Abstract Accurate estimation of mangrove ecosystem carbon stocks is essential for effective blue carbon management. Significant interspecific variations in carbon storage capacity and estimation methods arise due to species-specific biophysical characteristics, highlighting the need for precise mangrove species identification and species-level carbon stock assessment. However, limited studies assessed mangrove carbon stocks at species-level. This study, conducted in the Gaoqiao Mangrove Nature Reserve in Zhanjiang, Guangdong Province, applied UAV multispectral technology to simultaneously acquire spectral, structural and textural vegetation feature variables for mangrove species identification and established species-specific carbon stock models, thereby achieving species-level carbon stock estimation. Results showed that (1) by integrating spectral and structural features, the study achieved 89.87% overall accuracy in species identification. (2) Species-level carbon stock estimation models, incorporating spectral, structural and textural feature variables alongside field-measured carbon data, demonstrated strong predictive performance (R2 = 0.48-0.95). (3) The most effective vegetation feature variables for carbon estimation varied significantly across species, emphasizing the necessity of accounting for species heterogeneity in mangrove carbon stock estimations. (4) Carbon stocks exhibited significant interspecific variation, with Rhizophora stylosa demonstrating the highest aboveground (97.06 t hm⁻2) and belowground (37.22 t hm⁻2) stocks, compared to Aegiceras corniculatum’s minimum values of 49.14 and 19.88 t hm⁻2, respectively. This study established a UAV-based multispectral framework for mangrove species-level carbon stock estimation and provided new insights for mangrove carbon assessment and management by demonstrating the importance of considering species-specific influences on carbon stocks and their estimation.

Surface energy balance (SEB) models are widely employed for remote-sensing-based evapotranspiration estimation. A critical parameter in most SEB models is the surface temperature of wet or dry boundaries where sensible heat (H) or latent heat (LE) equals 0, which is difficult to measure or estimate. The wide application of SEB models is seriously limited due to this challenge. Therefore, this study introduces 'critical canopy temperature ( Tcc)', defined as the canopy temperature at which LE equals 0, corresponding to the dry boundary in SEB models. We develop a physics-constrained machine learning (ML) model (hybrid model) that conserves the SEB equation to predict Tcc using meteorological measurements from 103 eddy-covariance (EC) stations combined with remote-sensing data. The predicted Tcc is integrated into the Surface Energy Balance Algorithm for Land (SEBAL) model to replace the dry boundary, thereby to improve the estimation of LE estimation. Results demonstrate that the hybrid model effectively captures canopy temperature anomalies during stomatal closure and achieve better generalization than pure ML approaches in LE estimation, particularly under extreme conditions. Compared with conventional dry-boundary selection scheme without SEB constraints, incorporating Tcc significantly improve SEBAL performance, reducing the root mean square error for LE from 119.33 to 81.71 W m-2 against EC observations (at 31.52% reduction). At regional scales, the hybrid model enables pixel-level estimation of Tcc, addressing the long-standing challenge of dry-boundary underrepresentation. Overall, the Tcc hybrid model provides a robust and accurate framework for predicting theoretical dry-boundary temperatures while conserving the SEB, supporting improved monitoring of vegetation physiological status and enhancing the accuracy of SEB models.

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 (Gs) 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 Gs, 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.

Heatwaves are an increasing threat to urban health and comfort, and evapotranspiration by urban lawns and trees offers a potential solution. However, their distinct effects and mechanisms remain unclear. Using ten years of observations, we investigate the evapotranspiration responses of urban lawns and trees to 54 heatwave events in a subtropical city. We hypothesize that urban trees and lawns exhibit distinct evapotranspiration response patterns during heatwaves due to different water-use strategies and stomatal regulations. Our results show that (1) lawns, with high canopy stomatal conductance, rapidly increase evapotranspiration (+ 37.65

