18 MAR 2021 (THU) 09:00-09:45 | 10:10-10:55 | 11:20-12:05 | 12:30-13:15

Smart Specialization and Resilience

All sessions will be held virtually by Zoom



Ms Zhiying LI PhD Candidate, Department of Geography, The Ohio State University, Columbus, Ohio, USA

Zhiying Li is a Ph.D. candidate at the Department of Geography at The Ohio State University. Her research interests are hydro-climatology, applied climatology, and environmental data science. Her research focuses on investigating the water cycle from local to continental scale in a changing climate using multi-source observations. She is also interested in climate change implications on multiple sectors such as utility management and agriculture. Zhiying’s doctoral dissertation was funded by a National Science Foundation Doctoral Dissertation Research Improvement Grant. Zhiying has published in leading journals such as Earth-Science Reviews, Geomorphology, Anthropocene, and Energy. She has served as a manuscript reviewer for Physical Geography and Utilities Policy and a book reviewer for Elsevier. She has won numerous awards and fellowships for her research and presentation skills, such as the Presidential Fellowship at OSU, the Best Oral Presentation at the East Lakes Division of the American Association of Geographers, and the American Meteorological Society Annual Meeting Matthew J. Parker Travel Grant.

Before joining OSU, Zhiying received her master’s degree in Physical Geography at the Institute of Geographic Sciences and Natural Resources Research at the University of Chinese Academy of Sciences in China in 2017, and a bachelor’s degree in Soil and Water Conservation at Northwest A&F University in China in 2014. Her master’s research focuses on modeling the impacts of climate change and land-use change on watershed runoff and sediment yield.

Data science in geography: from hydrology to applied climatology


Climate change and human activities have changed long-term hydrological processes. Such changes are expected to continue in the future and pose a management challenge. At the same time, data science methods are increasingly important in geography and environmental science. The emerging large quantities of data change the ways scientists do research and allow them to address environmental problems in a new way. My research utilizes hydrological and climate models, machine learning, spatial analysis, and multiscale observations to inform water resources management. The climate change challenges as applied in the utility sector are also discussed. This seminar highlights two data science applications in hydrology and climatology. First, coupling hydrological framework, climate models, and geospatial data, I investigated the relative importance of drivers on streamflow changes over space and time in the continental United States in six decades from 1950 to 2009. Second, I addressed the short-term utility management challenge due to the increasing use of clean energy in a changing climate using machine learning algorithms. Overall, it is critical to use data science methods to understand hydrological change and provide implications to climate-sensitive sectors to better adapt to a changing environment.



Dr Hyunuk KIM Postdoctoral Associate, Information Systems, Questorm School of Business, Boston, MA, USA