Zhi “Allen” Li is the Stanford Doerr School of Sustainability Dean’s Postdoc Fellow. He studies surface water across scales, both spatially (local, continental, and global) and temporally (Hydrology, Hydrometeorology, and Hydroclimatology). His research focuses on flood prediction and monitoring by leveraging Remote Sensing platforms and Hydrologic-Hydraulic coupled models. He devotes himself to improving flood monitoring tools to deliver accurate and timely information for the community, especially under-represented communities.

Honors & Awards

  • Doerr School of Sustainability Dean's Postdoc Fellow, Stanford University (2023)
  • Hoving Fellowship, University of Oklahoma (2019)

Professional Education

  • Bachelor of Engineering, Hohai University (2017)
  • Doctor of Philosophy, University of Oklahoma (2022)
  • Master of Science, National University Of Singapore (2019)
  • PhD, University of Oklahoma, Hydrology and Remote Sensing (2022)
  • MS, National University of Singapore, Water Resources Management (2019)
  • BA, Hohai University, Hydraulic Engineering (2017)

Stanford Advisors

All Publications

  • Studying Brown Ocean Re-Intensification of Hurricane Florence Using CYGNSS and SMAP Soil Moisture Data and a Numerical Weather Model GEOPHYSICAL RESEARCH LETTERS Li, Z., Tiwari, A., Sui, X., Garrison, J., Marks, F., Niyogi, D. 2023; 50 (19)
  • Diverging identifications of extreme precipitation events from satellite observations and reanalysis products: A global perspective based on an object-tracking method REMOTE SENSING OF ENVIRONMENT Wang, T., Li, Z., Ma, Z., Gao, Z., Tang, G. 2023; 288
  • A decadal review of the CREST model family: Developments, applications, and outlook Journal of Hydrology X Li, Z., et al 2023; 20: 100159
  • Disentangling error structures of precipitation datasets using decision trees REMOTE SENSING OF ENVIRONMENT Sui, X., Li, Z., Tang, G., Yang, Z., Niyogi, D. 2022; 280
  • Spatiotemporal Characteristics of US Floods: Current Status and Forecast Under a Future Warmer Climate EARTHS FUTURE Li, Z., Gao, S., Chen, M., Gourley, J. J., Hong, Y. 2022; 10 (10)
  • Can re-infiltration process be ignored for flood inundation mapping and prediction during extreme storms? A case study in Texas Gulf Coast region ENVIRONMENTAL MODELLING & SOFTWARE Li, Z., Chen, M., Gao, S., Wen, Y., Gourle, J. J., Yang, T., Kolar, R., Hong, Y. 2022; 155
  • The conterminous United States are projected to become more prone to flash floods in a high-end emissions scenario COMMUNICATIONS EARTH & ENVIRONMENT Li, Z., Gao, S., Chen, M., Gourley, J. J., Liu, C., Prein, A. F., Hong, Y. 2022; 3 (1)
  • CREST-iMAP v1.0: A fully coupled hydrologic-hydraulic modeling framework dedicated to flood inundation mapping and prediction ENVIRONMENTAL MODELLING & SOFTWARE Li, Z., Chen, M., Gao, S., Luo, X., Gourley, J. J., Kirstetter, P., Yang, T., Kolar, R., McGovern, A., Wen, Y., Rao, B., Yami, T., Hong, Y. 2021; 141