School of Earth System Science
Associate professor
地下水科学与工程(Subsurface Hydrology)、土壤物理(Soil Physics)、水文与水资源(Hydrology and Water Resources)
ygzhang@tju.edu.cn
92 Weijin Road, Bldg 16, Room 309, Nankai District, Tianjin University
300072
Yonggen Zhang
Associate Professor and PhD Supervisor from School of Earth System Science, Tianjin University
Expertise Summary
Dr. Zhang specializes in computational and experimental approaches to solve fundamental physical problems in subsurface porous media, such as soil and groundwater. His work applies to practical engineering challenges including groundwater resources, soil and groundwater environments, and energy extraction. Key contributions include the development of the Rosetta3 model for predicting soil hydraulic parameters and the creation of global high-resolution soil hydraulic and thermal datasets.
Education
B.S. in Hydrogeology, Jilin University, Changchun, China (2007)
Ph.D. in Hydrogeology, China University of Geosciences (Beijing), China (2014)
Joint Ph.D. Training, Department of Hydrology and Water Resources, University of Arizona, Tucson, USA (2012–2014), under Prof. Shlomo P. Neuman and Prof. Marcel Schaap
Positions
Visiting Student, Department of Hydrology and Water Resources, University of Arizona (February 2012 – March 2014)
Research Specialist/Postdoctoral Researcher, Departments of Hydrology and Water Resources and Soil, Water, and Environmental Science, University of Arizona (September 2013 – July 2017); collaborated with Prof. Marcel Schaap, Regents' Prof. Shlomo P. Neuman, and Prof. Alberto Guadagnini (Politecnico di Milano, Italy)
Associate Professor and PhD Supervisor, School of Earth System Science, Tianjin University (July 2017 – present); selected for "Beiyang Scholars · Talented Associate Professor" and "Beiyang Scholars · Young Backbone Teacher" programs
Research Interests
Subsurface flow dynamics in saturated and unsaturated soils
Determination of saturated and unsaturated soil hydraulic properties
Parameter estimation, geostatistics, and uncertainty assessment
Development of soil parameterization schemes, including the Rosetta3 model
Global high-resolution land surface soil datasets for hydrological, soil, and climate models
Pore-scale simulations using methods like Lattice Boltzmann
For detailed resources:
Rosetta3 Model: Improves prediction accuracy of soil hydraulic parameters and provides covariance matrices for stochastic simulations, data assimilation, and fusion. Available implementations include:
Web interfaces: Handbook60 (by Todd Skaggs, USDA-ARS) and DSIWeb (by Ehsan Ghane, Michigan State University;
Python version (compatible with Python 2/3): GitHub Repository with user manual; works on Linux, Windows 7+, Mac OS X;
Rosetta-soil Python package: PyPI (install via pip install rosetta-soil); project page at GitHub;
R version: Installation and usage details at NCSS-Tech (by Dr. Todd Skaggs, USDA-ARS)
Global High-Resolution Soil Datasets: Parameters such as residual/saturated water content, permeability, field capacity, and plant-available water; supports models for water/pollutant transport, land surface, and climate simulations
Pore-Scale Simulations: Flow field visualizations for varying soil particle sizes using Lattice Boltzmann methods and pore network models
Awards and Honors
Three-time recipient of American Geophysical Union (AGU) most-read article author awards: Reviews of Geophysics (2017–2018), Water Resources Research (2018–2019), Journal of Advances in Modeling Earth Systems (2018–2019)
Selected ESI highly cited papers
Work featured in reports by Xinhua News Agency, People's Daily, National Natural Science Foundation of China, and AGU's EOS publication
Publications
Over 60 peer-reviewed publications (see Google Scholar for full list: Profile). Research focuses on soil hydraulic parameter prediction, global soil datasets, and subsurface modeling.
Professional Activities
Editor, Hydrology and Earth System Sciences (October 2025 – present)
Associate Editor, Water Resources Research (April 2026 – present)
Associate Editor, Vadose Zone Journal (American Society of Agronomy; January 2020 – present)
Executive Chair, 4th International Soil Modeling Conference (first held in China in 2024)
Co-Leader, Soil Parameters Working Group, International Soil Modeling Consortium (ISMC)
Committee Member, First Groundwater Working Committee, China Water Resources Strategy Research Association
Invited speaker in inernational conferences, such as European Geosciences Union (EGU) meeting, etc
Principal Investigator for funded projects: National Natural Science Foundation of China (two general projects, one youth project), Tianjin Natural Science Foundation, Tianjin University Young Backbone Teacher Project
We warmly welcome applications from students in subsurface hydrology, soil physics, agricultural water conservancy, hydrology and water resources, geological engineering, and physical geography for graduate studies.
