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289 results for “hydraulics”
Compilation of hydraulic models for the study of the spatial averaging on flow laws
<p><strong>1.Summary</strong></p> <p>Datasets used in the article written by Ernesto Rodríguez, Michael Durand and Renato Prata de Moraes Frasson entitled “Observing rivers with varying spatial scales”.</p> <p><strong>2.File description</strong></p> <p>Data will be contained in one NetCDF file per river. The file contains the following groups and variables:</p> <p><strong>/River_Info/</strong></p> <p>Name: River name, data type: char</p> <p>QWBM: Mean annual discharge from the water balance model WBMsed (Cohen et al., 2014)</p> <p>rch_bnd: Reach boundaries measured in meters from the upstream end of the model</p> <p>gdrch: Reaches used in the study. Used to exclude small reaches defined around low-head dams and other obstacles where Manning’s equation should not be applied.</p> <p><strong>/XS_Timeseries/</strong></p> <p>t: Time measured in days since the first day or “0-January-0000” for cases when specific dates were available. Dimension: 1,time step.</p> <p>Z: Bed elevation in meters. Dimension: Cross-section, time step.</p> <p>xs_rch: Reach number for each cross-section. Dimension: Cross-section,1.</p> <p>X: Flow distance measured from the most upstream end of the model to the cross-section (meters). Dimension: Cross-section, 1.</p> <p>W: River width in meters. Dimension: Cross-section, time step.</p> <p>Q: Discharge (m<sup>3</sup>/s). Dimension: Cross-section, time step.</p> <p>H: Water surface elevation in meters. Dimension: Cross-section, time step.</p> <p>A: Cross-sectional area of flow in m<sup>2</sup>. Dimension: Cross-section, time step.</p> <p>P: Wetted perimeter in meters. Dimension: Cross-section, time step.</p> <p>n: Manning’s roughness. Dimension: Cross-section, time step.</p> <p><strong>/Reach_Timeseries/</strong></p> <p>t: Time measured in days since the first day or “0-January-0000” for cases when specific dates were available. Dimension: 1,time step.</p> <p>W: Reach averaged river width in meters. Dimension: Reach, time step.</p> <p>Q: Reach averaged discharge (m<sup>3</sup>/s). Dimension: Reach, time step.</p> <p>H: Reach averaged water surface elevation in meters. Dimension: Reach, time step.</p> <p>S: Reach averaged water surface slope in meters per meter. Reach, time step.</p> <p>A: Reach averaged area of flow in m<sup>2</sup>. Dimension: Reach, time step.</p> <p>P: Reach averaged wetted perimeter in meters. Not available for all rivers. Fill value: NaN. Dimension: Reach, time step.</p> <p><strong>References</strong></p> <p>Cohen, S., A. J. Kettner, and J. P. M. Syvitski (2014), Global suspended sediment and water discharge dynamics between 1960 and 2010: Continental trends and intra-basin sensitivity, <em>Glob. Planet. Change</em>, <em>115</em>, 44-58, doi: <a href="https://doi.org/10.1016/j.gloplacha.2014.01.011">10.1016/j.gloplacha.2014.01.011</a>.</p> <p>Rodríguez, E., Durand, M. T., & Frasson, R. P. d. M. (2020). Observing rivers with varying spatial scales. Water Resources Research. doi: <a href="https://doi.org/10.1029/2019WR026476">10.1029/2019WR026476 </a></p>
CT-Scan Image Dataset of Residual Fluid-Driven Fracture in a Molasse de Villarlod Sandstone Core - Post-Radial Hydraulic Fracture Experiment - M04 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 (M04 Smaple). 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 M04 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> 13.5 cm to 23.3 cm (Coring direction)</li> </ul> <p>This spatial information specifies the exact location and orientation of the core extraction within the M04 cube sample.</p> <h4><strong>CT-scan instrument details:</strong></h4> <p>The M04 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 M04, 5 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>
Hydraulic mixing cell simulation of sources of surface water in the Weierbach catchment
<p>The dataset contains the meteorological forcing data and results of the hydraulic mixing cell (HMC) simulation presented and discussed in the research article: </p> <p><em>Sources of surface water in space and time: Identification of delivery processes and geographical sources with hydraulic mixing-cell modeling</em> (DOI:10.1029/2021WR030332)</p> <p>The simulation was performed with the model HydroGeoSphere and the HMC modelling was used to identify the delivery processes and geographical sources of surface water at specific points of interest (POI) within the riparian-stream continuum of the Weierbach catchment (Luxembourg). Detailed information on the model setup, the location of the POIs, the considered source types and source areas, and the processing we applied to the raw simulation output is given in the research article.</p>
Coordination of hydraulic and morphological traits across dominant grasses in eastern Australia
