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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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At least 217 records · Page 12

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DOE OSTI2025

SPRUCE Ground Observations of Phenology in Experimental Plots, 2024

This data set consists of one comma separated (*.csv) file containing phenological transition dates, as derived from direct observations of vegetative and reproductive phenology recorded by a human observer, from the SPRUCE experiment during 2024 (2025-03-06 to 2025-11-21), the ninth full year of whole-ecosystem warming (Hanson et al. 2017). Both spring and autumn phenological events are included. Since April 2016, human observers have been directly tracking the phenology of both woody and herbaceous species on a weekly schedule within the SPRUCE experimental chambers, these data are reported in annual ground observations data sets (see Related Data Sets). The observed date reported here is the first survey date in 2024 on which an event/phenophase was definitively observed. This data set also contains a companion file in HTML (*.html) containing figures showing the relationship between the day of year and temperature treatment for different phenological phases by species for 2024.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

SPRUCE Large-Collar In Situ CO2 and CH4 Flux Data for the SPRUCE Experimental Plots

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54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

SPRUCE Whole Ecosystem Warming (WEW) Peat Water Content and Temperature Profiles for Experimental Plot Cores Beginning June 2016

This data set provides the peat water content and peat temperature at time of sampling for peat cores collected before and during the SPRUCE Whole Ecosystem Warming (WEW) study. Cores for the current data set were collected during the following bulk peat sampling events: 13 June 2016 and 23 August 2016. Over time, this dataset will be updated with each new major bulk peat sampling event, and dates/methods will be updated accordingly.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

SPRUCE Vegetation Phenology in Experimental Plots from Phenocam Imagery, 2015-2017

The most recent version of these data are available https://doi.org/10.25581/spruce.100/1874948 and supersedes all previous versions. See Schädel et al., 2019. (https://doi.org/10.25581/spruce.071/1556082). This data set consists of PhenoCam data from the SPRUCE experiment from the beginning of whole ecosystem warming in August 2015 through the end of 2017. Digital cameras, or phenocams, installed in each SPRUCE enclosure track seasonal variation in vegetation “greenness”, a proxy for vegetation phenology and associated physiological activity. Regions of interest (ROIs) were defined for vegetation types (1) Picea trees (EN, evergreen needleleaf); (2) Larix trees (DN, deciduous needleleaf); and (3) the mixed shrub layer (SH, shrubs). This data set consists of two sets of data files: (1) standard “3-day summary product files” for each camera and each ROI (i.e. vegetation type), characterizing vegetation color at a 3-day time step and (2) a “transition date file” containing the estimated “greenness rising” (spring) and “greenness falling” (autumn) transition dates.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Stellar Interpretation of Meteoritic Data and PLotting for Everyone (SIMPLE): Isotope Mixing Lines for Six Sets of Core-collapse Supernova Models

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Physics↗

DOE OSTI2021

Strong temporal variation in treefall and branchfall rates in a tropical forest is related to extreme rainfall: results from 5 years of monthly drone data for a 50 ha plot

Abstract. A mechanistic understanding of how tropical-tree mortality responds to climate variation is urgently needed to predict how tropical-forest carbon pools will respond to anthropogenic global change, which is altering the frequency and intensity of storms, droughts, and other climate extremes in tropical forests. We used 5 years of approximately monthly drone-acquired RGB (red–green–blue) imagery for 50 ha of mature tropical forest on Barro Colorado Island, Panama, to quantify spatial structure; temporal variation; and climate correlates of canopy disturbances, i.e., sudden and major drops in canopy height due to treefalls, branchfalls, or the collapse of standing dead trees. Canopy disturbance rates varied strongly over time and were higher in the wet season, even though wind speeds were lower in the wet season. The strongest correlate of monthly variation in canopy disturbance rates was the frequency of extreme rainfall events. The size distribution of canopy disturbances was best fit by a Weibull function and was close to a power function for sizes above 25 m2. Treefalls accounted for 74 % of the total area and 52 % of the total number of canopy disturbances in treefalls and branchfalls combined. We hypothesize that extremely high rainfall is a good predictor because it is an indicator of storms having high wind speeds, as well as saturated soils that increase uprooting risk. These results demonstrate the utility of repeat drone-acquired data for quantifying forest canopy disturbance rates at fine temporal and spatial resolutions over large areas, thereby enabling robust tests of how temporal variation in disturbance relates to climate drivers. Further insights could be gained by integrating these canopy observations with high-frequency measurements of wind speed and soil moisture in mechanistic models to better evaluate proximate drivers and with focal tree observations to quantify the links to tree mortality and woody turnover.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013