Comprehensive Evaluation of Water‐Retention and Cooling Capacities of Urban Green Space Under Different Climatic Conditions Across China

Abstract

Urban green spaces play a crucial role in addressing pressing environmental challenges, such as alleviating the urban heat island effect and enhancing water retention. However, there remains a research gap in understanding the simultaneous benefits of water‐retention and cooling capacities, especially under the diverse climatic conditions across China. Utilizing robust methodologies and remote sensing data, our study evaluates the dynamic interplay between aridity index (AI) and retention‐cooling performances of urban green spaces in both cold and warm season from 2003 to 2018. Results demonstrated that water‐retention capacity is more effective in relatively arid regions, whereas cooling capacity is more pronounced in humid regions, with both effects being largely season‐dependent. In addition, green space proportion significantly influences the relationship between AI and retention‐cooling performances, particularly for cooling capacity, which exhibits opposite trends between cold and warm seasons. Future projection analysis indicate that climate change scenarios could significantly alter retention‐cooling performances, potentially leading to notable deviations from the patterns observed during the historical periods across different climate zones, with an increasing dependence on changes in local climatic conditions. The inconsistent performance of urban green spaces in terms of water‐retention and cooling across seasons and various climate regions, highlighting the importance of context‐specific greening strategies to sustain and enhance urban resilience to future climate change in China.

Introduction

Urbanization around the world has undergone unprecedented growth and transformation, with cities emerging as hotspots for future population expansion (Gao & O'Neill, 2020). These urban areas exhibit unique and complex climatic patterns and resource demand that distinguish them from their suburban and rural counterparts (Bowler et al., 2010; Wang et al., 2021). They profoundly influence local hydrological and energy balances, making cities a pivotal focus for exploring innovative strategies to confront their specific environmental challenges. Prominent among urban environmental challenges are the urban heat island (UHI) effect, which exacerbates urban warming (Nazarian et al., 2022; Zhou et al., 2022), and the urban stream syndrome (USS), reflecting heightened susceptibility to floods (Larsen et al., 2016; Tranmer et al., 2022). These challenges have garnered greater attention in recent years owing to the escalating repercussions of climate change.

To address the multifaceted challenges of these urban environments, strategies that enhance urban greening have garnered widespread recognition and implementation (Cruz et al., 2021). Initiatives such as increased vegetation cover in urban spaces are being promoted to reduce the UHI effect, mitigate flood risks, enhance local ecosystem services, and improve resident well-being (Demuzere et al., 2014; Sharifi et al., 2021; Webber & Smaras, 2022). While the urban greening offers a broad spectrum of benefits, two of the most critical advantages are its capacity for temperature reduction and water retention.

Urban greening emerges as a promising solution in moderating urban thermal environments, leveraging vegetation to reduce surface temperatures directly through shading provided by greening canopies, which limits the absorption of solar radiation, thus alleviating the intensity of urban heat (Cheung et al., 2020; Su et al., 2022). Simultaneously, vegetation within these green spaces can also contribute to thermal regulation through evapotranspiration, releasing water vapor that cools the surrounding air (McPherson et al., 1994; Shashua-Bar et al., 2009; Su et al., 2022; Wong et al., 2021). Nevertheless, achieving cooling efficiency necessitates elevated evapotranspiration, which, in turn, requires maintaining consistently moist soil conditions through either precipitation or irrigation (Kumar et al., 2024; Manoli et al., 2019). Water-retention capacity, another critical aspect of urban greening aimed at delaying and mitigating run-off generation, is also strongly influenced by antecedent conditions. The water-retention capacity of urban vegetation during and after rainfall depends on the relatively dry conditions preceding precipitation, as these conditions create the potential for greater water storage within the substrate and soil layers' pores (Kõiv-Vainik et al., 2022; Tang et al., 2016). Despite the commonalities in the underlying mechanisms of water-retention and cooling capacity, there is a significant lack of research systematically assessing their joint environmental benefits or comparing their relative effectiveness across regions with varying climatic differences. Nevertheless, these complementary functions of urban green spaces are intrinsically linked to the local climatic conditions, which are largely indicated by the aridity index (AI). The AI serves as a robust metric, enabling a more comprehensive evaluation of the effectiveness of specific urban greening interventions relative to the prevailing climatic conditions and their influence on water or energy balance (Larkin et al., 2016; Spronken-Smith & Oke, 1998; Zhu et al., 2023). It is essential to consider both aspects simultaneously, as considering any one of these aspects in isolation may inadvertently result in a potential detriment from another, given the possible interactions among various urban greening interventions (Cuthbert et al., 2022; Metselaar, 2012; Zhang et al., 2023). This research gap becomes particularly glaring when considering the unique complexities of Chinese urban environments.

