A recurrent neural networks approach using indices of low-frequency climatic variability to forecast regional annual runoff

This paper evaluates the potential of using low-frequency climatic mode indices to forecast regional annual runoff in northern Quebec and the Labrador region. The impact of climatic trends in the forecast accuracy is investigated using a recurrent neural networks (RNN) approach, time-series of inflow to eight large hydropower systems in Quebec and Labrador, and indices of selected modes of climatic variability: El Nino-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Pacific-North American (PNA), Baffin Island-West Atlantic (BWA) and sea-level pressure (SLP) at Iceland. A wavelet analysis is used to show that the selected climatic patterns are related to annual runoff from 1950 to 1996 in northern Quebec. The forecast results indicate that the use of BWA, PNA and ENSO indices results in better forecast skill than the use of SLP or NAO. Overall, the use of the BWA index is found to provide the best forecast improvement (38% on average), whereas the use of PNA provides 28% of improvement on average. Using the SLP index improves the forecast accuracy by 4%, and the use of an ENSO indicator leads to an improvement of 6%. The NAO index used here is found to provide only a modest improvement.