Articles | Volume 19, issue 1
https://doi.org/10.5194/hess-19-275-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-19-275-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
16 Jan 2015
Research article |
| 16 Jan 2015
The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models
Water Engineering and Management, Faculty of Engineering Technology, University of Twente, P.O. Box 217, 7500 AE Enschede, the Netherlands
current address: Portland State University, Department of Civil & Environmental Engineering, 1930 S. W. 4th Avenue, Suite 200, Portland, OR 97201, USA
M. J. Booij
Water Engineering and Management, Faculty of Engineering Technology, University of Twente, P.O. Box 217, 7500 AE Enschede, the Netherlands
A. Y. Hoekstra
Water Engineering and Management, Faculty of Engineering Technology, University of Twente, P.O. Box 217, 7500 AE Enschede, the Netherlands
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Short summary
This paper investigates the skill of 90-day low-flow forecasts using three models. From the results, it appears that all models are prone to over-predict runoff during low-flow periods using ensemble seasonal meteorological forcing. The largest range for 90-day low-flow forecasts is found for the GR4J model. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low-flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions.
This paper investigates the skill of 90-day low-flow forecasts using three models. From the...
