Surface Water Dynamics Research Team

Our research team (belongs to Land surface model development group led by Professor Yongjiu Dai) led by Professor Zhongwang Wei, focuses on enhancing our understanding of Earth’s surface water dynamics. We employ a multi-faceted approach that combines advanced numerical modeling techniques with innovative isotopic methods to unravel the complexities of the global water cycle.

Research Directions

Our primary areas of investigation include:

Land Surface Modeling: We explore the intricate interactions between land, atmosphere, and water. Through sophisticated numerical simulations, we study processes such as evapotranspiration, infiltration, and runoff, aiming to understand how land surface characteristics influence the water cycle and climate dynamics.

Artificial Intelligence Applications in Land Surface Hydrology: We actively explore and apply cutting-edge artificial intelligence methods to advance land surface hydrological research. This includes: utilizing symbolic regression techniques to discover underlying physical laws and empirical formulas in hydrological processes; employing deep learning and machine learning algorithms to generate high-quality hydrological datasets, filling gaps in observational data; developing AI-driven intelligent hydrological models to improve prediction accuracy of complex hydrological processes; and combining physical process understanding with data-driven approaches to construct hybrid AI hydrological models, achieving more accurate and efficient simulation of land surface water cycles.

Hydrological Hydrodynamics: Employing advanced hydrodynamic models, we investigate the physical processes governing diverse hydrological environments, including coastal regions and river confluences. Our research encompasses the interactions of tides, currents, and waves, as well as river flow convergence and material transport mechanisms, striving to understand the complex dynamics of hydrological systems and their responses to natural and anthropogenic factors.

Isotope Hydrology: We harness the power of stable isotopes as environmental tracers to unravel the mysteries of water movement and transformation. Through isotopic analysis, we track the sources, pathways, and alterations of water across various ecosystems, shedding light on hydrological processes, biogeochemical cycles, and paleoclimate conditions.

Large Eddy Simulation (LES) Modeling: Building on Prof. Wei's postdoctoral work, we develop and apply isotope-enabled LES models to study fine-scale atmospheric and hydrological processes, bridging the gap between micro-scale turbulence and larger-scale water cycle dynamics.

Our Approach

By integrating these diverse approaches, our group aims to contribute to a more comprehensive understanding of Earth's surface water dynamics and their far-reaching implications. We emphasize interdisciplinary collaboration, leveraging expertise in atmospheric sciences, hydrology, geochemistry, and environmental studies.

Our research seeks to inform effective water resource management strategies, improve climate change adaptation measures, and foster a more sustainable future for our planet. We are committed to training the next generation of scientists equipped to tackle the complex challenges facing our global water systems.

Collaboration and Opportunities

We maintain strong international collaborations, including ongoing partnerships with the University of Tokyo, Yale University, and ETH Zurich. Our group welcomes inquiries from potential graduate students, postdoctoral researchers, and collaborators who share our passion for understanding and protecting Earth's water resources.

For more information about our research or opportunities to join our group, please contact Prof. Zhongwang Wei at zhongwang007@gmail.com.

Address: Room A538, Building 2 Haiqin, Zhuhai Campus, Sun Yat-sen University, Tangjiawan, Zhuhai, Guangdong 519082, China

Latest News

Jul 21, 2025	Hong Kong Visit
Jul 09, 2025	Dr. Wei Zhongwang’s paper “OpenBench: a land models evaluation system” has been accepted for publication in Geoscientific Model Development. Congratulations!
May 21, 2025	Xu Qingchen’s paper “A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022” has been accepted for publication in Agricultural and Forest Meteorology. Congratulations!

Selected Publications

Earth’s futur

Assessing Climate Change Impacts on Crop Yields and Exploring Adaptation Strategies in Northeast China

Qingchen Xu, Hongbin Liang, Zhongwang Wei, and 8 more authors

Earth’s Future, 2024

Bib

@article{Xu:etal:EF2024,
  author = {Xu, Qingchen and Liang, Hongbin and Wei, Zhongwang and Zhang, Yonggen and Lu, Xingjie and Li, Fang and Wei, Nan and Zhang, Shupeng and Yuan, Hua and Liu, Shaofeng and Dai, Yongjiu},
  title = {Assessing Climate Change Impacts on Crop Yields and Exploring Adaptation Strategies in Northeast China},
  journal = {{E}arth’s {F}uture},
  year = {2024}
}

GRL

An Unstructured Mesh Generation Tool for Efficient High-Resolution Representation of Spatial Heterogeneity in Land Surface Models

Hanwen Fan, Qingchen Xu, Fan Bai, and 10 more authors

Geophysical Research Letters, 2024

Bib

@article{Fan:etal:GRL2024,
  author = {Fan, Hanwen and Xu, Qingchen and Bai, Fan and Wei, Zhongwang and Zhang, Yonggen and Lu, Xingjie and Wei, Nan and Zhang, Shupeng and Yuan, Hua and Liu, Shaofeng and Li, XianXiang and Li, Xueyan and Dai, Yongjiu},
  title = {An Unstructured Mesh Generation Tool for Efficient High-Resolution Representation of Spatial Heterogeneity in Land Surface Models},
  journal = {Geophysical Research Letters},
  volume = {51},
  number = {6},
  pages = {e2023GL107059},
  year = {2024},
}

GRL

Revisiting the contribution of transpiration to global terrestrial evapotranspiration

Zhongwang Wei, Kei Yoshimura, Lixin Wang, and 3 more authors

2017

Bib

@article{wei:etal:GRL2017,
  author = {Wei, Zhongwang and Yoshimura, Kei and Wang, Lixin and Miralles, Diego G. and Jasechko, Scott and Lee, Xuhui},
  title = {Revisiting the contribution of transpiration to global terrestrial evapotranspiration},
  journal = {Geophysical Research Letters},
  volume = {44},
  number = {6},
  pages = {2792-2801},
  issn = {00948276},
  doi = {10.1002/2016gl072235},
  year = {2017},
  dimensions = {true},
  type = {Journal Article}
}