袁华，副教授、博士生导师
 

联系方式：
通讯地址: 广东省珠海市唐家湾中山大学珠海校区海琴二号 (邮编519082)
Email: yuanh25@mail.sysu.edu.cn

 

研究方向：
陆面过程模式；植被与城市建模；陆面属性数据；植被、城市与气候

欢迎对陆面植被、城市及气候感兴趣的同学报考！


基于植物群落陆面过程建模

基于植物群落陆面过程建模示意图

 

 

三维城市建筑群落模型示意图

 

教育经历：
2010/10–2013/03, 美国德州大学奥斯汀分校，访问学者

2007/09–2013/07, 北京师范大学，地理学与遥感科学学院，博士

2001/09–2005/07, 北京师范大学，计算机科学与技术学院，学士

工作经历：
2016/09 -  今 ,  中山大学大气科学学院，副教授
2013/08 - 2016/09, 北京师范大学全球变化与地球系统科学研究院，讲师

讲授课程：
《陆面过程模拟》

《地球系统科学概论》

《大气科学软件基础与应用》
 

科研项目:

  • 国家自然科学基金气象联合基金：超级城市及城市群天气气候模式关键物理过程研究，2024.1-2027.12，项目负责人
  • 广东省基础与应用基础重大项目：高分辨率陆面环境模拟与预测研究, 2021.11-2026.11，中山大学校方负责人
  • 国家自然科学基金面上项目：基于植物群落的陆面过程建模，2021.1-2024.12，项目负责人
  • 国家重点研发计划：高分辨率全球陆面过程模式研发与应用, 2017.7-2022.6，骨干成员
  • 国家自然科学基金重点项目：高分辨率陆面水文过程模式研制，2018.1-2022.12，骨干成员
  • 国家自然科学基金青年基金：三维植被辐射传输模型的研制及其在陆面模式中的应用，2015.1-2017.12，项目负责人
  • 中国博士后科学基金面上一等资助：陆面模式中地表反照率参数化方案的改进，2014.9-2016.8 ，项目负责人
  • 公益性行业（气象）科研专项经费：GRAPES陆面数据同化系统建设，2012.1-2014.12，参与人
  • 公益性行业（气象）科研专项经费：陆地表面过程模型及其参数化方案研究，2007.7-2009.11，参与人
  • 国家科技支撑计划：京津冀业务预报模式中城市复杂下垫面和边界层物理方案的研发，2009.1-2012.12，参与人
 
代表性论文:
  1. Dai, Y., H. Yuan*, Q. Xin, D. Wang, W. Shangguan, S. Zhang, S. Liu, and N. Wei, 2019: Different representations of canopy structure—A large source of uncertainty in global land surface modeling. Agricultural and Forest Meteorology, 269–270, 119–135, https://doi.org/10.1016/j.agrformet.2019.02.006.
  2. Yuan, H., Y. Dai, Z. Xiao, D. Ji, and W. Shangguan, 2011: Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling. Remote Sensing of Environment, 115, 1171–1187, https://doi.org/10.1016/j.rse.2011.01.001.
  3. Yuan, H.*, R. E. Dickinson, Y. Dai, M. J. Shaikh, L. Zhou, W. Shangguan, and D. Ji, 2014: A 3D canopy radiative transfer model for global climate modeling: Description, validation, and application. Journal of Climate, 27, 1168–1192, https://doi.org/10.1175/JCLI-D-13-00155.1.
  4. Yuan, H., Y. Dai, R. E. Dickinson, B. Pinty, W. Shangguan, S. Zhang, L. Wang, and S. Zhu, 2017: Reexamination and further development of two-stream canopy radiative transfer models for global land modeling. Journal of Advances in Modeling Earth Systems, 9, 113–129, https://doi.org/10.1002/2016MS000773.
  5. Shi, J., H. Yuan and Coauthors, 2024: A flux tower site attribute dataset intended for land surface modeling. Earth Syst. Sci. Data Discuss., 1–24, https://doi.org/10.5194/essd-2024-77.
 
成果列表:

2024:

Cai, Y., and Coauthors, 2024: Reconciling Global Terrestrial Evapotranspiration Estimates From Multi‐Product Intercomparison and Evaluation. Water Resour. Res., 60, e2024WR037608, https://doi.org/10.1029/2024WR037608.

