Articles | Volume 22, issue 11
Hydrol. Earth Syst. Sci., 22, 5759–5779, 2018
https://doi.org/10.5194/hess-22-5759-2018
Hydrol. Earth Syst. Sci., 22, 5759–5779, 2018
https://doi.org/10.5194/hess-22-5759-2018

Research article 09 Nov 2018

Research article | 09 Nov 2018

Hybridizing Bayesian and variational data assimilation for high-resolution hydrologic forecasting

Felipe Hernández and Xu Liang

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Manuscript not accepted for further review
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Cited articles

Adams, R. M., Houston, L. L., McCarl, B. A., Tiscareño, M. L., Matus, J. G., and Weiher, R. F.: The benefits to Mexican agriculture of an El Niño-southern oscillation (ENSO) early warning system, Agr. Forest Meteorol., 115, 183–194, https://doi.org/10.1016/S0168-1923(02)00201-0, 2003. 
Andreadis, K. M. and Lettenmaier, D. P.: Assimilating remotely sensed snow observations into a macroscale hydrology model, Adv. Water Resour., 29, 872–886, https://doi.org/10.1016/j.advwatres.2005.08.004, 2006. 
Bannister, R. N.: A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics, Q. J. Roy. Meteorol. Soc., 134, 1971–1996, https://doi.org/10.1002/qj.340, 2008. 
Bannister, R. N.: A review of operational methods of variational and ensemble-variational data assimilation, Q. J. Roy. Meteorol. Soc., 29, 1–29, https://doi.org/10.1002/QJ.2982, 2016. 
Beven, K.: A manifesto for the equifinality thesis, J. Hydrol., 320, 18–36, https://doi.org/10.1016/j.jhydrol.2005.07.007, 2006. 
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Predicting floods requires first knowing the amount of water in the valleys, which is complicated because we cannot know for sure how much water there is in the soil. We created a unique system that combines the best methods to estimate these conditions accurately based on the observed water flow in the rivers and on detailed simulations of the valleys. Comparisons with popular methods show that our system can produce realistic predictions efficiently, even for very detailed river networks.