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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Vegetation responses to air dryness amplify future land surface warming

Temperature exerts a first-order control on vegetation photosynthesis and transpiration. Yet most studies investigating temperature impacts on plants rely on near-surface air temperature, rather than canopy temperature—the temperature plants actually experience. Because canopy temperature directly regulates ecosystem function, it provides a more accurate measure of vegetation–climate interactions. Combining Earth System Model (ESM) simulations and satellite observations in a dual emergent constraint, here we show that canopy temperature is projected to increase substantially more than air temperature (~0.11-degrees more or a 16% increase in their difference) over the 21st century. The ESM ensemble median fails to capture these stronger increases in the majority of vegetated regions. We find that the largest projected increases in the difference between canopy and air temperature are predicted to occur in regions where elevating moisture stress—particularly rising vapor pressure deficit—increasingly constrains vegetation growth and transpiration. This implies that future warming will impose stronger constraints on plant function than currently estimated. Relying on air temperature alone will therefore lead to systematic underestimation of temperature effects on photosynthesis, vegetation growth, and the land carbon sink. Accurate representation of canopy temperature in ESMs is thus essential to improve projections of ecosystem responses and feedbacks to climate change.

Green, Julia K↗
DOE OSTI2026

Understanding Subseasonal Moisture Recycling from Extreme MCSs Using a Land–Atmosphere Coupled Water Tracer Model

Extreme precipitation events often produce severe flooding and pose significant threats to our society. However, their subseasonal impacts on the terrestrial and atmospheric systems are not well understood. Here, we use a new land–atmosphere coupled water tracer tool embedded in the Weather Research and Forecasting (WRF) Model to “tag” precipitation produced by a series of extreme mesoscale convective systems (MCSs) in May 2015 over the southern Great Plains to reveal their contributions to different terrestrial water storages and moisture recycling during May–July. During May, we find that precipitation from earlier MCSs can contribute to 20%–50% of total evapotranspiration (ET) in later May and contribute ∼10% of local precipitation for later MCSs. After May, the water “tagged” to preceding May MCS events has the most active contribution to moisture recycling during the first 5 days in June, but this contribution diminishes quickly afterward. Both periods indicate a quick turnover of tagged precipitation, which may be representative of the southern Great Plains region. The quick turnover is closely associated with the direct evaporation from the soil surface due to the predominant shrubland and grassland coverage over this region. Over some forested areas, however, the tracer-ET flux is dominated by transpiration that stably supplies a small fraction (<1%) of moisture to the atmosphere throughout June and July. In conclusion, the tracer-contributed plume (through evaporation and transpiration) in atmospheric moisture can extend 500–700 km downstream between May and July, highlighting the far-reaching contribution to moisture recycling and precipitation from the tagged MCS events in the southern Great Plains.

Atmosphere-land interaction↗
DOE OSTI2026

Terrestrial laser scanning data (Levels 0 and 1) for Pasoh, Malaysia, Sep 2024

This data package contains data from terrestrial laser scanning (TLS) at the Pasoh Forest Reserve, Malaysia. The Pasoh Forest Reserve is a facility of the Forest Research Institute Malaysia, and contains evergreen lowland dipterocarp forest. The Next-Generation Ecosystem Experiments Tropics (NGEE-Tropics) study areas at Pasoh were established to study how different species respond to climatic variation and soil water availability. Two study areas were chosen representing different topography and species. The TLS data archived here were collected to provide detailed, three-dimensional information about forest structure. Specifically, data were collected to allow tree-level characterization of woody structure and leaf area for 12 focal trees with FloraPulse and sap flux sensors, facilitating estimation of woody biomass and leaf area to allow upscaling of water content and transpiration data to the tree-level. Scan positions were not selected to provide consistent data for non-focal trees with the study areas. This data package contains the following data: - High-level files document further details of the campaign and data package: 1_CampaignSummary.csv provides details about the campaign and study site, 2_ScanAreasDetail.csv provides details about each separate scan area (groups of scans post-processed into a single point cloud), 3_TerrestrialLidarSensor.csv provides further technical details about the Riegl VZ-400i TLS sensor, TLS_CSV_dd.csv is a CSV Data Dictionary providing information about the fields in CSV files following the ESS-DIVE CSV File Formatting Guidelines Reporting Format, TLS_flmd.csv is a File Level Metadata file providing information about each file in the data package following the ESS-DIVE File Level Metadata Reporting Format, and README.txt is a text file describing the overall project and file structure. - Level 0 data are the raw data (.PROJ folders) as recorded by the Riegl VZ-400i TLS instrument before scan co-registration and post-processing with the Riegl's proprietary RiSCAN PRO software, which requires a license. - Level 1 data contain post-processed, co-registered data from each scan area. The "PointClouds" folder for each scan area contains a .las file with 1 cm resolution point cloud data exported from RiSCAN PRO. These are the main files likely to be of interest to most users and can be further processed with any software capable of manipulating .las files (e.g. Python, R CloudCompare). The "Project Information" folder contains log files from post-processing in RiSCAN PRO that may be of interest to users who want to see detailed records of post-processing, including all PDF reports generated by RiSCAN PRO. The "ScanPositions" folder contains information about the final position of all TLS scans, after post-processing, in multiple formats. The file ScanPositions_*.csv provides final geo-referenced scan positions, and the file SOP_backup_*.csv can be used in RiSCAN PRO to restore the co-registered scan positions if users wish to re-process raw data (Level 0 .PROJ folders) with RiSCAN PRO software (e.g., subsample to a different resolution, exclude a certain scan position, or apply different filters on reflectance or deviation values) without redoing time-consuming co-registration steps.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X

