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.
01 — 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.
Milestones
2021
Ph.D., Environmental EngineeringUniversity of Oklahoma
2014 / 2011
M.S., Agricultural & Applied Economics · M.S., Environmental EngineeringUniversity of Illinois Urbana-Champaign
2010
B.S., Environmental System EngineeringPennsylvania State University
02 — 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.
03 — Selected Projects
Modeling, climate, and AI-enabled water systems
Interactive Atlas · Global Dataset
Hydrological Zones
Two decades of daily GLDAS and GPM IMERG data aggregated onto 57,646 HydroBASINS watersheds — per-basin temperature, precipitation, evapotranspiration, runoff, and groundwater storage, plus a HydroSHEDS-derived stream gradient — used to derive global hydrological zones from Köppen/UNEP/Budyko-grounded thresholds.
Open the hydrological zones →
Interactive Atlas · Ongoing / Unpublished
Solar + Wind Storage Atlas
An interactive map of 770 regions worldwide — click any region for its optimal solar/wind mix, storage requirement, and supply-vs-demand charts. USA states add real-units results (TWh, nameplate, land-use feasibility) from real EIA consumption. Preliminary, unpublished research.
Explore the interactive atlas →
Interactive Viewer · Published
CONUS CREST v3.0 Parameter Downloader
An interactive map of ~9,000 USGS streamgauges, color-coded by CREST hydrologic-model skill (NSCE) over the contiguous US. Click any gauge for its metrics and an interactive Water-Year-2019 hydrograph; select by point, HUC8 basin, or rectangle and download the simulation data. Based on published research.
Open the parameter downloader →
Interactive Demo · AI for Audio
AI Stem Splitter
Upload a song and split it into separate stems — vocals, drums, bass, and more — then download each as its own track. A built-in assistant recommends which stems to generate for your goal (karaoke, remix, instrument practice).
Try the stem splitter
Interactive Demo · AI for Hydrology
CREST AI Flood Dashboard
Describe a flood in plain language — or pick USGS gauges straight from the map — and watch the CREST/EF5 model simulate it live: 2-D streamflow animates over the map, hydrographs stream in as the model runs, and the AI assistant explains the event and can calibrate the model when the fit is poor.
Open the flood dashboard
Flood Inundation
CREST-iMAP flood mapping for Hurricane Harvey
A comprehensive flood inundation mapping of Hurricane Harvey using an integrated hydrologic and hydraulic model.
Read paper · J. Hydrometeorology, 2021
Flood Prediction
Flood predictability with QPF and U-Net nowcasts
Evaluating flood predictability of CREST-iMAP driven by quantitative precipitation forecasts and deep-learning (U-Net) precipitation nowcasts.
Read paper · J. Hydrology, 2022
Continental-scale Modeling
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.
Read paper · J. Hydrology, 2023
NSF INFEWS · 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.
Read paper · Frontiers in Env. Science, 2023
Climate Downscaling
Arequipa, Peru: water in the short future
Hyperresolution regional climate modeling and CREST-VEC simulations under SSP5-8.5 to assess flood, drought, and water-resource risk in the arid Peruvian Andes.
Read paper · Am. J. Water Resources, 2025
AI for Hydrology
AQUAH — an AI agent for hydrologic modeling
A language-based, vision-enabled agent that takes a natural-language prompt and autonomously retrieves data, configures and runs a hydrologic model, and writes a report.
Read paper · arXiv, 202504 — Community & Field
Conferences, collaborators, and community engagement
05 — Publications
Selected publications and research outputs
- Chen et al. CONUS-wide calibration and validation for CRESTv3.0. Journal of Hydrology, 2023. doi ↗
- Chen et al. Flood predictability using CREST-iMAP with quantitative precipitation forecasts and U-Net nowcasts. Journal of Hydrology, 2022. doi ↗
- Chen et al. A comprehensive flood inundation mapping for Hurricane Harvey using an integrated hydrological and hydraulic model. Journal of Hydrometeorology, 2021. doi ↗
- Chen et al. Arequipa's water in the short future: a hydrologic outlook for an arid Peruvian Andes region (CREST-VEC, SSP5-8.5). American Journal of Water Resources, 2025. doi ↗
- Yan et al. (incl. Chen) AQUAH: Automatic Quantification and Unified Agent in Hydrology. arXiv preprint, 2025. arXiv ↗
See the full, up-to-date list on Google Scholar.
06 — 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
07 — 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.