Cities in China are unique for their urban growth dynamics, experiencing unprecedented rural-to-urban migration, leading to the development of numerous megacities, and a significant expansion of existing urban areas (Cao et al., 2012; Huang et al., 2019). This rapid urbanization has created an urban environment characterized by high population density, heavy industrialization, and unique environmental pressures. Understanding the interplay between urban green spaces, temperature regulation, and water retention in this conditon is crucial because China's urban development path is different from that of many western countries (Hamnett, 2020). Simultaneously, the country's vast geographical and climatic diversity results in unparalleled climate challenges, ranging from extreme heat to water scarcity and flooding (Yu et al., 2023). Harnessing the potential of urban green spaces to address these challenges is crucial, yet the strategies required may differ widely across the regions with diverse local climatic conditions in China. Sustainable urban development and resilience to climate change are top priorities for China, and the government is committed to building ecologically sound cities and addressing environmental challenges (Liu et al., 2014; Siehr et al., 2022). Consequently, research into urban green spaces is crucial for informing policy decisions and urban planning practices aligned with national objectives. However, prior studies have explored the individual aspects of water-retention or cooling capacity within urban green spaces, few have conducted a comprehensive and systematic analysis that encapsulates the coupling effect of these two functions (Augusto et al., 2020; Kõiv-Vainik et al., 2022; Su et al., 2020; Xu & Zhao, 2023; Zhou et al., 2022). Furthermore, the relative performance of these two functions across diverse regions with different local climatic conditions in China has not been holistically evaluated (Cai et al., 2019; Ng et al., 2012; Xiao et al., 2018; Zhang et al., 2015). Neglecting these critical nuances could inadvertently lead to suboptimal strategies for urban green space management.

In consideration of these factors, we conduct a comprehensive data-driven analysis using seasonal remote sensing climate observations and re-analysis data to systematically evaluate the water-retention and cooling capacities of urban green spaces under different local climatic conditions, classified by the AI, across China from 2003 to 2018. Additionally, we perform a scenario analysis to explore the potential impacts of future climate changes on these two capacities. The aim of this study is to provide valuable insights into the nuanced performance of these two capacities with their local climatic conditions. These insights can guide the development of more effective urban adaptation strategies, contributing to the resilience and sustainability of cities across China.

Materials and Methods

Study Area

China encompasses diverse temperature zones stretching from the southern to the northern regions, characterized as tropical, subtropical, warm temperate, middle temperate, frigid-temperate, and plateau climate zones (Figure 1). Notably, the subtropical, warm temperate, and middle temperate climate zones collectively cover 70% of the mainland area. Precipitation and temperature exhibit spatial and temporal variations, profoundly impacted by both the winter and summer monsoons. Most of climate zones in China experience clearly delineated dry and wet seasons, with colder months typically associated with drier and less rainy conditions, while the warm season sees increased rainfall and humidity. However, the vast geographical expanse and diverse environmental features of each climate zone cause regional local climatic conditions to fluctuate unequally across seasons and geographical locations (Chen & Sun, 2017; Zhang et al., 2022).

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Data

In this study, we extracted the urban boundaries from high-density clusters (HDC) within the 2015 Global Human Settlement Layer (GHSL), which were delineated by contiguous grid cells of 1 km² with a minimum population of 50,000 and a density of at least 1,500 inhabitants per km² (Pesaresi et al., 2019; Teo et al., 2009). Within these 1-km² grid cells, we extracted land cover data from the Global Land Cover product with a fine classification system at 30 m (GLC_FCS30D). This data extraction process utilized time-series Landsat imagery captured between 2000 and 2020, with images acquired every 5 years (Zhang et al., 2024). In this study, we identified tree cover, grassland and shrubland in the land cover product as the urban green space, while the impervious surface type was classified according to the original classification of GLC_FCS30. For the sake of accuracy and scientific rigor, we first filtered grids with consistent land cover types from 2000 to 2020 before proceeding with the classification of urban green spaces and impervious surface. A grid cell with green space cover was classified as an urban greening sample site. After identifying and excluding the urban greening sample sites, we then classified grid cell with impervious surface cover >60% as an urban impervious surface sample site. A total 7,589 and 17,922 (out of a total of 126,457 urban grid cells) grid cells, across 210 cities in China, were classified as urban greening and impervious sample sites, respectively (Figure 1). Additionally, the proportion of land classified as urban space within each 1-km² greening sample site was calculated. We then categorized the proportion of green space for each site into five categories, ranging from 0% to 100% at 20% intervals, to facilitate the subsequent analysis about the impact of different green space proportions on water-retention and cooling capacities. It is worth noting that the spatial disparity in sample sites, particularly in the plateau and frigid-temperature zones, and parts of the warm temperate and middle temperate zones, is partly driven by the stringent criteria for consistent land cover types over the study period and the requirement for high population density. Additionally, the availability of meteorological data also influenced the distribution and number of identified sample sites, contributing to the observed imbalance.

