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School of Environment Science and Engineering

Home > Faculty > Browse by School/Department > School of Environment Science and Engineering > Han Feng


Han Feng

Research Assistant Professor

School of Environmental Science and Engineering


My research mainly focuses on improving the simulation, prediction and understanding of complex Earth system using watershed-scale models. I use and design new analytical methods (such as Bayesian analysis, data assimilation, and stochastic sampling) for model calibration and uncertainty analysis, with the goal to improve the theory of both modeling tools and the natural system. Recently, I have developed a new Bayesian analysis framework to jointly account for multiple sources of uncertainties. I have also contributed in developing an ecohydrological model, HEIFLOW, which is a physically-based, three-dimensional distributed model. My responsibility was to develop new ecological modules and couple them with the integrated surface water-groundwater model, GSFLOW. My research will involve high-performance scientific computing to solve the complex modeling problems.
Present Employment
Research Assistant Professor, School of Environmental Science and Engineering, Southern University of Science and Technology
2008/09 – 2015/07: Ph.d., Mechanics (Energy and Resources Engineering), Peking University
2004/09 – 2008/07: Bachelor, Theoretical and Applied Mechanics, Peking University
Work Experience                                                
2018-present: Research Assistant Professor, School of Environmental Science and Engineering, Southern University of Science and Technology
2015-2018: Post-doctoral research fellow, School of Environmental Science and Engineering, Southern University of Science and Technology


Research Interests
1) Watershed modelling and its uncertainty analysis
2) Integrated hydrological and ecological modeling at the watershed scale
3) Data-model fusion methods


[1] Han F, Zheng Y (2018), Joint analysis of input and parametric uncertainties in watershed water quality modeling: a formal Bayesian approach. Advances in Water Resources, 116:77-94. Doi: 10.1016/j.advwatres.2018.04.006.
[2] Li X, Cheng GD, Ge YC, Li HY, Han F, Hu XL, Tian W, Tian Y, Pan XD, Nian YY, Zhang YL, Ran YH, Zheng Y, Gao B, Yang DW, Zheng CM, Wang XS, Liu SM, Cai XM (2018), Hydrological cycle in the Heihe River basin and its implication for water resource management in endorheic basins. Journal of Geophysical Research: Atmospheres, 123(2):890-914. Doi: 10.1002/2017JD027889.
[3] Han F, Zheng Y (2016), Multiple-response Bayesian calibration of watershed water quality models with significant input and model structure errors. Advances in Water Resources, 88:109-123. Doi: 10.1016/j.advwatres.2015.12.007
[4] Zheng Y, Han F (2016), Markov Chain Monte Carlo (MCMC) uncertainty analysis for watershed water quality modeling and management. Stochastic Environmental Research and Risk Assessment, 30:293-308. Doi: 10.1007/s00477-015-1091-8
[5] Wu X, Zheng Y, Wu B, Tian Y, Han F, Zheng CM (2016) Optimizing conjunctive use of surface water and groundwater for irrigation to address human-nature water conflicts: A surrogate modeling approach. Agricultural Water Management, 163:380-392. Doi: 10.1016/j.agwat.2015.08.022
[6] Zheng Y, Luo XY, Zhang W, Wu X, Zhang J, Han F (2016) Transport mechanisms of soil-bound mercury in the erosion process during rainfall-runoff events. Environmental Pollution, 215:10-17. Doi: 10.1016/j.envpol.2016.04.101
[7] Wu B, Zheng Y, Wu X, Tian Y, Han F, Liu J, Zheng CM (2015), Optimizing water resources management in large river basins with integrated surface water-groundwater modeling: a surrogate-based approach. Water Resources Research, 51:2153-2173. Doi: 10.1002/2014WR016653.
[8] Luo XL, Zheng Y, Lin ZR, Wu B, Han F, Tian Y, Zhang W, Wang XJ (2015), Evaluating potential non-point source loading of PAHs from contaminated soils: A fugacity-based modeling approach. Environmental Pollution, 196:1-11. Doi: 10.1016/j.envpol.2014.09.011
[9] Wu B, Zheng Y, Tian Y, Yao YY, Han F, Liu J, Zheng CM (2014), Systematic assessment of the uncertainty in integrated surface water-groundwater modeling based on the probabilistic collocation method. Water Resources Research, 50:5848–5865. Doi: 10.1002/2014WR015366
[10] Luo XL, Zheng Y, Wu B, Lin ZR, Han F, Zhang W, Wang XJ (2013), Impact of carbonaceous materials in soil on the transport of soil-bound PAHs during rainfall-runoff events. Environmental Pollution, 182:233-241. Doi: 10.1016/j.envpol.2013.07.029
[11] Zheng Y, Luo XL, Zhang W, Wu B, Han F, Lin ZR, Wang XJ (2012), Enrichment behavior and transport mechanism of soil-bound PAHs during rainfall-runoff events. Environmental Pollution, 171:85-92. Doi: 10.1016/j.envpol.2012.07.030
[12] Zheng Y, Wang WM, Han F, Ping J (2011), Uncertainty assessment for watershed water quality modeling: A Probabilistic Collocation Method based approach. Advances in Water Resources, 34:887-898. Doi: 10.1016/j.advwatres.2011.04.016
[13] Lin ZR, Zheng Y, Xiang RJ, Zhang QM, Han F (2012), Simulation of the nonpoint sources load of heavy metals and its uncertainty analysis. Resources and Environment in the Yangtze Basin, 21:1112-1118. (In Chinese)
[14] Zheng Y, Wang XJ, Wu B, Han F (2010), Regulation, information and decision support for urban nonpoint source pollution management. Advances in Water Science, 21:726-732. (In Chinese)

[1] Zheng Y, Han F, Tian Y, Wu B, Lin ZR (2014). Addressing the uncertainty in modeling watershed nonpoint source pollution. In Jorgensen SE, Chang N and Xu F (Eds), Advances in ecological modelling and engineering - lakes and wetlands (Chapter 5, p113-p159). Elsevier, ISBN: 978-0-444-63249-4.

Research Fields

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