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289 results for “hydraulics”
Hydraulic Pathways in Leaves of Temperate Trees at Harvard Forest 2002
The transport of water, sugar and nutrients in trees is restricted to specific vascular pathways, and thus organs may be relatively isolated from one another (=sectored). Strongly sectored leaf-to-leaf pathways have been shown for the transport of sugar and signal molecules within a shoot, but not previously for water transport. The hydraulic sectoriality of leaf-to-leaf pathways was determined for current year shoots of six temperate deciduous tree species (three ring-porous: Castanea dentata, Fraxinus americana and Quercus rubra, and three diffuse-porous: Acer saccharum, Betula papyrifera and Liriodendron tulipifera). Hydraulic sectoriality was determined using dye staining and a hydraulic method. In the dye method, leaf blades were removed, and dye was forced into the most proximal petiole. For each petiole we counted the vascular traces shared with the proximal petiole. For other shoots, measurements were made of the leaf-area specific hydraulic conductivity for leaf-to-leaf pathways (kLL). In five of six species patterns of sectoriality reflected phyllotaxy; both the sharing of vascular bundles between leaves and kLL were higher for orthostichous than non-orthostichous leaf pairs. Species-differences in leaf-to-leaf sectoriality were determined as the proportional differences between non-orthostichous vs. orthostichous leaf pairs in their staining of shared vascular bundles and in their kLL; for the six species these two indices of sectoriality were strongly correlated (R2 = 0.94; P less than 0.001). Species varied 8-fold in their kLL-based sectoriality, and ring-porous species were more sectored than diffuse-porous species. Differential leaf-to-leaf sectoriality has implications for species-specific coordination of leaf gas exchange and water relations within a branch, especially during fluctuations in irradiance, water and nutrient availability.
Gas exchange velocities (k600), gas exchange rates (K600), and hydraulic geometries for streams and rivers derived from the NEON Reaeration field and lab collection data product (DP1.20190.001)
This dataset contains estimates of gas exchange velocity, gas exchange rate, and hydraulic parameters for streams calculated from tracer-gas experiments and conservative tracer injections collected by the National Ecological Observatory Network (NEON). All input data were collected by NEON and is available on the NEON data portal at https://data.neonscience.org. Specifically, the NEON Reaeration field and lab collection data product (DP1.20190.001) was used to calculate these estimates. Gas exchange was estimated in two ways: first, following an unpooled frequentist approach and second, following a partially pooled Bayesian approach. In addition, a salt-correction was applied to gas exchange estimates for sites where it was possible and necessary. All estimates of gas exchange are included in the file gasExchange_ds.csv. A recommended selection of these estimates is included in the dataset (best_k600_mPerDay and best_K600_mPerDay). The stanfit objects used for the partially pooled Bayesian approach are also included as site-specific model objects for gas exchange velocities and rates. In addition, water velocity was calculated from conservative tracer injections, and mean water depth was calculated from these water velocity estimates and measurements of wetted width and water discharge. All hydraulic parameters are included in the file hydraulics_ds.csv. All processing code is available in the reaRates R package. NEON is sponsored by the National Science Foundation (NSF) and operated under cooperative agreement by Battelle. This material is based in part upon work supported by NSF through the NEON Program.