Mangrove wetlands in China are naturally expanding seaward due to rapid sediment accretion, yet the dynamics of species competition and stand structure during this expansion remain unclear. Here, we developed an integrated remote sensing framework combining satellite imagery, unmanned aerial vehicle (UAV) data, and deep learning to investigate species composition and stand structure in China's largest contiguous mangrove forest. The algorithm achieved high accuracy in identifying two dominant species (Aegiceras corniculatum and Avicennia marina, overall accuracy: 87.7 %) and extracting individual crown parameter (precision > 70 %). Results show that A. corniculatum initially colonizes new mudflats but is later outcompeted by A. marina, forming monospecific stands over time. Interspecific competition intensity peaks during early succession (1-7 years) and declines with stand age, accompanied by a shift in spatial distribution from random to uniform. These findings reveal a distinct pattern of sequential species replacement and community succession during mangrove seaward expansion. The proposed framework and ecological insights provide valuable guidance for near-natural mangrove afforestation and the restoration of degraded coastal ecosystems, contributing to sustainable wetland conservation strategies.

Transpiration and canopy shading are the main ways that trees cool urban environments; this is crucial to human survival and improving urban livability in the context of global warming and rapid urbanization. So far, most studies focus on the combined cooling effect of transpiration and canopy shading, but their individual contributions have not been widely explored. Therefore, a quantitative framework was developed by carrying out a long-term field experiment and microenvironment simulations to investigate the cooling effect of a single Ficus concinna. The results show that the annual mean cooling effects of shading and transpiration are 0.17 ± 0.27 °C and 0.30 ± 0.13 °C, accounting for 21.2 ± 51.6% and 44.7 ± 26.3% of total cooling, respectively. Shade cooling demonstrates strong radiative dependence, reaching a peak of 0.63 °C with a cooling contribution of 77.1% during summer at noon due to solar radiation interception. In contrast, nighttime and winter conditions revealed shading-induced temperature increases up to 0.52 °C via longwave radiation reflection. By contrast, transpiration cooling demonstrated temperature dependence, which increased with air temperature and peaked at 1.03 °C (contributing 70.0% to the total cooling) before stomata closing. This mechanistic analysis quantitatively reveals that F. concinna provides cooling effects through a dynamic complementarity between transpiration and shading. These findings could offer a biophysically grounded basis for optimizing urban greening strategies and contribute to the theoretical advancement of nature-based urban climate solutions.

Urban evapotranspiration (ET) plays an important role in mitigating the adverse effects of urbanization and global warming. Precisely measuring urban ET is essential for understanding the mechanisms underlying these mitigation benefits. However, due to the lack of long-term continuous observations, there is limited knowledge regarding the seasonal and interannual variability in urban neighborhood ET and its driving forces, especially for tropical and subtropical cities. In this study, we investigated the dynamics of urban ET using eddy covariance and its influencing factors based on five years of data collected from 2017 to 2021 in Shenzhen, a subtropical megacity in China. Our results highlight the importance of urban neighborhood ET as a significant water consumption in urban areas. Over the five-year period, the mean daily ET value was 1.82 mm day(-1), with the lowest value in January and December (< 1 mm day(-1)) and the highest value between May and August (> 5 mm day(-1)). The annual ET ranged from 635 mm to 705 mm and averaged 664 mm, accounting for approximately 38 % of the total precipitation. The primary driving force behind urban neighborhood ET was the available energy, while water availability acted as a constraint in such urban environments with substantial annual precipitation (1761 mm). Furthermore, the variability in ET was observed to be influenced by vegetation coverage. These findings have significant implications for urban heat island mitigation and stormwater management.

We present a near-real-time daily European Consumption-based Power Carbon Intensity Dataset (ECON-PowerCI), developed from the CarbonMonitor power production dataset for Europe. Spanning from January 2015 to December 2024, the dataset encompasses 35 European countries, with daily updates and a one-day latency. ECON-PowerCI provides consumption-based power carbon intensity at the national level, accounting for cross-border electricity net imports in the country of consumption. By integrating ENTSO-E (The European Network of Transmission System Operators for Electricity) data, ECON-PowerCI enables comprehensive analysis of carbon intensity trends shaped by cross-border transmissions, extreme weather events, and disruptions like the COVID-19 pandemic and geopolitical conflicts. This dataset facilitates in-depth study of the effect of cross-border electricity flows on national carbon footprints, providing insights for energy policy and climate resilience. The dataset also holds extensive research potential for power-related analyses and policy-making in Europe’s interconnected power systems.

