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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

SPRUCE Soil Volumetric Water Content in Experimental Plots, Marcell Experimental Forest, Minnesota, 2017-2025

This dataset contains soil volumetric water content (VWC) measurements from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) Experiment. Measurements were made inside SPRUCE experimental plots in the S1 Bog at the US Forest Service Marcell Experimental Forest in northern Minnesota, USA from 2018-2025 (2018-06-14 to 2025-12-31). Observations were made with METER 10HS soil moisture sensors. To reduce bulk density related variability, 10HS sensors were placed inside mesh tubes filled with peat at a standard bulk density. The observations under standard bulk density represent relative differences in water content between hummock and hollow positions, and in response to experimental treatments. Productivity of peatlands, and their keystone species sphagnum, are highly dependent on water availability, which is affected by lateral inputs, precipitation, ground water depth and evapotranspiration. Knowledge of near surface and sphagnum water content is useful to understand peatland function. This dataset contains 8 data files in comma-separate values (.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma-separate values (*.csv) format and a user guide in PDF (*.pdf) format.

Warren, Jeffrey [ORNL] (ORCID:0000000206804697)↗
DOE OSTI2020

Field Test Bed for Vadose-Zone Monitoring Approaches - 20404

In the Central Plateau at the U.S. Department of Energy Hanford Site, a large inventory of contaminants resides in unsaturated sediments within the approximately 100-meter-thick vadose zone, posing a potential continuing risk to groundwater. Vadose zone remedies used to address these contaminants will require performance monitoring to provide feedback during implementation and for long-term verification that remedial action objectives have been met. Passive approaches may also need long-term monitoring to demonstrate that the flux of contaminants from the vadose zone to the groundwater are below thresholds established to meet groundwater protection goals. Collection of physical (e.g., groundwater or sediment) samples is a common method for identifying contaminant concentration distributions, but this approach is limited by the number of locations and the frequency with which data can be collected. In situ vadose zone measurements have evolved over the past few years to include key measurements of water content, soil water pressure, temperature, and chemical concentration. However, the current generation of sensors is designed for relatively short-term use in near-surface soils or sediments. Geophysical methods have been evolving but are also limited in that they have not been designed for the specific long-term vadose zone monitoring needs at the Hanford Site. Overall, monitoring under unsaturated conditions can be difficult due to the need to install and maintain instrumentation over a large area and depth and the need to identify preferential flow pathways due to geologic and chemical heterogeneities over long periods. Thus, a vadose zone monitoring test bed was initiated to address these challenges and identify cost-effective approaches for implementation and postclosure monitoring of the deep vadose zone. The monitoring test bed is expected to provide valuable field-scale information for the design of vadose zone monitoring systems. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Meteorological and Soil Data from Ecohydrology Sensor Towers at Pump House and Snodgrass Mountain in East River Watershed, Colorado, 2019-2025