Dataset of Soil hydraulic properties of Valle Telesina (Italy)
<p>The dataset contain a .xls file with the hydraulic properties georeferenced of 47 soil profiles of the "Valle Telesina (Italy) site, according to the parametrization of the van Genuthen-Mualem model (van Genuchten, 1980). Moreover a zipped folder with the shape files for the same area is provided.</p> <p>Following there is the description of the methods applied for the soil hydraulic characterization:</p> <p>Undisturbed soil samples were collected from the horizons using cylindrical steel samplers (8.5 cm diameter and 12.0 cm high). In the laboratory, the samples were saturated by slowly wetting from the bottom in order to remove all the air entrapped in the soil. The maximum water content,θ<sub>0</sub>, was gravimetrically determined and the saturated hydraulic conductivity, ks, was measured by a falling-head permeameter. Then, the Wind method was applied to simultaneously determine the water retention and hydraulic conductivity functions by subjecting the soil samples to an evaporation process. After sealing the bottom surface to prevent drainage, during the evaporation process - at appropriate pre-set time intervals - the weight of the whole sample and the pressure head at three different depths were measured. An iterative procedure was applied for estimating the water retention curve from these measurements. Then, the instantaneous profile method was applied to determine the unsaturated hydraulic conductivity. θr, θs, α and n parameters were derived by fitting the soil water retention data; under the restriction m=l−l/n, τ and k<sub>0</sub> parameters were derived by fitting the hydraulic conductivity data. Details of the tests and overall calculation procedures are described in Basile et al. (2012). The parameters obtained in the laboratory were then scaled to better reproduce the field behaviour by following the procedure suggested by Basile et al. (2003; 2006). Finally, for the few soils having considerable stone content, a correction of θs and k<sub>0</sub>, to take into account the stoniness, was applied (Coppola et al., 2013).</p> <p>References:</p> <p>Van Genuchten, M. T. (1980). A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal, 44(5), 892–898.</p> <p>Basile, A., Buttafuoco, G., Mele, G., & Tedeschi, A. (2012). Complementary techniques to assess physical properties of a fine soil irrigated with saline water. Environmental Earth Sciences,66(7), 1797–1807.</p> <p>Basile, A., Ciollaro, G., & Coppola, A.(2003). Hysteresis in soil water characteristics as a key to interpreting comparisons of laboratory and field measuredhydraulic properties.Water Resources Research, 39(12).</p> <p>Basile, A., Coppola, A., De Mascellis, R., & Randazzo, L. (2006). Scaling approach to deduce field unsaturated hydraulic properties and behavior from laboratory measurements on small cores. Vadose Zone Journal,5(3), 1005–1016.</p> <p>Coppola, A., Dragonetti, G., Comegna, A., Lamaddalena, N., Caushi, B., Haikal, M., & Basile, A. (2013). Measuring and modeling water content in stony soils. Soil and Tillage Research,128, 9–22.</p>
Data and Workflow to: Three-dimensional buoyant hydraulic fracture growth: constant release from a point source (Möri and Lecampion, (2022))
<p>This upload contains the relevant scripts, notebooks, and datasets to reproduce the numerically obtained results of the Journal article "Three-dimensional buoyant hydraulic fracture growth: constant release from a point source" by Möri and Lecampion, (2022).</p>
Below-ground hydraulic constraints during drought-induced decline in Scots pine
<p>Dataset from the paper 'Below-ground hydraulic constraints during drought-induced decline in Scots pine'.</p> <p><strong>Files:</strong></p> <p>DOY refers to day of year 2012, idtree is tree identity and Class is defoliation class. Tree characteristics can be found in the supplementary materials of the paper. Variables are expressed in the same units as in the paper.</p> <p><em>WaterPotentials.csv</em> - water potential data (predawn, PD, midday MD, difference)</p> <p><em>SapFlowDeltaPResistbc.csv</em> - daily sap flow per unit leaf area (Jl_daily), delta pressure (deltaP), VPD,SWC, belowcrown resistance (r_bc).</p> <p><em>Resistbc_percent.csv</em> - below-crown resistance as a percentage of total tree resistance</p> <p> </p>
Soil hydraulic and thermal properties determined in surface organic and mineral soils in the region near Toolik Lake on the North Slope of Alaska, 2016-2019
Soil cores of 5 cm diameter down to frozen soil were taken from a subset of sample sites for laboratory analysis. Determinations of hydraulic conductivity, thermal conductivity, porosity, and bulk density were made for each core. For a further subset of sites we developed soil moisture retention curves.