- Adjoint Ph.D. student| University of Arizona| Hydrology and Water Resources| 2013
- Doctoral degree| China University of Geosciences, Beijing, China| Subsurface hydrology| 2014
- Bachelor’s Degree| Jilin University, Changchun| Subsurface Hydrology| 2007
- 包气带与地下水水文学(大孔隙流、华北地下水漏斗区、海水入侵等)
- 土壤转换函数与土壤结构参数化(Rosetta3)
- 物理信息机器学习
- Pore Scale Simulation (Lattice Boltzmann Method)
- 物理信息机器学习
- Associate Editor, Water Resources Research (April 2026 – present)
- Editor, Hydrology and Earth System Sciences (October 2025 – present)
- Associate Editor, Vadose Zone Journal (January 2020 – present)
- Co-Leader of Working group on pedotransfer functions initiated by ISMC
- Member of AGU, EGU, ISMC, SSSA, Interpore
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2017.7-Now
 Associate Professor, School of Earth System Science, Tianjin University, China | Associate Professor  -
2013.10-2017.2
Hydrology and Water Resources | The University of Arizona, USA | Research Specialist 
- Papers
- [1] 【代表作】Wang, W., Feng, S., Zhang*, Y., Wei, Z., Dong, J., Weihermüller, L., Liu, C.-Q., Vereecken, H., 2026. Fusing ERA5-land and SMAP L4 for an improved global soil moisture product (1950–2025). Earth System Science Data 18, 1061–1088. https://doi.org/10.5194/essd-18-1061-2026
- [2] 【代表作】Xu, S., Zhang*, Y., Li, X., Li, J., Shi, W., Lian, S., Li, L., Weihermüller, L., Schaap, M., 2026. Quantification of delayed recharge by soil surface and riverbed infiltration in a deep groundwater depression zone in the north China plain. Hydrology and Earth System Sciences, 1–31. https://doi.org/10.5194/egusphere-2025-5651
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- [3] 【代表作】Shi, W., Zhang*, Y., Wei, Z., Weihermüller, L., Schaap, M., Vereecken, H., 2026a. Identifying and characterizing bimodal soil hydraulic properties through water retention curve analysis. Journal of Hydrology 666, 134808. https://doi.org/10.1016/j.jhydrol.2025.134808
- [4] 【代表作】【ESI高被引】Vereecken*, H., Amelung, W., Bauke, S.L., Bogena, H., Brüggemann, N., Montzka, C., Vanderborght, J., Bechtold, M., Blöschl, G., Carminati, A., Javaux, M., Konings, A.G., Kusche, J., Neuweiler, I., Or, D., Steele-Dunne, S., Verhoef, A., Young, M., Zhang, Y., 2022. Soil hydrology in the Earth system. Nature Reviews Earth & Environment 3, 573–587. https://doi.org/10.1038/s43017-022-00324-6
- [5] 【代表作】Zhang*, Y., Schaap*, M.G., Wei, Z., 2020. Development of Hierarchical Ensemble Model and Estimates of Soil Water Retention With Global Coverage. Geophysical Research Letters 47, e2020GL088819. https://doi.org/10.1029/2020GL088819
- [6] 【代表作】Zhang*, Y., Schaap*, M.G., Zha, Y., 2018. A high-resolution global map of soil hydraulic properties produced by a hierarchical parameterization of a physically based water retention model. Water Resources Research 54, 9774–9790. https://doi.org/10.1029/2018WR023539
- [7] 【代表作】【ESI高被引】Zhang, Y., Schaap*, M.G., 2017. Weighted recalibration of the Rosetta pedotransfer model with improved estimates of hydraulic parameter distributions and summary statistics (Rosetta3). Journal of Hydrology 547, 39–53. https://doi.org/10.1016/j.jhydrol.2017.01.004
- [8] 【代表作】【ESI高被引】Van Looy*, K., Bouma, J., Herbst, M., Koestel, J., Minasny, B., Mishra, U., Montzka, C., Nemes, A., Pachepsky, Y.A., Padarian, J., Schaap, M.G., Tóth, B., Verhoef, A., Vanderborght, J., Van Der Ploeg, M.J., Weihermüller, L., Zacharias, S., Zhang, Y., Vereecken, H., 2017. Pedotransfer Functions in Earth System Science: Challenges and Perspectives. Reviews of Geophysics 55, 1199–1256. https://doi.org/10.1002/2017RG000581
