Research Description
Professor Ximing Cai is a research pioneer in integrated hydrologic-economic modeling for river basin management and water systems operations. His current research areas include coupled human-natural system analysis with an emphasis on human interferences in hydrological processes, water-energy-food system modeling especially in dry areas, and sustainable water resources management particularly in developing countries. He has authored or co-authored more than 110 peer-reviewed journal papers, three books and several monographs. He currently serves as Editor for Water Resources Researchthe flagship journal of water resources, published by the American Geophysical Union, and on the editorial board of other major water journals. He has received several awards, including the National Science Foundation CAREER award and best paper awards from Water International and the Journal of Water Resources Planning and Management. He has worked as a consultant to the World Bank, the United Nations and other international agencies.
Courses Taught
Professor Cai teaches undergraduate and graduate courses in water resources engineering, surface water hydrology and application of geographic information systems, and river basin management.
Additional Campus Affiliations
Ben Chie Yen Professor, Civil and Environmental Engineering
Professor, Civil and Environmental Engineering
Professor, Center for East Asian and Pacific Studies
Professor, Center for Global Studies
Professor, National Center for Supercomputing Applications (NCSA)
Professor, Center for Digital Agriculture, National Center for Supercomputing Applications (NCSA)
External Links
Recent Publications
Chen, Y., & Cai, X. (2025). The Storage and Operation Changes of 256 Reservoirs Across the Contiguous United States. Water Resources Research, 61(2), Article e2024WR037372. https://doi.org/10.1029/2024WR037372
Li, D., Vora, A., & Cai, X. (2025). Streamflow simulation improvements enabled by a state-of-the-art algorithm for reservoir routing in the U.S. National Water Model. Journal of the American Water Resources Association, 61(1), Article e13244. https://doi.org/10.1111/1752-1688.13244
Razavi, S., Duffy, A., Eamen, L., Jakeman, A. J., Jardine, T. D., Wheater, H., Hunt, R. J., Maier, H. R., Abdelhamed, M. S., Ghoreishi, M., Gupta, H., Döll, P., Moallemi, E. A., Yassin, F., Strickert, G., Nabavi, E., Mai, J., Li, Y., Thériault, J. M., ... Grimm, V. (2025). Convergent and Transdisciplinary Integration: On the Future of Integrated Modeling of Human-Water Systems. Water Resources Research, 61(2), Article e2024WR038088. https://doi.org/10.1029/2024WR038088
Shi, H., & Cai, X. (2025). Extrapolability improvement of machine learning-based evapotranspiration models via domain-adversarial neural networks. Environmental Modelling and Software, 187, Article 106383. https://doi.org/10.1016/j.envsoft.2025.106383
Singh, V. P., Cai, X., & Vimal, S. (2025). Emerging Fields in Hydrology. Journal of Hydrologic Engineering, 30(2), Article 02525001. https://doi.org/10.1061/JHYEFF.HEENG-6423