In Review or Revision
Published
Ruehr S, Keenan TF, Williams C, Zhou Y, Lu X, Bastos A, Canadell JP, Prentice IC, Sitch S, Terrer. C Evidence and attribution of the enhanced land carbon sink. Nat Rev Earth Environ (2023). https://doi.org/10.1038/s43017-023-00456-3
Jennifer A. Rudgers, Anthony Luketich, Melissa Bacigalupa, Lauren E. Baur, Scott L. Collins, Kristofer M. Hall, Enqing Hou, Marcy E. Litvak, Yiqi Luo, Tom E. X. Miller, Seth D. Newsome, William T. Pockman, Andrew D. Richardson, Alex Rinehart, Melissa Villatoro-Castañeda, Brooke E. Wainwright, Samantha J. Watson, Purbendra Yogi, Yu Zhou. (2023) Infrastructure to Factorially Manipulate the Mean and Variance of Precipitation in the Field. Ecosphere 14( 7): e4603. https://doi.org/10.1002/ecs2.4603
Dong, Y., Zhou, Y., Zhang, L., Gu, Y., Sutrisno, D. (2023) Intensive land-use is associated with development status in port cities of Southeast Asia. Environmental Research Letters. https://doi.org/10.1088/1748-9326/acc2d2
Hou, E., Ma, S., Huang, Y., Zhou, Y., Kim, H.-S., López-Blanco, E., Jiang, L., Xia, J., Tao, F., Williams, C., Williams, M., Ricciuto, D., Hanson, P. J., & Luo, Y. (2023). Across-model spread and shrinking in predicting peatland carbon dynamics under global change. Global Change Biology, 00, 1– 17. https://doi.org/10.1111/gcb.16643.
Sun J, Wei X, Zhou Y, Chan C, Diao J. Hurricanes Substantially Reduce the Nutrients in Tropical Forested Watersheds in Puerto Rico. Forests. 2022; 13(1):71. https://doi.org/10.3390/f13010071.
Gourdji, S. M., Karion, A., Lopez-Coto, I., Ghosh, S., Mueller, K. L., Zhou, Y., et al. (2022). A modified Vegetation Photosynthesis and Respiration Model (VPRM) for the eastern USA and Canada, evaluated with comparison to atmospheric observations and other biospheric models. Journal of Geophysical Research: Biogeosciences, 127, e2021JG006290. https://doi.org/10.1029/2021JG006290.
Yu Zhou, Christopher A. Williams, Natalia Hasler, Huan Gu, Robert Kennedy. (2021). Beyond Biomass to Carbon Fluxes: Application and Evaluation of A Comprehensive Forest Carbon Monitoring System. https://doi.org/10.1088/1748-9326/abf06d. (pdf copy)
Yaxing Wei, and co-authors (2021). Atmospheric Carbon and Transport – America (ACT-America) data sets: Description, management, and delivery. Earth and Space Science, 8, e2020EA001634. https://doi.org/10.1029/2020EA001634.
Feng, S., Lauvaux, T., Williams, C., Davis, K., Zhou, Y., Baker, I., Barkley, Z., and Wesloh, D. (2021). Joint CO2 mole fraction and flux analysis confirms missing processes in CASA terrestrial carbon uptake over North America. Global Biogeochemical Cycles, 35, e2020GB006914. https://doi.org/10.1029/2020GB006914.
Gafforov, Y., Ordynets, A., Langer, E., Yarasheva, M., Gugliotta, A. D. M., Schigel, D., Pecoraro, L., Zhou, Y., Cai, L. & Zhou, L.-W. (2020). Species diversity with comprehensive annotations of wood-inhabiting poroid and corticioid fungi in Uzbekistan. Frontiers in Microbiology, 11, 3047, doi: 10.3389/fmicb.2020.598321. (pdf copy)
Zhou, Y., C. A. Williams, T. Lauvaux, K. J. Davis, S. Feng, I. Baker, S. Denning, and Y. Wei (2020), A Multiyear Gridded Data Ensemble of Surface Biogenic Carbon Fluxes for North America: Evaluation and Analysis of Results, Journal of Geophysical Research: Biogeosciences, 125(2), e2019JG005314, doi:10.1029/2019jg005314.(pdf copy)
Zhang, L., Xiao, J., Zheng, Y., Li, S., Zhou, Y. (2020). Increased carbon uptake and water use efficiency in global semi-arid ecosystems. Environmental Research Letters, https://doi.org/10.1088/1748-9326/ab68ec. (pdf copy)
Feng, S., T. Lauvaux, K. J. Davis, K. Keller, Y. Zhou, C. Williams, A. E. Schuh, J. Liu, and I. Baker (2019), Seasonal Characteristics of Model Uncertainties From Biogenic Fluxes, Transport, and Large-Scale Boundary Inflow in Atmospheric CO2 Simulations Over North America, Journal of Geophysical Research: Atmospheres, 124(24), 14325-14346, doi:10.1029/2019jd031165. (pdf copy)
Gu, H., Williams, C. A., Hasler, N., & Zhou, Y. ( 2019). The carbon balance of the southeastern U.S. forest sector as driven by recent disturbance trends. Journal of Geophysical Research: Biogeosciences, 124. https://doi.org/10.1029/2018JG004841. (pdf copy)
Zhou, Yu, Li Zhang, Jingfeng Xiao, Christopher A. Williams, Irina Vitkovskaya, and Anming Bao. (2019). Spatiotemporal transition of institutional and socioeconomic impacts on vegetation productivity in Central Asia over last three decades. Science of The Total Environment , doi 10.1016/j.scitotenv.2018.12.155. (pdf copy)
Yan, M., Z. Li, X. Tian, L. Zhang, and Y. Zhou. (2019). Improved simulation of carbon and water fluxes by assimilating multi-layer soil temperature and moisture into process-based biogeochemical model, Forest Ecosystems, 6(1), 12, doi:10.1186/s40663-019-0171-5. (pdf copy)
