A 10-year experimental study on the cooling effects of urban tree and lawn by transpiration on heatwaves and their mechanisms

A 10-year experimental study on the cooling effects of urban tree and lawn by transpiration on heatwaves and their mechanisms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A 10-year experimental study on the cooling effects of urban tree and lawn by transpiration on heatwaves and their mechanisms Guo Yu Qiu, Tao Fang, Weiting Hu, Chunhua Yan, Chao Zhang, Bei Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5532766/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Dec, 2025 Read the published version in Nature Cities → Version 1 posted You are reading this latest preprint version Abstract Heatwaves have become the serious threat to the comfort and lives of urban residents. The cooling effects of urban tree and lawn through transpiration are regarded as a potential way to address these challenges, but their effects on heatwaves and mechanism remained unclear. Here, using a 10-year observation, we investigate the transpiration responses of urban lawn and a tree to 54 heatwave events in a subtropical city. We hypothesize that urban trees and lawns exhibit distinct transpiration response patterns during heatwaves due to different water use strategies and stomatal regulations. The findings reveal that (1) the lawn maintained high canopy stomatal conductance (G s ) during heatwaves, resulting in a 42.3% increase in transpiration rates (from 2.39 to 3.40 mm day − 1 ). In contrast, the tree significantly reduced G s , maintaining relatively stable transpiration rates (slightly decreasing from 51.98 to 48.27 g m − 2 s − 1 ). (2) the lawn transpiration was highly dependent on soil water content (SWC), with rapid SWC depletion limiting sustained transpiration increases. Conversely, the tree accessed deeper soil water layers, enabling more stable transpiration throughout heatwaves. Urban tree responded to heatwaves much better than that of urban lawn. These results are of great importance for advancing knowledge in urban green space planning and water management. Earth and environmental sciences/Environmental sciences/Environmental impact Earth and environmental sciences/Natural hazards Earth and environmental sciences/Ecology/Urban ecology Earth and environmental sciences/Environmental social sciences/Climate-change adaptation Urban heatwave Urban green space Transpiration Cooling effects Water use strategy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Main As global temperatures continue to rise, heatwaves (HWs) have become increasingly frequent and severe 1 , posing significant risks to urban environments. From 1990 to 2019, HWs were responsible for 153,078 fatalities 2 . Meanwhile, rapid urbanization has reshaped cities by replacing natural landscapes with impervious surfaces, such as concrete and asphalt, which accumulate heat 3 and exacerbate extreme drought conditions 4 . This urban transformation has intensified the urban heat island (UHI) effect 5 , a phenomenon that becomes particularly pronounced during HWs 6 , 7 . HWs amplify evaporation rate, causing green spaces to dry out and exposing cities to heightened risks. putting urban areas at greater risk. Arid vegetation provides fuel for firewaves, overwhelming urban fire brigades 8 . Additionally, elevated temperatures drive up electricity demand in urban areas 9 , resulting in increased carbon emissions. The synergistic effects of HWs and the UHI effect further amplify the associated risks for urban residents, particularly vulnerable groups such as the elderly and individuals with pre-existing health conditions 10 . To overcome these challenges, urban green spaces have emerged as one of the most promising solutions 11 . They can reduced ambient temperatures by providing shade, enhancing evapotranspiration, and increasing solar reflectivity 12 . Among these cooling mechanisms, transpiration plays a pivotal role 13 . This process involves the movement of water from the soil, through vegetation, and into the atmosphere, thereby absorbing substantial amounts of heat. Research indicates that during periods of extreme heat, transpiration can convert up to 50% of incoming solar radiation into latent heat 14 , making it an exceptionally potent cooling force. Consequently, expanding urban green spaces is recognized as a viable and effective nature-based strategy for alleviating both heatwaves and the urban heat island effect 15 . While the benefits of urban green space are well accepted, the responses of different vegetation types to extreme heat varied significantly. For instance, Eyster and Beckage 16 found that during a HW in Canada, coniferous trees offered more effective cooling compared to broadleaf trees, shrubs, and lawns. Similarly, Teuling 17 demonstrated that during heatwaves, grasslands cool through evaporation but quickly deplete soil moisture, leading to higher temperatures, while forests conserve water, effectively mitigating prolonged extreme heat. Even among similar tree species, their performance can vary significantly across different situations, Ahongshangbam 18 showed that tree sap flow rates vary substantially from other underlying surfaces, such as grass, pavement, or other materials. Thess variations underscores the highly heterogeneous impact of urban green spaces on cooling efficiency within urban landscapes. On the other hand, urban green spaces are vulnerable to heat-induced stress. Specifically, lawns have been observed to deteriorate under extreme heat conditions, often losing their capacity to provide essential ecological benefits 19 – 21 . In contrast, trees employ a variety of adaptive strategies: some species close their stomata to conserve water 22 , while others maintain open stomata, enabling continuous transpiration and cooling 23 , 24 . Although the latter mechanism facilitates immediate temperature regulation, it can also lead to excessive water loss, potentially compromising the long-term health of the trees 25 . While these findings offer valuable case studies, effectively managing vegetation in urban green spaces remains a complex challenge 26 , especially in subtropical regions 27 . Urban areas face not only the escalating impacts of climate change but also a complex and heterogeneous mix of green spaces, buildings, and impervious surfaces, which complicates the study and management of urban vegetation 28 . Therefore, management strategies must thoroughly consider the transpiration characteristics of different plant species to better understand their cooling effects in response to climatic extremes 29 . Much of the existing research has focused on short-term assessments of heatwave impacts on plant health, leaving significant gaps in understanding how different vegetation types adapt during prolonged heatwaves 30 . This is especially relevant for synchronized root-to-canopy analyses, which require extensive observational studies. In this study, we analyzed 10-year observational data to investigate the responses of two prevalent urban species— Ficus concinna Miq. and Zoysia matrella (L.) Merr. —to HWs in a subtropical megacity. Specifically, we aimed to elucidate how the interaction between meteorological conditions and soil moisture influences their adaptive strategies. By exploring these mechanisms in detail, we seek to delineate the distinct strategies employed by lawns and trees in managing heat stress and their role in cooling urban environments. Our findings, based on long-term data, will reveal the contributions of different urban vegetation types to mitigating high temperatures and provide evidence-based recommendations for optimizing urban green space design to enhance resilience against heatwaves. Results Transpiration Responses of Lawn and Tree to Heatwaves During heatwave events, the transpiration rates of lawns and trees exhibited markedly different responses (Fig. 1 ). In the pre-heatwave (Pre-HW) period, the lawn transpiration rate (T lawn ) remained relatively low, averaging 2.39 mm day − 1 . Upon the onset of the heatwave (HW), T lawn increased sharply to 3.40 mm day − 1 (p < 0.001), representing a 42.26% rise. Subsequently, in the post-heatwave (Post-HW) period, T lawn declined swiftly to 2.30 mm day − 1 (p < 0.001), reverting to levels comparable to the Pre-HW period. In contrast, heatwaves exert an insignificant effect on the tree transpiration rate (J s ). J s remained relatively stable throughout the heatwaves (Fig. 1 ), with values of 51.98, 48.27, and 49.72 g m − 2 s − 1 for Pre-HW, HW and Post-HW periods, respectively. Stomatal Conductance Responses of Lawn and Tree to Heatwaves The canopy stomatal conductance (G s ) of the lawn remained relatively stable throughout the heatwave period, indicating low regulatory adjustment in response to extreme heat stress. Across the Pre-HW, HW, and Post-HW periods, the average G s of the lawn was 19.89, 20.43, and 19.59 mol m² s⁻¹, respectively (Fig. 2 a), with no significant differences observed between these phases (p > 0.05). Meanwhile, the stomatal sensitivity of Gs to ln(VPD) ( m ) in the lawn significantly increased during the heatwave, rising from 11.03 before the heatwave to 19.82 during the heatwave (Fig. 2 b). Conversely, trees exhibited a more dynamic and adaptive stomatal regulation strategy during the heatwaves. The G s of tree reduced 38.65% from 49.24 mmol m² s⁻¹ at Pre-HW to 30.21 mmol m² s⁻¹ during HW (p < 0.001, Fig. 2 a). This marked decline suggests that tree rapidly closed its stomata to minimize water loss under severe heat stress conditions. Subsequent to the HW, G s recovered to 50.33 mmol m² s⁻¹, aligning with pre-HW levels, which demonstrates an effective restoration of stomatal conductance once the stress abated. The "rapid closure-recovery" pattern exhibited by tree underscores its capacity for flexible and dynamic water regulation. Additionally, the m decreased from 1.19 in Pre-HW to 0.91 during HW (Fig. 2 b), indicating a conservative response to minimize water loss under extreme conditions. This diminished sensitivity suggests a strategy focused on maintaining water balance by downregulating stomatal responses during periods of high VPD and evaporative demand, reflecting a prudent water-use approach to mitigate dehydration risks. Responses of Soil Water to Heatwaves Using three representative heatwave events from the summer of 2018 (HW#21, HW#22, HW#23) as case studies, the time series of rhizosphere volumetric soil water content (SWC) at depths of 10 cm for lawns and 30 cm for trees, precipitation (P), lawn transpiration (T lawn ), and tree transpiration (J s ) revealed distinct water-use patterns between the lawn and trees (Fig. 3 ). These disparities were particularly pronounced during heatwave periods. As the heatwave progressed, T lawn increased significantly, leading to a rapid depletion of SWC. Despite intermittent rainfall events, the heightened transpiration demand hindered effective recovery of soil water content, highlighting a significant escalation in lawn water consumption during heatwaves. As soil water content continued to decrease, T lawn began to decrease in the later phases of the heatwave, indicating that transpiration was increasingly constrained by limited soil water availability. In contrast, the tree exhibited a more stable water-use strategy. Although J s exhibited some fluctuations, the SWC remains quite stable overall. The tree likely mitigated water loss by adjusting stomatal conductance, thereby preserving soil water reserves. This strategy enabled the tree to maintain a balanced water status, effectively preventing excessive water depletion under intense heat and drought conditions. Isotopic analysis reveals how the tree leverages its deep root systems to adapt to heatwaves, ensuring stable water uptake during periods of extreme heat stress. During the heatwave (HW), the isotopic composition (δD) and (δ¹⁸O) of stem water showed a marked shift away from the isotopic profiles of shallow soil water, aligning more closely with those of middle (10–30 cm) and deep (30–50 cm) soil layers (Fig. 4 a-c). This shift highlights a significant change in water uptake behavior, where the tree increasingly relied on deeper soil water content as surface water resources rapidly evaporated under high temperatures. Quantitatively, the MixSiar model analysis (Fig. 4 d) provided a detailed breakdown of water usage proportions from various soil depths. Pre-HW, the tree sourced water relatively evenly from shallow (28%), middle (36%), and deep (35%) soil layers, indicating diverse access to available water resources. However, during HW, a notable redistribution occurred: the contribution from shallow soil plummeted to 18%, while reliance on middle and deep soil layers increased to 44% and 39%, respectively. This substantial increase in uptake from deeper layers during HW suggests a physiological adaptation to mitigate water loss risks from the rapidly drying upper soil strata. The tree’s extensive root system became critical for accessing less volatile water reserves, thereby maintaining necessary hydration for photosynthetic activity and preventing severe hydraulic failure. Post-HW, the proportional contribution of shallow soil water partially recovered to 23%, while middle and deep layers contributed 32% and 45%, respectively. Although there was some recovery in shallow soil water content, deep soil water remained the predominant source, highlighting its sustained importance even as surface conditions ameliorated. This dependence on deep soil layers emphasizes their function as a buffer against surface moisture variability, providing a stable water supply essential for tree recovery following heat stress. Discussion Differential Stomatal Responses to Heatwaves in Lawns and Trees The contrasting physiological structures of lawns and trees result in markedly different responses to heatwaves. Lawns, characterized by a simple physiological architecture, maintain a high canopy stomatal conductance (G s ) both before and during heat events. The sensitivity of G s to vapor pressure deficit (VPD) increases significantly from 11.03 to 19.82, leading to a sharp rise (+ 42.26%) in transpiration rates—reaching up to 3.40 mm day⁻¹ during heatwaves. This suggests an inability to adequately limit transpiration under heat stress. Such limited regulatory capacity aligns with the findings of Joo 31 , who showed that grasses exhibit a physiological predisposition for rapid response to VPD increases, ultimately constraining water conservation during prolonged heat events. In contrast, trees adapt to heatwaves through more flexible stomatal regulation. Despite the increased evaporative demand of the environment, the tree in our study showed a slight decrease in transpiration rates from 51.98 to 48.27 g m⁻² s⁻¹. Their G s significantly decreased by 38.65%, from 49.24 to 30.21 µmol m² s⁻¹. This indicates that trees were capable of moderating water loss under increased VPD conditions, aligning with previous findings that trees usually reduce water loss by closing stomata to maintain a more stable level of transpiration 32 . Even with fewer leaf surface stomata, plants can enhance their cooling capacity through architectural adaptations such as reduced leaf thickness and increased stomatal spacing 33 . This is consistent with Buwalda and Lenz, who found that trees have a strong water coupling capacity to avoid reduced evapotranspiration due to high ambient water demand under high VPD conditions 34 . Despite the lawn trying to cool down by increasing transpiration during heatwaves, extensive research demonstrates that trees offer substantially superior cooling effects through a combination of shading, solar reflectance, and sustained transpiration. In our previous research 35 , we used unmanned aerial vehicle and infrared remote sensing to measure the observation area and found that trees have a significantly better cooling effect than lawns. This phenomenon has also been widely found in other research, Potchter 36 compared three parks in Tel Aviv and found that parks with trees can reduce temperatures by 2–3°C, while grass parks can even be warmer than built-up areas during the day, increasing heat stress values. Similarly, in Singapore, Nichol 37 observed that tree-covered areas maintained an average surface temperature of 32.9°C, compared to 35.6°C for turfgrass and 40.7°C for asphalt. Further simulation studies affirm that tree shading alone can lower near-surface urban temperatures by an average of 3.06°C, effectively reducing sensible heat loss and optimizing surface energy balance 38 . Collectively, these findings underscore the critical role of trees in urban temperature regulation, positioning them as an essential resource in mitigating the impacts of urban heatwaves. Differential Soil Water Reliance of Lawns and Trees As shallow-rooted plants, lawns rely heavily on surface soil water content (SWC). Our findings (Fig. 3 ) indicate that the surge in lawn transpiration during heatwaves rapidly depletes surface SWC. This observation aligns with studies on perennial herbaceous plants in high-temperature environments, where rapid declines in SWC lead to irreversible plant damage, such as reductions in leaf elongation rates, turf quality, and chlorophyll content 39 , 40 . Under such conditions, the water acquisition capacity of lawns is severely limited, rendering them dependent on external irrigation for survival in urban settings. Using hydrogen and oxygen isotope analyses, we revealed that trees shift their water uptake to deeper soil layers during heatwaves. The contribution of mid-layer(10-30cm) water increased from 36–44%, and deep soil water from 35–39%, while shallow soil water decreased from 28–18%. This deep-water(30-50cm) utilization strategy has been corroborated by field studies in Singapore 29 and Los Angeles 41 , where trees maintained stable transpiration and cooling benefits during heat waves through deep rooting systems. From an urban water resource management perspective, the differing water use mechanisms of lawns and trees directly impact the sustainability of urban green spaces. Lawns' high transpiration rates and shallow roots result in significant water demand spikes during heatwaves, necessitating frequent irrigation to sustain physiological activities. In water-scarce cities (with annual precipitation below 1000 mm), lawns’ high-water consumption is unsustainable in the long term. Studies in Melbourne 42 and London's Hyde Park 43 have shown that urban lawns rapidly deplete surface water under high temperatures, exacerbating urban water resource pressures. In many arid cities, landscape irrigation accounts for 40–70% of total water use 44 , leading to stringent water-saving practices during hot summers in places like Melbourne, Sydney, California, and Arizona 45 . These restrictions often result in lawn deterioration and damage 19 , 46 . Consequently, whether through ongoing irrigation during heatwaves or post-heatwave restoration, maintaining lawns demands considerable energy and resource inputs. Conversely, selecting appropriate tree species for urban environments can reduce reliance on artificial irrigation while providing stable ecological benefits during heatwaves. McCarthy 47 . suggested that choosing tree species with high water use efficiency and growth rates can maximize growth while conserving water. The extensive use of trees not only reduces irrigation needs under drought conditions but also maintains soil