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Tiantian Yang, Ph.D.
Hello, I am Tiantian Yang. I am an Associate professor at the University of Oklahoma, School of Civil Engineering and Environmental Science. My research focuses on hydrology, water resources, weather and climate, complex water-energy system, AI/DL development and application, especially for Subseasonal-to-Seasonal (S2S) forecasts and reservoir/lake operation, hydrologic modeling, and surface and sub-surface water resources management and planning problems across regional and global scales.
Annoucement:
We will be moving!
Starting at Jan 2026, I will be starting a new appointment as a Tenured Associate Professor at the School for Enviroment and Sustainability (SEAS) at the University of Michigan - Ann Arbor.
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I will be hiring two PhD students (Fall 2026 start) and two Postdocs in the areas of Hydrology, Water Resources, AI/DL, Hydrometeorology, and Hydroclimatetology.
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Please send your CV to: Tiantian.Yang@ou.edu
and copy to my new email address:
yangtt@umich.edu
Newly Accepted Articles / News
(New) Lujun Zhang and Team's paper accepted in Journal of Hydrology
entitled "Evaluation of Subseasonal-to-Seasonal (S2S) Precipitation Forecast from the North American Multi-Model Ensemble Phase II (NMME-2) over the contiguous U.S."
Tiantian Yang and Team's paper published in Journal of Hydrology
entitled "A large-scale comparison of Artificial Intelligence and Data Mining (AI&DM) techniques in simulating reservoir releases over the Upper Colorado Region"
Citation: Yang, T., Zhang, L., Kim, T., Hong, Y., Zhang, D., & Peng, Q. (2021). A large-scale comparison of Artificial Intelligence and Data Mining (AI&DM) techniques in simulating reservoir releases over the Upper Colorado Region. Journal of Hydrology, 602, 126723.
Tareem Kim and Team's paper accepted in the Journal of Hydrology
entitled "Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four watersheds with different hydro-climatic regions across the CONUS"
Citation: Kim, T., Yang, T., Gao, S., Zhang, L., Ding, Z., Wen, X., ... & Hong, Y. (2021). Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four watersheds with different hydro-climatic regions across the CONUS. Journal of Hydrology, 598, 126423.
Ziyu Ding and Team's paper accepted in the Applied Energy
entitled "A forecast-driven decision-making model for long-term operation of a hydro-wind-photovoltaic hybrid system"
Citation: Ding, Z., Wen, X., Tan, Q., Yang, T., Fang, G., Lei, X., ... & Wang, H. (2021). A forecast-driven decision-making model for long-term operation of a hydro-wind-photovoltaic hybrid system. Applied Energy, 291, 116820.
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