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At least 757 records · Page 42

Vegetation transect surveys from 2022 and 2023 within the Kougarok Fire Complex (KFC) on the Seward Peninsula in Alaska, USA

This dataset contains recorded vegetation classifications from 16 transects within the Kougarok Fire Complex (KFC) area in Alaska. Each transect is 50 meters long, and vertical vegetation profiles were sampled every 50cm, resulting in 101 points recorded for each transect. At each point, a vertical rod was inserted in the ground and any living plant or substrate touching the rod was recorded from top to bottom. For each plant, the plant functional type (PFT) and species name was identified and recorded. The file "flmd.xlsx" provides file level meta data for all other included files. The file (transect_metadata.csv) contains descriptive information about each of the 16 transects (field observation year and date; latitude and longitude GPS measurements of the 0, 25, and 50 meter points; and whether that area burned during the 1971, 1997, 2002, 2015, and/or 2019 fires). The files (PFT_names.csv and species_names.csv) map the short hand labels for the plant function type (PFT) and species used in the transect surveys to their full names. The remaining files are named for each transect, and contain the results of the vegetation survey for each transect. These files contain the plant functional type and the species identified at each point. Points along the transect with more than one species at that point are listed vertically from top to bottom in these files.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↗

Monthly Mean In Situ Surface Flux Observations Paired with Satellite-Derived and Reanalysis-Based Flux Data for the Great Lakes Region, 2001–2020

Surface radiative and turbulent heat fluxes over the Great Lakes strongly influence regional hydrological and meteorological processes, and their accurate representation is critical for numerical weather prediction and coupled atmosphere–lake modeling. However, direct flux observations are spatially sparse across the region, so gridded reanalysis and satellite-derived products are often used for climatological analyses and model evaluation despite differences in their flux representations. This dataset provides processed, quality-controlled, monthly mean surface flux observations from the Great Lakes Evaporation Network (GLEN), AmeriFlux, and the National Data Buoy Center, paired with spatiotemporally matched flux estimates from two reanalysis products, the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset (ERA5) and the Modern Era Reanalysis for Research and Applications, version 2 (MERRA-2), and two satellite-derived products, the Clouds and Earth's Radiant Energy Systems Energy Balanced and Filled (CERES-EBAF) and the Cloud, Albedo and Surface Radiation dataset from AVHRR data - Edition 3 (CLARA-A3). The dataset includes sixteen observational stations with variable temporal coverage within 2001–2020. For each station, a CSV file contains monthly time series of available flux variables, including surface downwelling shortwave radiation (SW), surface downwelling longwave radiation (LW), sensible heat (SH) flux, and latent heat flux (LH), alongside matched gridded product values where available. Columns in the CSV file correspond to different variables sourced from each dataset, with column titles structured as "{dataset}_{variable}". Columns with relevant metadata are also provided in each CSV file, including station latitude and longitude, monthly timestamps, and the name of the sourced observational data. These files are structured for direct use in common analysis tools, including Microsoft Excel, Python pandas, and Python matplotlib. This dataset supports climatological analysis of the Great Lakes regional surface energy budget, evaluation of satellite-derived and reanalysis-based flux products, and development or validation of flux representations in numerical weather prediction and coupled atmosphere–lake models.

Great Lakes↗

Sap Velocity Data for East River Watershed Sites (2023-2025)

This dataset includes sap velocity measurements for aspen (populus tremuloides), fir (abies lasiocarpa), spruce (Engelmann spruce) and lodgepole pine (pinus contorta) trees at nine sites in the East River Watershed near Gothic, CO. This dataset was generated following a similar method as a previous dataset (Dataset. doi:10.15485/1647654) but was conducted at different sites in the area and now includes lodgepole pine. Site selection was done to explicitly improve understanding of topographic controls of tree water use. The data collection began in June 2023 and we provide data until December 2025 - though data collection is ongoing. The sap flux data were collected using ICT SFM1 sensors and are presented in both units of cm h^-1 and as kg h^-1 by multiplying the sap flux by the sapwood area of the tree. All sap flow data has been been corrected using estimates of wounding diameter, water content of wood and sap wood depth. We also provide a normalized sap velocity estimate by subtracting each measurement from that trees' annual minimum and dividing by that trees' annual maximum. This provides data for each tree and year on a 0-1 scale. This data entry contains one CSV file that includes all available sap flow data. The timestamps are provided in local time as year, day of year and hour and each measurement contains an associated latitude, longitude and site number which can be used to identify trees in a given stand. Each species is given a numeric value as: 1= populus tremuloides, 2=Engelmann spruce, 3=abies lasiocarpa and 4=pinus contorta.

