Landscape simulation and prediction are crucial for understanding the dynamic evolution and future trends of wetlands. However, only a few existing studies have focused on the applicability and limitations of commonly used land-use/cover change (LUCC) simulation models in lake wetland landscapes. Taking Shengjin Lake Reserve in China as the study area, we firstly analyzed landscape variations during 2010–2020 using multisource remote sensing images. Then, the patch-generating land-use simulation (PLUS) model was employed to simulate wetland landscapes in 2020, the accuracy and limitation of which in simulating lacustrine wetlands were also explored. Lastly, the changing trends of wetland landscapes in 2030 under different development scenarios were predicted. The results show that the landscape of Shengjin Lake Reserve has changed significantly during 2010–2020, with increases in mudflats, reservoirs/ponds, woodlands, and built-up land, and there has been decreases in lakes, grass beaches, and croplands. The PLUS model demonstrated an ideal simulation accuracy for Shengjin Lake Reserve, with the overall accuracy exceeding 80%, kappa coefficient greater than 0.75, and figure of merit (FOM) coefficient of 0.35, indicating that the model can capture the dynamic changes in wetland landscapes accurately. The simulation accuracy can be effectively improved with the adjacent initial year, shorter time interval, and the primary driver factors. Under the natural development scenario, the number of patches in the Shengjin Lake Reserve increased sharply, and landscape fragmentation intensified. Under the urban development scenario, the expansion of built-up land increased, and the average patch area increased. In the ecological protection scenario, the Shannon diversity index and Shannon evenness index of the landscape improved significantly, and the natural wetlands such as grass beaches and lakes can be protected effectively. Our study confirms the applicability of the PLUS model in simulating and predicting lacustrine wetlands landscapes, and the conclusions provide a scientific basis for formulating reasonable development strategies to realize wetland resource conservation and management.

Investigating the influence of landscape evolution on river eutrophication is critical for optimizing spatial patterns to improve water quality. Machine learning (ML) models can capture the complex relationship between landscape metrics and water quality, but their black-box property restricts the interpretability of the underlying mechanisms and makes it difficult to forecast future trends in water quality. To address this, we developed a novel framework that, for the first time, couples an interpretable ML model with the Patch-generating Land Use Simulation (PLUS) model for eutrophication index (EI) prediction. This approach elucidates the response of river eutrophication to landscape dynamics and forecasts future river EI trends. The random forest regression (RFR) model outperformed other algorithms in quantifying these relationships (R² = 0.934 for training, 0.711 for testing). SHAP analysis revealed that landscape metrics contributed 81.78% to the river EI, far exceeding climate factors (18.22%). Consequently, landscape evolution emerged as the dominant explanatory factor. Scenario simulations indicated that while the ecological protection (EP) scenario effectively mitigates river eutrophication, the urban development (UD) scenario significantly exacerbates it. Specifically, under the UD scenario, the average EI in urban sub-watersheds is projected to reach 60.78 by 2040, approaching heavy eutrophic levels. Our findings inform spatial optimization strategies for river eutrophication management and facilitate the design of targeted, localized water ecological protection policies in subtropical monsoonal basins.