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113 results for “water potential”
WSC - Soil moisture, temperature, and water potential at Wibu field site
Soil moisture, temperature, and water potential measurements for 3 locations within Wibu field site: (1) WIBU-6, which is characterized by deep (greater than6 m) groundwater and coarse soil; (2) WIBU-7, which is characterized by intermediate (2-4 m) groundwater and intermediate soil; (3) WIBU-8, which is characterized by shallow (0-3 m) groundwater and fine soil. For more information about the soil and groundwater levels, see other datasets from this field site. The Wibu field site is a commercial agricultural field, which grew corn in the 2012, 2013, and 2014 growing seasons. See Zipper and Loheide (2014) Ag. For. Met. for more information about the field site.
Secchi depth data and discrete depth profiles of water temperature, dissolved oxygen, conductivity, specific conductance, photosynthetic active radiation, oxidation-reduction potential, and pH for Beaverdam Reservoir, Carvins Cove Reservoir, Falling Creek Reservoir, Gatewood Reservoir, and Spring Hollow Reservoir in southwestern Virginia, USA 2013-2025
Discrete depth profiles of water temperature, dissolved oxygen, oxidation-reduction potential, conductivity, specific conductance, and pH were collected with multiple handheld water quality probes and discrete depth profiles of photosynthetically active radiation (PAR) were collected with a LI-COR underwater light meter from 2013 to 2025 in five drinking water reservoirs in southwestern Virginia, USA. These reservoirs are: Beaverdam Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), Falling Creek Reservoir (Vinton, Virginia), Gatewood Reservoir (Pulaski, Virginia), and Spring Hollow Reservoir (Salem, Virginia). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia, and Gatewood Reservoir is a drinking water source for the Town of Pulaski, Virginia. All discrete depth profiles were collected on approximately 1-meter intervals. The data package consists of two datasets: 1) Secchi depth data; and 2) discrete depth profiles of multiple water quality variables measured by handheld sensors. The Secchi data and discrete depth profiles were measured at the deepest site of each reservoir adjacent to the dam, as well as other in-reservoir sites. Handheld sensor measurements were also collected at a gauged weir on the primary inflow tributary, other inflows, and outflows at Falling Creek Reservoir; inflows and outflows at Beaverdam Reservoir; and inflows at Carvins Cove Reservoir. In 2021, YSI handheld data were also collected from a littoral site in Beaverdam Reservoir. In 2025, YSI handheld data were collected monthly from June to October from nine littoral sites around the perimeter of Falling Creek Reservoir. From 2024 - 2025, additional within-reservoir depth profiles were collected in Carvins Cove Reservoir and multiple sites. Data were collected approximately fortnightly in the spring months (March - Ma
Hubbard Brook Experimental Forest: Droughtnet soil moisture, temperature and soil water potential
The forest drought experiment prototype at Hubbard Brook was constructed in 2015, as part of the International Drought Experiment (IDE) coordinated by the DroughtNet Research Coordination Network. The throughfall exclusion experiment was designed to simulate a 1-in-100-year drought during an average precipitation year by diverting ~50% of forest throughfall from each treatment plot starting in May 2015 (Asbjornsen et al., 2018). Throughfall was intercepted by reinforced polyethylene troughs and diverted passively to the downslope side of each plot. Each throughfall exclusion plot was 15 x 15 meters in area. TFE plots were designated with the labels 7 and 8 to avoid any confusion with the nearby CCASE plots (which are labeled 1-6). Plots were not trenched to isolate them from the surrounding soil. In May 2019 throughfall removal was increased to approximately 95% (i.e. full coverage but with stemflow not fully excluded). Throughfall exclusion treatments ended in February 2020. Recovery and return to baseline conditions were monitored during 2020 (when a natural drought occurred) and 2021 (a more normal year). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Supplementary dataset for "Potential of Wastewater Reuse to Alleviate Water Scarcity under Future Warming Scenarios"
<p>The folder contains water gap data relative to the paper:<br>Kahn, M., Sangiorgio, M., and Rosa, L. (2025) Potential of wastewater reuse to alleviate water scarcity under future warming scenarios. Environmental Research Letters, 20, 034012<br>https://doi.org/10.1088/1748-9326/adb31d</p> <p>All water gaps data are in km3/yr.</p> <p><br>Gridded data(NetCDF at 0.5°)</p> <ul> <li>baseline (2001-2010) <ul> <li>Water_gap_baseline_no_wastewater_reuse: Water gap under baseline climate scenario with no wastewater reuse.</li> <li>Water_gap_baseline_treated_wastewater_reuse: Water gap under baseline climate scenario with treated wastewater reuse.</li> <li>Water_gap_baseline_full_wastewater_reuse: Water gap under baseline climate scenario with full wastewater reuse.</li> </ul> </li> </ul> <p> </p> <ul> <li>1.5°C warming <ul> <li>Water_gap_15_no_wastewater_reuse: Water gap under 1.5°C warming scenario with no wastewater reuse.