Hydraulic scale model experiments on the two-dimensional run-up of impulse wave trains on steep to vertical slopes
<p>This dataset includes the experimental data and videos, which were generated during the study on the run-up of impulse wave trains at the Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich.</p>
HyG: A hydraulic geometry dataset derived from historical stream gage measurements across the conterminous United States
<p>Regional- and continental-scale models predicting variations in the magnitude and timing of streamflow are important tools for forecasting water availability as well as flood inundation extent and associated damages. Such models must define the geometry of stream channels through which flow is routed. These channel parameters, such as width, depth, and hydraulic resistance, exhibit substantial variability in natural systems. While hydraulic geometry relationships have been extensively studied in the United States, they remain unquantified for thousands of stream reaches across the country. Consequently, large-scale hydraulic models frequently take simplistic approaches to channel geometry parameterization. Over-simplification of channel geometries directly impacts the accuracy of streamflow estimates, with knock-on effects for water resource and hazard prediction.</p> <p>Here, we present a hydraulic geometry dataset derived from long-term measurements at U.S. Geological Survey (USGS) stream gages across the conterminous United States (CONUS). This dataset includes (a) at-a-station hydraulic geometry parameters following the methods of Leopold and Maddock (1953), (b) at-a-station Manning's n calculated from the Manning equation, (c) daily discharge percentiles, and (d) downstream hydraulic geometry regionalization parameters based on HUC4 (Hydrologic Unit Code 4). This dataset is referenced in Heldmyer et al. (2022); further details and implications for CONUS-scale hydrologic modeling are available in that article (https://doi.org/10.5194/hess-26-6121-2022). </p> <p><strong>At-a-station Hydraulic Geometry</strong></p> <p>We calculated hydraulic geometry parameters using historical USGS field measurements at individual station locations. Leopold and Maddock (1953) derived the following power law relationships:</p> <p>\(w={aQ^b}\)</p> <p>\(d=cQ^f\)</p> <p>\(v=kQ^m\)</p> <p>where Q is discharge, w is width, d is depth, v is velocity, and a, b, c, f, k, and m are at-a-station hydraulic geometry (AHG) parameters. We downloaded the complete record of USGS field measurements from the USGS NWIS portal (https://waterdata.usgs.gov/nwis/measurements). This raw dataset includes 4,051,682 individual measurements from a total of 66,841 stream gages within CONUS. Quantities of interest in AHG derivations are Q, w, d, and v. USGS field measurements do not include d--we therefore calculated d using d=A/w, where A is measured channel area. We applied the following quality control (QC) procedures in order to ensure the robustness of AHG parameters derived from the field data:</p> <ol> <li>We considered only measurements which reported Q, v, w and A.</li> <li>For each gage, we excluded measurements older than the most recent five years, so as to minimize the effects of long-term channel evolution on observed hydraulic geometry relationships.</li> <li>We excluded gages for which measured Q disagreed with the product of measured velocity and measured area by more than 5%. Gages for which \( Q\neq vA\) are often tidally influenced and therefore may not conform to expected channel geometry relationships.</li> <li>Q, v, w, and d from field measurements at each gage were log-transformed. We performed robust linear regressions on the relationships between log(Q) and log(w), log(v), and log(d). AHG parameters were derived from the regressed explanatory variables. <ol> <li>We applied an iterative outlier detection procedure to the linear regression residuals. Values of log-transformed w, v, and d residuals falling outside a three median absolute deviation (MAD) envelope were excluded. Regression coefficients were recalculated and the outlier detection procedure was reapplied until no new outliers were detected.</li> <li>Gages for which one or more regression had p-values >0.05 were excluded, as the relationships between log-transformed Q and w, v, or d lacked statistical significance.