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33 results for “soil hydraulics”
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>
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.
Dataset on soil hydraulic properties under contrasted plant covers and agricultural practices
<p>Complete dataset on the temporal variation of soil infiltrability along a homogeneous fluvisol, on bare soil or soil planted with two plant species with contrasted root systems (a Malvaceae with a tap-root system and a Poaceae with a fibrous root system), and impacted by three different management practices (burning, mowing, and chemical weeding). An original protocol, based on specific ring infiltrometers, able to measure the temporal dynamics of soil infiltrability was used. This dispositive takes into account the variability of the measurement across space.</p> <p> </p>
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>
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>
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>
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>
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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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>
Evaporation and permeameter data hydraulic properties stony soil
<p>We used the evaporation method to determine the hydraulic conductivity and the retention curve of a soil sample. The principle of this method is to simultaneously measure the matric head at different depths and the water content of an initially saturated soil sample submitted to evaporation.</p> <p>The experiments were performed over cylindrical Plexiglas samples of 1 L (height: 65 mm), perforated at the bottom to allow saturation from below and open to atmosphere on the upper side to allow evaporation of the soil moisture. Four 6 mm-long ceramic tensiometers (SDEC230) were introduced at 10, 25, 40 and 55 mm in height, respectively denoted T1 to T4 (the reference level is located at the bottom of the sample). In order to avoid preferential flow due to the introduction of the tensiometers on a same vertical line, each hole of the sample was horizontally shifted of 12 degrees vis-à-vis the center of the tube. The tensiometers are connected through a tube to a pressure transducer (DPT-100, DELTRAN). The setup was filled with degased water. The variation in pressure of the drying soil was recorded every 15 min by a CR800 (CAMPBELL SCIENTIFIC). Tensions beyond the consolidation point were not taken into account. The consolidation point refers to the state from which the measured pressure head starts to decrease as bubbles appear and water vapour accumulates (typically 68 kPa cm in this case).</p> <p>The total water loss as a function of time was monitored by a balance (OHAUS) with a sensitivity of 0.2 g with an accuracy of 1 g with a time resolution of 15 min. A 50 W infrared lamp was positioned 1 m above the sample surface to slightly speed up the evaporation process. The light was turned off for the first 24 hours of every experiment, as the evaporation rate is already high in a saturated sample. A measuring campaign lasted until 3 of the 4 tensiometers ran dry (the tension sharply drops down to approximately a null value). At the end of the experiment, the sample was oven dried for 24 hours at 105°C to estimate the .</p>
An in situ observation dataset of soil hydraulic properties and soil moisture in a high and cold mountainous area on the northeastern Qinghai-Tibet Plateau
<p>Based on soil profile data at depths of 5 cm and 25 cm from 238 sampling sites, and on soil data from 32 soil moisture monitoring stations at depths of 5 cm, 15 cm, 25 cm, 40 cm, and 60 cm, we have compiled a soil hydraulic properties and soil moisture dataset for a high and cold mountainous area, Northeastern Qinghai-Tibet Plateau. Specifically, the soil hydraulic properties include clay, silt, sand, soil organic carbon, soil saturated hydraulic conductivity, soil water retention curve parameters (Van Genuchten model) and soil dry bulk density.</p>
GSHP: Global database of soil hydraulic properties
<p>A total of 15,259 SWCCs from 2,702 sites were assembled from published literature and other sources, standardized, and quality-checked to obtain global database of soil hydraulic properties (GSHP). The GSHP database covers most regions across the globe, with the highest number of curves from North America followed by Africa, Europe, Asia, South America, Australia/Oceania. In addition to SWCCs, other soil variables such as soil texture (12,233 measurements), bulk density (15,125 measurements), and soil organic carbon (2,255 measurements) are also listed in the database.</p> <p>The R code used for this study is available here: https://github.com/ETHZ-repositories/GSHP-database</p> <p>For more details / to cite this dataset please use:</p> <ul> <li>Gupta, S., Papritz, A., Lehmann, P., Hengl, T., Bonetti, S., and Or, D., (2022): Global Soil Hydraulic Properties dataset based on legacy site observations and robust parameterization”. Manuscript accepted to <strong>Scientific Data.</strong></li> </ul> <p>Examples of using the GSHP database 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><strong>Description of the files</strong>:</p> <p>The datasets in this repository include: </p> <p><strong>WRC_dataset_surya_et_al_2021_final </strong>provides a global compilation of soil hydraulic properties and the information described in the<strong> Readme_GSHP file</strong>. <strong>Dataset_notebook </strong>shows the graphical representation of the GSHP database. </p> <p>The study was supported by ETH Zurich (Grant ETH-18 18-1). </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>
Data from: Soil hydraulic properties determined by inverse modeling of drip infiltrometer experiments extended with pedotransfer functions
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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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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>
Dataset for Estimating soil hydraulic properties from oven-dry to full saturation using inverse modeling and shortwave infrared imaging
<p>In this repository, we provide all the datasets that are needed to reproduce the analysis conducted in the paper entitled "Estimating soil hydraulic properties from oven-dry to full saturation using inverse modeling and shortwave infrared imaging."</p> <p><br> codes: This folder contains Python codes to run the forward and inverse modeling. Install the following packages.<br> notebook, fenics, numpy, pandas, matplotlib, scipy, numdifftools, and lmfit for inverse modeling (needs to be run on Linux).<br> data: This directory contains data used in the inverse modeling.<br> gif: This directory contains GIF movies of the upward infiltration experiments.</p> <p>readme.xlsx: This file explains which data are used for each figure in the paper.</p>
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