The monthly precipitation (P) and land surface temperature (T) data were obtained from a high-spatial-resolution monthly data set, which provided 1 km-resolution monthly precipitation and temperature data for China (1901–2021) (Peng et al., 2019a). This study utilizes the 2003–2018 monthly data on mean temperature and total precipitation in all urban green space sample sites. For evapotranspiration metrics, including actual evapotranspiration (AET) and potential evapotranspiration (PET), we utilized the Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data product (MOD16A2 Version 6 data set), with a spatial resolution of 500 m and 8-day accumulated values (Mu et al., 2011). We aggregated the 8-day data into monthly products spanning from 2003 to 2018, focusing on the seasonal changes to represent water-balance characteristics. The average data from December of the previous year to February of the following year represented the cold season, and the average data from June to August represented the warm season. To address potential gaps in MOD16A2 coverage for urban areas, we rigorously screened the data set to ensure the inclusion of only those grid cells with consistent long-term data coverage for urban green spaces. In cases where key representative urban areas had missing data, inverse distance weighting (IDW) interpolation was applied to fill the gaps, providing a more comprehensive and continuous data set.

The future monthly climate parameters, including precipitation, evapotranspiration, and potential evapotranspiration, were obtained from six global climate models (GCMs) (Table 1), which is provided by the 6th Coupled Model Intercomparison Project (CMIP6), in the projection period from 2021 to 2100 (). These climate projections originated from an updated suite of emissions and land use scenarios, formulated through integrated assessment models (IAMs) based on the Representative Concentration Pathways (RCPs) and the Shared Socioeconomic Pathways (SSPs) framework (Oneill et al., 2016). The SSPs delineate various trajectories for future societal evolution, assuming differing levels of climate change and policy interventions. In this study, we examined three integrated scenarios (SSP1-2.6, SSP2-4.5, and SSP3-7.0) to forecast future developments. Notably, SSP1-2.6 corresponds to a modified RCP2.6 pathway, emphasizing lower-range forcing pathways characterized by minimal vulnerability and challenges to mitigation and adaptation. Similarly, SSP2-4.5 and SSP3-7.0 depict scenarios of medium and high-level development with intermediate and high societal vulnerability, respectively (Chen et al., 2021; Zhang et al., 2022). The projected climate variables of CMIP6 were interpolated into gridded data at a resolution of 1.125° with bilinear method, for the analysis and interpretation of projected climatic conditions.

Table 1 Descriptions of the 6 Selected Global Climate Models (GCMs) From CMIP6

No.	GCM name	Institute	Resolution/km
1	BCC-CSM2-MR	Beijing Climate Center, China Meteorological Administration (China)	100
2	CAS.FGOALS-f3-L	Chinese Academy of Sciences (China)	100
3	CESM2	National Science Foundation, U.S. Department of Energy, and National Center for Atmospheric Research (United States)	100
4	CNRM-CM6-1-HR	CNRM and CERFACS (CNRM-CERFACS) (France)	50
5	EC-EARTH3	EC-Earth Consortium (Europe)	100
6	MRI-ESM2-0	Meteorological Research Institute (Japan)	100

Methods

Classification of the Climatic Conditions

In order to quantify the climatic conditions and comprehensively investigate its impact on water-retention and cooling capacities, we utilized the aridity index (AI), a widely adopted metric renowned for its robustness in assessing climate dryness levels (Gao & Giorgi, 2008; UNEP, 1992), to classify the specific region under different climatic condition. The AI is determined as the ratio of precipitation (P) to potential evapotranspiration (PET): 1 $$\text{AI}=\frac{P}{\text{PET}}$$

Based on the aforementioned gridded data, we calculated the average seasonally AI values for each greening sample site for both the cold and warm seasons during 2003 and 2018. Following the methodology outlined by the Food and Agriculture Organization (FAO, 1993), we classify the climatic conditions into five categories (arid, semi-arid, semi-humid, humid, and extreme humid types), based on AI thresholds, as outlined in Table 2.

Table 2 Classification of Aridity Index (AI) Categories

Climatic condition	Arid	Semi-arid	Semi-humid	Humid	Extreme-humid
AI	AI ≤ 0.5	0.5 < AI ≤ 1	1 < AI ≤ 1.5	1.5 < AI ≤ 2	2 < AI

Calculating the Water-Retention Capacity (R)

In the traditional sense within urban studies (Nawaz et al., 2015; Sims et al., 2016), water-retention capacity was computed as the proportion of the difference between incident precipitation and subsequent drainage (D) over the entire rainfall event, normalized by the total precipitation for that specific duration (Equation 2). However, when viewed over the longer timescales, this computation can be equivalently expressed using the mean actual evapotranspiration (AET) as Equation 3 (Cuthbert et al., 2022). 2 $$R=\frac{P-D}{P}$$ 3 $$R=\frac{AET}{P}$$

Calculating the Cooling Capacity (C)