</li> <li>Gages were omitted if regressed AHG parameters did not fulfill two additional relationships derived by Leopold and Maddock: \(b+f+m=1{\displaystyle \pm }0.1\) and \(a{\displaystyle \times }c{\displaystyle \times }k=1{\displaystyle \pm }0.1\).</li> </ol> </li> <li>If the number of field measurements for a given gage was less than 10, either initially or after individual measurements were removed via steps 1-4, the gage was excluded from further analysis.</li> </ol> <p>Application of the QC procedures described above removed 55,328 stream gages, many of which were short-term campaign gages at which very few field measurements had been recorded. We derived AHG parameters for the remaining 11,513 gages which passed our QC.</p> <p><strong>At-a-station Manning's n</strong></p> <p>We calculated hydraulic resistance at each gage location by solving Manning's equation for Manning's n, given by</p> <p>\(n = {{R^{2/3}S^{1/2}} \over v}\)</p> <p>where v is velocity, R is hydraulic radius and S is longitudinal slope. We used smoothed reach-scale longitudinal slopes from the NHDPlusv2 (National Hydrography Dataset Plus, version 2) ElevSlope data product. We note that NHDPlusv2 contains a minimum slope constraint of 10<sup>-5</sup> m/m--no reach may have a slope less than this value. Furthermore, NHDPlusv2 lacks slope values for certain reaches. As such, we could not calculate Manning's n for every gage, and some Manning's n values we report may be inaccurate due to the NHDPlusv2 minimum slope constraint. We report two Manning's n values, both of which take stream depth as an approximation for R. The first takes the median stream depth and velocity measurements from the USGS's database of manual flow measurements for each gage. The second uses stream depth and velocity calculated for a 50th percentile discharge (Q<sub>50</sub>; see below). Approximating R as stream depth is an assumption which is generally considered valid if the width-to-depth ratio of the stream is greater than 10<span>—</span>which was the case for the vast majority of field measurements. Thus, we report two Manning's n values for each gage, which are each intended to approximately represent median flow conditions.</p> <p><strong>Daily discharge percentiles</strong></p> <p>We downloaded full daily discharge records from 16,947 USGS stream gages through the NWIS online portal. The data includes records from both operational and retired gages. Records for operational gages were truncated at the end of the 2018 water year (September 30, 2018) in order to avoid use of preliminary data. To ensure the robustness of daily discharge percentiles, we applied the following QC:</p> <ol> <li>For a given gage, we removed blocks of missing discharge values longer than 6 months. These long blocks of missing data generally correspond to intervals in which a gage was temporarily decommissioned for maintenance.</li> <li>A gage was omitted from further analysis if its discharge record was less than 10 years (3,652 days) long, and/or less than 90% complete (>10% missing values after removal of long blocks in step 1.</li> </ol> <p>We calculated discharge percentiles for each of the 10,871 gages which passed QC. Discharge percentiles were calculated at increments of 1% between Q<sub>1</sub> and Q<sub>5</sub>, increments of 5% (e.g. Q<sub>10</sub>, Q<sub>15</sub>, Q<sub>20</sub>, etc.) between Q<sub>5</sub> and Q<sub>95</sub>, increments of 1% between Q<sub>95</sub> and Q<sub>99</sub>, and increments of 0.1% between Q<sub>99</sub> and Q<sub>100</sub> in order to provide higher resolution at the lowest and highest flows, which occur much less frequently.</p> <p><strong>HG Regionalization</strong></p> <p>We regionalized AHG parameters from gage locations to all stream reaches in the conterminous United States. This downstream hydraulic geometry regionalization was performed using all gages with AHG parameters in each HUC4, as opposed to traditional downstream hydraulic geometry--which involves interpolation of parameters of interest to ungaged reaches on individual streams. We performed linear regressions on log-transformed drainage area and Q at a number of flow percentiles as follows:</p> <p>\(log(Q_i) = \beta_1log(DA) + \beta_0\)</p> <p>where Q<sub>i</sub> is streamflow at percentile i, DA is drainage area and \(\beta_1\) and \(\beta_0\) are regression parameters. We report \(\beta_1\), \(\beta_0\) , and the r<sup>2</sup> value of the regression relationship for Q percentiles Q<sub>10</sub>, Q<sub>25</sub>, Q<sub>50</sub>, Q<sub>75</sub>, Q<sub>90</sub>, Q<sub>95</sub>, Q<sub>99</sub>, and Q<sub>99.9</sub>. Further discussion and additional analysis of HG regionalization are presented in Heldmyer et al. (2022).