Exploring scientific unknowns
my research agenda advances under the SURGE Flood Lab framework: Source Understanding diagnoses atmospheric contributions to flood-producing storms using multi-source data from remote sensing, in-situ measurements, and warning messages; Unifying Models integrates physical hydrologic-hydraulic (H&H) models with AI for rapid, interpretable flood computations; Risk Communication adopts a bottom-up approach, tailoring warnings to individual profiles for equitable dissemination; and Global & Sectoral Evaluation assesses cross-sectoral impacts holistically. Central to this is launching the Flood-Agriculture-Climate-Ecology-Diseases (FACED) Collaboratory.
For a full list of publication, please refer to Publication
Research Thrust 1: Remote Sensing and Hydrometeorology
My research has used many remote sensing products, such as satellites (GPM, SMAP, GRACE, Landsat, etc.) and weather radars (WSR-88D), to capture extreme hydrometeorological events and hydrologic water cycle.
Selected Reference:
Li, Zhi, Wen, Y., Liao, L., Wolff, D., Meneghini, R., & Schuur, T. (2023). Joint Collaboration on Comparing NOAA’s Ground-Based Weather Radar and NASA–JAXA’s Spaceborne Radar. Bulletin of the American Meteorological Society, 104(8), E1435-E1451. https://doi.org/10.1175/BAMS-D-22-0127.1
Li, Zhi, Tang, G., Kirstetter, P., Gao, S., Li, J.-L. F., Wen, Y., & Hong, Y. (2021). Evaluation of GPM IMERG and its constellations in extreme events over the conterminous United States. Journal of Hydrology, 267, 112725. https://doi.org/10.1016/j.jhydrol.2021.127357.
Li, Zhi, Tang, G., Hong, Z., Chen, M., Gao, S., Kirstetter, P., . . . Hong, Y. (2021). Two-decades of GPM IMERG early and final run products intercomparison: Similarity and difference in climatology, rates, and extremes. Journal of Hydrology, 594, 125975.
Wang, T., Li, Zhi, Ma, Z., Liu, C., Tang, G., 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, 288, 113490, doi: 10.1016/j.rse.2023.113490.
Gao, S., Li, Zhi, Chen, M., Lin, P., Hong, Z., Allen, D., . . . Hong, Y. (2021). Spatiotemporal variability of global river extent and the natural driving factors revealed by decades of Landsat observations, GRACE gravimetry observations, and land surface model simulations. Remote Sensing of Environment, 267, 112725.
Research Thrust 2: Flood Inundation Modeling
I developed a coupled hydrologic-hydraulic (H&H) framework – CREST-iMAP that features a two-way coupling between hydrology and hydrodynamics. I am currently working on developing Flood Foundation Model, a generalizable and transferrable deep learning based flood model
Selected Reference:
Li, Zhi, Chen, M., Gao, S., Wen, Y., Gourley, J. J., Yang, T., Kolar, R., & Hong, Y. (2022). 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, 155, 105450. https://doi.org/10.1016/j.envsoft.2022.105450.
Li, Zhi, Chen, M., Gao, S., Luo, X., Gourley, J. J., Kirstetter, P., . . . Hong, Y. (2021). CREST-iMAP v1. 0: A fully coupled hydrologic-hydraulic modeling framework dedicated to flood inundation mapping and prediction. Environmental Modelling & Software, 141, 105051.
Chen, M., Li, Zhi, Gao, S., Luo, X., Wing, O. E., Shen, X., . . . Hong, Y. (2021). A comprehensive flood inundation mapping for hurricane Harvey using an integrated hydrological and hydraulic model. Journal of Hydrometeorology, 22 (7), 1713–1726.
Chen, M., Li, Zhi, Gao, S., Xue, M., Gourley, J. J., Kolar, R. L., & Hong, Y. (2022). A flood predictability study for Hurricane Harvey with the CREST-iMAP model using high-resolution quantitative precipitation forecasts and U-Net deep learning precipitation nowcasts. Journal of Hydrology, 128168.
Research Thrust 3: Hydrologic Simulation and Flooding
My research involves using large-scale and hyper-resolution hydrologic models to study terrestrial water changes.
Selected Reference:
Li, Zhi, Gao, S., Chen, M., Gourley, J., Mizukami, N., and Hong, Y.: CREST-VEC: A framework towards more accurate and realistic flood simulation across scales, Geoscientific Model Development, https://doi.org/10.5194/gmd-2022-61, 2022.
