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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

SPRUCE Vegetation Phenology in Experimental Plots from Phenocam Imagery, 2015-2019

The most recent version of these data are available https://doi.org/10.25581/spruce.100/1874948 and supersedes all previous versions. This dataset consists of PhenoCam data from the SPRUCE experiment from the beginning of whole ecosystem warming in August 2015 through March-31 of 2020, with start- and end-of-season phenological transition dates derived through the end of autumn 2019. Digital cameras, or phenocams, installed in each SPRUCE enclosure track seasonal variation in vegetation “greenness”, a proxy for vegetation phenology and associated physiological activity. Three separate regions of interest (ROIs) were defined for each camera field of view, corresponding to different vegetation types and demarcating: Picea trees (vegetation type EN, for evergreen needleleaf); Larix trees (vegetation type DN, for deciduous needleleaf); and the mixed shrub layer (vegetation type SH).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

SPRUCE Vegetation Phenology in Experimental Plots from Phenocam Imagery, 2015-2020

The most recent version of these data are available https://doi.org/10.25581/spruce.100/1874948 and supersedes all previous versions. This data set consists of PhenoCam data from the SPRUCE experiment from the beginning of whole ecosystem warming (Hanson et al. 2017) in August 2015 through March 31 of 2021, with start- and end-of-season phenological transition dates derived through the end of autumn 2020. Digital cameras, or phenocams, installed in each SPRUCE enclosure track seasonal variation in vegetation “greenness”, a proxy for vegetation phenology and associated physiological activity. Three separate regions of interest (ROIs) were defined for each camera field of view, corresponding to different vegetation types and demarcating (1) Picea trees (vegetation type EN, for evergreen needleleaf); (2) Larix trees (vegetation type DN, for deciduous needleleaf); and (3) the mixed shrub layer (vegetation type SH). User note: A list of previous versions can be found in the Related Datasets section of the user guide.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Interplay of Topography, Fire History, and Climate on Interior Alaska Boreal Forest Vegetation Dynamics in the 21st Century: A Landsat Time-Series Analysis

This study investigates vegetation dynamics in boreal forests of Interior Alaska, focusing on topography, fire history, and climate influences. The study area includes Bonanza Creek Experimental Forest (BCEF) and surrounding region, categorized by topography (upland, floodplain, lowland) and fire history. Using Mann–Kendall trend and Theil–Sen slope analyses on Landsat-derived spectral metrics: Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR), we observed a shift from browning to greening trends, particularly in historically burned areas. The photosynthetic activity in burned upland converged with unburned areas ~30 years post-fire, coincident with a shift towards deciduous dominance during post-fire succession. Normalized Difference Moisture Index (NDMI) trends revealed a significant increase in vegetation moisture content across all topographies. We introduce Effective Seasonal Precipitation Index (ESPI), which combines prior-year annual precipitation with current-year spring snow depth. Its positive correlation with NDMI highlights its potential for monitoring vegetation moisture dynamics at the landscape scale. Furthermore, by correlating dendrochronology-based climate indices, we found strong correlation between NDMI and normalized Supplemental Precipitation Index (nSPI), across topographies. Overall, this research provides critical insights into how climate and fire influence interior boreal vegetation, highlighting the effects of increased precipitation, and topography on shaping differential vegetation responses across the landscape.

Google Earth Engine↗
DOE OSTI2020

Vegetation Detection Using Deep Learning and Conventional Methods

Land cover classification with the focus on chlorophyll-rich vegetation detection plays an important role in urban growth monitoring and planning, autonomous navigation, drone mapping, biodiversity conservation, etc. Conventional approaches usually apply the normalized difference vegetation index (NDVI) for vegetation detection. In this paper, we investigate the performance of deep learning and conventional methods for vegetation detection. Two deep learning methods, DeepLabV3+ and our customized convolutional neural network (CNN) were evaluated with respect to their detection performance when training and testing datasets originated from different geographical sites with different image resolutions. A novel object-based vegetation detection approach, which utilizes NDVI, computer vision, and machine learning (ML) techniques, is also proposed. The vegetation detection methods were applied to high-resolution airborne color images which consist of RGB and near-infrared (NIR) bands. RGB color images alone were also used with the two deep learning methods to examine their detection performances without the NIR band. The detection performances of the deep learning methods with respect to the object-based detection approach are discussed and sample images from the datasets are used for demonstrations.

