Urban evapotranspiration (ET) plays a critical role in regulating urban heat and governing water–energy exchanges. However, high-resolution ET estimation across entire cities remains challenging due to pronounced surface heterogeneity, which limits the applicability of physical models originally developed for homogeneous natural surfaces. Moreover, satellite-derived ET estimates are often spatially and temporally fragmented due to their dependence on clear-sky conditions and long revisit intervals. To address these limitations, we propose a transformer-based framework for generating spatiotemporally seamless, daily urban ET maps at 10 m resolution. The framework integrates 10 m vegetation greenness from Sentinel-2 NDVI, Google satellite embedding data, and gap-free daily meteorological variables (precipitation, air temperature, and radiation etc.). A key advantage of the proposed method is its use of a group-to-token transformer encoder, which learns nonlinear interactions among atmospheric demand, surface structure, and vegetation dynamics without requiring explicit urban parameterization. The model is trained on time-series observations from 64 globally distributed FluxNet towers and an urban flux site in Shenzhen, where the flux data are gap-free and suitable for continuous daily modeling. The trained model performs well, with an overall R² of 0.92 and RMSE of 0.37 mm d⁻¹. Comparative analyses further indicate that the proposed framework reduces RMSE by approximately 16% and improves spatial discrimination in heterogeneous urban settings. The trained model was applied to Shenzhen, China, generating a spatiotemporally seamless daily urban ET dataset at 10 m resolution for 2017–2024. Validation confirms the reliability of the approach for urban ET mapping, with a mean R² of 0.56 and RMSE of 0.45 mm d⁻¹. Overall, this study establishes a practical pathway for producing cloud-gap-free, 10 m daily urban ET estimates by integrating routinely available reanalysis data with satellite-derived geospatial embeddings. The framework robustly captures intra-urban heterogeneity, supports seasonal to interannual analyses and pixel-wise trend detection, and enables scalable applications in urban hydroclimate research and planning.

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.

Ground heat flux (G) governs the subsurface energy storage, influences evapotranspiration through the available energy A = Rn - G, and contributes to permafrost–carbon feedbacks, yet widely used parameterizations—either prescribing G as a fixed/state-dependent fraction of net radiation or inferring it from land-surface temperature via soil heat-conduction models—fail to represent the multi-scale “memory’’ of the soil–canopy system. We develop an explainable deep-learning framework that predicts and diagnoses G by explicitly incorporating memory at diurnal, daily, and weekly timescales. Three Long-Short-Term-Memory (LSTM) models are trained on multi-year observations from 112 eddy-covariance sites spanning diverse biomes, using routinely available drivers (net radiation, air temperature, vapor-pressure deficit, wind speed, precipitation), satellite LAI, and static soil/PFT attributes. Interpretation via Expected Gradients reveals scale- and lag-specific contributions of each driver. Across sites, memory-informed models substantially outperform an identical non-memory baseline that uses only current-time inputs, improving median Nash–Sutcliffe Efficiency (NSE) by ~0.1–0.3 from diurnal to weekly scales for both raw G and anomalies. The models reproduce the observed G–Rn hysteresis, capture diurnal phase lags and longer-scale phase leads (including snowmelt-driven surges), and exhibit biome-dependent gains, which are largest in croplands and forests where antecedent soil-moisture variability and canopy heat storage amplify memory effects, intermediate in savanna–shrublands, and most variable in grasslands. Attribution analyses further show a systematic reorganization of the drivers with the different time scales: the diurnal dominance of Rn and air temperature transitions toward increasing influence of accumulated thermal state (air temperature) and moderate growth in moisture-related effects (precipitation). These findings demonstrate that multi-scale memory is an intrinsic organizing principle of G dynamics and provide a physically motivated path to improve predictive modeling, ET retrievals, surface energy-budget closure, as well as their representation of subsurface heat storage in land-surface models.

Large-scale solar photovoltaic (PV) farms are widely promoted in semi-arid regions as a key strategy to meet the growing energy demand while reducing carbon emissions. However, it is hypothesized that this extensive land-cover transformation disrupt local energy balance, thereby modifying near-surface thermal conditions. To verify this hypothesis, we conducted a comprehensive field experiment in a semi-arid desert region in Inner Mongolia, China. Our observations reveal a persistent warming signal: (1) The PV farm exhibited a two-year mean air temperature increase of 0.8 °C relative to the reference site, with warming evident across all seasons. Notably, the increase in the daily minimum air temperature was greater than the increase in daily maximum temperature, resulting in a 1.9 °C reduction in the daily temperature range compared to non-PV areas. (2) UAV-based thermal mapping further corroborated these findings, detecting consistent land surface temperature increase of by 0.3-4.1 °C relative to adjacent non-PV areas, indicating enhanced surface warming and altered spatial thermal patterns. (3) The daily net radiation was increased by 8.3 W m-2 in the PV farm area, particularly during the daytime (18.5 W m-2). This increase in net radiation was primarily due to a decrease in albedo, which resulted in 24.6 W m-2 more net shortwave radiation. (4) The PV farm increased the outgoing longwave radiation by 6.1 W m-2 during the daytime and 4.6 W m-2 at night, which was considerably lower than the increased net shortwave radiation. In sum, this study demonstrates persistent site-scale warming associated with photovoltaic installations under the observed environmental conditions observed in a semi-arid desert, which may have implications for microclimatic regulation in similar dryland PV systems. These findings underscore the need to consider potential environmental trade-offs in future PV deployment strategies.

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

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.

