Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
46
datasets available to search
ShareScore release 0.9.0
Dataset results
46 results for “Hydraulic model”
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>
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>
Hydraulic scale model experiments on the two-dimensional run-up and overtopping of solitary waves at a vertical wall and a dam-like structure
<p>This dataset includes the experimental data, which were generated during the study on the run-up and overtopping of solitary waves at the Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich.</p>
Data supplement to "From Grains to Plastics: Modeling Nourishment Patterns and Hydraulic Sorting of Fluvially Transported Materials in Deltas"
<p>Supplementary data and codes for "From Grains to Plastics: Modeling Nourishment Patterns and Hydraulic Sorting of Fluvially Transported Materials in Deltas". Zipped files contain ANUGA hydrodynamic model outputs, dorado particle-routing simulation outputs, Python scripts for running additional dorado simulations, and other metadata used in the analysis of dorado outputs. See README for additional details about directory contents. Note that this directory does not contain the model software itself, which is available on GitHub and has been archived elsewhere (relevant links can be found in README).</p>
Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
<p>Information about the spatial distribution of soil hydraulic parameters is necessary for the accurate prediction of soil water flow and coupled movement of chemicals and heat at the field scale using a process-based model. Physics-informed neural networks (PINNs), which can provide physical constraints in deep learning to obtain a mesh-free solution, can be used to inversely estimate the soil hydraulic parameters from less and noisy training data. Previous studies using PINNs have successfully estimated soil hydraulic parameters for homogeneous soil but estimating such parameters of layered soil profiles where the interface depth and the parameters are unknown still has some difficulties. The objective of this study was to develop PINNs to inversely estimate the distribution of soil hydraulic parameters, such as saturated hydraulic conductivity and <em>α</em> and <em>n</em>, of the Mualem-van Genuchten model directly within layered soil profiles by predicting changes in pressure head from training data based on simulation results at given depths during infiltration. The impact of factors affecting PINNs performance, such as the weights assigned to each component of the loss function, the time range used in error computations, and the number of samples used to assess physical constraint was investigated. By assigning a larger weight to the physical constraint and excluding the earlier stage of infiltration in the loss function, the changes in pressure head and the three soil hydraulic parameter distributions within the layered soil profiles were successfully estimated. The developed PINNs can be further applied to more complex soils and can be improved.</p>
Data from: The PDI model system for parameterizing soil hydraulic properties
<p>The PDI ("Peters-Durner-Iden") model system represents a robust framework for parameterizing soil hydraulic properties, i.e. the water retention curve and the hydraulic conductivity curve, across the entire soil moisture spectrum. This model accounts for water retention and hydraulic conductivity in completely and partially-filled pores, including adsorption and film-flow. The model was developed in stages and a comprehensive overview of the model development and the model equations is provided in Peters et al. (2024). In this repository, we provide a Python file named "pdi.py" which can be used to compute the various submodels (PDI-VG, PDI-KOS, PDI-FX, ...) of the PDI model system. One MS Excel file is provided for easy access to one PDI model, the PDI-VG. The PYTHON functions contained in "pdi.py" can be used to calculate the water retention curve, the unsaturated hydraulic conductivity curve, the specific water capacity function, and the soil water diffusivity function. In addition, we provide five python scripts which illustrate how to call the various PDI functions in different contexts. Notably, "pdi.py" incorporates a utility function, 'export_hydrus_materin', which generates an ASCII file named "MATER.IN". This file serves as input for simulations with Hydrus-1D and Hydrus-2D3D, offering seamless integration with these simulation platforms. It's important to emphasize that the provided Python scripts and accompanying documentation are closely aligned with the research article by Peters et al. (2024). To streamline accessibility, the repository refrains from redundantly restating the theory or equations already detailed in the referenced publication.</p>
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>
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>
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.
Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
Open the record for dataset details and reuse information.
Data from: The PDI model system for parameterizing soil hydraulic properties
Open the record for dataset details and reuse information.
