Analysing the Performance of Various Radar-Rain Gauge Merging Methods for Modelling the Hydrologic Response of Upper Thames River Basin, Canada
Bibliographic record
Abstract
Accurate estimate of precipitation is of paramount importance for assessing the hydrologic response of a river basin. Weather radar data integrated with rain gauge measurements are applied to characterize the spatial feature of the storm event producing precipitation over the basin. Ordinary kriging of rain gauge data, mean field bias, Brandes spatial adjustment, conditional merging (CM), and local bias techniques are applied in this study to evaluate the performance of these radar-rain gauge merging methods for hydrologic modelling of the Upper Thames River basin (UTRb), south-western Ontario, Canada. Singularity-sensitive Bayesian merging method (SSBM) with a fine spatial resolution was also applied to retain the singularity character of the rainfall event. Rainfall–runoff simulations were carried out for three major storm events recorded in the UTRb using the HEC-HMS 4.0 hydrologic model. River flow analysis was performed for the comparison of results of HEC-RAS 4.1 hydraulic model with the observed rating curve. A novel methodology involving a dual-storage system is proposed to model three sub-basins of UTRb which displayed skewed and spiked observed runoff hydrographs. Using this dual-storage system for the three sub-basins it is found that CM and SSBM merging methods yielded optimal Nash-Sutcliffe efficiency coefficients for the prediction of runoff from these sub-basins.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".