Mapping in situ eddy covariance measurements of terrestrial land–atmosphere fluxes to the globe is a key method for diagnosing the Earth system from a data-driven perspective. We describe the first global products (called X-BASE) from a newly implemented upscaling framework, FLUXCOM-X, representing an advancement from the previous generation of FLUXCOM products in terms of flexibility and technical capabilities. The X-BASE products are comprised of estimates of CO 2 net ecosystem exchange (NEE), gross primary productivity (GPP), evapotranspiration (ET), and for the first time a novel, fully data-driven global transpiration product (ETT), at high spatial (0.05°) and temporal (hourly) resolution. X-BASE estimates the global NEE at −5.75 ± 0.33 Pg C yr −1 for the period 2001–2020, showing a much higher consistency with independent atmospheric carbon cycle constraints compared to the previous versions of FLUXCOM. The improvement of global NEE was likely only possible thanks to the international effort to increase the precision and consistency of eddy covariance collection and processing pipelines, as well as to the extension of the measurements to more site years resulting in a wider coverage of bioclimatic conditions. However, X-BASE global net ecosystem exchange shows a very low interannual variability, which is common to state-of-the-art data-driven flux products and remains a scientific challenge. With 125 ± 2.1 Pg C yr −1 for the same period, X-BASE GPP is slightly higher than previous FLUXCOM estimates, mostly in temperate and boreal areas. X-BASE evapotranspiration amounts to 74.7×10 3 ± 0.9×10 3 km 3 globally for the years 2001–2020 but exceeds precipitation in many dry areas, likely indicating overestimation in these regions. On average 57 % of evapotranspiration is estimated to be transpiration, in good agreement with isotope-based approaches, but higher than estimates from many land surface models. Despite considerable improvements to the previous upscaling products, many further opportunities for development exist. Pathways of exploration include methodological choices in the selection and processing of eddy covariance and satellite observations, their ingestion into the framework, and the configuration of machine learning methods. For this, the new FLUXCOM-X framework was specifically designed to have the necessary flexibility to experiment, diagnose, and converge to more accurate global flux estimates.

Nelson, Jacob A.↗
DOE OSTI2026

Impacts of Pasture Conversion to Sugarcane on Water Fluxes and Water Use Efficiency in the Southeastern US