<p>1. Leaf hydraulic traits characterize plant drought tolerance and responses to climate change. Yet, plant hydraulics are biased towards northern hemisphere woody species. We collected rhizomes of several perennial grass species along a precipitation gradient in eastern Australia and grew them in an experimental garden to investigate potential tradeoffs between drought tolerance and plant morphology.</p> <p>2. We measured the following leaf hydraulic traits: the leaf water potential (Ψleaf) at 50% and 88% loss of leaf hydraulic conductance (P50Kleaf and P88Kleaf), the Ψleaf at 50% loss of stomatal conductance (P50gs), leaf turgor loss point (TLP), leaf dry matter content (LDMC), leaf modulus of elasticity (ε), and the slope of the relationship between predawn and midday Ψleaf. We also measured basal area, tiller density, seed head density, root collar diameter, plant height, and aboveground biomass of each individual.</p> <p>3. As expected, grass species varied widely in leaf-level drought tolerance, with loss of 88% hydraulic conductance occurring at a Ψleaf ranging from -1.52 to -4.01 MPa. However, all but one species lost leaf turgor, and most reached P50gs before this critical threshold. Taller more productive grass species tended to have drought vulnerable leaves characterized by low LDMC and less negative P88Kleaf. Species with greater tiller production experienced stomatal closure and lost turgor at more negative Ψleaf. Although our sample size was limited, we found no relationships between these species' traits and their climate of origin.</p> <p>4. Overall, we identified important hydraulic and morphological tradeoffs in Australian grasses that were surprisingly similar to those observed for woody plants: (1) xylem of taller species was less drought tolerant and (2) turgor loss occurs and stomatal closure begins before significant loss of Kleaf. These data build upon a small yet growing field of grass hydraulics and may be informative of species responses to further drought intensification in Australia.</p>
GPS and hydraulic head measurement utilized in North China Plain research
<p>This dataset contains the raw data of the GPS and hydraulic head measurement <br> utilized in North China Plain research.</p> <p>## Included files</p> <p>The `gps_cmonoc.dat` file contains the horizontal and vertical velocities of <br> the 35 continuous GPS stations from the Crustal Movement Observation Network of <br> China (CMONOC) project.</p> <p>The `gps_bjcors.dat` file contains the horizontal and vertical velocities of <br> the 14 continuous GPS stations from the Beijing Continuously Operating Reference <br> Station (BJCORS) network.</p> <p>The `gps_campaign.dat` file contains the horizontal and vertical velocities of <br> the 432 campaign GPS stations from the Crustal Movement Observation Network of <br> China (CMONOC) project.</p> <p>The `hydraulic_datacenter.xlsx` file contains the 559 measurements from confined <br> well accessed from the National Earth System Science Data Center, National Science <br> & Technology Infrastructure of China (http://www.geodata.cn), recording during 2005-2018.</p> <p>The `hydraulic_yearbook.xlsx` file contains the 130 measurements from both confined<br> and unconfined well compiled from the yearbook 'the China Groundwater Level Yearbook <br> for Geo-environmental Monitoring', recoding during 2005-2016.</p>
Effects of powdered cactus pear pruning amendment on the physical and hydraulic properties of two contrasting Mediterranean soils
<p>Full database.</p> <p>A production and consumption paradigm known as "circular economy" (CE) emphasizes sharing, renting, reusing, repairing, refurbishing, and, in particular, recycling materials as much as feasible. Traditional agriculture relied totally on the CE, progressively the search for maximization of yields has produced more and more by-products. Their recovery and reuse are possible with approaches that refer to the CE, for example, with the use of pruning biomasses. The cultivation of the cactus pear annually produces large quantities of pruning residues, which have been shown to be useful for the recovery and reuse of nutrients. This study investigates the hydraulic properties of benchmark soils in which this by-product is incorporated. Here we show that the amendment with powdered cactus pear pruning waste (PCPPW) positively affects soil water retention. However, observable benefits require very high amendment proportions, more than 20% by volume. These quantities make use in the open field unrealistic but offer perspectives in the horticultural and floricultural sectors. These results reveal agreement in direct comparison to what was thought to be the case previously, i.e., a decrease in soil bulk density, an increase in plant available water capacity and an increase in soil swelling. A few per cent application of PCPPW improves the drainable water capacity only in the case of not very clayey soils, where their use becomes useless. The principles of the CE are important, but they must not be pursued a priori. For example, in the use of soil amendments, the behavior in the different soils conditions the suitability of their use.</p>
Supplementary data of article Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks
<p>This dataset was generated within the research thesis of Axel Hutomo, under the supervision of Leonardo Alfonso and Ioana Popescu at IHE Delft, and it is published as supplementary data for the article <em>Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks, </em>currently under review. </p> <p>The Excel sheet provides information about the datasets produced to integrate acoustic sensor data and hydraulic model output data, to be used by the Machine Learning model. The acoustic sensor data were obtained by extracting several features in time and frequency domains from each audio file coming from acoustic sensors, whereas hydraulic model data was obtained by modelling these leaks using a pressure-independent analysis.</p> <p>The Python code shows the building of the ANN for leakage modelling prediction, integrating the two datasets above, for different leak rates.</p>
Multi-scale effects on the hydraulic behaviour of a root-permeated and compacted soil
<p>Dataset obtained from multi-scale observations on the hydraulic behaviour of a root-permeated and compacted soil.</p>
Hydraulic (HEC-RAS) model of the Lower San Saba River between Harkeyville and San Saba, TX, USA
Open the record for dataset details and reuse information.
