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Rill and gully erosion on unpaved roads under heavy rainfall in agricultural watersheds on China’s Loess Plateau
- Zhang, Yan, Zhao, Yiyang, Liu, Baoyuan, Wang, Zhiqiang, Zhang, Shuai
- Agriculture, ecosystems & environment 2019 v.284 pp. 106580
- agricultural land, agricultural watersheds, geographic information systems, geomorphology, gully erosion, land degradation, land use, rain, regression analysis, remote sensing, rill erosion, roads, sediments, storms, unmanned aerial vehicles, vegetation, China
- Soil erosion causes agricultural land degradation. As one of the indispensable components of the agricultural system, unpaved roads are significant sediment sources, but road erosion is often overlooked because of the relatively small areas that roads cover. This study aims to investigate the severity of road erosion under extreme rainfall and ascertain the dominating factor. We investigated road erosion in two small watersheds after a heavy rainstorm to measure rill and gully erosion on three types of road in the field and determine the factors affecting that erosion with remote sensing images and geographic information system (GIS). Using Google images before the storm and unmanned aerial vehicle (UAV) images after the storm, we interpreted land use and measured geomorphological and vegetation factors. In 579 road cross-sections within 63 road erosion segments found along the 10.42 km roads surveyed, the average soil loss was 804.77 t ha^-1 from main unpaved roads, 471.78 t ha^-1 from secondary unpaved roads, and 147.46 t ha^-1 from trails. Gully erosion predominated over rill erosion on all three types of road. The average rill erosion was 0.41, 0.17, and 0.02 cm, compared with 4.88, 3.15, and 1.05 cm of gully erosion on the main unpaved roads, secondary unpaved roads, and trails, respectively. The contributing area dominated over other factors associated with road erosion under heavy rainfall and could explain 84.9% of the erosion from the road segment to which it drains based on the linear regression analysis. Furthermore, a nonlinear regression model with the contributing area and road segment gradient as predictors precisely predicted road erosion (coefficient of determination, 0.970).