Identifying and Mapping the Spatial Factors That Control Soil Erosion Changes in the Yellow River Basin of China

Guo, Jinwei; Qi, Yanbing; Zhang, Luhao; Zheng, Jiale; Sun, Jingyan; Tang, Yuanyuan; Yang, Xiangyun

doi:10.3390/land13030344

Land

2024

DOI: 10.3390/land13030344

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Identifying and Mapping the Spatial Factors That Control Soil Erosion Changes in the Yellow River Basin of China

Jinwei Guo,

Yanbing Qi,

Luhao Zhang

et al.

Abstract: The Yellow River Basin has been considered to have the most serious soil erosion in the world, and identifying and mapping the spatial controlling factors would be of great help in adopting targeting strategies for soil erosion prevention. This study used the Universal Soil Loss Equation (USLE) to estimate the spatial and temporal changes in soil erosion from 1985 to 2020 and analyzed the controlling factors. The results indicated that from 1985 to 2020, the average erosion modulus in the Yellow River Basin wa… Show more

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Spatiotemporal variation and driving force of gully erosion in the Pisha sandstone area

Zhang,

Li,

Liu

et al. 2024

Front. Environ. Sci.

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The experiment was conducted on gully slopes with slopes ranging from 80° to 90° to investigate the relationship between erosion rates, spatial and temporal changes in microtopography, and drivers of erosion on gully slopes in different seasons. To precisely characterize the microtopography of slopes where debris slides occur, we used the RIEGL VZ-400 3D laser scanner to scan the observation site and acquire point cloud data on the slope’s microtopography. Using the “data conversion module” of ArcGIS software, the point cloud data were transformed into raster data. Through the “3D analysis,” “hydrological analysis,” and “grid calculator” modules, the basic microgeomorphological indicators were extracted from the gully slope grid data, and the erosion rate and microterrain evolution mechanism of the gully slope in different seasons were also determined. The results revealed the following: (1) in the Pisha sandstone area, erosion was relatively strong in the first quarter, with 65% of the area being eroded. The average erosion rates over the four quarters followed the order of first quarter > fourth quarter > second quarter > third quarter, from fastest to slowest. (2) As the soil on the gully slope thawed, melt water increased soil moisture. This phenomenon sharply increased surface roughness in the first quarter. The correlation coefficients between the erosion rate and temperature in the first and fourth quarters were 0.75 and 0.82, respectively. Temperature mainly affected the erosion rate through surface roughness. The direct path coefficient of this effect was 0.72. (3) In the first and fourth quarters, temperature and wind speed were the main factors influencing the erosion rate; the relationship between surface roughness and other factors was evident, making surface roughness the best topographic factor for assessing slope erosion in the Pisha sandstone area. The results of this study aim to provide theoretical references for understanding the gravity erosion mechanism of gully slopes in the Pisha sandstone area and contribute to the high-quality development of the Yellow River Basin.

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Spatiotemporal variation and driving force of gully erosion in the Pisha sandstone area

Zhang,

Li,

Liu

et al. 2024

Front. Environ. Sci.

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Spatiotemporal Changes and Simulation Prediction of Ecological Security Pattern on the Qinghai–Tibet Plateau Based on Deep Learning

Liu,

Zhang,

Liu

et al. 2024

Land

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Over the past two decades, due to the combined effects of natural and human factors, the ecological environment and resources of the Qinghai–Tibet Plateau (QTP) have faced serious threats, profoundly impacting its ecosystem and the lives of its residents. Therefore, the establishment of the ecological security pattern (ESP) is crucial to cope with climate change, maintain ecosystem function, and sustainable development. Based on the Pressure–State–Response (PSR) model, this study constructed an evaluation index system for the ecological security (ES) of the QTP, evaluated the ES of the QTP during 2000–2020, and predicted the ES of the QTP during 2025–2035 based on the deep learning model. Combined with the residents’ perception of ES, the ES of the QTP was evaluated comprehensively. The results showed that: (1) From 2000 to 2020, the ES value of the QTP continued to rise, the number of dangerous and sensitive counties decreased, and the number of other counties increased. The overall spatial distribution features higher values in the southeast and lower values in the northwest and central regions. (2) From 2000 to 2020, both hot spots and cold spots on the QTP decreased, with the hot spots mainly concentrated in the southeast of the QTP, represented by Yunnan Province, and the cold spots shifting from west to east, mainly concentrated in the central QTP, represented by Qinghai Province. (3) The Long Short-Term Memory (LSTM) model demonstrates high prediction accuracy. Based on the prediction of LSTM, the ES value of the QTP will continue to rise from 2025 to 2035, and the number of safe counties will reach the highest level in history. The spatial distribution is still higher in the southeast and lower in the northwest and central regions. (4) By analyzing residents’ perception of 25 potential factors that may affect the ES of the QTP, the results show that residents generally believe that these factors have an important impact on ES, and their evaluation is between “important” and “very important”. In addition, there is a significant correlation between these factors and the predicted values of ES. The results of the study will help to improve our understanding of the overall ecological environment of the QTP, provide accurate positioning and reasonable help for the government to formulate relevant protection strategies, and lay a methodological and practical foundation for the sustainable development of the QTP.

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Identifying and Mapping the Spatial Factors That Control Soil Erosion Changes in the Yellow River Basin of China

Cited by 2 publications

References 55 publications

Spatiotemporal variation and driving force of gully erosion in the Pisha sandstone area

Spatiotemporal variation and driving force of gully erosion in the Pisha sandstone area

Spatiotemporal Changes and Simulation Prediction of Ecological Security Pattern on the Qinghai–Tibet Plateau Based on Deep Learning

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