moisture, reduces surface runoff, and prevents water loss through their root systems 48 . In conclusion, trees offer a more water-efficient means to alleviate urban heatwaves, reduce high-temperature exposure risks 49 , and enhance the ecological services of urban green spaces. Methods Study Site The study was conducted on the campus of the Shenzhen Graduate School of Peking University (PKUSZ), located in Shenzhen, Guangdong Province, China (22°26'59"-22°51'49"N, 113°45'44"-114°37'21"E). The site is situated at an average elevation of approximately 17 meters above sea level. Shenzhen, a densely populated city with a population of approximately 17.66 million, features a typical subtropical climate heavily influenced by the South Asian monsoon. The PKUSZ campus, which serves as the study site, has a vegetation coverage of approximately 50%, consisting of trees, shrubs, and lawns. The turfgrass species studied is Zoysia matrella (L.) Merr. , a rapidly spreading grass that forms dense lawns, characterized by its high tolerance to drought, shade, and its minimal soil requirements. These attributes make Z.matrella a popular choice for urban greening in tropical and subtropical regions. The tree species under study is Ficus concinna Miq., an evergreen species well-adapted to warm and humid climates. It is commonly found in subtropical regions of China, as well as in India, Vietnam, Myanmar, Malaysia, and the Philippines. Due to its strong survivability, rapid growth rate, long lifespan, and ease of transplantation, F. concinna is widely used as an urban greening species. The observational period for this study spanned from July 2014 to November 2023, covering nearly a decade of data collection. This long-term dataset provides a robust basis for analyzing the evapotranspiration responses of typical subtropical urban vegetation to heatwave conditions Environmental Measurements A Bowen ratio system was installed at the center of the lawn to monitor meteorological parameters, including air temperature and relative humidity at heights of 1.5 and 2 meters, solar radiation, net radiation, and soil heat flux. Each sensor took measurements every minute, which were subsequently recorded automatically by a CR1000 data logger (Campbell Scientific, Logan, UT, USA), with the system averaging and storing data every 10 minutes. Details on the sensor models and manufacturers can be found in the Appendix. In addition, volumetric soil water content at a depth of 30 cm (SWC) was monitored using time-domain reflectometry (TDR) probes (SM300, Delta-T Devices Ltd., Burwell, Cambridge, UK) located near both the Bowen ratio system and the target trees. All data were collected every 60 seconds and averaged or summed every 10 minutes before being stored in the CR1000 data logger (Campbell Scientific, Logan, UT, USA). Precipitation (P) was recorded using a 7852M-AB tipping bucket rain gauge (Davis Instruments, CA, USA). Hydrogen and Oxygen Isotope To determine the stable hydrogen and oxygen isotopic composition of soil and plant stem water, we collected both soil and plant samples from the study area. Specifically, healthy branches from the sun-exposed side of the target trees were sampled at a height of 3 meters using pruning shears. Soil samples were collected from a 5 × 5 m plot surrounding the target tree using a soil corer, with sampling conducted at 0.5 m intervals. In the laboratory, the soil samples were separated into specific depth layers: 0–2 cm and 2–5 cm for the surface layers, followed by increments of 5 cm up to a depth of 50 cm. After collection, water was extracted from both plant and soil samples using an LI-2100 vacuum condensation extraction system (LICA, China). The extracted water samples were then analyzed for stable isotopic ratios using a Liquid Water Isotope Analyzer (LWIA, Model 912-0008, Los Gatos Research, USA). The LWIA operates on the principle of cavity ring-down spectroscopy (CRDS), providing high-precision measurements of hydrogen (δD) and oxygen (δ 18 O) isotopes, with accuracies better than 0.1‰ and 0.3‰, respectively. Sampling and analysis were conducted on July 5th, July 10th, and July 14th, 2018. Heatwave Identification In accordance with the definition provided by the China Meteorological Administration, a heatwave (HW) is defined as a period of at least three consecutive days with daily maximum temperatures reaching or exceeding 35°C. Based on the air temperature recorded at a height of 1.5 m from the Bowen ratio system between July 2014 and November 2023, we identified 54 heatwave events (Fig. 6 ). Detailed time periods for each heatwave can be found in the Appendix. We defined the Pre-HW period as the 15 days preceding the onset of each heatwave. If the interval between two consecutive heatwaves was less than 15 days, the Pre-HW period was defined as the entire interval between the two heatwaves. Similarly, the Post-HW period was defined as the 15 days following the end of each heatwave. If the time gap between two consecutive heatwaves was less than 15 days, the Post-HW period was considered as the interval between the two heatwave events 66 . Calculation of Transpiration Rate Transpiration rate of the lawn (T lawn ) was calculated using parameters obtained from the Bowen ratio system. Transpiration rate of the tree (J s ) for the target tree species was measured using a sap flow system. The sap flow probes (SF-L probe sensor, Ecomatik, Munich, Bavaria, Germany) were installed on the northern side of the trunk at breast height. A CR1000 data logger (Campbell Scientific, Logan, UT, USA) recorded the temperature differential between the probes every minute, with data automatically averaged and stored every 5 minutes. These measurements were conducted continuously from July 2014 to November 2023. Data analysis and parameter calculation Transpiration rate of the lawn (T lawn ) was calculated based on the Bowen ratio energy balance 50 : $$\:{T}_{lawn}=\frac{{R}_{n}-G}{L(1+\beta\:)}$$ 1 where T lawn is the transpiration rate (mm s − 1 ), R n is the net radiation (W m − 2 ), G is the soil heat flux (W m − 2 ), L is the latent heat coefficient of vaporization (J kg − 1 ), and \(\:\beta\:\) is the Bowen ratio, defined as: $$\:\beta\:=\frac{H}{LE}=\frac{\rho\:{C}_{p}{K}_{h}\frac{\partial\:{T}_{a}}{\partial\:z}}{\epsilon\:L/P\rho\:{K}_{w}\frac{\partial\:e}{\partial\:z}}=\gamma\:\frac{\varDelta\:{T}_{a}}{\varDelta\:e}=\frac{{C}_{p}\varDelta\:{T}_{a}}{L\varDelta\:q}$$ 2 where ρ is the air density (kg m − 3 ), C p is the specific heat of air at constant pressure (kJ kg − 1 °C − 1 ), ε is the molecular weight ratio of water vapor to dry air, which is a constant 0.622; P is the atmospheric pressure (kPa), γ is the hygrometer constant; ΔT a , Δe, and Δq are the air temperature difference, vapor pressure difference, and humidity difference between the two heights the air temperature and relative humidity are recorded, respectively. They were 2 and 1.5 m in this study. During periods of sunrise, sunset, rainfall, and lower temperatures, the β often approaches − 1, which results in calculated latent heat flux values that become unreasonably large or even undefined. To address this, following the methodology of Perez et al. 51 , intraday outliers were excluded, and both latent and sensible heat flux anomalies were corrected via interpolation based on the characteristics of evapotranspiration dynamics and the theoretical basis of the Bowen ratio energy balance method. Transpiration rate of the tree (J s ) was calculated using Granier's empirical formula for sap flow density 52 : $$\:{J}_{s}=8.64\times\:119\times\:{\left(\frac{{\varDelta\:T}_{m}-\varDelta\:T}{\varDelta\:T}\right)}^{1.231}$$ 3 where J s is the instantaneous sap flow density (g m − 2 s − 1 ), ΔT is the instantaneous temperature difference between the probes, and ΔT m is the maximum temperature difference throughout the day. To account for potential errors due to nighttime flows and probe drift, local maximum values of ΔT m were calculated over a rolling 10-day period. A linear regression was then applied to these local maxima over time to estimate new ΔT m values. After the first linear interpolation, data points below the estimated values were excluded, and a second interpolation was applied to the remaining data points 53 , 54 . The canopy stomatal conductance (G s ) was estimated using a simplified version of the Penman-Monteith model 55 : $$\:{G}_{S}\:=\frac{{K}_{G}\left(Tair\right){T}_{lawn}or{E}_{L}}{VPD}$$ 4 $$\:{K}_{G}\left(Tair\right)=115.8+0.4236{T}_{air}$$ 5 $$\:{E}_{L}=\frac{{J}_{s}}{{A}_{s}\times\:LAI}$$ 6 where K G (Tair) is the conductance coefficient, T air is the air temperature, E L represents transpiration per unit leaf area 56 , A s is the projected canopy area, and LAI is the leaf area index. 