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Transpiration, the dominant component of terrestrial evapotranspiration (ET), directly connects the water, energy and carbon cycles and is typically restricted by soil and atmospheric (for example, the vapour pressure deficit (VPD)) moisture stresses through plant hydraulic processes. These sources of stress are likely to diverge under climate change, with a globally enhanced VPD but more variable and uncertain changes in soil moisture. Here, using a model–data fusion approach, we demonstrate that the common empirical approach used in most Earth system models to evaluate the ET response to soil moisture and VPD, which neglects plant hydraulics, underestimates ET sensitivity to VPD and compensates by overestimating the sensitivity to soil moisture stress. A hydraulic model that describes water transport through the plant better captures ET under high VPD conditions for wide-ranging soil moisture states. These findings highlight the central role of plant hydraulics in regulating the increasing importance of atmospheric moisture stress on biosphere–atmosphere interactions under elevated temperatures.Evapotranspiration links productivity with water cycling between land and atmosphere. A model including plant hydraulics better describes the response of evapotranspiration to stress from vapour pressure deficit and soil moisture under rising temperatures than approaches common in Earth system models.

Water availability plays a critical role in shaping terrestrial ecosystems, particularly in low- and mid-latitude regions. The sensitivity of vegetation growth to precipitation strongly regulates global vegetation dynamics and their responses to drought, yet sensitivity changes in response to climate change remain poorly understood. Here we use long-term satellite observations combined with a dynamic statistical learning approach to examine changes in the sensitivity of vegetation greenness to precipitation over the past four decades. We observe a robust increase in precipitation sensitivity (0.624% yr −1 ) for drylands, and a decrease (−0.618% yr −1 ) for wet regions. Using model simulations, we show that the contrasting trends between dry and wet regions are caused by elevated atmospheric CO 2 (eCO 2 ). eCO 2 universally decreases the precipitation sensitivity by reducing leaf-level transpiration, particularly in wet regions. However, in drylands, this leaf-level transpiration reduction is overridden at the canopy scale by a large proportional increase in leaf area. The increased sensitivity for global drylands implies a potential decrease in ecosystem stability and greater impacts of droughts in these vulnerable ecosystems under continued global change. Changes in vegetation responses to precipitation may be hydroclimate dependent. Here the authors reveal contrasting trends of vegetation sensitivity to precipitation in drylands vs. wetter ecosystems over the last 4 decades and identify increased CO2 as a major contributing factor.

This study investigated the spatiotemporal variation of vegetation growth and the influence of climatic drivers from 1982 to 2011 across China using datasets from the Normalized Difference Vegetation Index (NDVI) and climatic drivers. Long term trends, significance and abrupt change points of interannual NDVI time series were analyzed. We applied both simple regression and multi-regression models to quantify the effects of climatic drivers on vegetation growth and compare their relative contributions. Results show that on average, the growing season NDVI significantly increased by 0.0007 year-1, with 76.5% of the research area showed increasing NDVI from 1982 to 2011. Seasonally, NDVI increased at high rates during the spring and autumn while changed slightly during the summer. At a national scale, the growing season NDVI was significantly and positively correlated to temperature and precipitation, with temperature being the dominant factor. At regional scales, the growing season NDVI was dominated by increasing temperature in most forest-covered areas in southern China and dominated by precipitation in most grassland in northern China. Within the past three decades, the increasing trend of national mean NDVI abruptly changed in 1994, slowing down from 0.0008 year-1 to 0.0003 year-1. To be regional specific, the growing season NDVI in forest covered southern China has accelerated together with temperature since mid 1990s, while parts of the grassland in northern China have undergone stalled NDVI trends corresponding to slowed temperature increment and dropped precipitation since around 2000. Typical region analysis suggested that apart from long term trends and abrupt change points of climatic drivers, the processes of NDVI variation were also affected by other external factors such as drought and afforestation. Further studies are needed to investigate the nonlinear responses of vegetation growth to climatic drivers and effects of non-climate factors on vegetation growth.