58 GEOSCIENCES↗
DOE OSTI2020

A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra

Changes in vegetation distribution, structure, and function can modify the canopy properties of terrestrial ecosystems, with potential consequences for regional and global climate feedbacks. In the Arctic, climate is warming twice as fast as compared to the global average (known as ‘Arctic amplification’), likely having stronger impacts on arctic tundra vegetation. In order to quantify these changes and assess their impacts on ecosystem structure and function, methods are needed to accurately characterize the canopy properties of tundra vegetation types. However, commonly used ground-based measurements are limited in spatial and temporal coverage, and differentiating low-lying tundra plant species is challenging with coarse-resolution satellite remote sensing. The collection and processing of multi-sensor data from unoccupied aerial systems (UASs) has the potential to fill the gap between ground-based and satellite observations. To address the critical need for such data in the Arctic, we developed a cost-effective multi-sensor UAS (the ‘Osprey’) using off-the-shelf instrumentation. The Osprey simultaneously produces high-resolution optical, thermal, and structural images, as well as collecting point-based hyperspectral measurements, over vegetation canopies. In this paper, we describe the setup and deployment of the Osprey system in the Arctic to a tundra study site located in the Seward Peninsula, Alaska. We present a case study demonstrating the processing and application of Osprey data products for characterizing the key biophysical properties of tundra vegetation canopies. In this study, plant functional types (PFTs) representative of arctic tundra ecosystems were mapped with an overall accuracy of 87.4%. The Osprey image products identified significant differences in canopy-scale greenness, canopy height, and surface temperature among PFTs, with deciduous low to tall shrubs having the lowest canopy temperatures while non-vascular lichens had the warmest. The analysis of our hyperspectral data showed that variation in the fractional cover of deciduous low to tall shrubs was effectively characterized by Osprey reflectance measurements across the range of visible to near-infrared wavelengths. Therefore, the development and deployment of the Osprey UAS, as a state-of-the-art methodology, has the potential to be widely used for characterizing tundra vegetation composition and canopy properties to improve our understanding of ecosystem dynamics in the Arctic, and to address scale issues between ground-based and airborne/satellite observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The Pan-Arctic Vegetation Cover (PAVC) database v1.1

The Pan-Arctic Vegetation Cover (PAVC) database contains synthesized field-data observations of vegetation cover from 978 Arctic Alaska plots with observations from 2010 to 2021. The cover datasets contain plot data at both the plant functional type (PFT) and species-level resolution, with standardized PFT definitions and species names. We synthesized publicly available point-intercept and visual estimate plots from the Arctic Vegetation Archive of Alaska, the Alaska Vegetation Plots Database, the North Slope Science Catalog, and the National Ecological Observatory Network; as well as previously unpublished data from the Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic).Users will find four synthesized datasets, 4 associated data descriptor (dd) files, and 1 metadata file in the PAVC database:synthesized_species_fcover.csv contains fractional cover (fcover) for unique accepted species names, where names include vegetation identified at the family, genus, species, subspecies, and variety levels, as well as general functional types across all 5 data sources. The synthesized_species_fcover_dd.csv accompanies this dataset with header information.synthesized_pft_fcover.csv contains fcover for the following PFTs: non-vascular plants with lichen and bryophyte subcategories, trees with deciduous and evergreen subcategories, shrubs with deciduous and evergreen subcategories, graminoids (grasses), and forbs (herbaceous flowering plants) measured as total cover. Litter and “other” cover are also included as total cover. Additional “types” include water and bare ground, which were measured as top cover. The synthesized_pft_fcover_dd.csv accompanies this dataset with header information.species_pft_checklist.csv is a lookup table containing the translation from a dataset species name to an accepted species name and to a PFT. This table can be used to clarify our species to PFT adjudications, and to aid users in assigning their own PFTs. Any issues found in this checklist should be reported in the Issues tab of our github.survey_unit_information.csv contains auxiliary information about the plots synthesized in this database. It contains useful information for filtering plots of interest based on temporal, geospatial, and contextual information about the plot surveys.flmd.csv contains metadata information about each file in the database.This research was performed as a part of the NGEE Arctic project. The NGEE Arctic project was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Photosynthesis phenology, as defined by solar-induced chlorophyll fluorescence, is overestimated by vegetation indices in the extratropical Northern Hemisphere