This data package includes hourly meteorological and soil sensor data at eight ecohydrology monitoring sites in East River Watershed, Colorado as part of the Watershed Function Scientific Focus Area (WFSFA) research led by Lawrence Berkeley National Lab (LBNL). Four field sites were located on the hillslope of East River (ER) near Pump House (PH) at Mount Crested Butte (ER-PHS1 to 4), and the other four are in the Snodgrass Mountain (SG) area (SG-EHS5 to 8). In terms of vegetation cover, three sites are in montane grasslands (ER-PHS1, ER-PHS2, and SG-EHS5), three are below evergreen conifer canopy (ER-PHS3, SG-EHS6, and SG-EHS7), and two are below deciduous aspen canopy (ER-PHS4 and SG-EHS8). The monitoring period began in October 2019 at the East River sites, in October 2020 at SG-EHS5 and SG-EHS6, and in October 2021 at SG-EHS7 and SG-EHS8. In September 2024, all four East River sites were fully retired. The four Snodgrass Mountain sites remain active. Each site is equipped with a comprehensive suite of meteorological sensors on a tripod and soil sensors that measure weather, energy fluxes, and soil variables. This data package includes measurements from ten different types of sensors and up to thirteen individual sensors per site, including (1) a weather station (measurement height ranges from 2.8~3.8 meters (m) above ground), (2) a quantum sensor for photosynthetic active radiation (PAR) (2.4~3.3m), (3) a net radiometer (1.7~2.1m), (4) an infrared radiometer (1.6~2.2m), (5) a sonic distance sensor (1.5~1.9m), (6) a soil carbon dioxide (CO2) flux chamber (0m), (7) a soil heat flux plate (-0.05m below ground), (8) a soil oxygen sensor (-0.3m), (9) a soil water potential sensor (-0.3m), and (10) soil water content sensors at 3~4 depths (-1.15 ~ -0.1m). A total of twenty-three variables is reported in this data package, including (1) atmospheric variables: air temperature (TA), atmospheric pressure (PA), vapor pressure (VP), and vapor pressure deficit (VPD), (2) precipitation variables: rain precipitation (P) and snow depth (D_SNOW), (3) energy fluxes variables: four-component net radiation (NETRAD) (shortwave/longwave incoming/outgoing radiation, SW_IN, SW_OUT, LW_IN, LW_OUT), photosynthetic photon flux density (PPFD), and soil heat flux (G), (4) soil variables: soil water content (SWC), soil water potential (SWP), soil temperature (TS), soil bulk electrical conductivity (COND_SOIL), and soil gaseous oxygen concentration (O2_SOIL), (5) wind variables: two-dimensional wind speed (WS), gust speed (WS_MAX), and wind direction (WD), and (6) surface variables: surface infrared temperature (T_CANOPY) and soil CO2 flux (CO2_SOIL). Please see the Methods section for data processing and QA/QC steps taken to generate the hourly datasets. The following files are included in this data package (notes on version: v{x}-{y}, where x is the metadata version, and y is the data version, when applicable): (1) “metadata_site_v{x}-{y}.csv” - a site metadata file that summarizes location information of all sites, including site ID, description, coordinates, timeframe, elevation, and vegetation cover, (2) “metadata_instrument_v{x}-{y}.csv” - an instrument metadata file that summarizes sensor information of all sites, including sensor manufacturer and model, measurement height, and sampling and averaging interval of all variables, (3) "data_{SITE_ID}_v{x}-{y}.csv" - eight data files that contain hourly data of each site indicated by {SITE_ID} in the filename, (4) “/figure/data_{SITE_ID}_v{x}-{y}.png" - eight figures that help visualize data of each site indicated by {SITE_ID} in the filename, (5) “/photo/*” - photos of each site indicated by {SITE_ID} in the filename, and (6) four file level metadata (flmd.csv) and data dictionary (*_dd.csv) files that summarize file, header, column, and variable information of all files. Notes: (1) Measurement height: Each variable name is followed by conventional positional qualifiers “H_V_R”, where H indicates the relative horizontal positions of that specific variable, V the vertical positions, and R the replicates. In this data package, only the vertical qualifier V varies, and V increases from the highest vertical position (V=1) to the lowest. Variables with the same qualifier are not necessarily measured by the same sensor, and the same variable with the same qualifier across different sites are not necessarily measured at the same height. Please refer to “metadata_instrument.csv” for the sensor information and measurement heights, and whether a variable is measured below the canopy. (2) Variable availability: Snow depth is not available at ER-PHS3 and SG-EHS7. SWC, soil temperature, and soil bulk EC at the deepest depth (<-1m) are not available at SG-EHS6 and SG-EHS7. The missing value code for numeric variables is -9999, except for SWP. For SWP, the missing value code is +9999, because SWP values are negative. (3) Sampling frequency: Please refer to “metadata_instrument.csv” for the increase of sampling frequency of some variables from 30-min to 1-min at ER-PHS1 to 4 in July 2020. (4) Sensors: While the methods of each sensor are not detailed, all sensors are commercially available, and their methods can be found in their manuals. Please refer to “metadata_instrument.csv” for the sensor manufacturer and model information. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Hydraulic redistribution by deeply rooted grasses and its ecohydrologic implications in the southern Great Plains of North America

Perennial bioenergy crops with deep (>1 m) rooting systems, such as switchgrass (Panicum virgatum L.), are hypothesized to increase carbon storage in deep soil. Deeply rooted plants may also affect soil hydrology by accessing deep soil water for transpiration, which can affect soil water content and infiltration in deep soil layers, thereby affecting groundwater recharge. Using stable H and O isotope (δ 2 H and δ 18 O) and 3 H values, we studied the soil water conditions at 20–30 cm intervals to depths of 2.4–3.6 m in paired fields of switchgrass and shallow rooted crops at three sites in the southern Great Plains of North America. We found that soil under switchgrass had consistently higher soil water content than nearby soil under shallow-rooted annual crops by a margin of 15%–100%. Soil water content and isotopic depth profiles indicated that hydraulic redistribution of deep soil water by switchgrass roots explained these observed soil water differences. To our knowledge, these are the first observations of hydraulic redistribution in deeply rooted grasses, and complement earlier observations of dynamic soil water fluxes under shallow-rooted grasses. Hydraulic redistribution by switchgrass may be a strategy for drought avoidance, wherein the plant may actively prevent water limitation. Furthermore, this raises the possibility that deeply rooted grasses may be used to passively subsidize soil water to more shallow-rooted species in inter-cropping arrangements.