The expansion of sugarcane (cane), a high-yielding perennial crop, will likely reshape the bioenergy landscape in the Southeastern US. However, its ecohydrological implications, particularly following conversion from grazed pastures, a dominant land use in the region, remain highly uncertain. We investigated the impact of cane expansion on evapotranspiration (ET) and its partitioning, and the mechanisms influencing both ET components and water use efficiency (WUE) across multiple scales and growth cycles in subtropical Florida. We combined eddy covariance, biometric measurements, and process-based stomatal conductance (g s ) models. ET was 1.7% lower in cane than in improved pasture (IMP) but exceeded that in semi-native pasture (SN) by 21%. Transpiration (T) followed a similar pattern, consistent with lower g s in cane relative to IMP. Cane had more conservative water use and greater sensitivity of g s to vapor pressure deficit (VPD) compared to IMP pasture, suggesting cane may be more tolerant of increasing atmospheric water demand. In contrast, SN showed lower g s and weaker stomatal sensitivity to VPD compared to cane, resulting in lower T. In cane, stomatal regulation and T varied across growth cycles, with stomata becoming less water conservative as stands matured, highlighting the importance of incorporating stand age-dependent stomatal regulation into hydrological models. Evaporation (E) was higher in cane than pastures (19%–26%), partially offsetting WUE gains. Cane exhibited higher intrinsic WUE (GPP/g s ; Gross Primary Productivity), ecosystem WUE (GPP/ET), and harvest WUE (harvest/ET) than both pasture types. Large-scale pasture-to-cane conversion could produce widely contrasting hydrological outcomes. The net regional impact will depend on the proportion of each pasture type converted and on cane's high g s sensitivity to VPD, which triggers tight stomatal regulation and conservative water use, both of which will become increasingly consequential under intensifying atmospheric water demand.

bioenergy↗
DOE OSTI2026

Data From: "Warming and snow loss increase reliance on old groundwater in a Colorado River headwater"

This repository contains the data and code associated with the paper titled "Warming and snow loss increase reliance on old groundwater in a Colorado River headwater," published in Nature Geoscience, 2026. This study seeks to answer how various ages of groundwater interact with mountainous streamflow in mountainous headwaters such as the East River. It includes various model-data processing scripts, primarily for ParFlow-CLM analysis of simulated water years 2015-2021, and two numerical warming experiments (+2.5 and +4.0 degrees C), including run scripts, forcing scripts, and post-processing, as well as comparison to observation datasets, detailed below. This data requires the use of R (.r, .rmd), Python (.py), Jupyter Notebook or Jupyter Lab (.ipynb), ParFLOW-CLM, EcoSLIM. Further information on the use of all file formats mentioned below (e.g. .tff. .nc) are provided within the associated scripts and directory where the files are located. Contents & Usage ASO/: ​​Contains the bash and python scripts used to convert airborne snow observatory (ASO) data (ASO, 2023) in various data formats (georeferenced tiff file, NetCDF, UTM, and to latitude/longitude) then regrided to the ParFlow equivalent grid. Output data are in regrid_regll_data.zip and subsequently visualized and analyzed in plot_and_compare.py for Supplementary Figures A14 and A15. The wksht_ASO_comparison.xlsx spreadsheet is used to calculate the data for Supplementary Figure A16. EcoSLIM/: Contains the scripts and input files to run the EcoSLIM particle tracking simulations (/run_scripts) and the post-processing python script (/plot_scripts/eco_agedist_plots.ipynb). Jasechko et al./: Contains the jupyter notebook (Extract_Elevation.ipynb) to determine the outlet elevations of the 260 watersheds used in Jasechko et al. (2016), and the corresponding table, Table_S1_Watersheds_alt.csv. Used to create Supplementary Information Figure A2. PLM_Wells/: Contains the QA/QC-ed groundwater level time series of the PLM-1 and PLM-6 Monitoring Wells from Faybishenko et al. (2023), reformatted to water years used for Supplementary Figures A19 and and A20. ParFlow/: Contains the input files and run scripts to run ParFlow-CLM (/run_scripts), the python and tool command language (Tcl) scripts to create and distribute the ParFlow forcing simulation files (/forcing), and various scripts and intermediary files to analyze the model outputs (/post_process). SQUIRE/: Contains the processing scripts and intermediary files for the Surface QUantitatIve pRecipitation Estimation (SQUIRE) data (Grover, 2023) used to generate Supplementary Figure A18. USGS_Streamflow/: Contains the raw and gap-filled United States Geological Survey streamflow data (U.S. Geological Survey, 2026) used at the Almont station (site number 09112500). Gap-filling is performed in the R script with data from the Taylor station (site number 09110000). (/USGS_09112500_EAST_RIVER_AT_ALMONT_GAP_FILLED/code_almont_streamflow_gap_fill.Rmd). discharge/: Contains the gap-filled discharge data at the Watershed Function SFA East River pumphouse site (Newcomer et al., 2022) used to generate Supplementary Figure A13 and to compute hourly Nash-Sutcliffe model efficiency coefficients (NSE) in Table A4. snotel_and_flux_tower/: Contains the snow telemetry data (U.S. Department of Agriculture, 2024) from the Butte (site ID 380) and Schofield (site ID 737) stations, reformatted by water year, accessed with the snotelr R package. Used to create Supplementary Figure A17. Also contains the flux tower observational data (FluxTower_Pumphouse_ESS-DIVE.ET_only.h.txt) from Ryken et al. (2022) and sap flux transpiration data (MaxB_Transpiration_5Sites.daily_sums.h.txt) from Ryken (2021), used to create Supplementary Figures A22 and A23, respectively. Raw EcoSLIM model outputs are in excess of 24TB, and are stored on National Energy Research Scientific Computing Center (NERSC) and publicly available via the external link provided in the paper.

