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

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

doi:https://doi.org/10.5194/hess-27-3143-2023

Articles | Volume 27, issue 16

https://doi.org/10.5194/hess-27-3143-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-27-3143-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 27, issue 16

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

Supplement

https://doi.org/10.5194/hess-27-3143-2023-supplement

Data sets

Seasonal forecast daily and subdaily data on single levels Copernicus Climate Change Service, Climate Data Store https://doi.org/10.24381/cds.181d637e

SoilGrids International Soil Reference and Information Centre (ISRIC) - World Soil Information data hub https://www.isric.org/explore/soilgrids

National-scale crop- and land-cover map of Germany (2016) based on imagery acquired by Sentinel-2A MSI and Landsat-8 OLI P. Griffiths, C. Nendel, and P. Hostert https://doi.org/10.1594/PANGAEA.893195

Victorian Land Use Information System 2016 Victorian Government Data Directory, Agriculture Victoria Research Division in the Department of Economic Development, Jobs, Transport, and Resources, Spatial Sciences Group https://doi.org/10.4226/92/590abbe6ea3f1

Model code and software

tboas/CTSM: CLM_WW_CC (reelase_08_2020) B. Sacks, E. Kluzek, N. Sobhani, mvertens, S. Levis, S. C. Swenson, Y. Cheng, K. Oleson, B. Andre, J. Hamman, J. Edwards, M. Rothstein, J. Truesdale, D. Lawrence, ciceconsortium, L. van Kampenhout, nanr, C. Koven, B. Andre, R. Fischer, djk2120, W. Wieder, B. Kauffman, R. Dunlap, J. Perket, M. Barlage, S. P. Serbin, and D. Coleman https://doi.org/10.5281/zenodo.3978092

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.