A Statistical Model for Managing Water Temperature in Streams with Anthropogenic Influences
Bibliographic record
Abstract
Abstract Streams in the Pacific Northwest (Oregon, Washington, British Columbia) face rising summer temperatures and increasing anthropogenic influence, with consequences for fish populations. Guidance is needed in small managed watersheds for setting reservoir release rates or for the restriction of water extractions to meet the needs of fish and aquatic ecosystems. Existing environmental flow methods focus on discharge rates and do not typically consider water temperatures, and detailed thermal models are too complex for widespread implementation. We used multiple logistic regression to develop statistical models for estimating the probability of exceeding a salmonid stream temperature threshold of 22 °C as a function of discharge and maximum daily air temperatures. Data required are air temperature, stream temperature and stream discharge over a minimum of one summer. The models are used to make minimum discharge recommendations under varying forecast weather conditions. The method was applied to nine streams in the Pacific Northwest. Minimum recommended discharge generally ranged from 23% to 86% of mean annual discharge and was higher than observed low flows in most streams. Comparison of the new method to existing methods for Fortune Creek in British Columbia indicated that total season discharge volumes could be reduced while meeting thermal requirements. For other streams, it was evident that high water temperatures cannot be managed by increasing discharge, as the discharge required would be greater than natural discharge and higher than achievable by management. The statistical method described in this paper allows for a risk‐based approach to discharge management for fish habitat needs.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".