Soil Erosion Characteristics and Scenario Analysis in the Yellow River Basin Based on PLUS and RUSLE Models

Abstract

Soil erosion is an important global environmental issue that severely affects regional ecological environment and socio-economic development. The Yellow River (YR) is China’s second largest river and the fifth largest one worldwide. Its watershed is key to China’s economic growth and environmental security. In this study, six impact factors, including rainfall erosivity (R), soil erosivity (K), slope length (L), slope steepness (S), cover management (C), and protective measures (P), were used. Based on the revised universal soil loss equation (RUSLE) model, and combined with a geographic information system (GIS), the temporal and spatial distribution of soil erosion (SE) in the YR from 2000 to 2020 was estimated. The patch-generating land use simulation (PLUS) model was used to simulate the land-use and land-cover change (LUCC) under two scenarios (natural development and ecological protection) in 2040; the RUSLE factor P was found to be associated with LUCC in 2040, and soil erosion in the Yellow River Basin (YRB) in 2040 under the two scenarios were predicted and evaluated. This method has great advantages in land-use simulation, but soil erosion is greatly affected by rainfall and slope, and it only focuses on the link between land-usage alteration and SE. Therefore, this method has certain limitations in assessing soil erosion by simulating and predicting land-use change. We found that there is generally slight soil erosivity in the YRB, with the most serious soil erosion occurring in 2000. Areas with serious SE are predominantly situated in the upper reaches (URs), followed by the middle reaches (MRs), and soil erosion is less severe in the lower reaches. Soil erosion in the YRB decreased 11.92% from 2000 to 2020; thus, soil erosion has gradually reduced in this area over time. Based on the GIS statistics, land-use change strongly influences SE, while an increase in woodland area has an important positive effect in reducing soil erosion. By predicting land-use changes in 2040, compared to the natural development scenario, woodland and grassland under the ecological protection scenario can be increased by 1978 km2 and 2407 km2, respectively. Soil erosion can be decreased by 6.24%, indicating the implementation of woodland and grassland protection will help reduce soil erosion. Policies such as forest protection and grassland restoration should be further developed and implemented on the MRs and URs of the YR. Our research results possess important trend-setting significance for soil erosion control protocols and ecological environmental protection in other large river basins worldwide.

Assessing the Impact of Engineering Measures and Vegetation Restoration on Soil Erosion: A Case Study in Osmancık, Türkiye

“…This difference in the C factor value could be due to variations in topography, vegetation density, and climate. Rehabilitation works such as afforestation and planting carried out in the area for various reasons can reduce soil erosion by at least 6%, and the protection of the forested regions and grasslands can aid in reducing soil erosion [73].…”

Multi-Scenario Simulation and Eco-Environmental Effects Analysis of Land Use/Cover Change in China by an Integrated Cellular Automata and Markov Model

“…[21][22][23]. For example, the soil erosion characteristics of a basin can be analyzed in multiple scenarios based on PLUS models and RUSLE models [24]; an MLP-Markov model (a multi-layer perceptron-Markov chain) can be used to predict dynamic change in land use and land vulnerability in a region and obtain estimated values for different types of land in the future [25]; and Markov models can be used to simulate regional land use change and explore the relationship between urban growth and landscape change and population growth [26]. Among the different models, the system dynamics model [27] and the Markov model [28] mainly adopt numerical simulation model analysis, which has the advantage of quantitative prediction; the CLUE-S model belongs to the class of spatial prediction models, pays more attention to spatial data information, and has the ability to predict change in spatial locations [29], but it is based on the traditional logistic regression method, which may ignore the internal autocorrelation of spatial data during spatial analysis, affecting the simulation accuracy to a certain extent [30].…”