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SOIL An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/soil-2018-32
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-2018-32
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Review article 15 Oct 2018

Review article | 15 Oct 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal SOIL (SOIL).

A review on the global soil datasets for earth system modeling

Yongjiu Dai1, Wei Shangguan1, Dagang Wang2, Nan Wei1, Qinchuan Xin2, Hua Yuan1, Shupeng Zhang1, Shaofeng Liu1, and Fapeng Yan3 Yongjiu Dai et al.
  • 1Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China
  • 2School of Geography and Planning, Sun Yat-sen University, Guangzhou, China
  • 3College of Global Change and Earth System Science, Beijing Normal University, Beijing, China

Abstract. Global soil dataset is a pillar to the challenge of earth system modeling. But it is one of the most important uncertainty sources for Earth System Models (ESMs). Soil datasets function as model parameters, initial variables and benchmark datasets for model calibration, validation and comparison. For modeling use, the dataset should be geographically continuous, scalable and with uncertainty estimates. The popular soil datasets used in ESMs are often based on limited soil profiles and coarse resolution soil maps. Updated and comprehensive soil information needs to be incorporated in ESMs. New generation soil datasets derived by digital soil mapping with abundant soil observations and environmental covariates are preferred to those by the linkage method for ESMs. Because there is no universal pedotransfer function, an ensemble of them may be more suitable to provide secondary soil parameters to ESMs. Aggregation and upscaling of soil data are needed for model use but can be avoid by taking a subgrid method in ESMs at the cost of increases in model complexity. Uncertainty of soil data needs to be incorporated in ESMs.

Yongjiu Dai et al.
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Short summary
Global soil dataset is one of the most important uncertainty sources for Earth System Models (ESMs). Soil datasets are used for model calibration, validation and comparison. The popular soil datasets used in ESMs are often with limited accuracy. Updated and comprehensive soil information needs to be incorporated in ESMs. New generation soil datasets with abundant soil observations and environmental covariates are preferred. Uncertainty of soil data needs to be incorporated in ESMs.
Global soil dataset is one of the most important uncertainty sources for Earth System Models...
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