Vegetation phenology is highly sensitive to climate change, although the data and methods used to estimate key phenological states can influence this sensitivity. Because of its direct relation to leaf photosynthetic carbon uptake, remotely sensed solar-induced chlorophyll fluorescence (SIF) can provide new insight assessing changes in vegetation phenology. In this work, we investigated the potential of using a SIF time series product named contiguous SIF (CSIF) to estimate spring, summer, and autumn phenology in the extratropical Northern Hemisphere (>30°N) and compared the results with those based on Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) for the period 2001–2018. Overall, we found similar spatial patterns in phenological states. However, specific dates of key phenological events differed when using CSIF vs. MODIS NDVI data. NDVI data indicated that the growing season started earlier (by 10.1 days on average) and ended later (11.5 days on average) relative to CSIF data. This implies that actual periods for photosynthetic activity are shorter (by 21.6 days on average) than those estimated from vegetation indices more directly related to changes in canopy structure. These large differences between results from NDVI and that from CSIF suggest that vegetation indices such as NDVI seem to overestimate the period for active photosynthesis over the extratropical Northern Hemisphere. Furthermore, while phenology of the early growing season is dominated by temperature for both NDVI and CSIF data, phenology of the late growing season is mainly controlled by temperature for NDVI but by precipitation for CSIF. Our findings were further confirmed by other SIF (GOME-2 SIF) and vegetation index (MODIS EVI) datasets. Phenology modes in Earth system modelling are often parameterized using leaf unfolding and senescence from either station or satellite observations. Our results imply that canopy structure-based parameterization schemes may have overestimated photosynthesis active period, and thus productivity responses. We conclude that SIF data offers a novel and unique approach for assessing phenological change - one that is more directly tied to the carbon cycle and how it is being influenced by climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Satellite solar-induced chlorophyll fluorescence and near-infrared reflectance capture complementary aspects of dryland vegetation productivity dynamics

Mounting evidence indicates dryland ecosystems play an important role in driving the interannual variability and trend of the terrestrial carbon sink. Nevertheless, our understanding of the seasonal dynamics of dryland ecosystem carbon uptake through photosynthesis [gross primary productivity (GPP)] remains relatively limited due in part to the limited availability of long-term data and unique challenges associated with satellite remote sensing across dryland ecosystems. Here, we comprehensively evaluated longstanding and emerging satellite vegetation proxies in their ability to capture seasonal dryland GPP dynamics. Specifically, we evaluated: 1) reflectance-based proxies normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), near infrared reflectance index (NIR v ), and kernel NDVI (kNDVI) from the MODerate resolution Imaging Spectroradiometer (MODIS); and 2) newly available physiologically-based proxy solar-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI). As a performance benchmark, we used GPP estimates from a robust network of 21 western United States eddy covariance tower sites that span representative gradients in dryland ecosystem climate and functional composition. We found that NIR v and SIF were the best performing GPP proxies and captured complementary aspects of seasonal GPP dynamics across dryland ecosystem types. NIR v offered better performance than the other proxies across relatively low-productivity, sparsely non-evergreen vegetated sites (R 2 = 0.59 ± 0.13); whereas SIF best captured seasonal dynamics across relatively high-productivity sites, including evergreen-dominated sites (R 2 = 0.74 ± 0.07). Notably, across grass-dominated sites, all reflectance-based proxies (NDVI, SAVI, NIRv and kNDVI) showed significant seasonal bias (hysteresis) that strengthened with the total fraction of woody vegetation cover, likely due to seasonal patterns in woody vegetation reflectance that are unrelated to or decoupled from GPP. In conclusion, future efforts to fully integrate the complementary strengths of NIR v and SIF could significantly improve our understanding and representation of dryland GPP dynamics in satellite-based models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Persistent global greening over the last four decades using novel long-term vegetation index data with enhanced temporal consistency

