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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Real-Time, Simultaneous Soil Water Content and Meteorological Data Measurement to Support TRACER over Harris County, Texas (Field Campaign Report: Part I)

The main purpose of this project was to provide ground-truth and satellite-based soil water content data in the Houston, Texas, area to support the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s 2022 field campaign, the Tracking Aerosol Convection Interactions ExpeRiment (TRACER). This involved installing four soil monitoring stations at different locations in the Houston area, two of which were part of the ARM facility and two were ancillary. The two stations associated directly with ARM were installed alongside other facilities managed and maintained by the TRACER research team during the intensive operational period (IOP), one located at the La Porte, Texas airport and the other near Guy, Texas. Data from these stations were assimilated with similar data collected by the Harris County Flood Control District (HCFCD) to improve the spatial coverage of the real-time monitoring network. The ground-truth data were compared to the satellite-based (National Aeronautics and Space Administration [NASA]’s Soil Moisture Active Passive [SMAP] mission) data in the TRACER area of interest, and analyzed further to nowcast gridded data over the Houston area. The nowcasted, gridded data product was made available to TRACER researchers for use in climate and land-atmosphere interaction modeling that can help understand formation and persistence of convective storms in dense urban areas, and predict possible environmental events such as floods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Real-Time, Simultaneous Soil Water Content and Meteorological Data Measurement to Support TRACER over Harris County, Texas Field Campaign Report: Part II

The main purpose of this project was to provide ground-truth and satellite-based soil water content data in the Houston, Texas, area to support the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s 2022 field campaign, the Tracking Aerosol Convection Interactions ExpeRiment (TRACER). This involved installing four soil monitoring stations at different locations in the Houston area, two of which were part of the ARM facility and two were ancillary. The two stations associated directly with ARM were installed alongside other facilities managed and maintained by the TRACER research team during the intensive operational period (IOP), one located at the La Porte, Texas airport and the other located near Guy, Texas. Data from these stations were assimilated with similar data collected by the Harris County Flood Control District (HCFCD) to improve the spatial coverage of the real-time monitoring network. The ground-truth data were compared to the satellite-based (National Aeronautics and Space Administration [NASA]’s Soil Moisture Active Passive [SMAP] mission) data in the TRACER area of interest, and analyzed further to nowcast gridded data over the Houston area. The nowcasted, gridded data product was made available to TRACER researchers for use in climate and land-atmosphere interaction modeling that can help understand formation and persistence of convective storms in dense urban areas, and predict possible environmental events such as floods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Data Report: Meteorological and Evapotranspiration Data from Sagebrush and Pinyon Pine/Juniper Communities at Pahute Mesa, Nevada National Security Site, 2011-2012

Pahute Mesa is a groundwater recharge area at the Nevada National Security Site. Because underground nuclear testing was conducted at Pahute Mesa, groundwater recharge may transport radionuclides from underground test sites downward to the water table; the amount of groundwater recharge is also an important component of contaminant transport models. To estimate the amount of groundwater recharge at Pahute Mesa, an INFIL3.0 recharge-runoff model is being developed. Two eddy covariance (EC) stations were installed on Pahute Mesa to estimate evapotranspiration (ET) to support the groundwater recharge modeling project. This data report describes the methods that were used to estimate ET and collect meteorological data. Evapotranspiration was estimated for two predominant plant communities on Pahute Mesa; one site was located in a sagebrush plant community, the other site in a pinyon pine/juniper community. Annual ET was estimated to be 310±13.9 mm for the sagebrush site and 347±15.9 mm for the pinyon pine/juniper site (March 26, 2011 to March 26, 2012). Annual precipitation measured with unheated tipping bucket rain gauges was 179 mm at the sagebrush site and 159 mm at the pinyon pine/juniper site. Annual precipitation measured with bulk precipitation gauges was 222 mm at the sagebrush site and 227 mm at the pinyon pine/juniper site (March 21, 2011 to March 28, 2012). A comparison of tipping bucket versus bulk precipitation data showed that total precipitation measured by the tipping bucket rain gauges was 17 to 20 percent lower than the bulk precipitation gauges. These differences were most likely the result of the unheated tipping bucket precipitation gauges not measuring frozen precipitation as accurately as the bulk precipitation gauges. In this one-year study, ET exceeded precipitation at both study sites because estimates of ET included precipitation that fell during the winter of 2010-2011 prior to EC instrumentation and the precipitation gauges started collecting data in March 2011.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

