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Greening can usually have a cooling effect on urban space; but is this law also applicable to coastal sloping urban space? The coastal urban space of Qingdao Haizhifeng Square, with a sloping topography, was the area we selected to study. The study area contained two parts: a coastal green space and a residential area. ENVI-met was used to create six scenarios. Different lawns, black pine and ash were planted in the two areas to study the cooling effect. The results showed that the closer the area was to the sea, the better the thermal comfort. In both the coastal green area and the residential area, trees increased the PET of the site, and the higher the LAI of the trees, the more obvious the thermal effect. At 15:00, the hottest time during the summer, the highest PET at pedestrian height was lowest in the scenario without trees, reaching 28.3 °C, and the highest was with full ash, reaching 34.3 °C. At the same time, the average difference in PET between the two scenarios was 1.4 °C. The highest PET at pedestrian height was generated in the area of the building away from the sea breeze, especially in the case of the sloping topography behind it or dense street trees on the urban road. Finally, it was concluded that, in urban spaces with a coastal slope topography, lawns should be planted in the coastal green part and low LAI trees in residential areas, and shade trees should not be planted on the coastal walkway. This afforestation strategy can provide a basis to formulate a strategy for promoting the design of regions with similar geographical and climatic conditions in the future.

The determination of the evapotranspiration (ET) and its components in urban woodlands is crucial to mitigate the urban heat island effect and improve sustainable urban development. However, accurately estimating ET in urban areas is more difficult and challenging due to the heterogeneity of the underlying surface and the impact of human activities. In this study, we compared the performance of three types of classic two-source ET models on urban woodlands in Shenzhen, China. The three ET models include a pure physical and process-based ET model (Shuttleworth–Wallace model), a semi-empirical and physical process-based ET model (FAO dual-Kc model), and a purely statistical and process-based ET model (deep neural network). The performance of the three models was validated using an eddy correlation and stable hydrogen and oxygen isotope observations. The verification results suggested that the Shuttleworth–Wallace model achieved the best performance in the ET simulation at main urban area site (coefficient of determination (R2) of 0.75). The FAO-56 dual Kc model performed best in the ET simulation at the suburb area site (R2 of 0.77). The deep neural network could better capture the nonlinear relationship between ET and various environmental variables and achieved the best simulation performance in both of the main urban and suburb sites (R2 of 0.73 for the main urban and suburb sites, respectively). A correlation analysis showed that the simulation of urban ET is most sensitive to temperature and least sensitive to wind speed. This study further analyzed the causes for the varying performance of the three classic ET models from the model mechanism. The results of the study are of great significance for urban temperature cooling and sustainable urban development.

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