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Abstract Quantifying relative contributions of plant transpiration (T) and soil evaporation to evapotranspiration (ET) is crucial to better understand how vegetation influences and controlsET, the largest efflux of the terrestrial water balance. Here, we derive estimates of transpiration fraction (T/ET) using consistent isotope‐basedETpartitioning methods for 13 sites spanning five ecosystem types of the continental US, capturing 56 snapshots ofT/ETduring the growing season. We found transpiration dominated theETflux across all sites with a meanT/ETof 0.81 ± 0.08 (±standard error). Sites and dates with higher vegetation indices exhibited higherT/ETand transpiration rates, with the latter increasing 0.30 mm/day per unit Leaf Area Index and 2.9 mm/day per unit Normalized Difference Vegetation Index. Counter to expectations, antecedent precipitation had no effect onT/ET. Despite the breadth of ecosystems and conditions represented, evaporation exceeded transpiration only once, suggesting that evaporation rarely dominatesETin the growing season. 

Abstract Land surface models (LSMs) play a crucial role in elucidating water and carbon cycles by simulating processes such as plant transpiration and evaporation from bare soil, yet calibration often relies on comparing LSM outputs of landscape total evapotranspiration (ET) and discharge with measured bulk fluxes. Discrepancies in partitioning into component fluxes predicted by various LSMs have been noted, prompting the need for improved evaluation methods. Stable water isotopes serve as effective tracers of component hydrologic fluxes, but data and model integration challenges have hindered their widespread application. Leveraging National Ecological Observation Network measurements of water isotope ratios at 16 US sites over 3 years combined with LSM‐modeled fluxes, we employed an isotope‐enabled mass balance framework to simulateETisotope values (δET) within three operational LSMs (Mosaic, Noah, and VIC) to evaluate their partitioning. Models simulatingδETvalues consistent with observations were deemed more reflective of water cycling in these ecosystems. Mosaic exhibited the best overall performance (Kling‐Gupta Efficiency of 0.28). For both Mosaic and Noah there were robust correlations between bare soil evaporation fraction and error (negative) as well as transpiration fraction and error (positive). We found the point at which errors are smallest (x‐intercept of the multi‐site regression) is at a higher transpiration fraction than is currently specified in the models. Which means that transpiration fraction is underestimated on average. Stable isotope tracers offer an additional tool for model evaluation and identifying areas for improvement, potentially enhancing LSM simulations and our understanding of land‐surface hydrologic processes. 

Abstract Stable isotope ratios of H (δ²H), O (δ¹⁸O), and C (δ¹³C) are linked to key biogeochemical processes of the water and carbon cycles; however, the degree to which isotope-associated processes are reflected in macroscale ecosystem flux observations remains unquantified. Here through formal information assessment, new measurements ofδ¹³C of net ecosystem exchange (NEE) as well asδ²H andδ¹⁸O of latent heat (LH) fluxes across the United States National Ecological Observation Network (NEON) are used to determine conditions under which isotope measurements are informative of environmental exchanges. We find all three isotopic datasets individually contain comparable amounts of information aboutNEEandLHfluxes as wind speed observations. Such information from isotope measurements, however, is largely unique. Generally,δ¹³C provides more information aboutLHas aridity increases or mean annual precipitation decreases.δ²H provides more information aboutLHas temperatures or mean annual precipitation decreases, and also provides more information aboutNEEas temperatures decrease. Overall, we show that the stable isotope datasets collected by NEON contribute non-trivial amounts of new information about bulk environmental fluxes useful for interpreting biogeochemical and ecohydrological processes at landscape scales. However, the utility of this new information varies with environmental conditions at continental scales. This study provides an approach for quantifying the value adding non-traditional sensing approaches to environmental monitoring sites and the patterns identified here are expected to aid in modeling and data interpretation efforts focused on constraining carbon and water cycles’ mechanisms. 