</li> <li>Water_gap_15_treated_wastewater_reuse: Water gap under 1.5°C warming scenario with treated wastewater reuse.</li> <li>Water_gap_15_full_wastewater_reuse: Water gap under 1.5°C warming scenario with full wastewater reuse.</li> </ul> </li> </ul> <p> </p> <ul> <li>3°C warming (5 models + average) <ul> <li>Water_gap_3_no_wastewater_reuse: Water gap under 3°C warming scenario with no wastewater reuse.</li> <li>Water_gap_3_treated_wastewater_reuse: Water gap under 3°C warming scenario with treated wastewater reuse.</li> <li>Water_gap_3_full_wastewater_reuse: Water gap under 3°C warming scenario with full wastewater reuse.</li> </ul> </li> </ul> <p> </p> <p>Aggregated data (.xlsx)</p> <ul> <li>country_level_water_gaps.xlsx: Water gap aggregated by country for all the considered scenarios.</li> <li>city_water_gaps.xlsx: 0.5° pixels with populations greater than 5,000,000 and non-zero water gaps corresponding to urban center.</li> <li>seasonal_variations.xlsx: Monthly water gaps of the 5 most water scarce countries.</li> </ul> <p><br>Note: the global water gap obtained by summing all the countries is not completely equivalent to the sum of all the pixels because some pixels' center is outside the polygon of the corresponding country.</p>
WaterGAP2.2d model derived Potential evapotranspiration and Renewable water resources variables with standard and modified PET calculation methods
<p>This data set is produced as a part of the ''Improving the quantification of climate change hazards by hydrological models: A simple ensemble approach for considering the uncertain effect of vegetation response to climate change on potential evapotranspiration" journal publication (in preparation). WaterGAP2.2d global hydrological model with two different settings; 1) with standard PET method Priestley-Taylor (PT) and 2) with modified approach (PT-MA) (please refer to the publication for more details on the method) used to derive the data set. The bias-adjusted GCM-derived (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5) climate data under RCP2.6 and RCP8.5 emission scenarios were used as the input. The model-derived potential evapotranspiration and the renewable water resources variables are available from 1981 to 2099 on the monthly scale for each land grid cell (spatial resolution: 0.5 degrees x 0.5 degrees). The data files are in the netCDF format (.nc4). </p>
A "short blanket" dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the underlying many-body physics?
<p>Deep neural network (DNN) potentials have recently gained popularity in computer simulations of a wide range of molecular systems, from liquids to materials.<br> In this study, we explore the possibility of combining the computational efficiency of the DeePMD framework and the demonstrated accuracy of the MB-pol data-driven many-body potential to train a DNN potential for large-scale simulations of water across its phase diagram.<br> We find that the DNN potential is able to reliably reproduce the MB-pol results for liquid water but provides a less accurate description of the vapor-liquid equilibrium properties.<br> This shortcoming is traced back to the inability of the DNN potential to correctly represent many-body interactions.<br> An attempt to explicitly include information about many-body effects results in a new DNN potential that exhibits the opposite performance, being able to correctly reproduce the MB-pol vapor-liquid equilibrium properties but losing accuracy in the description of the liquid properties.<br> These results suggest that DeePMD-based DNN potentials are not able to correctly "learn" and, consequently, represent many-body interactions, which implies that DNN potentials may have limited ability to predict properties for state points that are not explicitly included in the training process.<br> The computational efficiency of the DeePMD framework can still be exploited to train DNN potentials on data-driven many-body potentials, which can thus enable large-scale, "chemically accurate" simulations of various molecular systems, with the caveat that the target state points must have been adequately sampled by the reference data-driven many-body potential in order to guarantee a faithful representation of the associated properties.</p>
Effects of drying temperature on potential carbon mineralization and water-extractable organic carbon in Iowa cropland and riparian buffer soils
Measuring carbon dioxide (CO2) produced after re-wetting a previously dried soil is an increasingly popular soil health assay, but there is disagreement on the optimal soil drying temperature. We tested whether soil drying temperature impacts water-extractable organic carbon (WEOC) and soil CO2 emissions (potential carbon mineralization) following rewetting of dried soil. Samples were collected at four sites in north-central Iowa, US, and each site had soils planted to corn/soybean or perennial vegetation. The dataset includes measurements of WEOC prior to the incubation experiment, and measurements of CO2 flux and its stable carbon isotope ratio over the course of a 28-day incubation. The manuscript describing these data is under review in Geoderma.
