Articles | Volume 27, issue 16
https://doi.org/10.5194/hess-27-3143-2023
https://doi.org/10.5194/hess-27-3143-2023
Research article
 | 
29 Aug 2023
Research article |  | 29 Aug 2023

Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach

Theresa Boas, Heye Reemt Bogena, Dongryeol Ryu, Harry Vereecken, Andrew Western, and Harrie-Jan Hendricks Franssen

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Cited articles

ABARES – Australian Bureau of Agricultural and Resource Economics and Sciences: Australian Crop Report, February 2021, Canberra, https://doi.org/10.25814/xqy3-sx57, 2020. 
Ash, A., McIntosh, P., Cullen, B., Carberry, P., and Smith, M. S.: Constraints and opportunities in applying seasonal climate forecasts in agriculture, Aust. J. Agric. Res., 58, 952–965, https://doi.org/10.1071/AR06188, 2007. 
Baatz, R., Hendricks Franssen, H.-J., Han, X., Hoar, T., Bogena, H. R., and Vereecken, H.: Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction, Hydrol. Earth Syst. Sci., 21, 2509–2530, https://doi.org/10.5194/hess-21-2509-2017, 2017. 
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. 
Bennett, A., Hamman, J., and Nijssen, B.: MetSim: A Python package for estimation and disaggregation of meteorological data, J. Open Source Softw., 5, 2042, https://doi.org/10.21105/joss.02042, 2020. 
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Short summary
In our study, we tested the utility and skill of a state-of-the-art forecasting product for the prediction of regional crop productivity using a land surface model. Our results illustrate the potential value and skill of combining seasonal forecasts with modelling applications to generate variables of interest for stakeholders, such as annual crop yield for specific cash crops and regions. In addition, this study provides useful insights for future technical model evaluations and improvements.
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