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>
Hellisheiði geothermal field: Hydraulic data for pseudo-prospective forecasting models (Dec. 2018 - Jan. 2021)
<p>This dataset comprises the compound volumes processed from injection and production rates in the Hellisheiði field between December 2018 and January 2021. This dataset corresponds to the input data for the ETAS-f and Seismogenic Index models of Ritz et al., 2023 (doi:<a href="https://doi.org/10.22541/essoar.168500354.49240043/v1">10.22541/essoar.168500354.49240043/v1</a>)</p><p>The hydraulic data was acquired and processed by Reykjavik Energy/ON power, the operator of the Hellisheiði geothermal field.</p>
Orthophotos and 2D hydraulic modelling results used for habitat suitability modelling of the River Inn section (river km 35.3-48) in SE Germany
<p>The aerial RGB picture acquisition was performed on October 7 (bypass channel) and 11 (side channel) 2022 using a DJI-Matrice 210 V2 RTK drone. For the image acquisition, the drone mounted the DJI Zenmuse X5S RGB camera. The flight was realized at an altitude of about 120 m, ensuring a lateral and longitudinal overlap of the images of about 80%. Gound Control Points (GCPs) have been disposed along the study site, and their position georeferenced using a Emlid Reach RS2 RTK GPS system. After data collection, an RGB orthomosaic with a spatial resolution of 25 cm was generated, using PIX4Dmapper v4.7.5 (www.pix4d.com).</p><p>The hydrodynamic simulations were performed with the freeware software BASEMENT v3.2 (https://basement.ethz.ch), which solves the 2D shallow-water equations using a finite volume approach over two-dimensional unstructured meshes. Computational meshes were created using the QGIS plugin BASEmesh 2, with spatially varying element sizes, which were set to be finer in areas expected to be suitable for spawning and as nursery grounds, or when needed to more accurately represent the local morphological complexity.</p>
Induced polarization and hydraulic tomography joint inversion results on a synthetic model
<p>These data are supplementary material for the following publication: Lukas Römhild, Gianluca Fiandaca, Peter Bayer (2024): Joint inversion of induced polarization and hydraulic tomography data for hydraulic conductivity imaging, <em>Geophysical Journal International</em>, Volume 238, Issue 2, August 2024, Pages 960–973, <a href="https://doi.org/10.1093/gji/ggae197">https://doi.org/10.1093/gji/ggae197</a></p> <p>The data set presents induced polarization (IP) and hydraulic tomography (HT) inversion results for a simple synthetic model. In addition, a joint inversion approach for both data types was implemented, so that individual and joint inversion results can be compared. We also show how the joint inversion procedure has the potential to correct a petrophysical bias within the IP inversion by incorporating the hydraulic data. The effect of different HT setups is also assessed by implementing different source and receiver positions.</p> <p>The file directory contains the original synthetic model, a subfolder for the synthetic data of IP and HT, and a subfolder for the individual HT and IP, as well as the joint inversion results. The HT results contain three different setups (1m, 2m-inside, 2m-outside). The IP results are shown for five different assumptions of petrophysical bias (-2, -1, 0, +1, +2). The joint inversion results contain all possible combinations of these test cases. For more information on the methodology, we refer to the paper (see above, currently under review).</p>
Dataset of "Hybrid modeling on 3D hydraulic features of a step-pool unit"
<p>In this repository you can find the data for the submission "Hybrid modeling on 3D hydraulic features of a step-pool unit" by Zhang et al. to Earth Surface Dynamics.</p> <p>The topographic models of the step-pool unit made of natural stones after FAVORization in FLOW3D for the six flow rates are kept in .stl files which were named after the flow rates (L/s). The mesh size for the step-pool feature is 2.5 mm for X, Y and Z directions. The locations, water level and flow velocity for the inlet boundary in all the numerical models are presented in the excel file. </p>
Dataset files for 'Tan et al., Hydraulic Fracturing Induced Seismicity in the Changning Shale Gas Field: Evidence From 3-D Seismic Velocity Structure and Pore Pressure Field Models'
