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@ARTICLE{Boas:1014302,
      author       = {Boas, Theresa and Bogena, Heye Reemt and Ryu, Dongryeol and
                      Vereecken, Harry and Western, Andrew and Hendricks Franssen,
                      Harrie-Jan},
      title        = {{S}easonal soil moisture and crop yield prediction with
                      fifth-generation seasonal forecasting system ({SEAS}5)
                      long-range meteorological forecasts in a land surface
                      modelling approach},
      journal      = {Hydrology and earth system sciences},
      volume       = {27},
      number       = {16},
      issn         = {1027-5606},
      address      = {Munich},
      publisher    = {EGU},
      reportid     = {FZJ-2023-03225},
      pages        = {3143 - 3167},
      year         = {2023},
      abstract     = {Long-range weather forecasts provide predictions of
                      atmospheric, ocean and land surface conditions that can
                      potentially be used in land surface and hydrological models
                      to predict the water and energy status of the land surface
                      or in crop growth models to predict yield for water
                      resources or agricultural planning. However, the coarse
                      spatial and temporal resolutions of available forecast
                      products have hindered their widespread use in such
                      modelling applications, which usually require
                      high-resolution input data. In this study, we applied
                      sub-seasonal (up to 4 months) and seasonal (7 months)
                      weather forecasts from the latest European Centre for
                      Medium-Range Weather Forecasts (ECMWF) seasonal forecasting
                      system (SEAS5) in a land surface modelling approach using
                      the Community Land Model version 5.0 (CLM5). Simulations
                      were conducted for 2017–2020 forced with sub-seasonal and
                      seasonal weather forecasts over two different domains with
                      contrasting climate and cropping conditions: the German
                      state of North Rhine-Westphalia (DE-NRW) and the Australian
                      state of Victoria (AUS-VIC). We found that, after
                      pre-processing of the forecast products (i.e. temporal
                      downscaling of precipitation and incoming short-wave
                      radiation), the simulations forced with seasonal and
                      sub-seasonal forecasts were able to provide a model output
                      that was very close to the reference simulation results
                      forced by reanalysis data (the mean annual crop yield showed
                      maximum differences of 0.28 and 0.36 t ha−1 for
                      AUS-VIC and DE-NRW respectively). Differences between
                      seasonal and sub-seasonal experiments were insignificant.
                      The forecast experiments were able to satisfactorily capture
                      recorded inter-annual variations of crop yield. In addition,
                      they also reproduced the generally higher inter-annual
                      differences in crop yield across the AUS-VIC domain
                      (approximately $50 \%$ inter-annual differences in
                      recorded yields and up to $17 \%$ inter-annual differences
                      in simulated yields) compared to the DE-NRW domain
                      (approximately $15 \%$ inter-annual differences in
                      recorded yields and up to $5 \%$ in simulated yields). The
                      high- and low-yield seasons (2020 and 2018) among the 4
                      simulated years were clearly reproduced in the forecast
                      simulation results. Furthermore, sub-seasonal and seasonal
                      simulations reflected the early harvest in the drought year
                      of 2018 in the DE-NRW domain. However, simulated
                      inter-annual yield variability was lower in all simulations
                      compared to the official statistics. While general soil
                      moisture trends, such as the European drought in 2018, were
                      captured by the seasonal experiments, we found systematic
                      overestimations and underestimations in both the forecast
                      and reference simulations compared to the Soil Moisture
                      Active Passive Level-3 soil moisture product (SMAP L3) and
                      the Soil Moisture Climate Change Initiative Combined dataset
                      from the European Space Agency (ESA CCI). These observed
                      biases of soil moisture and the low inter-annual differences
                      in simulated crop yield indicate the need to improve the
                      representation of these variables in CLM5 to increase the
                      model sensitivity to drought stress and other crop
                      stressors.},
      cin          = {IBG-3},
      ddc          = {550},
      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:001116760800001},
      doi          = {10.5194/hess-27-3143-2023},
      url          = {https://juser.fz-juelich.de/record/1014302},
}