Plant transpiration is a key element in the hydrological cycle. Widely used methods for its assessment comprise sap flux techniques for whole-plant transpiration and porometry for leaf stomatal conductance. Recently emerging approaches based on surface temperatures and a wide range of machine learning techniques offer new possibilities to quantify transpiration. The focus of this study was to predict sap flux and leaf stomatal conductance based on drone-recorded and meteorological data and compare these predictions with in-situ measured transpiration. To build the prediction models, we applied classical statistical approaches and machine learning algorithms. The field work was conducted in an oil palm agroforest in lowland Sumatra. Random forest predictions yielded the highest congruence with measured sap flux (r² = 0.87 for trees and r² = 0.58 for palms) and confidence intervals for intercept and slope of a Passing-Bablok regression suggest interchangeability of the methods. Differences in model performance are indicated when predicting different tree species. Predictions for stomatal conductance were less congruent for all prediction methods, likely due to spatial and temporal offsets of the measurements. Overall, the applied drone and modelling scheme predicts whole-plant transpiration with high accuracy. We conclude that there is large potential in machine learning approaches for ecological applications such as predicting transpiration.

Urban vegetation plays a role in offsetting urban CO₂ emissions, mitigating heat through tree transpiration and shading, and acting as deposition surfaces for pollutants. The frequent occurrence of heatwaves and of concurrent drought conditions significantly disrupts the processes of urban trees, particularly their photosynthesis and transpiration rates. Despite the pivotal role of urban tree functioning in delivering essential ecosystem services, the precise nature of their response remains uncertain. We conducted sap flux density (J_s) and leaf gas exchange measurements of four tree species (Tilia cordata, Tilia × europaea, Betula pendula, and Malus spp.) located in different urban green areas (Park, Street, Forest, and Orchard) in Helsinki, Finland. Measurements were made over two contrasting summers 2020 and 2021. Summer 2021 experienced a local heatwave and drought, whereas summer 2020 was more typical of Helsinki. In this study, we aimed to understand the responses of urban tree transpiration (measured with sap flux density) and leaf gas exchange to heatwave and drought conditions, and we examined the main environmental drivers controlling the tree transpiration rate during these periods. We observed varying responses of J_s during the heatwave period at the four urban sites. When comparing the heatwave and no heatwave periods, a 35 %–67 % increase in J_s was observed at the Park, Forest, and Orchard locations, whereas no significant change was seen at the Street site. Our results also showed that J_s was higher (31 %–63 %) at all sites under drought conditions compared with non-dry periods. The higher J_s values during the heatwave and dry periods were mainly driven by the high atmospheric demand for evapotranspiration, represented by the high vapor pressure deficit (VPD), suggesting that the trees were not experiencing severe enough heat or drought stress that stomatal control would have decreased transpiration. Accordingly, photosynthetic potential (A_max), stomatal conductance (g_s), and transpiration (E) at the leaf level did not change during heatwave and drought periods, excluding the Park site where a significant reduction in g_s was seen. VPD explained 55 %–69 % of the variation in the daily mean J_s during heatwave and drought periods at all sites. At the Forest site, the increase in J_s saturated after a certain VPD level, likely due to low soil water availability during these hot and dry periods. Overall, the heat and drought conditions were untypical of the region but not excessive enough to restrict stomatal control and transpiration, indicating that ecosystem services such as cooling were not at risk.

