Quantifying the evolution of ensemble water temperature forecasts as a function of weather forecast lead-time: case study on the Nechako River watershed
Bibliographic record
Abstract
Producing and improving hydrological and hydrodynamic forecasts while accounting for uncertainty through a probabilistic approach is useful in various applications, such as for water temperature forecasting. To produce such ensembles, probabilistic meteorological forecasts can be fed into hydrological and water temperature models for different lead-times. This study aims to gauge the impact of the meteorological forecast quality on the accuracy and reliability of water temperature forecast ensembles generated through the HEC-RAS process-based hydrothermal model. The Nechako River, a managed river system in British Columbia, Canada, is used in this case study. The thermal forecasts were generated and evaluated from 2017 to 2020. The tested hypothesis is that improvements in the meteorological forecasts would result in reducing the uncertainty and improving the accuracy of the water temperature forecast ensembles at various lead-times. The results of this study show that the thermal forecasts were indeed improved in terms of their sharpness and their individual accuracy, but no significant impacts were noted over the accuracy of the ensembles. Reliability was also investigated, and it was revealed that water temperature forecast ensembles were initially under-dispersed over the Nechako River, and that this issue was exacerbated when the HEC-RAS model was forced with better quality ensemble weather forecasts. The presence of lakes along the river and the meteorological forecast’s reliability are considered and discussed as causes for this issue. Overall, it was concluded that a reduction of the meteorological inputs’ uncertainty did not improve the uncertainty representation of water temperature forecasts for this system.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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; both teacher heads agree on what is shown here.
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".