Combining Stochastic Weather Generation and Ensemble Weather Forecasts for Short-Term Streamflow Prediction

Abstract

Ensemble streamflow predictions (ESPs) offer great potential benefits for water resource management, as they contain key probabilistic information for analyzing prediction uncertainty. Ensemble weather forecasts (EWFs) are usually incorporated into ESPs to provide climate information. However, there is no simple way to combine both of them, since EWFs are generally biased and under-dispersed. This study presents a new short-term (1 to 7 lead days) probabilistic streamflow prediction system combining stochastic weather generation and EWFs. The bias and under-dispersion of EWFs were first corrected using a weather generator-based post-processing approach (GPP). The corrected weather forecasts were then coupled with a hydrological model for streamflow forecasts. The proposed GPP forecast was compared against two other forecasts, one using the raw EWF (GFS), and the other using a stochastic weather generator (WG). The comparison was carried out over two Quebec watersheds, using a set of deterministic and probabilistic verification metrics. The deterministic metrics showed that the GPP forecast is consistently the best at predicting the ensemble mean streamflow for both watersheds and for all the leads ranging between 1 and 7 days, followed by the WG forecast. The probabilistic metrics showed negative or near zero skill retained by the GFS forecast for the first 7 lead days. The WG system was much more skillful than the GFS. The GPP forecast consistently displayed the highest skill and reliability in terms of all the metrics applied. With increasing lead days, the skill and reliability of the GPP forecast tend to converge toward that of the WG forecast, indicating that the short-term GPP forecast could easily be linked to a pure WG forecast to extend the forecast horizon.