The Surface Urban Energy and Water Balance Scheme (SUEWS) has recently been introduced to include a bottom-up approach to modeling carbon dioxide (CO₂) emissions and uptake in urban areas. In this study, SUEWS is evaluated against the measured eddy covariance (EC) turbulent fluxes of sensible heat (Q_H), latent heat (Q_E), and CO₂ (F_C) in a densely built neighborhood in Beijing. The model sensitivity to maximum conductance (g_max) and leaf area index (LAI) is examined. Site-specific g_max is obtained from observations over local vegetation species, and LAI parameters are extracted by optimization with remotely sensed LAI obtained from a Landsat 7 data product. For the simulation of anthropogenic CO₂ components, local traffic and population data are collected. In the model evaluation, the mismatch between the measurement source area and simulation domain is also considered. Using the optimized g_max and LAI, the modeling of heat fluxes is noticeably improved, showing higher correlation with observations, lower bias, and more realistic seasonal dynamics of Q_E and Q_H. The effect of the g_max adjustment is more significant than the LAI adjustment. Compared to heat fluxes, the F_C module shows lower sensitivity to the choices of g_max and LAI. This can be explained by the low relative contribution of vegetation to the net F_C in the modeled area. SUEWS successfully reproduces the average diurnal cycle of F_C and annual cumulative sums. Depending on the size of the simulation domain, the modeled annual accumulated F_C ranges from 7.4 to 8.7 $\mathrm{kg}\mathrm{C}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{yr}}^{-\mathrm{1}}$ , compared to 7.5 $\mathrm{kg}\mathrm{C}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{yr}}^{-\mathrm{1}}$ observed by EC. Traffic is the dominant CO₂ source, contributing 59 %–70 % to the annual total CO₂ emissions, followed by human metabolism (14 %–18 %), buildings (11 %–14 %), and CO₂ release by vegetation and soil respiration (6 %–10 %). Vegetation photosynthesis offsets only 5 %–10 % of the total CO₂ emissions. We highlight the importance of choosing the optimal LAI parameters and g_max when SUEWS is used to model surface fluxes. The F_C module of SUEWS is a promising tool in quantifying urban CO₂ emissions at the local scale and therefore assisting in mitigating urban CO₂ emissions.

Tropical rainforests comprise complex 3D structures and encompass heterogeneous site conditions; their transpiration contributes to climate regulation. The objectives of our study were to test the relationship between tree water use and crown metrics and to predict spatial variability of canopy transpiration across sites. In a lowland rainforest of Sumatra, we measured tree water use with sap flux techniques and simultaneously assessed crown metrics with drone-based photogrammetry. We observed a close linear relationship between individual tree water use and crown surface area (R² = 0.76, n = 42 trees). Uncertainties in predicting stand-level canopy transpiration were much lower using tree crown metrics than the more conventionally used stem diameter. 3D canopy segmentation analyses in combination with the tree crown–water use relationship predict substantial spatial heterogeneity in canopy transpiration. Among our eight study plots, there was a more than two-fold difference, with lower transpiration at riparian than at upland sites. In conclusion, we regard drone-based canopy segmentation and crown metrics to be very useful tools for the scaling of transpiration from tree- to stand-level. Our results indicate substantial spatial variation in crown packing and thus canopy transpiration of tropical rainforests.

Urban green areas have multiple benefits extending from heat mitigation and carbon sinks to human well-being. Due to their multi-benefits, they are an attractive natural solution to aid climate change adaptation and mitigation. In cities of Helsinki and Tampere located in Finland, intensive observations and modelling of urban water and carbon dioxide (CO2) fluxes have taken place to improve our understanding of the functioning and carbon sequestration potential of different urban green areas and provide science-based evidence for decision-makers on how urban green areas should be planned and constructed to maximize their climate benefits.Extensive eco-physiological observations were collected from different vegetation types (urban forest, park, garden, and street vegetation) in Helsinki during summers 2020-2022. The observations were made in the vicinity of the ICOS Associated Ecosystem Station FI-Kmp where eddy covariance (EC) measurements presenting the ecosystem level are conducted. The measurements included photosynthesis, sap flow, soil respiration, phenology, fine root growth, meteorology and soil properties. FI-Kmp represents mixed land use and vegetation, and to get more information of the behavior of lawns, additional EC measurements were conducted over urban lawn in the city of Espoo in 2021-2022. The observations are complemented by ecosystem modelling using SUEWS (Surface Urban Energy and Water balance Scheme). SUEWS is used to examine the impact of different urban green area planning options on carbon sinks and storages with focus on the city of Tampere. This work will highlight some of the findings made so far and provide examples on the carbon and water fluxes in different urban green areas. We also demonstrate how science-based knowledge can aid decision-making concerning urban green areas.