Evaluation of Statistical River Temperature Forecast Models for Fisheries Management
Bibliographic record
Abstract
Abstract Warming rivers and an improved knowledge of thermal impacts on fish are fueling a need for simple tools to generate water temperature forecasts that aid in decision making for the management of aquatic resources. Although there is strong evidence for temperature-dependent mortality in freshwater fish populations, the application of water temperature models for in-season fisheries management is still limited due to a lack of appropriate temperature thresholds and due to uncertainty in forecasts. We evaluated the ability of statistical models based on seasonal trends, air temperature, and discharge to produce daily forecasts of water temperature in the Fraser River, British Columbia, including explicit quantification of uncertainty in predictor variables. For all models evaluated (with and without air temperature and/or discharge predictor variables), the top model choice varied as a function of environmental conditions, uncertainty in the air temperature forecasts used to predict water temperature, and the selection of quantitative performance criteria (i.e., defining the “best” model based on the smallest mean raw error or based on the ability to accurately forecast extreme water temperatures). Water temperature forecasts averaged across 10 d produced by simple models that were fitted only to historical seasonal water temperature trends were as accurate as forecasts generated from uncertain air temperature predictions. Models fitted to air temperature were critical for forecasting high temperature thresholds; even the use of uncertain air temperature forecasts predicted high water temperatures with greater accuracy than models that lacked an air temperature covariate. In contrast, models that were fitted to discharge variables lowered the rate of false-negative and false-positive errors associated with estimating below-average temperatures. On the basis of our findings, we suggest that fisheries managers should quantify the effect of uncertainties in model predictor variables when assessing water temperature models and should evaluate model performance in the context of system-specific conditions and management objectives. Received May 15, 2013; accepted September 13, 2013 Published online January 31, 2014
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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 | 0.002 | 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".