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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2016-454
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
20 Sep 2016
Review status
This discussion paper has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.
Hybridizing sequential and variational data assimilation for robust high-resolution hydrologic forecasting
Felipe Hernández and Xu Liang Civil and Environmental Engineering Department, University of Pittsburgh, Pittsburgh, 15213, United States of America
Abstract. There are two main frameworks for the estimation of initial states in geophysical models for real-time and forecasting applications: sequential data assimilation and variational data assimilation. However, modern high-resolution models offer challenges, both in terms of indeterminacy and computational requirements, which render most traditional methods insufficient. In this article we introduce a hybrid algorithm called OPTIMISTS which combines advantageous features from both of these data assimilation perspectives. These features are integrated with a multi-objective approach for selecting ensemble members to create a probabilistic estimate of the state variables, which promotes the reduction of observational errors as well as the maintenance of the dynamic consistency of states. Additionally, we propose simplified computations as alternatives aimed at reducing memory and processor requirements. OPTIMISTS was tested on two models of real watersheds, one with over 1,000 variables and the second with over 30,000, on two distributed hydrologic modelling engines: VIC and the DHSVM. Our tests, consisting of assimilating streamflow observations, allowed determining which features of the traditional approaches lead to more accurate forecasts while at the same time making an efficient use of the available computational resources. The results also demonstrated the benefits of the coupled probabilistic/multi-objective approach, which proved instrumental in reducing the harmful effects of overfitting – especially on the model with higher dimensionality.

Citation: Hernández, F. and Liang, X.: Hybridizing sequential and variational data assimilation for robust high-resolution hydrologic forecasting, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-454, 2016.
Felipe Hernández and Xu Liang
Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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RC1: 'Review of HESS-2016-454', Anonymous Referee #1, 25 Oct 2016 Printer-friendly Version 
AC1: 'Response to referees', Felipe Hernández, 14 Dec 2016 Printer-friendly Version 
 
RC2: 'Review', Anonymous Referee #2, 01 Nov 2016 Printer-friendly Version 
AC2: 'Response to referees', Felipe Hernández, 14 Dec 2016 Printer-friendly Version 
Felipe Hernández and Xu Liang
Felipe Hernández and Xu Liang

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Short summary
We combine the best characteristics of the two established paradigms (sequential and variational) for estimating the initial conditions of geophysical models. Our algorithm also includes the ability to balance the physical consistency of the model with the fit to the observed data, and provides simplified approaches to enable running high-resolution simulations while making an efficient use of the computational resources. We validate the advantages of our approach on two hydrologic models.
We combine the best characteristics of the two established paradigms (sequential and...
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