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Parameter Analysis and Estimates for the MODIS Evapotranspiration Algorithm and Multiscale Verification

机译:MODIS EVAPOT散列算法和多尺度验证的参数分析和估计

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摘要

Accurate estimation of terrestrial evapotranspiration (E) is critical to understand the world's energy and water cycles. MOD16 is the core algorithm of the widely used global E data set (the Moderate Resolution Imaging Spectroradiometer [MODIS] E product). However, it exhibits considerable uncertainties in some regions. Based on the data from 175 flux towers, we identified the key parameters of the MOD16 algorithm using the Sobol' sensitivity analysis method across biomes. The output of the MOD16 algorithm was sensitive to eight parameters. Among them, beta, which is treated as a constant (0.2 kPa) across biomes in the original MOD16 algorithm, was identified as the parameter to which the algorithm was most sensitive. We used the differential-evolution Markov chain method to obtain the proper posterior distributions for each key parameter across a range of biomes. The values of the key parameters for the different biomes were accurately estimated by differential-evolution Markov chain in comparison with data from the flux towers. We then evaluated the performances of the original MOD16 and the optimized MOD16 and compared them at multiple spatial scales (i.e., site, catchment, and global). We obtained relatively consistent and more reliable E simulations using the optimized MOD16 at all three scales. In the future, more attention should be paid to uncertainties in the algorithm's structure and its parameterizations of soil moisture constraint, canopy resistance, and energy partitioning.
机译:准确估计陆地蒸散(E)对于了解世界的能源和水循环至关重要。 MOD16是广泛使用的全局E数据集的核心算法(适度分辨率成像光谱仪[MODIS] E产品)。然而,它在某些地区表现出相当大的不确定性。基于来自175个磁通塔的数据,我们通过跨越生物群系的Sobol'敏感性分析方法确定了Mod16算法的关键参数。 Mod16算法的输出对八个参数敏感。其中,在原始Mod16算法中跨越生物群体的常数(0.2kPa)的β,被识别为算法最敏感的参数。我们使用差动evolovelov链条方法来获得各种生物群体的每个关键参数的适当后分布。与来自磁通塔的数据相比,通过差分演进性马克可夫链精确地估计不同生物群体的关键参数的值。然后,我们评估了原始MOD16和优化的MOD16的性能,并在多个空间尺度(即,网站,集水区和全球)上进行比较。我们在所有三个尺度中使用优化的MOD16获得了相对一致和更可靠的E模拟。在未来,应更多地关注算法结构中的不确定性及其土壤水分限制,冠层电阻和能量分区的参数化。

著录项

  • 来源
    《Water resources research》 |2019年第3期|2211-2231|共21页
  • 作者单位

    Lanzhou Univ Coll Earth & Environm Sci Minist Educ Key Lab Western Chinas Environm Syst Lanzhou Gansu Peoples R China|Chinese Acad Sci Inst Tibetan Plateau Res Beijing Peoples R China;

    Lanzhou Univ Coll Earth & Environm Sci Minist Educ Key Lab Western Chinas Environm Syst Lanzhou Gansu Peoples R China;

    Lanzhou Univ Coll Earth & Environm Sci Minist Educ Key Lab Western Chinas Environm Syst Lanzhou Gansu Peoples R China;

    Tsinghua Univ Dept Hydraul Engn State Key Lab Hydrosci & Engn Beijing Peoples R China;

    Lanzhou Univ Coll Earth & Environm Sci Minist Educ Key Lab Western Chinas Environm Syst Lanzhou Gansu Peoples R China;

    Lanzhou Univ Coll Earth & Environm Sci Minist Educ Key Lab Western Chinas Environm Syst Lanzhou Gansu Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    terrestrial evapotranspiration; Sobol' sensitivity analysis; DE-MC optimization; multiscale evaluation;

    机译:陆地蒸发;索尔思敏感性分析;DE-MC优化;多尺度评估;

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