What are we doing?
Welcome to CACEE Lab @ Texas A&M University!
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.
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
2025-11-18
Graduate student Xiying Sun is awarded a Graduate Student Research and Presentation Travel Award funded by The Graduate and Professional School ($1,000), as well as a Doctoral Student Research, Travel, and Professional Development Award funded by The College of Art & Science ($1,000).
Graduate student Yating Li is awarded a Graduate Student Research and Presentation Travel Award funded by The Graduate and Professional School ($1,000).
Graduate student Xiying Sun is awarded a Graduate Student Research and Presentation Travel Award funded by The Graduate and Professional School ($1,000), as well as a Doctoral Student Research, Travel, and Professional Development Award funded by The College of Art & Science ($1,000).
Graduate student Yating Li is awarded a Graduate Student Research and Presentation Travel Award funded by The Graduate and Professional School ($1,000).
2025-11-06
Dr. Shuang Zhang contributes to a new paper in Science that provides the first CONUS-wide estimates of river metabolism using a machine-learning model. The study finds that U.S. rivers have an annual GPP of about 10.1 Tg-C yr⁻¹ and ER of 18.7 Tg-C yr⁻¹, and western rivers contribute more than 71% of the nation’s metabolism-driven CO₂ uptake, reflecting strong autotrophic and heterotrophic extremes in arid landscapes. Drought and flow variation strongly modulate carbon uptake and release, and feature-importance analyses highlight canopy cover, discharge, watershed area, land cover, and temperature as key controls. The work reveals that river autotrophy is far more widespread, especially in western landscapes, than previously recognized. Maavara, T., Yuan, Z., Johnson, A. M., Zhang, S., Aho, K. S., Brinkerhoff, C. B., Logozzo, L. A., & Raymond, P. River metabolism in the contiguous United States: A West of extremes. https://doi.org/10.1126/science.adu9843
Dr. Shuang Zhang contributes to a new paper in Science that provides the first CONUS-wide estimates of river metabolism using a machine-learning model. The study finds that U.S. rivers have an annual GPP of about 10.1 Tg-C yr⁻¹ and ER of 18.7 Tg-C yr⁻¹, and western rivers contribute more than 71% of the nation’s metabolism-driven CO₂ uptake, reflecting strong autotrophic and heterotrophic extremes in arid landscapes. Drought and flow variation strongly modulate carbon uptake and release, and feature-importance analyses highlight canopy cover, discharge, watershed area, land cover, and temperature as key controls. The work reveals that river autotrophy is far more widespread, especially in western landscapes, than previously recognized. Maavara, T., Yuan, Z., Johnson, A. M., Zhang, S., Aho, K. S., Brinkerhoff, C. B., Logozzo, L. A., & Raymond, P. River metabolism in the contiguous United States: A West of extremes. https://doi.org/10.1126/science.adu9843
2025-10-24
Graduate student Shihan Li, Xiying Sun and Yating Li hosted a workshop titled “Data Science Meets Geoscience: a case study in rock weathering and Riverine Carbon Fluxes”. The event was supported by the Texas A&M Institute of Data Science (TAMIDS) Student Ambassador Program, aiming to enhance data science literacy across disciplines at Texas A&M and beyond. More info can be found here.
Graduate student Shihan Li, Xiying Sun and Yating Li hosted a workshop titled “Data Science Meets Geoscience: a case study in rock weathering and Riverine Carbon Fluxes”. The event was supported by the Texas A&M Institute of Data Science (TAMIDS) Student Ambassador Program, aiming to enhance data science literacy across disciplines at Texas A&M and beyond. More info can be found here.