Spatiotemporal variations in soil erosion and their driving factors in Hunan province from 1990 to 2020

Abstract：Background As an important ecological barrier in the middle reaches of the Yangtze River, Hunan Province serves as a crucial ecological shield and major agricultural province in southern China. Its terrain is predominantly mountainous and hilly, characterized by a distinct subtropical monsoon humid climate and widespread red soil distribution. These natural conditions make it prone to frequent and severe soil erosion. Analyzing the spatiotemporal evolution of soil erosion in Hunan Province is essential for elucidating driving mechanisms and maintaining regional ecological security.This study provides a scientific basis for establishing robust ecological security patterns, regulating soil and water conservation measures, and ensuring sustainable watershed management.Methods This study employs the Revised Universal Soil Loss Equation (RUSLE) as the foundational model to conduct a quantitative assessment of soil erosion across Hunan Province. To systematically investigate the spatiotemporal evolution patterns and driving mechanisms from 1990 to 2020, we integrated multiple analytical approaches: the geographical detector for identifying factor interactions and dominant driving factors, transition matrices for precisely quantifying erosion intensity class conversions and landscape dynamics, and spatial autocorrelation analysis (including global Moran's I and local indicators/LISA) for detecting and mapping significant erosion clustering zones and spatial heterogeneity characteristics. Through this multi-method integrated framework, we achieved a rigorous multidimensional analysis of erosion processes and their causative factors over the three-decade period. Results The results indicate that from 1990 to 2020, soil erosion in Hunan was dominated by slighter and milder erosion classes, which together accounted for 91.63% of the provincial area; moderate and above classes comprised 8.37%. The multi-year mean soil erosion modulus was 2091.87 t/(km²·a). Temporal variation showed notable fluctuations with peaks in 2005 and 2020 likely linked to extreme rainfall events and intensified human activities. Transition-matrix analysis indicated substantial conversions between slighter and milder categories across decades, with notable recovery in some periods (e.g., 1990–2000: 12,787 km² of mild erosion converted to slighter; 2010–2020: 9,666 km² shifted from higher- to lower-intensity classes). Global Moran’s I values (0.131–0.250, p < 0.05) confirmed significant positive spatial autocorrelation each year. Local LISA hotspots (high–high clusters, mean 12.75% area) were concentrated in the Wuling Mountains (western Hunan), northern Xuefeng Mountains, and the western side of the Luoxiao Mountains; coldspots (low–low clusters, mean 28.97%) were located on the Dongting Lake plain, the mid–lower Xiang River valley and the lower Yuan River alluvial plain. Geomorphological type was the dominant explanatory factor (q = 0.056) for spatial variation in erosion, followed by slope (q = 0.042) and elevation (q = 0.041). Interaction detection showed that combining geomorphology and slope produced the strongest explanatory power (q = 0.069), indicating nonlinear enhancement from factor coupling. High-risk zones (erosion modulus > 3,500 t/(km²·a)) were characterized by slope gradients of ~6.32°–27.8°, silt content 38%–100%, elevations 557–1,980 m, annual precipitation 1,710–2,060 mm, and relatively low NDVI (0.559–0.598). Conclusions From 1990 to 2020, Hunan Province’s soil erosion was generally light, dominated by slighter and milder classes, but exhibited marked temporal fluctuations associated with extreme rainfall events. Spatially, erosion displays clear clustering with mountainous western regions as hotspots and eastern plains as coldspots. Geomorphology and its interaction with slope are the principal drivers of spatial heterogeneity. Targeted measures—such as contour farming, terracing, afforestation and soil amendments—in identified high-risk areas are recommended to reduce erosion and improve regional ecological security.