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Preprints
https://doi.org/10.5194/essd-2020-283
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2020-283
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  29 Oct 2020

29 Oct 2020

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This preprint is currently under review for the journal ESSD.

SoilErosionDB: A global database for surface runoff and soil erosion evaluation

Jinshi Jian1,, Xuan Du2,, Ryan D. Stewart3, Zeli Tan4, and Ben Bond-Lamberty1 Jinshi Jian et al.
  • 1Pacific Northwest National Laboratory, Joint Global Change Research Institute at the University of Maryland–College Park, 5825 University Research Court, Suite 3500, College Park, MD 20740, USA
  • 2Department of Hydraulic Engineering, Yangling Vocational & Technical College, Yang Ling, Shaanxi, China
  • 3School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, USA
  • 4Pacific Northwest National Laboratory, Richland, WA, USA
  • These authors contributed equally to this work.

Abstract. Soil erosion is a major threat to soil resources, continuing to cause environmental degradation and social poverty in many parts of the world. Many field and laboratory experiments have been performed over the past century to study spatio-temporal patterns of soil erosion caused by surface runoff under different environmental conditions. However, these historical data have never been integrated together in a way that can inform current and future efforts to understand and model soil erosion at different scales. Here, we designed a database (SoilErosionDB) to compile field and laboratory measurements of soil erosion caused by surface runoff, with data coming from sites across the globe. The SoilErosionDB includes 18 columns for soil erosion related indicators and 73 columns for background information that describe factors such as latitude, longitude, climate, elevation, and soil type. Currently, measurements from 99 geographic sites and 22 countries around the world have been compiled into SoilErosionDB. We provide examples of linking SoilErosionDB with an external climate dataset and using annual precipitation to explain annual soil erosion variability under different environmental conditions.

All data and code to reproduce the results in this study can be found at: Jian, J., Du, X., Stewart, R., Tan, Z. and Bond-Lamberty, B.: jinshijian/SoilErosionDB: First release of SoilErosionDB, Zenodo, https://doi.org/10.5281/zenodo.4030875, 2020b. All data are also available through GitHub: https://github.com/jinshijian/SoilErosionDB.

Jinshi Jian et al.

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jinshijian/ESSD: SRDB-V5 first release (Version v1.0.0) Jian, J., Du, X., Stewart, R., Tan, Z., and Bond-Lamberty, B. https://doi.org/10.5281/zenodo.4030875

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Latest update: 27 Nov 2020
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Short summary
Field soil loss due to surface runoff observations were compiled into a global database (SoilErosionDB). The database focuses on three erosion-related metrics – surface runoff, soil erosion, and nutrient leaching – and also records background information. Data from 99 geographic sites and 22 countries around the world have been compiled into SoilErosionDB. SoilErosionDB aims to be a data framework for the scientific community to share field-based soil erosion measurements.
Field soil loss due to surface runoff observations were compiled into a global database...
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