Accurately identifying and obtaining changes in ecosystem drivers and the spatial heterogeneity of their impacts on ecosystem services can provide comprehensive support information for ecological governance. In this study, we investigate the changes in the relationship between human and natural factors and water-related ecosystem services (WESs) in different sub-watersheds across various time periods, focusing on four aspects: single-factor effect, nonlinear effect, interactive effects, and spatial characteristics. Taking the southern basins, which have complex topographic, climatic, and economic characteristics, as a study area, the study area was divided into four sub-basins with different characteristics. WESs of water yield, soil conservation, and water purification were quantified using the InVEST model for five periods from 2000 to 2020, and the OPGD and MGWR models were integrated to assess the impacts of 15 factors on WESs and their spatial characteristics. The results show the following: (1) After comparing the data over multiple time periods, climate factors such as precipitation (0.4033) are the primary factors affecting WESs in the southern basins, and human factors such as construction area (0.0688) have a weaker influence. The direct impact of human factors on WESs is not significant in the short term but increases over time. (2) Different sub-watersheds have different impacts on WESs. For instance, human activity intensity (0.3518) is a key factor affecting WESs in the Inward Flowing Area, while precipitation is the primary factor influencing WESs in other sub-watersheds. (3) Influencing factors and WES changes are often nonlinearly correlated; however, once a certain threshold is exceeded, they may have adverse impacts on WESs. (4) When a single factor interacts with other factors, its explanatory power tends to increase. (5) Compared to traditional methods, the estimation accuracy of MGWR is higher. Intense human activities can adversely affect WESs, while abundant precipitation creates favorable conditions for the formation of WESs. Therefore, integrating long-time-series multi-remote sensing data with OPGD and MGWR models is suitable for identifying and analyzing the driving mechanisms of human and natural factors that influence changes in WESs. Against the backdrop of global change, elucidating the driving factors of ecosystem services can provide crucial insights for developing practical policies and land management applications.

The dynamic changes in land use/cover (LULC) significantly influence carbon storage, and assessing the vulnerability of carbon storage services in different basins is crucial for a comprehensive understanding of the impacts of human activities on ecosystems. The objective of this study is to propose a framework for optimizing LULC, simulating carbon storage, and assessing vulnerability by integrating the MOP, PLUS, and InVEST models. The results show that forests play a crucial role in enhancing carbon storage services in the Yangtze River Basin (YRB). Carbon storage in the upper reaches of the YRB is on the rise, counteracting the decrease in carbon storage caused by the expansion of built-up land. However, in the middle and lower reaches of the YRB, LULC has a negative impact on ecosystem carbon storage services. Under natural development scenarios, carbon storage is projected to decrease by 68.84 × 10⁶ tons, leading to increased vulnerability of ecosystem carbon storage services. Under the scenario of ecological and economic balance, carbon storage is expected to increase by 97 × 10⁶ tons. In the future, while restricting built-up land expansion, emphasis should be placed on expanding forest areas to more effectively enhance ecosystem services in basins.

To promote sustainable hierarchical management, it is essential to understand the complex relationships within and underlying causes of supply–demand changes in water-related ecosystem services (WESs) across different spatial scales and landscape patterns. Consequently, the Optimal Parameters-based Geographical Detector (OPGD) and Multi-Scale Geographically Weighted Regression (MGWR) are used to analyze the factors influencing changes in WESs supply–demand. The findings indicate that (1) at the macroscale, population size, and economic activity are the main driving factors, while at the microscale, precipitation becomes the primary factor influencing fluctuations in WESs supply–demand. (2) Furthermore, over time, the influence of social factors becomes increasingly significant. (3) The explanatory power of a single factor typically increases as it interacts with other factors. (4) Abundant precipitation helps in the generation and maintenance of WESs, but intense human activities may have negative impacts on them. Therefore, we have made significant progress in identifying and analyzing the natural and human-induced driving forces affecting changes in WESs by deeply integrating long-term multi-source remote sensing data with the OPGD and MGWR models.

Clarifying the spatiotemporal changes in the supply and demand of water-related ecosystem services (WESs) can provide comprehensive support information for ecological governance decisions. However, the spatial mismatch between the supply and demand of WESs is often overlooked, resulting in a lack of targeted decision-making. At the grid scale, while preserving both natural and social attributes, this study quantitatively analyzed the spatiotemporal changes in the supply and demand of WESs in the Southern River Basin from 2000 to 2020. Ecological zoning was performed based on the temporal changes in WESs supply and demand. The OPGD model was used to investigate the impacts of socio-economic and natural factors on different WESs supply factors and further explore the spatial correlation of WESs supply and demand changes in different zones. The results show that there is significant spatial heterogeneity in the changes in WESs supply and demand. Economic belts and megacities have experienced remarkable changes, with WESs supply decreasing and WESs demand increasing. WESs demand changes significantly affect WESs supply changes. The supply of WESs in all zones is influenced by WESs demand. In the high supply–low demand zone, WY has the highest explanatory power for WESs demand changes. From the high supply–middle demand zone to the low supply–middle demand zone and then to the high supply–high demand zone, the explanatory power of PE for WESs demand changes gradually increases. As WESs demand starts from the middle level, HAI gradually dominates WESs demand changes. The increase in land use changes may promote the impact of WESs demand changes on WESs supply changes. This study contributes to incorporating the supply and demand changes of WESs and their correlations into the ecological protection and restoration system, providing a new perspective and method for regional sustainable management.