atmospheric warming↗
DOE OSTI2026

Data and scripts associated with a manuscript analyzing ELM-FATES parameter sensitivity under pre-fire and postfire scenarios using machine learning

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Fire Severity-Dependent Shifts in Vegetation Parameter Sensitivity: A Pre- and Post-Fire Analysis Using ELM-FATES and Explainable AI” submitted to Journal of Advances in Modeling Earth Systems (Zahura et al. 2026). The study examines vegetation physiological parameters controlling pre-fire and post-fire vegetation dynamics. To support this analysis, 73 vegetation parameters in Functionally Assembled Terrestrial Ecosystem Simulator (FATES) (Fisher et al., 2018) , which is coupled with E3SM (Energy Exascale Earth System Model) land model (ELM, ELM-FATES), were perturbed using a Sobol sequence to generate 1,024 ensemble members for two plant functional types: needleleaf evergreen extratropical trees (NEET) and C3 grass. Simulations were conducted for the pre-fire period (2016) and post-fire period (2018–2023). Burn severity was represented by modifying the Nesterov index in FATES to 75,000, 150,000, and 300,000 for low, moderate, and high severity, respectively. A no-fire scenario was also included. Simulations were performed for 16 grid cells in the American River Watershed across different burn severities and plant functional types. XGBoost (eXtreme Gradient Boosting) models were trained using the parameter ensembles and ELM-FATES-simulated outputs, including leaf area index (LAI), gross primary productivity (GPP), aboveground biomass, vegetation evaporation, transpiration, and soil evaporation. Models were trained separately for each year and burn severity, followed by SHAP (SHapley Additive exPlanations) analysis to identify changes in dominant parameters after fire disturbance. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. The data package contains the ELM-FATES simulation data. The scripts and data related to the analysis will be added later. The inputs and outputs from ELM-FATES are inside the “FATES” folder. “FATES_domain_surface” contains the domain and surface netcdfs that were used to run ELM-FATES in the study area. “FATES_parameters” contains the 1024 ensembles that were generated using Sobol sequence. “FATES_outputs” folder contains ELM-FATES simulated variables. All files are .csv and .nc (NetCDF).

Aboveground biomass↗
DOE OSTI2025

A minor respiratory process with major global implications: is atmospheric methane oxidation in tree stems driven by stem respiration rather than microbial methanotrophy?

Tree stem surfaces are widely recognized as sites of carbon dioxide (CO₂) efflux and oxygen (O₂) influx, reflecting the dynamics of aerobic respiration of photosynthate substrates, such as sugars, delivered via the phloem. Stems are also largely considered passive conduits for methane (CH₄) produced in anoxic soils via microbial methanogenesis, where CH₄ is thought to be transported upward through the transpiration stream and/or diffusion and emitted through stem surfaces and the canopy. However, recent observations from dynamic stem chambers suggest that stems may also act as active sinks for atmospheric CH₄. Despite these findings, the extent and drivers of stem CH₄ consumption remain poorly characterized across biomes, species, and environmental gradients, and its quantitative relationship to stem respiration has not been established. Moreover, previous studies captured only snapshot fluxes, leaving diurnal patterns of CH₄ exchange uncharacterized. Here, we address these limitations by combining real-time measurements of stem CH₄ and O₂ uptake under ambient conditions in a California cherry tree, using a dynamic stem gas exchange system with three chambers receiving a continuous flow of ambient air and automated chamber and reference air sampling every 10 min. Our results confirm that stems of upland trees can actively consume both atmospheric CH₄ and O₂, but with decreasing temperature sensitivity as daily temperatures increase. Early mornings were marked by rapid influxes of both gases, followed by declining uptake as temperatures rose further. Methane uptake was tightly coupled with O₂ influx and represented a minor (0.012% ± 0.002%) fraction of stem respiratory activity, as determined by concurrent O₂ uptake. These findings suggest that while atmospheric CH₄ oxidation is a minor respiratory process in stems, it is strongly linked with stem physiological activity. This challenges the current assumption that terrestrial CH₄ uptake is driven solely by microbial methanotrophy and raises the possibility that living stem tissues may contribute to CH₄ oxidation through an as-yet-unidentified plant-based mechanism.

