Mountainous cities face various land use challenges, including complex topography, low land use efficiency, and the insufficient control of land use in small-scale areas at the urban fringe. Considering population changes, environmental conservation, and urban planning, this study first established three scenarios: economic priority (Econ. Prior.), ecological priority (Ecol. Prior.), and balanced development (BD), and then used the Multi-Objective Planning (MOP) model to calculate the optimal land use structure. Finally, it carried out land use spatial layout optimization based on the Patch-generating Land Use Simulation (PLUS) model in 2035, Caijia Cluster, Chongqing, China. This approach, known as MOP-PLUS modeling, aimed to optimize land use. Meanwhile, the applicability of the PLUS model in simulating land use changes was discussed in small-scale mountainous areas. The results show the following: (1) The “quantity + space” approach in the MOP-PLUS model demonstrated the feasibility of the PLUS model in simulating land use change in small-scale mountainous areas. The overall accuracy (OA) of land use change simulation reached 81.60%, with a Kappa value of 0.73 and a Figure of Merit (FoM) coefficient of 0.263. (2) Land use optimization: Under the Econ. Prior. scenario, economic benefits peaked at 4.06 × 10¹⁰ CNY. Urban expansion was the largest, leading to increased patch fragmentation. The Ecol. Prior. scenario yielded the highest ecological benefits, reaching 7.46 × 10⁷ CNY. The urban development pattern exhibited inward contraction, accompanied by urban retrogression. In the BD scenario, economic benefits totaled 3.89 × 10¹⁰ CNY, and ecological benefits amounted to 7.16 × 10⁷ CNY. Construction land tended to concentrate spatially, leading to relatively optimal land use efficiency. Therefore, based on a comprehensive consideration of the regional land use constraint policies and spatial layout, we believe that a balance point for land use demands can be found in the BD scenario. It can ensure economic growth without compromising the ecological environment.

The Gravity Recovery and Climate Experiment (GRACE) introduces a new approach to accurately monitor, in real time, regional groundwater resources, which compensates for the limitations of traditional hydrological observations in terms of spatiotemporal resolution. Currently, observations of groundwater storage changes in Jiangsu Province face issues such as low spatial resolution, limited applicability of the downscaling models, and insufficient water resource observation data. This study based on GRACE employs Random Forest Regression (RFR) and Geographically Weighted Regression (GWR) methods in order to obtain high-resolution information on groundwater storage change. The results indicate that among the established 66 × 158 local GWR models, the coefficient of determination (R²) ranges from 0.39 to 0.88, with a root mean squared error (RMSE) of approximately 2.60 cm. The proportion of downscaling models with an R² below 0.5 was 18.52%. Similarly, the RFR models trained on the above time series grid data achieved an R² of 0.50, with the RMSE fluctuating around 1.59 cm. In the results validation, the monthly correlation coefficients between the GWR downscaling results and the data of measured stations ranged from 0.37 to 0.66, with 53.33% of the stations having a coefficient greater than 0.5. The seasonal correlation coefficients ranged from 0.41 to 0.62, with 60% of the stations exceeding 0.5. The correlation coefficients for the RFR downscaling results ranged from 0.44 to 0.88, with seasonal correlation coefficients ranging from 0.49 to 0.84. Only one station had a correlation coefficient below 0.5 for both monthly and seasonal results. In the validation of the correlation accuracy between the downscaling results and the measured groundwater levels, the Random Forest model demonstrated better predictive performance, which offers distinct advantages in improving the spatial resolution of groundwater storage changes in Jiangsu Province.