Assessing sequential data assimilation techniques for integrating GRACE data into a hydrological model

Handle URI:
http://hdl.handle.net/10754/625176
Title:
Assessing sequential data assimilation techniques for integrating GRACE data into a hydrological model
Authors:
Khaki, M.; Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; Kuhn, M.; Awange, J.; Forootan, E.; van Dijk, A.; Schumacher, M.; Pattiaratchi, C.
Abstract:
The time-variable terrestrial water storage (TWS) products from the Gravity Recovery And Climate Experiment (GRACE) have been increasingly used in recent years to improve the simulation of hydrological models by applying data assimilation techniques. In this study, for the first time, we assess the performance of the most popular data assimilation sequential techniques for integrating GRACE TWS into the World-Wide Water Resources Assessment (W3RA) model. We implement and test stochastic and deterministic ensemble-based Kalman filters (EnKF), as well as Particle filters (PF) using two different resampling approaches of Multinomial Resampling and Systematic Resampling. These choices provide various opportunities for weighting observations and model simulations during the assimilation and also accounting for error distributions. Particularly, the deterministic EnKF is tested to avoid perturbing observations before assimilation (that is the case in an ordinary EnKF). Gaussian-based random updates in the EnKF approaches likely do not fully represent the statistical properties of the model simulations and TWS observations. Therefore, the fully non-Gaussian PF is also applied to estimate more realistic updates. Monthly GRACE TWS are assimilated into W3RA covering the entire Australia. To evaluate the filters performances and analyze their impact on model simulations, their estimates are validated by independent in-situ measurements. Our results indicate that all implemented filters improve the estimation of water storage simulations of W3RA. The best results are obtained using two versions of deterministic EnKF, i.e. the Square Root Analysis (SQRA) scheme and the Ensemble Square Root Filter (EnSRF), respectively improving the model groundwater estimations errors by 34% and 31% compared to a model run without assimilation. Applying the PF along with Systematic Resampling successfully decreases the model estimation error by 23%.
KAUST Department:
King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Citation:
Khaki M, Hoteit I, Kuhn M, Awange J, Forootan E, et al. (2017) Assessing sequential data assimilation techniques for integrating GRACE data into a hydrological model. Advances in Water Resources. Available: http://dx.doi.org/10.1016/j.advwatres.2017.07.001.
Publisher:
Elsevier BV
Journal:
Advances in Water Resources
Issue Date:
6-Jul-2017
DOI:
10.1016/j.advwatres.2017.07.001
Type:
Article
ISSN:
0309-1708
Additional Links:
http://www.sciencedirect.com/science/article/pii/S0309170816307564
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorKhaki, M.en
dc.contributor.authorHoteit, Ibrahimen
dc.contributor.authorKuhn, M.en
dc.contributor.authorAwange, J.en
dc.contributor.authorForootan, E.en
dc.contributor.authorvan Dijk, A.en
dc.contributor.authorSchumacher, M.en
dc.contributor.authorPattiaratchi, C.en
dc.date.accessioned2017-07-12T07:20:54Z-
dc.date.available2017-07-12T07:20:54Z-
dc.date.issued2017-07-06en
dc.identifier.citationKhaki M, Hoteit I, Kuhn M, Awange J, Forootan E, et al. (2017) Assessing sequential data assimilation techniques for integrating GRACE data into a hydrological model. Advances in Water Resources. Available: http://dx.doi.org/10.1016/j.advwatres.2017.07.001.en
dc.identifier.issn0309-1708en
dc.identifier.doi10.1016/j.advwatres.2017.07.001en
dc.identifier.urihttp://hdl.handle.net/10754/625176-
dc.description.abstractThe time-variable terrestrial water storage (TWS) products from the Gravity Recovery And Climate Experiment (GRACE) have been increasingly used in recent years to improve the simulation of hydrological models by applying data assimilation techniques. In this study, for the first time, we assess the performance of the most popular data assimilation sequential techniques for integrating GRACE TWS into the World-Wide Water Resources Assessment (W3RA) model. We implement and test stochastic and deterministic ensemble-based Kalman filters (EnKF), as well as Particle filters (PF) using two different resampling approaches of Multinomial Resampling and Systematic Resampling. These choices provide various opportunities for weighting observations and model simulations during the assimilation and also accounting for error distributions. Particularly, the deterministic EnKF is tested to avoid perturbing observations before assimilation (that is the case in an ordinary EnKF). Gaussian-based random updates in the EnKF approaches likely do not fully represent the statistical properties of the model simulations and TWS observations. Therefore, the fully non-Gaussian PF is also applied to estimate more realistic updates. Monthly GRACE TWS are assimilated into W3RA covering the entire Australia. To evaluate the filters performances and analyze their impact on model simulations, their estimates are validated by independent in-situ measurements. Our results indicate that all implemented filters improve the estimation of water storage simulations of W3RA. The best results are obtained using two versions of deterministic EnKF, i.e. the Square Root Analysis (SQRA) scheme and the Ensemble Square Root Filter (EnSRF), respectively improving the model groundwater estimations errors by 34% and 31% compared to a model run without assimilation. Applying the PF along with Systematic Resampling successfully decreases the model estimation error by 23%.en
dc.publisherElsevier BVen
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0309170816307564en
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Advances in Water Resources. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Advances in Water Resources, 6 July 2017. DOI: 10.1016/j.advwatres.2017.07.001. © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectData assimilationen
dc.subjectGRACEen
dc.subjectHydrological modellingen
dc.subjectKalman filteringen
dc.subjectParticle filteringen
dc.titleAssessing sequential data assimilation techniques for integrating GRACE data into a hydrological modelen
dc.typeArticleen
dc.contributor.departmentKing Abdullah University of Science and Technology, Thuwal, Saudi Arabiaen
dc.identifier.journalAdvances in Water Resourcesen
dc.eprint.versionPost-printen
dc.contributor.institutionWestern Australian Centre for Geodesy and The Institute for Geoscience Research, Curtin University, Perth, Australiaen
dc.contributor.institutionSchool of Earth and Ocean Sciences, Cardiff University, Cardiff, UKen
dc.contributor.institutionFenner School of Environment and Society, the Australian National University, Canberra, Australiaen
dc.contributor.institutionInstitute of Geodesy and Geoinformation, University of Bonn, Nussallee 17, 53115 Bonn, Germanyen
dc.contributor.institutionSchool of Civil, Environmental, and Mining Engineering / UWA Oceans Institute, The University of Western Australia, Crawley, Australiaen
kaust.authorHoteit, Ibrahimen
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