来源:生态与环境科学学院

2022年4月6日 王立新:Understanding vegetation water stress across multiple spatial scales

来源:生态与环境科学学院发布时间:2022-04-01浏览次数:64

讲座题目:Understanding vegetation water stress across multiple spatial scales
主 讲 人:王立新 Associate Professor
主 持 人:夏建阳 教授
开始时间:4月6日 上午10:00
讲座地址:腾讯会议809-235-932
主办单位:生态与环境科学学院

报告人简介:
        Dr. Lixin Wang is currently an associate professor at the Department of Earth Sciences, Indiana University-Purdue University Indianapolis. Before Indiana, he spent three years as a research associate at Princeton University and one and half years at the University of New South Wales as a Vice-Chancellor Research Fellow. He got his Ph.D. from the University of Virginia. Dr. Wang’s research field is ecohydrology. He has published more than 180 research articles in peer-reviewed journals including articles in Science, Science Advances, Nature Ecology and Evolution, Nature Climate Change, and Nature Communications. He is an editor of Hydrology and Earth System Sciences (HESS), and associate editor of the Journal of Hydrology, Journal of Arid Environments. Among other awards, he is a recipient of the NSF CAREER award in 2016 and the President’s Bicentennial Medal from Indiana University in 2021.

报告人照片

 

报告内容简介:
        Vegetation-water interaction plays a significant role in the global carbon cycle and energy balance. Vegetation water stress in different environments is a key in vegetation-water interactions. In this talk, I will discuss vegetation water stress ranging from desert environments to forests, ranging from individual plants to a global scale using stable isotope and remote sensing techniques. The results will highlight the important but often neglected role of fog and dew in dryland ecosystems, identity a new mechanism causing vegetation water stress in forests under acid deposition, and demonstrate the increasing water limitation in vegetation growth across the Northern Hemisphere. The results highlight the importance of understanding and quantification of vegetation water stress to better predict the ecosystem responses to climate change.