An approach for probabilistic forecasting of seasonal turbidity threshold exceedance

Though climate forecasts offer substantial promise for improving water resource oversight, additional tools are needed to translate these forecasts into water-quality-based products that can be useful to water utility managers. To this end, a generalized approach is developed that uses seasonal forecasts to predict the likelihood of exceeding a prescribed water quality limit. Because many water quality standards are based on thresholds, this study utilizes a logistic regression technique, which employs nonparametric or local estimation that can capture nonlinear features in the data. The approach is applied to a drinking water source in the Pacific Northwest United States that has experienced elevated turbidity values that are correlated with streamflow. The main steps of the approach are to (1) obtain a seasonal probabilistic precipitation forecast, (2) generate streamflow scenarios conditional on the precipitation forecast, (3) use a local logistic regression to compute the turbidity threshold exceedance probabilities, and (4) quantify the likelihood of turbidity exceedance corresponding to the seasonal climate forecast. Results demonstrate that forecasts offer a slight improvement over climatology, but that representative forecasts are conservative and result in only a small shift in total exceedance likelihood. Synthetic forecasts are included to show the sensitivity of the total exceedance likelihood. The technique is general and could be applied to other water quality variables that depend on climate or hydroclimate.