Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites
Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites
复制标题
DOI：
10.1016/j.agrformet.2021.108350
复制
发表时间：
2021-02-14
影响因子：
6.2
通讯作者：
Zona, Donatella
中科院分区：
文献类型：
--
作者：
Chu, Housen;Luo, Xiangzhong;Zona, Donatella
用涡动协方差技术测量的温室气体和能量地面-大气通量的大型数据集（例如，FLUXNET 2015，AmeriFlux BASE）被广泛用于基准模型和遥感产品。本研究解决了模型数据集成面临的主要挑战之一：在何种空间范围内个别涡度协方差网站采取的通量测量反映模型或卫星为基础的网格单元？我们评估通量足迹-有助于测量通量的时间动态源区域-以及这些足迹对目标区域的代表性（例如，在通量塔周围250-3000 m半径内），其经常用于通量数据合成和建模研究。我们研究的土地覆盖组成和植被特征，这里所代表的增强植被指数（EVI），在通量足迹和目标地区在214 AmeriFlux网站，并评估潜在的偏见作为一个后果的足迹，目标区域不匹配。每月80%的足迹气候在不同地点和不同时间有四个数量级的变化，从10（3）到10（7）m（2），这是由于测量高度、底层植被和地表特征、风向和大气湍流状态。很少有涡动协方差的网站位于一个真正均匀的景观。因此，在不同地点使用固定范围目标区域的常见模型数据整合方法对经济脆弱性指数和主要土地覆盖百分比分别产生了4%-20%和6%-20%的偏差。这些偏差是测量高度，目标区域范围和地表特征的特定功能。我们主张通量数据集需要与足迹意识一起使用，特别是在以具有明确空间信息的模型和数据产品为基准的研究和应用中。我们提出了一个简单的代表性指数的基础上，我们的评估，可以作为一个指南，以确定适合特定应用程序的网站周期，并提供一般指导数据的使用。
Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints-the temporally dynamic source areas that contribute to measured fluxes-and the representativeness of these footprints for target areas (e.g., within 250-3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 10(3) to 10(7) m(2) due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%-20% for EVI and 6%-20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applications and to provide general guidance for data use.