Potential effects of long-lead hydrologic predictability on Missouri River main-stem reservoirs

Abstract

Understanding the links between remote conditions, such as tropical sea surface temperatures, and regional climate has the potential to improve streamflow predictions, with associated economic benefits for reservoir operation. Better definition of land surface moisture states (soil moisture and snow water storage) at the beginning of the forecast period provides an additional source of streamflow predictability. The value of long-lead predictive skill added by climate forecast information and land surface moisture states in the Missouri River basin is examined. Forecasted flows were generated that represent predictability achievable through knowledge of climate, snow, and soil moisture states. For the current main-stem reservoirs (90 × 109 m3 storage volume) only a 1.8% improvement in hydropower benefits could be achieved with perfect forecasts for lead times up to one year. This low value of prediction skill is due to the system\u27s large storage capacity relative to annual inflow. To evaluate the effects of hydrologic predictability on a smaller system, a hypothetical system was specified with a reduced storage volume of 36 × 109 m3. This smaller system showed a 7.1% difference in annual hydropower benefits for perfect forecasts, representing $25.7 million. Using realistic streamflow predictability,$6.8 million of the $25.7 million are realizable. The climate indices provide the greatest portion of the$6.8 million, and initial soil moisture information provides the largest increment above climate knowledge. The results demonstrate that use of climate forecast information along with better definition of the basin moisture states can improve runoff predictions with modest economic value that, in general, will increase as the size of the reservoir system decreases