Combining Hydrologic Forecasts

Abstract
Forecasts of river flows are useful in optimizing the operation of multipurpose reservoir systems. Using two case studies, the usefulness of combination techniques for improving forecasts is examined. In the first study, a transfer function‐noise model, a periodic autoregressive model, and a conceptual model are employed to forecast quarter‐monthly river flows. These models all approach the modeling and forecasting problem from three different perspectives, and each has its own particular strengths and weaknesses. The forecasts generated by the individual models are combined in an effort to exploit the strengths of each model. The results of this case study indicate that significantly better forecasts can be obtained when forecasts from different types of models are combined. In the second study, periodic autoregressive models and seasonal autoregressive integrated moving average models are used to forecast monthly river flows. Combining the individual forecasts from these two statistical time series models does not result in significantly better forecasts.

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