Atmospheric greenhouse gases↗
DOE OSTI2025

Heterogeneous catalysis: Optimal performance at a phase boundary?

Most of the industrially used heterogeneous catalysts have been discovered by trial and error, and despite decades of experience, the discovery of new catalysts continues to be extremely challenging. The drive to uncover guiding principles in catalyst design is more present than ever. We share a series of observations indicating that optimal catalysts typically function at characteristic phase boundaries (e.g., abrupt changes in adsorbate coverage, catalyst structure, etc.) accessed in the reaction conditions. The catalyst exploits the associated instability—the desire to exist in multiple states simultaneously—as a driving force for chemical transformations. In other words, phase boundaries are good places to start the catalyst search, and indeed, we should focus on at least two phases at once rather than just one. Here, we substantiate this claim with several studies that combine statistical operando modeling and experiments. Transpiring from these observations is a hitherto unrecognized vector in catalyst discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

The Role of Leaf Area Changes Within Plant CO 2 Physiological Impacts on the Global Hydrological Cycle

Rising atmospheric CO 2 concentrations enhance greenhouse warming and drive changes to plant physiology, leading to innumerable climate impacts. This study explores the impacts of plant responses on hydrological cycling at 2x preindustrial CO 2 concentrations by analyzing simulations that isolate plant physiological effects using the Community Earth System Model versions 1 and 2. We find that leaf area growth increases canopy evaporation, which offsets transpiration declines, and dampens changes in global mean evapotranspiration, precipitation, and runoff in a CESM2 experiment with dynamic leaf area. These leaf area impacts are also evident in the differences between CESM1 and CESM2, with CESM2 better capturing observed leaf area magnitudes but potentially overestimating leaf area-CO 2 sensitivity, highlighting the importance of plant CO 2 physiology on hydrological cycle changes and the need to improve its representation in climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗
DOE OSTI2026

Integrating Maximum Entropy Production Theory and Machine Learning to Improve Global Evapotranspiration Modeling

Accurate estimation of terrestrial evapotranspiration (ET) is vital for understanding global water and energy cycles. However, current global ET estimations are not well constrained. This study introduces an integrated framework combining the Maximum Entropy Production (MEP) theory with Random Forest (RF) model to improve global ET estimation. Specifically, in contrast to direct ET estimation by the RF model, the integrated framework (MEP‐RF) trains to predict error of MEP‐simulated ET. MEP‐RF outperforms RF in spatiotemporal extrapolation. Attribution analysis with in situ observations reveals that the inputs of MEP are the most critical variables for the ET process, including net radiation, vegetated area, soil moisture, and surface temperature. We further drive MEP‐RF with global reanalysis and satellite data sets of these four inputs, yielding a global mean terrestrial ET of 548 mm/year, with 77% attributed to transpiration. The global ET increased at a rate of 0.85 mm/year per year during 2003–2021, primarily due to vegetation greening rather than rising temperature, while decreasing soil moisture led to decreasing regional ET. The integrated framework provides a novel approach for the estimation of global ET without the need for hard‐to‐obtain and thus uncertain inputs, such as wind speed, surface roughness, aerodynamic and canopy stomatal resistance. Therefore, MEP‐RF offers an independent method on existing global ET products. It represents a promising physically based approach that can be incorporated into Earth System Models to enhance water and energy cycle simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Hot droughts in the Amazon provide a window to a future hypertropical climate

