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Discussion papers | Copyright
https://doi.org/10.5194/hess-2017-431
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 11 Aug 2017

Research article | 11 Aug 2017

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Hydrology and Earth System Sciences (HESS).

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

Felipe Hernández and Xu Liang Felipe Hernández and Xu Liang
  • Civil and Environmental Engineering Department, University of Pittsburgh, Pittsburgh, 15213, United States of America

Abstract. The success of real-time estimation and forecasting applications based on geophysical models has been possible thanks to the two main frameworks for the determination of the models’ initial conditions: Bayesian data assimilation and variational data assimilation. However, while there have been efforts to unify these two paradigms, existing attempts struggle to fully leverage the advantages of both in order to face the challenges posed by modern high-resolution models – mainly related to model indeterminacy and steep computational requirements. In this article we introduce a hybrid algorithm called OPTIMISTS (Optimized PareTo Inverse Modeling through Integrated STochastic Search) which is targeted at non-linear high-resolution problems and that brings together ideas from particle filters, 4-dimensional variational methods, evolutionary Pareto optimization, and kernel density estimation in a unique way. Streamflow forecasting experiments were conducted to test which specific parameterizations of OPTIMISTS led to higher predictive accuracy. The experiments analysed two watersheds, one with a low resolution using the VIC (Variable Infiltration Capacity) model and one with a high-resolution using the DHSVM (Distributed Hydrology Soil Vegetation Model). By selecting kernel-based non-parametric sampling, non-sequential evaluation of candidate particles, and through the multi-objective minimization of departures from the streamflow observations and from the background states, OPTIMISTS was shown to outperform a particle filter and a 4D variational method. Moreover, the experiments demonstrated that OPTIMISTS scales well in high-resolution cases without imposing a significant computational overhead and that it was successful in mitigating the harmful effects of overfitting. With these combined advantages, the algorithm shows the potential to increase the accuracy and efficiency of operational prediction systems for the improved management of natural resources.

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Felipe Hernández and Xu Liang
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Felipe Hernández and Xu Liang
Felipe Hernández and Xu Liang
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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 is there 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 more realistic predictions efficiently, even for very detailed river networks.
Predicting floods requires first knowing the amount of water in the valleys, which is...
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