Streamflow forecasting using functional regression

- Functional linear models are adapted to the forecasting of streamflows.
- Total autumn streamflow are predicted using meteorological curves.
- The global shape of autumn streamflows is predicted using precipitations curves.
- Functional linear models are better than artificial neural network for predicting the global shape of a hydrograph.
- July precipitations can increase streamflows until the month of October.

SummaryStreamflow, as a natural phenomenon, is continuous in time and so are the meteorological variables which influence its variability. In practice, it can be of interest to forecast the whole flow curve instead of points (daily or hourly). To this end, this paper introduces the functional linear models and adapts it to hydrological forecasting. More precisely, functional linear models are regression models based on curves instead of single values. They allow to consider the whole process instead of a limited number of time points or features. We apply these models to analyse the flow volume and the whole streamflow curve during a given period by using precipitations curves. The functional model is shown to lead to encouraging results. The potential of functional linear models to detect special features that would have been hard to see otherwise is pointed out. The functional model is also compared to the artificial neural network approach and the advantages and disadvantages of both models are discussed. Finally, future research directions involving the functional model in hydrology are presented.