Preprints
https://doi.org/10.5194/essd-2022-217
https://doi.org/10.5194/essd-2022-217
08 Sep 2022
 | 08 Sep 2022
Status: this preprint was under review for the journal ESSD but the revision was not accepted.

Global soil moisture storage capacity at 0.5° resolution for geoscientific modelling

Kang Xie, Pan Liu, Qian Xia, Xiao Li, Weibo Liu, Xiaojing Zhang, Lei Cheng, Guoqing Wang, and Jianyun Zhang

Abstract. Soil moisture storage capacity (SMSC) links the atmosphere and terrestrial ecosystems, which is required as spatial parameters for geoscientific models. However, there are currently no available common datasets of the SMSC on a global scale, especially for hydrological models since conventional evapotranspiration-derived estimates cannot represent the extra storage capacity for the lateral flow and runoff generation. Here, we produce a dataset of the SMSC parameter for global hydrological models. Joint parameter calibration of three commonly used monthly water balance models provides the labels for a deep residual network. The global SMSC is constructed based on the deep residual network at 0.5° resolution by integrating 15 types of meteorological forcings, underlying surface properties, and runoff data. SMSC products are validated with the spatial distribution against root zone depth datasets and validated in the simulation efficiency on global grids and typical catchments from different climatic regions. We provide the global SMSC parameter dataset as a benchmark for geoscientific modelling by users.

Kang Xie, Pan Liu, Qian Xia, Xiao Li, Weibo Liu, Xiaojing Zhang, Lei Cheng, Guoqing Wang, and Jianyun Zhang

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-217', Anonymous Referee #1, 08 Oct 2022
    • AC1: 'Reply on RC1', Pan Liu, 13 Oct 2022
  • RC2: 'Comment on essd-2022-217', Anonymous Referee #2, 23 Oct 2022
    • AC2: 'Reply on RC2', Pan Liu, 27 Oct 2022
  • CC1: 'Comment on essd-2022-217', Chian Phạm, 29 Oct 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-217', Anonymous Referee #1, 08 Oct 2022
    • AC1: 'Reply on RC1', Pan Liu, 13 Oct 2022
  • RC2: 'Comment on essd-2022-217', Anonymous Referee #2, 23 Oct 2022
    • AC2: 'Reply on RC2', Pan Liu, 27 Oct 2022
  • CC1: 'Comment on essd-2022-217', Chian Phạm, 29 Oct 2022
Kang Xie, Pan Liu, Qian Xia, Xiao Li, Weibo Liu, Xiaojing Zhang, Lei Cheng, Guoqing Wang, and Jianyun Zhang

Data sets

Global soil moisture storage capacity at 0.5° resolution for geoscientific modelling Kang Xie https://zenodo.org/record/5598405

Kang Xie, Pan Liu, Qian Xia, Xiao Li, Weibo Liu, Xiaojing Zhang, Lei Cheng, Guoqing Wang, and Jianyun Zhang

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Latest update: 28 Mar 2024
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Short summary
There are currently no available common datasets of the Soil moisture storage capacity (SMSC) on a global scale, especially for hydrological models. Here, we produce a dataset of the SMSC parameter for global hydrological models. The global SMSC is constructed based on the deep residual network at 0.5° resolution. SMSC products are validated on global grids and typical catchments from different climatic regions.
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