Effects of plant hydraulic traits on the flammability of live fine canopy fuels in 62 Australian plant species
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Coordination of hydraulic and morphological traits across dominant grasses in eastern Australia
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Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
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Data from: The PDI model system for parameterizing soil hydraulic properties
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Datasets used in 'Streambed hydraulic conductivity estimated by spectral induced polarization imaging can help to improve groundwater modeling'
<p>These datasets pertain to the manuscript entitled 'Streambed hydraulic conductivity estimated by spectral induced polarization imaging can help to improve groundwater modeling', which is currently submitted for revision in Water Resources Research. They comprise raw data from measurements taken in the field at 2 sites in terms of (1) pressure time series during the performed slug tests, (2) impedance from spectral induced polarization, (3) submersion levels of the used electrodes below the top of the water column, and (4) three-dimensional Cartesian coordinates relating the measurements spatially. The coordinates have been projected to a local coordinate system for each site, to comply with a non-disclosure agreement of the measurement locations. The projection of the coordinates still allows to fully reproduce the presented results, if using the methods described in the manuscript. The naming convention throughout the datasets is consistent with site labels used in the manuscript. All data is given as comma-separated values with intuitive file names and self-explanatory headers containing a list of field names. The slug test data includes multiple repetitions of the same measurement, and the impedance measurements contain normal as well as reciprocal readings – as described in the manuscript.</p> <p>The data is separated into two compressed file archives, named according to the site names given in the manuscript. Each file pertaining to slug tests at a certain location, in the subfolder “slugTestRecordings” has the following naming convention: “<locationTag>_<finalDepth>.csv”, where <locationTag> corresponds to the local coordinates given in “coordinates.txt”, and where <finalDepth> is an integer describing the largest depth in cm at which a slug test was performed according to the protocol described in the manuscript. Each file pertaining to impedance measurements at a certain profile, in the subfolder “SIPRecordings”, has the following naming convention: “<locationTag>_<frequency>.dat”, where <locationTag> corresponds to the local coordinates given in “coordinates.txt”, and where <frequency> is a zero-padded integer describing the measurement frequency in Hz at which the measurement was performed according to the protocol described in the manuscript. Submersion levels of the electrodes below the top of the stream’s water column are given in m in the file “submersionLevels.txt”, corresponding to the local coordinates given in “coordinates.txt”. The local coordinates given in m in “coordinates.txt” have the following convention for the column “locationTag”: “<profileTag>-<electrodeNumber>, where <profileTag> pertains to a name of the electrical array and <electrodeNumber”> is a continuous number for the electrode. Slug tests were exclusively perfomed at the location of electrodes and files are, thus, as described above, named accordingly.</p>
Global soil saturated hydraulic conductivity map using random forest in a Covariate-based GeoTransfer Functions (CoGTF) framework at 1 km resolution
<p>The global Ksat map at 1 km resolution was developed by harnessing the technological advances in machine learning and availability of remotely sensed surrogate information such as terrain, climate, vegetation, and soil covariates. We merge concepts of predictive soil mapping with a large data set of Ksat measurements and local information (soil, vegetation, climate) into covariate-based “Geo Transfer Functions'' (CoGTFs) to generate global estimates of Ksat values (to highlight the impact of Geo-referenced covariates including various remote sensing maps, we use the term Geotransfer function GTF and not pedotransfer function PTF; in the latter case, typically only soil properties are used to estimate Ksat).