Preprints
https://doi.org/10.5194/essd-2022-222
https://doi.org/10.5194/essd-2022-222
 
19 Jul 2022
19 Jul 2022
Status: this discussion paper is a preprint. It has been under review for the journal Earth System Science Data (ESSD). The manuscript was not accepted for further review after discussion.

Long-time Series Dataset of Soil Conservation Capacity Preventing Water Erosion in China (1992–2019)

Jialei Li1,2, Hongbin He1, Liding Chen1,2, and Ranhao Sun1,2 Jialei Li et al.
  • 1State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
  • 2College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100190, China

Abstract. Soil conservation capacity (SC) is defined as the ability of the ecosystem to control soil erosion and protect soil function. A long-term and high-resolution estimation of soil conservation is urgent for ecological assessment and land management on a large scale. Here, a 300-m resolution SC dataset in China is established from 1992 to 2019 based on the Revised Universal Soil Loss Equation (RUSLE) model. The RUSLE modelling was conducted based on five key parameters, including the rainfall erosivity (interpolation of daily rainfall), land cover management (provincial data), conservation practices (weighted by terrain and crop types), topography (30 m), and soil properties (250 m). The dataset agrees with previous measurements (R2 > 0.5 in all the basins) and other regional simulations. The results show that China's SC decreased before 2003 and then increased up to now. The SC change exhibits the ecological effects of soil and water conservation policies in China, such as the Conversion of Farmland to Forests and Grass (Grain-for-Green), which unfolded many movements after 2000. Compared with current studies, the dataset has long-term, large-scale, and relatively high-resolution characteristics. This dataset will serve as a base to open out the mechanism of SC variations in China and could help assess the ecological effects of land management policies. This dataset is available at https://doi.org/10.11888/Terre.tpdc.272668 (Li et al., 2022).

Jialei Li et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on essd-2022-222', Song Leng, 05 Aug 2022
    • CC2: 'Reply on CC1', Jialei Li, 05 Aug 2022
  • CC3: 'Comment on essd-2022-222', Yuxia Liu, 07 Aug 2022
    • CC4: 'Reply on CC3', Jialei Li, 08 Aug 2022
  • CC5: 'Comment on essd-2022-222', Rong Gan, 08 Aug 2022
    • CC6: 'Reply on CC5', Jialei Li, 08 Aug 2022
  • CC7: 'Comment on essd-2022-222', Sicong Gao, 08 Aug 2022
    • AC1: 'Reply on CC7', Jialei Li, 20 Nov 2022
  • RC1: 'Comment on essd-2022-222', Anonymous Referee #1, 18 Nov 2022
  • RC2: 'Comment on essd-2022-222', Anonymous Referee #2, 04 Dec 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on essd-2022-222', Song Leng, 05 Aug 2022
    • CC2: 'Reply on CC1', Jialei Li, 05 Aug 2022
  • CC3: 'Comment on essd-2022-222', Yuxia Liu, 07 Aug 2022
    • CC4: 'Reply on CC3', Jialei Li, 08 Aug 2022
  • CC5: 'Comment on essd-2022-222', Rong Gan, 08 Aug 2022
    • CC6: 'Reply on CC5', Jialei Li, 08 Aug 2022
  • CC7: 'Comment on essd-2022-222', Sicong Gao, 08 Aug 2022
    • AC1: 'Reply on CC7', Jialei Li, 20 Nov 2022
  • RC1: 'Comment on essd-2022-222', Anonymous Referee #1, 18 Nov 2022
  • RC2: 'Comment on essd-2022-222', Anonymous Referee #2, 04 Dec 2022

Jialei Li et al.

Data sets

Dataset of Soil Conservation Capacity Preventing Water Erosion in China (1992–2019) Jialei Li, Hongbin He, Liding Chen, Ranhao Sun https://doi.org/10.11888/Terre.tpdc.272668

Jialei Li et al.

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Short summary
Soil conservation is determined by land use management and climate change. A long-term (1992–2019) and high-precision (300 m) soil conservation capacity dataset in China was constructed on the observed, remote-sensing, and statistical data. The dataset agrees with the observations in hydrological stations and other regional research on soil conservation.