3H↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Trail Creek in Taylor River Watershed, Colorado 2024-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Trail Creek. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format TR-X-Y, where TR refers to Trail Creek, X is the treatment block identifier, and Y is the location identifier. Specifically, TR-ASCC1 is the control treatment block under the Adaptive Silviculture for Climate Change (ASCC) project, and TR-ASCC2 is the clear-cut treatment block. TR-ASCC-EHSn is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and TR-ASCC-ERTn (upslope n=1) are ecohydrology sites along the electrical resistivity tomography transects. The sample and location information can be found in metadata.csv, and the data from the soil sensors will be included in a future data version when the observation period becomes sufficiently long for data analysis. Sampling and Measurements Each sample falls into one of the two sampling methods – (1) intact cores or (2) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. The intact cores were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Pump House in East River Watershed, Colorado 2019-2024

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Pump House at Mount Crested Butte in the East River Watershed. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format ER-X-Y, where ER refers to East River, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, ER-PHS, ER-LMC, ER-LMF, and ER-SMN are associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and ER-RBTn (upslope n=1) are sampling transects during the 2019 Rootball Campaign. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Snodgrass Mountain in East River Watershed, Colorado 2020-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors at Snodgrass Mountain. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format SG-X-Y, where SG refers to Snodgrass Mountain, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, SG-EHS is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and SG-ERTn (upslope n=1) are points along the Snodgrass electrical resistivity tomography transect not associated with the existing site names in the directory. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2025

TARP Identifying Critical Thresholds for Acute Response of Plants and Ecosystems to Water Stress at Walker Branch Watershed, 2002-2005

This dataset contains data from a manipulative field study aimed at identifying critical thresholds for acute response of plants and ecosystems to water stress (TARP) that took place at Walker Branch Watershed in Oak Ridge, Tennessee from 2002-2005 (2002-06-20 to 2005-12-16). The study used understory tents for the removal of 100% of the growing-season throughfall and stem flow, to provide data on the impact of acute drought on mechanisms responsible for growth and mortality of deciduous forest canopy trees representative of common plant functional types (Liriodendron and Quercus). Through three years of manipulation (2003, 2004 and 2005; pretreatment measurements in 2002) various measures of tree response to surface moisture deficits were recorded including root and leaf traits, plant nonstructural carbohydrates status, hourly sapflow, basal area, and periodic observations of foliar photosynthesis and conductance. Additionally, environmental data such as air and soil temperature, soil water content, and soil matric potential were recorded. This dataset contains data 16 files in comma-separate (*.csv) format. Additional metadata are provided: 16 data dictionaries and a file-level metadata file in comma separate (*.csv) format and a user guide in PDF (*.pdf) format.

basal area↗
NASA NTRS1980

Agricultural soil moisture experiment, Colby, Kansas 1978: Measured and predicted hydrological properties of the soil

Predictive procedures for developing soil hydrologic properties (i.e., relationships of soil water pressure and hydraulic conductivity to soil water content) are presented. Three models of the soil water pressure-water content relationship and one model of the hydraulic conductivity-water content relationship are discussed. Input requirements for the models are indicated, and computational procedures are outlined. Computed hydrologic properties for Keith silt loam, a soil typer near Colby, Kansas, on which the 1978 Agricultural Soil Moisture Experiment was conducted, are presented. A comparison of computed results with experimental data in the dry range shows that analytical models utilizing a few basic hydrophysical parameters can produce satisfactory data for large-scale applications.

