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5 results for “soil water retention curve”

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zenodo48/100

Input Dataset for Estimating Continuous Soil Water Retention Curves Using Physics-Informed Neural Networks

<p>This dataset was used as input to a physics-informed neural network (PINN) model developed to estimate continuous soil water retention curves (SWRCs). It includes basic soil properties such as particle-size distribution (sand, silt, clay), organic carbon content (OC), bulk density (BD), and measurements of soil water retention at various matric potentials. These inputs allow the model to learn the relationship between soil properties and water retention, via both data and embedded physical constraints. This data set consists of 4,200 Danish soil samples with measurements spanning the wet and dry ends of the SWRC.&nbsp;</p>

opencc-by-4.0Nov 2024View details →
zenodo36/100

The observed data used in paper titled "An analytic method for calculating parameters of the van Genuchten model for soil water retention curve"

<p>In the file, the 46 soil samples from UNSODA were used to test the proposed method of estimating the parameters of VG model, including the physical and hydraulic properties data, PSD, SWRC, saturated hydraulic conductivity, porosity and saturated water content. Also, this data file includes some&nbsp;process data and results data, <em>D</em><sub><em>i</em>, psd</sub>, predicted&nbsp;<em>K<sub>s</sub></em>&nbsp;, the data relate to&nbsp;<em>a </em>and <em>n&nbsp;</em>and sensitivity analysis.</p>

opencc-by-4.0Jul 2020View details →
zenodo36/100

Reproducibility of the wet part of the soil water retention curve: a European interlaboratory comparison (dataset)

<p>The soil water retention curve (SWRC) is a key soil property required for predicting basic hydrological processes. The SWRC is often obtained in the laboratory with non-harmonized methods. Moreover, procedures associated with each method are not standardized. This can induce a lack of reproducibility between laboratories using different methods and procedures or using the same methods with different procedures. The goal here&nbsp;was to estimate the inter- and intralaboratory variability of the measurement of the wet part (from 10 to 300 hPa) of the SWRC.&nbsp;An interlaboratory comparison was carried out between 14 laboratories, using artificially constructed, porous reference samples that were transferred between laboratories according to a statistical design.&nbsp;The retention measurements were modelled by a series of linear mixed models using a Bayesian approach. This allowed the detection of sample-to-sample variability, interlaboratory variability, intralaboratory variability and the effects of sample changes between measurements. Here we present the dataset containing&nbsp;measured&nbsp;data from the interlaboratory comparison, R code to implement models, and&nbsp;final model results.</p>

opencc-by-4.0Dec 2022View details →
dryad36/100

Soil water retention curves and soil physicochemical properties

<p class="MsoPlainText"><span>To identify the contribution of soil organic and inorganic fractions to soil water retention, we compiled the data of soil water retention curves and soil physicochemical properties. Data includes site information (Table 1), soil pH, soil C, cation exchange capacity (CEC), exchangeable basic cations, particle size distribution, soil texture class, contents of short-range-order minerals (Table 2), and soil water retention at different pressures, saturated hydraulic conductivity, and termite nest percent in soil profile (Table 3).</span></p>

opencc-zeroAug 2023View details →
dryad36/100

Soil water retention curves and soil physicochemical properties

Open the record for dataset details and reuse information.

publicAug 2023View details →

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