Landscape Evolution and Ecosystem Service Value Responses Under Multi-Scenario Simulations in the Erhai Lake Basin, China

The evolution of landscape patterns in plateau lake basins directly influences the sustainable provision of ecosystem services. Revealing and predicting the impacts of landscape changes on ecosystem service value (ESV) under different development scenarios are essential for maintaining regional ecological security, enhancing ESV, and formulating policies for ecological conservation and restoration. As a typical representative of China’s plateau lake basin, the Erhai Lake Basin faces multiple challenges arising from rapid urbanization, tourism commercialization, and agricultural modernization. It is therefore crucial to understand its potential future landscape dynamics and their effects on ecosystem services. Based on landscape data, natural environmental data, and socio-economic data, we applied GIS-based spatial analysis and the equivalent factor method to simulate and assess landscape pattern changes and corresponding variations in ESV in 2030, 2040, and 2050 under three distinct scenarios. Local spatial autocorrelation analysis was further employed to identify the spatial clustering patterns of ESV. There were three findings: (1) From 2030 to 2050, forest increased continuously under the natural evolution scenario (NES) and ecological protection scenario (EPS) but declined under the economic growth scenario (EGS). Farmland expanded under the NES and EGS, whereas it decreased under the EPS. Grassland declined across all three scenarios, while built-up area showed consistent expansion. (2) In all simulated years, the total ESV of the Erhai Lake Basin ranked as EPS > NES > EGS. Between 2030 and 2050, total ESV exhibited an increasing trend under the EPS but declined under the other two scenarios, with the sharpest reduction under the EGS. Forests and water body were the main contributors to total ESV, while farmland and grassland played a critical role in driving ESV dynamics—the scale and direction of their transformation directly determined the overall ESV trends. (3) Across the three scenarios, ESVs all exhibit significant spatial heterogeneity. Local Moran’s I analysis indicated a dominant cluster of high values (HH) or a cluster of low values (LL), with LL clusters mainly concentrated in the northern basin and the western side of Erhai Lake, and HH clusters primarily located within the lake area. This study, through multi-scenario simulations, elucidates the spatiotemporal dynamics of landscape and ESV changes, providing valuable insights for green transformation, landscape spatial allocation, ecological restoration, and sustainable development in the Erhai Lake Basin.