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    Non-parametric Data Assimilation Scheme for Land Hydrological Applications

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    Type
    Article
    Authors
    Khaki, M.
    Hamilton, F.
    Forootan, E.
    Hoteit, I.
    Awange, J.
    Kuhn, M.
    KAUST Department
    King Abdullah University of Science and Technology; Thuwal Saudi Arabia
    Date
    2018-07-25
    Online Publication Date
    2018-07-25
    Print Publication Date
    2018-07
    Permanent link to this record
    http://hdl.handle.net/10754/628427
    
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    Abstract
    Data assimilation, which relies on explicit knowledge of dynamical models, is a well-known approach that addresses models' limitations due to various reasons, such as errors in input and forcing data sets. This approach, however, requires intensive computational efforts, especially for high-dimensional systems such as distributed hydrological models. Alternatively, data-driven methods offer comparable solutions when the physics underlying the models are unknown. For the first time in a hydrological context, a nonparametric framework is implemented here to improve model estimates using available observations. This method uses Takens delay coordinate method to reconstruct the dynamics of the system within a Kalman filtering framework, called the Kalman-Takens filter. A synthetic experiment is undertaken to fully investigate the capability of the proposed method by comparing its performance with that of a standard assimilation framework based on an adaptive unscented Kalman filter (AUKF). Furthermore, using terrestrial water storage (TWS) estimates obtained from the Gravity Recovery And Climate Experiment mission, both filters are applied to a real case scenario to update different water storages over Australia. In situ groundwater and soil moisture measurements within Australia are used to further evaluate the results. The Kalman-Takens filter successfully improves the estimated water storages at levels comparable to the AUKF results, with an average root-mean-square error reduction of 37.30% for groundwater and 12.11% for soil moisture estimates. Additionally, the Kalman-Takens filter, while reducing estimation complexities, requires a fraction of the computational time, that is, ∼8 times faster compared to the AUKF approach.
    Citation
    Khaki M, Hamilton F, Forootan E, Hoteit I, Awange J, et al. (2018) Nonparametric Data Assimilation Scheme for Land Hydrological Applications. Water Resources Research 54: 4946–4964. Available: http://dx.doi.org/10.1029/2018wr022854.
    Sponsors
    We would like to thank Tyrus Berry and Timothy Sauer for their valuable help in this study. M. Khaki is grateful for the research grant of Curtin International Postgraduate Research Scholarships (CIPRS)/ORD Scholarship provided by Curtin University (Australia). F. Hamilton is supported by National Science Foundation grant RTG/DMS-1246991. This work is a TIGeR publication. The GRACE data are acquired from the ITSG-Grace2014 gravity field model (Mayer-Gürr et al., 2014). In situ groundwater and soil moisture measurements are obtained from the New South Wales Government (NSW; http://waterinfo.nsw.gov.au/pinneena/gw.shtml) and the OzNet network (http://www.oznet.org.au/), respectively. Meteorological forcing data are provided by Princeton University (http://hydrology.princeton.edu). Other data used in this study can be found at DOI: 10.6084/m9.figshare.5942548. A more detailed discussion of the results can be found in the supporting information (Huffman et al., 2007; Mu et al., 2011).
    Publisher
    American Geophysical Union (AGU)
    Journal
    Water Resources Research
    DOI
    10.1029/2018wr022854
    Additional Links
    https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018WR022854
    ae974a485f413a2113503eed53cd6c53
    10.1029/2018wr022854
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