- [9] Shi, W., Zhang*, Y., Weihermüller, L., Nemes, A., Schaap, M.G., Vereecken, H., 2026b. Correlation of pedotransfer function residuals with input variables and the effect of database similarity on predictive performance. Journal of Hydrology 671, 135192. https://doi.org/10.1016/j.jhydrol.2026.135192
- [10] Zhang, L., Zhang*, Y., Li, J., Lian, S., Li, L., Zhang, T., Feng, P., Ren, Y., Li, Y., Weihermüller, L., 2026a. Multi-stage flood utilization framework to support ecological flow protection and groundwater recovery mechanisms. Journal of Hydrology 664, 134494. https://doi.org/10.1016/j.jhydrol.2025.134494
- [11] Zhang, L., Zhang*, Y., Li, J., Lian, S., Li, X., Feng, P., Weihermüller, L., 2026b. Integrating deep learning and multi-objective optimization for floodwater utilization: a coordinated surface water-groundwater regulation framework for groundwater recovery. Journal of Hydrology 666, 134749. https://doi.org/10.1016/j.jhydrol.2025.134749
- [12] Feng, S., Qin, W., Shi, W., Chen, W., Li, Y., Li, X., Zhang*, Y., 2025. 天津市水资源现状分析与多情景演化规律研究 [Analysis of water resources status quo and multi-scenario evolution of Tianjin City]. Water Saving Irrigation (4), 73–80. https://doi.org/10.12396/jsgg.2024383
- [13] Li, P., Zha*, Y., Zuo, B., Zhang*, Y., 2023. A Family of Soil Water Retention Models Based on Sigmoid Functions. Water Resources Research 59, e2022WR033160. https://doi.org/10.1029/2022WR033160
- [14] Yan, Z., Wang, Z., Fu, Z., Zhang*, Y., Peng, X., Zheng, J., 2023. Microscale heterogeneity controls macroscopic soil heterotrophic respiration by regulating resource availability and environmental stress. Biogeochemistry 1–19. https://doi.org/10.1007/s10533-023-01044-9
- [15] Chen, W., Yao, R., Shi, W., Li, Q., Zhao, J., Zhang*, Y., 2023. 基于系统动力学模型的天津市水资源承载力模拟分析 [Simulation analysis of water resources carrying capacity of Tianjin based on system dynamics model]. Journal of Water Resources & Water Engineering 34(2), 42–51. https://doi.org/10.11705/j.issn.1672-643X.2023.02.06
- [16] Li, Q., Chen, W., Shi, W., Sun, S., Zhang*, Y., 2023. 基于单一和集合土壤转换函数模型对土壤含水量的模拟性能分析 [Performance analysis of simulating soil moisture content based on single and ensemble pedotransfer function]. Soils 55(3), 658–670. https://doi.org/10.13758/j.cnki.tr.2023.03.024
- [17] Zhang*, Y., Weihermüller, L., Toth, B., Noman, M., Vereecken, H., 2022. Analyzing dual porosity in soil hydraulic properties using soil databases for pedotransfer function development. Vadose Zone Journal 21, e20227. https://doi.org/10.1002/vzj2.20227
- [18] Shi, W., Li, Q., Han, Q., Wang, T., Chen, X., Zhang*, Y., 2020. 基于不同优化算法的土壤水分特征曲线模型模拟性能分析 [Performance analysis of soil water retention curve models based on different fitting optimization algorithms]. Journal of Water Resources & Water Engineering 31(4), 157–165. https://doi.org/10.11705/j.issn.1672-643X.2020.04.23
- [19] Dai*, Y., Xin*, Q., Wei*, N., Zhang*, Y., Shangguan, W., Yuan, H., Zhang, S., Liu, S., Lu, X., 2019. A global high‐resolution data set of soil hydraulic and thermal properties for land surface modeling. Journal of Advances in Modeling Earth Systems 11, 2996–3023. https://doi.org/10.1029/2019MS001784