Study region: The study was conducted in the middle reach of the Heihe River Basin, located in the Hexi Corridor of Gansu Province, Northwest China. Study focus: Accurate partitioning of evapotranspiration (ET) into soil evaporation (LE) and plant transpiration (LT) is essential for water resource management, particularly in arid and semi-arid regions. However, multi-scale ET partitioning remains challenging due to landscape heterogeneity. In this study, we applied the three-temperature (3T) model, a resistance-free method requiring minimal inputs, to partition ET over the heterogeneous oasis-desert landscape using aerial (3 m), ASTER (90 m), and MODIS (1000 m) thermal remote sensing data. The model's performance was validated by isotope-based measurements and compared across multi-scales. New hydrological insights for the region: The 3T model showed good agreement with isotope-based measurements in oasis croplands (MAE = 3.0 %). A key contribution of this study is demonstrating the consistent performance of the 3T model across three spatial resolutions. While finer-resolution data captured greater spatial variability in LE and LT, mean values remained relatively stable across scales. The strong consistency in LE and LT values between aggregated high-resolution and native coarse-resolution images (R-2 = 0.59-0.88, MAE < 50 W m(-2)) highlights the potential of the 3T model for regional and global ET assessments using moderate-to coarse-resolution satellite data.

The urban heat island (UHI) effect has become a global issue, attracting widespread attention in recent years. Urban vegetation is an effective way to mitigate UHI through evapotranspiration (ET). However, the seasonal variation in vegetation cooling effect and its driving factors have not been well investigated. Therefore, the seasonal UHI intensity (UHII) on typical clear days in 2021 was calculated for Shenzhen based on the land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The slope between UHII and vegetation coverage was then used to characterize the vegetation cooling effect. Results showed that: (1) there is a significant negative linear correlation between UHII and vegetation coverage, with slopes mostly less than -1 and coefficients of determination (R-2) larger than 0.4. (2) The slopes varied seasonally, indicating a larger vegetation cooling effect during summer daytime. A 10 % increase in vegetation coverage can reduce the daytime UHII by 0.16 degrees C in January and 0.59 degrees C in June, while at nighttime, the cooling effect varied between 0.12 degrees C in January and 0.27 degrees C in June. The daytime and nighttime UHII for the whole year can be reduced by 0.43 degrees C and 0.24 degrees C, respectively. (3) The seasonal variation in vegetation cooling effects during the daytime can be largely explained by that of ET, which is mainly driven by leaf area index (LAI).

With rapid urbanization and climate change, water consumption and land-use pattern has dramatically changed, resulting in altered eco-hydrological processes and high ecological water requirements in megacities. However, the water uptake strategies may differ in urban and natural environment, and which remains largely unknown. Therefore, this study investigated the water use patterns of two greening plants species (Ficus concinna and Ligustrum vicaryi) and their responses to rainfall events in a megacity of subtropical China using the stable isotope methods. The results indicated that the two greening plants species showed different water use strategies. F. concinna mainly absorbed water from the shallower soil layer (0-20 cm, 56.29 %) in the wet season and deeper soil water (30-50 cm, 41.13 %) in the dry season, whereas L. vicaryi mainly relied on the shallower soil water (0-20 cm, 48.28 %) throughout the whole year. L. vicaryi absorbed water from the shallower soil layer (0-20 cm) before rainfall events and changed into deeper soil water (30-50 cm) after rainfall events in both dry and wet season; on the contrary, F. concinna did not show these dynamics throughout the year. These results suggested that the water use pattern of F. concinna showed more ecological plasticity, facilitating the adaptation of the plant to seasonal drought and other environment fluctuations in subtropical China urban areas.

