Bio

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

Contact

  • Academic
    li1995@stanford.edu
    University - Scholar Department: Department of Earth System Science Position: Postdoctoral Scholar

Additional Info

Links

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)

    View details for DOI 10.1029/2023GL105102

    View details for Web of Science ID 001078109700001

  • 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

    View details for DOI 10.1016/j.rse.2023.113490

    View details for Web of Science ID 000942342600001

  • A decadal review of the CREST model family: Developments, applications, and outlook Journal of Hydrology X Li, Z., et al 2023; 20: 100159

    View details for DOI 10.1016/j.hydroa.2023.100159

  • 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

    View details for DOI 10.1016/j.rse.2022.113185

    View details for Web of Science ID 000930771300002

  • 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)

    View details for DOI 10.1029/2022EF002700

    View details for Web of Science ID 000865794800001

  • 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

    View details for DOI 10.1016/j.envsoft.2022.105450

    View details for Web of Science ID 000841773600003

  • 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)

    View details for DOI 10.1038/s43247-022-00409-6

    View details for Web of Science ID 000779137900002

  • 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

    View details for DOI 10.1016/j.envsoft.2021.105051

    View details for Web of Science ID 000655692100003

https://orcid.org/0000-0003-4760-842X