Towards mapping large scale soil erodibility by using pedological knowledge

• Published in: Archiv für Acker- und Pflanzenbau und Bodenkunde Vol. 67; no. 6; pp. 809 - 821
• Main Authors: Gu, Zhijia, Huang, Yong, Feng, Detai, Duan, Xingwu, Xue, Mengyu, Li, Yuxiang, Li, Yawen
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 12.05.2021; Taylor & Francis Ltd
• Subjects: China; erodibility; Heterogeneity; Interpolation; k factors; Land use; large scale mapping; latitude; Mapping; mountains; pedological knowledge; Redundancy; Revised Universal Soil Loss Equation; Soil erodibility; Soil erosion; soil map; Soil maps; Soil properties; Soil types; Soil water; Spatial heterogeneity; spatial variation; temperature; Universal Soil Loss Equation; Water erosion; China
• ISSN: 0365-0340; 1476-3567; 1476-3567
• DOI: 10.1080/03650340.2020.1759799

Soil erodibility is a key parameter to measure soil susceptibility to water erosion and thus to soil erosion modelling, introduced as K factor in the Universal Soil Loss Equation (USLE) and the Revised Universal Soil Loss Equation (RUSLE). However, subject to soil spatial heterogeneity and data availability, large-scale K value acquisition has not been properly addressed. In this study, authors aimed to construct a new large-scale soil erodibility mapping technique to provide methodological support for regional soil erosion surveys. Taking the mountainous Yunnan province as a case, 743 soil samples from 252 typical soil series were collected. A 'GIS-based pedological method' was developed to construct a large-scale soil erodibility database by linking a soil map with soil properties. K value in Yunnan varied between 0.0004 and 0.0169 t ha h ha −1 MJ −1  mm −1 , with an average of 0.0065 t ha h ha −1 MJ −1  mm −1 . Significantly different K values were found among different types of soils and land use (p < 0.05). Redundancy analysis indicated that annual average temperature and latitude were the dominant control factors. The study showed that regional K value estimated by 'GIS-based pedological method' manifested an improvement compared to the traditional spatial interpolation method.