Effects of gully head height and soil texture on gully headcut erosion in the Loess Plateau of China

CATENA ◽  
2021 ◽  
Vol 207 ◽  
pp. 105674
Author(s):  
Rui Wang ◽  
Peng Li ◽  
Zhanbin Li ◽  
Kunxia Yu ◽  
Jianchun Han ◽  
...  
Author(s):  
Jiaying Zhai ◽  
Yahui Song ◽  
Wulan Entemake ◽  
Hongwei Xu ◽  
Yang Wu ◽  
...  

Analyzing the dynamics of soil particle size distribution (PSD) and erodibility is important for understanding the changes of soil texture and quality after cropland abandonment. This study aimed to determine how restoration age and latitude affect soil erodibility and the multifractal dimensions of PSD during natural recovery. We collected soil samples from grassland, shrubland, and forests with different restoration ages in the steppe zone (SZ), forest-steppe zone (FSZ), and forest zone (FZ). Various analyses were conducted on the samples, including multifractal analysis and erodibility analysis. Our results showed that restoration age had no significant effect on the multifractal dimensions of PSD (capacity dimension (D0), information dimension (D1), information dimension/capacity dimension ratio (D1/D0), correlation dimension (D2)), and soil erodibility. Multifractal dimensions tended to increase, while soil erodibility tended to decrease, with restoration age. Latitude was negatively correlated with fractal dimensions (D0, D2) and positively correlated with K and D1/D0. During vegetation restoration, restoration age, precipitation, and temperature affect the development of vegetation, resulting in differences in soil organic carbon, total nitrogen, soil texture, and soil enzyme activity, and by affecting soil structure to change the soil stability. This study revealed the impact of restoration age and latitude on soil erosion in the Loess Plateau.


Author(s):  
Cunjie Guo ◽  
Laibin Zhang ◽  
Wei Liang ◽  
Zhong Lu ◽  
Detian Liu ◽  
...  

2019 ◽  
Vol 34 (3) ◽  
pp. 718-729 ◽  
Author(s):  
Qianhua Shi ◽  
Wenlong Wang ◽  
Mingming Guo ◽  
Zhuoxin Chen ◽  
Lanqian Feng ◽  
...  

2021 ◽  
Vol 13 (3) ◽  
pp. 421
Author(s):  
Chengcheng Jiang ◽  
Wen Fan ◽  
Ningyu Yu ◽  
Yalin Nan

Gully head erosion causes serious land degradation in semiarid regions. The existing studies on gully head erosion are mainly based on measuring the gully volume in small-scale catchments, which is a labor-intensive and time-consuming approach. Therefore, it is necessary to explore an accurate method quantitatively over large areas and long periods. The objective of this study was to develop a model to assess gully head erosion in the Loess Plateau of China using a method based on the SBAS-InSAR technique. The gully heads were extracted from the digital elevation model and validated by field investigation and aerial images. The surface deformation was estimated with SBAS-InSAR and 22 descending ALOS PALSAR datasets from 2007 to 2011. A gully head erosion model was developed; this model can incorporate terrain factors and soil types, as well as provides erosion rate predictions consistent with the SBAS-InSAR measurements (R2 = 0.889). The results show that gully head erosion significantly depends on the slope angle above the gully head, slope length, topographic wetness index, and catchment area. The relationship between these factors and the gully head erosion rate is a power function, and the average rate of gully head erosion is 7.5 m3/m2/year, indicating the high erosional vulnerability of the area. The accuracy of the model can be further improved by considering other factors, such as the stream power factor, curvature, and slope aspect. This study indicates that the erosion rate of gully heads is almost unaffected by soil type in the research area. An advantage of this model is that the gully head area and surface deformation can be easily extracted and measured from satellite images, which is effective for assessing gully head erosion at a large scale in combination with SBAS-InSAR results and terrain attributes.


CATENA ◽  
2019 ◽  
Vol 172 ◽  
pp. 148-157 ◽  
Author(s):  
Nannan Ge ◽  
Xiaorong Wei ◽  
Xiang Wang ◽  
Xuetong Liu ◽  
Mingan Shao ◽  
...  

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