Advanced Very High-Resolution Radiometer (AVHRR) satellite observations have provided the longest global daily records from 1980s, but the remaining temporal inconsistency in vegetation index datasets has hindered reliable assessment of vegetation greenness trends. To tackle this, we generated novel global long-term Normalized Difference Vegetation Index (NDVI) and Near-Infrared Reflectance of vegetation (NIRv) datasets derived from AVHRR and Moderate Resolution Imaging Spectroradiometer (MODIS). We addressed residual temporal inconsistency through three-step post processing including cross-sensor calibration among AVHRR sensors, orbital drifting correction for AVHRR sensors, and machine learning-based harmonization between AVHRR and MODIS. After applying each processing step, we confirmed the enhanced temporal consistency in terms of detrended anomaly, trend and interannual variability of NDVI and NIRv at calibration sites. Our refined NDVI and NIRv datasets showed a persistent global greening trend over the last four decades (NDVI: 0.0008 yr -1 ; NIRv: 0.0003 yr -1 ), contrasting with those without the three processing steps that showed rapid greening trends before 2000 (NDVI: 0.0017 yr -1 ; NIRv: 0.0008 yr -1 ) and weakened greening trends after 2000 (NDVI: 0.0004 yr -1 ; NIRv: 0.0001 yr -1 ). These findings highlight the importance of minimizing temporal inconsistency in long-term vegetation index datasets, which can support more reliable trend analysis in global vegetation response to climate changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Deforestation Increases Vegetation Vulnerability to Drought Across Biomes

Land use and land cover changes have altered terrestrial ecosystem carbon storage, but their impacts on ecosystem sensitivity to drought and temperature fluctuations have not been evaluated spatially over the globe. We estimate drought and temperature sensitivities of ecosystems using vegetation greenness from satellite observations and vegetation biomass from dynamic global vegetation model (DGVM) simulations. Using a space-for-time substitution with satellite data, we first illustrate the effects of vegetation cover changes on drought and temperature sensitivity and compare them with the effects estimated from DGVMs. We also compare simulations forced by scenarios with and without land cover changes to estimate the historical land cover change effects. Satellite data and vegetation models both show that converting forests to grasslands results in a more negative or decreased positive sensitivity of vegetation greenness or biomass to drought. Significant variability exists among models for other types of land cover transitions. We identify substantial effects of historical land cover changes on drought sensitivity from model simulations with a generally positive direction globally. Deforestation can lead to either an increased negative sensitivity, as drought-tolerant forests are replaced by grasslands based on model ensemble mean, or a decreased negative sensitivity, since forests under current land cover are predicted to exhibit greater drought resistance compared to those under pre-industrial land cover. Overall, our findings emphasize the critical role of forests in maintaining ecosystem stability and resistance to drought and temperature fluctuations, thereby implying their importance in stabilizing the carbon stock under increasingly extreme climate conditions.

Xiao, Chenwei [Max Planck Institute for Biogeochem↗
DOE OSTI2024

Mapping the global distribution of C 4 vegetation using observations and optimality theory

Plants with the C 4 photosynthesis pathway typically respond to climate change differently from more common C 3 -type plants, due to their distinct anatomical and biochemical characteristics. These different responses are expected to drive changes in global C 4 and C 3 vegetation distributions. However, current C 4 vegetation distribution models may not predict this response as they do not capture multiple interacting factors and often lack observational constraints. Here, we used global observations of plant photosynthetic pathways, satellite remote sensing, and photosynthetic optimality theory to produce an observation-constrained global map of C 4 vegetation. We find that global C 4 vegetation coverage decreased from 17.7% to 17.1% of the land surface during 2001 to 2019. This was the net result of a reduction in C 4 natural grass cover due to elevated CO 2 favoring C 3 -type photosynthesis, and an increase in C 4 crop cover, mainly from corn (maize) expansion. Using an emergent constraint approach, we estimated that C 4 vegetation contributed 19.5% of global photosynthetic carbon assimilation, a value within the range of previous estimates (18–23%) but higher than the ensemble mean of dynamic global vegetation models (14 ± 13%; mean ± one standard deviation). Our study sheds insight on the critical and underappreciated role of C 4 plants in the contemporary global carbon cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Contrasting effects of urbanization on vegetation between the Global South and Global North

