To control and improve the phenomena of rocky desertification and soil erosion in karst landform areas, which are caused by a series of human factors that include social and economic development and human activities, China has successively introduced many policies, resulting in spatial and temporal changes in the landscape pattern of the southern karst area. In this study, land use transfer intensity maps, the grid method, the sample line method, the semivariogram method, and the Spearman analysis method are used to explore the spatial and temporal evolutions in surface runoff as responses to landscape pattern and policy factors in karst landform area. Therefore, this study provides theoretical and policy support for improving the regional landscape structure, optimizing the landscape layout, introducing regional policies, reducing surface runoff, and alleviating soil erosion. The results show that the best scale for the study of landscape patterns in the southern karst area is 3000 m. Forests are the land type that make up the highest proportion in the southern karst area, and they have the strongest interception capacity for surface runoff. The spatial and temporal distributions of the surface runoff are significantly different, and urban expansion has led to an increase in impervious runoff year over year. Runoff is positively correlated with the Shannon diversity index (SHDI), patch density (PD), and landscape shape index (LSI). The stronger the landscape heterogeneity, the more runoff. DIVISION is positively correlated with forest runoff and negatively correlated with other land types. The higher is the degree of aggregation of impervious patches, the higher the regional runoff rate. The more dispersed the forest patches are, the smaller the area proportion, and the greater the runoff. In addition, policy factors have a significant impact on surface runoff.

With the rapid advancement of industrialization and urbanization, the systematic assessment of water environment security in lake-type basins and the identification of key restoration zones have become critical scientific challenges for sustainable watershed management. This study focused on the Dongting Lake Basin, where a comprehensive evaluation system comprising 24 indicators was developed based on the Driving forces–Pressure–State–Impact–Response model. Indicator weights were determined using the entropy method. An obstacle degree model was applied to quantitatively identify the primary factors constraining water environment security. Additionally, spatial autocorrelation analysis was introduced to examine spatial dependency characteristics, enabling the delineation of priority restoration areas. The results demonstrated the following: (1) During 2000–2020, the Dongting Lake Basin exhibited significant spatial heterogeneity, with higher water environment security risks in the southeastern region, while the central-eastern region showed a continuous improvement trend. (2) Quantitative analysis identified the core obstacle factors affecting regional water environment security: wastewater treatment capacity (obstacle degree: 16.87%), ecological water use proportion (12.71%), effective irrigation area ratio (9.29%), environmental protection investment as a percentage of GDP (8.54%), and wastewater treatment rate (7.10%). (3) Based on these key constraints, targeted governance strategies are proposed, including enhancing wastewater treatment capacity, optimizing ecological water allocation, and increasing environmental protection investment. This study established an integrated “diagnosis–restoration–regulation” analytical framework for assessing water environment security and identifying priority restoration zones in lake-type basins, providing both theoretical foundations and practical references for global lake-type basin management.