Abstract
The ensemble Kalman filter (EnKF) recursively integrates field data into simulation models to obtain a better characterization of the model’s state and parameters. These are generally estimated following a state-parameters joint augmentation strategy. In this study, we introduce a new smoothing-based joint EnKF scheme, in which we introduce a one-step-ahead smoothing of the state before updating the parameters. Numerical experiments are performed with a two-dimensional synthetic subsurface contaminant transport model. The improved performance of the proposed joint EnKF scheme compared to the standard joint EnKF compensates for the modest increase in the computational cost.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Gharamti, M.E., Kadoura, A., Valstar, J., Sun, S., Hoteit, I.: Constraining a compositional flow model with flow-chemical data using an ensemble-based Kalman filter. Water Resour. Res. 50, 2444–2467 (2014)
Gharamti, A., Valstar, J., Hoteit, I.: An adatpive hybrid EnKF-OI scheme for efficient state-parameter estimaton of reactive contaminant transport models. Adv. Water Resour. 71, 1–15 (2014)
Gharamti, M.E., Hoteit, I.: Complex step-based low-rank extended Kalman filtering for state-parameter estimation of subsurface transport models. J. Hydrol. 509, 588–600 (2013)
Hendricks-Franssen, H., Kinzelbach, W.: Real-time groundwater flow modeling with the ensemble kalman filter: Joint estimation of states and parameters and the filter inbreeding problem. Water Resour. Res. 44, W09408 (2008)
Gómez-Hernández, J.J., Journel, A.G.: Joint sequential simulation of multigaussian fields. Geostatistics Troia. 92, 85–94 (1993)
Li, L., Zhou, H., Gómez-Hernández, J.J., Hendricks-Franssen, H.-J.: Jointly mapping hydraulic conductivity and porosity by assimilating concentration data via ensemble kalman filter. J. Hydrol. 428, 152–169 (2012)
Moradkhani, H., Sorooshian, S., Gupta, H.V., Houser, P.R.: Dual state-parameter estimation of hydrological models using ensemble Kalman filter. Adv. Water Resour. 28(2), 135–147 (2005)
Desbouvries, F., Petetin, Y., Ait-El-Fquih, B.: Direct, prediction- and smoothing-based kalman and particle filter algorithms. Signal Process. 91(8), 2064–2077 (2011)
Lee, W., Farmer, C.: Data assimilation by conditioning of driving noise on future observations. IEEE Trans. Signal Process. 62(15), 3887–3896 (2014)
Reichle, R.H., McLaughlin, D.B., Entekhabi, D.: Hydrologic data assimilation with the ensemble Kalman filter. Mon. Weather Rev. 130(1), 103–114 (2002)
Smidl, Y., Quinn, A.: Variational Bayesian filtering. IEEE Trans. Signal Process. 56, 5020–5030 (2008)
Smidl, Y., Quinn, A.: The Variational Bayes Method in Signal Processing. Springer, Heidelberg (2006)
Cohn, S.E., Sivakumaran, N.S.: Ricardo todling.: a fixed-lag kalman smoother for retrospective data assimilation. Mon. Weather Rev. 122, 2838–2867 (1994)
Polson, N.G., Stroud, J.R., Müller, P.: Practical filtering with sequential parameter learning. J. Roy. Stat. Soc. 70(2), 413–428 (2008)
Cuzol, A., Mémin, E.: Monte Carlo fixed-lag smoothing in state-space models. Nonlin. Process. Geophys. 21, 633–643 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Gharamti, M.E., Ait-El-Fquih, B., Hoteit, I. (2015). A One-Step-Ahead Smoothing-Based Joint Ensemble Kalman Filter for State-Parameter Estimation of Hydrological Models. In: Ravela, S., Sandu, A. (eds) Dynamic Data-Driven Environmental Systems Science. DyDESS 2014. Lecture Notes in Computer Science(), vol 8964. Springer, Cham. https://doi.org/10.1007/978-3-319-25138-7_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-25138-7_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25137-0
Online ISBN: 978-3-319-25138-7
eBook Packages: Computer ScienceComputer Science (R0)