage-discharge relationships at all hydrometric stations in the area. However, existing hydrodynamic models of the IND typically use a static stage-discharge relationship as the downstream boundary condition during both the rise and recession of the flood, which leads to potential inaccuracies when trying to predict the flood extent. This paper explores how the simulation results of the flood and connectivity dynamics in the IND can be improved by using a looped rating curve at the downstream model boundary. The looped rating curve is described using the dimensionless discharges and water levels (DLRC) method. The results show that simulation with DLRC improves the accuracy in predicting floodplain extent and connectivity dynamics between the Niger river system and an important lake in the IND. The improvement in water level predictions decreased steadily with the distance from the downstream boundary of the modelled area.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".