</p> <p>The Ksat dataset is provided in GeoTIFF format. A total of 4 files that represent different soil depths (0, 30, 60, and 100 cm) are provided. The Ksat values are log-transformed (log10 Ksat) and cm/day was selected as a standardized unit.</p> <p>The Global Ksat training dataset used for this study is available here:<br> <a href="https://doi.org/10.5281/zenodo.3752721">https://doi.org/10.5281/zenodo.3752721</a></p> <p>The R code used for this study is available here:<br> <a href="https://github.com/ETHZ-repositories/Ksat_mapping_2020">https://github.com/ETHZ-repositories/Ksat_mapping_2020</a></p> <p>For more details / to cite this dataset please use:</p> <ul> <li>Gupta, S., Lehmann, P., Bonetti, S., Papritz, A., and Or, D., (2020): <strong>Global prediction of soil saturated hydraulic conductivity using random forest in a Covariate-based Geo Transfer Functions (CoGTF) framework</strong>. Journal of Advances in Modeling Earth Systems,<strong> </strong>13(4), e2020MS002242. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020MS002242</li> </ul> <p>Other datasets related to this project:</p> <p>The Global vG training dataset is available here:</p> <p><a href="https://doi.org/10.5281/zenodo.5547338">10.5281/zenodo.5547338</a></p> <p>Examples of using this dataset to generate van Genuchten parameters maps can be found in <a href="https://doi.org/10.5281/zenodo.6343570">10.5281/zenodo.6343570</a>.</p> <p>The study was supported by ETH Zurich (Grant ETH-18 18-1). We would like to thank Zhongwang Wei, Samuel Bickel and Simone Fatichi (ETH Zurich) for insightful discussions.</p> <p> </p> <p> </p>
Mechanical and hydraulic transport properties of transverse-isotropic Gneiss deformed under deep reservoir stress and pressure conditions.
<p>"This is the ReadMe file corresponding to the study entitled: "Mechanical and hydraulic transport properties of transverse-isotropic<br> Gneiss deformed under deep reservoir stress and pressure conditions" <br> <br> "By M. Acosta, & M. Violay." <br> <br> This study has been published in the International Journal of Rock Mechanics and Mining Sciences in June 2020. <br> https://doi.org/10.1016/j.ijrmms.2020.104235<br> <br> This Read-Me file has been last edited on 2020-06-31 <br> <br> This readme file describes the data repository and supplementary files accompanying the above publication. <br> For any further queries please contact mateo.acosta@epfl.ch <br> <br> The following files are included: <br> <br> --- Regarding Figure 3. <br> <br> "1) ""Acosta_et_al_2020_Figure3Data.xlsx"" " <br> This is the processed data from the experiments described in Figure1 of the article. <br> "In this .xlsx File, each sheet corresponds to one figure panel as follows: " <br> <br> Fig.3: One experiment example<br> Column A: Axial strain in (%) ; Column B: Effective axial stress (in MPa); Column C: Axial strain for AE's in (%) ; Column D: Acoustic emission hits (in #); Column D: Axial strain for porosity change in (%) ; Column E: Porosity change (in %);</p> <p>--- Regarding Figure 4. <br> <br> "1) ""Acosta_et_al_2020_Figure4Data.xlsx"" " <br> This is the processed data from the experiments described in Figure1 of the article. <br> "In this .xlsx File, each sheet corresponds to one figure panel as follows: " <br> <br> Fig.4a&g: Beta=0deg<br> Column A: Axial strain in (%) ; Column B: Effective axial stress (in MPa); Column C: Axial strain for AE's in (%) ; Column D: Acoustic emission hits (in #); Column D: Axial strain for porosity change in (%) ; Column E: Porosity change (in %);</p> <p>Fig.4b&h: Beta=30deg<br> Column A: Axial strain in (%) ; Column B: Effective axial stress (in MPa); Column C: Axial strain for AE's in (%) ; Column D: Acoustic emission hits (in #); Column D: Axial strain for porosity change in (%) ; Column E: Porosity change (in %);</p> <p>Fig.4c&i: Beta=45deg<br> Column A: Axial strain in (%) ; Column B: Effective axial stress (in MPa); Column C: Axial strain for AE's in (%) ; Column D: Acoustic emission hits (in #); Column D: Axial strain for porosity change in (%) ; Column E: Porosity change (in %);</p> <p>Fig.4d&j: Beta=60deg<br> Column A: Axial strain in (%) ; Column B: Effective axial stress (in MPa); Column C: Axial strain for AE's in (%) ; Column D: Acoustic emission hits (in #); Column D: Axial strain for porosity change in (%) ; Column E: Porosity change (in %);</p> <p>Fig.4e&k: Beta=90deg<br> Column A: Axial strain in (%) ; Column B: Effective axial stress (in MPa); Column C: Axial strain for AE's in (%) ; Column D: Acoustic emission hits (in #); Column D: Axial strain for porosity change in (%) ; Column E: Porosity change (in %);</p> <p>Fig.4f&l: LPG<br> Column A: Axial strain in (%) ; Column B: Effective axial stress (in MPa); Column C: Axial strain for AE's in (%) ; Column D: Acoustic emission hits (in #); Column D: Axial strain for porosity change in (%) ; Column E: Porosity change (in %);</p> <p>--- Regarding all other Figures, the tables provided in the article allow reproduction of these.</p> <p> </p>