Tropical forests represent the warmest and wettest of Earth’s biomes, but with continued anthropogenic warming, they will be pushed to climate states with no current analogue. Droughts in the tropics are already becoming more intense as they occur at successively higher temperatures. Here, in this study, we synthesize multiple datasets to assess the effects of hot droughts on a central Amazon forest. First, a more than 30-year record of annually resolved forest demographic data from a selective logging experiment showed higher tree mortality during intense droughts, particularly among fast-growing pioneer species with low wood density. Second, analysis of ecophysiological field measurements from the 2015 and 2023 El Niño droughts identified a soil moisture threshold beyond which transpiration rates rapidly declined. As rainless days beyond this threshold continued, drought conditions intensified, increasing the potential for tree mortality from hydraulic failure and carbon starvation. Third, analyses from the Coupled Model Intercomparison Project Phase 6 demonstrated that under high-emission scenarios, a large area of tropical forest will shift to a hotter ‘hypertropical’ climate by 2100. Last, under a hypertropical climate, temperature and moisture conditions during typical dry season months will more frequently exceed identified drought mortality thresholds, elevating the risk of forest dieback. Present-day hot droughts are harbingers of this emerging climate, offering a window for studying tropical forests under expected extreme future conditions.

drought↗
DOE OSTI2024

A variational method for the sheath potential of hypersonic leading edges with space-charge limitations

Electron transpiration cooling for the leading edges (LE) of hypersonic aircraft utilizes thermionic emission; however, space-charge effects limit the electron emission rate, potentially diminishing the efficiency of this cooling mechanism. We develop a variational weak form of the Poisson equation that describes the sheath potential and then numerically solve it using the finite element method. This formulation has two main benefits: (1) the space-charge limit condition can be incorporated as a constraint and (2) it allows for the analysis of three-dimensional geometries with complex boundary conditions. We demonstrate that the current emitted from the surface of an LE is generally a small fraction of the Child–Langmuir limit due to space charge. We then propose several methods to enhance the emitted current from the surface and to boost the cooling effect of thermionic emission. These include increasing the plasma density, applying a negative surface potential, and using fringe fields under suitable geometric conditions. For a LaB6 emitting LE, the total emitted current is shown to be minimal and independent of the temperature of a surface with floating potential. However, when a negative potential is applied and the surface is heated, the emitted current follows the Richardson–Dushman relationship up to a critical temperature, beyond which it remains constant. At an applied surface potential of −5 V, the critical temperature is around 1700 K.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Stomata in-sight: Integrating live confocal microscopy with leaf gas exchange and environmental control

Stomatal anatomy (aperture area, length, and width) influences leaf-level physiology traits including conductance to water vapor. Stomatal anatomy can be visualized in situ by microscopy, but the difficulty of regulating the atmospheric environment of a microscope stage means that the conditions under which imaging is done are rarely physiologically relevant. Alternatively, leaf gas exchange instruments that measure gas fluxes reflect stomatal anatomical characteristics in aggregate, but the relative strengths of anatomical traits to control water use (e.g. size vs density) cannot be firmly established. To reconcile the microscopic stomatal characteristics with leaf-level gas exchange, we describe a tool that combines laser scanning confocal microscopy, gas exchange instruments, and machine-learning image analysis to simultaneously observe anatomical characteristics of many (>40) stomata alongside leaf-level traits like photosynthesis, transpiration, and stomatal conductance. We demonstrate how the tool has the resolution capable of quantifying aperture sizes and variability in maize (Zea mays) leaves under 5 steady-state light/pCO 2 treatments while tightly controlling other environmental variables like relative humidity and temperature. A model used to calculate stomatal conductance from measured apertures and stomatal density accurately matched stomatal conductance measured by gas exchange. This technical advancement will provide insight on how stomatal anatomy and function trade off to influence stomatal conductance and leaf-level water use efficiency.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Elucidating the drought-responsive changes in Poplar cuticular waxes: A GWAS analysis of genes involved in fatty acid biosynthesis