</p> <p><strong>Dataset description</strong></p> <p>We present the HyG dataset in a comma-separated value (csv) format. Each row corresponds to a different USGS stream gage. Information in the dataset includes gage ID (column 1), gage location in latitude and longitude (columns 2-3), gage drainage area (from USGS; column 4), longitudinal slope of the gage's stream reach (from NHDPlusv2; column 5), AHG parameters derived from field measurements (columns 6-11), Manning's n calculated from median measured flow conditions (column 12), Manning's n calculated from Q50 (column 13), Q percentiles (columns 14-51), HG regionalization parameters and r<sup>2</sup> values (columns 52-75), and geospatial information for the HUC4 in which the gage is located (from USGS; columns 76-87). Users are advised to exercise caution when opening the dataset. Certain software, including Microsoft Excel and Python, may drop the leading zeros in USGS gage IDs and HUC4 IDs if these columns are not explicitly imported as strings.</p> <p> </p> <p><strong>Errata</strong></p> <p>In version 1, drainage area was mistakenly reported in cubic meters but labeled in cubic kilometers. This error has been corrected in version 2.</p>
Roughness and Energy Losses Induced by Mussel Growth on the Walls of Hydraulic Structures and Application to a Water Transfer Project
<p>This file contains the ADV data of <em>Roughness and Energy Losses Induced by Mussel Growth on the Walls of Hydraulic Structures and Application to a Water Transfer Project</em>.</p>
GeoERA RESOURCE H3O-PLUS data set which contains hydraulic properties of prime aquifers and aquitards in the Dutch-Flemish-German cross-border area
<p>Dataset which contains information about hydraulic properties of harmonized hydrogeological units in the Dutch-Flemish-German cross-border region which was compiled in the GeoERA RESOURCE project under WP3 H3O-PLUS. The harmonization of the 3D geometry of the cross-border hydrogeological units in the H3O projects constituted a major step towards a common hydrogeological dataset of the Roer Valley Graben and thus the harmonization of groundwater flow models. The database that was compiled provides the characterization of these hydrogeological units with respect to their hydraulic properties, primarily their hydraulic conductivity.<br> The associated report and appendices describe the database of hydraulic properties of aquifers and aquitards based on common criteria. Attention is also given to the characterization of hydraulic properties of faults.</p>
Periodic Hydraulic Testing Dataset for "Borehole-based fracture unclogging experiment: bridging the gap between laboratory- and field-scale evidence (FRANC)"
<p>This dataset is associated with the SNSF-SPARK project “Borehole-based fracture unclogging experiment: bridging the gap between laboratory- and field-scale evidence (FRANC)”. Please read the ReadMe file for more information.</p>
Computed Basic Statistics of Hydraulics and Discharge Measures at USGS River Monitoring Stations
<p>The shared table contains basic statistics (average, standard deviation, minimum, maximum, and coefficient of variation [%]) river channel hydraulics and discharge records of the 4472 USGS river monitoring stations. The required raw data are free to access by the USGS-<em>National Water Information System</em> (<a href="https://waterdata.usgs.gov/nwis">https://waterdata.usgs.gov/nwis</a>). Hydraulics and discharge records that measured at each USGS monitoring site, given a long time period, were assembled, assessed, and finally used for computing the basic statistics. </p>
Hydraulic burst pressure test of Type IV composite pressure vessel