Urban evapotranspiration (ET) is one of the most important components of water and energy balance, and carbon cycle in urbans. It is also a natural process that is powerful enough to possibly mitigate the negative effects caused by urbanization and global warming. Increasing or regulating urban ET could possibly be a solution to overcome the negative impacts caused by urbanization and global warming. Since 2000, researches on urban ET have been increasing and significant progresses have been achieved. A review of these progresses will certainly further promote the related researches and social practices, however, there is yet no such a review article available. Therefore, this article reviewed almost all the published papers on urban ET over the world, summarized its current progresses, scientific understandings, and forecasted the possible challenges in the future. Results achieved from this review would be helpful to use the power of ET to improve the livability of cities, guide the practices of sponge city construction and low impact development, mitigate the negative effect of urban heat island, and reduce urban carbon emission.

The urban green infrastructure such as the low impact development (LID) facility and traditional garden that are relatively small and characterized by decentralized distributions has been proposed as the most effective way to mitigate urban heat through its evaporative cooling effect. Recently, there have been increasing studies on its temperature reduction and evapotranspiration (ET) rate, but few of them correlate ET with external surface temperature reductions. Therefore, this study investigated the evaporative cooling effects, ET rates, and their relationships by the three-temperature (3T) model and ground-based thermal infrared remote sensing. Results show that the cooling effect of both vegetated LID facilities and traditional gardens is significantly stronger than that of non-vegetated LID facilities. Due to a thinner soil layer and lower water connectivity of LID facilities, their ET rates are significantly reduced in the dry period while the evaporative cooling effect of traditional gardens covered by the same vegetation can maintain high. The dependency of their cooling effect can be largely explained by the ET rates. When ET < 0.6 mm h(-1), an increase in ET of 0.1 mm h(-1) can enhance the cooling effect by 3.66 degrees C. When ET exceeds 0.6 mm h(-1), the evaporative cooling effect saturates. Vegetation types and soil water conditions are two main factors that govern evaporative cooling effect. Specifically, shrubs with higher ET rates are more efficient in urban heat mitigation than herbs. The responses of the evaporative cooling effect to soil water availability vary among species, which may require species-specific irrigation regime. These results may have implications on the best management practices for urban heat mitigation by the small widely- distributed green spaces.

Dynamics in long-term evapotranspiration (ET) and its controlling variables are essential for understanding how a high-altitude wetlands ecosystem responds to climate change. The rising temperature is expected to agitate the regional hydrological cycle and water balance, particularly in the subalpine wetland valley of Jiuzhaigou, located in the transition zone between the northeast Qinghai-Tibet Plateau and the Sichuan Basin, Southwest China. Here, we used growing season multi-year (2013-2021) Bowen ratio data to assess the variability in ET and its key controlling parameters at different timescales in Jiuzhaigou valley. This study also explored the ratio of ET to precipitation (P). The wetland daily mean ET varied from 0.06 to 6.77 mm d-1, with a mean value of 2.64 mm d-1 for the nine years. Fluctuations in daily ET were primarily driven by available energy (net radiation, Rn), explaining 86 % of the variation. Seasonal patterns in ET were largely similar to environmental parameters, i.e., Rn, air temperature (Ta), and vapor pressure deficit (VPD), peaking in August with an interannual monthly mean value of 3.48 mm d-1. Interannual monthly mean ET had a strong positive linear relationship with Rn, Ta, and VPD, while there was no significant correlation with P on a growing season basis. Furthermore, monthly ET was shown to be regulated by Ta largely in high-temperature months and minimally in low-temperature months. The growing season ET varied interannually, and the ET to P ratio (i.e., ET/P) ranged between 0.52 and 1.16. Interannual variation in annual ET was controlled by Ta and P, which individually explained 73 and 61 % of the variation, respectively. The multiple regression model indicated that Ta and P together elucidated 92 % of the variation in annual ET. The increased sensitivity (e.g., regression slopes) of ET to P over 2014-2021 indicates that ET consumed most of P, which leads to decreasing runoff and streams drying up. This study clarifies the temporal dynamics in ET for wetlands and its environmental controls at multiple timescales. It is demonstrated that the proportion of ET could increase in response to increasing temperature without an associated increase in P, affecting local water balance. These results could potentially contribute to sustainable water management in high-altitude wetlands and environmental planning under future climate change.