Evapotranspiration (ET) plays a critical role in water and energy budgets at regional to global scales. ET is composed of direct evaporation (E) and plant transpiration (T) where the latter is regulated via stomatal conductance (gsc), which depends on a multitude of plant physiological processes and hydrometeorological forcings. In recent years, significant advances have been made toward estimating gsc using a variety of models, ranging from relatively simple empirical models to more complex and data‐intensive plant hydraulic models. Using machine learning (ML) and eddy covariance flux tower data of 642 site years across 84 sites distributed across 10 land covers globally, here we show that structural constraints inherent in current empirical and plant hydraulic models of gsc limit their effectiveness for predicting ET. These constraints also prevent the models from fully utilizing the available hydrometeorological data at eddy covariance sites. Even if these gsc models are calibrated locally, structural simplifications inherent in them limit their capability to accurately capture gsc dynamics. In contrast, a ML approach, wherein the model structure is learned from the data, outperforms traditional models, thus highlighting that there still is significant room for improvement in the structure of traditional models for predicting ET. These results underscore the need to prioritize improvements in gsc models for more accurate ET estimation. This, in turn, will help reduce uncertainties in the assessments of plants' role in regulating the Earth's climate. Key Points Current stomatal conductance models underutilize the site‐specific hydrometeorological data Structural constraints in empirical models are more restrictive compared to plant hydraulic models Enhancements are needed for the simplified depiction of the water potential gradient across the root‐xylem‐leaf continuum in plant hydraulic models

Vegetation composition shifts, and in particular, shrub expansion across the Arctic tundra are some of the most important and widely observed responses of high-latitude ecosystems to rapid climate warming. These changes in vegetation potentially alter ecosystem carbon balances by affecting a complex set of soil–plant–atmosphere interactions. In this review, we synthesize the literature on (a) observed shrub expansion, (b) key climatic and environmental controls and mechanisms that affect shrub expansion, (c) impacts of shrub expansion on ecosystem carbon balance, and (d) research gaps and future directions to improve process representations in land models. A broad range of evidence, including in-situ observations, warming experiments, and remotely sensed vegetation indices have shown increases in growth and abundance of woody plants, particularly tall deciduous shrubs, and advancing shrublines across the circumpolar Arctic. This recent shrub expansion is affected by several interacting factors including climate warming, accelerated nutrient cycling, changing disturbance regimes, and local variation in topography and hydrology. Under warmer conditions, tall deciduous shrubs can be more competitive than other plant functional types in tundra ecosystems because of their taller maximum canopy heights and often dense canopy structure. Competitive abilities of tall deciduous shrubs vs herbaceous plants are also controlled by variation in traits that affect carbon and nutrient investments and retention strategies in leaves, stems, and roots. Overall, shrub expansion may affect tundra carbon balances by enhancing ecosystem carbon uptake and altering ecosystem respiration, and through complex feedback mechanisms that affect snowpack dynamics, permafrost degradation, surface energy balance, and litter inputs. Observed and projected tall deciduous shrub expansion and the subsequent effects on surface energy and carbon balances may alter feedbacks to the climate system. Land models, including those integrated in Earth System Models, need to account for differences in plant traits that control competitive interactions to accurately predict decadal- to centennial-scale tundra vegetation and carbon dynamics.

Atmospheric dryness (i.e., high vapor pressure deficit, VPD), together with soil moisture stress, limits plant photosynthesis and threatens ecosystem functioning. Regions where rainfall and soil moisture are relatively sufficient, such as the rainfed part of the U.S. Corn Belt, are especially prone to high VPD stress. With globally projected rising VPD under climate change, it is crucial to understand, simulate, and manage its negative impacts on agricultural ecosystems. However, most existing models simulating crop response to VPD are highly empirical and insufficient in capturing plant response to high VPD, and improved modeling approaches are urgently required. In this study, by leveraging recent advances in plant hydraulic theory, we demonstrate that the VPD constraints in the widely used coupled photosynthesis‐stomatal conductance models alone are inadequate to fully capture VPD stress effects. Incorporating plant xylem hydraulic transport significantly improves the simulation of transpiration under high VPD, even when soil moisture is sufficient. Our results indicate that the limited water transport capability from the plant root to the leaf stoma could be a major mechanism of plant response to high VPD stress. We then introduce a Demand‐side Hydraulic Limitation Factor (DHLF) that simplifies the xylem and the leaf segments of the plant hydraulic model to only one parameter yet captures the effect of plant hydraulic transport on transpiration response to high VPD with similar accuracy. We expect the improved understanding and modeling of crop response to high VPD to help contribute to better management and adaptation of agricultural systems in a changing climate. Key Points Coupled photosynthesis‐stomatal conductance models alone underestimate vapor pressure deficit (VPD) stress effects on crop stomatal conductance and transpiration Limited plant hydraulic transport capability could play a role in plant response to high VPD A simplified representation of plant hydraulic model for capturing VPD stress on plants is proposed