High-Resolution Floating Solar PV Data

This dataset contains over two years of 1-minute resolution data collected from four floating solar sites, as well as data from a land-based PV system co-located with one of the floating sites. The dataset includes highly granular module temperature measurements - five modules per floating site, with three sensors per module, totaling 15 module temperature sensors per floating site. In addition to the module temperature data, meteorological data collected at the floating sites is also included, along with traditional PV system-level parameters. The data is intended for analysis of solar energy production, efficiency, and performance degradation over time. For information about the data file usage see the "README" resource below. See "Metadata File" for information about individual files and other metadata information.

Array↗
DOE OSTI2020

Subsurface Flow Across Polygonal Tundra Measured by Bromide Tracer, Utqiagvik (Barrow), Alaska, 2015-2016

A bromide tracer study with the primary focus on hydrology in the active layer of polygonal tundra in the Barrow Environmental Observatory in Utqiagvik (Barrow), Alaska from July 2015 to September 2016. Sampling devices placed at various depths in the polygon centers, rims, and troughs were used to monitor tracer concentrations during thaw seasons. The tracer was continually monitored simultaneously on a low- and high-centered polygon throughout thaw seasons. Included is one data file of the bromide tracer concentrations. Also included are measurements taken throughout the experiment - one data file each of frost table depth and precipitation; and twelve water table elevation data files. Water level data, meteorological data, and thaw depth measurements were taken throughout the experiment. Our approach permits a comparison of hydrological behaviors in low- and high-centered polygons over the same time period and meteorological conditions.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) 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 OSTI2012

Recommended Parameter Values for GENII Modeling of Radionuclides in Routine Air and Water Releases

The GENII v2 code is used to estimate dose to individuals or populations from the release of radioactive materials into air or water. Numerous parameter values are required for input into this code. User-defined parameters cover the spectrum from chemical data, meteorological data, agricultural data, and behavioral data. This document is a summary of parameter values that reflect conditions in the United States. Reasonable regional and age-dependent data is summarized. Data availability and quality varies. The set of parameters described address scenarios for chronic air emissions or chronic releases to public waterways. Considerations for the special tritium and carbon-14 models are briefly addressed. GENIIv2.10.0 is the current software version that this document supports.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Air Dispersion Modeling for the INL Application for a Synthetic Minor Sitewide Air Quality Permit to Construct with a Facility Emission Cap Component

The Department of Energy Idaho Operations Office (DOE-ID) is applying for a synthetic minor, Sitewide, air quality permit to construct (PTC) with a facility emission cap (FEC) component from the Idaho Department of Environmental Quality (DEQ) for Idaho National Laboratory (INL) to limit its potential to emit to less than major facility limits for criteria air pollutants (CAPs) and hazardous air pollutants (HAPs) regulated under the Clean Air Act. This document is supplied as an appendix to the application, Idaho National Laboratory Application for a Synthetic Minor Sitewide Air Quality Permit to Construct with a Facility Emissions Cap Component, hereafter referred to as “permit application” (DOE-ID 2015). Air dispersion modeling was performed as part of the permit application process to demonstrate pollutant emissions from the INL will not cause a violation of any ambient air quality standards. This report documents the modeling methodology and results for the air dispersion impact analysis. All CAPs regulated under Section 109 of the Clean Air Act were modeled with the exception of lead (Pb) and ozone, which are not required to be modeled by DEQ. Modeling was not performed for toxic air pollutants (TAPs) as uncontrolled emissions did not exceed screening emission levels for carcinogenic and non-carcinogenic TAPs. Modeling for CAPs was performed with the EPA approved AERMOD dispersion modeling system (Version 14134) (EPA 2004a) and five years (2000-2004) of meteorological data. The meteorological data set was produced with the companion AERMET model (Version 14134) (EPA 2004b) using surface data from the Idaho Falls airport, and upper-air data from Boise International Airport supplied by DEQ. Onsite meteorological data from the Grid 3 Mesonet tower located near the center of the INL (north of INTEC) and supplied by the local National Oceanic and Atmospheric Administration (NOAA) office was used for surface wind directions and wind speeds. Surface data (i.e., land use data that defines roughness, albedo, Bowen ratio, and other parameters) were processed using the AERSURFACE utility (Version 13016) (EPA 2013). Emission sources were modeled as point sources using actual stack locations and dimensions. Emissions, flow rates and exit temperatures were based on the design operating capacity of each source. All structures close enough to produce an area of wake effect were included for all sources. For multi-tiered structures, the heights of the tiers were included or the entire building height was assumed to be equal to the height of the tallest tier. Concentrations were calculated at 1,352 receptor locations provided by DEQ. All receptors were considered for each pollutant and averaging period. Maximum modeled CAP concentrations summed with average background concentration values were presented and compared to National Ambient Air Quality Standards (NAAQS). The background concentration values used were obtained using the Washington State University’s Laboratory for Atmospheric Research North West Airquest web-based retrieval tool (http://lar.wsu.edu/nw airquest/lookup.html). The air dispersion modeling results show the maximum impacts for CAPs are less than applicable standards and demonstrate the INL will not cause a violation of any ambient air quality standards.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Comparison of Triton SODAR Data to Meteorological Tower Wind Measurement Data in Hebei Province, China