<p>The present dataset belongs to a hydraulic burst pressure test of one Type IV vessel designed to burst at 200 bar. Before testing, the vessel was inspected by ultrasonic measurements. During burst pressure test, strain gauges at nine positions within the cylindrical part and on one dome of the tank recorded the deformation behavior of the vessel. The dataset provides information on the nominal tank design and winding layup, data of geometrical measurement of a nominal identical vessel, data of the ultrasonic inspection and the pressure and strain gauge data recorded during burst pressure test. Assembling this information, the data set provides an experimental validation basis for simulation methods aiming to predict deformation and damage behavior of composite pressure vessels.</p>
CT-Scan Image Dataset of Residual Fluid-Driven Fracture in a Molasse de Villarlod Sandstone Core - Post-Radial Hydraulic Fracture Experiment - M03 Sample
<h3><strong>Dataset Description</strong></h3> <p>This dataset contains high-resolution CT-scan images that capture the residual fracture surface within a core sample of Molasse de Villarlod Sandstone. The core sample was extracted after conducting a radial hydraulic fracture experiment on a 25 × 25 × 25 cm cubic block of sandstone (M03 sample). The experiment was designed to simulate fluid-driven fracture propagation and closure, and the resulting fracture path was preserved in the core sample.</p> <p><strong>Core Location in the M03 Cube Sample:</strong></p> <ul> <li><strong>Z:</strong> 12.5 cm</li> <li><strong>South-North:</strong> 12.5 cm</li> <li><strong>West-East:</strong> 11.5 cm to 1.36 cm (Coring direction)</li> </ul> <p>This spatial information specifies the exact location and orientation of the core extraction within the M03 cube sample.</p> <h4><strong>CT-scan instrument details:</strong></h4> <p>The M03 sample was analyzed using an X-ray micro-CT scanner (RX-Solutions Ultratom) under consistent scanning protocols and parameters. A reflective 230 kV microfocus X-ray source (Hamamatsu L10801) equipped with a 0.2 mm thick copper filter, a tungsten cathode, and a tungsten target was employed for the imaging process. The scans were conducted with a voltage of 120 kV and a current intensity of 80 mA.</p> <p>The volume data acquisition was performed in continuous helical mode, ensuring complete coverage of the sample’s height. For sample M03, 6 full rotations were executed, with 1312 projections captured for each 360° rotation, allowing for highly precise volume reconstruction. The X-ray beam attenuation was recorded by an XL Varex Paxscan 2530HE plane detector with a resolution of 2176 x 1792 pixels, and an exposure time of 0.50 seconds per projection.</p> <p>The acquired projections were processed using RX-Solutions X-act software with Filtered Backprojection to reconstruct a corrected volume. This reconstruction yielded approximately 9000 slices in 16-bit TIFF format, with voxel dimensions of 10 x 10 x 10 microns, providing detailed insights into the internal structure of the sample.</p> <h4><strong>Key Features:</strong></h4> <ul> <li> <p><strong>Fracture Characteristics</strong>: The fracture observed in the CT-scans represents a residual opening that remains post-fracturation. It is entirely contained within the core, showcasing the internal fracture geometry resulting from the hydraulic fracturing process.</p> </li> <li> <p><strong>CT-Scan Details</strong>: The CT-scans were taken perpendicular to the fracture surface, offering a detailed cross-sectional view of the fracture at different depths. This orientation is critical for accurately capturing the fracture morphology and allows for the reconstruction of the fracture surface in 3D.</p> </li> <li> <p><strong>Material Information</strong>: The core sample is composed of Molasse de Villarlod Sandstone, a sedimentary rock which is porous (18% porosity) and permeable. This material choice is relevant for studying fracture closure subjected to the leak-off of the fluid inside the porous medium.</p> </li> <li> <p><strong>Experimental Context</strong>: The radial hydraulic fracture experiment aimed to simulate the propagation of hydraulic fracture and its closure due to the leakage of fluid inside fracture into the porous medium. The dataset provides valuable insights into fracture propagation patterns, surface roughness, and the effects of fluid-driven fractures in porous media.