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@INPROCEEDINGS{Boas:917349,
      author       = {Boas, Theresa and Bogena, Heye and Ryu, Dongryeol and
                      Vereecken, Harry and Western, Andrew and Hendricks-Franssen,
                      Harrie-Jan},
      title        = {{U}sing {SEAS}5 {S}easonal {W}eather {F}orecasts for
                      {R}egional {C}rop {Y}ield {P}rediction in a {L}and {S}urface
                      {M}odelling {A}pproach},
      reportid     = {FZJ-2023-00577},
      year         = {2022},
      abstract     = {Seasonal weather forecasts can provide important
                      information for water resources and agricultural planning.
                      However, their coarse spatial and temporal resolution limit
                      the usage for modelling applications such as crop and land
                      surface models and have hindered their widespread use in
                      such models. In this study, we applied sub-seasonal and
                      seasonal weather forecasts from the latest ECMWF SEAS5
                      forecasting system in a land surface modelling approach
                      using the Community Land Model version 5.0 (CLM5).
                      Simulations were conducted for multiple years forced with
                      sub-seasonal and seasonal weather forecasts over two
                      different domains, one over the German state of North
                      Rhine-Westphalia characterized by heterogeneous land cover
                      and diverse agricultural land use, the other over the
                      Australian state of Victoria that is dominated by large
                      agricultural fields of mostly rainfed winter grain crops.
                      Our results show that the simulations forced with seasonal
                      and sub-seasonal forecasts were able to reproduce recorded
                      inter-annual trends of crop yield, but the inter-annual
                      variability of crop yields was significantly lower compared
                      to the records. The forecast-forced simulations were able to
                      reproduce the generally higher inter-annual variability in
                      crop yield throughout the Australian domain (approx. 50 $\%$
                      inter-annual variability in recorded yields and 20 $\%$ in
                      simulated yields) compared to the German domain (approx. 15
                      $\%$ inter-annual variability in recorded yields and 5 $\%$
                      in simulated yields). Also, sub-seasonal and seasonal
                      simulations reflected the early harvest in the drought year
                      of 2018 throughout the German domain, thus capturing one of
                      the main contributing factors to the low annual crop yield.
                      While general soil moisture trends, such as the European
                      drought in 2018, were reproduced in the results from the
                      sub-seasonal and seasonal experiments, we found systematic
                      biases compared to satellite products that could also be
                      observed in the reference simulations forced with reanalysis
                      weather data. The observed biases in the representation of
                      soil moisture, as well as the relatively low inter-annual
                      variability of simulated crop yield, indicate that the
                      representation of these variables in CLM5 still needs to be
                      improved to increase the model sensitivity to drought stress
                      and other crop stressors (e.g., pests, hail, wind).},
      month         = {Dec},
      date          = {2022-12-12},
      organization  = {American Geophysical Union Fall
                       Meeting, Chicago, IL (United States),
                       12 Dec 2022 - 16 Dec 2022},
      subtyp        = {Other},
      cin          = {IBG-3},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217) / 2B2 - TERENO (CTA - CCA) (POF4-2B2)},
      pid          = {G:(DE-HGF)POF4-2173 / G:(DE-HGF)POF4-2B2},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/917349},
}