Arya, L. M.↗
DOE OSTI2026

Soil physical and chemical measurements for topsoils collected during NEON campaign in East River, CO (06/14/2018-06/28/2018)

The package is part of the DOE Watershed Function Science Focus Area (SFA) project and includes soil physical and chemical measurements from topsoils collected at the East River, Colorado, in conjunction with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey conducted in June 2018. The soil measurements include soil bulk density, soil volumetric water content, soil microbial biomass C (Carbon), N (Nitrogen) and C:N (C to N ratio), soil DNA yield, soil total extractable organic C, soil total extractable N, soil extractable nitrate, soil extractable ammonium, soil dissolved inorganic N, soil dissolved organic N, soil pH, soil TOC400 (total organic carbon at 400°C), soil ROC (residual oxidizable carbon), soil TIC (total inorganic carbon), soil TOC (total organic carbon), soil TC (total carbon), soil N, soil OM (organic matter) loss on ignition. Additional associated site metadata can be found in the ESS-DIVE package 10.15485/1618130. The dataset includes (1) 2018_NEON_soil_physical_chemical_measurements.csv: soil physical and chemical measurements indexed by soil sample IGSNs; (2) samples.csv: sample metadata file used to register International Generic Sample Numbers (IGSNs); (3) flmd.csv: file level metadata file; and (4) dd.csv: data dictionary file. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS (Catchment Hydrology and ↗
NASA NTRS2017

How Accurately Do Maize Crop Models Simulate the Interactions of Atmospheric CO2 Concentration Levels With Limited Water Supply on Water Use and Yield?

This study assesses the ability of 21 crop models to capture the impact of elevated CO2 concentration [CO2] on maize yield and water use as measured in a 2-year Free Air Carbon dioxide Enrichment experiment conducted at the Thunen Institute in Braunschweig, Germany (Manderscheid et al. 2014). Data for ambient [CO2] and irrigated treatments were provided to the 21 models for calibrating plant traits, including weather, soil and management data as well as yield, grain number, above ground biomass, leaf area index, nitrogen concentration in biomass and grain, water use and soil water content. Models differed in their representation of carbon assimilation and evapotranspiration processes. The models reproduced the absence of yield response to elevated [CO2] under well-watered conditions, as well as the impact of water deficit at ambient [CO2], with 50 percent of models within a range of plus/minus 1 Mg ha(exp. -1) around the mean. The bias of the median of the 21 models was less than 1 Mg ha(exp. -1). However under water deficit in one of the two years, the models captured only 30 percent of the exceptionally high [CO2] enhancement on yield observed. Furthermore the ensemble of models was unable to simulate the very low soil water content at anthesis and the increase of soil water and grain number brought about by the elevated [CO2] under dry conditions. Overall, we found models with explicit stomatal control on transpiration tended to perform better. Our results highlight the need for model improvement with respect to simulating transpirational water use and its impact on water status during the kernel-set phase.

grain number↗
NASA NTRS1975

The utility of surface temperature measurements for the remote sensing of surface soil water status

Experiments carried out on an Avondale loam soil indicated that the thermal inertia concept of soil water content detection is reasonably sound. The volumetric water contents of surface soil layers between 2 and 4 cm thick were found to be linear functions of the amplitude of the diurnal surface soil temperature wave for clear day-night periods. They were also found to be linear functions of the daily maximum value of the surface soil-air-temperature differential. Tests on three additional soils ranging from sandy loam to clay indicated that the relations determined for Avondale loam could not be accurately applied to these other soil types. When the moisture characteristic curves of each soil were used to transform water contents into pressure potentials, however, it was found that soil water pressure potential could be determined without prior knowledge of soil type, and thus its value as a potential soil water status survey tool was significantly enhanced.

Idso, S. B.↗
DOE OSTI2025

Evapotranspiration Partitioning Using Flux Tower Data in a Semi-Arid Ecosystem

Information about evapotranspiration (ET) and its components, that is, evaporation and transpiration, is crucial for a wide range of water and ecosystem management applications. However, partitioning ET into its two components is often challenging because of their spatiotemporal variabilities and lack of process understanding. This study developed a machine learning (ML) framework to shed light on ET processes and assess the relative importance of different drivers by incorporating hydrometeorology and biomass productivity variables. The Shapley Additive Explanations (SHAP) approach was applied to enhance explainability and rank the importance of ET drivers and their components. A total of 62 variables covering hydrometeorological and biomass productivity dimensions were considered from the Reynolds Creek Critical Zone Observatory (CZO) station in Idaho. The variable importance assessment identified the leading drivers individually for evaporation, transpiration and ET (soil water content for evaporation, vapour pressure deficit for transpiration and soil water content for ET). The results further highlighted the value of combining hydrometeorological and biomass productivity variables to achieve better predictability of ET processes.