Global warming significantly impacts forest growth. However, commonly used spatially interpolated gridded air temperature datasets may not fully capture these effects due to their coarse spatial resolution and because air temperature may not accurately reflect the conditions that influence the tree growth process. Although finer spatial resolution land surface temperature (LST) datasets may capture more detailed temperature variations, their potential to assess forest growth responses to global warming has not been thoroughly explored. We evaluated the performance of air temperature and LST datasets with various spatial resolutions, including Climatic Research Unit gridded Time Series (CRU), TerraClimate, the land component of the fifth-generation European ReAnalysis (ERA5-Land), and MODIS LST (MOD11A2), in capturing the relationships between tree radial growth and temperature variations across 555 sites in the Northern Hemisphere. Our results showed that the finer spatial resolution MOD11A2 significantly outperformed the widely used CRU air temperature in modeling tree radial growth, with mean and maximum temperatures increasing the coefficient of determination (R2) by 16.32 % and 18.14 %, respectively. This improvement was especially apparent in high-elevation areas where R2 increased by 35.70 % and 36.97 %. We suggested that commonly used spatially interpolated gridded air temperature datasets (e.g., CRU and TerraClimate) may underestimate the impact of rising temperatures on forest growth. Our findings highlight the necessity of integrating high-resolution LST to accurately model forest growth responses to global warming.

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.

Terrestrial Water Storage (TWS) plays a pivotal role in water resource management by providing a comprehensive measure of both surface water and groundwater availability. This study investigates changes in TWS driven by human activities from 2003 to 2023, and forecasts future TWS trends under various climate change and development scenarios. Our findings reveal a continuous decline in China's TWS since 2003, with an average annual decrease of approximately 1.36 mm. This reduction is primarily attributed to the combined effects of climate change and human activities, including irrigation, industrial water use, and domestic water consumption. Notably, TWS exhibits significant seasonal and annual fluctuations, with variations ranging +/- 10 mm. For the future period (2024-2030), we project greater disparities between water resource supply and demand in specific years for the Songliao, Southwest, and Yangtze basins. Consequently, future water resource management must prioritize water conservation during wet seasons, particularly in years when supply-demand conflicts for limited water resources intensify. This study is valuable for effective planning and sustainable utilization of water resources.

Salix sand barriers have been widely used in arid and semi-arid regions of northern China as an environmentally friendly approach to control wind erosion. However, the inherent variability in length and thickness of Salix branches complicates achieving the desired fence porosity, typically resulting in an error of approximately 10% or more. This defect affects the fence's wind protection, thereby limiting their broader application. To address these challenges, we conducted a field experiment in the Hobq Desert, where we established Salix fences with porosities of 23%, 37%, and 44%, and measured the airflow velocities around these fences. The results indicated that the fence with 37% porosity exhibited the most effective sheltering effect, providing a protection distance up to 7.14 times of the fence height. A total of 44% porosity fences are also reasonably effective for practical purposes. On the other end of the spectrum, fences with 23% porosity demonstrated a better sediment interception than its high porosity peers, achieving an interception rate of 81.46%. Therefore, we conclude that organic fences made from Salix branches can serve as efficient windbreaks. Our findings provide foundational data for the application of organic fences in arid and semi-arid regions of northern China, and potentially similar environments globally.

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.

以深圳市为例,研究分析城市水系统的能源消耗及其经济成本,比较分析不同水资源开发利用方式的节能潜力及经济可行性.结果发现,深圳市居民生活用水系统每年总能耗约为39.6亿度,经济总成本约为60.1亿元,其中,终端生活用水、自来水生产与供应能耗分别占比78.3%和12.0%,其经济成本分别占比47.3%和29.4%.其次,污水再生回用与雨水收集利用的单位能耗要远远低于远程调水.因此,能源消耗是城市水系统运行的主要成本来源,节约居民终端生活用水,不仅能节约终端用水本身的能耗,也能节约远程调水、生产处理及管网输送等前端环节的能耗,从而带来双重节能效果.同时,再生水回用与雨水收集利用要比远程调水更节能.

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.

Accurate estimation of desert vegetation biomass is crucial for monitoring changes in carbon stocks and productivity status. Unmanned aerial vehicle (UAV) remote sensing allows large-scale biomass surveys at the individual or patch scale. However, since desert shrubs are short and sparse, the UAV-based techniques do not always accurately capture biomass-related indicators at any flight height. This study investigated the effects of flight height on above-ground biomass (AGB) estimation using UAV images of typical shrub communities (Reaumuria soongarica) captured at different heights (i.e., 30 m, 50 m, 70 m, 90 m, 110 m, 130 m, and 150 m) in desert-grassland ecosystems. Several structural indicators associated with shrub allometric growth were extracted for AGB modeling, including canopy area (horizontal properties), canopy height (vertical properties), and canopy volume. Results revealed that the values of canopy height and volume decreased with increasing flight height, which made the poor performance of AGB models based on these indicators worse. For example, the variance explained (VE) of the models based on the mean canopy height decreased from about 62% to -137%, while the root mean square error (RMSE) increased from about 39 g to 92 g. In contrast, the canopy area was less affected by flight height, maintaining stable AGB models with VE around 72% and RMSE at 33 g. Adjusting the coefficients of linear models based on canopy height and volume with flight height significantly improved their predictive performance, with VE between 54% and 77% and RMSE between 30 g and 43 g for the optimized models based on mean canopy height. Furthermore, a higher flight height (e.g., 90-110 m) could be chosen to enhance operational efficiency while ensuring the accuracy of biomass observation. Our study offers valuable insights and guidance for vegetation surveys and research in desert-grassland ecosystems.

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.