Influences of leaf area index and albedo on estimating energy fluxes with HOLAPS framework

JOURNAL OF HYDROLOGY

卷:580

Article

Peng, Jian^1,2,3  Kharbouche, Said⁴  Muller, Jan-Peter⁴  Danne, Olaf⁵  Blessing, Simon⁶  Giering, Ralf⁶  Gobron, Nadine⁷  Ludwig, Ralf²  Mueller, Benjamin²  Leng, Guoyong⁸  Lees, Thomas¹  Dadson, Simon¹ 

[1] Univ Oxford, Sch Geog & Environm, Oxford OX1 3QY, England

[2] Univ Munich LMU, Dept Geog, D-80333 Munich, Germany

[3] Max Planck Inst Meteorol, D-20146 Hamburg, Germany

[4] Univ Coll London, Dept Space & Climate Phys, Imaging Grp, Holmbury RH5 6NT, England

[5] Brockmann Consult GmbH, Chrysanderstr 1, D-21029 Hamburg, Germany

[6] FastOpt GmbH, Ispra, Italy

[7] European Commiss, JRC, Ispra, Italy

[8] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China

关键词: HOLAPS;    Albedo;    Leaf area index;    QA4ECV;    MODIS;    GlobAlbedo;    GLOBMAP;    FLUXNET;   

DOI  :  10.1016/j.jhydrol.2019.124245

来源: Elsevier

【 摘 要 】

The High resOlution Land Atmosphere surface Parameters from Space (HOLAPS) programme provides a modeling system to maximize the use of satellite-based products and ensure internally consistent estimation of surface water and energy fluxes. Leaf area index (LAS) and land surface albedo are two key parameters for estimation of latent and sensible heat fluxes with HOLAPS. Thus, to facilitate the generation of long-term high accuracy latent and sensible heat fluxes, high quality global long-term LAI and land surface albedo datasets are essential. The Quality Assurance for Essential Climate Variables (QA4ECV) project released quality-assured longterm LAI and albedo datasets with traceable and reliable uncertainty information provided in the dataset. Taking MODIS-BNU-LAI and MODIS albedo as reference, different global long-term LAI and albedo datasets including GlobAlbedo, QA4ECV and GLOBMAP were investigated for estimation of latent/sensible heat fluxes with HOLAPS in this study. The results show that all albedo datasets show similar accuracy for estimation of latent and sensible heat fluxes when validated against FLUXNET observations. The QA4ECV LAI leads to worse latent heat flux estimation due to its use of effective LAI rather than green leaf LAI. Sensitivity analysis also shows that the HOLAPS estimated latent heat flux (LE) is more sensitive to uncertainty in LAI than land surface albedo. Overall, the combined use of QA4ECV albedo and GLOBMAP LAI is suggested for estimation of latent/sensible heat fluxes with HOLAPS. The root mean square differences (RMSD) between estimations and FLUXNET measurements are 54 (30) W/m(2) for hourly (monthly) latent heat flux, and 80.5 (24.5) W/m(2) for sensible heat flux, which are comparable to estimates with MODIS and other reported studies.

【 授权许可】

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