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@ARTICLE{Weihermller:14523,
      author       = {Weihermüller, L. and Huisman, J.A. and Graf, A. and
                      Herbst, M. and Vereecken, H.},
      title        = {{E}rrors in modelling carbon turnover induced by temporal
                      temperature aggregation},
      journal      = {Vadose zone journal},
      volume       = {10},
      issn         = {1539-1663},
      address      = {Madison, Wis.},
      publisher    = {SSSA},
      reportid     = {PreJuSER-14523},
      pages        = {195 - 205},
      year         = {2011},
      note         = {We would like to thank Horst Hardelauf for the
                      modifications of the RothC/SOILCO2 code and Jan Vanderborght
                      for the fruitful discussions. This research was supported by
                      the German Research Foundation DFG (Transregional
                      Collaborative Research Centre 32-Patterns in
                      Soil-Vegetation-Atmosphere Systems: Monitoring, Modelling,
                      and Data Assimilation).},
      abstract     = {Modeling of C turnover is a common tool for the prediction
                      of C stocks and CO2 efflux. It is well recognized that the
                      choice of the input data (e. g., C pool sizes, hydraulic
                      parameters, atmospheric boundary conditions) determines the
                      outcome of these prediction. Temperature is known to be one
                      of the most important driving factors and it varies in a
                      range of temporal scales. Typically, the time discretization
                      of most models is flexible and can range from minutes to
                      months. However, the implications of variable time
                      discretization for predicted soil C turnover are seldom
                      discussed. In this study, we demonstrated that averaging of
                      input temperature data will lead to changes in predicted C
                      turnover in terms of daily amplitude and the impact of
                      extreme temperatures. The results indicate that averaging
                      from hourly to daily or monthly temperatures will lead to
                      relative errors $>4\%$ yr(-1) for cumulative CO2 efflux.
                      Instantaneous CO2 fluxes are even more affected, where daily
                      and monthly averaging will lead to estimation errors
                      exceeding $20\%.$ We also show that a constant or daily
                      variable temperature amplitude for rescaling daily average
                      temperature did not decrease the error when using daily or
                      monthly mean temperature instead of hourly data. Therefore,
                      instantaneous fluxes are only accurately predicted when
                      hourly temperature input is used. For long-term modeling
                      (e.g., years to centuries), the relative error in cumulative
                      efflux, and therefore in C stocks loss, is reasonably low
                      (similar to $4-5\%$ annual error) but will accumulate with
                      time again.},
      keywords     = {J (WoSType)},
      cin          = {IBG-3},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {Terrestrische Umwelt},
      pid          = {G:(DE-Juel1)FUEK407},
      shelfmark    = {Environmental Sciences / Soil Science / Water Resources},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000287573300017},
      doi          = {10.2136/vzj2009.0157},
      url          = {https://juser.fz-juelich.de/record/14523},
}