Does model complexity of a pedotransfer function for soil hydraulic properties hamper its transferability?

Testing soil water retention estimation with the MUUF pedotransfer model using data from the southern United States

New pedotransfer functions for soil water retention curves that better account for bulk density effects

Functional evaluation of PTF prediction uncertainty: An application at hillslope scale

Water retention and saturated hydraulic conductivity are soil properties that are key determinants in crop growth and hydrological modelling. They are commonly estimated from basic soil characteristics such as bulk density, organic carbon content and texture by means of pedotransfer functions (PTFs). In order to assess and compare the inherent performance and the functional applicability in the Zambezi River Basin (ZRB) of the widely used Saxton & Rawls PTFs and a set of newly developed PTFs, we compiled measurements of water retention at pF0.0, 1.0, 2.0, 2.8, 3.4 and 4.2 and of saturated hydraulic conductivity (Ksat) on 631 soil samples throughout the ZRB. A total of 329 of the samples were related to 55 soil profiles available in the Africa Soil Profile database, whereas our own field campaign carried out in a 2,426‐km 2 subbasin of the ZRB provided the remaining 302 samples related to 119 soil profiles. Apart from evaluating the Saxton & Rawls PTFs, we developed multiple linear regression (MLR) PTFs, and PTFs derived by three machine learning (ML) models: artificial neural network (ANN), random forest (RF) and support vector machine (SVM). All PTFs were first evaluated based on a comparison of the estimated and measured property values by means of R 2 , mean absolute error (MAE) and root mean squared error (RMSE). For the ensemble of MLR‐PTF and ML‐PTFs, the R 2 of the six water content variables and the Ksat ranged from 0.55 to 0.85, whereas for the Saxton & Rawls PTFs the range was between 0.10 and 0.50. Secondly, all PTFs were subjected to a functional evaluation using the Food and Agriculture Organization (FAO) AquaCrop crop growth model. Dry season irrigation requirements for maize as computed by AquaCrop with measured versus estimated soil hydraulic properties revealed that ANN‐PTFs provide AquaCrop outputs that come closest to AquaCrop outputs generated with measured soil hydraulic properties. This study shows the importance of performing functional evaluation of pedotransfer functions before their widespread application. Highlights Developed machine learning and multiple linear regression pedotransfer functions (PTFs). The Saxton & Rawls PTFs are not recommended for use in the Zambezi River Basin. PTFs were functionally evaluated through use of estimated soil hydraulic properties in AquaCrop. More accurate PTFs have better functional performance, although differences are small.

New pedotransfer approaches to predict soil bulk density using WoSIS soil data and environmental covariates in Mediterranean agro-ecosystems

We reviewed the use of the van Genuchten–Mualem (VGM) model to parameterize soil hydraulic properties and for developing pedotransfer functions (PTFs). Analysis of literature data showed that the moisture retention characteristic (MRC) parameterization by setting shape parameters m = 1 − 1/ n produced the largest deviations between fitted and measured water contents for pressure head values between 330 (log 10 pressure head [pF] 2.5) and 2500 cm (pF 3.4). The Schaap–van Genuchten model performed best in describing the unsaturated hydraulic conductivity, K The classical VGM model using fixed parameters produced increasingly higher root mean squared residual, RMSR, values when the soil became drier. The most accurate PTFs for estimating the MRC were obtained when using textural properties, bulk density, soil organic matter, and soil moisture content. The RMSR values for these PTFs approached those of the direct fit, thus suggesting a need to improve both PTFs and the MRC parameterization. Inclusion of the soil water content in the PTFs for K only marginally improved their prediction compared with the PTFs that used only textural properties and bulk density. Including soil organic matter to predict K had more effect on the prediction than including soil moisture. To advance the development of PTFs, we advocate the establishment of databases of soil hydraulic properties that (i) are derived from standardized and harmonized measurement procedures, (ii) contain new predictors such as soil structural properties, and (iii) allow the development of time‐dependent PTFs. Successful use of structural properties in PTFs will require parameterizations that account for the effect of structural properties on the soil hydraulic functions.

New empirical-point pedotransfer functions for water retention data for a wide range of soil texture and climates

<p>Modelling is often used to acquire information on water and nutrient fluxes within and out of the root zone. The models require detailed information on the spatial variability of soil hydraulic properties derived from soil texture and other soil characteristics using pedotransfer functions (PTFs). Soil texture can vary considerably within a field and is cumbersome and expensive to map in details using traditionally measurements in the laboratory. The electrical conductivity (EC) of the soil have shown to correlate with its textural composition.</p><p>This study investigates the ability of electromagnetic induction (EMI) methods to predict clay content in three soil layers of the root zone. As the clay fraction often is a main predictor in PTFs predicting soil hydraulic properties this parameter is of high interest. EMI and soil textural surveys on four Danish agricultural fields with varying textural composition were used. Sampling density varied between 0.5 and 38 points per hectare. The EMI data was gathered with a Dualem21 instrument with a sampling density 200-3000 points per hectare. The EC values were used together with the measured values of the clay content creating a statistical relationship between the two variables. Co-kriging of the clay content from the textural sampling points with the EC as auxiliary variable produces clay content maps of the fields. Unused (80%) texture points were used for validation. EMI-predicted clay content maps and clay content maps based on the survey were compared. The two sets of soil texture maps are used as predictors for PTF models to predict soil hydraulic properties as input in field-scale root zone modelling.</p><p>The comparisons between EC and clay content show some degree of correlation with an R<sup>2</sup> in the range of 0.55 to 0.80 for the four fields. The field with the highest average clay content showed the best relationship between the two parameters. Co-kriging with EC decreased mean error by 0.016 to 0.52 and RMSE by 0.04 to 1.80 between observed and predicted clay maps.</p>