Summary Intraspecific variation in functional traits may mediate tree species' drought resistance, yet whether trait variation is due to genotype (G), environment (E), or G×E interactions remains unknown. Understanding the drivers of intraspecific trait variation and whether variation mediates drought response can improve predictions of species' response to future drought.Using populations of quaking aspen spanning a climate gradient, we investigated intraspecific variation in functional traits in the field as well as the influence of G and E among propagules in a common garden. We also tested for trait‐mediated trade‐offs in growth and drought stress tolerance.We observed intraspecific trait variation among the populations, yet this variation did not necessarily translate to higher drought stress tolerance in hotter/drier populations. Additionally, plasticity in the common garden was low, especially in propagules derived from the hottest/driest population. We found no growth–drought stress tolerance trade‐offs and few traits exhibited significant relationships with mortality in the natural populations, suggesting that intraspecific trait variation among the traits measured did not strongly mediate responses to drought stress.Our results highlight the limits of trait‐mediated responses to drought stress and the complex G×E interactions that may underlie drought stress tolerance variation in forests in dry environments. 

Abstract Forest productivity projections remain highly uncertain, notably because underpinning physiological controls are delicate to disentangle. Transient perturbation of global climate by large volcanic eruptions provides a unique opportunity to retrospectively isolate underlying processes. Here, we use a multi‐proxy dataset of tree‐ring records distributed over the Northern Hemisphere to investigate the effect of eruptions on tree growth and photosynthesis and evaluate CMIP6 models. Tree‐ring isotope records denoted a widespread 2–4 years increase of photosynthesis following eruptions, likely as a result of diffuse light fertilization. We found evidence that enhanced photosynthesis transiently drove ring width, but the latter further exhibited a decadal anomaly that evidenced independent growth and photosynthesis responses. CMIP6 simulations reproduced overall tree growth decline but did not capture observed photosynthesis anomaly, its decoupling from tree growth or the climate sensitivities of either processes, highlighting key disconnects that deserve further attention to improve forest productivity projections under climate change. 

Abstract The National Ecological Observatory Network (NEON) provides open-access measurements of stable isotope ratios in atmospheric water vapor (δ²H, δ¹⁸O) and carbon dioxide (δ¹³C) at different tower heights, as well as aggregated biweekly precipitation samples (δ²H, δ¹⁸O) across the United States. These measurements were used to create the NEON Daily Isotopic Composition of Environmental Exchanges (NEON-DICEE) dataset estimating precipitation (P; δ²H, δ¹⁸O), evapotranspiration (ET; δ²H, δ¹⁸O), and net ecosystem exchange (NEE; δ¹³C) isotope ratios. Statistically downscaled precipitation datasets were generated to be consistent with the estimated covariance between isotope ratios and precipitation amounts at daily time scales. Isotope ratios in ET and NEE fluxes were estimated using a mixing-model approach with calibrated NEON tower measurements. NEON-DICEE is publicly available on HydroShare and can be reproduced or modified to fit user specific applications or include additional NEON data records as they become available. The NEON-DICEE dataset can facilitate understanding of terrestrial ecosystem processes through their incorporation into environmental investigations that require daily δ²H, δ¹⁸O, and δ¹³C flux data. 

Abstract Mechanistic representations of biogeochemical processes in ecosystem models are rapidly advancing, requiring advancements in model evaluation approaches. Here we quantify multiple aspects of model functional performance to evaluate improved process representations in ecosystem models. We compare semi‐empirical stomatal models with hydraulic constraints against more mechanistic representations of stomatal and hydraulic functioning at a semi‐arid pine site using a suite of metrics and analytical tools. We find that models generally perform similarly under unstressed conditions, but performance diverges under atmospheric and soil drought. The more empirical models better capture synergistic information flows between soil water potential and vapor pressure deficit to transpiration, while the more mechanistic models are overly deterministic. Although models can be parameterized to yield similar functional performance, alternate parameterizations could not overcome structural model constraints that underestimate the unique information contained in soil water potential about transpiration. Additionally, both multilayer canopy and big‐leaf models were unable to capture the magnitude of canopy temperature divergence from air temperature, and we demonstrate that errors in leaf temperature can propagate to considerable error in simulated transpiration. This study demonstrates the value of merging underutilized observational data streams with emerging analytical tools to characterize ecosystem function and discriminate among model process representations. 