Drought and episodic drought events are major impending impacts of climate change, limiting the productivity of plants and especially trees due to their inherent high transpiration rates. One common mechanism used by plants to cope with drought stress is to change the composition of their leaf cuticular waxes. Cuticular waxes are essential for controlling non-stomatal water loss and are typically composed of a homologous series of very-long-chain fatty acid-derived compounds, as well as flavonoids, tocopherols, triterpenoids, and phytosterols. In this study, we compared the cuticular waxes of 339 natural accessions of Populus trichocarpa (black cottonwood) grown under control and drought conditions in a common garden. A Genome-Wide Association Study (GWAS) was then used to identify candidate genes associated with cuticular wax biosynthesis and/or its regulation. Although no major differences were observed in total wax load when subject to drought conditions, the amounts of the individual wax constituents were indeed responsive to drought. Specifically, changes in alkenes, alcohols, esters, and aldehydes were evident, and suggest that they contribute to the drought response/tolerance in poplar. GWAS uncovered several genes linked to fatty acid biosynthesis, including CER1, CER3, CER4, FATB, FAB1, FAR3, FAR4, KCS, and a homolog of SOH1, as well as other candidate genes that may be involved in coordinating the drought responses in poplar trees. Our findings provide new evidence that genotype-specific shifts in wax composition, rather than total wax accumulation, contribute to drought adaptation in poplar. Additionally, we show that genetic variation in key wax biosynthetic genes drives cuticular wax plasticity in P. trichocarpa under drought, identifying putative molecular targets for improving stress resilience in trees. This study expands our understanding of the adaptative mechanisms of the cuticle and their potential for enhancing drought tolerance in poplar species.

Alkanes↗
DOE OSTI2025

Variation in Flooding Tolerance in Populus deltoides ‘D-124’ and P. trichocarpa x P. deltoides Hybrid ‘52–225’

Flooding poses a substantial challenge to plant survival and productivity, particularly in riparian genus like Populus. This study examines the physiological, morphological, metabolic, and molecular responses of Populus deltoides ‘D-124’ and P. trichocarpa x P. deltoides hybrid clone ‘52–225’ under control and inundated conditions to identify differences in flooding tolerance. Under flooding conditions, physiological and cellular stress was more pronounced in P. deltoides ‘D-124’ than in the hybrid clone ‘52–225,’ as evidenced by lower transpiration (E), photosynthesis (A), and chlorophyll content. In contrast, ‘52–225’ showed reduced ROS accumulation suggesting better cellular function under stress. Morphologically, ‘52–225’ produced more shoot-born roots, which likely enhance oxygen transport and metabolic activity during flooding. Metabolite profiling revealed both overlapping and distinct patterns of sugar and amino acid accumulation between genotypes. Gene expression analysis revealed that flooding-responsive genes, including ALCOHOL DEHYDROGENASE 1 and HYPOXIA RESPONSIVE ERF 2, were activated in both genotypes, with a more pronounced response noted in ‘52–225.’ These findings extend our understanding of flooding tolerance mechanisms in Populus by connecting physiological traits, stress responses, and genetic regulation. This research contributes to the development of more flooding-resilient poplar varieties, with potential applications in breeding and restoration programs for flooding-prone environments.

Flooding stress↗
DOE OSTI2026

Engineered Production of Hydroxycinnamoyl Tyramine Conjugates Limits the Growth of the Pathogen Pseudomonas syringae in Arabidopsis

Hydroxycinnamoyl tyramine conjugates are phenolamides produced by plants in response to pathogen attack and biotic stresses. Their proposed mechanisms of action include cytotoxicity towards pathogens, cell wall reinforcement to restrict pathogen proliferation, and signaling activity to trigger general stress responses. Here, we engineered the production of the tyramine conjugates p-coumaroyltyramine (CT) and feruloyltyramine (FT) in Arabidopsis to gain insight into their mode of action. Co-expression of feedback-insensitive 3-deoxy-D-arabino-heptulosonate 7-phosphate synthase and tyrosine decarboxylase increased tyramine content. Additional expression of tyramine hydroxycinnamoyltransferase led to de-novo production of CT and FT, which were found as soluble and cell-wall-bound forms. FT was associated with lignin in stems. The growth of pathogenic Pseudomonas syringae was reduced in rosettes of the Arabidopsis CT- and FT-producing lines compared to wild type. These lines also exhibited increased transpirational water loss in excised rosettes. Transcriptomic analysis of transgenic lines grown under normal conditions revealed alterations in the expression of genes associated with the biological circadian clock. These changes led to a reduction in flavonoids and an early flowering phenotype. Important changes in the expression of genes related to abiotic stress such as drought, cold, heat, and hypoxia potentially contribute to reduced growth of P. syringae in engineered Arabidopsis.

Phenolamides↗