16 Mar 2021

16 Mar 2021

Review status: a revised version of this preprint is currently under review for the journal ESSD.

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

Jiao Lu1, Guojie Wang1, Tiexi Chen1, Shijie Li1, Daniel Fiifi Tawia Hagan1, Giri Kattel1, Jian Peng2,3, Tong Jiang1, and Buda Su1 Jiao Lu et al.
  • 1Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • 2Department of Remote Sensing, Helmholtz Centre for Environmental Research – UFZ, Permoserstrasse 15, 04318, Leipzig, Germany
  • 3Remote Sensing Centre for Earth System Research, Leipzig University, Talstr. 35, 04103, Leipzig, Germany

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 (Lu et al., 2021).

Jiao Lu et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on essd-2021-61', Savin Chand, 17 May 2021
    • AC3: 'Reply on CC1', Jiao Lu, 07 Sep 2021
  • RC1: 'Comment on essd-2021-61', Anonymous Referee #1, 17 May 2021
    • AC1: 'Reply on RC1', Jiao Lu, 07 Sep 2021
  • CC2: 'Comment on essd-2021-61', Qichun Yang, 18 May 2021
    • AC4: 'Reply on CC2', Jiao Lu, 07 Sep 2021
  • RC2: 'Comment on essd-2021-61', Anonymous Referee #2, 17 Aug 2021
    • AC2: 'Reply on RC2', Jiao Lu, 07 Sep 2021

Jiao Lu et al.

Data sets

A Harmonized Global Land Evaporation Dataset from Reanalysis Products Covering 1980-2017 Jiao Lu, Guojie Wang, Tiexi Chen, Shijie Li, Daniel Fiifi Tawia Hagan, Giri Kattel, Jian Peng, Tong Jiang, and Buda Su

Jiao Lu et al.


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