- [20] Zhang*, Y., Schaap*, M.G., 2019. Estimation of saturated hydraulic conductivity with pedotransfer functions: A review. Journal of Hydrology 575, 1011–1030. https://doi.org/10.1016/j.jhydrol.2019.05.058
- [21] Zhang, Y., Schaap*, M.G., Guadagnini, A., Neuman, S.P., 2016. Inverse modeling of unsaturated flow using clusters of soil texture and pedotransfer functions: inverse modeling using clusters of soil texture. Water Resources Research 52, 7631–7644. https://doi.org/10.1002/2016WR019016
- [22] Zhang, Y., Liu*, M., Chen, H., Hou, G., 2014. Source identification of polycyclic aromatic hydrocarbons in different ecological wetland components of the qinkenpao wetland in northeast China. Ecotoxicology and environmental safety 102, 161–167. https://doi.org/10.1016/j.ecoenv.2014.01.030
- [23] Li, M., Shao*, W., Yu, W., Su, Y., Song, Q., Zhang, Yiping, Gao, H., Zhang, Yonggen, Dong, J., 2025. Optimization of soil hydraulic parameters within a constrained sampling space. Geoderma 455, 117210. https://doi.org/10.1016/j.geoderma.2025.117210
- [24] Liu, Y., Zha*, Y., Ran, G., Zhang, Y., Shi, L., 2025. SMRFR: A global multilayer soil moisture dataset generated using Random Forest from multi-source data. Scientific Data 12, 1170. https://doi.org/10.1038/s41597-025-05511-w
- [25] Zhou, J., Wang*, Y., Qi, P., Ma, R., Vereecken, H., Minasny, B., Zhang, Y., 2025. Soil Physics-Informed Neural Networks to Estimate Bimodal Soil Hydraulic Properties. Water Resources Research 61, e2024WR039337. https://doi.org/10.1029/2024WR039337
- [26] Bai, F., Wei*, Z., Wei, N., Lu, X., Yuan, H., Zhang, S., Liu, S., Zhang, Y., Li, X., Dai, Y., 2024. Global assessment of atmospheric forcing uncertainties in the common land model 2024 simulations. Journal of Geophysical Research: Atmospheres 129, e2024JD041520. https://doi.org/10.1029/2024JD041520
- [27] Cai, Y., Xu, Q., Bai, F., Cao, X., Wei*, Z., Lu*, X., Wei, N., Yuan, H., Zhang, S., Liu, S., Zhang, Y., Li, X., Dai, Y., 2024. Reconciling global terrestrial evapotranspiration estimates from multi-product intercomparison and evaluation. Water Resources Research 60, e2024WR037608. https://doi.org/10.1029/2024WR037608
- [28] Chen, S., Li*, L., Wei, Z., Wei, N., Zhang, Y., Zhang, Shupeng, Yuan, H., Shangguan, W., Zhang, Shulei, Li, Q., Dai, Y., 2024. Exploring topography downscaling methods for hyper‐resolution land surface modeling. Journal of Geophysical Research: Atmospheres 129, e2024JD041338. https://doi.org/10.1029/2024JD041338
- [29] Fan, H., Xu, Q., Bai, F., Wei*, Z., Zhang, Y., Lu, X., Wei, N., Zhang, S., Yuan, H., Liu, S., Li, XianXiang, Li, Xueyan, Dai, Y., 2024. An unstructured mesh generation tool for efficient high-resolution representation of spatial heterogeneity in land surface models. Geophysical Research Letters 51, e2023GL107059. https://doi.org/10.1029/2023GL107059
- [30] Li, L., Dai*, Y., Wei, Z., Shangguan, W., Wei, N., Zhang, Y., Li, Q., Li, X.-X., 2024a. Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models. Advances in Atmospheric Sciences 1–16. https://doi.org/10.1007/s00376-023-3181-8
- [31] Li, L., Dai*, Y., Wei, Z., Shangguan, W., Zhang, Y., Wei, N., Li, Q., 2024b. Enforcing Water Balance in Multitask Deep Learning Models for Hydrological Forecasting. Journal of Hydrometeorology 25, 89–103. https://doi.org/10.1175/JHM-D-23-0073.1
