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@PHDTHESIS{Li:1034169,
      author       = {Li, Fang},
      title        = {{A}ssimilation of groundwater level and cosmic-ray neutron
                      sensor soil moisture measurements into integrated
                      terrestrial system models for better predictionst},
      volume       = {650},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2024-06980},
      isbn         = {978-3-95806-796-7},
      series       = {Reihe Energie $\&$ Umwelt / Energy $\&$ Environment},
      pages        = {xvii, 172},
      year         = {2024},
      note         = {Dissertation, RWTH Aachen University, 2024},
      abstract     = {Groundwater and soil moisture (SM) play a crucial role in
                      the hydrological cycle, and therefore the dynamics of these
                      two variables need to be accurately quantified on spatial
                      and temporal scales. In situ observation networks can
                      provide direct and accurate information on groundwater level
                      (GWL) and SM. However, observations from observation
                      networks are not sufficient to fully represent the Earth’s
                      hydrological system without the help of models. Integrated
                      models such as the Terrestrial System Modelling Platform
                      (TSMP) can simulate the hydrological system from the
                      subsurface to the atmosphere and accurately capture the full
                      terrestrial hydrological cycle. Current model estimates of
                      GWL and SM are highly uncertain due to data limitations and
                      model uncertainties. The main sources of uncertainty are
                      related to atmospheric forcings, model structural errors,
                      and uncertain parameterization. Data assimilation (DA) can
                      merge numerical models with observations, resulting in a
                      correction of hydrological states and fluxes and improved
                      parameter estimates. Different sources of uncertainty may
                      lead to unsatisfactory simulations of groundwater
                      hydrodynamics with hydrological models. The goal of first
                      study is to investigate the impact of assimilating
                      groundwater data into TSMP for improving hydrological
                      modelling in a real-world case. Daily groundwater table
                      depth (WTD) measurements from the year 2018 for the Rur
                      catchment in Germany were assimilated by the Localized
                      Ensemble Kalman Filter (LEnKF) into TSMP. The LEnKF is used
                      with a localization radius so that the assimilated
                      measurements only update model states in a limited radius
                      around the measurements, in order to avoid unphysical
                      updates related to spurious correlations. Due to the
                      mismatch between groundwater measurements and the coarse
                      model resolution (500 m), the measurements need careful
                      screening before DA. Based on the spatial autocorrelation of
                      the WTD deduced from the measurements, three different
                      filter localization radii (2.5 km, 5 km and 10 km) were
                      evaluated for assimilation. The bias in the simulated water
                      table and the root mean square error (RMSE) are reduced
                      after DA, compared with runs without DA (i.e., open loop
                      (OL) runs). The best results at the assimilated locations
                      are obtained for a localization radius of 10km, with an
                      $81\%$ reduction of RMSE at the measurement locations, and
                      slightly smaller RMSE reductions for the 5 km and 2.5 km
                      radius. The validation with independent WTD data showed the
                      best results for a localization radius of 10 km, but
                      groundwater table characterization could only be improved
                      for sites less than 2.5 km from measurement locations. In
                      case of a localization radius of 10km the RMSE-reduction was
                      $30\%$ for those nearby sites. Simulated soil moisture was
                      validated against soil moisture measured by cosmic-ray
                      neutron sensors (CRNS), cut no RMSE reduction was observed
                      for DA-runs compared to OL-run. However, in both cases, the
                      correlation between measured and simulated soil moisture
                      content was high (between 0.70 and 0.89, except for the
                      Wüstebach site). CRNS fill the gap between locally measured
                      in situ SM and remotely sensed (RS) SM by providing accurate
                      SM estimation at the field scale. This is promising for
                      improving hydrologic model predictions, as CRNS can provide
                      valuable information on SM in the root zone at the typical
                      scale of a model grid cell. In a second study of this
                      PhD-work, SM measurements from a network of 12 CRNS in the
                      Rur catchment (Germany) were assimilated into TSMP to
                      investigate its potential for improving SM,
                      evapotranspiration (ET) and river discharge characterization
                      and estimating soil hydraulic parameters at the larger
                      catchment scale. DA experiments (with and without parameter
                      estimation) were conducted in both a wet year (2016) and a
                      dry year (2018) with the Ensemble Kalman Filter (EnKF), and
                      later verified with an independent year (2017) without DA.
