FORECASTING QUARTER‐MONTHLY RIVERFLOW1

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Abstract
Recent developments with respect to transfer function‐noise models are reviewed and used to model and forecast quarter‐monthly (i.e., near‐weekly) natural inflows to the Lac St‐Jean reservoir in the Province of Quebec, Canada. The covariate series are rainfall and snowmelt, the latter being a novel derivation from daily rainfall, snowfall and temperature series. It is clearly demonstrated using the residual variance and the Akaike information criterion that modeling is improved as one starts with a deseasonalized ARMA model of the inflow series and successively adds transfer functions for the rainfall and snowmelt series. It is further demonstrated that the transfer function‐noise model is better than a periodic autoregressive model of the inflow series. A split‐sample experiment is used to compare one‐step‐ahead forecasts from this transfer function‐noise model with forecasts from other stochastic models as well as with forecasts from a so‐called conceptual hydrological model (i.e., a model which attempts to mathematically simulate the physical processes involved in the hydrological cycle). It is concluded that the transfer function‐noise model is the preferred model for forecasting the quarter‐monthly Lac St‐Jean inflow series.

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