Urban vegetation, the core component of green infrastructure and critical for sustainable cities, is profoundly affected by the process of urbanization. Urbanization not only leads to substantial vegetation loss (direct impact) but also fosters urban vegetation growth (indirect impact). However, the extent to which these direct and indirect impacts affect vegetation dynamics across cities worldwide and how urban greening will change in the future remain unclear. Using satellite-based greenness and impervious surface datasets, we show that positive indirect impacts mitigated 56.85% of the negative direct impacts across 4,718 cities worldwide from 2000 to 2019. Notably, the offsetting coefficient is much greater in Global North cities (79.13%) than in Global South cities (38.01%) partly due to their socioeconomic differences. This disparity in urban greening dynamics will continue in the future. Approximately 60% of Global North cities and 30% of Global South cities will become greener by 2040. Our results reveal the divergent trade-offs between vegetation loss and enhanced vegetation growth in cities of different socioeconomic levels and stages of urbanization. In conclusion, such insights are crucial for a comprehensive understanding of urban greening dynamics and for devising strategies to attain sustainable development goals.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Modeling strategies and data needs for representing coastal wetland vegetation in land surface models

Vegetated coastal ecosystems sequester carbon rapidly relative to terrestrial ecosystems. Coastal wetlands are poorly represented in land surface models, but work is underway to improve process-based, predictive modeling of these ecosystems. Here, in this work, we identify guiding questions, potential simulations, and data needs to make progress in improving representation of vegetation in terrestrial–aquatic interfaces, with a focus on coastal and estuarine ecosystems. We synthesize relevant plant traits and environmental controls on vegetation that influence carbon cycling in coastal ecosystems. We propose that models include separate plant functional types (PFTs) for mangroves, graminoid salt marshes, and succulent salt marshes to adequately represent the variation in aboveground and belowground productivity between common coastal wetland vegetation types. We also discuss the drivers and carbon storage consequences of shifts in dominant PFTs. We suggest several potential approaches to represent the diversity in vegetation tolerance and adaptations to fluctuations in salinity and water level, which drive key gradients in coastal wetland ecosystems. Finally, we discuss data needs for parameterizing and evaluating model implementations of coastal wetland vegetation types and function.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Vegetation Warming Experiment: Environmental Conditions, Utqiagvik (Barrow), Alaska, 2018

Environmental conditions measured in five warming chambers and paired ambient control plots located on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska from 16 June - 24 September, 2018. These data were recorded in support of the Zero Power Warming (ZPW) vegetation warming experiment, a series of single season vegetation warming treatments conducted over four years from 2017-2021 (no experiment in 2020). Air temperature and humidity, infrared surface (canopy) temperature, soil temperature, soil moisture, NDVI (normalized difference vegetation index), PRI (photochemical reflectance index), solar radiation and chamber venting were recorded in each chamber at 1 minute intervals. Ambient air temperature, humidity, solar radiation and uplooking PRI and NDVI were measured at a centrally located meteorology station. Vapor pressure deficit (VPD) was calculated and included in the final processed data products. Data has undergone full QA/QC and is presented as 1 minute data, and hourly and daily aggregate data products. This data package includes unprocessed raw data (*.dat files), processed data (*.csv) and metadata including a full description of sensors, calculations and processing (*.csv, *.pdf). See related NGEE-Arctic "Vegetation Warming Experiment" data packages for leaf-level gas exchange and other leaf trait data; chamber, plot and landscape phenocamera images; thaw depth, and GPS locations of chambers and ambient plots.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Vegetation Warming Experiment: Environmental conditions, Utqiagvik (Barrow), Alaska, 2019

Environmental conditions measured in five warming chambers and paired ambient control plots located on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska from 19 June – 25 September, 2019. These data were recorded in support of the Zero Power Warming (ZPW) vegetation warming experiment, a series of single season vegetation warming treatments conducted over four years from 2017–2021 (no experiment in 2020). Air temperature and humidity, infrared surface (canopy) temperature, soil temperature, soil moisture, NDVI (normalized difference vegetation index), PRI (photochemical reflectance index), solar radiation and chamber venting were recorded in each chamber at 1 minute intervals. Ambient air temperature, humidity, solar radiation and uplooking PRI and NDVI were measured at a centrally located meteorology station. Vapor pressure deficit (VPD) was calculated and included in the final processed data products. Data has undergone full QA/QC and is presented as 1 minute data, and hourly and daily aggregate data products. This data package includes unprocessed raw data (*.dat files), processed data (*.csv) and metadata including a full description of sensors, calculations and processing (*.csv, *.pdf). See related NGEE-Arctic "Vegetation Warming Experiment" data packages for leaf-level gas exchange and other leaf trait data; chamber, plot and landscape phenocamera images; thaw depth, and GPS locations of chambers and ambient plots.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Vegetation Warming Experiment: Environmental Conditions, Utqiagvik (Barrow), Alaska, 2017

