Providing Regional Climate Services to British Columbia

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Chao Li

Research Associate

Chao Li is a hydroclimatology scientist at Pacific Climate Impacts Consortium. His research interests focus on the dynamics and impacts of weather and climate extremes. The topics that he is currently exploring include: 1) estimation of changes in short-duration precipitation extremes, 2) detection, attribution, and projection of weather and climate extremes, and 3) evaluation of how changes in weather and climate extremes affect natural and human systems.

Chao Li joined the Pacific Climate Impacts Consortium in August 2016 after completing a post-doc at the Carnegie Institution for Science at Stanford University. He gained his PhD degree in Hydrology and Water Resources from Texas A&M University (2013), and MSc in Hydraulic Engineering from Tsinghua University, China (2009).


PhD, Hydrology and Water Resources, Texas A&M University, USA

MSc, Hydraulic Engineering, Tsinghua University, China

BSc, Hydrology and Water Resources, Lanzhou University, China

Selected Publications: 
  • Li, C., Y. Y. Fang, K. Caldeira, X. B. Zhang, N. S. Diffenbaugh, and A. M. Michalak, 2015, Widespread, persistent changes to temperature extremes have occurred earlier than predicted, in review by Environmental Research Letters.
  • J. Chen, C. Li, F. Brissette, H. Chen, M. Wang, and G. Essou, 2016, Impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling, in review by Journal of Hydrology.
  • Z. Liu, C. Li, P. Zhou, and X. Chen, 2016, A probabilistic assessment of the likelihood of vegetation drought under varying climate conditions across Chine, in review by Scientific Report.
  • Li, C., E. Sinha, D. E. Horton, N. S. Diffenbaugh, and A. M. Michalak, 2015, Joint bias correction of temperature and precipitation in climate model simulations, Journal of Geophysical Research – Atmospheres, 119, doi:10.1002/2014JD022514.
  • Li, C. and V. P. Singh, 2014, A multi-model regression-sampling algorithm for generating rich monthly streamflow scenarios, Water Resources Research, 50, doi:10.1002/2013WR013969.
  • Li, C., V. P. Singh, and A. K. Mishra, 2013, Monthly river flow simulation with a joint conditional density estimation network, Water Resources Research, 49, doi:10.1002/wrcr.20146.
  • Li, C., V. P. Singh, and A. K. Mishra, 2013, A bivariate mixed distribution with a heavy-tailed component and its application for single-site daily rainfall simulation, Water Resources Research, 49, doi:10.1002/wrcr.20063.
  • Li, C., V. P. Singh, and A. K. Mishra, 2012, Entropy theory-based criterion for hydrometric network evaluation and design: Maximum information minimum redundancy, Water Resources Research, 48, doi:10.1029/2011WR011251.
  • Li, C., V. P. Singh, and A. K. Mishra, 2012, Simulation of the entire range of daily precipitation using a hybrid probability distribution, Water Resources Research, 48, W03521, doi:10.1029/2011WR011446.
  • Yang, Y., S. Shang, and C. Li, 2010, Correcting the smoothing effect of ordinary Kriging estimates in soil moisture interpolation, Advances in Water Science, 21(2), 208-213 (in Chinese).
  • Yamamoto, J. K., and C. Li, 2009, Comparacão de métodos para teste de bigaussian dade, revista Geociências (coded the computation program).
  • Li, C., S. Shang, F. Yi, and C. Sun, 2009, Uncertainty analysis of sample semivariogram for field soil water content and its application, Journal of Sichuan University, 41(S2), 84-91.