at the invitation of prof. gou xiaohua and prof. wei liang from the key laboratory of western environment (ministry of education), college of earth and environmental sciences, lanzhou university, dr. yu kailiang, an of princeton university, usa, came to our college to conduct academic exchange and gave an academic report.
title: leveraging a data-model approach to assessing carbon storage under global change
time: august 26, 2021 (thursday) 9:00 a.m. - 10:30 a.m.
site: lecture room 502, qilian building
reporter profile:
yu kailiang is an associate professor at princeton university. he got his master’s degree of science in ecology from lanzhou university and doctoral degree in environmental science (ecohydrology) from the university of virginia. he has conducted postdoctoral research at the university of utah, eth zurich, and the laboratory of climate and environmental change in france. his research focuses on ecohydrology, competition and coexistence of metabolic plants of tree/grass/scentidae, and global forest carbon and nutrient cycling. he has been awarded the university of virginia hydrology award, joseph k. roberts award, and publication award. he has published more than 50 papers in international journals such as pnas, science advances, nature geoscience, and new phytologist.
report profile:
land carbon storage plays a prominent role in global carbon cycling, but its dynamics and projections remain highly uncertain. thus we are unable to confidently predict changes in land carbon storage and its feedbacks to climate under global change. in this talk, i will show how the ground-based observations and a model-data integrated approach help us better understand the crucial ecological processes such as forest demography and structure (size vs density) and biogeography of soil microbial composition and root nutrients in influencing land carbon storage under global change. first, i will present the examples of leveraging large-scale forest inventory datasets, earth system models, and dynamic vegetation models to understand spatiotemporal dynamics of forest net primary productivity and carbon turnover time, which together impact carbon stock change in forests. second, i will show how the biogeography of soil microbial composition and root nutrients by linking above-ground and below-ground processes improve our predictions of land carbon storage.