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Growth and physiological responses of temperate pasture species to consecutive heat and drought stresses. Plants 8, 227 (2019). Jiang, Y. & Huang, B. Drought and heat stress injury to two cool-season turfgrasses in relation to antioxidant metabolism and lipid peroxidation. Crop science 41, 436–442 (2001). Bijoor, N. S., McCarthy, H. R., Zhang, D. & Pataki, D. E. Water sources of urban trees in the Los Angeles metropolitan area. Urban Ecosystems 15, 195–214 (2012). Cheung, P. K., Meili, N., Nice, K. A. & Livesley, S. J. Identifying the mechanisms by which irrigation can cool urban green spaces in summer. Urban Climate 55, 101914 (2024). Scharfstädt, L. et al. From Oasis to Desert: The Struggle of Urban Green Spaces Amid Heatwaves and Water Scarcity. Sustainability 16, 3373 (2024). Hilaire, R. S. et al. Efficient water use in residential urban landscapes. HortScience 43, 2081–2092 (2008). Ignatieva, M., Haase, D., Dushkova, D. & Haase, A. Lawns in cities: from a globalised urban green space phenomenon to sustainable nature-based solutions. Land 9, 73 (2020). Haase, D. et al. A quantitative review of urban ecosystem service assessments: concepts, models, and implementation. Ambio 43, 413–433 (2014). McCarthy, H. R., Pataki, D. E. & Jenerette, G. D. Plant water-use efficiency as a metric of urban ecosystem services. Ecological Applications 21, 3115–3127 (2011). Bartens, J., Day, S. D., Harris, J. R., Wynn, T. M. & Dove, J. E. Transpiration and root development of urban trees in structural soil stormwater reservoirs. Environmental Management 44, 646–657 (2009). Ettinger, A. K. et al. Street trees provide an opportunity to mitigate urban heat and reduce risk of high heat exposure. Scientific Reports 14, 3266 (2024). Bowen, I. S. The ratio of heat losses by conduction and by evaporation from any water surface. Physical review 27, 779 (1926). Perez, P., Castellvi, F., Ibanez, M. & Rosell, J. Assessment of reliability of Bowen ratio method for partitioning fluxes. Agricultural and Forest Meteorology 97, 141–150 (1999). Granier, A. Evaluation of transpiration in a Douglas-fir stand by means of sap flow measurements. Tree physiology 3, 309–320 (1987). Lu, P., Urban, L. & Zhao, P. Granier's thermal dissipation probe (TDP) method for measuring sap flow in trees: theory and practice. ACTA BOTANICA SINICA-ENGLISH EDITION- 46, 631–646 (2004). Granier, A. & Gross, P. Mesure du flux de sève brute dans le tronc du Douglas par une nouvelle méthode thermique. Ann. Sci. For. 44, 1–14 (1987). Monteith, J. L., Unsworth, M. H. & Webb, A. Principles of environmental physics. Q. J. R. Meteorol. Soc. 120, 1699 (1994). Phillips, N. & Oren, R. A comparison of daily representations of canopy conductance based on two conditional time-averaging methods and the dependence of daily conductance on environmental factors. Ann. Sci. For. 55, 217–235 (1998). Oishi, A. C., Hawthorne, D. A. & Oren, R. Baseliner: an open-source, interactive tool for processing sap flux data from thermal dissipation probes. SoftwareX 5, 139–143 (2016). Oren, R. et al. Survey and synthesis of intra-and interspecific variation in stomatal sensitivity to vapour pressure deficit. Plant, cell & environment 22, 1515–1526 (1999). Additional Declarations There is NO Competing Interest. Supplementary Files Appendix.docx Appendix Cite Share Download PDF Status: Published Journal Publication published 09 Dec, 2025 Read the published version in Nature Cities → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5532766","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":414315030,"identity":"9c5a4bf2-9faf-492f-8b28-7c2cff9a9792","order_by":0,"name":"Guo Yu Qiu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYDACZhBRwWAA4bERreUMSVpAgLGNFC18x3mMX/POu2NscP74A4YPZYcZ+Gc34NcieZjHzJp32zMzgxsJCYwzzh1mkLhzAL8WA6AWY95th20MbjAcYOZtO8xgIJFAjJY5QC3nDzYw/yVSi/Fj3obDZgYHkhmYGYnRInmYrYxxzrFnxpI30hgO9pxL55G4QUAL3/nDmz+8qblj2Hf++MMHP8qs5fhnENDCcICBTYoHSELYDAw8BNSDlTF//AHVMgpGwSgYBaMAKwAAaQZGc3c3KJ4AAAAASUVORK5CYII=","orcid":"","institution":"Peking University","correspondingAuthor":true,"prefix":"","firstName":"Guo","middleName":"Yu","lastName":"Qiu","suffix":""},{"id":414315031,"identity":"bda6caca-cd40-4fc9-8b42-3f5c3917830e","order_by":1,"name":"Tao Fang","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Fang","suffix":""},{"id":414315032,"identity":"f3d9efbb-1801-4123-bc45-92971b4f07bd","order_by":2,"name":"Weiting Hu","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Weiting","middleName":"","lastName":"Hu","suffix":""},{"id":414315033,"identity":"53e06cf7-f036-471e-8a88-28d76c463f02","order_by":3,"name":"Chunhua Yan","email":"","orcid":"","institution":"Shenzhen Campus of Sun Yat-sen University; Sun Yat-sen University,","correspondingAuthor":false,"prefix":"","firstName":"Chunhua","middleName":"","lastName":"Yan","suffix":""},{"id":414315034,"identity":"f533037f-4787-4846-94aa-1f664634de97","order_by":4,"name":"Chao Zhang","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Zhang","suffix":""},{"id":414315035,"identity":"6b5df3e2-8520-4e86-8aaf-540a408c4969","order_by":5,"name":"Bei Wang","email":"","orcid":"","institution":"AT\u0026M Environmental Engineering Technology Co., Ltd,","correspondingAuthor":false,"prefix":"","firstName":"Bei","middleName":"","lastName":"Wang","suffix":""},{"id":414315036,"identity":"9b502e0b-0150-4042-81ac-c8c0c8c0dc14","order_by":6,"name":"Muhammad Hayat","email":"","orcid":"","institution":"Xinjiang Institute of Ecology and Geography,Xinjiang Institute of Ecology and Geography; Fukang National Station of Observation and Research for Desert Ecosystem","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Hayat","suffix":""}],"badges":[],"createdAt":"2024-11-27 07:05:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5532766/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5532766/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s44284-025-00353-4","type":"published","date":"2025-12-09T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":76112966,"identity":"904d9c1c-b715-41cd-99f8-1ea597b3da72","added_by":"auto","created_at":"2025-02-12 12:23:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":157418,"visible":true,"origin":"","legend":"\u003cp\u003eChanges of lawn transpiration rate (T\u003csub\u003elawn\u003c/sub\u003e) and tree transpiration rate (J\u003csub\u003es\u003c/sub\u003e) before (Pre-HW), during (HW), and after (Post-HW) 54 heatwave events for a 10 years period (2014-2023). The left panel shows lawn transpiration, while the right panel presents tree transpiration. In the box plots, the box represents the interquartile range, the line indicates the median, and stars indicate the Wilcoxon paired test results (***: p \u0026lt; 0.001). \"ns\" denotes no significant difference.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5532766/v1/8e39cfd91b6afb14fdbdfead.png"},{"id":76112956,"identity":"d444d34d-e41b-4d46-95db-6f4b98416244","added_by":"auto","created_at":"2025-02-12 12:23:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61749,"visible":true,"origin":"","legend":"\u003cp\u003eChanges of canopy stomatal conductance (G\u003csub\u003es\u003c/sub\u003e) (a) and stomatal sensitivity (\u003cem\u003em\u003c/em\u003e) to VPD (b) of lawn and tree before (Pre-HW), during (HW), and after (Post-HW) during the different 54 heatwave events for a 10 years period (2014-2023).\u003c/p\u003e\n\u003cp\u003eBox plots depict the interquartile range, with the median represented by the central line. Stars indicate significant differences from the Wilcoxon paired test (***: p \u0026lt; 0.001), while \"ns\" indicates no statistically significant difference. Error bars indicate standard errors.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5532766/v1/29cc1570cf41f9af89760c55.png"},{"id":76112953,"identity":"7d5288e6-4f71-449f-ba40-7a49288b1b68","added_by":"auto","created_at":"2025-02-12 12:23:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":105740,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the characteristics of transpiration, volumetric soil water content (SWC) , and precipitation (P) before, during and after 3 heat wave events in 2018 (HW#21, HW#22, HW#23), where a is for lawn and b is for tree. The dashed line represents soil water content, the bars show precipitation, and the solid line indicates the daily changes in either lawn transpiration (T\u003csub\u003elawn\u003c/sub\u003e) or tree transpiration (J\u003csub\u003es\u003c/sub\u003e).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5532766/v1/0fb2daeb42670c3a79197933.png"},{"id":76112988,"identity":"ab06c1da-5ef8-4e36-b9c2-74654aecb38b","added_by":"auto","created_at":"2025-02-12 12:23:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":127313,"visible":true,"origin":"","legend":"\u003cp\u003eIsotopic composition (δD and δ¹⁸O) of soil and stem water, and proportional water uptake by tree across heatwave periods. (a-c) Depth profiles of soil water (solid lines) and stem water (dashed-dotted lines) for hydrogen (δD) and oxygen (δ¹⁸O) isotopes. (d) Proportional contribution of water from shallow (0-10 cm), middle (10-30 cm), and deep (30-50 cm) soil layers to tree uptake during each period, based on our measured data and MixSiar model analysis.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5532766/v1/81653ee86e7826cd88bce457.png"},{"id":76112961,"identity":"1579b69d-11dc-4eac-9970-47a7733a7e55","added_by":"auto","created_at":"2025-02-12 12:23:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1602904,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the study site (left), the objective tree and the sap flow system (upper right), and the objective lawn and the Bowen ratio system (lower right).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5532766/v1/0ed196805211e7dc50ef84b9.png"},{"id":76114294,"identity":"5e97eb17-debc-4d9b-aa44-8ca92a964c01","added_by":"auto","created_at":"2025-02-12 12:31:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":113749,"visible":true,"origin":"","legend":"\u003cp\u003eIdentified heatwave periods in Shenzhen University Town based on 10 years (2014-2023) of Bowen ratio System, using air temperature recorded at a height of 1.5 m.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5532766/v1/d10e8102a23cd22de9d97d58.png"},{"id":97857878,"identity":"bf481405-fcfc-471e-b356-0db430904d1c","added_by":"auto","created_at":"2025-12-10 08:09:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2625112,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5532766/v1/d5481ce7-c1d7-477b-a6a0-61d4148203a2.pdf"},{"id":76112989,"identity":"366f816c-e040-472c-8651-be44c683d0f0","added_by":"auto","created_at":"2025-02-12 12:23:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":71086,"visible":true,"origin":"","legend":"Appendix","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-5532766/v1/ba8ee44f4e60df64629a726f.