                      The results show that SM characterization was significantly
                      improved at measurement locations (with up to $60\%$ RMSE
                      reduction), and that joint state-parameter estimation
                      improved SM simulation more than state estimation one (more
                      than $15\%$ additional RMSE reduction). Jackknife
                      experiments showed that SM at verification locations had
                      lower and different improvements in the wet and dry years
                      (an RMSE 2 reduction of $40\%$ in 2016 and $16\%$ in 2018).
                      In addition, SM assimilation was found to improve ET
                      characterization to a much lesser extent, with a $15\%$ RMSE
                      reduction of monthly ET in the wet year and $9\%$ in the dry
                      year. In a third study, we tested different approaches for
                      joint DA of observed SM data from CRNS and GWL data into the
                      TSMP model. A experiments (with and without parameter
                      estimation) were conducted with LEnKF for the Rur catchment
                      in Germany for the years 2016-2018, followed by
                      cross-validation (if parameters were estimated) in
                      independent years. Univariate SM assimilation reduced the
                      RMSE of SM over the assimilation locations by more than
                      $50\%.$ Univariate GWL assimilation reduced the monthly RMSE
                      of GWL at assimilation locations by $70\%.$ Within 5 km of
                      the assimilated sites, GWL estimation was still obviously
                      improved, with RMSE reductions $2-50\%.$ However, the
                      univariate assimilation of GWL degraded the characterization
                      of SM, and the univariate assimilation of SM also diminished
                      the simulation of GWL. A new multivariate DA approach that
                      assimilates GWL and SM separately is proposed. GWL data are
                      assimilated and used to estimate the interface between the
                      unsaturated and saturated zones, and update the states (and
                      possibly parameters) of the saturated zone. SM measurements
                      are assimilated to update states of the unsaturated zone. In
                      addition, observation specific localization is proposed.
                      With multivariate DA, at the assimilation locations the
                      estimates of variables (GWL, SM, and ET) are close to those
                      in univariate assimilation. However, there were more than
                      $15\%$ RMSE reductions for GWL at 2.5~5 km validation
                      locations compared to univariate assimilation. In addition,
                      only SM assimilation (univariate or multivariate) improves
                      very slightly ET estimates, with an overall RMSE reduction
                      of $3\%.$ Parameter updating reduced the RMSE of variable
                      estimates by up to $17\%$ compared to updating states alone.
                      This work was carried out for the Rur catchment (2354 km²),
                      which has a well-established monitoring infrastructure and
                      considerable regional diversity in climate, soil types, and
                      land use. In contrast to previously reported small-domain
                      tests, which were primarily conducted in synthetic
                      experiments or oversimplified real-world cases, the
                      assimilation of real-world data at the larger catchment
                      scale faces additional complexities and challenges. The
                      effectiveness of DA can be limited by the uneven
                      distribution of monitoring stations, coarse model
                      resolution, and model structure errors. This thesis, using
                      EnKF and its variants, proposes specific strategies for the
                      assimilation of GWL and SM (from CRNS) separately or jointly
                      in the integrated terrestrial model TSMP. Overall, the
                      results of this thesis provide insights for improving the
                      characterization of multiple variables and parameters (GWL,
                      SM, ET, and saturated hydraulic conductivity (Ks)) by DA.
                      Possible promising approaches for future improvement of DA
                      performance in coupled models are: (i) improving the
                      accuracy of terrestrial system modeling, including the
                      addition of an atmospheric model, the inclusion of more
                      detailed agro-ecological processes into land surface models
                      and increasing model resolution; (ii) attempting to
                      assimilate data from more diverse sources such as RS,
                      unmanned aerial vehicles (UAVs), and small satellites to
                      address the sparse distribution of in situ observations; and
                      (iii) exploring advanced DA algorithms, potentially EnKF
                      variants or hybrid methods with machine learning (ML)
                      integration, to address the issues and challenges of
                      multivariate assimilation at large scales.},
      cin          = {IBG-3},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:0001-20250106151927669-2497821-0},
      doi          = {10.34734/FZJ-2024-06980},
      url          = {https://juser.fz-juelich.de/record/1034169},
}