With the increased interest in remote sensing of wind information in recent years, it is important to determine the reliability and accuracy of new wind measurement technologies if they are to replace or supplement conventional tower-based measurements. In view of this, HydroChina Corporation and the United States National Renewable Energy Laboratory (NREL) conducted a comparative test near awind farm in Hebei Province, China. We present the results of an analysis characterizing the measurement performance of a state-of-the-art Sound Detection and Ranging (sodar) device when compared to a traditional tower measurement program. NREL performed the initial analysis of a three-month period and sent the results to HydroChina. When another month of data became available, HydroChina andtheir consultant Beijing Millenium Engineering Software (MLN) repeated NREL's analysis on the complete data set, also adding sensitivity analysis for temperature, humidity, and wind speed (Section 6). This report presents the results of HydroChina's final analysis of the four-month period.

17 WIND ENERGY↗
DOE OSTI2019

Calculating Dose Release Factors to Obtain the Worst-Case Maximally Exposed Location by Sector

The aim of this project was to update the SRS Dose Release Factors (DRF's) for the Maximally Exposed Individual (MEI) for 20 area release points onsite. To update, potential MEIs now include onsite locations and modeled stack heights were updated to match onsite operations. The DRF's and MEI locations are used to ensure DOE and EPA compliance is met for dose standards. The Environmental Protection Agency (EPA) requires the use of the computer model CAP88 to estimate doses for demonstrating compliance with 40 CFR 61, the National Emission Standard for Hazardous Air Pollutants (NESHAP). The location of the maximally exposed individual (MEI) by sector and distance is required for execution of CAP88. The worst-case sector is the sector and distance where an offsite individual would receive the highest dose due to a potential release from a designated release location at a facility. The worst-case sector analysis addresses potential ground-level and stack-height releases from 20 areas of Savannah River Site (SRS). Weather patterns were analyzed at each of these areas relative to sixteen compass points. The analysis makes use of recently updated distance data and meteorological data. The computer model, CAP88 is EPA's software system used by the Department of Energy (DOE) to calculate the receptor dose due to routine atmospheric releases via a gaussian plume at the Savannah River Site (SRS). The dose-release factors (DRFs) are used to estimate the offsite and onsite dose from facility-specific releases and represent the dose to a receptor exposed to 1 Ci of a specified radionuclide being released into the atmosphere. The DRFs are simply a ratio of the TED to the release rate and, therefore, are directly scalable. The DRF values listed in the Appendix can be applied to expected release values from each area to estimate the potential dose to an individual adult at each location. NESHAP DRFs have been supplied to Environmental Compliance and Area Closure Projects (EC and ACP) for many years and are periodically updated to include changes in Cap88. estimate the potential dose to an individual adult at each location DRF in mrem Ci{sup -1} are estimated by modeling the total effective dose (TED) in mrem yr{sup -1} assuming an annual atmospheric release rate of 1 Ci yr-1 of the associated radionuclides. TED = Effective Dose (ED) + Committed Effective Dose (CED). TED = DRF/RR. Approach and Methods: Determine possible MEI locations Onsite/Offsite including buildings and farms; Determine the distances from the release area to the possible MEI location and the direction from the release to the MEI location; Using the most recent meteorological data (2007-2011) and parameters, run CAP88 for each release area for each compass sector and previously calculated distances to MEI locations using a 1 Ci release rate for tritium; Use the calculated the air concentration values to determine the MEI for each sector from CAP88. The relationship between air concentration and received dose is directly proportional and can be used to determine which locations to calculate DRF's and a dose for an MEI; The DRFs are found for each compass sector from 20 SRS release areas using CAP88-PC Version 4.0; Each location is calculated with ground level releases evaluated for each area for a list of radionuclides, some with and some without progenies; Compare the DRFs of the potential onsite MEIs to offsite MEIs. Based on the 2018 MEI Report, the Site worst case sector MEI is located 593 mW of B-Area. This MEI location represents a point along the main road that travels through a tract of land leased to the SC National Guard. While this location is estimated to have the highest exposure, minimal radioactivity is released from B-Area. In 2017, SRS requested and was approved by the EPA to change the location for modeling site releases from the Center of Site to H-Area, and to use six stack heights. H-Area emission account for approximately 90% of all radioactive operations onsite. The MEI for H-Area was found to be 11,458 m NNE which locates the Savannah River Ecology Lab Conference Center. Comparisons for 2018 data and 2014 shows the new methods of calculation are more conservative yet still remain well below the DOE 100 mrem limit.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

