Prediction Of Soil Water Retention Research Articles

Effects of vegetation growth on soil microstructure and hydro-mechanical behaviour

Literature studies are presenting counterposed effects of vegetation on soil hydraulic behaviour. Besides, root impacts at the micro-scale are rarely correlated to phenomenological observations due to the complexity of adopting up-scaling factors. In this study, the microstructural causes behind observed changes in vegetated compacted clayey sand’s hydraulic and volume change behaviour were explored. Laboratory experiments were carried out on compacted samples seeded with Cynodon dactylon. Significant changes in soil water-saturated permeability, water retention and shrinkage upon drying were observed after root growth. Laboratory measurements were complemented by the quantification of root morphological features and observations at the micro-scale for different soil hydraulic states. X-ray scans and mercury intrusion porosimetry allowed to cover seven orders of magnitude of pore sizes, shedding light on soil fabric changes at the soil–root interface and the clay aggregate scale. The effects of roots on the soil pore size distribution consisted of Multiphysics phenomena: fissure generation, void clogging and soil aggregation due to roots’ chemical interaction. Furthermore, a good correlation was found between the normalised volume of roots and the micro-pores volume. This new expression was included in a framework to predict soil water retention behaviour considering the aggregated structure and soil–root hydro-chemical interactions.

Artificial neural networks for predicting soil water retention data of various Brazilian soils

Artificial neural networks for predicting soil water retention data of various Brazilian soils

An improved pore-solid fractal model for predicting coastal saline soil hydraulic properties based on changepoints determined by genetic algorithm-support vector regression

Coastal soil is an important reserve land resource. The accurate prediction of soil hydraulic properties plays an important role in understanding the improvement of soil properties after reclamation in coastal areas. With the development of modern mathematical theory, the pore-solid fractal (PSF) model has become an important basis for simulating soil hydraulic properties. The accuracy of PSF model depends on the accurate acquisition of changepoints and fractal dimensions. Previous studies have found that micro-CT scanning combined with image processing could accurately obtain fractal dimensions. However, we still need soil water retention data to determine changepoints, which greatly limits the application of fractal models for estimating soil hydraulic properties. In this case, we determined the relationship between changepoints and soil physical and chemical properties and established a genetic algorithm-support vector regression (GA-SVR) model to predict changepoints. Then an improved PSF model was adopted to predict soil water retention curve in coastal areas of Jiangsu Province based on image processing. The results showed that soil physical and chemical properties changed the soil water movement and changepoints by affecting soil pore distribution. In coastal saline soil, four parameters (BD, silt, EC, SOM) were selected to predict changepoints. The mass fractal dimension (Dm) was mainly influenced by the porosity and the heterogeneity of pore space. The porosity, pore diameter and specific surface area were the determining factors of DS value. Reclamation activities in coastal reclamation area changed Dm value but had no clear effect on DS value. By combining predicted changepoints, measured water content at the suction of changepoints and fractal dimensions, the accuracy of the PSF model had been greatly improved.

Evaluation of Different Models for Predicting Soil Water Retention from A Semiarid Region of Duhok

A study was carried out to examine the capability of bi and uni- models to explore water retention data for two different textured soils from a semiarid region Duhok-Iraqi Kurdistan. A soil hydraulic model, can be used to derive the parameters of soil hydraulic properties for describing soil water movement. The program "SWRC Fit," which performs nonlinear fitting of soil water retention curves for the two examined soils using six models, was employed for this purpose. The six models are the Brooks Corey, van Genuchten, Kosugi, Fredlund and Xing model as uni models and Durner and Seki as a bimodal model used for this purpose. The program can be carried out straightly from a web page at http://purl.org/net/swrc/; The program was used for determining Fifteen parameters of soil hydraulic for two different textured soils. As related to Berderash sandy loam soil the LN uni-model revealed better performance than the FX, BC and VG models and at same time the FX, BC and VG models exhibit similar fitting precision on average. Whereas bi-models is shown that the Durner’s bi- model (DB) revealed very well fitting performance than that of the bi-log-normal distribution model (BL) models, regarding to Zawita clay soil it revealed that the uni -models, of BC, VG, FX and LN revealed good fitting performance with similar fitting precision on average. Whereas in bi- models it was noticed that the log-normal distribution model (BL) gave very well-suitable performance than that of the Durner’s b- model (DB). After comparing between the uni-models and bi-models of the six mentioned soil hydraulic models from accuracy and coefficient determination (R2), for two different textured soils under the study it can be illustrated: firstly, for fine texture soil (clay soil) the most precision fitting performance was found by model (BL), whereas for light texture soil (sandy loam soil) the most very well-fitting performance was noticed by (DB) model, and secondly the bi-models hydraulic were better than uni-models in fitting performance.

