The new urbanization (NU) will lead to changed land use types, affecting the ecological environment and ecosystem service value (ESV). The NU is affected by the ecological environment because of resource scarcity when the ecological environment is damaged. NU levels and ESV were used to evaluate the degree of urbanization development and value provided by ecosystem services (ES), respectively, and to analyze their coupling and coordination relationships. This study shows that (1) the Nanchang city NU increases annually, at an accelerated rate, while the city scale continuously expands. Among the various NU subsystems, economy and spatial urbanization are primary, and the remaining subsystems are secondary. (2) In terms of the area of each land use in Nanchang, arable land is the most widely distributed, followed by forest land, and water. The land type with the greatest change was development land, followed by arable land. (3) ESV declined during the study period, with water and forest land being the main ESV components. Hydrological regulation had the greatest contribution among the individual services, while maintaining the nutrient cycle had the minimal contribution. The high-value areas of Nanchang ecology were mainly located in the northeast corner and the water location in the southeast, while the low-value areas were mainly located in the central Nanchang county area. (4) The coupling degree (CD) of Nanchang’s NU and ES showed an inverted U-shaped development trend, first increasing and then decreasing. The coupling coordination degree also showed the same trend and exhibited fluctuation in the evolution process.

The evolution of regional land use is a complex process under the combined effect of multiple factors, and it is important to understand this evolution process, as well as its characteristics and future trends, through land use change models in order to achieve scientific use of land space and optimize the regional development pattern. In this study, the PLUS model is used to simulate the land use in 2035 for the natural development scenario, the urban expansion scenario and the ecological protection scenario using the middle reaches of Yangtze River urban agglomeration (MRYRUA) as the study area, and then to calculate the ecosystem service values (ESV) and analyze the contribution of each driver to each land type and the spatial autocorrelation of the ESV at the grid scale. The results show that (1) the land use changes in the study area from 2015 to 2020 are mainly: the rapid expansion of construction land with an increase of 200,221 hm² and an increase in arable land, specifically 85,982 hm², and a decrease in all other land types. (2) The ESV of the study area was CNY 3,837,282 million and CNY 3,774,162 million from 2015 to 2020, respectively, with an general decreasing trend. (3) Three scenarios are simulated for the study area in 2035, and the ESVs under the natural development scenario, urban expansion scenario and ecological conservation scenario are CNY 3,618,062 million, CNY 3,609,707 million and CNY 3,625,662 million, respectively, which are all lower than those in 2020. (4) The global autocorrelation indices for 2020 and the three scenarios are 0.7126, 0.7104, 0.7144 and 0.7104, respectively, which are significantly positive. The simulation of MRYRUA land use and the comparative analysis of ESV provide some help in the strategic optimization of the spatial distribution pattern of land use in large regional urban agglomerations.

This study examined the characteristics of rainfall fields in a monsoon climate using seven years of daily rainfall data from 95 stations in eastern China. The stations were located in a region with a flat, low-lying topography, covering approximately 250 km by 150 km. The spatial and temporal properties of the rainfall fields were analyzed from three perspectives: time scales, spatial distance, and direction. Multiple linear regression was applied to explore linear relationships and predict rainfall amounts at selected sites, considering four time scales: 1 day, 3 days, 7 days, and 15 days. The analysis assumed isotropy when examining the distance–decay in the correlation field. The results showed that the distance–decay of the correlation coefficient followed an exponential pattern, with decay rates decreasing as time scales increased. Spatially, north–south variations were more pronounced than east–west variations, and the correlation extended further in latitude than in longitude, highlighting spatial heterogeneity. The correlation coefficient in the wet season was smaller than for the entire year due to dry days and rainfall variability. Additionally, a multi-site linear model was used to interpolate missing rainfall data. The analysis revealed that the goodness of fit improved over longer time scales. Nevertheless, it was also observed that the error ratio simultaneously increased. This implies that when applying distance-weighted interpolation methods, one must exercise due caution.