We adopted a window-searching strategy, a methodology previously employed and examined by Li et al. (2015), to find out all the nearby impervious surface sample sites for each urban greening sample site. This strategy ensures that all greening and impervious surface sample sites, located in close distances with similar climate backgrounds, undergo a meaningful comparison (Jaganmohan et al., 2016). Using each greening sample site as a base point, we identified the corresponding referenced impervious surface sample sites by moving the window with a specified range of 5 km × 5 km. Each greening sample site and its referenced impervious surface sample sites are treated as a valid pair of study sites, resulting a total 7,484 pairs, consistent with the number of greening sample sites mentioned above. We then calculated the temperature of all referenced impervious surface sample sites using the inverse distance weighting (IDW) method to obtain a comparation temperature ($$\overline{{T}_{\text{imp}}}$$). Consequently, the cooling/warming effect ($$\mathit{{\increment}}T$$) of urban greening sample site is defined as the temperature of the urban green space sample site ($${T}_{\text{green}}$$) minus the comparation temperature ($$\overline{{T}_{\text{imp}}}$$), as expressed in Equation 4: 4 $$\mathit{{\increment}}T={T}_{\text{green}}-\overline{{T}_{\text{imp}}}$$

When this result is less than 0, the greening sample site has a cooling effect. Conversely, when the result is equal to or greater than 0, the sample site has no cooling effect or exhibits a warming effect. To facilitate better evaluation on the cooling capacity (C) with the dimensionless scale, we normalized the cooling/warming effect of all sample sites to a range between 0 and 1, using the min-max normalization method with 0.5 as the midpoint. Sample sites with a better cooling capacity ($$\mathit{{\increment}}T< $$ 0) are assigned values greater than 0.5 (C > 0.5), while those with poor cooling capacity ($$\mathit{{\increment}}T\mathit{\ge }$$ 0) are assigned values less than 0.5 (C ≤ 0.5). Note that a value of 0.5 in the normalized scale indicates no temperature difference between the green space and impervious surface, representing a baseline condition for cooling capacity. Values of 0 and 1 correspond to the extreme ends of cooling and warming effects, respectively. To account for potential asymmetry in the temperature difference range, normalization of cooling capacity was performed relative to the median value rather than directly using the minimum and maximum. Therefore, these values should be interpreted as relative indicators rather than implying identical magnitudes of cooling or warming.

Performance Metrics for Water-Retention and Cooling Capacities

In order to comprehensively and effectively evaluate the performances of water-retention and cooling capacities in urban green spaces, we established a threshold value at 0.5 for the metrics measuring the retention-cooling performances (Cuthbert et al., 2022). Four categories about the performances were conducted in this study: those with better water-retention and cooling capacities (R > 0.5 & C > 0.5), solely exhibiting better cooling capacity (R ≤ 0.5 & C > 0.5), solely exhibiting better water-retention capacity (R > 0.5 & C ≤ 0.5), and those with poor performance in both water-retention and cooling capacities (R ≤ 0.5 & C ≤ 0.5).

Forecast of the Water-Retention and Cooling Capacities in the Future

Owing to the resolution limitations of CMIP6 Global Climate Models (GCMs) output data, directly utilizing temperature differences between urban green spaces and impervious surfaces for estimating cooling capacity in future scenarios is not feasible. Moreover, many studies highlight the significant impact of local background climate on the extent of UHI effects through changes in evaporative cooling (Manoli et al., 2019; Martilli et al., 2020; Zhao et al., 2014). Therefore, we sought a parsimonious heuristic metric of relative cooling performance of urban green spaces with the meteorological parameters that are more widely available. Initial exploration found that ratio of normalized cooling capacity and water-retention capacity has a very strong correspondence with aridity index (Figure S1 in Supporting Information S1). According to the fitted relationship, we can project the normalized cooling capacity values using the CMIP6 GCMs output data. However, it is important to note that this calculation is based on the assumption that this fitted relationship remains consistent under climate change condition. A 1.125-degree grid cell containing urban green space sample sites was considered as the projected study site for the evaluating future scenarios, resulting in a total of 256 projected study sites for this study. We opted to focus on the climate zones with the highest grid density for our study area, including the subtropical, warm temperate, and middle temperate regions. These three regions were collectively cover the majority of our study sites (approximately 96%), with 133 study sites in the subtropical region, 52 in the warm temperate region, and 59 in the middle temperate region. Given the limited number of study sites in other climate regions (4 in tropical, 1 in frigid-temperate, and 7 in plateau climate regions) and the constraints of CMIP6 GCMs output data, focusing on these three regions ensures a robust analysis and the reliability of our findings.

We categorized future scenarios into three phases: near-term (2021–2040), mid-term (2051–2070), and long-term (2081–2100), with the historical period (2003–2018) serving as our baseline for comparative analysis. To ensure the accuracy of future projections, we performed a bias correction on the CMIP6 data. In brief, the baseline data was resampled to a 1.125-degree resolution to match the CMIP6 data. We then extracted the CMIP6 historical data (2004–2015) for the 256 projected study sites and compared it with the resampled baseline data. A fitting equation was derived from this comparison, which was subsequently used to adjust the CMIP6 projected data under the three future scenarios (SSP1-2.6, SSP2-4.5, and SSP3-7.0).

