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32 results for “hydraulic conductance”
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.
Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity
<p><strong>Version 1.0 - This version is the final revised one.</strong></p> <p>This is the dataset accompanying the paper: Zeitfogel et al., Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity, published at Geoderma, 2023 (https://doi.org/10.1016/j.geoderma.2023.116418).</p> <p>Soil property and Ksat maps for Austria. The digital soil maps were generated based on a Machine Learning and PTF-based approach (indirect approach) and a pure Machine Learning based approach (direct approach). By downloading the datasets, you agree that we nor the provider of the used source datasets cannot be liable for the data provided.</p> <p>This study was funded by the Austrian Federal Ministry of Agriculture, Regions and Tourism (Project InfCapAT), the Austrian Academy of Science (Project RechAUT) and the Austrian Science Fund project P 31213.</p> <p> </p>
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>
Experiment and analysis data of Study on the Effect of Pore-Clogging Caused by Fenton's Reagent on the Hydraulic Conductivity Using Time-lapsed Hydraulic Tomography
<p>The Microsoft Office Excel data file ('HT data.xlsx') contains two sheets: the first sheet includes the raw data from the first and second HT experiments, labeled as '1st' and '2nd' respectively; the second sheet contains their processed data.</p> <p>The compressed file ('Models.rar') includes all the models, such as the forward and inverse models. Each model contains its own input files and an executable file.</p>
Observed soil water retention and soil hydraulic conductivity data and fits to those data by RIAfitter and KRIAfitter
<p>Weber soils SWRC.7z: zipped file with the Windows folders that hold all input and output data related to fitting the RIA parameterization (de Rooij, 2022, 2024a) to soil water retention data of 13 soils.</p> <p>Weber soils UHCC.7z: the zipped folder structure with fits of several models (de Rooij, 2024b, c) for the unsaturated hydraulic conductivity for the same 13 soils as the other file. The fitting code (KRIAfitter) used the fitted parameters of the other file plus soil hydraulic conductivity data to produce the fits.</p> <p>Both files contain Excel files that process the input and output of all fits.</p> <p>The soils are taken from Weber et al. (2019). The support of T.K.D. Weber in providing the data is gratefully acknowledged,</p> <p>N.B. The output format may differ somewhat from that reported in the User Manual of de Rooij (2024c) because the fits were used to improve details of the code and the way information is provided in the output files. These variations do not affect the parameter fitting process.</p> <p>References</p> <p>de Rooij, G. H.: Technical note: A sigmoidal soil water retention curve without asymptote that is robust when dry-range data are unreliable, Hydrol. Earth Syst. Sci., 26, 5849–5858, doi: 10.5194/hess-26-5849-2022, 2022.</p> <p>de Rooij, G.: Fitting the parameters of the RIA parameterization of the soil water retention curve (2.0), Zenodo [code], doi: 10.5281/zenodo.6491978, 2024a.</p> <p><br>de Rooij, G. H.: Averaging or adding domain conductivities to calculate the unsaturated soil hydraulic conductivity, Vadose Zone J., e20329, doi: 10.1002/vzj2.20329, 2024b.</p> <p><br>de Rooij, G.: Fitting the junction model and other models for the unsaturated hydraulic conductivity curve: KRIAfitter, Zenodo [code], doi: 10.5281/zenodo.14047942, 2024c.</p> <p><br>Weber, T. K. D., Durner, W., Streck, T., and Diamantopoulos, E.: A modular framework for modeling unsaturated soil hydraulic properties over the full moisture range, Water Resour. Res., 55, 4994-5011, doi: 10.1029/2018WR024584, 2019.</p> <p> </p>
Soil hydraulic conductivity measurement using Ksat with falling head method