IDC Re-Engineering Phase 2 Iteration E2 Use Case Realizations

This architecturally significant use case describes how the System acquires meteorological data to build atmospheric models used in automatic and interactive processing of infrasound data. The System requests the latest available high-resolution global meteorological data from external data centers and puts it into the correct formats for generation of infrasound propagation models. The system moves the meteorological data from Data Acquisition Partition to the Data Processing Partition and stores the meteorological data. The System builds a new atmospheric model based on the meteorological data. This use case is architecturally significant because it describes acquiring meteorological data from various sources and creating dynamic atmospheric transmission model to support the prediction of infrasonic signal detection

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2016

IDC Re-Engineering Phase 2 Iteration E2 Use Case Realizations

This architecturally significant use case describes how the System acquires meteorological data to build atmospheric models used in automatic and interactive processing of infrasound data. The System requests the latest available high-resolution global meteorological data from external data centers and puts it into the correct formats for generation of infrasound propagation models. The system moves the meteorological data from Data Acquisition Partition to the Data Processing Partition and stores the meteorological data. The System builds a new atmospheric model based on the meteorological data. This use case is architecturally significant because it describes acquiring meteorological data from various sources and creating dynamic atmospheric transmission model to support the prediction of infrasonic signal detections.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Riverbank temperatures on the Selawik River, Alaska 2010-2012, and Koyukuk River, Alaska June to July 2018.

The data package includes temperature measurements from riverbanks along the Selawik and Koyukuk Rivers in northwest Alaska. The Selawik River data also includes meteorological data collected locally and the Koyukuk River includes bulk density and ice content data from the riverbanks. The Selawik River data was collected between 2010 and 2013 with meteorological data collected for the entire time period and riverbank temperatures collected in shorter time intervals between 2010 to 2012. All of the Selawik River data were collected a single river bend. The Koyukuk River temperature data was collected from late June to early July 2018 at a total of five locations on three riverbanks. Ice content measurements on the Koyukuk were made at five riverbank locations. The data collection on both rivers was done in support of studies on the influence of permafrost on riverbank erosion. The temperature data was collected to monitor the thermal conditions of the river banks both seasonally and during periods of erosion. The ice content of the banks was measured to better understand how bank erosion rates are influenced by ice in the frozen riverbanks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Dataset: "Widespread Drought-driven Declines in Streamflows and Water quality in the Upper Colorado River Basin (1998-2022)"