Chen, S., and Coauthors, 2024: Exploring Topography Downscaling Methods for Hyper‐Resolution Land Surface Modeling. JGR Atmospheres, 129, e2024JD041338, https://doi.org/10.1029/2024JD041338.

Fan, H., and Coauthors, 2024: An Unstructured Mesh Generation Tool for Efficient High‐Resolution Representation of Spatial Heterogeneity in Land Surface Models. Geophys. Res. Lett., 51, e2023GL107059, https://doi.org/10.1029/2023GL107059.

Li, Q., and Coauthors, 2024: LandBench 1.0: A benchmark dataset and evaluation metrics for data-driven land surface variables prediction. Expert Syst. Appl., 243, 122917, https://doi.org/10.1016/j.eswa.2023.122917.

Liu, Z., and Coauthors, 2024: Assessing daytime discrepancies and key factors in urban thermal environments: A local climate zones-based modeling study in five Chinese cities. Urban Clim., 55, 101993, https://doi.org/10.1016/j.uclim.2024.101993.

Shi, J., and Coauthors, 2024: A flux tower site attribute dataset intended for land surface modeling. Earth Syst. Sci. Data Discuss., 1–24, https://doi.org/10.5194/essd-2024-77.

Xu, Q., and Coauthors, 2024: Assessing Climate Change Impacts on Crop Yields and Exploring Adaptation Strategies in Northeast China. Earth’s Future, 12, e2023EF004063, https://doi.org/10.1029/2023EF004063.

2023:

Li, J., and Coauthors, 2023: Impact of Reservoirs on Local Precipitation‐Temperature Coupling Relationships. Geophys. Res. Lett., 50, e2023GL103453, https://doi.org/10.1029/2023GL103453.

Liao, J., J. Hang, Q. Luo, H. Luo, T. Ma, Z. Wei, and H. Yuan, 2023: Seasonal variability of forest cooling and warming effects and response to drought in mid-to-high latitudes of the Northern Hemisphere. Forest Ecol. Manag., 546, 121324, https://doi.org/10.1016/j.foreco.2023.121324.

Lin, W., and Coauthors, 2023: Reprocessed MODIS version 6.1 leaf area index dataset and its evaluation for land surface and climate modeling. Remote Sens., 15, 1780, https://doi.org/10.3390/rs15071780.

Liu, S., and Coauthors, 2023: Scale‐Dependent Estimability of Turbulent Flux in the Unstable Surface Layer for Land Surface Modeling. J. Adv. Model. Earth Syst., 15, e2022MS003567, https://doi.org/10.1029/2022MS003567.

Xiong, Z., and Coauthors, 2023: Assessing the Reliability of Global Carbon Flux Dataset Compared to Existing Datasets and Their Spatiotemporal Characteristics. Climate, 11, 205, https://doi.org/10.3390/cli11100205.

李淑津, 袁华, 孔冬冬, 董文宗, 黄丽娜, and 戴永久, 2023: 基于PML-V2模型和站点观测数据的植被水利用率及其趋势差异分析. qhyhjyj, 28, 89–102, https://doi.org/10.3878/j.issn.1006-9585.2022.22009.

2022:

Huang, L., and Coauthors, 2022: A catchment-based hierarchical spatial tessellation approach to a better representation of land heterogeneity for hyper-resolution land surface modeling. Water Resour. Res., 58, e2021WR031589, https://doi.org/10.1029/2021WR031589.

Liu, R., and Coauthors, 2022a: The Effect of Surface Heating Heterogeneity on Boundary Layer Height and Its Dependence on Background Wind Speed. JGR Atmospheres, 127, e2022JD037168, https://doi.org/10.1029/2022JD037168.

Liu, S., X. Zeng, Y. Dai, H. Yuan, N. Wei, Z. Wei, X. Lu, and S. Zhang, 2022b: A Surface Flux Estimation Scheme Accounting for Large-Eddy Effects for Land Surface Modeling. Geophys. Res. Lett., 49, e2022GL101754, https://doi.org/10.1029/2022GL101754.

Liu, X., and Coauthors, 2022c: Plant drought tolerance trait is the key parameter in improving the modeling of terrestrial transpiration in arid and semi-arid regions. Atmospheric and Oceanic Science Letters, 15, 100139, https://doi.org/10.1016/j.aosl.2021.100139.