</p> </li> </ul> <h4><strong>Applications:</strong></h4> <p>This dataset is particularly valuable for researchers and engineers involved in:</p> <ul> <li>Fracture mechanics and surface characterization</li> <li>3D reconstruction and visualization of fracture surfaces</li> <li>Surface roughness analysis</li> <li>Hydraulic fracturing studies</li> <li>Geomechanical modeling</li> </ul> <h4><strong>File Structure:</strong></h4> <p>The dataset is organized into zip-folder contains .tif images corresponding to different depths within the core. Each tif-image is a CT-scan for that specific depth, labeled according to their position along the fracture path.</p> <h4><strong>Processing code:</strong></h4> <p>Follow the <strong>URL repository</strong> in the software section to access to the code for processing these images and reconstructing the fracture surfaces.</p> <p><strong>Acknowledgment:</strong></p> <p>We would like to extend our deepest thanks to Gary Perrenoud, Albert Taureg, and Lionel Pittet, the technical specialists of the PIXE platform at École Polytechnique Fédérale de Lausanne (EPFL). Their expertise and support in operating the CT-scan machine were important to the success of this research. We greatly appreciate their dedication and the high-quality work they provided.</p> <p><strong>Contact and Support:</strong></p> <p>Email:</p> <p>Brice Lecampion: brice.lecampion@epfl.ch</p> <p>Mohsen Talebkeikhah: m.talebkeikhah@gmail.com</p>
Dataset for manuscript: Rock anisotropy promotes hydraulic fracture containment at depth
<p>This is the experimental data used in manuscript: "Rock anisotropy promotes hydraulic fracture containment at depth".</p> <p>This data set has 5 folders: </p> <p>K1, T2, M3 and M4 contain the raw data collected by active acoustic monitoring system and various pumps and transducers. <br> The readings of the Top Industrie syringe pump (for fluid injection), pressure transducers at downstream, and GDS pumps that provide confining stresses along all three directions via flatjacks are provided by two excel spreadsheets - Low_frequency_measurements.csv and Pump_reading.csv.<br> For active acoustic data, there are four different files named by the starting time for each experiment. The .json file contains the basic information of each experiment. The .txt file contains the acquisition time for each active acoustic sequence. The .bin file contains the collected waveforms of all acoustic sequences.</p> <p>Processed_data includes the synchronized pressure data (Pressure_data.xlsx) and detailed acoustic emission results (AE_K1.csv, AE_T2.csv, AE_M3.csv, AE_M4.csv).<br> </p>
Data_Schönauer et al. (2023)_Root and branch hydraulic functioning and trait coordination across organs in drought-deciduous and evergreen tree species of a subtropical highland forest
<p>Data used in</p> <p>Schönauer, M., Hietz, P., Schuldt, B., and Rewald, B. (2023). Root and branch hydraulic functioning and trait coordination across organs in drought-deciduous and evergreen tree species of a subtropical highland forest. Frontiers in plant science 14, 1127292. doi: 10.3389/fpls.2023.1127292</p>
Seismicity catalog of hydraulic-fracturing-induced earthquakes in the Kiskatinaw area, northeast British Columbia
<p>Seismicity catalog of 8,731 hydraulic-fracturing-induced earthquakes in the Kiskatinaw area, northeast British Columbia between 1 July 2017 to 31 Dec 2020. The Study area covers the Kiskatinaw area, wich extends over parts of the Montney Formation, a major shale gas play within the Western Canada Sedimentary Basin. Catalog corresponds to:</p> <p>Roth, M. P., A. Verdecchia, R. M. Harrington, and Y. Liu (2020). High-Resolution Imaging of Hydraulic-Fracturing-Induced Earthquake Clusters in the Dawson-Septimus Area, Northeast British Columbia, Canada, Seismol. Res. Lett. 91, 2744–2756, doi: 10.1785/0220200086.</p>
The role of the intraspecific variability of hydraulic traits for modelling the plant water use in different European forest ecosystems: scripts, model output, and parameter files
