Bias-corrected short-range Member-to-Member ensemble forecasts of reservoir inflow

Abstract

• We develop a short-term ensemble reservoir inflow forecasting system. • The ensemble attempts to sample all sources of uncertainty in the modeling chain. • Increasing the diversity of the ensemble greatly improves ensemble quality. • Bias correction of ensemble members offers significant additional improvement. • For the flashy case study basin, only recent errors are important in bias correction. A Member-to-Member ensemble forecasting system is developed for inflows to hydroelectric reservoirs that incorporates multiple numerical weather prediction models and multiple distributed hydrological models linked by a variety of downscaling schemes. Each hydrological model uses multiple differently-optimized parameter sets and begins each daily forecast from several different initial conditions. The ensemble thereby attempts to sample all sources of error in the modeling chain. The importance of sampling all sources of error is illustrated by comparing this ensemble with an ensemble comprised of single ‘best’ parameterization for each hydrological model. Degree-of-mass-balance bias correction schemes trained using data windows of varying lengths are applied to the individual ensemble members. Based on examination of various verification metrics, we determine that a bias corrector that uses a linearly-weighted combination of past errors calculated over a three-day moving window is able to significantly improve forecast quality for the flashy case study watershed in southwestern British Columbia, Canada. Incorporation of all sources of modeling uncertainty is found to greatly improve ensemble resolution and discrimination. The full potential for these improvements using ensembles is only realized after removal of bias.