A Harmonized Global Land Evaporation Dataset from Reanalysis Products Covering 1980-2017

Lu Jiao, Wang Guojie, Chen Tiexi, Li Shijie, Hagan Fiifi T. Daniel, Kattel Giri, Peng Jian, Jiang Tong, S. Buda
{"title":"A Harmonized Global Land Evaporation Dataset from Reanalysis Products Covering 1980-2017","authors":"Lu Jiao, Wang Guojie, Chen Tiexi, Li Shijie, Hagan Fiifi T. Daniel, Kattel Giri, Peng Jian, Jiang Tong, S. Buda","doi":"10.5281/ZENODO.4595941","DOIUrl":null,"url":null,"abstract":"Abstract. Land evaporation (ET) plays a crucial role in hydrological and energy cycle. However, the widely used numerical products are still subject to great uncertainties due to imperfect model parameterizations and forcing data. Lack of available observed data has further complicated the estimation. Hence, there is an urgency to define the global benchmark land ET for climate-induced hydrology and energy change. In this study, we have used the coefficient of variation (CV) and carefully selected merging regions with high consistency of multiple data sets. Reliability Ensemble Averaging (REA) method has been used to generate a long-term (1980–2017) daily ET product with a spatial resolution of 0.25 degree by merging the selected three data sets, ERA5, GLDAS2 and MERRA2. GLEAM3.2a and flux tower observation data have been selected as the data for reference and evaluation, respectively. The results showed that the merged product performed well under a variety of vegetation cover conditions as the weights were distributed across the east-west direction banding manner, with greater differences near the equator. The merged product also captured well the trend of land evaporation over different areas, showing the significant decreasing trend in Amazon plain in South America and Congo Basin in central Africa, and the increasing trend in the east of North America, west of Europe, south of Asia and north of Oceania. In addition to model performance, REA method also successfully worked for the model convergence showing as an outstanding reference for data merging of other variables. Data can be accessed at https://doi.org/10.5281/zenodo.4595941 (Lu et al., 2021).","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth System Science Data Discussions","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5281/ZENODO.4595941","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Abstract. Land evaporation (ET) plays a crucial role in hydrological and energy cycle. However, the widely used numerical products are still subject to great uncertainties due to imperfect model parameterizations and forcing data. Lack of available observed data has further complicated the estimation. Hence, there is an urgency to define the global benchmark land ET for climate-induced hydrology and energy change. In this study, we have used the coefficient of variation (CV) and carefully selected merging regions with high consistency of multiple data sets. Reliability Ensemble Averaging (REA) method has been used to generate a long-term (1980–2017) daily ET product with a spatial resolution of 0.25 degree by merging the selected three data sets, ERA5, GLDAS2 and MERRA2. GLEAM3.2a and flux tower observation data have been selected as the data for reference and evaluation, respectively. The results showed that the merged product performed well under a variety of vegetation cover conditions as the weights were distributed across the east-west direction banding manner, with greater differences near the equator. The merged product also captured well the trend of land evaporation over different areas, showing the significant decreasing trend in Amazon plain in South America and Congo Basin in central Africa, and the increasing trend in the east of North America, west of Europe, south of Asia and north of Oceania. In addition to model performance, REA method also successfully worked for the model convergence showing as an outstanding reference for data merging of other variables. Data can be accessed at https://doi.org/10.5281/zenodo.4595941 (Lu et al., 2021).
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于1980-2017年再分析产品的统一全球土地蒸发数据集
摘要土地蒸发(ET)在水文和能量循环中起着至关重要的作用。然而,由于模型参数化和强迫数据的不完善,广泛使用的数值产品仍然存在很大的不确定性。缺乏可用的观测数据使估计进一步复杂化。因此,迫切需要确定气候引起的水文和能源变化的全球基准陆地ET。在本研究中,我们使用了变异系数(CV),并仔细选择了多数据集一致性高的合并区域。选取ERA5、GLDAS2和MERRA2 3个数据集,采用可靠性集合平均(REA)方法合成空间分辨率为0.25度的长期(1980-2017)日ET产品。选取GLEAM3.2a和通量塔观测数据分别作为参考和评价数据。结果表明,在各种植被覆盖条件下,合并后的结果均表现良好,权重呈东西向带状分布,赤道附近差异较大。合并后的产物也很好地反映了不同区域的土地蒸发趋势,南美洲亚马逊平原和中非刚果盆地的土地蒸发呈显著减少趋势,北美东部、欧洲西部、亚洲南部和大洋洲北部的土地蒸发呈增加趋势。除了模型性能外,REA方法还成功地实现了模型的收敛性,为其他变量的数据合并提供了很好的参考。数据可访问https://doi.org/10.5281/zenodo.4595941 (Lu et al., 2021)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
New SMOS SSS maps in the framework of the Earth Observation data For Science and Innovation in the Black Sea LGHAP: a Long-term Gap-free High-resolution Air Pollutants concentration dataset derived via tensor flow based multimodal data fusion Pre- and post-production processes along supply chains increasingly dominate GHG emissions from agri-food systems globally and in most countries Last Interglacial sea-level data points from Northwest Europe A machine learning approach to address air quality changes during the COVID-19 lockdown in Buenos Aires, Argentina
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1