Enhancing Ensemble Seasonal Streamflow Forecasts in the Upper Colorado River Basin Using Multi-Model Climate Forecasts

Abstract

In the Colorado River Basin (CRB), ensemble streamflow prediction (ESP) forecasts drive operational planning models that project future reservoir system conditions. CRB operational seasonal streamflow forecasts are produced using ESP, which represents climate using an ensemble of meteorological sequences of historical temperature and precipitation, but do not typically leverage additional real-time subseasonal-to-seasonal climate forecasts. Any improvements to streamflow forecasts would help stakeholders who depend on operational projections for decision making. We explore incorporating climate forecasts into ESP through variations on an ESP trace weighting approach, focusing on Colorado River unregulated inflows forecasts to Lake Powell. The k-nearest neighbors (kNN) technique is employed using North American Multi-Model Ensemble one- and three-month temperature and precipitation forecasts, and preceding three-month historical streamflow, as weighting factors. The benefit of disaggregated climate forecast information is assessed through the comparison of two kNN weighting strategies; a basin-wide kNN uses the same ESP weights over the entire basin, and a disaggregated-basin kNN applies ESP weights separately to four subbasins. We find in general that climate-informed forecasts add greater marginal skill in late winter and early spring, and that more spatially granular disaggregated-basin use of climate forecasts slightly improves skill over the basin-wide method at most lead times.