<p>These files are the data and result files for the manuscript entitled<strong> 'Hydraulic Fracturing Induced Seismicity in the Changning Shale Gas Field: Evidence From 3-D Seismic Velocity Structure and Pore Pressure Field Models'</strong> by Tan et al., including</p> <p>catalog.dat : the seismic phase catalog used in seismic tomography</p> <p>station.dat : the station coordinates of the local seismic network</p> <p>relocation.dat : the earthquake relocations obtained by double-difference seismic tomography</p> <p>1-D Vs.xlsx : the 1-D Vs model in the shale gas field</p> <p>3-D Vp.dat: the 3-D Vp model obtained by DD seismic tomography</p> <p>3-D Vs.dat: the 3-D Vs model obtained by DD seismic tomography</p> <p>3-D VpVs.sgy: the 3-D Vp/Vs model obtained by DD seismic tomography (3-5 km)</p> <p>3-D pressure.sgy: the 3-D pore pressure field model obtained by focal mechanism tomography (3-5 km)</p>
Dataset files for 'Tan et al., Hydraulic Fracturing Induced Seismicity in the Changning Shale Gas Field: Evidence From 3-D Seismic Velocity Structure and Pore Pressure Field Models'
<p>These files are the data and result files for the manuscript entitled<strong> 'Hydraulic Fracturing Induced Seismicity in the Changning Shale Gas Field: Evidence From 3-D Seismic Velocity Structure and Pore Pressure Field Models'</strong> by Tan et al., including</p> <p><strong>station.dat</strong> : the station coordinates of the local seismic network (including the station ID, longitude, latitude, elevation(negative)/depth(positive), X, Y)</p> <p><strong>catalog.dat</strong> : the seismic phase catalog used in double-difference (DD) seismic tomography</p> <p><strong>relocation.dat </strong>: the earthquake relocations obtained by DD tomography</p> <p><strong>1-D Vs.xlsx</strong> : the 1-D Vs model in the shale gas field</p> <p><strong>3-D Vp.dat</strong>: the 3-D Vp model obtained by DD tomography</p> <p><strong>3-D Vs.dat</strong>: the 3-D Vs model obtained by DD tomography</p> <p><strong>3-D VpVs.sgy</strong>: the 3-D Vp/Vs model (interpolated, within 3-5 km)</p> <p><strong>3-D pressure.sgy</strong>: the 3-D pore pressure field model (interpolated, within 3-5 km)</p>
SurEau database : A database of hydraulic and stomatal traits for modelling drought resistance in plants
<p>This file contains a database of hydraulic and stomatal traits that accompanies the paper untitled "Pant resistance to drought relies on timely stomatal closure" publish in Ecology Letters. This database was used to built the Figure of this manuscript.</p> <p>> The first page ("Stem_VCurves") contains the parameter of vulnerability curve to embolism for 150 species. Family, genus and species names as well as original reference are provided.</p> <p>> The second page ("Pgs90") contains a first proxy for the water potential causing stomatal closure, it is the value of water potential causing 90% stomatal closure computed from gs versus water potential. Family, genus and species names as well as original reference are provided.</p> <p>> The third page (Ptlp) contains a second proxy for the water potential causing stomatal closure, it is the turgor loss point computed from pressure volume curves. Family, genus and species names as well as original reference are provided.</p> <p>> The fourth page (ALL) contains all the three previous pages together, allowing to reconstruct Figure 1.</p> <p>> The fifth page (PitlpAdultSeedlings) contains values of turgor loss point for adults and seedilngs for 15 species.</p> <p>> The sixth page (P50AdultSeedlings) contains values of embolism resistance for adults and seedilngs for 14 species.</p> <p>> The seventh page (Emin) contains values of minimum (i.e. cuticular) conductance and minimum transpiration for 33 species as well as embolism resistance values for theese species.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Hydraulic model (HEC-RAS) of downstream of Tuttle Creek Reservoir at the confluence of the Big Blue River and the Kansas River near Manhattan, KS
<p>A 2D Hydraulic model (HEC-RAS) for below Tuttle Creek Reservoir at the confluence of the Kansas River and the Big Blue River near Manhattan, KS is presented. Model geometry is based on United States Geological Survey (USGS) 3DEP data (2015), with underwater bathymetry "burned" in using cross-sections sampled in the field in April of 2023. The model was calibrated based on water surface measured during data collection. The hydraulic simulations correspond to streamflows during which fish monitoring data were collected by researchers at Kansas State University (L. Rowley and K. Gido, to be published). Results from the hydraulic model, coupled with a sediment transport model, will be used to study fish and macroinvertabrate ecological response to streamflow.</p>
ScienceDex guides
Understand access before you commit
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.