Dongmei Feng

Dongmei Feng serves as an Assistant Professor in the Department of Chemical and Environmental Engineering within the College of Engineering and Applied Science at the University of Cincinnati. Her research focuses on terrestrial hydrology, specializing in river systems through innovative integration of satellite remote sensing, process-based models, and machine learning to address freshwater resource challenges.

Dr. Feng earned her Ph.D. (2018), M.S. (2013), and B.S. (2010) in Environmental Engineering from Northeastern University and Tongji University. Her research interests span terrestrial hydrology, river dynamics, satellite remote sensing applications, machine learning in hydrological modeling, freshwater ecosystem health, inland water carbon cycling, and harmful algal blooms monitoring. She develops cutting-edge methodologies to quantify river water quantity and quality while advancing understanding of complex river dynamics under changing environmental conditions.

Her publication portfolio demonstrates strong focus on satellite-based river monitoring, with recent work analyzing global river discharge changes, Arctic hydrology, suspended sediment quantification, and phosphorus cycling using machine learning. This research trajectory reflects increasing integration of NASA's SWOT mission data with traditional hydrological models to address continental-scale water challenges.

• 2023 CEAS Faculty Development Award
• 2024 NASA Early Career Investigator Award
• 2024 CEAS Distinguished Researcher Award
• 2025 CEAS Research Award for Early Career Faculty

As Principal Investigator, Dr. Feng has secured over $2.2 million in federal research funding since 2022, including multiple NASA grants (totaling $1.9M), USGS projects, and University of Cincinnati initiatives. Her current portfolio includes lead roles in pan-Arctic sediment mapping, Ohio River Basin phosphorus monitoring, and global inland water carbon emission studies. She directs the Terrestrial Hydrology Lab, which employs UAVs, satellite data, and machine learning to study Earth's water systems with emphasis on river dynamics and ecosystem health.