This data package contains the associated data and scripts for Nagamoto, E., Ombadi, M., Ciulla, F. et al. Widespread drought-driven declines in streamflows and water quality in the Upper Colorado River Basin during 1998-2022. Commun Earth Environ 7, 734 (2026). https://doi.org/10.1038/s43247-026-03890-5. This purpose of this study was to investigate the impact of the 21st century drought on water quantity and quality at catchments throughout the Upper Colorado River Basin (UCRB). We used stream flow, water temperature, specific conductance, air temperature, precipitation, and catchment attribute data for over 200 sites in the UCRB, collected from the National Water Information System using Basin3D (Varadharajan, 2023), GAGESII (Falcone, 2010), and the Google Earth Engine. We identified years of severe drought between 1998 and 2022 using the Standardized Precipitation Evaporation Index (SPEI), then calculated the relative change percentage of the stream flow, water temperature, and specific conductance from drought versus non-drought years. We used the attribute information from GAGESII to investigate what physical traits of catchments are associated streamflow vulnerability (greater relative change) or resilience to drought. We used land cover data from the National Land Cover Database (USGS, 2024) to assess any changes to physical attributes that may not be represented in the static attributes information in GAGESII. To increase data availability, we modeled stream temperature using methods from Willard, 2023. While the study period is water years 1998 to 2022, the raw water quantity and quality data extends to 1950 and the meteorological data extends to 1980. The data and code can be downloaded via the UCRB_drought.zip. Within the zip, the files are organized as follows: - INPUTS: Contains all input data used in UCRB_Drought_Workflow.ipynb - OUTPUTS: Contains all intermediate data created from UCRB_Drought_Workflow.ipynb as well as final products including the calculated Standardized Evapotranspiration Index (SPEI) - climatic_variables: The code used to collect meteorologic data from Google Earth Engine - feature_importance: The code used for the catchment attributes analysis - preprocessing: Code used in UCRB_Drought_Workflow_Preprocessing.ipynb - pyeto: Code used in UCRB_Drought_Workflow_Preprocessing.ipynb - calculations: Code used in UCRB_Drought_Workflow_Impacts.ipynb - plotting: Code used in UCRB_Drought_Workflow_Impacts.ipynb - README.md - UCRB_Drought_Workflow_Preprocessing.ipynb: The code used to prep raw data for the analysis - UCRB_Drought_Workflow_Impact.ipynb: The code which uses the prepped raw data for analysis, and plots all figures - requirements_ucrb-drought_v2.yml: The requirements file to create a virtual environment and Jupyter Lab kernel to run the code The INPUTS folder is organized into the following major directories and sub-directories. The "RDC_WT_SC_RAW" folder contains raw data for streamflow, water temperature, and specific conductance in a ".h5" file. The "NLCD_RAW" folder contains ".csv" files with annual land cover percentages for counties within the UCRB. The "MET_RAW" folder contains a ".csv" file with monthly meteorological data (air temperature and precipitation) for the sites in the UCRB which was obtained from code in the climatic_variables folder. The "GAGESII" folder contains ".csv" files with physical catchment attribute variables for catchments across the country. The "WT_LSTM_data" folder contains ".csv" files with calculated WT (Willard, 2023) and the associated RMSEs. The "Upper_Colorado_River_Basin_Boundary" folder contains geographic data including a shapefile for plotting in the UCRB_Drought_Workflow.ipynb. The "RESERVOIRS_RAW" folder contains ".csv" files for each reservoir in the UCRB with daily reservoir storage. There are also two files in the INPUTS folder that have combined reservoir storage data and reservoir metadata. The OUTPUTS folder is organized into the following major directories and sub-directories. The "RDC_WT_SC_data" folder contains a folder "Water_year" with the associated cleaned data, metadata, and data availability information in ".csv" files, a folder "Median_Relchange" with the relative change comparing drought to non-drought years in ".csv" files, and a folder "Peak95_Min5_Relchange" that has ".csv" files for the relative change in peak (95th %) and minimum (5th %) variables. The "NLCD_data" folder contains the difference in land cover from the beginning to end of the study period and the percentage of the county that is within UCRB bounds can be found in Nagamoto et al (2025)). The "MET_data" folder contains separated monthly air temperature and precipitation data and the calculated PET in ".csv" files. The "SPEI_data" folder contains ".csv" files with calculated SPEI values (one restricted to the study period and the other with information from the entire MET data period). The "Paper_Tables" folder contains two ".csv" files containing site information and data availability and information about the GAGESII trait aggregated categories. The base directory includes the file “flmd.csv” for a list and description of all files and the file “dd.csv” for data dictionaries. Scripts for preprocessing, analysis, and figure generation are located in the associated GitHub repository found at [https://github.com/iNAIADS/drought-impacts/tree/develop/UCRB-drought]. UPDATE 1: Title and code file updated to match submitted manuscript 10-15-2025. UPDATE 2: Code and data files updated to match revised manuscript 3-4-2026. UPDATE 3: Code and data files updated to match revised manuscript 6-7-2026. ** NOTE: DD and FLMD have not been updated yet. UPDATE 4: Added associated Manuscript information and DD and FLMD have been updated. To cite this code, please use the following BibTeX: @misc{nagamoto2025drought, author = {Emily Nagamoto and Fabio Ciulla and Mohammad Ombadi and Jared Willard and Rosemary Carroll and Charuleka Varadharajan}, title = {Dataset: "Widespread Drought-driven Declines in Streamflows and Water quality in the Upper Colorado River Basin (1998-2022)"}, year = {2025}, doi = {10.15485/2551894}, publisher = {ESS-DIVE Repository}, url = {https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2551894} }