Results

Water-Retention and Cooling Capacities Under Different Climatic Condition

Our analysis reveals a significant negative relationship between AI and water-retention capacity across both cold and warm seasons (Figures 2a and 2b), highlighting the impact of climatic conditions on urban green spaces. The influence of seasonality is evident, particularly in relatively arid regions (AI < 1), where water-retention capacity is higher during the cold season compared to the warm season. Additionally, during the cold season, As AI increases, the water-retention capacity markedly decreases. By contrast, during the warm season, a pronounced decline in water-retention capacity is primarily observed in the AI range of 1–1.5 (Figure 2b). However, for AI values above 2, representing extreme humid conditions, the sensitivity of water-retention capacity to changes in AI significantly diminishes during both cold and warm seasons compared to other AI categories (Figures 3a–3j).

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The relationship between AI and the cooling capacity of urban green spaces exhibits contrasting trends between the cold and warm seasons (Figures 2c and 2d). During the cold season, cooling capacity gradually decreases as AI values increase, with some green spaces occasionally showing signs of warming effect (C < 0.5). In contrast, during the warm season, although signs of warming are observed in some relatively arid regions, the cooling capacity generally increases with rising AI values. Compared to the exponential relationship observed with water-retention capacity (Figures 3a–3j), the relationship between AI and cooling capacity appears more linear (Figures 3k–3t), suggesting a steadier response of cooling capacity to changes in AI.

Water-Retention and Cooling Capacities Under Different Proportion of Green Spaces

Although water-retention and cooling capacities each exhibit similar patterns in the shape of their relationships with AI across different green space proportions in both cold and warm seasons (Figures 3a–3t), distinct differences emerge between AI categories. Therefore, we further examined how local climatic conditions influence the relationship between green space proportions and both water-retention and cooling capacities across all study sites (Figures 3I–3IV). For water-retention capacity, in humid and extreme humid regions (AI > 1), green space proportion exerts a positive influence, with higher proportions correlating with greater water-retention capacity in both cold and warm seasons. However, in arid region (AI ≤ 1), this positive influence is only evident in the warm season (Figures 3I and 3II). For cooling capacity, a season-dependent dynamic was observed in the influence of green space proportion. Although positive influences of green space proportion on mean cooling capacity are found under different climatic conditions in the cold season, the effects are comparatively weaker, suggesting that cooling capacity is less dependent on the proportion of green space. During the warm season, study sites with lower proportions of green space in arid regions did not exhibit effective cooling effects. However, as the proportion of green space increased, there was a notable improvement in mean cooling capacity, particularly in humid and extreme humid regions (Figures 3III and 3IV).

Retention-Cooling Integrative Performance of Urban Green Space

Our analysis reveals that 14.16% of urban green space study sites have water-retention capacity above the breakpoint in the cold season, compared to 7.68% in the warm season. In terms of cooling capacity, 63.61% and 68.56% of urban green space study sites exceeded the breakpoint for the cold and warm seasons, respectively. The data displayed in Figure 4 elucidates the spatial dynamics of the retention-cooling relationship in both cold and warm seasons. In areas with a high density of green space study sites, such as those located in the subtropical zone, approximately one-third of the study sites do not simultaneously achieve water-retention and cooling capacities above the 0.5 breakpoint. However, in these subtropical areas, the cooling capacity generally perform well above 0.5 during both the cold and warm seasons in most sites (Figures 4c and 4f). Conversely, these regions exhibit poor performance in water retention, falling below the 0.5 breakpoint. Notably, significant difference in the retention-cooling relationship can be observed between the cold and warm seasons in temperate zone. Such location, with relatively low humidity conditions (Figures S2 and S3 in Supporting Information S1) compared to subtropical zone, favors retention more than cooling in the cold season, whereas favors cooling more than retention in the warm season (Figures S4 in Supporting Information S1).

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Retention-Cooling Integrative Performance Under Climate Change

In the subtropical zone, urban green spaces consistently exhibited favorable cooling capacity during both the cold and warm seasons in the baseline period (Figures 5a and 5d and Figure S4 in Supporting Information S1). However, under future climatic conditions, the proportion of green spaces meeting specific thresholds shows varying trends across different timeframes. During the cold season, there is a notable increase in the proportion of green spaces achieving effective retention-cooling capacity compared to baseline period, but this proportion declines progressively from the near-term to the long-term (Figure 5a). In contrast, during the warm season, the proportion of green spaces with effective water retention remains relatively low, and there is a decline in the proportion of sites with effective cooling capacity across all timeframes (Figure 5d). Additionally, the impact of the three climate scenarios on the proportion of green spaces meeting good retention-cooling performances exhibited significant variability across the cold and warm seasons. During the cold season, the harsher climate scenarios resulted in a decline in the proportion of sites achieving both effective retention and cooling capacities, while the proportion of sites demonstrating good cooling performance alone increases. In contrast, during the warm season, the harsher scenarios lead to fewer sites maintaining effective cooling capacity, accompanied by a notable increase in the proportion of sites failing to reach the 0.5 threshold for both retention and cooling capacities.