Comparison of specific and universal linear regression models for predicting soil water retention in eastern Croatia

We analyzed 96 samples from 48 soil profiles (Gleysols, Chernozems, Anthrosols) on arable land in eastern Croatia (Europe) in order to develop specific pedotransfer functions (PTFs) for the local-scale prediction of soil water retention at field capacity (FC) and wilting point (WP). Our easy-to-use multiple linear regression models performed very well (RMSEs 2.7% and 1.8%, MAEs 2.1% and 1.5%; for FC and WP, respectively), in spite relying only on the contents of clay, silt, and organic C as predictors. These three are routinely measured, fundamental soil properties for which the data is widely-available. Because these PTFs were developed for the soils on the most common groups of parent materials in continental Croatia (loess derivates and fluvial deposits with mostly silt loam, silt clay loam, and silt clay textures), they should have wide applicability. We have also tested the prediction performance of the universal regression-based PTFs (EU-PTFs), created by Toth et al. (2015) to predict FC and WP at the European scale. Although the soils of Croatia are not included in the EU-HYDI database, which was used for the development of the EU-PTFs, we found that the continental model predicts the FC of our soils almost equally well (RMSE = 3.3%, MAE = 2.7%) as our local model. However, for predicting the WP of our soils, the continental model is not as accurate, overestimating this parameter notably more (RMSE = 4.9%, MAE = 4.4%) than the local model. Given that the WP values of our (and many other European) soils are relatively low, the obtained prediction errors may be even more problematic. However, our results suggest that the use of EU-PTFs can partially compensate for the development of specific (local) PTFs, at least in the European regions with soilscapes similar to those in our study. To help increase the reliability of the EU-PTFs, the soil database used for their development should be expanded to include new countries and regions of Europe.

Pedotransfer functions for estimating soil hydraulic properties from saturation to dryness

Current pedotransfer functions (PTFs) for estimating soil hydraulic curves are mostly developed to predict parameters of the Mualem-van Genuchten hydraulic functions. The Mualem-van Genuchten functions are recognised to be inadequate in representing soil water retention hydraulic conductivity curves at low pressure head ranges. This study presents neuroFX, a suite of PTFs, for estimating soil water retention and unsaturated hydraulic conductivity curves from saturation to complete dryness based on the Fredlund-Xing-Wang (FXW) model. The PTFs were calibrated using the neuro-m neural networks approach with three different sets of inputs: (1) SSCBD uses the sand, silt, and clay fractions, and bulk density; (2) SSC uses the sand, silt and clay fractions, and (3) soil textural class input. NeuroFX PTFs were trained using fitted parameters of the FXW model from selected data in the UNSODA database and validated using 5-fold cross-validation that was repeated 10 times. NeuroFX provides an uncertainty estimate of hydraulic parameters. The prediction quality of neuroFX was compared with two existing PTFs: ROSETTA and Brunswick-Weber (BW) PTFs. Based on multiple criteria, we found that neuroFX performed better than ROSETTA and BW PTFs in the same test sample. NeuroFX PTF with the SSCBD input yielded the best prediction of soil water retention and unsaturated hydraulic conductivity curves with RMSE in water content of 0.052 cm3 cm−3 and RMSE in log10(K) = 0.732 (in the magnitude of cm day−1), indicating the importance of including bulk density in the input of PTFs. NeuroFX was then used to map parameters of the FXW model for the whole of the continental USA over 6 depth intervals. The code of neuroFX in R software is available in Supplementary Material 1.

