Sensitivity analysis and calibration of a semi-distributed HBV model in the data-limited and regulated Nile River Basin
Bibliographic record
Abstract
Study region The Nile River Basin Study focus Developing hydrologic models for large transboundary basins characterized by significant spatial variability, complexity, and limited data is a particularly challenging task. This study conducts a parameter sensitivity analysis and multi-site calibration of the Nile River Basin (NRB) semi-distributed HBV hydrologic model emulated by the Raven framework. The sensitivity analysis included 40 hydrologic and routing parameters to identify the most significant parameters at nine subbasins using the Normalized Sensitivity Coefficient (NSC) method. We then investigated five different calibration approaches, which are based on subbasin types (natural or regulated), calibration procedure, and dam operation rules’ simulation method, at fifteen subbasin outlets. New hydrologic insights for the region The results revealed 21 significant parameters to be tuned in the model calibration and therefore indicate the most relevant data to be collected in the study area. Soil parameters, including hygroscopic minimum saturation, field capacity saturation, and topsoil thickness, were top-ranked in most subbasins, except the Lake Tana subbasin, where the lake control parameter was most significant. Model calibration and validation showed good performances at natural subbasins and Blue Nile’s regulated subbasins, where dam operation rules were available with adequate details. However, performance varied at other regulated subbasins where data limitation is more severe. The calibration approach, which involves separately tuning natural subbasin parameters and simulating the annual cycle of monthly streamflow, achieved the overall best performance. This study’s findings help guide future hydrologic modelling studies in the NRB and similar basins.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".