Heavy metal contamination in soils can pose severe challenges to the safety of geotechnical engineering projects. Loess, which is widely distributed in Northwest China, is a preferred engineering construction material for anti-fouling barriers. Therefore, research on the influence of heavy metal ions on its seepage performance is urgently required. To obtain new insights into the seepage behavior of heavy metal-contaminated loess and its underlying geochemical mechanism, laboratory investigations were performed on the saturated hydraulic conductivity (Ksat), leaching, and microstructural characteristics of loess contaminated with Cu2+ and Zn2+. The results indicate that the hydrolysis of Zn2+ creates an acidic environment, which promotes the dissolution of carbonate minerals in loess, enhances the leaching capacity, and leads to the quantitative transformation of small pores (2–8 μm) to mesopores (8–32 μm). Meanwhile, the alternating adsorption of Zn2+ and its diffuse double-layer effect compresses the diffusion layer, increasing the abundance of free water channels. Thus, the Ksat of Zn-contaminated loess increases by 81.2% during the seepage period. As for Cu-contaminated loess, its seepage behavior is the opposite of that of Zn-contaminated loess, with a Ksat decrease of nearly 50%. The primary factor controlling this phenomenon is the formation and enrichment of Cu2O in the lower part of the soil, which inhibits the enlargement of pores and reduces the effective connectivity of pores. The findings of this work provide insight into the seepage behavior of saturated loess under erosion by heavy metals and the underlying geochemical mechanism thereof.

Wetland evapotranspiration (ET), which involves the land-atmosphere exchange of energy and water, is dynamic and affects the spatiotemporal distribution of water resources. However, due to the variability and complexity of wetlands, accurate estimation of patch-scale ET and its spatial variability remain insufficiently characterized. To overcome this challenge, an advanced unmanned aerial vehicle (UAV) technology was developed by combining the three-temperature (3T) model, which is robust to estimate transpiration and its spatial variability with UAVbased thermal infrared remote sensing, and Penman equation, which is commonly used to estimate open water evaporation. The combined approach was verified using the Bowen ratio system over a subalpine wetland. The results show that the proposed method is simple and applicable for estimating wetland ET and its spatial variability, with a determination coefficient (R2) of 0.93, mean absolute percentage error (MAPE) of 7.90%, root mean squared error (RMSE) of 0.05 mm h-1, and Nash-Sutcliffe efficiency (NSE) of 0.93. It depicts a large spatial variability in wetland ET with respect to surface vegetation characteristics, water regimes, meteorological factors, and larger transpiration rates than open water evaporation. With its limited inputs and no calibration requirements, the proposed method is concluded to be simple and to easily reveal the high temporal and spatial resolution characteristics of patch-scale ET and its components.

Using less water to generate more power is a goal of the worldwide power industry, but this is difficult to achieve because of the lack of long-term, operational data-based studies. This challenge is especially severe for megacities facing water shortages. This study used long-term data (2005–2015) from Shenzhen, a megacity of over 20 million people that faces severe water shortages, to determine the relationship between water and energy for different types of power generation. It was found that power generation consumed huge amounts of water and that cooling water was the biggest water use category. Smaller power plants, such as the Yueliangwan power plant, which uses the closed cooling method, consume 2.36 million m3 of tap water per year, equivalent to the water supply of a small reservoir. However, larger power plants, such as the Mawan power plant and Dayawan nuclear power plant (using the open cooling method), use 0.92 and 3.42 billion m3 of seawater for cooling every year, respectively, equivalent to about 60% and 200% of the total annual water supply in Shenzhen, respectively. Therefore, large thermal power plants and nuclear power plants should be built in coastal areas with rich water resources rather than in arid or semi-arid areas. Additionally, the water use efficiency of nuclear power plants was found to be 0.22 m3/kWh, which was significantly lower than that of coal-fired power plants (0.10 m3/kWh) and gas-fired power plants (0.09 m3/kWh). Third, the water use efficiency of the closed cooling method was ten times higher than that of the open cooling method. Therefore, the closed cooling method is suitable for power plants constructed in areas without rich water resources. These results are useful for balancing the water and energy demands in the changing world.

