Gully erosion susceptibility considering spatiotemporal environmental variables: Midwest U.S. region

• Published in: Journal of hydrology. Regional studies 2022-10, Vol.43, p.101196, Article 101196
• Main Authors: Han, Jeongho, Guzman, Jorge A., Chu, Maria L.
• Format: Article
• Language: English
• Subjects: agricultural land; atmospheric precipitation; environmental factors; Gully erosion; Gully erosion susceptibility mapping; hydrology; Illinois; land cover; land use; leaf area index; LiDAR; Machine learning; MaxEnt model; model validation; organic matter; prediction; remote sensing; risk; rowcrops; Soil erosion; topography; vegetation
• ISSN: 2214-5818; 2214-5818
• DOI: 10.1016/j.ejrh.2022.101196

The study was tested in Jefferson County in Illinois, USA, whose land use is a typical representation of row crop cultivation in the Midwestern USA. This study aimed to predict the gully erosion susceptibility in agricultural land using remote sensed environmental data (topographic, pedologic, land cover, precipitation, and vegetation development) considering their spatio-temporal variability in a modeling framework based on the maximum entropy model MaxEnt. The methodology thoroughly evaluated each environmental factor contributing to gully erosion prediction and used a set of rules based on accuracy, transferability, and efficiency to evaluate the model performance. This study developed a data-driven modeling framework that can be applied across other regions. The modeling framework indicates that fifteen factors were the most relevant for developing the gully erosion susceptibility map, where 7.4% of the agricultural land in the study area was found at elevated risk of developing gully erosion. Slope, land cover, organic matter, seasonal LAI, and maximum daily precipitation were the most contributing environmental factors to the study area. Furthermore, this study identified the importance of high temporal resolution in varying seasonal factors (i.e., leaf area index and precipitation) to improve model predictability compared to annual temporal discretization. [Display omitted] •Assessment of the spatiotemporal environmental variables driving gully erosion in agricultural lands.•Prediction and mapping of gully erosion susceptibility using the Maxent model.•Analysis of geospatial features of vulnerable areas subjected to gully erosion development.