Effects of microarthropod exclusion and water amendments on fluffgrass rhizosphere nitrogen mineralization potential at the Jornada Basin LTER site in 1987
This data package contains data on soil nitrogen mineralization potential in the rhizosphere of fluffgrass (Dasyochloa pulchella) plants from 1987. This study was conducted at the Chihuahuan Desert Rangeland Research Center to test how watering, and changing densities of soil microarthropods and nematodes, impacts fluffgrass growth and nutrient cycling. Twenty 6 x 6 m plots were established with a 3 m buffer between plots. Five plots were randomly assigned to one of four treatments:1) Chlordane (to exclude microarthropods), 2) Chlordane and water, 3) Water, and 4) Control. Three randomly located subsamples were taken from each plot and consisted of the fluff grass plant, a 10 cm in diameter and 15 cm deep soil core centered on each plant. Samples were incubated for 0, 2, 4, or 8 weeks. This data set contains information on the incubation time, soil nitrate (NO3-), soil ammonium (NH4+), and soil water content for each subsample. This data were collected in May 1987 and the study is complete.
Pre-dawn water potential of leaves from reduced and enhanced precipitation treatments at the Jornada Basin LTER site, 2007-2008
This data package contains pre-dawn water potential data for leaves collected from mesquite shrubs and black grama grasses at the Jornada Basin LTER site in 2007-2008. The purpose of this study was to quantify ecophysiological performance of shrubs and grasses in the context of precipitation manipulations. One mesquite and one black grama leaf from each plot (n = 20 plots; 5 PPT treatments X 4 replicate plots/treatment) were measured 5 times beginning July 2007 and ending in August 2008. The precipitation treatments consisted of +/-80%, +/-50%, and ambient/control. Leaves were excised from plants and immediately measured for water potential (MPa) with a pressure chamber at least 0.5 h before dawn. The data set is complete.
Data for "The potential of water security in leveraging Agenda 2030"
<p>Data set of the linkages that water security has on other SDGs. The data are based on literature review and final data consists of 120 publications. The linkages are graded from -3 (cancelling) to +3 (indivisible) according Nilsson et al. classification. </p> <p>Prior to data collection, all the reviewers participated in training on SDGs and water security, and on review training before the work. Only papers with a link from water security to SDGs (not vice versa) were included. The resulting subset of 170 papers was then read and evaluated by all the authors independently, with a 10% random overlap on the papers (i.e. 17 of the papers were reviewed at least twice, by different people). During this process, another 50 papers were found to be irrelevant (e.g. due to full paper inaccessibility or questionnaire analysis with no inclusion of SDGs) and were therefore eliminated. The results are thus based on the 120 reviewed papers that we found to be suitable for the analysis. See the journal paper methods for more information, and Supplementary material for author bias analysis. </p> <p>The list of paper ID's can be found rom the journal paper in One Earth. Column "Review" indicates whether the analyzed publication was a review or an original research paper.</p> <p>1_waterresources_sec = an identified linkage to the water resources dimension of the water security concept</p> <p>2_human_sec = an identified linkage to the human security dimension of the water security concept</p> <p>3_national_sec = = an identified linkage to the national security dimension of the water security concept</p> <p>4_climate_sec = an identified linkage to the climate security dimension of the water security concept</p> <p>5_energy_sec = an identified linkage to the energy security dimension of the water security concept</p> <p>6_food_sec = an identified linkage to the food security dimension of the water security concept</p> <p>sdg_counts = the total number of SDGs with reported linkage of water security</p> <p>year = publication year </p>
FIG. 1 in Marine cyanobacteria diversity and biotechnological potential in Caribbean waters
FIG. 1. — Map showing Caribbean countries were studies on cyanobacteria have been reported (gray) and those that have no reports yet (white).
