Combining large-scale and regional hydrological forecasts using simple methods

Abstract

The development and expanded application of large-scale hydrological models has produced forecasts that often overlap with more targeted, regional hydrological forecasts. Here the possibility is explored for using simple methods to combine forecasts from a large-scale model, the Great Lakes portion of the National Surface and River Prediction System (NSRPS), and a regional system, the Système de Prévision Hydrologique (SPH) which covers southern Quebec, to improve regional forecasts. Outputs from the two forecasting systems are combined using multiple methods, including the simple mean, a weighted average in which the weights are optimized using the Kling-Gupta Efficiency (KGE), the Reduced Continuous Ranked Probability Score (RCRPS), and Ignorance Score (IGN) as cost functions, and weights calculated from the residual errors of the models. Bayesian Model Averaging (BMA) is also used to combine the probabilistic forecasts from both systems. The results show that it is possible to improve regional hydrological forecasts by using simple weighted combinations with forecasts from the large-scale system, even though the regional system performs clearly better. Performance is assessed via many well-known metrics, such as Nash-Sutcliffe Efficiency (NSE), KGE, RCRPS, and IGN. Results are averaged over 40 gauging stations and analyzed at lead times from 3 to 120 h. Improvements in all criteria for lead times over 60 h are observed, and there is no loss in performance at any lead times. Finally, the methods are used in a leave-one-out setup containing 29 validation basins to simulate performance on ungauged basins. The performance gain for ungauged basins is similar to that of the gauged basins, demonstrating that these simple methods can also improve forecasts in more remote territories where no gauging is available.