- [32] Li, P., Zha*, Y., Zhang, Y., Michael Tso, C.-H., Attinger, S., Samaniego, L., Peng, J., 2024. Deep learning integrating scale conversion and pedo-transfer function to avoid potential errors in cross-scale transfer. Water Resources Research 60, e2023WR035543. https://doi.org/10.1029/2023WR035543
- [33] Schaap*, M.G., Zhang, Y., Rasmussen, C., 2024. Soil hydraulic property maps for the contiguous united states at 100‐m resolution and seven depths: Code design and preliminary results. Vadose Zone Journal 23, e20377. https://doi.org/10.1002/vzj2.20377
- [34] Weber*, T.K.D., Weihermüller, L., Nemes, A., ..., Zhang, Y., et al., 2024. Hydro-pedotransfer functions: A roadmap for future development. Hydrology and Earth System Sciences 28, 3391–3433. https://doi.org/10.5194/hess-28-3391-2024
- [35] Wu, L., Wang, P., Zhang, Q., ..., Zhang, Yonggen, et al., 2024. Dominant contribution of combustion-related ammonium during haze pollution in Beijing. Science Bulletin 69, 978–987. https://doi.org/10.1016/j.scib.2024.01.002
- [36] Xu, Q., Liang, H., Wei*, Z., Zhang, Y., Lu, X., Li, F., Wei, N., Zhang, S., Yuan, H., Liu, S., Dai, Y., 2024. Assessing climate change impacts on crop yields and exploring adaptation strategies in northeast China. Earth’s Future 12, e2023EF004063. https://doi.org/10.1029/2023EF004063
- [37] Zhao, J., Chen*, W., Liu, Z., Liu, W., Li, K., Zhang, B., Zhang, Y., Yu, L., Sakai, T., 2024. Urban expansion, economic development, and carbon emissions: trends, patterns, and decoupling in mainland china’s provincial capitals (1985–2020). Ecological Indicators 169, 112777. https://doi.org/10.1016/j.ecolind.2024.112777
- [38] Chen, H., Wang*, T., Zhang, Y., Bai, Y., Chen, X., 2023. Dynamically weighted ensemble of geoscientific models via automated machine-learning-based classification. Geoscientific Model Development 16, 5685–5701. https://doi.org/10.5194/gmd-16-5685-2023
- [39] Shao, W., Chen, S., Li, M., Su, Y., Ni, J., Dong, J., Zhang, Y., Yang*, Z., 2023a. Reducing uncertainties in hydromechanical modeling with a recently developed rosetta 3 podeotransfer function. Engineering Geology 324, 107250. https://doi.org/10.1016/j.enggeo.2023.107250
- [40] Shao*, W., Chen, S., Su, Y., Dong, J., Ni, J., Yang, Z., Zhang, Y., 2023b. Reduce uncertainty in soil hydrological modeling: a comparison of soil hydraulic parameters generated by random sampling and pedotransfer function. Journal of Hydrology 623, 129740. https://doi.org/10.1016/j.jhydrol.2023.129740
- [41] Sun, R., Liu, Y., Sonke, J.E., Feifei, Z., Zhao, Y., Zhang, Y., Chen, J., Liu, C.-Q., Shen, S., Anbar, A.D., Zheng*, W., 2023. Mercury isotope evidence for marine photic zone euxinia across the end-permian mass extinction. Communications Earth & Environment 4, 159. https://doi.org/10.1038/s43247-023-00821-6
- [42] Wu*, Y., Cheng*, T., Zheng, L., Zhang, Y., Zhang, L., 2023. Particle size amplification of black carbon by scattering measurement due to morphology diversity. Environmental Research Letters 18, 24011. https://doi.org/10.1088/1748-9326/acaede
- [43] Schaap*, M.G., Zhang, Y., Nemes, A., 2023. Pedotransfer functions and their application to soil water dynamics. In: Goss, Michael and Oliver, Margaret (eds.) Encyclopedia of Soils in the Environment Second Edition. UK: Elsevier, vol. 4, pp. 642-654. https://doi.org/10.1016/B978-0-12-822974-3.00210-X