Estimation of soil water retention in conservation agriculture using published and new pedotransfer functions

Conservation agriculture has been developed as a means to improve the sustainability of agricultural systems and reduce drawbacks of conventional agricultural practices. Cropping practices can have a large influence on soil properties such as water retention. Proper tools are needed to assess and model effects of conservation agriculture on soil properties. As measuring soil water retention is expensive and time consuming, pedotransfer functions (PTFs) are now commonly used to predict them. The objectives of this study were to (i) present a new dataset of conservation agriculture data, (ii) assess performances of existing PTFs in predicting soil water retention of soils under conservation agriculture and (iii) develop new specific PTFs to predict water retention in conservation agriculture more accurately. We used data collected only in fields under conservation agriculture in France to evaluate several published PTFs with three evaluation criteria (RMSE, prediction bias (ME) and Nash-Sutcliffe Efficiency (EF)). We then developed new PTFs using three methods ― multiple linear regression, regression tree and random forest ― to predict soil water content at matric heads of -100 (θ100, field capacity for sandy soils), -330 (θ330, field capacity for other soils) and -15 000 cm (θ15 000, wilting point). Soil tillage, presence of a cover crop, rotation length and previous reduced/no tillage were used as predictors in addition to basic soil properties for regression trees and random forests. The quality of prediction (RMSE, ME and EF) was calculated for each new PTF using a cross-validation procedure. Generally, predictions of wilting point had lower absolute error than those of sandy-soil field capacity (RMSE = 0.044 and 0.066 cm3/cm3, respectively). EF was usually negative for all water contents. The cross-validation performance of the new PTFs was similar for multiple linear regression (RMSE: 0.028, ME: 0.000, EF: 0.34 for θ100) and random forest (RMSE: 0.027, ME: 0.000, EF: 0.36 for θ100), and generally worse for regression tree (especially EF). Multiple linear regression that did not consider cropping practices performed as well as random forest and thus did not identify any major influence of agricultural management on predicted water content. Future research on developing PTFs should focus on identifying more relevant predictors.

Introducing a Kriging-based Gaussian Process approach in pedotransfer functions: Evaluation for the prediction of soil water retention with temperate and tropical datasets

Data mining algorithms such as Artificial Neural Networks (ANN) and k-Nearest Neighbour (kNN) have proven their merits in pedotransfer function modelling. Kriging is a well-known algorithm for spatial interpolation, but in this study it is proposed as an alternative data mining technique. It was compared to kNN as a benchmark pedotransfer function to predict soil water retention for a wide range of datasets, containing soil data from both temperate and tropical regions. The performance of both methods was compared through Monte Carlo cross-validation and the precision of the predictions was assessed with an ensemble procedure. Across all datasets, a significant improvement in prediction bias, accuracy and precision was found with Kriging, compared to kNN. Moreover, it was demonstrated how predictions with Kriging are more robust and insensitive to non-correlated predictor variables, and how the optimized hyperparameters provide additional insight in the training dataset properties. Kriging was found to be a accurate, precise and robust data mining solution for pedotransfer function modelling.

Capability of Diffuse Reflectance Spectroscopy to Predict Soil Water Retention and Related Soil Properties in an Irrigated Lowland District of Southern Italy

In this study, we examined the potential of vis-NIR reflectance spectroscopy, coupled with partial least squares regression (PLSR) analysis, for the evaluation and prediction of soil water retention at field capacity (FC) and permanent wilting point (PWP) and related basic soil properties [organic carbon (OC), sand, silt, and clay contents] in an agricultural irrigated land of southern Italy. Soil properties were determined in the laboratory with reference to the Italian Official Methods for Soil Analysis. Vis-NIR reflectance spectra were measured in the laboratory, using a high-resolution spectroradiometer. All soil variables, with the exception of silt, evidently affected some specific spectral features. Multivariate calibrations were performed to predict the soil properties from reflectance spectra. PLSR was used to calibrate the spectral data using two-thirds of samples for calibration and one-third for validation. Spectroscopic data were pre-processed [multiplicative scatter correction (MSC), standard normal variance (SNV), wavelet detrending (WD), first and second derivative transformation, and filtering] prior to multivariate calibration. The results revealed very good models (2.0 < RPD < 2.5) for the prediction of FC, PWP and sand, and excellent (RPD > 2.5) models for the prediction of clay and OC, whereas a poor (RPD < 1.4) prediction model was obtained for silt.