Environmental conditions measured in five warming chambers and paired ambient control plots located on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska from 22 June - 17 September, 2017. These data were recorded in support of the Zero Power Warming (ZPW) vegetation warming experiment, a series of single season vegetation warming treatments conducted over four years from 2017-2021 (no experiment in 2020). Air temperature and humidity, infrared surface (canopy) temperature, soil temperature, soil moisture, NDVI (normalized difference vegetation index), PRI (photochemical reflectance index), solar radiation and chamber venting were recorded in each chamber at 1 minute intervals. Ambient air temperature, humidity, solar radiation and uplooking PRI and NDVI were measured at a centrally located meteorology station. Vapor pressure deficit (VPD) was calculated and included in the final processed data products. Data has undergone full QA/QC and is presented as 1 minute data, and hourly and daily aggregate data products. This data package includes unprocessed raw data (*.dat files), processed data (*.csv) and metadata including a full description of sensors, calculations and processing (*.csv, *.pdf). See related NGEE-Arctic Vegetation Warming Experiment data packages for leaf-level gas exchange and other leaf trait data; chamber, plot and landscape phenocamera images; thaw depth, and GPS locations of chambers and ambient plots. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Vegetation Warming Experiment: Environmental conditions, Utqiagvik (Barrow), Alaska, 2021

Environmental conditions measured in five warming chambers and paired ambient control plots located on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska from 19 June - 17 September, 2021. These data were recorded in support of the Zero Power Warming (ZPW) vegetation warming experiment, a series of single season vegetation warming treatments conducted over four years from 2017-2021 (no experiment in 2020). Air temperature and humidity, infrared surface (canopy) temperature, soil temperature, soil moisture, NDVI (normalized difference vegetation index), PRI (photochemical reflectance index), solar radiation and chamber venting were recorded in each chamber at 1 minute intervals. Ambient air temperature, humidity, solar radiation and uplooking PRI and NDVI were measured at a centrally located meteorology station. Vapor pressure deficit (VPD) was calculated and included in the final processed data products. Data has undergone full QA/QC and is presented as 1 minute data, and hourly and daily aggregate data products. This data package includes unprocessed raw data (*.dat files), processed data (*.csv) and metadata including a full description of sensors, calculations and processing (*.csv, *.pdf). See related NGEE-Arctic "Vegetation Warming Experiment" data packages for leaf-level gas exchange and other leaf trait data; chamber, plot and landscape phenocamera images; thaw depth, and GPS locations of chambers and ambient plots. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Analytical simulation of meander morphology from equilibrium to long-term evolution: Impacts of channel geometry and vegetation-induced coarsening

This study introduces an innovative approach to modeling meandering river morphology, integrating and investigating the effects of geometric characteristics and vegetation-induced channel coarsening. The developed comprehensive framework combines several advanced techniques: Genetic Programming for refining the scour factor of transverse bed slope, a Leaf Area Index (LAI)-enhanced analytical model for quantifying vegetative flow resistance, and an upstream-weighted moving average method for efficient approximation of the convolution integral in meander migration calculations. The model is validated against both an idealized Kinoshita meander and a natural bend of the Tumen River (China) in equilibrium, demonstrating its robustness across diverse scales and conditions. The model's ability to simulate the long-term evolution, including cutoff events, provides valuable insight for river management strategies. The current findings demonstrate that channel geometry, particularly width-to-depth ratio, plays a dominant role in meander evolution, with wider channels prone to more complex and rapid morphological changes. Vegetation effects are most pronounced in channels with moderate width-to-depth ratios, where they can significantly influence migration rates and bed topography. A combination of channel widening and deepening, coupled with strategic vegetation management, can effectively enhance navigability while maintaining channel stability in the studied Tumen River reach. Sensitivity analyses highlight the complex interplay between hydraulic conditions, sediment characteristics, and vegetation in shaping river morphology. This research advances understanding of the multifaceted nature of meandering river systems and offers practical tools for informed decision-making in river engineering and environmental management, particularly in the context of climate change and increasing anthropogenic pressures on fluvial ecosystems.

54 ENVIRONMENTAL SCIENCES↗