Li, Zhi, Chen, M., Gao, S., Gourley, J. J., Yang, T., Shen, X., . . . Hong, Y. (2021). A multi-source 120-year us flood database with a unified common format and public access. Earth System Science Data, 13 (8), 3755–3766. doi: 10.5194/essd-13-3755-2021.
Sun, A. Y., Li, Zhi, Lee, W., Huang, Q., Scanlon, B. R., & Dawson, C. (2023). Rapid Flood Inundation Forecast Using Fourier Neural Operator. ArXiv. /abs/2307.16090
Chen, M., Li, Zhi, Vergara, H., Gourley, J. J., Xue, M., Hong, Y., Hu, X., Novoa, H., Martin, E.R., McPherson, R.A., Gao, S., Perez, A. V., Morales, I. Y. (2023). CONUS-wide model calibration and validation for CRESTv3.0 – an improved Coupled Routing and Excess STorage distributed hydrological model. Journal of Hydrology, in press.
Research Thrust 4: Land-Atmosphere Interaction
I am keen to investigate the land-atmosphere interactions, in particular, how soil moisture exerts impacts on extreme rainfall. A phenomenon called the “Brown Ocean” Effect indicates that anomalously wet soil moisture can re-intensify landfilling hurricanes.
Selected Reference:
Li, Zhi, Tiwari, A., Sui, X., Garrison, J., Marks, F., Niyogi, D., Studying Brown Ocean Re-intensification of Hurricane Florence Using CYGNSS and SMAP Soil Moisture Data and a Numerical Weather Model, Geophysical Research Letters, doi: 10.1029/2023GL105102.
Research Thrust 5: Climate Change Impact on Extreme Events
Climate change has significant impacts on hydrology and increases the frequency and intensity of flooding. As temperatures rise, more precipitation falls as rain instead of snow, leading to higher river flows and flood risks. Furthermore, the warmer atmosphere can hold more moisture, resulting in more frequent and severe precipitation events, further exacerbating flood risk. Additionally, rising sea levels, combined with more frequent and intense storms, increase the likelihood of coastal flooding. To mitigate the impacts of climate change on hydrology, adaptation measures such as improved flood management, resilient infrastructure, and land-use planning are essential.
Selected Reference:
Li, Zhi, Gao, S., Chen, M., Gourley, J. J., Liu, C., Prein, A. F., & Hong, Y. (2022). The conterminous United States are projected to become more prone to flash floods in a high-end emissions scenario. Communications Earth & Environment, 3(1), 1-9.
Li, Zhi, Gao, S., Chen, M., Gourley, J. J., & Hong, Y. (2022). Spatiotemporal characteristics of US floods: Current status and forecast under a future warmer climate. Earth’s Future, 10, e2022EF002700. https://doi.org/10.1029/2022EF002700
Research Thrust 6: Environmental and Climate Justice
I have been working with Tribal Nations in Oklahoma and Florida to advocate environmental and climate justice. Given my upbringing and identity as an Indigenous People in China, I am deeply committed to addressing injustice issues by promoting open science, open data, and integrating scientific knowledge and indigenous knowledge. I deeply believe that solutions to a community lie within the community, so I have been working with stakeholders and decision-makers in local communities to co-produce actionable science.
Selected Reference:
Li, Zhi, Tsoodle, T., Chen, M., Gao, S., Zhang, J., Wen, Y., Yang, T., Hong, Y., Future extreme rainfall and flood risks for Native Americans under climate and demographic change: A case study in Oklahoma, in press, Weather, Climate, and Society, doi: 10.1175/WCAS-D-23-0005.
Research Thrust 7: AI in Hydrologic Science
Hydrologic Models or Physical Models are good at predicting rainfall-streamflow response but it can be tedious to set up and computationally expensive to run. I am developing hydrologic foundation models that hope to disrupt existing hydrologic science by developing a hydrology-focused LLM (brain) and agents to execute, discover hydrologic sciences.
Selected Reference:
Yan, S., Li, Zhi., Zhu, S., Wen, Y., Zhang, M., Chen, M., … & Hong, Y. (2025). AQUAH: Automatic Quantification and Unified Agent in Hydrology. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 2926-2935).
Research Visualization
North America Flashiness-Intensity-Duration-Frequency values