Long-Lead Water Supply Forecast Using Large-Scale Climate Predictors and Independent Component Analysis

Abstract

Interest in water supply forecasting has grown prominently due to population growth and increasing demands for water. One important aspect of successfully managing the supply of water is accurate and reliable forecasts of seasonal streamflow volumes. Much of the streamflow in mountainous regions is a result of the collection of seasonal snowpack over the winter months and the melting of this snowpack over the spring and summer. However, there has been increasing pressure on operational agencies to issue longer-lead water supply forecasts that would be released in late fall or early winter preceding the runoff season. Longer-lead forecasts are difficult to make due to the uncertainty in future winter and spring climate conditions and the lack of snowpack information. During the late fall and early winter, large-scale oceanic and atmospheric information can provide insight into future climate conditions and spring runoff and have shown to be useful in developing long-lead forecast. In this study, statistical models are developed that incorporate large-scale climate signals into seasonal streamflow forecasting scheme. The objective of this study is to increase the skill of longer-lead water supply forecasts and also to increase the skill of current shorter-lead forecasts that rely on season-to-date hydroclimate information (e.g., precipitation since October, forecast month accumulated snowpack, and antecedent streamflow as a soil-moisture proxy). We employ a principal component regression model, with the addition of independent component analysis (ICA) climate predictors, for forecasting in two Pacific Northwest basins. It was found that incorporating large-scale climate predictors selected by ICA into a forecast model allowed a longer-lead-time forecast (beginning in September of previous year) and also contributed a significant amount of skill during the spring runoff season through April.