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In the warm temperate zone, similar performances are observed between baseline period and future periods in cold season, but a significant increase in the proportion of sites with effective cooling capacity emerges in warm season across future timeframes (Figures 5b and 5c). In the middle temperate zone, contrasting trends emerged across different timeframes. During the cold season, compared to the baseline, the proportion of sites achieving good retention-cooling performance showed a slight increase in the near-term under the SSP1-2.6 scenario, with a more pronounced increase under harsher climate scenarios. However, the mid-term and long-term projections show a decline in the proportion of green spaces meeting both thresholds, especially under harsher climate scenarios, returning to levels similar to the baseline. Additionally, the proportion of sites with good water-retention performance significantly decreases compared to the baseline and other periods. During the warm season, the proportion of sites with effective cooling performance remains largely stable across future timeframes, though sites with effective water-retention performance continued to be limited, especially during the long-term period (Figures 5c and 5f).

The above results are closely related to projected climatic changes. Under the projected future climate change scenarios, the AI across these three climate zones shows a significant decline compared to the baseline period during warm season, with a noticeable reduction in occurrences of extreme humidity and, in some cases, a shift toward drier conditions (Figure S2 in Supporting Information S1). Projected precipitation in the subtropical zone decreases in both the cold and warm seasons, while the warm temperate and middle temperate zones display differing trends: an increase in precipitation during the cold season and a decrease during the warm season compared to the baseline period (Figure S3 in Supporting Information S1).

Discussion

Influence of Local Climatic Condition on the Water-Retention and Cooling Capacities

Previous studies have shown that water-retention and cooling capacities of urban green space highly depends on the ambient climatic conditions of urban areas (Cai et al., 2019; Su et al., 2020; Yan et al., 2023). In this study, we employed the AI to evaluate the impact of local climatic conditions on the water-retention and cooling capacities within the urban green spaces. Results showed that water-retention capacity performed better in the relatively arid regions during both cold and warm seasons. However, cooling capacity was more pronounced in humid regions, especially during the warm season (Figure 2). This may be attributed to the limited water availability in relatively direr regions (ratio of P/PET less than 1), where insufficient precipitation constrains evapotranspiration (AET) despite the high atmospheric evaporative demand (PET) (Cuthbert et al., 2022). Consequently, soil moisture swiftly transfers into the atmosphere through evaporation, driven by ample energy (high PET). This process enables the soil substrate to create additional space to buffer and retain rainwater for the subsequent precipitation events. Conversely, in humid regions, ample precipitation combined with limited energy for evapotranspiration leads to reduced water-retention capacity, as most rainfall saturates the soil rather than evaporating. On the other hand, the cooling process relies primarily on the conversion of sensible heat into latent heat during evaporation (Wang et al., 2022; Yan et al., 2023). Therefore, as the AI values increase, we observe a corresponding escalation in the cooling capacity in the warm season. But in the cold season, the lower background temperature and the relatively low conversion between sensible heat and latent heat, combined with the dry and less rainy characteristics of winter in most part of China, result in a less pronounced cooling effect, and even a warming effect may occur (Li et al., 2015; Solcerova et al., 2017; Su et al., 2022).

Influence of Green Space Proportion on the Water-Retention and Cooling Capacities

In addition to the influence of local climatic condition, vegetation within urban green spaces plays a crucial role in the water and energy cycles, and its impact should not be underestimated. The effect of increased greening coverage (i.e., the proportion of green space) on water retention remains a topic of debate (Andréassian, 2004; Zhou et al., 2015). Some scholars argue that increased greening coverage amplifies vegetation transpiration, facilitating the transfer of water from the soil to the atmosphere, thereby reducing soil moisture and creating additional space for water retention (Jackson et al., 2005; Sun et al., 2006). Conversely, other studies have observed a positive correlation between vegetation coverage and runoff coefficient, suggesting a potential decrease in water retention (Onuchin et al., 2021; Wang et al., 2011). In this study, green space proportion positively influenced water retention in both cold and warm seasons in humid and extreme humid regions (AI > 1). This may be attributed to the relatively stable soil moisture conditions in these regions, providing favorable water availability for vegetation growth, which in turn increases vegetation coverage, slows water flow, and allows more time for soil infiltration (Wu et al., 2019). However, in the relatively arid regions, the impact of green space proportion on water retention differs between seasons (Figure 3II). During the cold season, the effect is less pronounced, primarily due to reduced precipitation and limited vegetation growth, which diminish the influence of green space proportion on water retention, resulting in an insignificant effect in some areas. In contrast, during the warm season, areas with higher green space proportion generally show enhanced water retention. This is largely because elevated temperatures amplify the positive impact of increased green space proportion, further enhancing water-retention capacity through intensified evapotranspiration.

