Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations-猫眼课题宝

DOI：

10.5194/essd-11-1263-2019

发表时间：

2019-02

期刊：

Earth System Science Data

影响因子：

11.4

通讯作者：

中科院分区：

地球科学1区

文献类型：

作者：

O. Peltola;T. Vesala;Yao Gao;Olle Räty;P. Alekseychik;M. Aurela;B. Chojnicki;A. Desai;A. Dolman;E. Euskirchen;T. Friborg;M. Göckede;M. Helbig;E. Humphreys;R. Jackson;G. Jocher;F. Joos;Janina Klatt;S. Knox;N. Kowalska;L. Kutzbach;Sebastian Lienert;A. Lohila;I. Mammarella;D. Nadeau;M. Nilsson;W. Oechel;M. Peichl;T. Pypker;W. Quinton;J. Rinne;T. Sachs;M. Samson;H. Schmid;O. Sonnentag;C. Wille;D. Zona;T. Aalto

抽象的。自然湿地是大气中最大且最不确定的甲烷 (CH4) 来源，其中很大一部分位于北纬地区。这些排放通常使用过程（“自下而上”）或反演（“自上而下”）模型来估计。然而，这两种类型的模型的估计并不相互独立，因为自上而下的估计通常依赖于通过过程模型获得的这些排放的先验估计。因此，需要独立的空间显式验证数据。在这里，我们利用随机森林 (RF) 机器学习技术来升级 25 个地点的 CH4 涡流协方差通量测量值，以估算北纬地区（北纬 45°以北）的 CH4 湿地排放量。 2005年至2016年的涡度协方差数据用于模型开发。然后使用该模型来预测 2013 年和 2014 年期间的排放量。使用留一站点交叉验证方案来评估 RF 模型的预测性能。其性能（Nash-Sutcliffe 模型效率=0.47）与之前升级二氧化碳净生态系统交换的研究以及将过程模型输出与场地水平 CH4 排放数据进行比较的研究相当。湿地的全球分布是甲烷升级不确定性的主要来源之一。因此，在升级中使用了三个湿地分布图。根据湿地分布图，北部湿地的年排放量为 32（22.3-41.2，根据 RF 模型集合计算的 95% 置信区间）、31（21.4-39.9）或 38（25.9-49.5）Tg(CH4)yr−1。为了进一步评估升级后的 CH4 通量数据产品的不确定性，我们还将它们与两个过程模型（LPX-Bern 和 WetCHART）的输出进行了比较，并讨论了与 CH4 通量升级相关的方法问题。每月升级的 CH4 通量数据产品可在 https://doi.org/10.5281/zenodo.2560163 上获取（Peltola 等人，2019）。

Abstract. Natural wetlands constitute the largest and most uncertain source of methane (CH4) to the atmosphere and a large fraction of them are found in the northern latitudes. These emissions are typically estimated using process (“bottom-up”) or inversion (“top-down”) models. However, estimates from these two types of models are not independent of each other since the top-down estimates usually rely on the a priori estimation of these emissions obtained with process models. Hence, independent spatially explicit validation data are needed. Here we utilize a random forest (RF) machine-learning technique to upscale CH4 eddy covariance flux measurements from 25 sites to estimate CH4 wetland emissions from the northern latitudes (north of 45∘ N). Eddy covariance data from 2005 to 2016 are used for model development. The model is then used to predict emissions during 2013 and 2014. The predictive performance of the RF model is evaluated using a leave-one-site-out cross-validation scheme. The performance (Nash–Sutcliffe model efficiency =0.47) is comparable to previous studies upscaling net ecosystem exchange of carbon dioxide and studies comparing process model output against site-level CH4 emission data. The global distribution of wetlands is one major source of uncertainty for upscaling CH4. Thus, three wetland distribution maps are utilized in the upscaling. Depending on the wetland distribution map, the annual emissions for the northern wetlands yield 32 (22.3–41.2, 95 % confidence interval calculated from a RF model ensemble), 31 (21.4–39.9) or 38 (25.9–49.5) Tg(CH4) yr−1. To further evaluate the uncertainties of the upscaled CH4 flux data products we also compared them against output from two process models (LPX-Bern and WetCHARTs), and methodological issues related to CH4 flux upscaling are discussed. The monthly upscaled CH4 flux data products are available at https://doi.org/10.5281/zenodo.2560163 (Peltola et al., 2019).

Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations

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