This work aims to investigate the application of advanced deep learning algorithms and image recognition technologies to enhance language analysis tools in secondary education, with the goal of providing educators with more effective resources and support. Based on artificial intelligence, this work integrates data mining techniques related to deep learning to analyze and study language behavior in secondary school education. Initially, a framework for analyzing language behavior in secondary school education is constructed. This involves evaluating the current state of language behavior, establishing a framework based on evaluation comments, and defining indicators for analyzing language behavior in online secondary school education. Subsequently, data mining technology and image and character recognition technology are employed to conduct data mining for online courses in secondary schools, encompassing the processing of teaching video images and character recognition. Finally, an experiment is designed to validate the proposed framework for analyzing language behavior in secondary school education. The results indicate specific differences among the grouped evaluation scores for each analysis indicator. The significance p values for the online classroom discourse’s speaking rate, speech intelligibility, average sentence length, and content similarity are −0.56, −0.71, −0.71, and −0.74, respectively. The aim is to identify the most effective teaching behaviors for learners and enhance the support for online course instruction.

Anaplastic thyroid cancer (ATC), one of the most aggressive solid tumors, is characterized by rapid tumor growth and severe metastasis to other organs. Owing to the lack of effective treatment options, ATC has a mortality rate of ∼100% and median survival of less than 5 months. RNAi nanotechnology represents a promising strategy for cancer therapy through nanoparticle (NP) -mediated delivery of RNAi agents (e.g., siRNA) to solid tumors for specific silencing of target genes driving growth and/or metastasis. Nevertheless, the clinical success of RNAi cancer nanotherapies remains elusive in large part because of the suboptimal systemic siRNA NP delivery to tumors and the fact that tumor heterogeneity produces variable NP accumulation and thus, therapeutic response. To address these challenges, we here present an innovative theranostic NP platform composed of a near-infra-red (NIR) fluorescent polymer for effective in vivo siRNA delivery to ATC tumors and simultaneous tracking of the tumor accumulation by noninvasive NIR imaging. The NIR polymeric NPs are small (∼50 nm), show long blood circulation and high tumor accumulation, and facilitate tumor imaging. Systemic siRNA delivery using these NPs efficiently silences the expression of V-Raf murine sarcoma viral oncogene homolog B (BRAF) in tumor tissues and significantly suppresses tumor growth and metastasis in an orthotopic mouse model of ATC. These results suggest that this theranostic NP system could become an effective tool for NIR imaging-guided siRNA delivery for personalized treatment of advanced malignancies.

Fully distributed hydrologic models are often used to simulate hydrologic states at fine spatio-temporal resolutions. However, simulations based on these models may become computationally expensive, constraining their applications to smaller domains. This study demonstrates that a nested-discretization based modeling strategy can be used to improve the efficiency of distributed hydrologic simulations, especially for applications where fine resolution estimates of hydrologic states are of the focus only within a part of a watershed. To this end, we consider two applications where the goal is to capture the groundwater dynamics within a defined target area. Our results show that at the target locations, a nested simulation is able to competently replicate the estimates of groundwater table as obtained from the fine simulation, while yielding significant computational savings. The results highlight the potential of using nested discretization for a detailed yet computationally efficient estimation of hydrologic states in part of the model domain.

Arctic shrub expansion alters carbon budgets, albedo, and warming rates in high latitudes but remains challenging to predict due to unclear underlying controls. Observational studies and models typically use relationships between observed shrub presence and current environmental suitability (bioclimate and topography) to predict shrub expansion, while omitting shrub demographic processes and non-stationary response to changing climate. Here, we use high-resolution satellite imagery across Alaska and western Canada to show that observed shrub expansion has not been controlled by environmental suitability during 1984–2014, but can only be explained by considering seed dispersal and fire. These findings provide the impetus for better observations of recruitment and for incorporating currently underrepresented processes of seed dispersal and fire in land models to project shrub expansion and climate feedbacks. Integrating these dynamic processes with projected fire extent and climate, we estimate shrubs will expand into 25% of the non-shrub tundra by 2100, in contrast to 39% predicted based on increasing environmental suitability alone. Thus, using environmental suitability alone likely overestimates and misrepresents shrub expansion pattern and its associated carbon sink. Shrub encroachment trends are widespread yet complex. Here the authors demonstrate that not considering dispersal and fire leads to overestimating shrub expansion in Arctic tundra and therefore its role as carbon sink.