Our results also indicate a higher proportion of green space leads to a more pronounced increase of cooling capacity (Figures 3III and 3IV), particularly during the warm season in the humid/extreme humid regions. This aligns with previous studies that emphasize the significant role of green space in urban heat mitigation (Huang & Cadenasso, 2016; Masoudi & Tan, 2019; Yang et al., 2014). Notably, the influence of the AI on cooling capacity is influenced by the proportion of green space, exhibiting opposite trends in different seasons. In the warm season, a higher proportion of green space correlates with a more significant increase in cooling capacity as AI increases (Figures 3p–3t), likely due to intensified evapotranspiration and shading during warmer months when plants are in their growing season. In contrast, in the cold season, this relationship reverses, with cooling capacity decreasing as AI increases. This counterintuitive result may be influenced by factors such as reduced evapotranspiration rates and increased sensible heat flux in colder months (Hathway & Sharples, 2012; Solcerova et al., 2017; Su et al., 2022). Furthermore, while our study offers valuable insights into the general relationship between urban green space and its effectiveness in water-retention and cooling capacities, it is important to recognize the distinct contributions of different types of green space and their spatial layout (Xiao et al., 2018). These aspects of urban green spaces may play unique roles in these greening functions, and a more nuanced understanding is necessary for developing targeted urban planning and climate adaptation strategies in the future research.

Retention-Cooling Integrative Performance of Urban Green Space Across Major Climate Zones in China

The retention-cooling integrative performance of urban green space across stropical and warm/middle temperate zones in China reveals distinct patterns that highlight the complexity of urban greening functionality under varying climatic conditions. In our study, the retention performance in the warm and middle temperate zones was found to be superior to that in the subtropical zone, while the cooling performance was more prominent in the subtropical zone, especially during the warm season (Figure 4 and Figure S4 in Supporting Information S1). However, when considering the overall performance, the favorable retention-cooling performance of urban green space is more pronounced in the temperate zone compared to the subtropical zone (Figure 4). These differences can be explained by the variations in ambient climatic conditions across these regions (Cuthbert et al., 2022; Yang et al., 2024).

The subtropical zone consistently demonstrates strong cooling capacity in both cold and warm seasons, likely due to the higher levels of humidity in this region (Figures S2 and S3 in Supporting Information S1), which enhance the cooling capacity of green spaces through processes such as evapotranspiration and shading. However, the superior cooling performance in these humid regions often comes at the expense of water-retention capacity, which remains at a low level in many subtropical regions. As climate change progresses, cooling capacity in the future period is projected to be lower than during the baseline period and shows a decline trend across near-term, mid-term, and long-term projections, particularly under the influence of the three scenarios (SSP1-2.6, SSP2-4.5, and SSP3-7.0) in the warm season (Figures 5b and 5e). This trend aligns with the projected changes in humidity condition and rainfall patterns across the different future scenarios, where reductions in rainfall intensity and environmental humidity are expected to impact the hydrological cycle in subtropical zones (Figures S2 and S3 in Supporting Information S1). Though the low water-retention performance might show some improvement under future climatic conditions during the cold season, contributing to an increase in proportion of sites achieving effective retention-cooling performance. However, the trade-off between cooling and retention still emphasizes the need for tailored urban planning strategies that account for specific climatic conditions in this climate zone. A nuanced understanding of temporal variations is also critical, as the near-term, mid-term, and long-term differences can inform when adaptive interventions should be most effectively introduced to sustain the functionality of urban green spaces.

In temperate zones, urban green spaces are projected to be more effective at water retention during the cold season, which may be due to the lower humidity levels and relatively higher atmospheric demand for evapotranspiration compared to those in subtropical zone (Yan et al., 2024). This suggests that urban green spaces in temperate climates could play a vital role in regulating hydrological cycles under changing seasonal dynamics. The pronounced seasonal variation in retention and cooling performance observed in temperate zones underscores the differential functionality of urban green spaces: during the warm season, water-retention capacity significantly decreases, while cooling capacity remains stable. The shifts toward cooling capacity surpassing water retention as the dominant function is likely related to increased rainfall and higher humidity, leading to a relatively reduced rate of evapotranspiration. Under the influence of future climate change scenarios, the proportion of sites in temperate zones achieving effective retention-cooling performance differed notably between cold and warm seasons across different timeframes, with higher proportions in the cold season and much lower or absent in the warm season. Compared to the baseline period, the future period is marked by increased rainfall during the cold season, leading to a more humid environment in urban green spaces. This heightened humidity may weaken water-retention capacity, particularly in the long-term period, while simultaneously enhancing cooling performance, as seen in the warm temperate zone. Conversely, the warm season is characterized by reduced rainfall and a drier environment. However, the reduction in rainfall does not necessarily lead to improved water-retention capacity, as the warm season's inherent climatic conditions limit the potential for significant gains. Instead, the drier conditions during the warm season may enhance cooling performance, as reduced soil moisture and lower humidity facilitate more effective heat dissipation. These effects become more pronounced as the intensity of the three climate scenarios increases. These distinct seasonal patterns and variations across different future projection periods suggest that urban green space planning must not only consider current climatic conditions but also anticipate future shifts that could require rethinking green space design and functionality over timeframes aligned with climate projections.