<p>Experimental serie: Soil hydraulic conductivity measurement using Ksat with falling head method.</p> <p>Serie experimental: Medición de conductividad hidráulica saturada de suelo en laboratorio con Ksat con método falling head.</p>
Dataset for: Novel Physics Informed-Neural Networks for Estimation of Hydraulic Conductivity of Green Infrastructure as a Performance Metric by Solving Richards-Richardson PDE
<p><strong>Based on the Github respostitory: <a href="https://github.com/Khadrawi/Physics-Informed-Neural-Networks-for-Estimation-of-Hydraulic-Conductivity/tree/main">https://github.com/Khadrawi/Physics-Informed-Neural-Networks-for-Estimation-of-Hydraulic-Conductivity/tree/main</a></strong></p> <p>This repository contains the data used for the paper "Novel Physics Informed-Neural Networks for Estimation of Hydraulic Conductivity of Green Infrastructure as a Performance Metric by Solving Richards-Richardson PDE"<br> You'll find the csv files for the three simulated (Hydrus 1D) scenarios explained in the paper. These files were processed from the 'Nod_Inf.out' files to csv format.</p> <p><strong>Acknowledgments</strong><br> The publicly available data used for this study (scenario 1 & 2) as well as the code for the second PINN architecture (based on Dr. Maziar Raissi PINN code) and the code used to transform “Nod_inf.out” files from Hydrus 1D to csv files created by Dr. Toshiyuki Bandai and Dr. Teamrat A. Ghezzehei were helpfulfor this study.</p>
Predicting soil interpedal macroporosity and hydraulic conductivity dynamics: A model for integrating laser-scanned profile imagery with soil moisture sensor data
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Saturated Hydraulic Conductivity Pedotransfer Models
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Growth, gas exchange and hydraulic conductivity of greenhouse grown willows under pre-drought conditions: Investigating patterns of habitat specialization in fifteen co-occurring willow and poplar species.
Thirteen willow (Salix) species occur in southeastern Minnesota and often co-occur within the same wetlands. This high local diversity is challenging to explain since closely related species are often functionally similar and density-dependent interactions such as competition and susceptibility to pests and pathogens should limit their co-occurrence. However, if willow species are partitioning resources, or if they are phylogenetically structured so that closely related species rarely co-occur, then the impact of these density-dependent processes could be reduced. In this study, I examined the role of niche partitioning in maintaining local willow diversity by documenting species distributions in plots across a water availability gradient and comparing species physiology in the field and greenhouse. By taking a phylogenetic approach, I also investigated whether willow communities exhibit phylogenetic community structure and whether there is evidence for environmental filtering.
Saturated and unsaturated hydraulic conductivity for Saddle Stream Network, 2017
Saturated and unsaturated hydraulic conductivity are important surface hydrologic properties that dictate the how fast water moves into the subsurface. Saturated and unsaturated hydraulic conductivity were measured at approximately 25 locations throughout the Niwot Ridge Saddle Stream Catchment in 2017. Saturated hydraulic conductivity was calculated from saturated infiltration rates determined using a single-ring infiltrometer. Unsaturated hydraulic conductivity was calculated from unsaturated infiltration rates determined using a mini-disk portable tension infiltrometer. Both measurements had over an order of magnitude variation across the catchment, suggesting a wide range in hydrologic function. Replicate measurements within 1m^2 were often comparable, but at some sites showed large differences. These differences likely reflect the small-scale variability in subsurface characteristics across the catchment.
