Articles | Volume 21, issue 11
Hydrol. Earth Syst. Sci., 21, 5493–5502, 2017
Hydrol. Earth Syst. Sci., 21, 5493–5502, 2017

Technical note 08 Nov 2017

Technical note | 08 Nov 2017

Technical note: Combining quantile forecasts and predictive distributions of streamflows

Konrad Bogner, Katharina Liechti, and Massimiliano Zappa Konrad Bogner et al.
  • Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland

Abstract. The enhanced availability of many different hydro-meteorological modelling and forecasting systems raises the issue of how to optimally combine this great deal of information. Especially the usage of deterministic and probabilistic forecasts with sometimes widely divergent predicted future streamflow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple streamflow forecast information will be aggregated based on several different predictive distributions, and quantile forecasts. For this combination the Bayesian model averaging (BMA) approach, the non-homogeneous Gaussian regression (NGR), also known as the ensemble model output statistic (EMOS) techniques, and a novel method called Beta-transformed linear pooling (BLP) will be applied. By the help of the quantile score (QS) and the continuous ranked probability score (CRPS), the combination results for the Sihl River in Switzerland with about 5 years of forecast data will be compared and the differences between the raw and optimally combined forecasts will be highlighted. The results demonstrate the importance of applying proper forecast combination methods for decision makers in the field of flood and water resource management.

Short summary
The enhanced availability of many different weather prediction systems nowadays makes it very difficult for flood and water resource managers to choose the most reliable and accurate forecast. In order to circumvent this problem of choice, different approaches for combining this information have been applied at the Sihl River (CH) and the results have been verified. The outcome of this study highlights the importance of forecast combination in order to improve the quality of forecast systems.