PEDOFUNCTIONS APPLIED TO THE LEAST LIMITING WATER RANGE TO ESTIMATE SOIL WATER CONTENT AT SPECIFIC POTENTIALS

ABSTRACT The least limiting water range (LLWR) is a soil physical quality indicator that receives much attention. It has been criticized and put to the test regarding mathematical models that compose it since they describe the behavior of soil physical attributes in a simplified way. This study aimed to assess the efficiency of some pedofunctions proposed in the literature and artificial neural networks on the accuracy in predicting soil water retention at potentials equivalent to field capacity (θFC) and permanent wilting point (θPWP). In other words, to apply the best models to LLWR of two soil types (Oxisol and Ultisol) and verify changes in their structure. The results indicated that pedofunctions using sand, silt, clay, bulk density, and soil organic matter contents are more efficient in estimating θFC and θPWP. However, the use of multiple linear regression models to predict θFC values below 0.20 m3 m−3 may present a slight tendency to overestimate it, which is not observed in the neural networks. As in R2, equations from neural networks were more efficient in estimating θFC and θPWP. Pedofunctions used to calculate LLWR differ in the establishment of the critical soil bulk density, exposing the limitations of the model.

Evaluation of point and parametric pedotransfer functions for predicting soil water retention and availability in soils of southwestern Saudi Arabia

Simulation of water flow and solute transport in the soil environment requires accurate estimates of the soil water retention characteristics (SWRC). In this study, multiple linear regression (MLR) was used to develop two site-specific pedotransfer functions (PTFs), a point (MLRP) and a parametric (MLRF), using soil properties of 43 soil samples collected from the Jazan region, southwest of Saudi Arabia. The accuracy of the developed PTFs and four existing PTFs to determine the SWRC, predict soil water content (SWC) at − 10, − 33, and − 1500 kPa, and estimate available water content (AWC) was assessed. The MLRP and the Schaap PTFs produced the best estimate of SWC, with smaller root mean square error (RMSE) (0.023–0.053 cm3 cm−3), and larger D-index (0.8–0.9) and Akaike information criterion (AIC) (− 303.7 to − 240.7), respectively. The largest prediction errors in the estimation of SWC were observed at matric potential close to field capacity (FC). For the AWC, the Schaap PTF provided the best prediction (RMSE = 0.014 cm3 cm−3, D-index = 0.93, AIC = − 359.9), followed by the MLRP PTF (RMSE = 0.027 cm3 cm−3, D-index = 0.83, AIC = − 302.1), whereas the Vereecken, Gupta and Larson, and the MLRF PTFs produced less accurate predictions of the AWC. The MLRP PTF proved to be more accurate compared to other tested PTFs in the prediction of SWC at both FC and permanent wilting point (PWP). In contrast, the MLRF PTF produced a relatively large error in the estimation of SWC at FC.

A Simple and Improved Model for Describing Soil Hydraulic Properties from Saturation to Oven Dryness

Core Ideas Soil hydraulic properties from saturation to oven dryness are needed to simulate soil water movement. A new soil hydraulic model was compared with the classic van Genuchten–Mualem models. The new model with different fitted parameters was more efficient than the van Genuchten–Mualem models. Soil water retention characteristics and hydraulic conductivity from saturation to oven dryness are needed for simulating soil water movement and solute transport, especially in arid and semiarid regions. The aim of this study was to derive a new soil hydraulic model and compare this model with the classic van Genuchten–Mualem (VGM) models (M1). A total of 12 soils with varying basic properties were selected from the Unsaturated Soil Hydraulic Database (UNSODA) for the evaluation of the new model. Five pairs of soil hydraulic models were considered, including M1 and the new model with different parameters fitted (M2–M5). Correlation analysis was conducted to analyze the relationships between the new model parameters and basic soil properties. In addition, the Morris method was applied to quantify the parameter sensitivity of the new model. Results showed that M2 to M5 gave slightly better performance than M1 in predicting soil water retention characteristics, with a decrease of ∼15% in the root mean squared error (RMSE). However, they produced substantially better performance than M1 in estimating soil hydraulic conductivities, with a decrease of ∼40% in the RMSE. The new model with all of the model parameters being fitted (M2) was more efficient than those with one or two model parameters not fitted. However, an over‐parameterization problem was detected with M2. Physically unrealistic parameters (e.g., the saturated soil water content and effective grain diameter) exist in M2. These parameters were found to have important influences on the results of the soil hydraulic properties prediction based on the Morris‐based sensitivity analysis. Overall, the new model with two parameters not fitted (M5) can best describe the soil hydraulic properties from saturation to oven dryness and had the potential to simulate water and solute transport in the vadose zone.

