Assessment of monthly to daily streamflow disaggregation methods: A case study of the Nile River Basin
Bibliographic record
Abstract
Observed streamflow data is critical for calibrating and validating hydrologic and water management models. However, the lack of such data with the required temporal accuracy is a common challenge, impacting the developed model's reliability and accuracy. Watershed dynamics are usually well captured at a daily time scale. Therefore, properly disaggregating streamflow data from larger into shorter time scales is a crucial area of research. Many disaggregation methods were previously investigated, resulting in various relative performances without a clear indication of which one to use. Furthermore, most of these methods were tested for disaggregating variables other than streamflow. This study aims to disaggregate monthly to daily streamflow data at 14 NRB major subbasins’ outlets, i.e., Target Stations (TSs). A total of eight disaggregation methods, classified into interpolation and proportionality methods, were iteratively investigated to identify the best performing for each TS. The interpolation methods performed very well, far better than the proportionality method. However, their performances decrease at stations with high daily streamflow fluctuations. The interpolation methods’ performances are similar in mimicking the daily values but significantly different in preserving the mass balance. Therefore, the latter aspect was mainly used to select the most adequate method to be applied at the TSs. In general, the mean preserving interpolation method (i.e., Lai 22) was found the best-performing approach for disaggregating monthly to daily streamflow in the NRB.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".