摘要
基于2007年12月22日～2009年12月31日黑河流域阿柔冻融观测站的气象驱动数据,利用通用陆面模型(Common Land Model,CoLM)模拟的地表通量结果,研究地表通量对模型参数(叶面积指数、地表反照率和植被覆盖度)的不确定性与敏感性。结果表明,叶面积指数、地表反照率和植被覆盖度对地表感热和潜热通量不同组分的影响存在较大的差异。其中,植被层的感热和潜热通量对叶面积指数的敏感性程度较高,敏感系数均达到0.7以上;与潜热通量相比,感热通量对反照率更加敏感,土壤感热、植被感热和总感热通量对反照率的敏感系数分别达到-0.96、-0.97和-0.66,而土壤潜热和总潜热通量对地表反照率的敏感系数仅为0.1左右;植被潜热通量对植被覆盖度的敏感性程度很高,敏感系数范围为0.92～0.96,而土壤感热通量对植被覆盖度最不敏感,敏感系数只有0.18左右。
The uncertainty and sensitivity analysis of surface turbulent fluxes to model parameters（leaf area index,surface albedo and vegetation coverage fraction） is studied,which uses meteorological forcing data from December 22,2007 to December 31,2009 at the Arou observation station in Heihe river basin.The result shows that surface turbulent fluxes are all quite sensitive to leaf area index,surface albedo and vegetation coverage fraction.As for leaf area index,the sensible and latent heat fluxes from canopy are more sensitive to leaf area index than other turbulent fluxes,the sensitivity coefficients are all above 0.7.As for surface albedo,sensible heat fluxes are more sensitive to surface albedo in comparison with latent heat fluxes.The sensitivity coefficients of ground,canopy,and total sensible heat fluxes to surface albedo are about-0.96,-0.97 and-0.66,respectively.The sensitivity coefficients of ground and total latent heat fluxes to surface albedo are very low and the value is only about 0.1.In terms of vegetation coverage fraction,latent heat flux from canopy is the most sensitive to vegetation coverage fraction in comparison with other turbulent fluxes,and the sensitivity coefficient ranges from 0.92 to 0.96,while the sensitivity coefficient of sensible heat flux from ground to vegetation coverage fraction is very low and the value is only about 0.18.
出处
《遥感技术与应用》
CSCD
北大核心
2011年第5期569-576,共8页
Remote Sensing Technology and Application
基金
国家自然科学基金项目(40801126)
中国科学院"百人计划"项目(29Y127D01)
中国科学院知识创新重要方向项目(KZCX2-YW-Q10\2)
国家973计划项目(2009CB723905)共同资助
关键词
地表通量
叶面积指数
地表反照率
植被覆盖度
遥感
Surface turbulent fluxes
Leaf area index
Surface albedo
Vegetation coverage fraction
Remote sensing
