Morphology and influencing factors of rills in the steep slope in Yuanmou Dry-Hot Valley (SW China)

Rao, Hong; Deng, Qi; Zhang, Bin; Liu, Hui; Wang, Lei; Luo, Mingliang; Qin, Fanglu

doi:10.1016/j.catena.2018.01.017

Cited by 31 publications

(15 citation statements)

References 33 publications

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“…On a hillslope, runoff concentrates and wash away the soil and parent material, then cuts into the surface to form gullies. Gullies can be classified as either ephemeral or permanent ( Poesen et al, 2003 ), and their development is often controlled by multiple factors ( Deng et al, 2015 ; Ran et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, based on 3S technologies, pioneering research has been conducted on the spatial pattern and distribution of gullies and their topographic differentiation in the Loess Plateau ( Tang et al, 2015 ; Yi et al, 2016 ) and black soil region of northeastern China ( Huang & Fan, 2017 ; Wang et al, 2008 ). The distribution of gullies at different spatial scales in China is inhomogeneous, and, at the national scale, they are concentrated mainly in the Loess Plateau, northeastern China, and the southwestern dry-hot valleys ( Deng et al, 2015 ; Ran et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

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Spatial differentiation of gully clusters based on the regional scale: an example from northeastern China

Zhao

Zhang

Qin

et al. 2020

PeerJ

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Gully erosion represents a serious environmental threat around the world, but their spatial distribution law are unclear at the regional scale. To quantitatively characterize the spatial distribution model of gullies and determine the regularity of regional differentiation, this paper used spatial analyst and statistics method to study the spatial distribution of gullies in 34 sample areas of northeastern China based on interpretations of high-resolution remote sensing images. The results showed that the kernel density could quantitatively describe the continuous spatial clustering of gullies. Gullies in northeastern China had the characteristics of a spatially unbalanced distribution at the scale of the sample area. The average kernel density of the 34 sample areas (Moran’s I was 0.43, P¡0.01P < 0.01) also indicated clustering distribution at the regional scale. The horizontal clustering characteristics of gullies exhibited an azonal distribution of being low values in the middle plain and high values on the three mountainous areas. The average kernel density in the southeastern part of the study area was highest (maximum value of 2.38). In the vertical direction, gullies were relatively undeveloped in low- and high-altitude areas, while middle-altitude areas were beneficial to the development of gullies. The effect of height differences on gully development was more significant than altitude. As the height difference increased, gullies tended to be more clustered, which can be expressed by a power function. The results of this study will not only help to understand the regional differentiation characteristics of gullies but will also provide a scientific reference for the study of spatial distribution of gullies in future.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial differentiation of gully clusters based on the regional scale: an example from northeastern China

Zhao

Zhang

Qin

et al. 2020

PeerJ

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“…The soil types in the valley include dry-red soil and vertisol. The vegetation distributed below an altitude of 1,600 m is mainly shrub savanna, with over 90% coverage, while forest coverage is approximately 3.4%-6.3% (Deng et al, 2015b;Ran et al, 2018). Gully erosion is well developed due to special climate and lower vegetation coverage; it is also a very serious issue that is fragmenting land in the Dry-hot valley.…”