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"A 10-year experimental study on the cooling effects of urban tree and lawn by transpiration on heatwaves and their mechanisms","fulltext":[{"header":"Main","content":"\u003cp\u003eAs global temperatures continue to rise, heatwaves (HWs) have become increasingly frequent and severe\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, posing significant risks to urban environments. From 1990 to 2019, HWs were responsible for 153,078 fatalities\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Meanwhile, rapid urbanization has reshaped cities by replacing natural landscapes with impervious surfaces, such as concrete and asphalt, which accumulate heat\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e and exacerbate extreme drought conditions\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. This urban transformation has intensified the urban heat island (UHI) effect\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, a phenomenon that becomes particularly pronounced during HWs\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. HWs amplify evaporation rate, causing green spaces to dry out and exposing cities to heightened risks. putting urban areas at greater risk. Arid vegetation provides fuel for firewaves, overwhelming urban fire brigades\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Additionally, elevated temperatures drive up electricity demand in urban areas\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, resulting in increased carbon emissions. The synergistic effects of HWs and the UHI effect further amplify the associated risks for urban residents, particularly vulnerable groups such as the elderly and individuals with pre-existing health conditions \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo overcome these challenges, urban green spaces have emerged as one of the most promising solutions\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. They can reduced ambient temperatures by providing shade, enhancing evapotranspiration, and increasing solar reflectivity\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Among these cooling mechanisms, transpiration plays a pivotal role\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. This process involves the movement of water from the soil, through vegetation, and into the atmosphere, thereby absorbing substantial amounts of heat. Research indicates that during periods of extreme heat, transpiration can convert up to 50% of incoming solar radiation into latent heat\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, making it an exceptionally potent cooling force. Consequently, expanding urban green spaces is recognized as a viable and effective nature-based strategy for alleviating both heatwaves and the urban heat island effect\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile the benefits of urban green space are well accepted, the responses of different vegetation types to extreme heat varied significantly. For instance, Eyster and Beckage\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e found that during a HW in Canada, coniferous trees offered more effective cooling compared to broadleaf trees, shrubs, and lawns. Similarly, Teuling\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e demonstrated that during heatwaves, grasslands cool through evaporation but quickly deplete soil moisture, leading to higher temperatures, while forests conserve water, effectively mitigating prolonged extreme heat. Even among similar tree species, their performance can vary significantly across different situations, Ahongshangbam\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e showed that tree sap flow rates vary substantially from other underlying surfaces, such as grass, pavement, or other materials. Thess variations underscores the highly heterogeneous impact of urban green spaces on cooling efficiency within urban landscapes.\u003c/p\u003e \u003cp\u003eOn the other hand, urban green spaces are vulnerable to heat-induced stress. Specifically, lawns have been observed to deteriorate under extreme heat conditions, often losing their capacity to provide essential ecological benefits\u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In contrast, trees employ a variety of adaptive strategies: some species close their stomata to conserve water\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, while others maintain open stomata, enabling continuous transpiration and cooling\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Although the latter mechanism facilitates immediate temperature regulation, it can also lead to excessive water loss, potentially compromising the long-term health of the trees\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile these findings offer valuable case studies, effectively managing vegetation in urban green spaces remains a complex challenge\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, especially in subtropical regions\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Urban areas face not only the escalating impacts of climate change but also a complex and heterogeneous mix of green spaces, buildings, and impervious surfaces, which complicates the study and management of urban vegetation\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Therefore, management strategies must thoroughly consider the transpiration characteristics of different plant species to better understand their cooling effects in response to climatic extremes\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Much of the existing research has focused on short-term assessments of heatwave impacts on plant health, leaving significant gaps in understanding how different vegetation types adapt during prolonged heatwaves\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. This is especially relevant for synchronized root-to-canopy analyses, which require extensive observational studies.\u003c/p\u003e \u003cp\u003eIn this study, we analyzed 10-year observational data to investigate the responses of two prevalent urban species\u0026mdash;\u003cem\u003eFicus concinna\u003c/em\u003e Miq. and \u003cem\u003eZoysia matrella\u003c/em\u003e (L.) \u003cem\u003eMerr.\u003c/em\u003e \u0026mdash;to HWs in a subtropical megacity. Specifically, we aimed to elucidate how the interaction between meteorological conditions and soil moisture influences their adaptive strategies. By exploring these mechanisms in detail, we seek to delineate the distinct strategies employed by lawns and trees in managing heat stress and their role in cooling urban environments. Our findings, based on long-term data, will reveal the contributions of different urban vegetation types to mitigating high temperatures and provide evidence-based recommendations for optimizing urban green space design to enhance resilience against heatwaves.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTranspiration Responses of Lawn and Tree to Heatwaves\u003c/p\u003e \u003cp\u003eDuring heatwave events, the transpiration rates of lawns and trees exhibited markedly different responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the pre-heatwave (Pre-HW) period, the lawn transpiration rate (T\u003csub\u003elawn\u003c/sub\u003e) remained relatively low, averaging 2.39 mm day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Upon the onset of the heatwave (HW), T\u003csub\u003elawn\u003c/sub\u003e increased sharply to 3.40 mm day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), representing a 42.26% rise. Subsequently, in the post-heatwave (Post-HW) period, T\u003csub\u003elawn\u003c/sub\u003e declined swiftly to 2.30 mm day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reverting to levels comparable to the Pre-HW period. In contrast, heatwaves exert an insignificant effect on the tree transpiration rate (J\u003csub\u003es\u003c/sub\u003e). J\u003csub\u003es\u003c/sub\u003e remained relatively stable throughout the heatwaves (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with values of 51.98, 48.27, and 49.72 g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for Pre-HW, HW and Post-HW periods, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStomatal Conductance Responses of Lawn and Tree to Heatwaves\u003c/p\u003e \u003cp\u003eThe canopy stomatal conductance (G\u003csub\u003es\u003c/sub\u003e) of the lawn remained relatively stable throughout the heatwave period, indicating low regulatory adjustment in response to extreme heat stress. Across the Pre-HW, HW, and Post-HW periods, the average G\u003csub\u003es\u003c/sub\u003e of the lawn was 19.89, 20.43, and 19.59 mol m\u0026sup2; s⁻\u0026sup1;, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), with no significant differences observed between these phases (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Meanwhile, the stomatal sensitivity of Gs to ln(VPD) (\u003cem\u003em\u003c/em\u003e) in the lawn significantly increased during the heatwave, rising from 11.03 before the heatwave to 19.82 during the heatwave (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eConversely, trees exhibited a more dynamic and adaptive stomatal regulation strategy during the heatwaves. The G\u003csub\u003es\u003c/sub\u003e of tree reduced 38.65% from 49.24 mmol m\u0026sup2; s⁻\u0026sup1; at Pre-HW to 30.21 mmol m\u0026sup2; s⁻\u0026sup1; during HW (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). This marked decline suggests that tree rapidly closed its stomata to minimize water loss under severe heat stress conditions. Subsequent to the HW, G\u003csub\u003es\u003c/sub\u003e recovered to 50.33 mmol m\u0026sup2; s⁻\u0026sup1;, aligning with pre-HW levels, which demonstrates an effective restoration of stomatal conductance once the stress abated. The \"rapid closure-recovery\" pattern exhibited by tree underscores its capacity for flexible and dynamic water regulation. Additionally, the \u003cem\u003em\u003c/em\u003e decreased from 1.19 in Pre-HW to 0.91 during HW (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), indicating a conservative response to minimize water loss under extreme conditions. This diminished sensitivity suggests a strategy focused on maintaining water balance by downregulating stomatal responses during periods of high VPD and evaporative demand, reflecting a prudent water-use approach to mitigate dehydration risks.