Abstract Forests are currently a substantial carbon sink globally. Many climate change mitigation strategies leverage forest preservation and expansion, but rely on forests storing carbon for decades to centuries. Yet climate‐driven disturbances pose critical risks to the long‐term stability of forest carbon. We quantify the climate drivers that influence wildfire and climate stress‐driven tree mortality, including a separate insect‐driven tree mortality, for the contiguous United States for current (1984–2018) and project these future disturbance risks over the 21st century. We find that current risks are widespread and projected to increase across different emissions scenarios by a factor of >4 for fire and >1.3 for climate‐stress mortality. These forest disturbance risks highlight pervasive climate‐sensitive disturbance impacts on US forests and raise questions about the risk management approach taken by forest carbon offset policies. Our results provide US‐wide risk maps of key climate‐sensitive disturbances for improving carbon cycle modeling, conservation and climate policy. 

Abstract Classifying the diverse ways that plants respond to hydrologic stress into generalizable ‘water‐use strategies’ has long been an eco‐physiological research goal. While many schemes for describing water‐use strategies have proven to be quite useful, they are also associated with uncertainties regarding their theoretical basis and their connection to plant carbon and water relations. In this review, we discuss the factors that shape plant water stress responses and assess the approaches used to classify a plant's water‐use strategy, paying particular attention to the popular but controversial concept of a continuum from isohydry to anisohydry.A generalizable and predictive framework for assessing plant water‐use strategies has been historically elusive, yet recent advances in plant physiology and hydraulics provide the field with a way past these obstacles. Specifically, we promote the idea that many metrics that quantify water‐use strategies are highly dynamic and emergent from the interaction between plant traits and environmental conditions, and that this complexity has historically hindered the development of a generalizable water‐use strategy framework.This idea is explored using a plant hydraulics model to identify: (a) distinct temporal phases in plant hydraulic regulation during drought that underpin dynamic water‐use responses, and (b) how variation in both traits and environmental forcings can significantly alter common metrics used to characterize plant water‐use strategies. This modelling exercise can bridge the divide between various conceptualizations of water‐use strategies and provide targeted hypotheses to advance the understanding and quantification of plant water status regulation across spatial and temporal scales.Finally, we describe research frontiers that are necessary to improve the predictive capacity of the plant water‐use strategy concept, including further investigation into the below‐ground determinants of plant water relations, targeted data collection efforts and the potential to scale these concepts from individuals to whole regions. A freePlain Language Summarycan be found within the Supporting Information of this article. 

Summary Optimal stomatal control models have shown great potential in predicting stomatal behavior and improving carbon cycle modeling. Basic stomatal optimality theory posits that stomatal regulation maximizes the carbon gain relative to a penalty of stomatal opening. All models take a similar approach to calculate instantaneous carbon gain from stomatal opening (the gain function). Where the models diverge is in how they calculate the corresponding penalty (the penalty function). In this review, we compare and evaluate 10 different optimization models in how they quantify the penalty and how well they predict stomatal responses to the environment. We evaluate models in two ways. First, we compare their penalty functions against seven criteria that ensure a unique and qualitatively realistic solution. Second, we quantitatively test model against multiple leaf gas‐exchange datasets. The optimization models with better predictive skills have penalty functions that meet our seven criteria and use fitting parameters that are both few in number and physiology based. The most skilled models are those with a penalty function based on stress‐induced hydraulic failure. We conclude by proposing a new model that has a hydraulics‐based penalty function that meets all seven criteria and demonstrates a highly predictive skill against our test datasets.