Section: The Study Areamentioning

confidence: 99%

Effect of Sampling Spacing on the Accuracy of Volume of a Medium-Size Gully

Luo¹,

Zhang²,

Du³

et al. 2019

J. Ecol. Eng.

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Accurate morphological parameters are key to understand the process and mechanism of gully erosion, but little information exists regarding the accuracy of gully volume influenced by sampling spacing. In order to assess the influence of sampling spacing on volume of medium gully under the condition of different shapes of gully cross-sections, as well as to provide information for proposed surveys, a MultiStation was utilized to obtain the point cloud of a U-and V-shaped gully. The coordinates were subsequently rarefied to a certain sampling spacing, and the gully volumes of three modes (Mode 1 only including rarefied terrain points; Mode 2 including detailed terrain and rarefied terrain points; Mode 3 including the rarefied detailed terrain points and terrain points with a given sampling spacing) were calculated in ArcGIS. The results showed that the error of gully volume increased non-monotonously with sampling spacing increased, and that the detail points had greater influence on the gully volume of a U-shaped than a V-shaped gully. As to Mode 1 and 2, the relative error (RE) of U-shaped gully volume increased faster with sampling spacing increasing, and presented high positive correlation. However, in the case of a V-shaped gully, a middle positive correlation for Mode 1 can be observed, and the RE was below 3% for Mode 2. For Mode 3, an increase in the sampling spacing of the terrain led to the larger RE, average RE, and variance of absolute error. If extremely high accuracy (2.5% of RE) is required, sampling spacing is less than 0.5 m; if high accuracy (5% of RE) is set, the sampling spacing of terrain points is below 0.8 m for a U-shaped gully, while the corresponding sampling spacing of terrain points and detail points are 2.0 m and 1.85 m, 3.0 m and 1.80 m, and 4.0 m and 0.75 m for a V-shaped gully. The shape of the gully's cross-section, as well as the exact location of the shoulder line will affect the accuracy of the gully's volume. This study will increase the efficiency of detail survey in the field for medium gully.

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“…Field surveys and measurements of rills have been conducted by scientists to explore rill morphology and erosion intensity in the field (Bruno et al, 2008;Cao et al, 2014;Hofer et al, 2012;Torri et al, 2006). The development of cross-sectional areas (CSAs) along rills can be quantified, and the effects of factors such as upslope area, slope gradient, soil erodibility and vegetation can be explored (Ran et al, 2018). Field surveys can acquire important rill parameters, although the traditional profile meter method reportedly generates inaccurate measurements (Bewket & Sterk, 2003;Vinci et al, 2016).…”

mentioning

confidence: 99%

“…Using terrestrial laser scanning (TLS) technology in field surveys, the roughness of the underlying terrain and rill morphology factors can be precisely measured (Stenberg et al, 2016;Vinci et al, 2015). Furthermore, the effects of terrain factors, including slope gradient, length and shape, on rill development can be accurately determined (Ran et al, 2018;Schneider et al, 2012). However, in cases where the terrain is being altered by human activity, the soil erosion process is expected to be changed and show unique characteristics (Tarolli, 2016).…”

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confidence: 99%

Variations in rill morphology on unpaved road surfaces and their controlling factors

Cao

Wang

et al. 2021

Earth Surf Processes Landf

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Rills play important roles in hillslope soil loss as both sediment sources and concentrated flow routes. The construction of unpaved roads in hilly regions changes the underlying surface conditions and leads to unique rill development. Via a terrestrial laser scanning-aided field survey of eight typical unpaved road segments in the hilly red soil region of China, road surface rill morphology was quantified in detail based on 0.05 m-resolution digital elevation models. The results illustrated that the rill network on a road segment was mainly composed of one major rill. The average rill network bifurcation ratio was 5.04, and the ratio was closely correlated with road surface area. Rill width and cross-sectional area (CSA) generally increased along roads, and ephemeral gullies formed in the lower part of some road segments. Slope length, gradient and surface roughness of the underlying road were closely related to the rill morphology factors. The winding roads exhibited higher rill widths, depths and CSAs in straight or highly curved road segments than in other segments. Intergraded factors composed of slope length and gradient were proposed to establish prediction equations for rill and gully CSA. The critical slope gradient and length for rill initiation were negatively related to each other, and the average critical slope was 7.4%. Similarly, the positions of the peak CSA were determined by a function of slope gradient and contributing road length (L = 46.98/sin[S] À 174.3). These results can help guide the prediction of road surface rill soil loss and the design of conservation treatments.

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Morphology and influencing factors of rills in the steep slope in Yuanmou Dry-Hot Valley (SW China)

Cited by 31 publications

References 33 publications

Spatial differentiation of gully clusters based on the regional scale: an example from northeastern China

Spatial differentiation of gully clusters based on the regional scale: an example from northeastern China

Effect of Sampling Spacing on the Accuracy of Volume of a Medium-Size Gully

Variations in rill morphology on unpaved road surfaces and their controlling factors

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