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Summary statement We recommend that stomatal slope parameters (g₁) be inferred by inversion so that variations ing₁may be attributed to variations physiological and environmental conditions. Understandingg₁will advance predictions of plant gas exchange and performance under global climate. 

Abstract Plant responses to water stress is a major uncertainty to predicting terrestrial ecosystem sensitivity to drought. Different approaches have been developed to represent plant water stress. Empirical approaches (the empirical soil water stress (or Beta) function and the supply‐demand balance scheme) have been widely used for many decades; more mechanistic based approaches, that is, plant hydraulic models (PHMs), were increasingly adopted in the past decade. However, the relationships between them—and their underlying connections to physical processes—are not sufficiently understood. This limited understanding hinders informed decisions on the necessary complexities needed for different applications, with empirical approaches being mechanistically insufficient, and PHMs often being too complex to constrain. Here we introduce a unified framework for modeling transpiration responses to water stress, within which we demonstrate that empirical approaches are special cases of the full PHM, when the plant hydraulic parameters satisfy certain conditions. We further evaluate their response differences and identify the associated physical processes. Finally, we propose a methodology for assessing the necessity of added complexities of the PHM under various climatic conditions and ecosystem types, with case studies in three typical ecosystems: a humid Midwestern cropland, a semi‐arid evergreen needleleaf forest, and an arid grassland. Notably, Beta function overestimates transpiration when VPD is high due to its lack of constraints from hydraulic transport and is therefore insufficient in high VPD environments. With the unified framework, we envision researchers can better understand the mechanistic bases of and the relationships between different approaches and make more informed choices. 

Eddy covariance data are invaluable for determining ecosystem water use strategies under soil water stress. However, existing stress inference methods require numerous subjective data processing and model specification assumptions whose effect on the inferred soil water stress signal is rarely quantified. These uncertainties may confound the stress inference and the generalization of ecosystem water use strategies across multiple sites and studies. In this research, we quantify the sensitivity of soil water stress signals inferred from eddy covariance data to the prevailing data and modeling assumptions (i.e., their robustness) to compile a comprehensive list of sites with robust soil water stress signals and assess the performance of current stress inference methods. To accomplish this, we identify the most prevalent assumptions from the literature and perform a digital factorial experiment to extract probability distributions of plausible soil water stress signals and model performance at 151 FLUXNET2015 and AmeriFlux-FLUXNET sites. We develop a new framework that summarizes these probability distributions to classify and rank the robustness of each site’s soil water stress signal, which we display with a user-friendly heat map. We estimate that only 5%–36% of sites exhibit a robust soil water stress signal due to deficient model performance and poorly constrained ecosystem water use parameters. We also find that the lack of robustness is site-specific, which undermines grouping stress signals by broad ecosystem categories or comparing results across studies with differing assumptions. Lastly, existing stress inference methods appear better suited for eddy covariance sites with grass/annual vegetation. Our findings call for more careful and consistent inference of ecosystem water stress from eddy covariance data. 

Abstract Urban trees are increasingly used by cities for cooling and climate adaptation. However, efforts to increase tree cover across cities have neglected to account for the trees' health and function, which are known to control their associated environmental benefits but have been difficult to assess at scales relevant for management. Here, we use remotely sensed, high resolution canopy temperature as a proxy for tree health and function and evaluate its relation to the built environment across Minneapolis‐St. Paul (MSP) using machine learning analyses. We develop a new index that incorporates information on urban trees' health and function, in addition to their presence. This index, when applied across MSP, suggests that canopy benefits may not be distributed equally even in neighborhoods with similar canopy cover. Furthermore, accounting for tree health and function can yield more effective and equitable benefits by guiding the location and magnitude of intervention for urban tree management. 

A previous study demonstrated that atmospheric rivers (ARs) generate substantial air-sea fluxes in the northeast Pacific. Since the southeast Indian Ocean is one of the active regions of ARs, similar air-sea fluxes could be produced. However, the spatial pattern of sea surface temperature (SST) in the southeast Indian Ocean, especially along the west coast of Australia, is different from that in the northeast Pacific because of the poleward flowing Leeuwin Current, which may cause different air-sea fluxes. This study investigates AR-associated air-sea fluxes in the southeast Indian Ocean and their relation with SST variability. The large-scale spatial pattern of latent heat flux (evaporation) associated with ARs in the southeast Indian Ocean is similar to that in the northeast Pacific. A significant difference is however found near the coastal area where relatively warm SSTs are maintained in all seasons. While AR-induced latent heat flux is close to zero around the west coast of North America where the equatorward flowing coastal current and upwelling generate relatively cold SSTs, a significant latent heat flux induced by ARs is evident along the west coast of Australia due to the relatively warm surface waters. Temporal variations of coastal air-sea fluxes associated with landfalling ARs are investigated based on the composite analysis. While the moisture advection reduces the latent heat during landfalling, the reduction of air humidity with strong winds enhances large evaporative cooling (latent heat flux) after a few days of the landfalling. A significant SST cooling along the coast is found due to the enhanced latent heat flux. 

Abstract Stomata have recently been theorized to have evolved strategies that maximize turgor-driven growth over plants’ lifetimes, finding support through steady-state solutions in which gas exchange, carbohydrate storage and growth have all reached equilibrium. However, plants do not operate near steady state as plant responses and environmental forcings vary diurnally and seasonally. It remains unclear how gas exchange, carbohydrate storage and growth should be dynamically coordinated for stomata to maximize growth. We simulated the gas exchange, carbohydrate storage and growth that dynamically maximize growth diurnally and annually. Additionally, we test whether the growth-optimization hypothesis explains nocturnal stomatal opening, particularly through diel changes in temperature, carbohydrate storage and demand. Year-long dynamic simulations captured realistic diurnal and seasonal patterns in gas exchange as well as realistic seasonal patterns in carbohydrate storage and growth, improving upon unrealistic carbohydrate responses in steady-state simulations. Diurnal patterns of carbohydrate storage and growth in day-long simulations were hindered by faulty modelling assumptions of cyclic carbohydrate storage over an individual day and synchronization of the expansive and hardening phases of growth, respectively. The growth-optimization hypothesis cannot currently explain nocturnal stomatal opening unless employing corrective ‘fitness factors’ or reframing the theory in a probabilistic manner, in which stomata adopt an inaccurate statistical ‘memory’ of night-time temperature. The growth-optimization hypothesis suggests that diurnal and seasonal patterns of stomatal conductance are driven by a dynamic carbon-use strategy that seeks to maintain homeostasis of carbohydrate reserves. 

Abstract Observations show vulnerability segmentation between stems and leaves is highly variable within and between environments. While a number of species exhibit conventional vulnerability segmentation (stem leaf ), others exhibit no vulnerability segmentation and others reverse vulnerability segmentation (stem leaf ). We developed a hydraulic model to test hypotheses about vulnerability segmentation and how it interacts with other traits to impact plant conductance. We do this using a series of experiments across a broad parameter space and with a case study of two species with contrasting vulnerability segmentation patterns:Quercus douglasiiandPopulus trichocarpa. We found that while conventional vulnerability segmentation helps to preserve conductance in stem tissues, reverse vulnerability segmentation can better maintain conductance across the combined stem‐leaf hydraulic pathway, particularly when plants have more vulnerable s and have hydraulic segmentation with greater resistance in the leaves. These findings show that the impacts of vulnerability segmentation are dependent upon other plant traits, notably hydraulic segmentation, a finding that could assist in the interpretation of variable observations of vulnerability segmentation. Further study is needed to examine how vulnerability segmentation impacts transpiration rates and recovery from water stress.