Articles | Volume 23, issue 8
https://doi.org/10.5194/hess-23-3335-2019
https://doi.org/10.5194/hess-23-3335-2019
Research article
 | 
14 Aug 2019
Research article |  | 14 Aug 2019

Potential application of hydrological ensemble prediction in forecasting floods and its components over the Yarlung Zangbo River basin, China

Li Liu, Yue Ping Xu, Su Li Pan, and Zhi Xu Bai

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Cited articles

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Aminyavari, S., Saghafian, B., and Delavar, M.: Evaluation of TIGGE Ensemble Forecasts of Precipitation in Distinct Climate Regions in Iran, Adv. Atmos. Sci., 35, 457–468, 2018. 
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Arheimer, B., Lindström, G., and Olsson, J.: A systematic review of sensitivities in the Swedish flood-forecasting system, Atmos. Res., 100, 275–284, https://doi.org/10.1016/j.atmosres.2010.09.013, 2011. 
Arnal, L., Cloke, H. L., Stephens, E., Wetterhall, F., Prudhomme, C., Neumann, J., Krzeminski, B., and Pappenberger, F.: Skilful seasonal forecasts of streamflow over Europe?, Hydrol. Earth Syst. Sci., 22, 2057–2072, https://doi.org/10.5194/hess-22-2057-2018, 2018. 
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The ensemble flood forecasting system can skillfully predict annual maximum floods with a lead time of more than 10 d and has skill in forecasting the snowmelt-related components about 7 d ahead. The accuracy of forecasts for the annual first floods is inferior, with a lead time of only 5 d. The snowmelt-induced surface runoff is the most poorly captured component by the system, and the well-predicted rainfall-related components are the major contributor to good performance.