Rill network development on loessial hillslopes in China

Abstract

Rill network development not only potentially affects hillslope and drainage network evolution, but also causes severe soil degradation. However, the studies on rill network development remain inconclusive. This study aimed to investigate the temporal and spatial development of hillslope rill networks and their characteristics based on rainfall simulations and field observations. A soil pan (10.0m long × 3.0m wide × 0.5m deep) on a 20°slope was applied three successive simulated rains at two intensities of 50 and 100mmh-1. The field observations were performed on two bare hillslope runoff plots (10.0m long × 3.0m wide) at 20°. Three typical erosive natural rainfall events were observed in the field, and rills were measured in detail, similar to the laboratory rainfall simulation. The results indicated that with increases in rainfall events, the rill network morphology varied from incipient formation to the maximum drainage network density. Four rill network development indicators (rill distribution density, distance between rills, rill bifurcation number, and confluence point number) exhibited different changes over time and space. Among the four indicators, the rill bifurcation number was the best indicator for describing rill network development. Rill flow energy increased and decreased cyclically on a slope ranging between~3 and 4m. Moreover, rill networks on loessial hillslopes generally evolved into dendritic rather than parallel forms. The development characteristics of the rill network were relatively similar between the laboratory simulation and natural field conditions. Over time, rill erosion control measures become increasingly difficult to implement as the rill network develops. The morphology of eroding rills evolved over time and space, which led to corresponding rill network development. Further study should quantify the impacts of rill network development on soil degradation and land development.

Rill Erosion on Slope of Spoil tips: experimental study of runoff scouring erosion in multiple times

“…7) is consistent with the findings of Fang et al (2015) and Tian et al (2020). However, Shen et al(2020) showed that the rill network mainly exhibits a dendritic pattern. The difference may be due to the fact that slope surface flow under scour conditions is surfaceproduced flow and is point-produced flow under rainfall conditions (Zhang et al, 2013), and the difference in the way they produce flow may lead to a different development of the rill network.…”

“…The parameters of rill erosion such as rill depth, rill width and width-to-depth ratio were selected to quantify the development characteristics of the rill network on the slope (Cerdan et al, 2002;Shen et al, 2020) and to reflect the intensity of rill erosion along the vertical and horizontal directions. Based on the 3D photo reconstruction technology Di Stefano et al, 2019), the photos taken by digital cameras were imported into Agisoft Photoscan Professional 1.2.4 (Agisoft LLC, St. Petersburg, Russia) for aligning photos, generating dense point clouds, generating grid textures, generating and exporting DEMs, and so on.…”

Rill investigation on landslide scar highlights the shortcomings of current knowledge on erosion of shallow landslides

“…Rill width, depth, width/depth ratio, density, and degree of dissection (Shen et al, 2020) were selected to assess rill morphology. The rill width (cm) and depth (cm) were computed using the mean values of the total measuring points of each rill.…”

“…Rill width, depth, width/depth ratio, density, and degree of dissection (Shen et al, 2020) were selected to assess rill morphology.…”

Reply on RC3

“…The parameters of rill erosion such as rill depth, rill width and width-to-depth ratio were selected to quantify the development characteristics of the rill network on the slope (Cerdan et al, 2002;Shen et al, 2020) and to reflect the intensity of rill erosion along the vertical and horizontal directions. Based on the 3D photo reconstruction technology Di Stefano et al, 2019), the photos taken by digital cameras were imported into Agisoft Photoscan Professional 1.2.4 (Agisoft LLC, St. Petersburg, Russia)…”