Hydrologic & Hydraulic Modeling
Process-based simulation, flood routing, inundation mapping, model calibration, and large-domain water system evaluation.
Hydrology • Climate Extremes • AI for Water Systems
Mengye Chen, Ph.D.
Water resources scientist working at the intersection of hydrologic modeling, flood prediction, remote sensing, climate risk, and AI-assisted environmental analysis.
About
Researcher in hydrology, climate, and data-driven environmental systems.
I am a researcher at the Hydrometeorology and Remote Sensing Laboratory at the University of Oklahoma, with a background in environmental engineering, water resources, climate impacts, and applied economics.
My work combines physics-based hydrologic and hydraulic models, remote sensing, large-scale environmental datasets, and AI-assisted workflows to improve flood prediction, climate-risk assessment, and water-hazard preparedness.
Research Areas
What I work on
AI for Environmental Systems
Machine learning, AI-assisted modeling workflows, flood prediction, parameter estimation, and scalable data processing.
Climate Extremes & Risk
Climate-driven flood and drought hazards, future scenario analysis, and regional water-resource vulnerability.
Remote Sensing & Geospatial Data
Large-scale raster and NetCDF workflows, precipitation and temperature datasets, regridding, validation, and spatial analytics.
Food–Energy–Water Systems
Interdisciplinary assessment of climate extremes, water resources, renewable energy, agriculture, and economic impacts.
Decision Support
Transforming scientific models into practical tools for emergency response, planning, infrastructure, and resilience decisions.
Selected Projects
Modeling, climate, and AI-enabled water systems
CREST-iMAP hydrologic–hydraulic flood modeling
Coupled hydrologic and hydraulic modeling for flood prediction and inundation mapping, including applications to extreme precipitation and hurricane flood events.
CRESTv3.0 CONUS-wide calibration and validation
Large-scale hydrologic model calibration and evaluation across the contiguous United States to support water-resource and flood-risk applications.
C-FEWS climate, food, energy, and water systems
Interdisciplinary modeling of climate extremes and their impacts across water resources, agriculture, renewable energy, and economic systems.
Peru Andes flood and water-resource risk
WRF dynamic downscaling, CMIP6 climate information, and hydrologic modeling to assess mountain-region flood and water-resource vulnerability.
FLASH flood monitoring and prediction workflows
Hydrometeorological data preparation, model configuration, QA/QC, and workflow improvement for regional to CONUS-scale real-time flood monitoring.
AI-assisted hydrologic modeling
Development of AI-enabled workflows and model-support tools for environmental data analysis, hydrologic simulation, and scientific decision support.
Publications
Selected publications and research outputs
- Chen et al. CONUS-wide calibration and validation for CRESTv3.0. Journal of Hydrology, 2023.
- Chen et al. Flood predictability using CREST-iMAP with quantitative precipitation forecasts and U-Net nowcasts. Journal of Hydrology, 2022.
- Chen et al. Integrated hydrologic–hydraulic mapping for Hurricane Harvey. Journal of Hydrometeorology, 2021.
- Li et al. U.S. flash flood risk under a high-end climate scenario. Nature Communications Earth & Environment, 2022.
- Additional work on CREST-iMAP, CREST-VEC, flood risk, climate extremes, and Food–Energy–Water systems.
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CV
Experience & education
Researcher, Hydrometeorology and Remote Sensing Laboratory
University of Oklahoma
Postdoctoral Researcher, Center for Analysis and Prediction of Storms
University of Oklahoma
Instructor / Teaching Assistant, Civil Engineering & Environmental Science
University of Oklahoma
Ph.D. Environmental Engineering
University of Oklahoma
M.S. Agricultural & Applied Economics; M.S. Environmental Engineering
University of Illinois Urbana-Champaign
B.S. Environmental System Engineering
Pennsylvania State University
Contact
Let’s connect.
I am interested in research, applied AI, climate-risk modeling, environmental data science, flood prediction, and decision-support systems for water and climate resilience.