Machine Learning Forecasts to Reduce Risk of Entrainment Loss of Endangered Salmonids at Large-Scale Water Diversions in the Sacramento–San Joaquin Delta, California
- Author(s): Tillotson, Michael D.;
- Hassrick, Jason;
- Collins, Alison L.;
- Phillis, Corey
- et al.
Published Web Locationhttps://doi.org/10.15447/sfews.2022v20iss2art3
Incidental entrainment of fishes at large-scale state and federal water diversion facilities in the Sacramento-San Joaquin Delta, California, can trigger protective management actions when limits imposed by environmental regulations are approached or exceeded. These actions can result in substantial economic costs, and likewise they can affect the status of vulnerable species. Here, we examine data relevant to water management actions during January–June; the period when juvenile salmonids are present in the Delta. We use a quantile regression forest approach to create a risk forecasting tool, which can inform adjustments of diversions based on near real-time predictions. Models were trained using historical entrainment data (Water Years 1999–2019) for Sacramento River winter-run Chinook Salmon or Central Valley Steelhead and a suite of environmental and water operations metrics. A range of models was developed; their performance was evaluated by comparison of a quantile loss metric. The models were validated through examination of partial dependence plots, cross-validation procedures, and further evaluated through WY 2019 pilot testing, which integrated real-world uncertainty in environmental parameters into model predictions. For both species, the strongest predictor of loss was the previous week’s entrainment loss. In addition, risk increased with higher water exports and more negative Old and Middle Rivers (OMR) flows. Point estimates of loss were modestly correlated with observations (R2 0.4 to 0.6), but the use of a quantile regression approach provided reliable prediction intervals. For both species, the predicted 75th quantile appears to be a robust and conservative estimator of entrainment risk, with overprediction occurring in fewer than 20% of cases. This quantile balances the magnitude of over- and under-prediction and results in a low probability (< 5% of predictions) of unexpected high-take events. These models, and the web-based application through which they are made accessible to non-technical users, can provide a useful and complementary approach to the current system of managing entrainment risk.