Combination of Global and Regional Hydrological Forecasts

In recent years, the number of large-scale hydrological forecasting systems has been steadily growing. This may lead to regions having numerous models spatially overlapping each other. Some of these regions have what we will refer to as a regional, more specialized, model for the area that performs generally better than their large-scale counterpart, considering the coarser spatial resolution and sometimes lack of calibration of the latter. Our work explored the possibility of using simple methods to retrieve hydrological information from a large-scale model, namely the National Surface and River Prediction System (NSRPS) that will eventually cover the Canadian territory, in order to improve the forecasts from a local system, namely the Système de Prévision Hydrologique (SPH) that covers most of the province of Quebec. Outputs from the two forecasting systems were thus combined using methods including the simple mean, a weighted average in which the weights are optimized either using the Kling-Gupta Efficiency (KGE) or the Continuous Ranked Probability Score (CRPS) as cost functions, or weights calculated from the residual errors of the forecasts. Bayesian Model Averaging (BMA) was also explored to combine the ensemble forecasts from both systems. The results show that it is possible to improve the local hydrological forecasts by using simple weighted combinations with forecasts from the large-scale system. Performance was assessed using many well-known criteria such as the Nash-Sutcliffe Efficiency (NSE), KGE and CRPS. Results were averaged over the 61 available gauging stations and analyzed at lead times ranging from 3 to 120 hours. We observed improvements in all criteria for lead times over 60 hours as well as no loss in performance at any lead times. Finally, the methods were also used in a leave-one-out setup to simulate performance on ungauged basins. The performance gain for ungauged basins is similar to that of the gauged basins, hinting at the fact that these simple methods could also improve forecasts in more remote territories where no measurements are available.