Shijie Jiang

Dr. Shijie Jiang



Department of Computational Hydrosystems (CHS)
Helmholtz Centre for Environmental Research - UFZ

Permoserstraße 15
04318 Leipzig

Building 7.1, Room 412

What I do

My research focuses on transdisciplinary applications of advanced artificial intelligence techniques (e.g., explainable AI and computer vision) into geoscientific fields (specifically hydrology), including (1) interpretive machine learning for scientific discovery, (2) physics-informed deep learning, and (3) computer vision in hydrology. In the UFZ, I am associated with the project COMPOUNDX that aims to detect compound climate features of extreme impacts. The complete description of the project can be seen here.


2017 - 2021 National University of Singapore
Ph.D. in Hydrology (NUS-SUSTech Collaborative Ph.D. Program)
Thesis: New paradigms for application of intelligent techniques in hydrology: bridging monitoring, modeling, and understanding
2013 - 2016 Beijing Normal University, China
M.Sc. in Groundwater Science and Engineering
2009 - 2013 Wuhan University, China
B.Sc. in Agricultural Water Resources Engineering


  • Jiang, S., Bevacqua, E., & Zscheischler, J. (2022). River flooding mechanisms and their changes in Europe revealed by explainable machine learning. Hydrology and Earth System Sciences, 26, 6339–6359. DOI: 10.5194/hess-26-6339-2022
  • Jiang, S., Zheng, Y., Wang, C., & Babovic, V. (2022). Uncovering flooding mechanisms across the contiguous United States through interpretive deep learning on representative catchments. Water Resources Research, 58(1), e2021WR030185. DOI: 10.1029/2021WR030185
  • Jiang, S., Zheng, Y., & Solomatine, D. (2020) Improving AI system awareness of geoscience knowledge: Symbiotic integration of physical approaches and deep learning. Geophysical Research Letters, 47(13), e2020GL088229. DOI: 10.1029/2020GL088229
  • Jiang, S., Babovic, V., Zheng, Y., & Xiong, J. (2019) Advancing opportunistic sensing in hydrology: a novel approach to measuring rainfall with ordinary surveillance cameras. Water Resources Research, 55(4), 3004–3027. DOI: 10.1029/2018WR024480 (This paper is highlighted as the Research Spotlight in Eos: Ordinary security cameras could keep an eye on rainfall).
  • Jiang, S., Zheng, Y., Babovic, V., Tian, Y., & Han, F. (2018) A computer vision-based approach to fusing spatiotemporal data for hydrological modeling. Journal of Hydrology, 567, 25–40. DOI: 10.1016/j.jhydrol.2018.09.064
  • Jiang, S., Zhai, Y., Leng, S., Wang, J., & Teng, Y. (2016) A HIVE model for regional integrated environmental risk assessment: A case study in China. Human and Ecological Risk Assessment, 22(4), 1002–1028. DOI: 10.1080/10807039.2015.1122510
  • Xiong, R., Zheng, Y., Chen, N., Tian, Q., Liu, W., Han, F., Jiang, S., Lu, M., & Zheng, Y. (2022) Predicting dynamic riverine nitrogen export in unmonitored watersheds: Leveraging insights of AI from data-rich regions. Environmental Science & Technology, 56(14), 10530–10542. DOI: 10.1021/acs.est.2c02232
  • Cai, H., Liu, S., Shi, H., Zhou, Z., Jiang, S., & Babovic, V. (2022) Toward improved lumped groundwater level predictions at catchment scale: mutual integration of water balance mechanism and deep learning method. Journal of Hydrology, 613(B), 128495. DOI: 10.1016/j.jhydrol.2022.128495
  • Tian, Y., Xiong, J., He, X., Pi, X., Jiang, S., Han, F.,& Zheng, Y. (2018) Joint operation of surface water and groundwater reservoirs to address water conflicts in arid regions: An integrated modeling study. Water, 10(8), 1105. DOI: 10.3390/w10081105
  • Leng, S., Zhai, Y., Jiang, S., Lei, Y., & Wang, J. (2017) Water-environmental risk assessment of the Beijing–Tianjin–Hebei collaborative development region in China. Human and Ecological Risk Assessment, 23(1), 1002–1028. DOI: 10.1080/10807039.2016.1229119