Spatial prediction of soil water retention in a Páramo landscape: Methodological insight into machine learning using random forest

Soils of Páramo ecosystems regulate the water supply to many Andean populations. In spite of being a necessary input to distributed hydrological models, regionalized soil water retention data from these areas are currently not available. The investigated catchment of the Quinuas River has a size of about 90km2 and comprises parts of the Cajas National Park in southern Ecuador. It is dominated by soils with high organic carbon contents, which display characteristics of volcanic influence. Besides providing spatial predictions of soil water retention at the catchment scale, the study presents a detailed methodological insight to model setup and validation of the underlying machine learning approach with random forest. The developed models performed well predicting volumetric water contents between 0.55 and 0.9cm3cm−3. Among the predictors derived from a digital elevation model and a Landsat image, altitude and several vegetation indices provided the most information content. The regionalized maps show particularly low water retention values in the lower Quinuas valley, which go along with high prediction uncertainties. Due to the small size of the dataset, mineral soils could not be separated from organic soils, leading to a high prediction uncertainty in the lower part of the valley, where the soils are influenced by anthropogenic land use.

Application and recalibration of soil water retention pedotransfer functions in a tropical upstream catchment: case study in Bengawan Solo, Indonesia

Abstract Hydrological models often require input data on soil-water retention (SWR), but obtaining such data is laborious and costly so that SWR in many places remains unknown. To fill the gap, a prediction of SWR using a pedotransfer function (PTF) is one of the alternatives. This study aims to select the most suitable existing PTFs in order to predict SWR for the case of the upper Bengawan Solo (UBS) catchment on Java, Indonesia. Ten point PTFs and two continuous PTFs, which were developed from tropical soils elsewhere, have been applied directly and recalibrated based on a small soil sample set in UBS. Scatter plots and statistical indices of mean error (ME), root mean square error (RMSE), model efficiency (EF) and Pearson’s correlation (r) showed that recalibration using the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm can help to improve the prediction of PTFs significantly compared to direct application of PTFs. This study is the first showing that improving SWR-PTFs by recalibration for a new catchment based on around 50 soil samples provides an effective parsimonious alternative to developing a SWR-PTF from specifically collected soil datasets, which typically needs around 100 soil samples or more.

Scheduling irrigation using an approach based on the van Genuchten model

Crop irrigation which results in high water use efficiencies typically uses science-based irrigation scheduling tools to determine irrigation application timing and quantities. Although a large variety of sensors are available for measuring soil moisture status, there are a few easy-to-use irrigation scheduling tools which provide a yes/no irrigation decision or recommend how much water should be applied to return the soil profile to an optimal soil moisture condition. The work described here developed a method which uses soil water tension data from soil moisture sensors and the van Genuchten model to provide irrigation scheduling recommendations. The strength of the method is that it can use data readily available from USDA-NRCS soil surveys to predict soil water retention curves and calculate the volumetric water content and soil water tension of a soil at field capacity. Those parameters are then used to translate measured soil water tension into irrigation recommendations which are specific to the soil moisture status of the soil. The method was validated by comparing its results to other published methods and with continuous soil water tension data with multiple wetting and drying cycles from six fields in southern Georgia, USA. Finally, the model was incorporated into a web-based irrigation scheduling tool and used in conjunction with a wireless soil moisture sensing system to schedule irrigation in a large commercial field during 2015. By the van Genuchten model, we used about two thirds of the irrigation water and produced about the same yields as a commonly used yes/no irrigation decision tool. The presented method can be used to build resiliency to climate variability because it provides growers with data which they can use to make informed decisions about managing their water resources.

Predicting available water of soil from particle-size distribution and bulk density in an oasis–desert transect in northwestern China