54 ENVIRONMENTAL SCIENCES↗
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↗
NASA NTRS1980

Thermography for estimating near-surface soil moisture under developing crop canopies

Previous investigations of thermal infrared techniques using remote sensors (thermography) for estimating soil water content have been limited primarily to bare soil. Ground-based and aircraft investigations were conducted to evaluate the potential for extending the thermography approach to developing crop canopies. A significant exponential relationship was found between the volumetric soil water content in the 0-4 cm soil layer and the diurnal difference between surface soil temperature measured at 0230 and 1330 LST (satellite overpass times of NASA's Heat Capacity Mapping Mission - HCMM). Surface soil temperatures were estimated using minimum air temperature, percent cover of the canopy and remote measurements of canopy temperature. Results of the investigation demonstrated that thermography can potentially be used to estimate soil temperature and soil moisture throughout a complete growing season for a number of different crops and soils.

Heilman, J. L.↗
DOE OSTI2025

TRACER Soil Moisture Data, Guy 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 the Guy, Texas station that the Bureau of Economic Geology at UT Austin installed in the area of study during period of performance. This station produces two data files. First, the file ending with “_Soil_flagged.dat” contains mean hourly volumetric soil water content and temperature measured at 5, 10, 20 and 50 cm depths. The file ending with “_Meteoro_flagged.dat” contains 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.

precipitation↗
DOE OSTI2024

High temperatures and low soil moisture synergistically reduce switchgrass yields from marginal field sites and inhibit fermentation

‘Marginal lands’ are low productivity sites abandoned from agriculture for reasons such as low or high soil water content, challenging topography, or nutrient deficiency. To avoid competition with crop production, cellulosic bioenergy crops have been proposed for cultivation on marginal lands, however on these sites they may be more strongly affected by environmental stresses such as low soil water content. In this study we used rainout shelters to induce low soil moisture on marginal lands and determine the effect of soil water stress on switchgrass growth and the subsequent production of bioethanol. Five marginal land sites that span a latitudinal gradient in Michigan and Wisconsin were planted to switchgrass in 2013 and during the 2018–2021 growing seasons were exposed to reduced precipitation under rainout shelters in comparison to ambient precipitation. The effect of reduced precipitation was related to the environmental conditions at each site and biofuel production metrics (switchgrass biomass yields and composition and ethanol production). During the first year (2018), the rainout shelters were designed with 60% rain exclusion, which did not affect biomass yields compared to ambient conditions at any of the field sites, but decreased switchgrass fermentability at the Wisconsin Central–Hancock site. In subsequent years, the shelters were redesigned to fully exclude rainfall, which led to reduced biomass yields and inhibited fermentation for three sites. When switchgrass was grown in soils with large reductions in moisture and increases in temperature, the potential for biofuel production was significantly reduced, exposing some of the challenges associated with producing biofuels from lignocellulosic biomass grown under drought conditions.

09 BIOMASS FUELS↗
DOE OSTI2024

Data from: High temperatures and low soil moisture synergistically reduce switchgrass yields from marginal field sites and inhibit fermentation

‘Marginal lands’ are low productivity sites abandoned from agriculture for reasons such as low or high soil water content, challenging topography, or nutrient deficiency. To avoid competition with crop production, cellulosic bio- energy crops have been proposed for cultivation on marginal lands, however on these sites they may be more strongly affected by environmental stresses such as low soil water content. In this study we used rainout shelters to in- duce low soil moisture on marginal lands and determine the effect of soil water stress on switchgrass growth and the subsequent production of bioetha- nol. Five marginal land sites that span a latitudinal gradient in Michigan and Wisconsin were planted to switchgrass in 2013 and during the 2018–2021 growing seasons were exposed to reduced precipitation under rainout shelters in comparison to ambient precipitation. The effect of reduced precipitation was related to the environmental conditions at each site and biofuel produc- tion metrics (switchgrass biomass yields and composition and ethanol pro- duction). During the first year (2018), the rainout shelters were designed with 60% rain exclusion, which did not affect biomass yields compared to ambient conditions at any of the field sites, but decreased switchgrass fermentability at the Wisconsin Central–Hancock site. In subsequent years, the shelters were redesigned to fully exclude rainfall, which led to reduced biomass yields and inhibited fermentation for three sites. When switchgrass was grown in soils with large reductions in moisture and increases in temperature, the potential for biofuel production was significantly reduced, exposing some of the chal- lenges associated with producing biofuels from lignocellulosic biomass grown under drought conditions.

bioethanol↗