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.
Three widely used land ET datasets were selected for merging, including the fifth-generation ECMWF reanalysis (ERA5; Hersbach et al., 2020), the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2; Gelaro et al., 2017), and Global Land Data Assimilation System Version 2 ET (GLDAS2; Sheffield and Wood, 2007). The differences in spatial and temporal resolution among the ET products were rescaled to a daily timescale and 0.25°, with the time span from 1980 to 2017.
In this study, we investigated whether more accurate ET results could be obtained through the weighted combination of ERA5, GLDAS and MERRA-2 estimation. Ideally, the weight assigned to each product should be based on an accurate description of the uncertainty during the merging process. Therefore, the three datasets have been weighted with respect to GLEAM, and the performance of the merged ET products has been studied at the selected sites. A set of weights need to be defined for the weighted combination of three products, usually based on the individual uncertainty in each product. The simplest strategy used in previous studies has been to assume that all three products have the same uncertainty; thus the merged product is a simple average of each product. A more detailed strategy used in this study is to weigh the products based on their uncertainties. The expected goal is to develop a product that minimizes root mean square deviation (RMSD), which we call optimal in the context of our merging strategy.
The data quality is good.
| # | number | name | type |
| 1 | 41875094 | National Natural Science Foundation of China |
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.ZENODO.DB6664.2024
10.5281/zenodo.4595941
