Dataset for Monthly Gridded Data Product of Northern Wetland Methane Emissions Based on Upscaling Eddy Covariance Observations
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This record is for the dataset “Dataset for Monthly Gridded Data Product of Northern Wetland Methane Emissions Based on Upscaling Eddy Covariance Observations” at https://doi.org/10.5281/zenodo.2560164. This dataset provides wetland methane (CH4) emissions, their uncertainties and underlying CH4 flux densities north from 45 N using three different wetland maps. The data products are derived using data from several eddy covariance CH4 flux sites, random forest machine learning algorithms and three prescribed wetland maps. The data are at 0.5 by 0.5 deg or 1 by 1 deg resolution, depending on the wetland map used. The dataset covers years 2013 and 2014. CH4 flux densities are provided only for grid cells with > 5 % wetland coverage. The three data products are provided in netCDF format files (.nc). Please see more details in the attributes saved in the netCDF files. RF-DYPTOP.nc Upscaling based on DYPTOP dynamic wetland map. At 1 by 1 deg resolution. RF-GLWD.nc Upscaling using GLWD static wetland map. At 0.5 by 0.5 deg resolution. RF-PEATMAP.nc Upscaling using PEATMAP static wetland map. At 0.5 by 0.5 deg resolution. This data can be downloaded at https://doi.org/10.5281/zenodo.2560164
本数据集对应链接https://doi.org/10.5281/zenodo.2560164 下的《基于涡度协方差观测向上尺度转换的北半球湿地甲烷(CH4)排放逐月格点数据产品数据集(Dataset for Monthly Gridded Data Product of Northern Wetland Methane Emissions Based on Upscaling Eddy Covariance Observations)》。 本数据集提供北纬45°以北区域的湿地甲烷(CH4)排放数据、排放不确定性及对应甲烷通量密度,共采用三套不同的湿地地图进行生成。 该数据产品基于多组涡度协方差(eddy covariance)甲烷通量观测站点数据、随机森林(random forest)机器学习算法以及三套预设湿地地图构建而成。 数据分辨率依据所采用的湿地地图而定,为0.5°×0.5°或1°×1°。 数据集覆盖2013年与2014年两个年度。 甲烷通量密度仅针对湿地覆盖率大于5%的格点进行提供。 三套数据产品均以网络通用数据格式(netCDF,后缀.nc)文件形式提供。 更多详细信息请参阅netCDF文件中存储的属性信息。 RF-DYPTOP.nc:基于DYPTOP动态湿地地图开展向上尺度转换,分辨率为1°×1°。 RF-GLWD.nc:采用GLWD静态湿地地图开展向上尺度转换,分辨率为0.5°×0.5°。 RF-PEATMAP.nc:采用PEATMAP静态湿地地图开展向上尺度转换,分辨率为0.5°×0.5°。 本数据集可通过链接https://doi.org/10.5281/zenodo.2560164 下载。