Transferable Water Potentials Using Equivariant Neural Networks
<p>Training configurations used in the development of the potential described in the related publication.</p>
Deep potential model and datasets used in the publication "Plastic Deformation of Superionic Water Ices"
<p>The file training_data_for_deepmd.zip contains the training set and deep potential model for superionic ices used in the publication "Plastic Deformation of Superionic Water Ices". The deep potential (DP) neural-network model was generated using the DeepMD-kit package. The training set was generated using AIMD/CP2K calculations for both undeformed and deformed states of the fcc superionic phase at temperatures of 2500, 3000, 3500 and 4000 K. </p> <p> </p> <p>The file cp2k_shear.zip contains instructions to modify the CP2K code to calculate the shear vs strain deformation in continuous mode. </p> <p> </p> <p>The file lammps_dislocation.zip contains the files needed to run the dislocation mobility calculation. </p> <p> </p>
MADFORWATER: WP1: Water and water-related vulnerabilities in Egypt, Morocco and Tunisia: Task1.2: Analysis and mapping of water stress, water vulnerability and potential for water reuse in Egypt, Morocco and Tunisia: Subtask1.2.b: Data collection on water stress and vulnerability: Souss-Massa Region Subset
<p>This folder contains the dataset that I used to write my conference paper "Groundwater Resources Scarcity in Souss-Massa Region and Alternative Solutions for Sustainable Agricultural Development"</p>
Data and model scripts for "Temporal shifts in iso/anisohydry revealed from daily observations of plant water potential in a dominant desert shrub"
<p>Model code and data as used for the first revision submitted to New Phytologist, Sept. 2019. </p> <p>Models are coded in JAGS and run in R via the package "rjags." Two model versions are presented:</p> <p>1) "mod_SAM.R" and "script_SAM.R" run the full, time-varying SAM model, utilizing both the plant water potential ("res.Rdata") and the environmental covariate data ("cov2.Rdata") and associated initial values ("initsSAM.Rdata")</p> <p>2) "mod_SIMPLE.R" and "script_SIMPLE.R" run the simple, time-invariant model, utilizing only the plant water potential data ("res.Rdata") and associated initial values ("initsSIMPLE.Rdata")</p>
FIGURE 1 in Assessment of potentially toxic metals in water, sediment, and the tissues of seven important fish species from neotropical brazilian river
FIGURE 1 | Location of the study area in the Sorocaba River drainage, São Paulo State, Brazil, indicating the Sorocaba river, the fish collection points, in addition to the urban area and areas of contamination.
Neural-network-based molecular dynamics simulations reveal that proton transport in water is doubly gated by sequential hydrogen-bond exchange: Neural network potentials training data
<h1>Neural network potentials of an excess proton in bulk water, training data</h1> <p>This dataset contains 2188 configurations labeled at two hybrid DFT levels (revPBE0-D3 and B3LYP-D3).</p> <p>The configurations are given as a single XYZ file: configurations.xyz</p> <p>The box dimensions are written in box.txt</p> <p>The energies for all configurations at a given level of theory are written in energies_LEVEL.txt (one configuration per line)</p> <p>The atomic forces for each configuration at a given level of theory are gathered in a XYZ file: forces_LEVEL.xyz</p> <p>The relative displacements of the Wannier centroids, with respect to the closest oxygen atom, for each configuration at a given level of theory, are in the following XYZ file: wannier-centroids-displacements_LEVEL.xyz</p>
Range-wide climate risk and adaptive potential in an iconic cold-water species (Part 2/2)
Open the record for dataset details and reuse information.
Range-wide climate risk and adaptive potential in a cold-water fish species (Part 1/2)
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Gas exchange, dieback, leaf water potential and chlorophyll content during a greenhouse drought experiment: An evolutionary perspective on functional diversity in co-occuring willow(salix) species
Thirteen willow (Salix) species occur in southeastern Minnesota and often co-occur within the same wetlands. This high local diversity is challenging to explain since closely related species are often functionally similar and density-dependent interactions such as competition and susceptibility to pests and pathogens should limit their co-occurrence. However, if willow species are partitioning resources, or if they are phylogenetically structured so that closely related species rarely co-occur, then the impact of these density-dependent processes could be reduced. In this study, I examined the role of niche partitioning in maintaining local willow diversity by comparing species physiology in a greenhouse.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.