Evaluation of the Hybrid Air2stream Model for Simulating Daily Stream Temperature During Extreme Summer Heat Wave and Autumn Drought Conditions
Bibliographic record
Abstract
ABSTRACT As climatic conditions change globally, so too will stream thermal regimes, with implications for water quality and habitat suitability for aquatic life. Stream temperature measurements are sparse in many regions, motivating the development of models that are able to extrapolate to past and future climatic conditions to support decision‐making for aquatic resource management. This study assesses the performance of air2stream, a hybrid, at‐a‐site stream temperature model that was developed to simplify the data requirements of process‐based models while maintaining their predictive performance. The air2stream model requires only time series of daily mean air temperature and stream discharge as input variables, and was calibrated for 23 streams in British Columbia, Canada, using data recorded at Water Survey of Canada gauging stations for the available periods of record up to 2020. Daily mean air temperature time series were interpolated to each monitoring site from the ERA‐5 gridded surface data product. Air2stream was validated with data from the years 2021 and 2022, which included an extreme summer heat wave and autumn drought conditions that fall outside the range of conditions observed during the calibration period. The validation results were compared to those of a set of linear mixed‐effects models with the same predictor variables, as well as a simplified version of air2stream that only uses air temperature as an input variable. The air2stream model produced higher errors during the extreme weather conditions compared to the calibration period, though its performance under extreme conditions overall remained superior to that of the statistical models and the simplified air2stream model. The results highlight the importance of representing hydrological and thermal processes and their seasonal variation in models for predicting stream temperature under changing climatic conditions.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".