Data for A Deviation-Frequency-Trend Framework for Multi-Scale Assessment of Soil Erosion Dynamics
Description
Research Hypothesis This study hypothesizes that soil erosion is driven by a combination of environmental, climatic, and socio-economic factors. It suggests that the interaction of these factors, such as precipitation, land use, and vegetation cover, significantly influences soil erosion patterns across regions and time periods. Data Overview The dataset consists of two main components: Soil Erosion Change Trajectories: Time-series data showing changes in soil erosion over multiple phases, categorized by trends such as increasing, decreasing, or stable erosion levels. 18 Driving Factors: Data on 18 variables, including climatic factors (e.g., precipitation), land use characteristics (e.g., vegetation cover), and socio-economic factors (e.g., population density), that impact soil erosion. These factors were collected through remote sensing, surveys, and publicly available sources. Findings and Observations Temporal Trends: Regions with higher precipitation and land disturbance show an increasing trend in soil erosion, while areas with improved land cover exhibit stable or declining erosion. Regional Differences: Erosion levels vary across regions, with some areas showing more severe erosion due to steep slopes and intensive agriculture. Climate and Land Use: Precipitation intensity is a major driver of soil erosion, followed by land use factors like vegetation cover and land management practices. Data Interpretation Soil Erosion Trajectories: Trajectories show the direction and magnitude of erosion changes, helping to predict future trends and identify high-risk areas. Driving Factors: The analysis helps identify which factors most influence erosion in specific areas, guiding targeted interventions. Data Collection and Usage The data was collected using remote sensing, field surveys, and climate data, covering a multi-year period. It can be used by researchers and policymakers to identify erosion-prone areas, assess land management practices, and develop predictive models for future erosion under different scenarios.