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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Aug 2023
Research article |
| 29 Aug 2023
| 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
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Research Centre Jülich, 52425 Jülich, Germany
Centre for High-Performance Scientific Computing in Terrestrial
Systems: HPSC TerrSys, Geoverbund ABC/J, 52425 Jülich, Germany
Department of Infrastructure Engineering, University of Melbourne,
Parkville, VIC 3010, Australia
Heye Reemt Bogena
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Research Centre Jülich, 52425 Jülich, Germany
Dongryeol Ryu
Department of Infrastructure Engineering, University of Melbourne,
Parkville, VIC 3010, Australia
Harry Vereecken
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Research Centre Jülich, 52425 Jülich, Germany
Centre for High-Performance Scientific Computing in Terrestrial
Systems: HPSC TerrSys, Geoverbund ABC/J, 52425 Jülich, Germany
Andrew Western
Department of Infrastructure Engineering, University of Melbourne,
Parkville, VIC 3010, Australia
Harrie-Jan Hendricks Franssen
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Research Centre Jülich, 52425 Jülich, Germany
Centre for High-Performance Scientific Computing in Terrestrial
Systems: HPSC TerrSys, Geoverbund ABC/J, 52425 Jülich, Germany
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Cited
13 citations as recorded by crossref.
- Evaluation of machine learning-dynamical hybrid method incorporating remote sensing data for in-season maize yield prediction under drought Y. Luo et al. 10.1007/s11119-024-10149-6
- Near‐Term Forecasting of Terrestrial Mobile Species Distributions for Adaptive Management Under Extreme Weather Events R. Dobson et al. 10.1111/gcb.17579
- Adaptive rotations for improving soil productivity and profits C. Francis et al. 10.1079/cabireviews.2024.0045
- Assessing seasonal forecast performance to predict crop irrigation requirements to support water management decision-making in the Mediterranean region D. Garcia et al. 10.1016/j.agwat.2025.109467
- Hydrometeorological Insights into the Forecasting Performance of Multi-Source Weather over a Typical Hill-Karst Basin, Southwest China C. Mo et al. 10.3390/atmos15020236
- Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe C. Poppe Terán et al. 10.5194/gmd-18-287-2025
- Seasonal forecasts have sufficient skill to inform some agricultural decisions A. Kondal et al. 10.1088/1748-9326/ad8bde
- Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data K. Reddy et al. 10.5194/gmd-18-763-2025
- Assessment of seasonal soil moisture forecasts over the Central Mediterranean L. Silvestri et al. 10.5194/hess-29-925-2025
- A Deep–Learning Network for Wheat Yield Prediction Combining Weather Forecasts and Remote Sensing Data D. Peng et al. 10.3390/rs16193613
- A linear model for predicting olive yield using root characteristics M. Nasiri et al. 10.1016/j.rhisph.2024.100859
- Remote Sensing Data Assimilation in Crop Growth Modeling from an Agricultural Perspective: New Insights on Challenges and Prospects J. Wang et al. 10.3390/agronomy14091920
- Comparing Satellite-Derived and Model-Based Surface Soil Moisture for Spring Barley Yield Prediction in Central Europe F. Reuß et al. 10.3390/rs17081394
13 citations as recorded by crossref.
- Evaluation of machine learning-dynamical hybrid method incorporating remote sensing data for in-season maize yield prediction under drought Y. Luo et al. 10.1007/s11119-024-10149-6
- Near‐Term Forecasting of Terrestrial Mobile Species Distributions for Adaptive Management Under Extreme Weather Events R. Dobson et al. 10.1111/gcb.17579
- Adaptive rotations for improving soil productivity and profits C. Francis et al. 10.1079/cabireviews.2024.0045
- Assessing seasonal forecast performance to predict crop irrigation requirements to support water management decision-making in the Mediterranean region D. Garcia et al. 10.1016/j.agwat.2025.109467
- Hydrometeorological Insights into the Forecasting Performance of Multi-Source Weather over a Typical Hill-Karst Basin, Southwest China C. Mo et al. 10.3390/atmos15020236
- Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe C. Poppe Terán et al. 10.5194/gmd-18-287-2025
- Seasonal forecasts have sufficient skill to inform some agricultural decisions A. Kondal et al. 10.1088/1748-9326/ad8bde
- Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data K. Reddy et al. 10.5194/gmd-18-763-2025
- Assessment of seasonal soil moisture forecasts over the Central Mediterranean L. Silvestri et al. 10.5194/hess-29-925-2025
- A Deep–Learning Network for Wheat Yield Prediction Combining Weather Forecasts and Remote Sensing Data D. Peng et al. 10.3390/rs16193613
- A linear model for predicting olive yield using root characteristics M. Nasiri et al. 10.1016/j.rhisph.2024.100859
- Remote Sensing Data Assimilation in Crop Growth Modeling from an Agricultural Perspective: New Insights on Challenges and Prospects J. Wang et al. 10.3390/agronomy14091920
- Comparing Satellite-Derived and Model-Based Surface Soil Moisture for Spring Barley Yield Prediction in Central Europe F. Reuß et al. 10.3390/rs17081394
Latest update: 23 Apr 2025
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.
In our study, we tested the utility and skill of a state-of-the-art forecasting product for the...
