A range of continental-scale soil datasets exists in Europe with different spatial representation and based on different principles. We developed comprehensive pedotransfer functions (PTFs) for applications principally on spatial datasets with continental coverage. The PTF development included the prediction of soil water retention at various matric potentials and prediction of parameters to characterize soil moisture retention and the hydraulic conductivity curve (MRC and HCC) of European soils. We developed PTFs with a hierarchical approach, determined by the input requirements. The PTFs were derived by using three statistical methods: (i) linear regression where there were quantitative input variables, (ii) a regression tree for qualitative, quantitative and mixed types of information and (iii) mean statistics of developer-defined soil groups (class PTF) when only qualitative input parameters were available. Data of the recently established European Hydropedological Data Inventory (EU-HYDI), which holds the most comprehensive geographical and thematic coverage of hydro-pedological data in Europe, were used to train and test the PTFs. The applied modelling techniques and the EU-HYDI allowed the development of hydraulic PTFs that are more reliable and applicable for a greater variety of input parameters than those previously available for Europe. Therefore the new set of PTFs offers tailored advanced tools for a wide range of applications in the continent.
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http://dx.doi.org/10.1111/ejss.12192 | DOI Listing |
PLoS One
November 2024
Faculty of Agriculture, Department of Soil Science and Engineering, Shiraz University, Shiraz, IR Iran.
Characterization of near (field) saturated hydraulic conductivity (Kfs) of the soil environment is among the crucial components of hydrological modeling frameworks. Since the associated laboratory/field experiments are time-consuming and labor-intensive, pedotransfer functions (PTFs) that rely on statistical predictors are usually integrated with the existing measurements to predict Kfs in other areas of the field. In this study some of the most appropriate machine learning approaches, including variants of artificial neural networks (ANNs) were used for predicting Kfs by some easily measurable soil attributes.
View Article and Find Full Text PDFSci Total Environ
April 2024
Institute for Soil Sciences, HUN-REN Centre for Agricultural Research, Herman Ottó út 15, 1022 Budapest, Hungary; National Laboratory for Water Science and Water Safety, Herman Ottó út 15, 1022 Budapest, Hungary.
Spatially explicit, quantitative information on soil hydraulic properties is required in various modelling schemes. At European scale, EU-SoilHydroGrids proved its applicability in a number of studies, in ecological predictions, geological and hydrological hazard assessment, agri-environmental models, among others. Inspired by its continental antecedent, an analogous, but larger scale, national, 3D soil hydraulic database was elaborated for the territory of Hungary (HU-SoilHydroGrids) supported by various improvements (i-iv) in the computation process.
View Article and Find Full Text PDFPLoS One
January 2024
Department of Biosystems Engineering, College of Agriculture, Shiraz University, Shiraz, Iran.
Hydraulic conductivity (Kψ) is one of the most important soil properties that influences water and chemical movement within the soil and is a vital factor in various management practices, like drainage, irrigation, erosion control, and flood protection. Therefore, it is an essential component in soil monitoring and managerial practices. The importance of Kψ in soil-water relationship, difficulties for its measurement in the field, and its high variability led us to evaluate the potential of stepwise multiple linear regression (SMLR), and multilayer perceptron (MLPNNs) and radial-basis function (RBFNNs) neural networks approaches to predict Kψ at tensions of 15, 10, 5, and 0 cm (K15, K10, K5, and K0, respectively) using easily measurable attributes in calcareous soils.
View Article and Find Full Text PDFData Brief
December 2023
Earth and Life Institute Environnemental Sciences, Université catholique de Louvain, Place Croix du Sud 1, 1348 Louvain-la-Neuve, Belgium.
This data article provides high spatial resolution (1 cm) datasets and related figures of the penetrometer resistance (PR) and soil bulk density (BD) data of nine agricultural 50 × 160 cm soil profiles exposed to three tillage treatments and including a wheel track. Soil treatments are moldboard plowing (MP), deep loosening (DL), and minimum tillage (MT). It also provides bulk density data, soil moisture content at various suctions and the parameters of van Genuchten's model for 27 soil cores, and saturated hydraulic conductivity (Ks) of 49 soil cores.
View Article and Find Full Text PDFIntegr Environ Assess Manag
July 2024
Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina.
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