School of Earth System Science
Professor
Hydrological Remote Sensing
dongjianzhi@tju.edu.cn
92 Weijin Road, Bldg 16, Room 304, Nankai District, Tianjin University
300072
Professor Dong Jianzhi graduated from Delft University of Technology in the Netherlands. He has conducted extensive research on the remote sensing observation, simulation, and assimilation of large-scale terrestrial hydrological processes at the USDA-ARS Hydrology and Remote Sensing Laboratory (HRSL) in the United States and at the Massachusetts Institute of Technology (MIT). Prof. Dong is an associate editor for the leading hydrology journal Water Resources Research. He has published over 20 articles in top-tier journals including Nature Communications, Geophysical Research Letters, Remote Sensing of Environment, and Water Resources Research. His work has been featured in a special report by NASA.
The research group led by Professor Dong focuses on the hydrological responses and feedback processes under global change. Their research directions include:
1. Acquisition, uncertainty analysis, and optimal integration of large-scale (watershed, national, and global) hydrological information;
2. Simulation and assimilation of large-scale terrestrial hydrological processes;
3. Hydrological responses and land-atmosphere coupling processes under global change.
Professor Dong has developed a new generation precipitation data optimization and merging framework, the Statistical Uncertainty analysis-based Precipitation mERging framework (SUPER), published in Remote Sensing of Environment.
The paper can be accessed at: Remote Sensing of Environment.
Data can be downloaded from: www.ctrehr.com
- Doctoral degree| Delft University of Technology| Hydrology and Water Resources| 2016
- Master’s Degree| Beijing Normal University| geography| 2012
- Bachelor’s Degree| Sun Yat-sen University| geography| 2009
- Land surface modeling and data assimilation
- Land-atmosphere coupling
- Hydrological Remote Sensing
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2022.5-Now
School of Earth System Science,Tianjin University, China | Professor  -
2021.6-2022.4
Department of Civil and Environmental Engineering | Massachusetts Institute of Technology, USA  -
2017.4-2021.5
Hydrology and Remote Sensing Laboratory | United States Department of Agriculture, USA 
- Papers
- [1] Xin Tian, Jianzhi Dong, Xi Chen, Jianhong Zhou, Man Gao, Lingna Wei, Xiaoqi Kang et al. County-level evaluation of large-scale gridded data sets of irrigated area over China, J. Geophys. Res. Atmos., 2024, e2023JD040333.
- [2] Xiaoqi Kang, Jianzhi Dong, Wade T. Crow, Lingna Wei, and Huiwen Zhang. The conditional bias of extreme precipitation in multi-source merged data sets, GRL, 2024, e2024GL111378.
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- [3] Jianzhi Dong, J., Xi Chen, Y. Li, M. Gao, L. Wei, N. Tangdamrongsu, and W. Crow. Inter-Basin Water Transfer Effectively Compensates for Regional Unsustainable Water Use, WRR, 2023, e2023WR035129.
- [4] Xin Tian, Jianzhi Dong, S. Jin, Hai He, Hao Yin, and Xi Chen. Climate change impacts on regional agricultural irrigation water use in semi-arid environments, Ag.Wat. Man., 2023, e2022WR032472. 2
- [5] Jianzhi Dong, Ruzbeh Akbar, Andrew F. Feldman, Daniel Short Gianotti, and Dara Entekhabi. Land surfaces at the tipping-point for water and energy balance coupling, WRR, 2023, e2023WR035129.
- [6] Jianzhi Dong, W. Crow, Xi Chen, N. Tangdamrongsub, M. Gao, S. Sun, J. Qiu, L. Wei, H. Gao, and Z. Duan. Statistical uncertainty analysis-based precipitation merging (SUPER): A new framework for improved global precipitation estimation, RSE, 2022, 113299.
- [7] Wade T. Crow, Jianzhi Dong, and Rolf H. Reichle. Leveraging pre‐storm soil moisture estimates for enhanced land surface model calibration in ungauged hydrologic basins, WRR., 2022, 58(8): e2021WR031565.
- [8] Jianzhi Dong, Ruzbeh Akbar, Daniel J. Short Gianotti, Andrew F. Feldman, Wade T. Crow, and Dara Entekhabi. Can surface soil moisture information identify evapotranspiration regime transitions?, GRL, 2022, e2021GL097697.
- [9] Jianzhi Dong, Fangni Lei, and Wade T. Crow. Land transpiration-evaporation partitioning errors responsible for modeled summertime warm bias in the central United States, Nat. Commun., 2022, 13, 336.