Water potential gradient, root conduit size and root xylem hydraulic conductivity determine the extent of hydraulic redistribution in temperate trees
1. Hydraulic redistribution (HR) of soil water through plant roots is widely described, however its extent, especially in temperate trees, remains unclear. Here, we quantified redistributed water of five temperate tree species. We hypothesized that both, HR within a plant and into the soil increases with higher water-potential gradients, larger root conduit diameters and root-xylem hydraulic conductivities. 2. Saplings of conifer (<i>Picea abies</i>, <i>Pseudotsuga menziesii</i>), diffuse-porous (<i>Acer pseudoplatanus</i>) and ring-porous species (<i>Castanea sativa</i>, <i>Quercus robur</i>) were planted in split-root systems, where one plant had its roots split between two pots with different water-potential gradients (0.23 to 4.20 MPa). Hydraulic redistribution was quantified via deuterium labeling. 3. On average, species redistributed 0.39 ± 0.14 ml water overnight (0.08 ± 0.01 ml g<sup>-1</sup> root mass). Higher pre-dawn water-potential gradients, xylem hydraulic conductivities and larger conduit diameters significantly increased HR. Hydraulic conductivity had the greatest influence on HR, within the plants (0.03 ± 0.01 ml g<sup>-1</sup>) and into the soil (0.06 ± 0.01 ml g<sup>-1</sup>). 4. Additional factors as soil-root contact should be considered, especially when calculating water transfer into the soil. Nevertheless, trees maintaining high xylem hydraulic conductivity showed higher HR amounts, potentially making them valuable 'silvicultural tools' to improve plant water-status.
Model for Predicting the Hydraulic Conductivity of Frozen Soils Using the Soil Freezing Characteristic Curve
<p>This is the data used in this manuscript.</p>
Assessment and application of an alternative formula to describe the hydraulic conductivity over the full moisture range
<p>This is the dataset used to produce Figures 1-4 and Figures S1-S4 in the manuscript entitled "Assessment and application of an alternative formula to describe the hydraulic conductivity over the full moisture range" (submitted to <em>Hydrological Processes</em>).</p>
Supplementary material for "Can the anisotropic hydraulic conductivity of an aquifer be determined using surface displacement data? A case study"
<p>Supplementary material for ”Can the anisotropic hydraulic conductivity of an aquifer be determined using surface displacement data? A case study".</p> <p>The <a href="https://github.com/sonasalehian/AHC-Poroelastic-Model.git" target="_blank" rel="noopener">AHC-Poroelastic-Model</a> codes from repository is included in the AHC-Poroelastic-Model-main.zip file.</p>
The source code for a new capillary and adsorption‒force model predicting hydraulic conductivity of soil during freeze‒thaw processes
<p>The source code is related to "A New Capillary and Adsorption‒Force Model Predicting Hydraulic Conductivity of Soil during Freeze‒thaw Processes" (Shufeng Qiao, Rui Ma, Yunquan Wang, Ziyong Sun, Helen Kristine French, Yanxin Wang)</p>
Hydraulic conductivity technical note
<p>Experimental data and code related to paper "<strong>A new experimental setup to measure hydraulic conductivity of plant segments":</strong> <a href="https://doi.org/10.1093/aobpla/plad024">https://doi.org/10.1093/aobpla/plad024</a></p><p>The interactive version can be found at: https://renkulab.io/projects/louis.krieger/hydraulic-conductivity-technical-note</p><p>Once there, you can start the project and interact.</p><p>If you would like to run it locally in a python 3.6 environment, you will need to install the <a href="https://renkulab.io/gitlab/louis.krieger/hydraulic-conductivity-technical-note/-/blob/cc1a351b1fb6e2a731fcc22d1fb406bd92d1ceb1/requirements.txt">requirements.txt</a> before using notebooks.</p><p> </p><p><strong>Contents:</strong></p><p>All <a href="https://renkulab.io/gitlab/louis.krieger/hydraulic-conductivity-technical-note/-/blob/cc1a351b1fb6e2a731fcc22d1fb406bd92d1ceb1/renku_commands">renku commands</a> are run from the home folder of the repository to create the graphs and the paper itself.