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Prediction of Potential and Actual Evapotranspiration Fluxes Using Six Meteorological Data-Based Approaches for a Range of Climate and Land Cover Types

Evapotranspiration is the major component of the water cycle, so a correct estimate of this variable is fundamental. The purpose of the present research is to assess the monthly scale accuracy of six meteorological data-based models in the prediction of evapotranspiration (ET) losses by comparing the modelled fluxes with the observed ones from eight sites equipped with eddy covariance stations which differ in terms of vegetation and climate type. Three potential ET methods (Penman-Monteith, Priestley-Taylor, and Blaney-Criddle models) and three actual ET models (the Advection-Aridity, the Granger and Gray, and the Antecedent Precipitation Index method) have been proposed. The findings show that the models performances differ from site to site and they depend on the vegetation and climate characteristics. Indeed, they show a wide range of error values ranging from 0.18 to 2.78. It has been not possible to identify a single model able to outperform the others in each biome, but in general, the Advection-Aridity approach seems to be the most accurate, especially when the model calibration in not carried out. It returns very low error values close to 0.38. When the calibration procedure is performed, the most accurate model is the Granger and Gray approach with minimum error of 0.13 but, at the same time, it is the most impacted by this process, and therefore, in a context of data scarcity, it results the less recommended for ET prediction. The performances of the investigated ET approaches have been furthermore tested in case of lack of measured data of soil heat fluxes and net radiation considering using empirical relationships based on meteorological data to derive these variables. Results show that, the use of empirical formulas to derive ET estimates increases the errors up to 200% with the consequent loss of model accuracy.

net radiation↗
DOE OSTI2025

Soil Moisture Data for TRACER project (Houston, TX)

The purpose of this study was to collect and distribute ground-truth soil water content and meteorological data in the Houston, TX, area, supporting the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility, and the 2022 field campaign for the Tracking Aerosol Convection Interactions ExpeRiment (TRACER). The files herein contain soil water content and meteorological data for three stations that the Bureau of Economic Geology at UT Austin installed in the Houston, TX, area during the period of performance. The data includes soil moisture, volumetric water content, electrical conductivity of soil, soil temperature, rain precipitation, air temperature and other parameters. Data files within this data set contain measurements at both sub-hourly and 1-hour resolution measurements. The sub-hourly meteorological data file names end with “TRACER_SubHourly_met.dat”, and the sub-hourly soil data files end with “TRACER_SubHourly_soil.dat”. For the 1-hour resolution data, the files ending with "_Soil_flagged.dat” contain mean hourly volumetric soil water content and temperature measured at 5, 10, 20 and 50 cm depths. The files ending with “_Meteoro_flagged.dat” contain mean hourly measured precipitation, air temperature and humidity, wind speed and direction, and solar radiation. All data have undergone QA/QC procedures that are described by Caldwell et al. (2019) and Dorigo et al. (2013) for the soil-specific data, and EPA (2008) for the meteorological data.

Air temperature↗
DOE OSTI2018

FACE-MDS Phase 2: Model Output

Model output from Phase 2 of the Free Air CO2 Enrichment Model Data Synthesis (FACE-MDS) project. Results from 12 models simulating six elevated CO2 experiments using the protocols, met data, and other data supplied in the accompanying dataset: Phase 2 Free Air CO2 Enrichment Model-Data Synthesis: Meteorological Data and Protocols. File naming convention can be found in the file 'facemds-file-table-modeldata.xml' and information about variable naming convention can be found in the file 'facemds-standard-name-table-modvars.xml'. We followed the ALMA v3 convention for meteorological variables and developed a hierarchical naming scheme for ecosystem variables. Additional info can be found in the protocols in the accompanying meteorological dataset (doi:10.15485/1480328). We replaced the original naming convention mentioned in the protocol with the one described in 'facemds-standard-name-table-modvars.xml'.

54 ENVIRONMENTAL SCIENCES↗