Evapotranspiration (ET) cooling of urban spaces is an effective and economical way to improve the urban thermal environment. However, the distribution of urban ET rate is typically unknown owing to the high heterogeneity of urban land covers, which limits the application of many conventional techniques for measuring ET, such as ground-based observations and satellite remote sensing. In this study, an improved approach called "UAV + IRs + 3T", combining unmanned aerial vehicle (UAV), thermal infrared remote sensing, and a threetemperature model (3T), was developed for estimating urban ET and validated by Bowen ratio method. Results showed that the proposed method could accurately measure urban LE with R2 = 0.95, MAE = 21.98 W m- 2, RMSE = 30.33 W m- 2, RRMSE =19.65%. The proposed method could obtain urban ET with an ultra-high spatial resolution (approximately 15.5 cm) and temporal resolution (once per hour). Furthermore, 9 plant species distributed across the 18 sample plots showed significant differences in mean intra-day ET rates. Even for the same plant species at different sites, such as Ficus concinna and Zoysia matrella, their average intra-day ET rates differed by 50% and 400%, respectively. These large differences could be attributed to artificial pavement and infrastructure, different artificial irrigation methods, and difference in artificial and natural shade. In conclusion, there is spatio-temporal variability in urban ET rates, which can be precisely revealed by the proposed method. Therefore, the "UAV + IRs + 3T" method has the potential for a wide range of applications in urban environmental planning.

Aims Parameterization of reference temperature has always been the key and difficult part in calculating evapotranspiration and its evaporation and transpiration components by using three-temperature model.In this paper, the best value of reference temperature was determined by quantifying and comparing the influence of different reference temperature values on the accuracy of transpiration estimation by three-temperature model. MethodsBased on the Bowen ratio and thermal infrared observation data of a typical urban lawn, sensitivity analysis and error analysis were carried out on the input variables involved in the sub-model of the three-temperature model to determine the most critical variables for the accuracy of transpiration estimation.Then the influence of input variables parameterization on the calculation of transpiration was quantified and compared to determine the best value of reference temperature.Important findings When using the three-temperature model, the best estimation is to select the maximum temperature of the whole piece of paper as the reference leaf temperature (R 2 = 0.91, root mean square error (RMSE) = 0.078 mm•h -1 ).When the maximum value of the vegetation canopy temperature was used as the reference temperature, it is directly assumed that the transpiration at the maximum temperature of the vegetation is zero (there is a certain transpiration rate in fact).Therefore, it is easy to underestimate the actual transpiration, resulting in that the estimation accuracy of the three-temperature model was slightly lower than the accuracy of using the maximum value of the reference leaf temperature, but the estimation effect is still good (R 2 = 0.87, RMSE = 0.080 mm•h -1 ).Therefore, considering the limitations of the reference leaf settings, if the reference leaf temperature cannot be measured in practical applications, the maximum temperature of the vegetation canopy as

利用一对设置在相同气象条件下的黑白标准A型蒸发器皿,观测两者气象要素、水温及蒸发量等动态特征,并根据现有的6个蒸发模型,探究水温对器皿蒸发量的影响.结果表明:1)设计的观测方法可以用于研究水温对器皿蒸发的影响.黑皿与白皿之间的水温和蒸发速率呈现明显的差异,在50天的观测期内,二者平均水温差为0.4℃,平均日蒸发量差为1.1 mm/d;2)在太阳辐射及其他气象要素相同的条件下,黑白皿水温差每升高1.0℃,由水温驱动的日蒸发量差异为0.808 mm/(d·℃);3)在水温升高条件下,没有考虑水温的经典蒸发模型的估值小于观测值,并且估值的误差也有所增加.