Skillful mid-term temperature predictions (up to five years out) offer a potential opportunity for water managers, especially in the Colorado River Basin (CRB), where streamflows are sensitive to temperature. The purpose of this paper is to develop and demonstrate a framework for how mid-term temperature predictions can be incorporated into streamflow forecasting and operational projections. The framework consists of three steps. First, 5-year average temperature predictions are obtained from two large ensemble climate model datasets. Second, hindcasts from the Ensemble Streamflow Predictions (ESP), an operationally used forecast method in the CRB, are post-processed using the 5-year average temperature predictions; specifically, a tercile-based block bootstrap resampling approach generates weighted streamflow ensembles called WeighESP. Third, ESP and WeighESP are run through an operational model, the Colorado River Mid-term Modeling System (CRMMS). Compared to ESP, WeighESP marginally improves streamflow forecast accuracy in the multi-year hindcasts up to five years out (i.e., years 1-5, 2-5, 2-4, and 2-3). The multi-year hindcasts show median annual root mean square error (RMSE) improvements between 437,000 and 771,000 m3 (354 and 625 thousand acre-feet). Improvements in streamflow accuracy are more pronounced for the most recent hindcast run dates through 2016, partially due to ESP being run with climate time series data from 1981 to 2010. Next, CRMMS translates the streamflow forecasts into operational projections of end of calendar year (EOCY) pool elevations. WeighESP improves the accuracy of EOCY predictions, but mainly for longer leads of 3- and 4-years. For the 4-year lead, the median RMSE improves by 1.1 and 0.7 m (3.5 and 2.3 ft) for Lakes Powell and Mead, respectively. Although marginal improvements in pool elevation could be beneficial, not being realized until longer leads is a limitation. This study describes the need for better predictive tools at the mid-term timescale and underscores the importance of evaluating improvements in streamflow forecasts in decision-relevant terms.
