Current research projects Spruce and Peatland Responses Under Climatic and Environmental Change. ORNL, 2024-2028 (Co-I, PIs: Paul Hanson and Daniel Ricciuto, Oak Ridge National Laboratory)TECO modeling, data assimilation, and ecological forecasting at Sevilleta (SEV) LTER site. NSF, 2018-2024 (Co-PI, PI: Jennifer Rudgers, University of New Mexico)Constraining the continental-scale terrestrial carbon cycle using NEON data. NSF, 2020-2024 (PI: Jingfeng Xiao, University of New Hampshire)USDA Climate Smart Commodities NYS Connects: Climate Smart Farms and Forests Project. NYS Department of Agriculture and Markets, 2023-2028 (PI) Empirical measurements and model representation of hydraulic redistribution as a control on function of semiarid woody ecosystems. DOE, 2022-2025 (PI: William Pockman, University of New Mexico)

About

Find out what this lab does

Our research program is designed to advance predictive understanding of ecosystem ecology and biogeochemistry under the global environmental change via data-model integration. Major issues we are addressing include (1) how global change alters structure and functions of terrestrial ecosystems, (2) how terrestrial ecosystems feedback to regulate climate change, and 3) how ecosystem processes can be effectively manipulated to offer nature-based solutions to mitigate climate change.

We have been using diverse approaches to our research, including global change experiments, observation, data synthesis, process-based modeling, data-model fusion, knowledge-guided artificial intelligence (AI), and theoretical analysis.

Our current research is focused on 1) developing and using knowledge-guided AI tools to discover new mechanisms underlying terrestrial ecosystem dynamics from big data; 2) identifying carbon dioxide removal (CDR) strategies that effectively lengthen carbon residence time (or permanence); 3) developing measurement, monitoring, reporting, and verification (MMRV) methods to evaluate CDR practices; and 4) integrating data from various global change experiments coherently with models according to biogeochemical and ecological principles.

News/Events

Lab news and upcoming events

7th Training Course (Virtual) on New Advances in Land Carbon Cycle Modeling, June 3-14, 2024

Nature paper "Microbial carbon use efficiency promotes global soil carbon storage" is online on May 24, 2023

Release data assimilation algorithm for "Methods for estimating temperature sensitivity of soil organic matter based on incubation data: A comparative evaluation".

The 1st version of the protocol for model intercomparison on terrestrial biogeochemistry has been released.

Work

Most recent publications

  • Feng Tao, Benjamin Z. Houlton, Yuanyuan Huang, Ying-Ping Wang, Stefano Manzoni, Bernhard Ahrens, Umakant Mishra, Lifen Jiang, Xiaomeng Huang, Yiqi Luo. 2024. Convergence in simulating global soil organic carbon by structurally different models after data assimilation. Glob Change Biol., 30: e17297. [Download]
  • Xingzhao Huang, Muhammed Mustapha Ibrahim, Yiqi Luo, Lifen Jiang, Ji Chen, and Enqing Hou. 2024. Land Use Change Alters Soil Organic Carbon: Constrained Global Patterns and Predictors. Earth's Future, 12: e2023EF004254. [Download]
  • Yuanyuan Huang, Xiaodong Song, Ying-Ping Wang, Josep G. Canadell, Yiqi Luo, Philippe Ciais, Anping Chen, Songbai Hong, Yugang Wang, Feng Tao, Wei Li, Yiming Xu, Reza Mirzaeitalarposhti, Heba Elbasiouny, Igor Savin, Dmitry Shchepashchenko, Raphael A. Viscarra Rossel, Daniel S. Goll, Jinfeng Chang, Benjamin Z. Houlton, Huayong Wu, Fei Yang, Xiaoming Feng, Yongzhe Chen, Yu Liu, Shuli Niu, Gan-Lin Zhang. 2024. Size, distribution, and vulnerability of the global soil inorganic carbon. Science, 384: 233–239. [Download]