abies↗

Soil Water Content (m3 m-3) located in the Dominican Republic , February 2013 - December 2016

Daily data that has been processed to monthly. Data represents near-surface soil moisture (depth of 0-10 cm) at a latitude of 17.90 N, and longitude -71.67 E. Data was downloaded from the PBO soil moisture data network and is publicly available. Data from this site is referenced as Dominican Republic (2) in Table 1 and Figure 4 of Solander et al., 2020, The pan-tropical soil moisture response to El Nino, Hydrol. Earth Syst. Sci., in press.

54 ENVIRONMENTAL SCIENCES↗

Soil Water Content (m3 m-3) from the Dominican Republic, February 2013 - August 2017.

Daily data that has been processed to monthly. Data represents near-surface soil moisture (depth of 0-10 cm) at a latitude of 19.76 N, and longitude -70.57 E. Data was downloaded from the PBO soil moisture data network and is publicly available. Data from this site is referenced as Dominican Republic (1) in Table 1 and Figure 4 of Solander et al., 2020, The pan-tropical soil moisture response to El Nino, Hydrol. Earth Syst. Sci., in press.

54 ENVIRONMENTAL SCIENCES↗

Soil Water Content (m3 m-3) from Australia, May 2014 - March 2017

Hourly data processed to monthly. Data collected from Cosmic Neutron Probes at variable depth averaging 28 cm that was interpolated to near-surface (0-10 cm). Site located at latitude -17.12 deg N, longitude 145.63 deg E. Data was downloaded from COSMOS data portal and is publicly available. Data from this site is referenced as Australia (1) in Table 1 and Figure 4 of Solander et al., 2020, The pan-tropical soil moisture response to El Nino, Hydrol. Earth Syst. Sci., in press.

54 ENVIRONMENTAL SCIENCES↗

Soil Water Content (m3 m-3) from Australia, June 2011 - December 2016

Hourly data processed to monthly. Data collected from Cosmic Neutron Probes at variable depth averaging 38 cm that was interpolated to near-surface (0-10 cm). Site located at latitude -14.16 deg N, longitude 131.39 deg E. Data was downloaded from the The COsmic-ray Soil Moisture Observing System (COSMOS) soil moisture data network and is publicly available. Data from this site located at, referenced as Australia (2) in Table 1 and Figure 4 of Solander et al., 2020, The pan-tropical soil moisture response to El Nino, Hydrol. Earth Syst. Sci., in press.

54 ENVIRONMENTAL SCIENCES↗

Soil Water Content (m3 m-3) from Guyaflux site, French Guiana, January 2007 - December 2014

Daily data processed to monthly. Data was collected from Eddy Covariance Flux Tower at the near-surface (0-10 cm). Site located at latitude 5.28 deg N, longitude -52.92 deg E. Data was downloaded from the FluxNet soil moisture data network and is publicly available. Data from this site is referenced as French Guiana (2) in Table 1 and Figure 4 of Solander et al., 2020, The pan-tropical soil moisture response to El Nino, Hydrol. Earth Syst. Sci., in press.

54 ENVIRONMENTAL SCIENCES↗

Soil Water Content (m3 m-3) from Australia, November 2007 - May 2009

Hourly data processed to monthly. Data collected from Eddy Covariance Flux Tower at near-surface (0-10 cm). Site located at latitude -13.08 deg N, longitude 131.12 deg E. Data was downloaded from the FluxNet soil moisture data network and is publicly available. Data from this site located at, referenced as Australia (3) in Table 1 and Figure 4 of Solander et al., 2020, The pan-tropical soil moisture response to El Nino, Hydrol. Earth Syst. Sci., in press.

54 ENVIRONMENTAL SCIENCES↗

Soil Water Content (m3 m-3) located in Santarem, Brazil K67, July 2000 - February 2004

Hourly data processed to monthly. Data collected from Eddy Covariance Flux Tower at the near-surface (0-10 cm). Site located at a latitude -3.02 deg N, longitude -54.97 deg E. Data was downloaded from the FluxNet soil moisture data network and is publicly available. Data from this site is referenced as Brazil (3) in Table 1 and Figure 4 of Solander et al., 2020, The pan-tropical soil moisture response to El Nino, Hydrol. Earth Syst. Sci., in press.