Summary A detailed understanding of soil hydraulic properties, particularly the available water content of soil, (AW, cm3 cm−3), is required for optimal water management. Direct measurement of soil hydraulic properties is impractical for large scale application, but routinely available soil particle-size distribution (PSD) and bulk density can be used as proxies to develop various prediction functions. In this study, we compared the performance of the Arya and Paris (AP) model, Mohammadi and Vanclooster (MV) model, Arya and Heitman (AH) model, and Rosetta program in predicting the soil water characteristic curve (SWCC) at 34 points with experimental SWCC data in an oasis–desert transect (20 × 5 km) in the middle reaches of the Heihe River basin, northwestern China. The idea of the three models emerges from the similarity of the shapes of the PSD and SWCC. The AP model, MV model, and Rosetta program performed better in predicting the SWCC than the AH model. The AW determined from the SWCCs predicted by the MV model agreed better with the experimental values than those derived from the AP model and Rosetta program. The fine-textured soils were characterized by higher AW values, while the sandy soils had lower AW values. The MV model has the advantages of having robust physical basis, being independent of database-related parameters, and involving subclasses of texture data. These features make it promising in predicting soil water retention at regional scales, serving for the application of hydrological models and the optimization of soil water management.

Towards Retrieving Soil Hydraulic Properties by Hyperspectral Remote Sensing

In this study, we developed spectrotransfer functions (STFs) that relate soil hydraulic properties (SHPs) to spectral reflectance values to estimate hydraulic parameters of the Mualem–van Genuchten (MvG) model. We investigated the general potential of airborne as well as space‐borne remote sensors to retrieve MvG hydraulic parameters of a bare soil agricultural field. Based on the ASD full spectrum (Scenario I), simple spectral signatures were generated mimicking the hyperspectral EnMAP sensor (Scenario II), and the multispectral Sentinel‐2 sensor (Scenario III). A stepwise multiple linear regression method was used for each scenario to derive STFs. We further tested laboratory‐ and soil‐map‐based HYPRES and Rosetta pedotransfer functions (PTFs) to parameterize MvG parameters and thus provide soil water characteristics and hydraulic conductivity functions in the region. The best results were obtained for Scenarios I and II, with similar R2 values for shape parameters α* and n and the lognormal saturated hydraulic conductivity (Ks*). The R2 values were highest for Ks* in Scenarios I and II (0.58 and 0.57, respectively). The R2 values for α* and n were 0.30 and 0.34 in Scenario I and 0.39 and 0.31 in Scenario II, respectively. In all scenarios, the lowest R2 values were obtained for saturated water content (θs), with values around 0.10 for Scenarios I and II and almost zero in Scenario III. Compared with HYPRES and Rosetta PTFs, the spectral approach performed reasonably well in terms of predicting soil water retention characteristics and unsaturated hydraulic conductivity. These findings suggest that spectral reflectance data provide a promising indirect and quick method for large‐scale parameter estimation.

Revisiting Field Capacity (FC): variation of definition of FC and its estimation from pedotransfer functions

Taking into account the nature of the hydrological processes involved in in situ measurement of Field Capacity (FC), this study proposes a variation of the definition of FC aiming not only at minimizing the inadequacies of its determination, but also at maintaining its original, practical meaning. Analysis of FC data for 22 Brazilian soils and additional FC data from the literature, all measured according to the proposed definition, which is based on a 48-h drainage time after infiltration by shallow ponding, indicates a weak dependency on the amount of infiltrated water, antecedent moisture level, soil morphology, and the level of the groundwater table, but a strong dependency on basic soil properties. The dependence on basic soil properties allowed determination of FC of the 22 soil profiles by pedotransfer functions (PTFs) using the input variables usually adopted in prediction of soil water retention. Among the input variables, soil moisture content θ (6 kPa) had the greatest impact. Indeed, a linear PTF based only on it resulted in an FC with a root mean squared residue less than 0.04 m³ m-3 for most soils individually. Such a PTF proved to be a better FC predictor than the traditional method of using moisture content at an arbitrary suction. Our FC data were compatible with an equivalent and broader USA database found in the literature, mainly for medium-texture soil samples. One reason for differences between FCs of the two data sets of fine-textured soils is due to their different drainage times. Thus, a standardized procedure for in situ determination of FC is recommended.

New generation of hydraulic pedotransfer functions for Europe.

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.

Prediction of soil water retention properties using pore-size distribution and porosity

Several models have been suggested to link a soil's pore-size distribution to its retention properties. This paper presents a method that builds on previous techniques by incorporating porosity and particles of different sizes, shapes, and separation distances to predict soil water retention properties. Mechanisms are suggested for the determination of both the main drying and wetting paths, which incorporate an adsorbed water phase and retention hysteresis. Predicted results are then compared with measured retention data to validate the model and to provide a foundation for discussing the validity and limitations of using pore-size distributions to predict retention properties.