- [10] Jianzhi Dong, Paul A. Dirmeyer, Fangni Lei, Martha C. Anderson, Thomas RH Holmes, Christopher Hain, and Wade T. Crow. Soil evaporation stress determines soil moisture-evapotranspiration coupling strength in land surface modeling, GRL, 2020, e2020GL090391.
- [11] Jianzhi Dong, Wade T. Crow, and Rolf Reichle, Thomas RH Holmes, Christopher Hain, and Wade T. Crow. Improving rain/no-rain detection skill by merging precipitation estimates from different sources, JHM, 2020, 21, 2419-2429.
- [12] Jianzhi Dong, Wade T. Crow, Kenneth J. Tobin, Michael H. Cosh, David D. Bosch, Patrick J. Starks, Mark Seyfried, and Chandra Holifield Collins. Comparison of microwave remote sensing and land surface modeling for surface soil moisture climatology estimation, RSE, 2020, 242, 111756.
- [13] Jianzhi Dong, Lingna Wei, Xi Chen, Zheng Duan, and Yang Lu. An instrument variable based algorithm for estimating cross-correlated hydrological remote sensing errors, J. Hydro., 2020, 124413.3
- [14] Jianzhi Dong, Wade Crow, Rolf Reichle, Qing Liu, Fangni Lei, and Michael H. Cosh. A global assessment of added value in the SMAP Level 4 soil moisture product relative to its baseline land surface model, GRL, 2019, 6604-6613.
- [15] Jianzhi Dong, Wade Crow, Zheng Duan, Lingna Wei, and Yang Lu. A double instrumental variable algorithm for geophysical product error estimation, RSE, 2019, 225, 217–228.
- [16] Jianzhi Dong, Wade Crow. L-band remote sensing increases sampled levels of global soil moisture–air temperature coupling strength, RSE, 2019, 220, 51–58.
- [17] Jianzhi Dong, Wade Crow. Use of satellite soil moisture to diagnose climate model representations of European soil moisture–air temperature coupling strength, GRL, 2018, 45, 884–891.
- [18] Jianzhi Dong, Wade Crow. The added value of assimilating remotely sensed soil moisture for estimating summertime soil moisture–air temperature coupling strength, WRR, 2018, 54, 6072–6084.
- [19] Jianzhi Dong, Wade Crow, and Rajat Bindlish. The error structure of the SMAP single and dual channel soil moisture retrievals, GRL, 2018, 45, 758–765.
- [20] Jianzhi Dong, Wade Crow. An improved triple collocation algorithm for decomposing autocorrelated and random observation errors, J. Geophys. Res. Atmos., 2017, 122(13), 081–13,094.
- [21] Jianzhi Dong, Rosa Agliata, Susan C. Steele-Dunne, Olivier Hoes, Thom Bogaard, and Nick van de Giesen. Impacts of heating strategies on soil moisture estimation using actively heated fiber optic cables, Sensors, 2017, 17(9).
- [22] Jianzhi Dong, Susan C. Steele-Dunne, Tyson E. Ochsner, Christine Hatch, John Selker, Scott Tyler, Michael H. Cosh, and Nick van de Giesen. Mapping high-resolution soil moisture and properties using distributed temperature sensing data and an adaptive particle batch smoother, WRR, 2016, 52, 7690–7710.
- [23] Jianzhi Dong, Susan C. Steele-Dunne, Tyson E. Ochsner, and Nick van de Giesen. Determining soil moisture and soil properties in vegetated areas by assimilating soil temperatures, WRR, 2016, 52, 4280–4300.
- [24] Jianzhi Dong, Susan C. Steele-Dunne, Tyson E. Ochsner, and Nick van de Giesen. Estimating soil moisture and soil thermal and hydraulic properties by assimilating soil temperatures using a particle batch smoother, Adv. Water Resour., 2016, 91, 104–116. 4
- [25] Jianzhi Dong, Susan C. Steele-Dunne, Tyson E. Ochsner, and Nick van de Giesen. Determining soil moisture by assimilating soil temperature measurements using the ensemble Kalman filter, Adv. Water Resour., 2015, 86, 340–353.
- [26] Jianzhi Dong, Susan C. Steele-Dunne, Jasmeet Judge, and Nick van de Giesen. A particle batch smoother for soil moisture estimation using soil temperature observations, Adv. Water Resour., 2015, 83, 111–122.
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