</p><p>First the <a href="https://renkulab.io/gitlab/louis.krieger/hydraulic-conductivity-technical-note/-/blob/cc1a351b1fb6e2a731fcc22d1fb406bd92d1ceb1/notebooks/extra_calculations/Intrinsic_permeability.ipynb">equations</a> used in other notebooks were derived and exported.</p><p>Then the <a href="https://renkulab.io/gitlab/louis.krieger/hydraulic-conductivity-technical-note/-/blob/cc1a351b1fb6e2a731fcc22d1fb406bd92d1ceb1/notebooks/calibrations/P_sensor_calibration.ipynb">calibrations of the pressure sensors</a> were run.</p><p>Next the graph are created from the notebook <a href="https://renkulab.io/gitlab/louis.krieger/hydraulic-conductivity-technical-note/-/blob/cc1a351b1fb6e2a731fcc22d1fb406bd92d1ceb1/notebooks/paper/graphs_for_paper.ipynb">graphs_for_paper.ipynb</a> using the first renku command.</p><p>Followed by using those graphs to compile the paper itself from <a href="https://renkulab.io/gitlab/louis.krieger/plantwatertransport/-/blob/498ceaa281eda57ba6f53eb015d722182a142d80/writing/paper1/paper1.tex">paper1.tex</a> with the second command.</p><p>Finally, the <a href="https://renkulab.io/gitlab/louis.krieger/hydraulic-conductivity-technical-note/-/blob/cc1a351b1fb6e2a731fcc22d1fb406bd92d1ceb1/paper/SI.tex">SI latex</a> is created from the <a href="https://renkulab.io/gitlab/louis.krieger/hydraulic-conductivity-technical-note/-/blob/cc1a351b1fb6e2a731fcc22d1fb406bd92d1ceb1/notebooks/paper/SI.ipynb">notebook itself</a>.</p><p>Additional notebooks where the graphs are a bit more in context can be found in the <a href="https://renkulab.io/gitlab/louis.krieger/hydraulic-conductivity-technical-note/-/tree/cc1a351b1fb6e2a731fcc22d1fb406bd92d1ceb1/notebooks/additional_notebooks">subfolder in notebooks</a>:</p><p>All the <a href="https://renkulab.io/projects/louis.krieger/hydraulic-conductivity-technical-note/files/blob/data/my_data">raw data files</a> are also available, as well as <a href="https://renkulab.io/gitlab/louis.krieger/hydraulic-conductivity-technical-note/-/tree/cc1a351b1fb6e2a731fcc22d1fb406bd92d1ceb1/paper/images">additional images</a> used in the write-ups.</p><p> </p>
Water potential gradient, root conduit size and root xylem hydraulic conductivity determine the extent of hydraulic redistribution in temperate trees
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Data from: Evaluation of parametric and nonparametric machine-learning techniques for prediction of saturated and near-saturated hydraulic conductivity
Parametric and nonparametric supervised machine learning techniques were used to estimate saturated and near saturated hydraulic conductivities (Ks, K10) from easily measurable soil properties including name of pedological horizon (HOR), soil texture (sand, silt & clay), organic matter (OM), bulk density (BD) and water contents (θpF1, θpF2, θpF3 and, θpF4.2) measured at four different matric heads (-10, -100, -1000, and -15848 cm). Using a stepwise linear model (SWLM) and the Lasso regression as parametric methods with 316 data in training and 135 data in testing phase, four pedotransfer functions (PTFs) were obtained in which water contents for both methods play an important role compared to other variables. SWLM showed better performance than Lasso in the testing phase for log(Ks) and log(K10) prediction with RMSE of 0.666 and 0.551 cm d-1 and R2 of 0.26 and 0.65. Nonparametric supervised machine learning methods trained and tested with similar data set significantly improved the accuracy of Ks prediction with R2 of 0.52, 0.36 and 0.53 for Gaussian regression process (GPR), support vector machine (SVM) and Ensemble (ENS) method in the testing stage. These methods also described 74.9, 66.7 and 72.5% of the variation of log(K10). Bootstrapping method validated the strong performance of nonparametric techniques. Feature selection capability of GPR determined that instead of using a model with all predictors, HOR, silt, θpF1 and θpF3 are sufficient for the prediction of log(Ks) or log(K10), HOR, silt, and OM can predict as accurate as the comprehensive model with all variables.
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