Articles | Volume 27, issue 7
https://doi.org/10.5194/hess-27-1583-2023
© 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-27-1583-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Apr 2023
Research article |
| 17 Apr 2023
| 17 Apr 2023
Improving regional climate simulations based on a hybrid data assimilation and machine learning method
Xinlei He
State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Yanping Li
School of Environment and Sustainability, University of Saskatchewan, Saskatoon, SK, Canada
Shaomin Liu
CORRESPONDING AUTHOR
State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Tongren Xu
State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Fei Chen
National Center for Atmospheric Research, Boulder, CO, USA
Zhenhua Li
School of Environment and Sustainability, University of Saskatchewan, Saskatoon, SK, Canada
Zhe Zhang
School of Environment and Sustainability, University of Saskatchewan, Saskatoon, SK, Canada
Rui Liu
Institute of Urban Study, School of Environmental and Geographical
Sciences (SEGS), Shanghai Normal University, Shanghai, China
Lisheng Song
School of Geography and Tourism, Anhui Normal University, Wuhu, China
Ziwei Xu
CORRESPONDING AUTHOR
State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Zhixing Peng
State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Chen Zheng
Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing, China
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- Regional simulation of water-carbon fluxes and winter wheat yield with assimilation of multi-source remote sensing data in the Haihe River Basin G. Zhang et al. 10.1016/j.jag.2025.104578
- Emilio Porcu, Horst Simon, and Youssef Wehbe’s contribution to the Discussion of ‘Inference for extreme spatial temperature events in a changing climate with application to Ireland’ by Healy et al. E. Porcu et al. 10.1093/jrsssc/qlae088
- Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN Y. Zhang et al. 10.3390/s24010035
- Sim2DSphere: A novel modelling tool for the study of land surface interactions G. Petropoulos et al. 10.1016/j.envsoft.2024.106086
- Modeling the effects of the plastic-mulched cropland over the arid and semi-arid areas of China on the East Asian regional climate S. Lu & W. Shi 10.1016/j.jhydrol.2024.131123
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- Innovative approach for estimating evapotranspiration and gross primary productivity by integrating land data assimilation, machine learning, and multi-source observations X. He et al. 10.1016/j.agrformet.2024.110136
- Spatio-temporal variations and multi-scale correlations of climate, water, land, and vegetation resources over the past four decades in the Heihe River Basin D. Jiao et al. 10.1016/j.ejrh.2024.101941
- Enhancing water-carbon fluxes and yield predictions of winter wheat using irrigation and data assimilation techniques in a land surface model T. Xu et al. 10.1016/j.compag.2024.109140
10 citations as recorded by crossref.
- Simulating oasis-desert interactions in artificial and natural oasis-desert areas: Integration of remote sensing data and CFD methodology Z. Peng et al. 10.1016/j.agrformet.2025.110516
- Regional simulation of water-carbon fluxes and winter wheat yield with assimilation of multi-source remote sensing data in the Haihe River Basin G. Zhang et al. 10.1016/j.jag.2025.104578
- Emilio Porcu, Horst Simon, and Youssef Wehbe’s contribution to the Discussion of ‘Inference for extreme spatial temperature events in a changing climate with application to Ireland’ by Healy et al. E. Porcu et al. 10.1093/jrsssc/qlae088
- Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN Y. Zhang et al. 10.3390/s24010035
- Sim2DSphere: A novel modelling tool for the study of land surface interactions G. Petropoulos et al. 10.1016/j.envsoft.2024.106086
- Modeling the effects of the plastic-mulched cropland over the arid and semi-arid areas of China on the East Asian regional climate S. Lu & W. Shi 10.1016/j.jhydrol.2024.131123
- Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions H. Tao et al. 10.1016/j.engappai.2023.107559
- Innovative approach for estimating evapotranspiration and gross primary productivity by integrating land data assimilation, machine learning, and multi-source observations X. He et al. 10.1016/j.agrformet.2024.110136
- Spatio-temporal variations and multi-scale correlations of climate, water, land, and vegetation resources over the past four decades in the Heihe River Basin D. Jiao et al. 10.1016/j.ejrh.2024.101941
- Enhancing water-carbon fluxes and yield predictions of winter wheat using irrigation and data assimilation techniques in a land surface model T. Xu et al. 10.1016/j.compag.2024.109140
Latest update: 01 Jun 2025
Short summary
This study highlights the role of integrating vegetation and multi-source soil moisture observations in regional climate models via a hybrid data assimilation and machine learning method. In particular, we show that this approach can improve land surface fluxes, near-surface atmospheric conditions, and land–atmosphere interactions by implementing detailed land characterization information in basins with complex underlying surfaces.
This study highlights the role of integrating vegetation and multi-source soil moisture...