Zhang, X., and Coauthors, 2022: Influences of 3D sub-grid terrain radiative effect on the performance of CoLM over Heihe river basin, Tibetan Plateau. J. Adv. Model. Earth Syst., 14, e2021MS002654, https://doi.org/10.1029/2021MS002654.

胡颖, 殷娴, 陈剑桥, 袁华, and 段志方, 2022: 基于GIS的云南省1km精细化暴雨灾害风险评估. 气象科技, 50, 742–750, https://doi.org/10.19517/j.1671-6345.20210513.

郭琦, 刘少锋, 袁华, and 李红梅, 2022: 基于FLUXNET数据集对陆面模式CoLM能量通量的单点评估. 气候与环境研究, 27, 688–706.

2021:

Blyth, E. M., and Coauthors, 2021: Advances in Land Surface Modelling. Curr. Clim. Change Rep., 7, 45–71, https://doi.org/10.1007/s40641-021-00171-5.

Dong, W., H. Yuan, R. Zhang, H. Li, L. Huang, S. Zhu, J. Peng, and Y. Dai, 2021: Effects of Incorporating Measured Leaf Optical Properties in Land Surface Models. Front. Earth Sci., 9, 384, https://doi.org/10.3389/feart.2021.663917.

Li, H., and Coauthors, 2021: New Representation of Plant Hydraulics Improves the Estimates of Transpiration in Land Surface Model. Forests, 12, 722, https://doi.org/10.3390/f12060722.

Menard, C. B., and Coauthors, 2021: Scientific and Human Errors in a Snow Model Intercomparison. Bulletin of the American Meteorological Society, 102, E61–E79, https://doi.org/10.1175/BAMS-D-19-0329.1.

Zhu, S., H. Chen, Y. Dai, X. Lu, W. Shangguan, H. Yuan, and N. Wei, 2021: Evaluation of the Effect of Low Soil Temperature Stress on the Land Surface Energy Fluxes Simulation in the Site and Global Offline Experiments. J. Adv. Model. Earth Syst., 13, e2020MS002403, https://doi.org/10.1029/2020MS002403.

2020:

Essery, R., and Coauthors, 2020: Snow cover duration trends observed at sites and predicted by multiple models. The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020.

Li, L., and Coauthors, 2020: A causal inference model based on random forests to identify the effect of soil moisture on precipitation. J. Hydrometeor., 21, 1115–1131, https://doi.org/10.1175/JHM-D-19-0209.1.

Niu, G.-Y., Y.-H. Fang, L.-L. Chang, J. Jin, H. Yuan, and X. Zeng, 2020: Enhancing the Noah-MP Ecosystem Response to Droughts With an Explicit Representation of Plant Water Storage Supplied by Dynamic Root Water Uptake. Journal of Advances in Modeling Earth Systems, 12, e2020MS002062, https://doi.org/10.1029/2020MS002062.

Xin, Q., X. Zhou, N. Wei, H. Yuan, Z. Ao, and Y. Dai, 2020: A Semiprognostic Phenology Model for Simulating Multidecadal Dynamics of Global Vegetation Leaf Area Index. Journal of Advances in Modeling Earth Systems, 12, e2019MS001935, https://doi.org/10.1029/2019MS001935.

Yuan, H., Y. Dai, R. E. Dickinson, S. Zhang, W. Shangguan, S. Liu, X. Lu, and N. Wei, 2020a: Towards a new sub-grid structure of vegetation canopies in land surface modeling. 100th American Meteorological Society Annual Meeting, AMS.

——, ——, and S. Li, 2020b: Reprocessed MODIS Version 6 Leaf Area Index data sets for land surface and climate modelling. Sun Yat-sun University.

2019:

Dai, Y., and Coauthors, 2019a: A global high-resolution data set of soil hydraulic and thermal properties for land surface modeling. Journal of Advances in Modeling Earth Systems, 11, 2996–3023, https://doi.org/10.1029/2019MS001784.

——, and Coauthors, 2019b: A review of the global soil property maps for Earth system models. SOIL, 5, 137–158, https://doi.org/10.5194/soil-5-137-2019.

——, N. Wei, H. Yuan, S. Zhang, W. Shangguan, S. Liu, X. Lu, and Y. Xin, 2019c: Evaluation of soil thermal conductivity schemes for use in land surface modeling. Journal of Advances in Modeling Earth Systems, 11, 3454–3473, https://doi.org/10.1029/2019MS001723.

