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@PHDTHESIS{Zhang:850282,
      author       = {Zhang, Hongjuan},
      title        = {{I}mproved characterization of root zone soil moisture by
                      assimilating groundwater level and surface soil moisture
                      data in an integrated terrestrial system model},
      volume       = {427},
      school       = {RWTH Aachen},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2018-04324},
      isbn         = {978-3-95806-335-8},
      series       = {Schriften des Forschungszentrums Jülich Reihe Energie $\&$
                      Umwelt / Energy $\&$ Environment},
      pages        = {x, 125 S.},
      year         = {2018},
      note         = {RWTH Aachen, Diss., 2018},
      abstract     = {Soil moisture is an important variable for the cycling of
                      water and energy at the catchment/regional/global scale.
                      Soil moisture content is usually simulated by land surface
                      models, monitored by ground-based sensors, or observed by
                      remote sensing techniques. However, land surface models
                      often have high uncertainties due to simplified
                      parameterizations and uncertainties from input forcing data
                      and hydraulic parameters. For example, in most land surface
                      models, the interaction between groundwater and root zone
                      soil moisture is neglected. The availabilities of in situ
                      monitoring networks are limited because of high costs.
                      Remote sensing can provide soil moisture at the global scale
                      but is limited to the top 5 cm and resolution is coarse.
                      Data assimilation can take advantage of these three
                      different sources of information by assimilating the
                      observations (e.g. from the ground sensors or remote sensing
                      data) into the land surface models to improve soil moisture
                      predictions in both space and time. Furthermore, soil
                      hydraulic parameters in land surface models can also be
                      estimated jointly with soil moisture by data assimilation to
                      further improve soil moisture characterization. In order to
                      make land surface models more robust, integrated land
                      surface-subsurface models have been developed which consider
                      the effect of groundwater on root zone soil moisture in a
                      fully two-way coupled fashion. In this work, we firstly
                      compared four data assimilation methods in terms of joint
                      estimation of soil moisture and soil hydraulic parameters
                      for two land surface models. The four assimilation methods
                      included Ensemble Kalman Filter (EnKF) with state
                      augmentation (EnKF-AUG) or dual estimation (EnKF-DUAL), the
                      residual resampling Particle Filter (RRPF) and the
                      MCMC-based parameter resampling method (PMCMC). The two land
                      surface models used were the Variable Infiltration Capacity
                      Model (VIC) and the Community Land Model (CLM version 4.5).
                      Real world data (soil properties, soil moisture measurements
                      at 5, 20 and 50 cm depth, climate forcing data) from the
                      Rollesbroich site located in the western Germany were used.
                      We evaluated the usefulness and applicability of the four
                      different data assimilation methods for joint parameter and
                      state estimation of the VIC and the CLM using a 5-month
                      calibration (assimilation) period of the soil moisture
                      measurements. The performance of the “calibrated” VIC
                      and CLM were investigated using water moisture measurements
                      of a 5-month evaluation period. Results from the first study
                      showed that all of the four assimilation methods were able
                      to improve the model predictions of soil moisture after soil
                      hydraulic parameters (for VIC) or sand/ clay/ organic matter
                      fraction (for CLM) were jointly estimated with soil
                      moisture. Overall, EnKF (EnKF-AUG and EnKF-DUAL) performed
                      better than PF (RRPF and PMCMC). The differences between the
                      soil moisture simulations of VIC and CLM were much larger
                      than the discrepancies among the four data assimilation
                      methods. CLM performed better than VIC in the soil moisture
                      simulations at 50 cm depth. The large systematic
                      underestimation of water storage at 50cm depth in VIC is
                      most probably related to the fact that groundwater is not
                      well represented in VIC. [...]},
      cin          = {IBG-3},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255)},
      pid          = {G:(DE-HGF)POF3-255},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:0001-2018080300},
      url          = {https://juser.fz-juelich.de/record/850282},
}