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 is currently under review for the journal ESSD.

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

Kang Xie1,2,3, Pan Liu1,2,3, Qian Xia1,2,3, Xiao Li1,2,3, Weibo Liu1,2,3, Xiaojing Zhang1,2,3, Lei Cheng1,2,3, Guoqing Wang4, and Jianyun Zhang4 Kang Xie et al.
  • 1State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
  • 2Hubei Provincial Key Lab of Water System Science for Sponge City Construction, Wuhan University, Wuhan 430072, China
  • 3Research Institute for Water Security (RIWS), Wuhan University, Wuhan 430072, China
  • 4State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China

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 et al.

Status: open (until 03 Nov 2022)

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Kang Xie et al.

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

Kang Xie et al.

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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.