Feng, Qi, Liangjiang Zhou, Erxue Chen, Xingdong Liang, Lei Zhao, and Yu Zhou. “The Performance of Airborne C-Band PolInSAR Data on Forest Growth Stage Types Classification.” Remote Sensing 9, no. 9 (2017): 955. (pdf copy)
Zhang, L., Xiao, J., Zhou, Y., Zheng, Y., Li, J., & Xiao, H. (2016). Drought events and their effects on vegetation productivity in China. Ecosphere, 7(12). (pdf copy)
Zhou, Y., Zhang, L., Fensholt, R., Wang, K., Vitkovskaya, I., & Tian, F. (2015). Climate contributions to vegetation variations in central Asian drylands: Pre-and post-USSR collapse. Remote Sensing, 7(3), 2449-2470. (pdf copy)
Xiao, J., Zhou, Y., & Zhang, L. (2015). Contributions of natural and human factors to increases in vegetation productivity in China. Ecosphere, 6(11), 1-20. (pdf copy)
Zhou, Y., Zhang, L., Xiao, J., Chen, S., Kato, T., & Zhou, G. (2014). A Comparison of Satellite-Derived Vegetation Indices for Approximating Gross Primary Productivity of Grasslands. Rangeland Ecology & Management, 67, 9-18. (pdf copy)
Zhang, B., Zhang, L., Guo, H., Leinenkugel, P., Zhou, Y., Li, L., & Shen, Q. (2014). Drought impact on vegetation productivity in the Lower Mekong Basin. International Journal of Remote Sensing, 35, 2835-2856. (pdf copy)
Zheng, Y., Zhang, L., Zhou, Y., & Zhang, B. (2017). Vegetation change and its driving factors in global drylands during the period of 1992-2012. Arid Zone Research, 34, no. 1 (2017): 59-66. DOI: 10.13866/j.azr.2017.01.08. (pdf copy)
Liu, C., Zhang, L., Zhou, Y., Zhang, B., & Hou, X. Retrieval and analysis of grassland coverage in arid Xinjiang, China and five countries of Central Asia. Pratacultural Science (in Chinese),2016,33(5): 861-870. DOI: 10.11829/j.issn.1001-0629.2015.0503. (pdf copy)
Zhang, J., Zhang. L., Zheng, Y., Tian X., Zhou, Y. Simulation of vegetation net primary productivity and evapotranspiration based on LPJ model in Central Asia. Pratacultural Science (in Chinese),2015,32(11): 1721-1729. DOI: 10.11829\j.issn.1001-0629.2015-0103. (pdf copy)
Liu, S., Zhang, L., Wang, C., Yan, M., Zhou, Y., & Lu, L. Analysis of Vegetation Phenology in the Tibetan Plateau Using MODIS data (2000-2010). Remote sensing information (in Chinese), 2014,(6):25-30.DOI:10.3969/j.issn.1000-3177.2014.06.006. (pdf copy)
Awards
NASA Group Achievement Award, ACT-America, 2020.
Read article. a, Schematic representation of the processes enhancing the land carbon sink by stimulating biomass growth and supporting either larger trees or more individuals. b, The processes limiting the net land carbon sink by supporting fewer, smaller individuals. The past, present and future of the sink are determined by the combined result of enhancing and limiting processes.
Read article. Across-model spread in ecosystem C dynamics are shrinkage after standardizing parameter values. Simulated ecosystem C storage, net ecosystem production, and C residence time in the (a–c) original model run and (d–r) model runs after Steps 1–5, respectively. Origin model run indicates model simulations with the default parameter values.
Read article. An inverted V-shaped relationship between fragmentation and development level was found at the landscape level and for cultivated land, in which both turning points (TPs) occurred in the mid-developed stage (ANLI = 41.1 and 20.0, respectively). Artificial surfaces tended to be more aggregated in later developed stages, showing a TP of ANLI around 53.2.
Read article. National forest carbon monitoring system that involves (a) the training of CASA model to match biomass accumulation with stand development from the FIA data, and the use of that trained model to calculate carbon stocks and fluxes with stand age for each possible combination of forest-type, productivity level, pre-disturbance conditions, and disturbance type, (b) the determination of pixel‐level characteristics for all forested pixels across the Pacific Northwest (PNW) United States at a 30 m resolution, and the assignment of stand age, carbon stocks and fluxes for each 30 m forested pixels according to its specific attributes, and (c) the estimate of regional carbon stock balance, carbon stock potentials and emission risks using the WoodCarb II model.
Read article. A new perturbed-parameter model ensemble with the CASA model to estimate surface biogenic carbon fluxes at monthly and 3-hourly scales for North America at ~500-m and 5-km resolutions. The initial range for each parameter is broadly sampled for the L1 ensemble, but then we pruned Emax with site-level primary productivity to derive an L2 ensemble with narrower uncertainty ranges. Ensembles are strongly correlated with site-level results at both monthly and 3-hourly scales, and the spread across L1/L2 ensemble members encompasses the range of AmeriFlux observations. Monthly variability in the L2 ensemble mean is 85% of the observed variability. The L2 ensemble outperforms diverse data products with the highest Taylor skill scores at diurnal to annual scales.