Identifying differences in evapotranspiration and ecosystem water use efficiency between different types of urban green spaces: A case study of the main urban area of Beijing

The main urban area of Beijing, China. Refined modeling is crucial for estimating carbon sequestration and water consumption in urban green spaces. This study used Sentinel-2 data, PT-JPLim, and analytical WUE models to estimate monthly ET and WUE in the main urban area of Beijing (2016–2021) at a 10 × 10 m resolution. Machine learning quantified the effects of environmental factors on ET and WUE across various green space types. Simulations assessed the impacts of different vegetation scenarios on ET and WUE, while urban ET and WUE were predicted for 2022–2100 under different climate scenarios using CMIP6. This study shows that vegetation, especially LAI, is the primary driver of spatiotemporal variations in ET and WUE across urban green spaces in Beijing, contributing 64.7 % and 62.5 % to these changes, respectively. Simulations of a 25 % vegetation increase revealed that residential green spaces are most sensitive, with ET and WUE rising by 6.2 % and 30 %, respectively. The study also estimated water requirements, with net irrigation needs of 95.18 million m³ in normal years and 125 million m³ in dry years. Projections under the SSP126 scenario indicate a near-equilibrium state for carbon sequestration and CO 2 concentrations, achieving greater carbon sinks and water conservation with minimal increases in water consumption. These insights are valuable for optimizing water resource management and ecological planning in urban green spaces. • Urban ET and WUE were estimated at 10 m × 10 m spatial resolution. • LAI is the largest influence on ET and WUE in different types of urban green spaces. • The ET and WUE of residential green spaces are more sensitive to vegetation changes than others. • Changes in urban vegetation carbon sequestration and CO 2 concentration were most balanced in the SSP126 scenario.