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eResponses of Soil Water to Heatwaves\u003c/p\u003e \u003cp\u003eUsing three representative heatwave events from the summer of 2018 (HW#21, HW#22, HW#23) as case studies, the time series of rhizosphere volumetric soil water content (SWC) at depths of 10 cm for lawns and 30 cm for trees, precipitation (P), lawn transpiration (T\u003csub\u003elawn\u003c/sub\u003e), and tree transpiration (J\u003csub\u003es\u003c/sub\u003e) revealed distinct water-use patterns between the lawn and trees (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These disparities were particularly pronounced during heatwave periods. As the heatwave progressed, T\u003csub\u003elawn\u003c/sub\u003e increased significantly, leading to a rapid depletion of SWC. Despite intermittent rainfall events, the heightened transpiration demand hindered effective recovery of soil water content, highlighting a significant escalation in lawn water consumption during heatwaves. As soil water content continued to decrease, T\u003csub\u003elawn\u003c/sub\u003e began to decrease in the later phases of the heatwave, indicating that transpiration was increasingly constrained by limited soil water availability.\u003c/p\u003e \u003cp\u003eIn contrast, the tree exhibited a more stable water-use strategy. Although J\u003csub\u003es\u003c/sub\u003e exhibited some fluctuations, the SWC remains quite stable overall. The tree likely mitigated water loss by adjusting stomatal conductance, thereby preserving soil water reserves. This strategy enabled the tree to maintain a balanced water status, effectively preventing excessive water depletion under intense heat and drought conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIsotopic analysis reveals how the tree leverages its deep root systems to adapt to heatwaves, ensuring stable water uptake during periods of extreme heat stress. During the heatwave (HW), the isotopic composition (δD) and (δ\u0026sup1;⁸O) of stem water showed a marked shift away from the isotopic profiles of shallow soil water, aligning more closely with those of middle (10\u0026ndash;30 cm) and deep (30\u0026ndash;50 cm) soil layers (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-c). This shift highlights a significant change in water uptake behavior, where the tree increasingly relied on deeper soil water content as surface water resources rapidly evaporated under high temperatures.\u003c/p\u003e \u003cp\u003eQuantitatively, the MixSiar model analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed) provided a detailed breakdown of water usage proportions from various soil depths. Pre-HW, the tree sourced water relatively evenly from shallow (28%), middle (36%), and deep (35%) soil layers, indicating diverse access to available water resources. However, during HW, a notable redistribution occurred: the contribution from shallow soil plummeted to 18%, while reliance on middle and deep soil layers increased to 44% and 39%, respectively. This substantial increase in uptake from deeper layers during HW suggests a physiological adaptation to mitigate water loss risks from the rapidly drying upper soil strata. The tree\u0026rsquo;s extensive root system became critical for accessing less volatile water reserves, thereby maintaining necessary hydration for photosynthetic activity and preventing severe hydraulic failure.\u003c/p\u003e \u003cp\u003ePost-HW, the proportional contribution of shallow soil water partially recovered to 23%, while middle and deep layers contributed 32% and 45%, respectively. Although there was some recovery in shallow soil water content, deep soil water remained the predominant source, highlighting its sustained importance even as surface conditions ameliorated. This dependence on deep soil layers emphasizes their function as a buffer against surface moisture variability, providing a stable water supply essential for tree recovery following heat stress.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDifferential Stomatal Responses to Heatwaves in Lawns and Trees\u003c/p\u003e \u003cp\u003eThe contrasting physiological structures of lawns and trees result in markedly different responses to heatwaves. Lawns, characterized by a simple physiological architecture, maintain a high canopy stomatal conductance (G\u003csub\u003es\u003c/sub\u003e) both before and during heat events. The sensitivity of G\u003csub\u003es\u003c/sub\u003e to vapor pressure deficit (VPD) increases significantly from 11.03 to 19.82, leading to a sharp rise (+\u0026thinsp;42.26%) in transpiration rates\u0026mdash;reaching up to 3.40 mm day⁻\u0026sup1; during heatwaves. This suggests an inability to adequately limit transpiration under heat stress. Such limited regulatory capacity aligns with the findings of Joo\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, who showed that grasses exhibit a physiological predisposition for rapid response to VPD increases, ultimately constraining water conservation during prolonged heat events.\u003c/p\u003e \u003cp\u003eIn contrast, trees adapt to heatwaves through more flexible stomatal regulation. Despite the increased evaporative demand of the environment, the tree in our study showed a slight decrease in transpiration rates from 51.98 to 48.27 g m⁻\u0026sup2; s⁻\u0026sup1;. Their G\u003csub\u003es\u003c/sub\u003e significantly decreased by 38.65%, from 49.24 to 30.21 \u0026micro;mol m\u0026sup2; s⁻\u0026sup1;. This indicates that trees were capable of moderating water loss under increased VPD conditions, aligning with previous findings that trees usually reduce water loss by closing stomata to maintain a more stable level of transpiration\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Even with fewer leaf surface stomata, plants can enhance their cooling capacity through architectural adaptations such as reduced leaf thickness and increased stomatal spacing\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. This is consistent with Buwalda and Lenz, who found that trees have a strong water coupling capacity to avoid reduced evapotranspiration due to high ambient water demand under high VPD conditions\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite the lawn trying to cool down by increasing transpiration during heatwaves, extensive research demonstrates that trees offer substantially superior cooling effects through a combination of shading, solar reflectance, and sustained transpiration. In our previous research\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, we used unmanned aerial vehicle and infrared remote sensing to measure the observation area and found that trees have a significantly better cooling effect than lawns. This phenomenon has also been widely found in other research, Potchter\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e compared three parks in Tel Aviv and found that parks with trees can reduce temperatures by 2\u0026ndash;3\u0026deg;C, while grass parks can even be warmer than built-up areas during the day, increasing heat stress values. Similarly, in Singapore, Nichol\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e observed that tree-covered areas maintained an average surface temperature of 32.9\u0026deg;C, compared to 35.6\u0026deg;C for turfgrass and 40.7\u0026deg;C for asphalt. Further simulation studies affirm that tree shading alone can lower near-surface urban temperatures by an average of 3.06\u0026deg;C, effectively reducing sensible heat loss and optimizing surface energy balance\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Collectively, these findings underscore the critical role of trees in urban temperature regulation, positioning them as an essential resource in mitigating the impacts of urban heatwaves.\u003c/p\u003e \u003cp\u003eDifferential Soil Water Reliance of Lawns and Trees\u003c/p\u003e \u003cp\u003eAs shallow-rooted plants, lawns rely heavily on surface soil water content (SWC). Our findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) indicate that the surge in lawn transpiration during heatwaves rapidly depletes surface SWC. This observation aligns with studies on perennial herbaceous plants in high-temperature environments, where rapid declines in SWC lead to irreversible plant damage, such as reductions in leaf elongation rates, turf quality, and chlorophyll content\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Under such conditions, the water acquisition capacity of lawns is severely limited, rendering them dependent on external irrigation for survival in urban settings.\u003c/p\u003e \u003cp\u003eUsing hydrogen and oxygen isotope analyses, we revealed that trees shift their water uptake to deeper soil layers during heatwaves. The contribution of mid-layer(10-30cm) water increased from 36\u0026ndash;44%, and deep soil water from 35\u0026ndash;39%, while shallow soil water decreased from 28\u0026ndash;18%. This deep-water(30-50cm) utilization strategy has been corroborated by field studies in Singapore\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e and Los Angeles\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, where trees maintained stable transpiration and cooling benefits during heat waves through deep rooting systems.