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@ARTICLE{Rudi:201154,
      author       = {Rudi, J. and Pabel, R. and Jager, G. and Koch, R. and
                      Kunoth, A. and Bogena, H.},
      title        = {{M}ultiscale {A}nalysis of {H}ydrologic {T}ime {S}eries
                      {D}ata using the {H}ilbert–{H}uang {T}ransform},
      journal      = {Vadose zone journal},
      volume       = {9},
      number       = {4},
      issn         = {1539-1663},
      address      = {Madison, Wis.},
      publisher    = {SSSA},
      reportid     = {FZJ-2015-03459},
      pages        = {925 - 942},
      year         = {2010},
      abstract     = {For the analysis of time series data from hydrology, we
                      used a recently developed technique that is by now widely
                      known as the Hilbert–Huang transform (HHT). Specifically,
                      it is designed for nonlinear and nonstationary data. In
                      contrast to data analysis techniques using the short-time,
                      windowed Fourier transform or the continuous wavelet
                      transform, the new technique is empirically adapted to the
                      data in the following sense. First, an additive
                      decomposition, called empirical mode decomposition (EMD), of
                      the data into certain multiscale components is computed.
                      Second, to each of these components, the Hilbert transform
                      is applied. The resulting Hilbert spectrum of the modes
                      provides a localized time–frequency spectrum and
                      instantaneous (time-dependent) frequencies. In this study,
                      we applied the HHT to hydrological time series data from the
                      Upper Rur Catchment Area, mostly German territory, taken
                      during a period of 20 yr. Our first observation was that a
                      coarse approximation of the data can be derived by
                      truncating the EMD representation. This can be used to
                      better model patterns like seasonal structures. Moreover,
                      the corresponding time–frequency energy spectrum applied
                      to the complete EMD revealed seasonal events in a particular
                      apparent way together with their energy. We compared the
                      Hilbert spectra with Fourier spectrograms and wavelet
                      spectra to demonstrate a better localization of the energy
                      components, which also exhibit strong seasonal components.
                      The Hilbert energy spectrum of the three measurement
                      stations appear to be very similar, indicating little local
                      variability in drainage.},
      cin          = {IBG-3},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {246 - Modelling and Monitoring Terrestrial Systems: Methods
                      and Technologies (POF2-246) / 255 - Terrestrial Systems:
                      From Observation to Prediction (POF3-255)},
      pid          = {G:(DE-HGF)POF2-246 / G:(DE-HGF)POF3-255},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000287739800012},
      doi          = {10.2136/vzj2009.0163},
      url          = {https://juser.fz-juelich.de/record/201154},
}