Dr. Tiantian Yang

EPSCoR Research Focus: 
Variable & Marginal Quality Water Supplies
Asst. Professor
School of Civil Engineering & Environmental Science
University of Oklahoma
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Research Interests: 
Dr. Tiantian Yang, assistant professor of civil engineering and environmental science at the University of Oklahoma, is a member of the OK NSF EPSCoR Track-1 RII Award titled Socially Sustainable Solutions for Water, Carbon, and Infrastructure Resilience in Oklahoma. The $20 million research project is a social science-led, multi-disciplinary collaboration among social, physical, biological, engineering, and computational scientists. More than thirty researchers from across the state are working together on the project, which began July 1, 2020.
Dr. Yang's research focuses on water resources management, reservoir operation and optimization, coupled natural and human systems, and the interaction among water-energy-climate. 
Dr. Yang's work supports the OK NSF EPSCoR research project's Focus Area 3: Variable and Marginal Quality Water Supplies (V-MQW). The V-MQW Supplies focus area addresses issues surrounding Oklahoma’s water demands, which are projected to increase 600,000 acre-feet per year between 2007-2060. Reliable water supplies are needed to provide for these demands while meeting the state’s goal of capping freshwater use to 2010 levels. However, freshwater supplies are declining due to reservoir sedimentation and groundwater overdraft and are increasingly vulnerable to S2S variability. Concurrently, volumes of oil and gas ‘produced water,’ municipal wastewater, and stormwater are increasing with continued oil and gas development and urbanization. Disposal of produced waters has been correlated with seismicity, potentially impacting infrastructure and resulting in energy production curtailment in some regions. The challenge is finding a mix of solutions that allow Oklahoma’s diverse array of MQW to be economically treated for beneficial use to address water scarcity related to changing seasonal to sub-seasonal weather patterns, waste disposal, and infrastructure risk while supporting continued energy production and economic growth. 
Key Publications: 
  • Yang T., Hsu K., Duan Q., Sorooshian S., Wang C. 2018. Method to Estimate Optimal Parameters. In: Duan Q., Pappenberger F., Thielen J., Wood A., Cloke H., Schaake J. (eds) Handbook of Hydrometeorological Ensemble Forecasting. Springer, Berlin, Heidelberg, DOI: https://doi.org/10.1007/978-3-642-40457-3_26-1, Online ISBN: 978-3-642-40457-3. 
  • Rahnamay. M, Yang,T., Sadege,M., AghaKouchak, A., Hsu, K., Sorooshian, S. 2018. A Shuffled Complex-Self Adaptive Hybrid EvoLution Optimization Framework. Environmental Modeling and Software. 104:215-235. 
  • Asanjan A.A., Yang, T., K Hsu, S Sorooshian, J Lin, Q Peng. 2018. Short‐Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks. Journal of Geophysical Research: Atmospheres. 123 (22):12,543-12,563. 
  • (ESI Highly Cited Paper) Yang, T., Asanjan, A.A., Welles, E., Gao, X., Sorooshian, S. and Liu, X. 2017. Developing Reservoir Monthly Inflow Forecasts Using Artificial Intelligence and Climate Phenomenon Information. Water Resources Research. 53(4):2786-2812. 
  • (ESI Highly Cited Paper) Yang, T., Asanjan, A.A., Faridzad, M., Hayatbini, N., Gao, X. and Sorooshian, S. 2017. An Enhanced Artificial Neural Network with A Shuffled Complex Evolutionary Global Optimization with Principal Component Analysis. Information Sciences. https://doi.org/10.1016/j.ins.2017.08.003. 
  • Yang, T., Tao, Y., Li, J., Zhu, Q., Su, L., He, X. and Zhang, X. 2017. Multi-criterion Model Ensemble of CMIP5 Surface Air Temperature over China. Theoretical and Applied Climatology. 1-16. 
  • Tao, Y., Yang, T.*, Faridzad, M., Jiang, L., He, X. and Zhang, X. 2017. Non-stationary Bias Correction of Monthly CMIP5 Temperature Projections over China using a Residual-based Bagging Tree Model. Int. J. Climatol. doi:10.1002/joc.5188. 
  • (ESI Highly Cited Paper) Liu, X., Yang, T., Hsu, K., Liu, C. and Sorooshian, S. 2017. Evaluating the Streamflow Simulation Capability of PERSIANN-CDR Daily Rainfall Products in Two River Basins on the Tibetan Plateau. Hydrol. Earth Syst. Sci. 21(1):169-181. 
  • Yang, T., Gao, X., Sorooshian, S. and Li, X. 2016. Simulating California Reservoir Operation Using the Classification and Regression-tree Algorithm Combined with a Shuffled Cross-validation Scheme. Water Resources Research. 52(3):1626-1651. 
  • Yang, T., Gao, X., Sellars, S.L. and Sorooshian, S. 2015. Improving the Multi-objective Evolutionary Optimization Algorithm for Hydropower Reservoir Operations in the California Oroville–Thermalito Complex. Environmental Modelling & Software. 69:262-279.
Curriculum Vitae: