The Urban Heat Island (UHI) effect has been extensively studied as a global issue. The urbanization process has been proved to be the main reason for this phenomenon. Over the past 20 years, the built-up area of Zhengzhou city has grown five times larger, and the UHI effect has become increasingly pressing for the city’s inhabitants. Therefore, mitigating the UHI effect is an important research focus of the expanding capital city of the Henan province. In this study, the Landsat 8 image of July 2019 was selected from Landsat collection to obtain Land Surface Temperature (LST) by using Radiative Transfer Equation (RTE) method, and present land cover information by using spectral indices. Additionally, high-resolution Google Earth images were used to select 123 parks, grouped in five categories, to explore the impact factors on park cooling effect. Park Cooling Intensity (PCI) has been chosen as an indicator of the park cooling effect which will quantify its relation to park patch metrics. The results show that: (1) Among the five studied park types, the theme park category has the largest cooling effect while the linear park category has the lowest cooling effect; (2) The mean park LST and PCI of the samples are positively correlated with the Fractional Vegetation Cover (FVC) and with Normalized Difference Water Index (NDWI), but these are negatively correlated with the Normalized Difference Impervious Surface Index (NDISI). We can suppose that the increase of vegetation cover rate within water areas as well as the decrease of impervious surface in landscape planning and design will make future parks colder. (3) There is a correlation between the PCI and the park characteristics. The UHI effect could be mitigated by increasing of park size and reducing park fractal dimension (Frac_Dim) and perimeter-area ratio (Patario). (4) The PCI is influenced by the park itself and its surrounding area. These results will provide an important reference for future urban planning and urban park design to mitigate the urban heat island effect.

Ecosystem services are pivotal in assessing environmental health and societal well-being. Focusing on Lake Dianchi Basin (LDB), China, our research evaluated the IESV (Integrated Ecosystem Service Value) from 2000 to 2020, utilizing remote sensing and multiple statistical datasets. The analysis incorporates LSV (Landscape Service Value), CSV (Carbon Sequestration Value), and NPPV (Net Primary Productivity Value). The results show that LSV and CSV exhibited an expansion of low-yield zones near urban areas, contrasted by NPPV’s growth in high-yield outskirt areas. LSV’s normal distribution indicates stability, while CSV’s bimodal structure points to partial integration and systemic divergence. IESV pronounced clustering in both low- and high-yield regions, with low-yield zones congregating near urban centers and high-yield zones dispersed along the basin’s periphery. Despite an overall downward trajectory in IESV, NPPV’s augmentation suggested an underlying systemic resilience. A southeastward shift in IESV’s focus was driven by patterns of urban expansion. Finally, we produced projections with the CA-MC (Cellular Automata–Markov Chain) model to analyze the ongoing distribution of IESV areas around Kunming. By 2030, IESV’s aggregate value is expected to modestly diminish, with NPPV’s ascension mitigating the declines in LSV and CSV. In essence, IESV fluctuations within the LDB are intricately linked to urban development.

Variations in urban surface runoff are often attributed to static infrastructure, neglecting the non-stationary hydrological responses induced by rapid urbanization and intricate micro-topography. This study introduces a Pressure–Trend–Pulse (PTP) framework to examine surface runoff dynamics in Kunming, China. By integrating continuous Soil and Water Assessment Tool (SWAT) simulations (2005–2024), Sen’s slope estimation, the Mann–Kendall test, robust residual analysis, and Self-Organizing Map (SOM) clustering, we quantify these multi-dimensional changes. The findings indicate: (1) runoff displays a structural north–south gradient, with the generation centroid migrating northward at a rate of 2.3 km per decade; (2) non-stationary positive trends (0.16 mm/year) are exclusively concentrated in northern sub-basins, which constitute 26.74% of the total area, thereby exacerbating long-term cumulative pressure; and (3) detrended residual analysis reveals high-frequency pulse volatility predominantly in the southern sink areas. Overlaying 139 historical waterlogging points confirms that trend-driven and pulse-driven risks account for 30.22% and 13.67% of urban disasters, respectively. Furthermore, approximately 22% of waterlogging occurrences fall within non-significant downstream zones, implying a potential upstream-downstream source–sink decoupling. The PTP framework highlights the necessity of differentiated resilience planning: upstream source-control and downstream adaptive buffering.