54 ENVIRONMENTAL SCIENCES↗

Soil Water Content (m3 m-3) from Australia, August 2001 - December 2014

Hourly data processed to monthly. Data collected from Eddy Covariance Flux Tower at near-surface (0-10 cm). Site located at latitude -12.49 deg N, longitude 131.15 deg E. Data was downloaded from the FluxNet soil moisture data network and is publicly available. Data from this site is referenced as Australia (4) in Table 1 and Figure 4 of Solander et al., 2020, The pan-tropical soil moisture response to El Nino, Hydrol. Earth Syst. Sci., in press.

54 ENVIRONMENTAL SCIENCES↗

Soil Water Content (m3 m-3), October 2011 - May 2017

Hourly data processed to monthly. Data collected from Cosmic Neutron Probes at variable depth averaging 15 cm that was interpolated to near-surface (0-10 cm). Site located at latitude 0.28 deg N, longitude 36.87 deg E. Data was downloaded from the The COsmic-ray Soil Moisture Observing System (COSMOS) soil moisture data network and is publicly available. Data was made available by the COsmic-ray Soil Moisture Observing SysteM (COSMOS). Data from this site is referenced as Kenya in Table 1 and Figure 4 of Solander et al., 2020, The pan-tropical soil moisture response to El Nino, Hydrol. Earth Syst. Sci., in press.

54 ENVIRONMENTAL SCIENCES↗

LAI, EVI, NDVI, and kNDVI in 23 pantropical forests affected by 21 cyclones

Statement of purpose: Cyclones alter the function and composition of tropical forests, making effects of intensifying cyclones on carbon-rich forests a critical topic of study. Here, we quantified cyclone-induced damage and recovery of 21 cyclone disturbances affecting 23 pantropical forest sites between 1988-2017 utilizing leaf area index (LAI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and transformed NDVI (kNDVI) values from Google Earth Engine. Field observations collected in a meta-analysis (Bomfim et al., 2022, in review) were used to ground-truth and test effects of soil resource availability and disturbance factors on damage and recovery. This meta-analysis also served as the basis to begin vegetation index extraction, utilizing unique site and date combinations, from tropical forests effect by cyclone disturbances. We began collecting NDVI (5km resolution) from the NOAA Climate Data Record (CDR) of AVHRR Normalized Difference Vegetation Index (NDVI), Version 5 data product (Vermote, 2019) for all case studies included, 42. Next, we began extracting Landsat data from Landsat 4, 5, and 8, courtesy of the U.S. Geological Survey, in search of higher resolution data. We selected a 3 by 3 Landsat pixel area, leading to a 90m resolution data extraction. The specific imagery used includes Landsat 4 USGS Landsat 4 TM Collection 1 Tier 1 TOA (top of atmosphere) Reflectance, Landsat 5 USGS Landsat 5 TM (thematic mapper) Collection 1 Tier 1 TOA Reflectance, and Landsat 8 USGS Landsat 8 Collection 1 Tier 1 TOA Reflectance. Within Google Earth Engine, we selected the date and location (latitude and longitude), calculated NDVI, kNDVI, and EVI utilizing Landsat bands (see metadata_NGEE-tropics_cyclones), and extracted post- and pre-cyclone values for each case study to calculate cyclone-induced change in the vegetative index. Due to limited spatial resolution of Landsat remote sensing data, MODIS products were investigated next. First, the MOD13Q1.006 Terra Vegetation Indices 16-Day Global 250m product was used to extract 250m EVI and NDVI (Didan, 2015) and then the MCD15A3H.006 MODIS Leaf Area Index/FPAR 4-Day Global 500m product product was used to extract LAI 500m (Myneni et al., 2015). Pre- and post-cyclone values, change in the vegetative index, and standard deviation for all values are included in the main csv (see case_study_data.csv) for all vegetative indices collected, including LAI 500m, EVI 250m, NDVI 250m, NDVI 90m, kNDVI 90m, EVI 90m, and NDVI 5km. Lastly, recovery values were calculated utilizing a standardization method (see metadata_NGEE-tropics_cyclones) and recovery values for MODIS (see MODIS_recovery.csv) and Landsat (Landsat_recovery.csv) data are included.

