A Temperature-Duration-Curve model for the real-time estimation of extreme river water temperatures at ungauged sites
Bibliographic record
Abstract
Water temperature is an important environmental variable that has impacts on the physical, chemical, and biological processes in streamflows. Extreme river water temperatures affect the spawning, development and survival of several fish species, and are considered as important indicators of the health of a river and essential variables in all habitat models. Unfortunately, river water temperature data is characterised by its limited availability: measurement sites are often scarce, and records are regularly very short when available. It is hence crucial to develop regional thermal data estimation models for ungauged and partially gauged locations. Very few studies in the literature focused on the estimation of extreme water temperatures at sites where thermal data are limited or inexistent. A Temperature-Duration-Curve (TDC) model is proposed in this work to provide real-time estimates of river water temperature at ungauged locations during extreme events. The TDCs are estimated at the ungauged locations using a Generalised Additive Model and are then used to provide continuous estimates of river water temperature at these sites based on a spatial interpolation model. The model is developed based on a data base of 126 river thermal stations from Canada. The performance of the method is compared to a simpler approach and results indicate that the developed TDC model is robust and useful in practice.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".