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@ARTICLE{Shrestha:857133,
      author       = {Shrestha, P. and Kurtz, W. and Vogel, G. and Schulz, J.-P.
                      and Sulis, M. and Hendricks Franssen, H.-J. and Kollet,
                      Stefan and Simmer, C.},
      title        = {{C}onnection {B}etween {R}oot {Z}one {S}oil {M}oisture and
                      {S}urface {E}nergy {F}lux {P}artitioning {U}sing {M}odeling,
                      {O}bservations, and {D}ata {A}ssimilation for a {T}emperate
                      {G}rassland {S}ite in {G}ermany},
      journal      = {Journal of geophysical research / Biogeosciences
                      Biogeosciences [...]},
      volume       = {123},
      number       = {9},
      issn         = {2169-8953},
      address      = {[Washington, DC]},
      reportid     = {FZJ-2018-06377},
      pages        = {2839 - 2862},
      year         = {2018},
      abstract     = {Land surface models (LSMs) with different degrees of
                      complexity are in use as lower boundary conditions for
                      atmospheric models with the simpler LSMs preferentially used
                      in numerical weather forecasting. This study evaluates the
                      second‐generation TERRA Multi‐Layer and the
                      third‐generation Community Land Model (CLM) to better
                      understand the connection between root zone soil moisture
                      and surface energy fluxes, which is important for
                      predictions. Both LSMs were compared in multiyear,
                      observation‐driven simulations at the Falkenberg grassland
                      site (Germany), and their results were compared to
                      observations. With their default settings for the site, both
                      LSMs tend to overestimate the Bowen ratio, while CLM
                      additionally exhibited a wet bias and a too low soil
                      moisture variance. With modified photosynthetic parameters
                      in CLM, the Bowen ratio improved considerably, but the soil
                      moisture bias and its too low variance remained. Joint data
                      assimilation with soil parameter update significantly
                      improved the soil moisture variance but degraded the Bowen
                      ratio. We could identify the default shallow root fraction
                      distribution to be responsible for the overestimated Bowen
                      ratio, which could be largely reduced by increasing the root
                      fractions in deeper layers. This study demonstrates how
                      observations and data assimilation with joint
                      state‐parameter updating can be used to improve the
                      realism of third‐generation LSMs and thus our
                      understanding of the connection between root zone soil
                      moisture and surface energy flux partitioning.},
      cin          = {IBG-3},
      ddc          = {550},
      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)16},
      UT           = {WOS:000447644800013},
      doi          = {10.1029/2016JG003753},
      url          = {https://juser.fz-juelich.de/record/857133},
}