54 ENVIRONMENTAL SCIENCES↗

AmeriFlux BR-SM1 Cachoeira do Sul Rice Paddy

This is the AmeriFlux version of the carbon flux data for the site BR-SM1 Cachoeira do Sul Rice Paddy. Site Description - The site is a flooded rice paddy located in the Cachoeira do Sul, Rio Grande do Sul State, Brazil (latitude: -30.27°; longitude: -53.14°; altitude 40.5 m). The total cultivation area is approximately 1000 ha, subdivided into 100 mx100m plots. Flooded rice paddies have been used in this area since the 1980s and are a part of a wide lowland area susceptible to flooding by a stream (Pedro Paulo Stream). Channels and artificial reservoirs provide the water used.

Roberti, Debora Regina [Universidade Federal de Sa↗

AmeriFlux FLUXNET-1F BR-SM1 Cachoeira do Sul Rice Paddy

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site BR-SM1 Cachoeira do Sul Rice Paddy. This is the FLUXNET version of the carbon flux data for the site BR-SM1 Cachoeira do Sul Rice Paddy produced by applying the standard ONEFlux (1F) software. Site Description - The site is a flooded rice paddy located in the Cachoeira do Sul, Rio Grande do Sul State, Brazil (latitude: -30.27°; longitude: -53.14°; altitude 40.5 m). The total cultivation area is approximately 1000 ha, subdivided into 100 mx100m plots. Flooded rice paddies have been used in this area since the 1980s and are a part of a wide lowland area susceptible to flooding by a stream (Pedro Paulo Stream). Channels and artificial reservoirs provide the water used.

Roberti, Debora Regina [Universidade Federal de Sa↗

Core Characterization of the Whipkey ST1 Well - Marcellus Shale

CT and geophysical data of the Marcellus from the Whipkey ST 1 well in Greene Co PA API No: 37-059-24715 latitude 39.9000020N, longitude -80.0199970W Please see the following technical report for further details. Crandall, D.; Moore, J.; Brown, S.; Mackey, P.; Carr, T. Computed Tomography Scanning and Geophysical Measurements of the Marcellus Formation from the Whipkey ST 1 Well NETL-TRS-X-2018; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV, 2018; p 54.

Computed Tomography↗

U.S. Freight Transload Facilities Dataset

The U.S. Freight Transload Facilities Dataset provides location information (latitude, longitude, zip, city, county, state)for more than 9,000 facilities across 50 U.S. States where freight may be transferred between waterways, railways, and roadways. The dataset lists the known modes and available direction(s) for freight transfers at each facility as of 2024. The U.S. Freight Transload Facilities dataset was built by mining and fusing several public sources, such as the USACE Master Docks Plus, the USDOT National Transportation Atlas Database (NTAD), files from the Intermodal Association of North America (IANA), and the industry publication Bulk Transloader. The dataset constitutes a key piece of a multimodal freight transportation network and routing algorithm developed by USACE-ERDC. The dataset is shared as a .csv file. The dataset is published for research purposes and should not be considered exhaustive or authoritative.

Peterson, Steven [ORNL] (ORCID:0000000287672998)↗

Surface Cloud Grid Version 2 (SFCCLDGRID2) Value-Added Product: Description of Update"

This document describes the algorithm used for the Surface Cloud Grid Value-Added Product (VAP). This VAP uses as input the 15-min. output from the Shortwave (SW) Flux Analysis VAP (see Long 2001; Long and Ackerman 2000; Long et al. 1999) from the Atmospheric Radiation Measurement (ARM) Climate Reseach Facility (ACRF) Southern Great Plains (SGP) Central Facility and extended facilities. This network of 21 sites is unevenly spaced over northern Oklahoma into southern Kansas, covering an area from 95.5° to 99.5° west longitude and 34.5° to 38.5° north latitude. For research applications such as single-column modeling, an estimate of the cloud and cloud effects distribution over this entire domain is desirable. The Surface Cloud Grid VAP applies a multi-pass weighted sum analytic approximation technique (Caracena 1987), which uses Gaussian weighting and an imposed scale length, to interpolate to a 0.25° by 0.25° lat/long grid over the SGP domain. The output, like the input, includes solar elevation angles of 10° or greater.

97 MATHEMATICS AND COMPUTING↗