Our group broadly uses data-driven and model-driven approaches to quantify the patterns of element flux and isotope behavior involved in the global carbon and biogeochemical cycles, especially under periods of climatic perturbations. Extensive data mining, data assimilation, large-scale spatial-temporal statistical analysis, and machine learning are frequently used in our research projects. We hope geo-statistics and machine learning could reveal the intrinsic patterns of nature’s processes that are sometimes extremely difficult to be captured by classical physical process models. With that being said, in areas where data is extremely limited or data-driven approaches are not suitable, numerical modeling (e.g., modeling the global carbon cycle) also serves as a critical tool in our research.
Recent News
2024-08-01 Dr. Zhang contributes to a new publication in Nature Water. The study provides a comprehensive assessment of greenhouse gas (GHG) emissions associated with irrigation across the United States, offering valuable insights for targeted GHG mitigation strategies in agriculture. Driscoll, A.W., Marston, L.T., Ogle, S.M., Planavsky, N.J., Siddik, M.A.B., Spencer, S., Zhang, S., Mueller, N.D., 2024. Hotspots of irrigation-related US greenhouse gas emissions from multiple sources. Nat Water 1–11. https://doi.org/10.1038/s44221-024-00283-w
2024-03-11 Graduate Student Shihan Li publishes anew paper in Global and Planetary Change. Li, S., Zeebe, R. E., & Zhang, S., (2024). iLOSCAR: interactive Long-term Ocean-atmosphere-Sediment CArbon cycle Reservoir model v1.0. Global and Planetary Change, 236, 104413. https://doi.org/10.1016/j.gloplacha.2024.104413