- [44] Guo*, B., Zeng, J., Brusseau, M.L., Zhang, Y., 2022. A screening model for quantifying PFAS leaching in the vadose zone and mass discharge to groundwater. Advances in Water Resources 160, 104102. https://doi.org/10.1016/j.advwatres.2021.104102
- [45] Sun, S., Wang, Y., Song*, Z., Chen, C., Zhang, Y., Chen, X., Chen, W., Yuan, W., Wu, X., Ran, X., Wang, Yidong, Li, Q., Wu, L., 2021. Modelling aboveground biomass carbon stock of the bohai rim coastal wetlands by integrating remote sensing, terrain, and climate data. Remote Sensing 13, 4321. https://doi.org/10.3390/rs13214321
- [46] Li, L., Shangguan*, W., Deng, Y., Mao, J., Pan, J., Wei, N., Yuan, H., Zhang, S., Zhang, Y., Dai, Y., 2020. A causal inference model based on random forests to identify the effect of soil moisture on precipitation. Journal of Hydrometeorology 21, 1115–1131. https://doi.org/10.1175/JHM-D-19-0209.1
- [47] Li, P., Zha*, Y., Shi, L., Tso, C.-H.M., Zhang, Y., Zeng, W., 2020a. Comparison of the use of a physical-based model with data assimilation and machine learning methods for simulating soil water dynamics. Journal of Hydrology 584, 124692. https://doi.org/10.1016/j.jhydrol.2020.124692
- [48] Li, P., Zha*, Y., Tso, C.-H.M., Shi, L., Yu, D., Zhang, Y., Zeng, W., 2020b. Data assimilation of uncalibrated soil moisture measurements from frequency-domain reflectometry. Geoderma 374, 114432. https://doi.org/10.1016/j.geoderma.2020.114432
- [49] Sun, S., Song*, Z., Chen, X., Wang, T., Zhang, Y., Zhang, D., Zhang, H., Hao, Q., Chen*, B., 2020a. Multimodel-based analyses of evapotranspiration and its controls in China over the last three decades. Ecohydrology 13, e2195. https://doi.org/10.1002/eco.2195
- [50] Sun, S., Zhang, Yonggen, Song*, Z., Chen, B., Zhang, Yangjian, Yuan, W., Chen, C., Chen, W., Ran, X., Wang, Y., 2020b. Mapping coastal wetlands of the bohai rim at a spatial resolution of 10 M using multiple open-access satellite data and terrain indices. Remote Sensing 12, 4114. https://doi.org/10.3390/rs12244114
- [51] Wei*, Z., He, X., Zhang, Y., Pan, M., Sheffield, J., Peng, L., Yamazaki, D., Moiz, A., Liu, Y., Ikeuchi, K., 2020. Identification of uncertainty sources in quasi-global discharge and inundation simulations using satellite-based precipitation products. Journal of Hydrology 589, 125180. https://doi.org/10.1016/j.jhydrol.2020.125180
- [52] Zha, Y., Shi*, L., Liang, Y., Tso, C.-H.M., Zeng, W., Zhang, Y., 2020. Analytical sensitivity map of head observations on heterogeneous hydraulic parameters via the sensitivity equation method. Journal of Hydrology 591, 125282. https://doi.org/10.1016/j.jhydrol.2020.125282
- [53] Zhu*, L., Shen, T., Ma, R., Fan, D., Zhang, Y., Zha, Y., 2020. Development of cracks in soil: an improved physical model. Geoderma 366, 114258. https://doi.org/10.1016/j.geoderma.2020.114258
- [54] Li, K., Zhang, Y., Yeh*, T.-C.J., Zhao, L., Su, X., Wang, Y.-L., Qi, Y., 2019. An iterative scheme to map and incorporate geologic information of discontinuous heterogeneity in hydraulic tomography. Journal of Hydrology 579, 124143. https://doi.org/10.1016/j.jhydrol.2019.124143
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- [57] Xu, C., Zeng, W., Zhang, H., Huang*, J., Zhang, Y., Wu, J., Schaap, M.G., 2017. Inversion of root zone soil hydraulic parameters with limited calibration data. Soil Science Society of America Journal 81, 734–746. https://doi.org/10.2136/sssaj2016.09.0308
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