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

Authors :
Jiao Lu
Guojie Wang
Tiexi Chen
Shijie Li
Daniel Fiifi Tawia Hagan
Giri Kattel
Jian Peng
Tong Jiang
Buda Su
Publication Year :
2021
Publisher :
Copernicus GmbH, 2021.

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).

Details

ISSN :
18663516
Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....e65fee13d3f999b67b7b50df00a6e4d9
Full Text :
https://doi.org/10.5194/essd-2021-61