<p>This repository contains the model outputs and R scripts used to process the data to analyze the impact of the plant hydraulic parameterization of the manuscript: "The role of the intraspecific variability of hydraulic traits for modelling the plant water use in different European forest ecosystems". The following is a detailed description of the content of this repository:</p> <p>model_output.zip: This compressed file contains the results of all the individual numerical experiments per experimental site as produced by the Comunity Land Model version 5. The files are stored in NETCDF format per year. The folder is arranged with subfolders containing the individual results from each experimental site as follows:</p> <ul> <li>rc: model output with the results of the resistant configuration of experiment 1 (RC)</li> <li>vc: model output with the results of the vulnerable configuration of experiment 1 (VC)</li> <li>k_dc: model output with the results of the default configuration used for experiments 1 and 2 (DC or DC<em>k</em><sub>max</sub>)</li> <li>k_rc: model output with the results of the low plant hydraulic conductance (L<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_irc: model output with the results of the intermediate low plant hydraulic conductance (IL<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_vc: model output with the results of the high plant hydraulic conductance (H<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_ivc: model output with the results of the intermediate high plant hydraulic conductance (IH<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_iirc: model output with the results of the additional intermediate low plant hydraulic conductance (IIL<em>k</em><sub>max</sub>) for experiment 2</li> <li>ko_dc: model output with the results of the best <em>k</em><sub>max</sub> and the default configuration of the PVC used in experiment 3</li> <li>ko_rc: model output with the results of the best <em>k</em><sub>max</sub> and the resistant configuration of the PVC used in experiment 3</li> <li>ko_vc: model output with the results of the best <em>k</em><sub>max</sub> and the vulnerable configuration of the PVC used in experiment 3</li> </ul> <p>The scripts were written for use in RStudio, and each contains a detailed description of the data requirements and outputs. Each script was developed to read directly the netcdf files of the model output and the csv files containing the transpiration estimates calculated from the SAPFLUXNET per experimental site (script 1).</p>
Water quality measurements, stream order, channel slope and hydraulic equations of conterminous USGS sites: 1919-2009.
Streams and rivers emit petagrams of CO2 yet there is little known about how discharge (Q) variability impacts stream CO2 at broad scales. Herein, we compiled historical water quality (including pH, alkalinity and temperature) measurements for conterminous USGS sites and coupled them with daily Q for this analysis (the water_quality.csv dataset, 10,822 sites). Based on this dataset, NHDplus channel slopes (NHDplus_slopeSO.csv, 24,764 sites) and hydraulic geometry equations (lm_vQ.csv, 12,854 sites), we calculated partial pressure of dissolved CO2 (pCO2), gas transfer velocity (k) and CO2 effluxes (F) for a total of 813 USGS sites across conterminous US. We derived hydrologic responses (log-linear regressions) for pCO2, k and F versus Q at each site and explored how these responses varied across stream order and different regions. Ancillary datasets provided coordinates (coor_sites.xls), hydrologic unit code (HUC.csv), and watershed area of conterminous USGS sites (watersheds_area.csv).
Hubbard Brook Experimental Forest: Watershed 3 Saturated Hydraulic Conductivity
This is a dataset of soil saturated hydraulic conductivity (Ksat) collected from augered boreholes or installed groundwater wells in Watershed 3 of the Hubbard Brook Experimental Forest. Hydraulic conductivity describes the ability of a porous medium such as soil to transmit fluid. It is dependent on both fluid (e.g., viscosity) and porous medium properties, and is a key property for estimating subsurface flow rates. Measurements were collected from near the soil surface (10-15 cm depth) to several meters below the surface. Locations are provided for sites where the confidence in coordinates established by GPS was high. Soil horizons without subordinate designators are approximate since the characterization skill of observers varied. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES) and several other NSF grants over the period from approximately 2007 to 2019. 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.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.