Data from: Six co-occurring conifer species in northern Idaho exhibit a continuum of hydraulic strategies during an extreme drought year
As growing seasons in the northwestern USA lengthen, on track with climate predictions, the mixed conifer forests that dominate this region will experience extended seasonal drought conditions. The year of 2015, which had the most extreme drought for the area on record, offered a potential analog of future conditions. During this period, we measured the daily courses of water potential and gas exchange as well as the hydraulic conductivity and vulnerability to embolism of six dominant native conifer species, Abies grandis, Larix occidentalis, Pinus ponderosa, Pinus monticola, Pseudotsuga menziesii, and Thuja occidentalis, to determine their responses to 5 months of record low precipitation. The deep ash-capped soils of the region allowed gas exchange to continue without significant evidence of water stress for almost two months after the last rainfall event. Midday water potentials never fell below -2.2 MPa in the evergreen species and -2.7 MPa in the one deciduous species. Branch xylem was resistant to embolism, with P50 values ranging from -3.3 to -7.0 MPa. Root xylem, however, was more vulnerable, with P50 values from -1.3 to -4.6 MPa. With predawn water potentials as low as -1.3 MPa, the two Pinus species likely experienced declines in root hydraulic conductivity. Stomatal conductance of all six species was significantly responsive to vapor pressure only in the dry months (August-October), with no response evident in the wet months (June-July). While there were similarities among species, they exhibited a continuum of isohydry and safety margins. Despite the severity of this drought, all species were able to continue photosynthesis until mid-October, likely due to the mediating effects of the meter-deep, ash-capped silty-loam soils with large water storage capacity. Areas with these soil types, which are characteristic of much of the Northwest USA, could serve as refugia under drier and warmer future conditions.
The Characteristics and Its Implications of Hydraulic Fracturing in Hydrate-Bearing Clayey Silt
<p>Datasets of the figures in the manuscript 'The Characteristics and Its Implications of Hydraulic Fracturing in Hydrate-Bearing Clayey Silt' to Journal of Natural Gas Science and Engineering.</p>
Hydraulic jump
<p>Description of the movies</p> <p>File: Oscillations-jump.mp4Oscillations-jump.mp4</p> <p>The video shows the oscillatory flow patterns between B-jump and Wave jump.</p> <p>File: Und-jump_large_channel.mp4</p> <p>The video shows a hydraulic jump in the large channel of the LIC – Coastal Engineering Laboratory of the Polytechnic University of Bari, Italy.</p>
A catastrophic tropical drought kills hydraulically vulnerable tree species
<p>Drought-related tree mortality is now a widespread phenomenon predicted to increase in magnitude with climate change. However, the patterns of which species and trees are most vulnerable to drought, and the underlying mechanisms have remained elusive, in part due to the lack of relevant data and difficulty of predicting the location of catastrophic drought years in advance. We used long‐term demographic records and extensive databases of functional traits and distribution patterns to understand the responses of 20 to 53 species to an extreme drought in a seasonally dry tropical forest in Costa Rica, which occurred during the 2015 El Niño Southern Oscillation event. Overall, species-specific mortality rates during the drought ranged from 0% to 34%, and varied little as a function of tree size. By contrast, hydraulic safety margins correlated well with probability of mortality among species, while morphological or leaf economics spectrum traits did not. This firmly suggests hydraulic traits as targets for future research.</p>
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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.