\u003c/p\u003e \u003cp\u003eFrom an urban water resource management perspective, the differing water use mechanisms of lawns and trees directly impact the sustainability of urban green spaces. Lawns' high transpiration rates and shallow roots result in significant water demand spikes during heatwaves, necessitating frequent irrigation to sustain physiological activities. In water-scarce cities (with annual precipitation below 1000 mm), lawns\u0026rsquo; high-water consumption is unsustainable in the long term. Studies in Melbourne\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e and London's Hyde Park\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e have shown that urban lawns rapidly deplete surface water under high temperatures, exacerbating urban water resource pressures. In many arid cities, landscape irrigation accounts for 40\u0026ndash;70% of total water use\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, leading to stringent water-saving practices during hot summers in places like Melbourne, Sydney, California, and Arizona\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. These restrictions often result in lawn deterioration and damage\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Consequently, whether through ongoing irrigation during heatwaves or post-heatwave restoration, maintaining lawns demands considerable energy and resource inputs.\u003c/p\u003e \u003cp\u003eConversely, selecting appropriate tree species for urban environments can reduce reliance on artificial irrigation while providing stable ecological benefits during heatwaves. McCarthy\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. suggested that choosing tree species with high water use efficiency and growth rates can maximize growth while conserving water. The extensive use of trees not only reduces irrigation needs under drought conditions but also maintains soil moisture, reduces surface runoff, and prevents water loss through their root systems\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. In conclusion, trees offer a more water-efficient means to alleviate urban heatwaves, reduce high-temperature exposure risks\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, and enhance the ecological services of urban green spaces.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Site\u003c/p\u003e \u003cp\u003eThe study was conducted on the campus of the Shenzhen Graduate School of Peking University (PKUSZ), located in Shenzhen, Guangdong Province, China (22\u0026deg;26'59\"-22\u0026deg;51'49\"N, 113\u0026deg;45'44\"-114\u0026deg;37'21\"E). The site is situated at an average elevation of approximately 17 meters above sea level. Shenzhen, a densely populated city with a population of approximately 17.66\u0026nbsp;million, features a typical subtropical climate heavily influenced by the South Asian monsoon.\u003c/p\u003e \u003cp\u003eThe PKUSZ campus, which serves as the study site, has a vegetation coverage of approximately 50%, consisting of trees, shrubs, and lawns. The turfgrass species studied is \u003cem\u003eZoysia matrella\u003c/em\u003e (L.) \u003cem\u003eMerr.\u003c/em\u003e, a rapidly spreading grass that forms dense lawns, characterized by its high tolerance to drought, shade, and its minimal soil requirements. These attributes make \u003cem\u003eZ.matrella\u003c/em\u003e a popular choice for urban greening in tropical and subtropical regions. The tree species under study is \u003cem\u003eFicus concinna\u003c/em\u003e Miq., an evergreen species well-adapted to warm and humid climates. It is commonly found in subtropical regions of China, as well as in India, Vietnam, Myanmar, Malaysia, and the Philippines. Due to its strong survivability, rapid growth rate, long lifespan, and ease of transplantation, \u003cem\u003eF. concinna\u003c/em\u003e is widely used as an urban greening species.\u003c/p\u003e \u003cp\u003eThe observational period for this study spanned from July 2014 to November 2023, covering nearly a decade of data collection. This long-term dataset provides a robust basis for analyzing the evapotranspiration responses of typical subtropical urban vegetation to heatwave conditions\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEnvironmental Measurements\u003c/p\u003e \u003cp\u003eA Bowen ratio system was installed at the center of the lawn to monitor meteorological parameters, including air temperature and relative humidity at heights of 1.5 and 2 meters, solar radiation, net radiation, and soil heat flux. Each sensor took measurements every minute, which were subsequently recorded automatically by a CR1000 data logger (Campbell Scientific, Logan, UT, USA), with the system averaging and storing data every 10 minutes. Details on the sensor models and manufacturers can be found in the Appendix.\u003c/p\u003e \u003cp\u003eIn addition, volumetric soil water content at a depth of 30 cm (SWC) was monitored using time-domain reflectometry (TDR) probes (SM300, Delta-T Devices Ltd., Burwell, Cambridge, UK) located near both the Bowen ratio system and the target trees. All data were collected every 60 seconds and averaged or summed every 10 minutes before being stored in the CR1000 data logger (Campbell Scientific, Logan, UT, USA). Precipitation (P) was recorded using a 7852M-AB tipping bucket rain gauge (Davis Instruments, CA, USA).\u003c/p\u003e \u003cp\u003eHydrogen and Oxygen Isotope\u003c/p\u003e \u003cp\u003eTo determine the stable hydrogen and oxygen isotopic composition of soil and plant stem water, we collected both soil and plant samples from the study area. Specifically, healthy branches from the sun-exposed side of the target trees were sampled at a height of 3 meters using pruning shears. Soil samples were collected from a 5 \u0026times; 5 m plot surrounding the target tree using a soil corer, with sampling conducted at 0.5 m intervals. In the laboratory, the soil samples were separated into specific depth layers: 0\u0026ndash;2 cm and 2\u0026ndash;5 cm for the surface layers, followed by increments of 5 cm up to a depth of 50 cm.\u003c/p\u003e \u003cp\u003eAfter collection, water was extracted from both plant and soil samples using an LI-2100 vacuum condensation extraction system (LICA, China). The extracted water samples were then analyzed for stable isotopic ratios using a Liquid Water Isotope Analyzer (LWIA, Model 912-0008, Los Gatos Research, USA). The LWIA operates on the principle of cavity ring-down spectroscopy (CRDS), providing high-precision measurements of hydrogen (δD) and oxygen (δ\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eO) isotopes, with accuracies better than 0.1\u0026permil; and 0.3\u0026permil;, respectively. Sampling and analysis were conducted on July 5th, July 10th, and July 14th, 2018.\u003c/p\u003e \u003cp\u003eHeatwave Identification\u003c/p\u003e \u003cp\u003eIn accordance with the definition provided by the China Meteorological Administration, a heatwave (HW) is defined as a period of at least three consecutive days with daily maximum temperatures reaching or exceeding 35\u0026deg;C. Based on the air temperature recorded at a height of 1.5 m from the Bowen ratio system between July 2014 and November 2023, we identified 54 heatwave events (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Detailed time periods for each heatwave can be found in the Appendix.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe defined the Pre-HW period as the 15 days preceding the onset of each heatwave. If the interval between two consecutive heatwaves was less than 15 days, the Pre-HW period was defined as the entire interval between the two heatwaves. Similarly, the Post-HW period was defined as the 15 days following the end of each heatwave. If the time gap between two consecutive heatwaves was less than 15 days, the Post-HW period was considered as the interval between the two heatwave events\u003csup\u003e66\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCalculation of Transpiration Rate\u003c/p\u003e \u003cp\u003eTranspiration rate of the lawn (T\u003csub\u003elawn\u003c/sub\u003e) was calculated using parameters obtained from the Bowen ratio system. Transpiration rate of the tree (J\u003csub\u003es\u003c/sub\u003e) for the target tree species was measured using a sap flow system. The sap flow probes (SF-L probe sensor, Ecomatik, Munich, Bavaria, Germany) were installed on the northern side of the trunk at breast height. A CR1000 data logger (Campbell Scientific, Logan, UT, USA) recorded the temperature differential between the probes every minute, with data automatically averaged and stored every 5 minutes. These measurements were conducted continuously from July 2014 to November 2023.