I am currently an Assistant Professor at CU Boulder. My research spans from remote sensing estimates of precipitation, to fundamental hydrology, to hydrologic modelling, and to flood inundation mapping, and to social perspectives of flood impacts. I am actively looking for collaborations in these areas 👯.
Not updated after 2022, a full list of publication is at my website
Li, Z., Gao, S., Chen, M. et al. The conterminous United States are projected to become more prone to flash floods in a high-end emissions scenario. Commun Earth Environ 3, 86 (2022). https://doi.org/10.1038/s43247-022-00409-6
Li, Z., Guoqiang Tang, Pierre Kirstetter, Shang Gao, J.L. Li, Yixin Wen, Yang Hong, 2022. Evaluation of GPM IMERG and its constellations in extreme events over the conterminous united states, Journal of Hydrology, 606, 127357. https://doi.org/10.1016/j.jhydrol.2021.127357
Li, Z., Chen, M., Gao, S., Luo, X., Gourley, J., Kirstetter, P., Yang, T., Kolar, R., McGovern, A., Wen, Y., Rao, B., Yami, T., Hong, Y., 2021. CREST-iMAP v1.0: A fully coupled hydrologic-hydraulic modeling framework dedicated to flood inundation mapping and prediction, Environmental Modelling and Software, 141, 105051.
Li, Z., Chen, M., Gao, S., Gourley, J. J., Yang, T., Shen, X., Kolar, R., and Hong, Y.: A multi-source 120-year U.S. flood database with a unified common format and public access, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2021-36, in review, 2021.
Li, Z., Tang, G., Hong, Z., Chen, M., Gao, S., Kirstetter, P., Gourley, J.J., Wen, Y.,Yami, T., Nabih, S., Hong, Y., Two-decades of GPM IMERG Early and Final Run Products Intercomparison:Similarity and Difference in Climatology, Rates, and Extremes, Journal of Hydrology (2021). https://doi.org/10.1016/j.jhydrol.2021.125975
Li, Z., Wen, Y., Schreier, M., Behrangi, A., Hong, Y., & Lambrigtsen, B. (2020). Advancing satellite precipitation retrievals with data driven approaches: is black box model explainable?. Earth and Space Science, 7, e2020EA001423. https://doi.org/10.1029/2020EA001423
Li, Z.; Chen, M.; Gao, S.; Hong, Z.; Tang, G.; Wen, Y.; Gourley, J.J.; Hong, Y. Cross-Examination of Similarity, Difference and Deficiency of Gauge, Radar and Satellite Precipitation Measuring Uncertainties for Extreme Events Using Conventional Metrics and Multiplicative Triple Collocation. Remote Sens. 2020, 12, 1258. LINK
Sui, X.; Li, Z.; Ma, Z.; Xu, J.; Zhu, S.; Liu, H. Ground Validation and Error Sources Identification for GPM IMERG Product over the Southeast Coastal Regions of China. Remote Sens. 2020, 12, 4154.
Cai, S., Zhou, S., Wu, P., Li, Z., Deng, S., 2019. BIBLIOMETRIC ANALYSIS OF RESEARCH ON FISH METAL FROM 1997 TO 2016, FRESENIUS ENVIRONMENTAL BULLETIN
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A differentiable, coupled hydrologic–hydraulic flood model in PyTorch. v2 replaces the ANUGA solver with a well-balanced finite-volume scheme in ~2k lines of PyTorch, keeping the CREST water balance — so the model is now differentiable end to end. Validated on Hurricane Harvey against 813 USGS high-water marks.
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Ensemble Framework For Flash Flood Forecasting. Continental-scale distributed hydrologic modeling with rapid forecast updates — SAC-SMA, CREST, and HP water balances coupled to linear-reservoir or kinematic-wave routing. My fork · manual · CREST family
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Does your AI hydrologic model conserve mass, energy and momentum? A benchmark that asks one question of any AI hydrologic model — not whether it fits a hydrograph, but whether its budgets close. Open to community probes and model submissions; contribute a probe, join the paper. flood-lab.github.io/HydroTuring
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RL fine-tuning of LLMs for hydrologic model calibration. Trains tool-calling language models to calibrate EF5/CREST via multi-turn GRPO with online simulation feedback, so the model internalizes calibration reasoning instead of relying on prompt engineering. Docs
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