Articles | Volume 22, issue 11
https://doi.org/10.5194/hess-22-5759-2018
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

Related authors

Hydroclimatic variability and predictability: a survey of recent research
Randal D. Koster, Alan K. Betts, Paul A. Dirmeyer, Marc Bierkens, Katrina E. Bennett, Stephen J. Déry, Jason P. Evans, Rong Fu, Felipe Hernandez, L. Ruby Leung, Xu Liang, Muhammad Masood, Hubert Savenije, Guiling Wang, and Xing Yuan
Hydrol. Earth Syst. Sci., 21, 3777–3798, https://doi.org/10.5194/hess-21-3777-2017,https://doi.org/10.5194/hess-21-3777-2017, 2017
Short summary
Hybridizing sequential and variational data assimilation for robust high-resolution hydrologic forecasting
Felipe Hernández and Xu Liang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-454,https://doi.org/10.5194/hess-2016-454, 2016
Manuscript not accepted for further review
Short summary

Related subject area

Subject: Hillslope hydrology | Techniques and Approaches: Modelling approaches
Recession discharge from compartmentalized bedrock hillslopes
Clément Roques, David E. Rupp, Jean-Raynald de Dreuzy, Laurent Longuevergne, Elizabeth R. Jachens, Gordon Grant, Luc Aquilina, and John S. Selker
Hydrol. Earth Syst. Sci., 26, 4391–4405, https://doi.org/10.5194/hess-26-4391-2022,https://doi.org/10.5194/hess-26-4391-2022, 2022
Short summary
Frozen soil hydrological modeling for a mountainous catchment northeast of the Qinghai–Tibet Plateau
Hongkai Gao, Chuntan Han, Rensheng Chen, Zijing Feng, Kang Wang, Fabrizio Fenicia, and Hubert Savenije
Hydrol. Earth Syst. Sci., 26, 4187–4208, https://doi.org/10.5194/hess-26-4187-2022,https://doi.org/10.5194/hess-26-4187-2022, 2022
Short summary
On the similarity of hillslope hydrologic function: a clustering approach based on groundwater changes
Fadji Z. Maina, Haruko M. Wainwright, Peter James Dennedy-Frank, and Erica R. Siirila-Woodburn
Hydrol. Earth Syst. Sci., 26, 3805–3823, https://doi.org/10.5194/hess-26-3805-2022,https://doi.org/10.5194/hess-26-3805-2022, 2022
Short summary
Spatiotemporal changes in flow hydraulic characteristics and soil loss during gully headcut erosion under controlled conditions
Mingming Guo, Zhuoxin Chen, Wenlong Wang, Tianchao Wang, Qianhua Shi, Hongliang Kang, Man Zhao, and Lanqian Feng
Hydrol. Earth Syst. Sci., 25, 4473–4494, https://doi.org/10.5194/hess-25-4473-2021,https://doi.org/10.5194/hess-25-4473-2021, 2021
Short summary
Estimation of rainfall erosivity based on WRF-derived raindrop size distributions
Qiang Dai, Jingxuan Zhu, Shuliang Zhang, Shaonan Zhu, Dawei Han, and Guonian Lv
Hydrol. Earth Syst. Sci., 24, 5407–5422, https://doi.org/10.5194/hess-24-5407-2020,https://doi.org/10.5194/hess-24-5407-2020, 2020
Short summary

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. 
Download
Short summary
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.