——, H. Yuan, Q. Xin, D. Wang, W. Shangguan, S. Zhang, S. Liu, and N. Wei, 2019d: Different representations of canopy structure—A large source of uncertainty in global land surface modeling. Agricultural and Forest Meteorology, 269–270, 119–135, https://doi.org/10.1016/j.agrformet.2019.02.006.

——, S. Zhang, H. Yuan, and N. Wei, 2019e: Modeling variably saturated flow in stratified soils with explicit tracking of wetting front and water table locations. Water Resources Research, 55, 7939–7963, https://doi.org/10.1029/2019WR025368.

Pan, J., W. Shangguan, L. Li, H. Yuan, S. Zhang, X. Lu, N. Wei, and Y. Dai, 2019: Using data-driven methods to explore the predictability of surface soil moisture with FLUXNET site data. Hydrological Processes, 33, 2978–2996, https://doi.org/10.1002/hyp.13540.

2018:

Krinner, G., and Coauthors, 2018: ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks. Geoscientific Model Development, 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018.

戴永久, 魏楠, 黄安宁, 朱司光, 上官微, 袁华, 张树鹏, and 刘少锋, 2018: 通用陆面模式 (CoLM) 湖泊过程方案与性能评估. 科学通报, 63, 3002.

2017:

Shangguan, W., T. Hengl, J. Mendes de Jesus, H. Yuan, and Y. Dai, 2017: Mapping the global depth to bedrock for land surface modeling. Journal of Advances in Modeling Earth Systems, 9, 65–88, https://doi.org/10.1002/2016MS000686.

Yuan, H., Y. Dai, R. E. Dickinson, B. Pinty, W. Shangguan, S. Zhang, L. Wang, and S. Zhu, 2017: Reexamination and further development of two-stream canopy radiative transfer models for global land modeling. Journal of Advances in Modeling Earth Systems, 9, 113–129, https://doi.org/10.1002/2016MS000773.

Zhang, X., and Coauthors, 2017: Evaluating common land model energy fluxes using FLUXNET data. Advances in Atmospheric Sciences, 34, 1035–1046.

Zhu, S., and Coauthors, 2017: Incorporating root hydraulic redistribution and compensatory water uptake in the Common Land Model: Effects on site level and global land modeling. Journal of Geophysical Research: Atmospheres, 122, 2016JD025744, https://doi.org/10.1002/2016JD025744.

2016以前:

Shangguan, W., Y. Dai, B. Liu, A. Ye, and H. Yuan, 2012: A soil particle-size distribution dataset for regional land and climate modelling in China. Geoderma, 171–172, 85–91, https://doi.org/10.1016/j.geoderma.2011.01.013.

——, and Coauthors, 2013: A China data set of soil properties for land surface modeling. Journal of Advances in Modeling Earth Systems, 5, 212–224, https://doi.org/10.1002/jame.20026.

——, Y. Dai, Q. Duan, B. Liu, and H. Yuan, 2014a: A global soil data set for earth system modeling. Journal of Advances in Modeling Earth Systems, 6, 249–263, https://doi.org/10.1002/2013MS000293.

——, Y. Dai, C. García-Gutiérrez, and H. Yuan, 2014b: Particle-size distribution models for the conversion of Chinese data to FAO/USDA system. The Scientific World Journal, 2014, e109310, https://doi.org/10.1155/2014/109310.

Widlowski, J.-L., and Coauthors, 2011: RAMI4PILPS: An intercomparison of formulations for the partitioning of solar radiation in land surface models. J. Geophys. Res., 116, G02019, https://doi.org/10.1029/2010JG001511.

Yuan, H., Y. Dai, Z. Xiao, D. Ji, and W. Shangguan, 2011: Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling. Remote Sensing of Environment, 115, 1171–1187, https://doi.org/10.1016/j.rse.2011.01.001.

——, R. E. Dickinson, Y. Dai, M. J. Shaikh, L. Zhou, W. Shangguan, and D. Ji, 2014: A 3D canopy radiative transfer model for global climate modeling: Description, validation, and application. Journal of Climate, 27, 1168–1192, https://doi.org/10.1175/JCLI-D-13-00155.1.

 

(更新日期：2024年11月)