A harmonized global land evaporation dataset from model-based products covering 1980–2017
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Published:2021-12-21
Issue:12
Volume:13
Page:5879-5898
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ISSN:1866-3516
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Container-title:Earth System Science Data
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language:en
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Short-container-title:Earth Syst. Sci. Data
Author:
Lu JiaoORCID, Wang Guojie, Chen Tiexi, Li Shijie, Hagan Daniel Fiifi Tawia, Kattel GiriORCID, Peng Jian, Jiang TongORCID, Su Buda
Abstract
Abstract. Land evaporation (ET) plays a crucial role in the hydrological and energy cycle. However, the widely used model-based
products, even though helpful, are still subject to great uncertainties
due to imperfect model parameterizations and forcing data. The lack of
available observed data has further complicated estimation. Hence, there
is an urgency to define the global proxy land ET with lower uncertainties
for climate-induced hydrology and energy change. This study has combined
three existing model-based products – the fifth-generation ECMWF reanalysis
(ERA5), Global Land Data Assimilation System Version 2 (GLDAS2), and the
second Modern-Era Retrospective analysis for Research and Applications
(MERRA-2) – to obtain a single framework of a long-term (1980–2017) daily ET
product at a spatial resolution of 0.25∘. Here, we use the
reliability ensemble averaging (REA) method, which minimizes errors using
reference data, to combine the three products over regions with high
consistencies between the products using the coefficient of variation (CV).
The Global Land Evaporation Amsterdam Model Version 3.2a (GLEAM3.2a) and flux
tower observation data were selected as the data for reference and
evaluation, respectively. The results showed that the merged product
performed well over a range of vegetation cover scenarios. The merged
product also captured the trend of land evaporation over different areas
well, showing the significant decreasing trend in the 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 demonstrating a good performance, the REA method also
successfully converged the models based on the reliability of the inputs.
The resulting REA data can be accessed at
https://doi.org/10.5281/zenodo.4595941 (Lu et al., 2021).
Funder
Key Technologies Research and Development Program National Natural Science Foundation of China Graduate Research and Innovation Projects of Jiangsu Province Nanjing University of Information Science and Technology
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences
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