Recognizing the inconsistent performance of urban green spaces in terms of water retention and cooling across seasons, timeframes, and various climate regions, it becomes evident that a simplistic one-size-fits-all approach falls short in addressing the multifaceted challenges posed by varying climatic conditions. A more customized, region-specific strategy that aligns with the unique environmental conditions, particularly in the context of future climate change, is highly recommended.

Limitation

This study presents a comprehensive evaluation on the water-retention and cooling capacities of green space across China's diverse urban areas, influenced by local climatic conditions and greening coverage. Nevertheless, several limitations should be acknowledged. Primarily, the study's reliance on remote sensing data, despite its extensive coverage and utility, may not capture the intricate details of local microclimates and urban green space dynamics. The spatial resolution of the data further constrains the analysis, potentially oversimplifying the complex interactions between urban green spaces and their surrounding environments. Additionally, while the current analysis primarily focuses on assessing the direct impacts of projected climatic conditions on water retention and cooling capacities of urban green spaces, we recognize the importance of considering potential feedback mechanisms. Green spaces can influence local climate conditions through processes such as evapotranspiration and albedo changes, which could, in turn, affect the broader climate system. This is an area requiring further exploration, and we have noted it as a limitation of the current study. Another limitation lies in the assumption that the relationship between normalized cooling capacity and climatic conditions remains consistent under future climate change scenarios. Future research could benefit from integrating ground-based observations and local climate simulations to enhance the granularity and accuracy of findings, providing a more nuanced understanding of green space functionalities within urban ecosystems. Moreover, utilizing more comprehensive data sets will enable broader coverage of urban green space samples, offering better representation of diverse climatic conditions across more regions. Given the current limitations in data resolution limitations, which prevented differentiation between specific green space types and their spatial layout within the 1-km study grid, future research should delve to these variations in greater detail. Such exploration could provide a more comprehensive understanding of how diverse forms of green spaces contribute to urban resilience, facilitating more precise and effective planning interventions. Optimizing the design and implementation of urban green spaces in this way may be crucial for addressing the specific climatic and environmental challenges faced by different regions.

Conclusions

In summary, our findings demonstrate that water-retention capacity is more effective in relatively arid regions, whereas cooling capacity is more pronounced in humid regions, with both effects significantly influenced by seasonal variations. The proportion of green space plays a crucial role, particularly in influencing cooling capacity, which exhibits opposite trends between cold and warm seasons. This complex interplay between water-retention and cooling capacities, influenced by local climatic conditions, underscores the need for adaptive greening strategies to maintain the functionality of urban green spaces and support the development of sustainable, resilient cities. Future scenarios analysis suggest that climate change will significantly alter retention-cooling performances, potentially leading to notable deviations compared to patterns observed in the historical periods across different climate zones in China. This further emphasizing the need for tailored region-specific strategies to enhance urban resilience in the face of climate change.

Given China's unique urban growth dynamics, characterized by rapid urbanization and diverse environmental pressures, understanding the interplay between urban green spaces, temperature regulation, and water retention is crucial for developing effective urban planning and policy decisions. This research provides valuable insights that can help inform sustainable urban development and resilience to climate change, aligned with China's national objective of building an ecological civilization.

Acknowledgments

This work was supported by the Natural Science Foundation of Guangdong Province (Grant 2024A1515030190), National Natural Science Foundation of China (Grant 42471326, 42130712), Young Talent Project of GDAS (Grant 2023GDASQNRC-0217), GDAS' Project of Science and Technology Development (Grant 2024GDASZH-2024010102, 2022GDASZH-2022010105), and Science and Technology Program of Guangzhou, China (Grant 2024A04J3347).

Data Availability Statement

All data used in this study are publicly available. Meteorological data like monthly precipitation data can be found in Peng (2019b), and monthly temperature data is available in Peng (2019c). Data for calculating monthly actual evapotranspiration and potential evapotranspiration were collected from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16A2 Version 6.1 Evapotranspiration/Latent Heat Flux product (Running et al., 2021). Data used for urban boundaries demarcation was based on Global Human Settlement Layer (GHSL) GHS-SMOD R2019 A product (version 1.0) available in Pesaresi et al. (2019). Land cover data are extracted from the Global 30 m Land-Cover dynamic monitoring products with fine classification system from 1985 to 2020 V1.0 (GLC_FCS30D) available in Liu et al. (2023). CMIP6 GCMa output for predicted monthly precipitation, evapotranspiration, and potential evapotranspiration data are available in Copernicus Climate Change Service and Climate Data Store (2021).

Erratum

The originally published version of this article contained an error in the affiliation for coauthor Yongxian Su. The affiliation has been corrected as follows: State Key Laboratory for Ecological Security of Regions and Cities, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China. This may be considered the authoritative version of record.

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