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@ARTICLE{Patil:903175,
      author       = {Patil, Amol and Fersch, Benjamin and Hendricks-Franssen,
                      Harrie-Jan and Kunstmann, Harald},
      title        = {{A}ssimilation of {C}osmogenic {N}eutron {C}ounts for
                      {I}mproved {S}oil {M}oisture {P}rediction in a {D}istributed
                      {L}and {S}urface {M}odel},
      journal      = {Frontiers in water},
      volume       = {3},
      issn         = {2624-9375},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {FZJ-2021-04895},
      pages        = {729592},
      year         = {2021},
      abstract     = {Cosmic-Ray Neutron Sensing (CRNS) offers a non-invasive
                      method for estimating soil moisture at the field scale, in
                      our case a few tens of hectares. The current study uses the
                      Ensemble Adjustment Kalman Filter (EAKF) to assimilate
                      neutron counts observed at four locations within a 655 km2
                      pre-alpine river catchment into the Noah-MP land surface
                      model (LSM) to improve soil moisture simulations and to
                      optimize model parameters. The model runs with 100 m spatial
                      resolution and uses the EU-SoilHydroGrids soil map along
                      with the Mualem–van Genuchten soil water retention
                      functions. Using the state estimation (ST) and joint
                      state–parameter estimation (STP) technique, soil moisture
                      states and model parameters controlling infiltration and
                      evaporation rates were optimized, respectively. The added
                      value of assimilation was evaluated for local and regional
                      impacts using independent root zone soil moisture
                      observations. The results show that during the assimilation
                      period both ST and STP significantly improved the simulated
                      soil moisture around the neutron sensors locations with
                      improvements of the root mean square errors between 60 and
                      $62\%$ for ST and $55–66\%$ for STP. STP could further
                      enhance the model performance for the validation period at
                      assimilation locations, mainly by reducing the Bias.
                      Nevertheless, due to a lack of convergence of calculated
                      parameters and a shorter evaluation period, performance
                      during the validation phase degraded at a site further away
                      from the assimilation locations. The comparison of modeled
                      soil moisture with field-scale spatial patterns of a dense
                      network of CRNS observations showed that STP helped to
                      improve the average wetness conditions (reduction of spatial
                      Bias from –0.038 cm3 cm−3 to –0.012 cm3 cm−3) for
                      the validation period. However, the assimilation of neutron
                      counts from only four stations showed limited success in
                      enhancing the field-scale soil moisture patterns.},
      cin          = {IBG-3},
      ddc          = {333.7},
      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)16},
      UT           = {WOS:000702043300001},
      doi          = {10.3389/frwa.2021.729592},
      url          = {https://juser.fz-juelich.de/record/903175},
}