\u003c/p\u003e \u003cp\u003eData analysis and parameter calculation\u003c/p\u003e \u003cp\u003eTranspiration rate of the lawn (T\u003csub\u003elawn\u003c/sub\u003e) was calculated based on the Bowen ratio energy balance\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{T}_{lawn}=\\frac{{R}_{n}-G}{L(1+\\beta\\:)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere T\u003csub\u003elawn\u003c/sub\u003e is the transpiration rate (mm s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), R\u003csub\u003en\u003c/sub\u003e is the net radiation (W m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), G is the soil heat flux (W m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), L is the latent heat coefficient of vaporization (J kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e is the Bowen ratio, defined as:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\beta\\:=\\frac{H}{LE}=\\frac{\\rho\\:{C}_{p}{K}_{h}\\frac{\\partial\\:{T}_{a}}{\\partial\\:z}}{\\epsilon\\:L/P\\rho\\:{K}_{w}\\frac{\\partial\\:e}{\\partial\\:z}}=\\gamma\\:\\frac{\\varDelta\\:{T}_{a}}{\\varDelta\\:e}=\\frac{{C}_{p}\\varDelta\\:{T}_{a}}{L\\varDelta\\:q}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere ρ is the air density (kg m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), C\u003csub\u003ep\u003c/sub\u003e is the specific heat of air at constant pressure (kJ kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e \u0026deg;C\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), ε is the molecular weight ratio of water vapor to dry air, which is a constant 0.622; P is the atmospheric pressure (kPa), γ is the hygrometer constant; ΔT\u003csub\u003ea\u003c/sub\u003e, Δe, and Δq are the air temperature difference, vapor pressure difference, and humidity difference between the two heights the air temperature and relative humidity are recorded, respectively. They were 2 and 1.5 m in this study.\u003c/p\u003e \u003cp\u003eDuring periods of sunrise, sunset, rainfall, and lower temperatures, the β often approaches \u0026minus;\u0026thinsp;1, which results in calculated latent heat flux values that become unreasonably large or even undefined. To address this, following the methodology of Perez et al. \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, intraday outliers were excluded, and both latent and sensible heat flux anomalies were corrected via interpolation based on the characteristics of evapotranspiration dynamics and the theoretical basis of the Bowen ratio energy balance method.\u003c/p\u003e \u003cp\u003eTranspiration rate of the tree (J\u003csub\u003es\u003c/sub\u003e) was calculated using Granier's empirical formula for sap flow density\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{J}_{s}=8.64\\times\\:119\\times\\:{\\left(\\frac{{\\varDelta\\:T}_{m}-\\varDelta\\:T}{\\varDelta\\:T}\\right)}^{1.231}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere J\u003csub\u003es\u003c/sub\u003e is the instantaneous sap flow density (g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), ΔT is the instantaneous temperature difference between the probes, and ΔT\u003csub\u003em\u003c/sub\u003e is the maximum temperature difference throughout the day. To account for potential errors due to nighttime flows and probe drift, local maximum values of ΔT\u003csub\u003em\u003c/sub\u003e were calculated over a rolling 10-day period. A linear regression was then applied to these local maxima over time to estimate new ΔT\u003csub\u003em\u003c/sub\u003e values. After the first linear interpolation, data points below the estimated values were excluded, and a second interpolation was applied to the remaining data points\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e .\u003c/p\u003e \u003cp\u003eThe canopy stomatal conductance (G\u003csub\u003es\u003c/sub\u003e) was estimated using a simplified version of the Penman-Monteith model\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{G}_{S}\\:=\\frac{{K}_{G}\\left(Tair\\right){T}_{lawn}or{E}_{L}}{VPD}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:{K}_{G}\\left(Tair\\right)=115.8+0.4236{T}_{air}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:{E}_{L}=\\frac{{J}_{s}}{{A}_{s}\\times\\:LAI}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere K\u003csub\u003eG\u003c/sub\u003e(Tair) is the conductance coefficient, T\u003csub\u003eair\u003c/sub\u003e is the air temperature, E\u003csub\u003eL\u003c/sub\u003e represents transpiration per unit leaf area\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, A\u003csub\u003es\u003c/sub\u003e is the projected canopy area, and LAI is the leaf area index.\u003c/p\u003e \u003cp\u003eTo further analyze the response characteristics of the demand side of transpiration for lawns and trees, the response of G\u003csub\u003es\u003c/sub\u003e to VPD (vapor pressure deficit) was fitted using the following relationships\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e :\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\:{G}_{s}={G}_{sref}-m\\text{l}\\text{n}\\left(\\text{V}\\text{P}\\text{D}\\right)\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere G\u003csub\u003esref\u003c/sub\u003e is the reference stomatal conductance when VPD is 1 kPa, and m is the stomatal sensitivity to ln(VPD).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePerkins, S. E., Alexander, L. \u0026amp; Nairn, J. Increasing frequency, intensity and duration of observed global heatwaves and warm spells. Geophysical Research Letters 39 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, Q. \u003cem\u003eet al.\u003c/em\u003e Global, regional, and national burden of heatwave-related mortality from 1990 to 2019: A three-stage modelling study. PLoS medicine 21, e1004364 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSethi, S. S. \u0026amp; Vinoj, V. Urbanization and regional climate change-linked warming of Indian cities. Nature Cities, 1\u0026ndash;4 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, S. \u003cem\u003eet al.\u003c/em\u003e Widespread global exacerbation of extreme drought induced by urbanization. Nature Cities 1, 597\u0026ndash;609 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRizwan, A. M., Dennis, L. Y. \u0026amp; Chunho, L. 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A comparison of daily representations of canopy conductance based on two conditional time-averaging methods and the dependence of daily conductance on environmental factors. Ann. Sci. For. 55, 217\u0026ndash;235 (1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOishi, A. C., Hawthorne, D. A. \u0026amp; Oren, R. Baseliner: an open-source, interactive tool for processing sap flux data from thermal dissipation probes. SoftwareX 5, 139\u0026ndash;143 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOren, R. \u003cem\u003eet al.\u003c/em\u003e Survey and synthesis of intra-and interspecific variation in stomatal sensitivity to vapour pressure deficit. Plant, cell \u0026amp; environment 22, 1515\u0026ndash;1526 (1999).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Urban heatwave, Urban green space, Transpiration, Cooling effects, Water use strategy","lastPublishedDoi":"10.21203/rs.3.rs-5532766/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5532766/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHeatwaves have become the serious threat to the comfort and lives of urban residents. The cooling effects of urban tree and lawn through transpiration are regarded as a potential way to address these challenges, but their effects on heatwaves and mechanism remained unclear. Here, using a 10-year observation, we investigate the transpiration responses of urban lawn and a tree to 54 heatwave events in a subtropical city. We hypothesize that urban trees and lawns exhibit distinct transpiration response patterns during heatwaves due to different water use strategies and stomatal regulations. The findings reveal that (1) the lawn maintained high canopy stomatal conductance (G\u003csub\u003es\u003c/sub\u003e) during heatwaves, resulting in a 42.3% increase in transpiration rates (from 2.39 to 3.40 mm day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). In contrast, the tree significantly reduced G\u003csub\u003es\u003c/sub\u003e, maintaining relatively stable transpiration rates (slightly decreasing from 51.98 to 48.27 g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). (2) the lawn transpiration was highly dependent on soil water content (SWC), with rapid SWC depletion limiting sustained transpiration increases. Conversely, the tree accessed deeper soil water layers, enabling more stable transpiration throughout heatwaves. Urban tree responded to heatwaves much better than that of urban lawn. These results are of great importance for advancing knowledge in urban green space planning and water management.\u003c/p\u003e","manuscriptTitle":"A 10-year experimental study on the cooling effects of urban tree and lawn by transpiration on heatwaves and their mechanisms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-12 12:23:05","doi":"10.21203/rs.3.rs-5532766/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-cities","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natcities","sideBox":"Learn more about [Nature Cities](https://www.springer.com/journal/44284)","snPcode":"44284","submissionUrl":"","title":"Nature Cities","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1c289e9f-910f-4c84-87cd-d2b2d5eca4f8","owner":[],"postedDate":"February 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":44181365,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":44181366,"name":"Earth and environmental sciences/Natural hazards"},{"id":44181367,"name":"Earth and environmental sciences/Ecology/Urban ecology"},{"id":44181368,"name":"Earth and environmental sciences/Environmental social sciences/Climate-change adaptation"}],"tags":[],"updatedAt":"2025-12-10T08:09:39+00:00","versionOfRecord":{"articleIdentity":"rs-5532766","link":"https://doi.org/10.1038/s44284-025-00353-4","journal":{"identity":"nature-cities","isVorOnly":false,"title":"Nature Cities"},"publishedOn":"2025-12-09 05:00:00","publishedOnDateReadable":"December 9th, 2025"},"versionCreatedAt":"2025-02-12 12:23:05","video":"","vorDoi":"10.1038/s44284-025-00353-4","vorDoiUrl":"https://doi.org/10.1038/s44284-025-00353-4","workflowStages":[]},"version":"v1","identity":"rs-5532766","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5532766","identity":"rs-5532766","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}