A probabilistic inflow forecasting system for operation of hydroelectric reservoirs in complex terrain

This dissertation presents a reliable probabilistic forecasting system designed to predict inflows to hydroelectric reservoirs. Forecasts are derived from a Member-to-Member (M2M) ensemble in which an ensemble of distributed hydrologic models is driven by the gridded output of an ensemble of numerical weather prediction (NWP) models. Multiple parameter sets for each hydrologic model are optimized using objective functions that favour different aspects of forecast performance. On each forecast day, initial conditions for each differently-optimized hydrologic model are updated using meteorological observations. Thus, the M2M ensemble explicitly samples inflow forecast uncertainty caused by errors in the hydrologic models, their parameterizations, and in the initial and boundary conditions (i.e., meteorological data) used to drive the model forecasts. Bias is removed from the individual ensemble members using a simple degree-of-mass-balance bias correction scheme. The M2M ensemble is then transformed into a probabilistic inflow forecast by applying appropriate uncertainty models during different seasons of the water year. The uncertainty models apply ensemble model output statistics to correct for deficiencies in M2M spread. Further improvement is found after applying a probability calibration scheme that amounts to a re-labelling of forecast probabilities based on past performance. Each component of the M2M ensemble has an associated cost in terms of time and/or money. The relative value of each ensemble component is assessed by removing it from the ensemble and comparing the economic gains associated with the reduced ensembles to those achieved using the full M2M system. Relative value is computed using a simple (static) cost-loss decision model in which the reservoir operator takes action (lowers the reservoir level) when significant inflows are predicted with probability exceeding some threshold. The probabilistic reservoir inflow forecasting system developed in this dissertation is applied to the Daisy Lake hydroelectric reservoir located in the complex terrain of southwestern British Columbia, Canada. The hydroclimatic regime of the case study watershed is such that flashy fall and winter inflows are driven by Pacific frontal systems, while spring and summer inflows are dominated by snow and glacier melt. Various aspects of ensemble and probabilistic forecast performance are evaluated over a period of three water years.

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