Estimation of Continuous Streamflow in Ontario Ungauged Basins: Comparison of Regionalization Methods
Bibliographic record
Abstract
Regionalization, a process of transferring hydrological information [i.e., parameters of a conceptual rainfall-runoff model, namely, the McMaster University-Hydrologiska Byråns Vattenbalansavdelning (MAC-HBV)] from gauged to ungauged basins, was applied to estimate continuous flows in ungauged basins across Ontario, Canada. To identify appropriate regionalization methods, different regionalization methods were applied, including the spatial proximity [i.e., kriging, inverse distance weighted (IDW), and mean parameters], physical similarity, and regression-based approaches. Furthermore, an approach coupling the spatial-proximity (IDW) method and the physical similarity approach is proposed. The analysis results show that the coupled regionalization approach as well as the IDW and kriging produce better model performances than the remaining three. Further investigations show that the coupled-regionalization approach provides slightly better performances than the other two spatial proximity methods. In addition, a modified Monte Carlo simulation method is used to assess the estimated flow confidence intervals. The prediction confidence intervals provide additional information on the range of variability of the simulated continuous streamflow in the ungauged basins, and this can be particularly useful for decision making, such as environmental flow determination in ungauged 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".