生态水文土地再分析
Ecohydrological land reanalysis
摘要：
陆地水和植被的精确估算是水文气象学中的一个重大挑战。以往的许多研究开发了陆地数据同化系统（LDAS），并提供全球尺度的陆面数据集，通过集成数值模拟和卫星数据。然而，植被动态还没有明确解决这些土地再分析数据集。在这里，我们提出了新开发的土地再分析数据集，生态水文土地再分析（ECHLA）。ECHLA是通过将C波段和X波段微波亮温卫星观测资料依次同化到一个陆面模式中来生成的，该模式可以显式地模拟植被生物量的动态演变。ECHLA数据集提供了从地表到1.95米深度的半全球土壤水分、叶面积指数（LAI）和植被含水量，数据时间为2003年至2010年和2013年至2019年。我们评估的性能ECHLA估计土壤水分和植被动态的ECHLA数据集与独立的卫星和原位观测数据进行比较。我们发现，我们的数据同化的顺序更新大大提高了重现植被的季节性周期的技能。数据同化还有助于提高模拟土壤水分的技能，主要是在浅层（0- 0.15米深）。ECHLA数据集将向公众开放，预计将有助于了解陆地生态水文循环和干旱等与水有关的自然灾害。
英文摘要：
The accurate estimation of terrestrial water and vegetation is a grand challenge in hydrometeorology. Many previous studies developed land data assimilation systems (LDASs) and provided global-scale land surface datasets by integrating numerical simulation and satellite data. However, vegetation dynamics has not been explicitly solved in these land reanalysis datasets. Here we present the newly developed land reanalysis dataset, ECoHydrological Land reAnalysis (ECHLA). ECHLA is generated by sequentially assimilating C- and X- band microwave brightness temperature satellite observations into a land surface model which can explicitly simulate the dynamic evolution of vegetation biomass. The ECHLA dataset provides semi-global soil moisture from surface to 1.95m depth, Leaf Area Index (LAI), and vegetation water content and is available from 2003 to 2010 and from 2013 to 2019. We assess the performance of ECHLA to estimate soil moisture and vegetation dynamics by comparing the ECHLA dataset with independent satellite and in-situ observation data. We found that our sequential update by data assimilation substantially improves the skill to reproduce the seasonal cycle of vegetation. Data assimilation also contributes to improving the skill to simulate soil moisture mainly in the shallow soil layers (0-0.15m depth). The ECHLA dataset will be publicly available and expected to contribute to understanding terrestrial ecohydrological cycles and water-related natural disasters such as drought.
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