Many catchment basins have insufficient data to make conservation planning for sustainable natural resources use. In addition, intensive soil sampling is neither feasible nor economical in a large studied area. Therefore, interpolation models and pedotransfer functions (PTFs) have important roles in data mining. Main objectives of this study were to estimate and evaluate the estimates of basic soil hydraulic properties in Buyukcay Watershed (Turkey) using different interpolation geostatistic models and PTFs. Three interpolation models (inverse distance weighting IDW, ordinary kriging and cokriging) and pedotransfer functions (continuous and class) were applied and compared in this study. In 84 soil samples, soil depth, texture, organic matter, pH, electric conductivity, saturation degree, field capacity and permanent wilting point were determined. According to the results, among geostatistical algorithms, ordinary cokriging yields better results for all soil hydraulic properties with lower MAE and MSE values than ordinary kriging and IDW. Within PTFs, soil hydraulic properties were estimated with high reliability using continuous approach; however, class PTFs showed less sensitivity to space variability than geostatistical methods.

Soil hydraulic properties play crucial role for having sustainable growth of vegetation and also required for carrying out environmental studies especially subsurface ground water. Since, its direct measurement is time consuming and therefore costly, indirect methods such as pedotransfer functions have been used to estimate hydraulic properties. Soil hydraulic properties: bulk density, available water, permanent wilting point, field capacity and water retention capacity and saturated hydraulic conductivity are essential to be studied. These factors affect soil productivity and the environment. The knowledge of soil water properties for different land-use and land cover types based on soil textural classes are important for re-growth of plants and agriculture crops and ultimately efficient soil and water management. The aim of this paper is to measure soil hydraulic properties based on soil textural classes for different land use types. Furthermore, the use of the soil texture triangle: hydraulic properties calculator has been assessed. The comparison of the soil hydraulic properties obtained from textural triangle for different land use land and cover types has also been analyzed. Moreover, an analysis of variance (ANOVA) of soil hydraulic properties among different land use types has been performed. The soil reaction (pH) value ranges from 4.69 to 6.69 and as a mean of 5.51 indicating slight acidic. The analysis showed barren land occupying 14.3 percent of total land use/land cover is found to be suitable for plant growth since it has loam texture having bulk density less than 1.6 g/cm3.The analysis of bulk density showed that barren land is significantly different at the 0.05 level from both agriculture and forest, whereas agriculture and forests are not different even at 0.05 significant levels. It verifies the fact that mean bulk density at the depth of 0.15 cm is affected by land use land cover types.The Geographical Journal of NepalVol. 11: 63-76, 2018

Accurate information about soil water retention curves (SWRCs) of sands is essential for evaluating groundwater recharge and vulnerability to contamination in many shallow sandy aquifers which are widespread on post glacial areas in Northern Europe and North America. Pedotransfer functions (PTFs) allow to estimate SWRC from basic physical characteristics of soils, such as textural composition. However, in the case of clean sands which are dominated by a single textural fraction, PTFs should be based on more detailed information given by the particle size distribution. In this study we evaluated three parametric PTFs, which estimate parameters of the van Genuchten SWRC based on empirical correlations to the parameters of soil particle size distribution, and five semi‐physical PTFs, which derive the pore size distribution from particle size distribution. PTFs were compared to SWRCs fitted to the results of drainage experiments on sandy soil samples from six locations in Gdańsk region (northern Poland). Although in all samples the content of silt and clay fractions was low (<3.5%), the differences in actual content of fines strongly influenced the shape of SWRC. In contrast, the amount of gravel fraction (varying from 1% to 35%) did not have significant effect on SWRC. Semi‐physical PTFs were found to be more accurate than parametric PTFs. The best overall performance was shown by the semi‐physical Chang and Cheng PTF. Among the parametric PTFs the best accuracy was obtained with the Schaap and Bouten method. However, all considered functions showed limited accuracy in higher suction range. Additionally, infiltration experiments were performed on four sites. SWRCs were obtained from ring infiltrometer tests using the Beerkan estimation of soil transfer parameters (BEST) method and from the tension infiltrometer (TI) tests using numerical solution of the inverse problem based on the Richards equation. In almost all cases the wetting SWRCs were characterized by higher values of the pressure scaling parameter α compared to SWRCs measured in drainage experiments, which is consistent with the well‐known phenomenon of hysteresis in soils. However, the BEST method resulted in significantly higher α and hydraulic conductivity K s than TI, probably due to activation of the largest soil pores during ponded infiltration.

Estimation of soil hydraulic properties using proximal spectral reflectance in visible, near-infrared, and shortwave-infrared (VIS–NIR–SWIR) region

Soil hydraulic properties are needed in the modeling of water flow and solute movement in the vadose zone. Pedotransfer functions (PTFs) have received the attention of many researchers for indirect determination of hydraulic properties from basic soil properties as an alternative to direct measurement. The objective of this study was to compare the performance of cascade forward network (CFN), multiple-linear regression (MLR), and seemingly unrelated regression (SUR) methods using prediction capabilities of point and parametric PTFs developed by these methods. The point PTFs estimated field capacity (FC), permanent wilting point (PWP), available water capacity (AWC), and saturated hydraulic conductivity (Ks) and the parametric PTFs estimated the van Genuchten retention parameters. A total of 180 soil samples was extracted from the UNSODA database and divided into two groups as 135 for the development and 45 for the validation of the PTFs. The model performances were evaluated with three statistical tools: the maximum error (ME), the model efficiency (EF), and the D index (D) using the observed and predicted values of a given parameter. Despite the fact that the differences among the three methods in prediction accuracies of the point and parametric PTFs were not statistically significant (p > 0.05) except θr and α (p < 0.05) based on the ANOVA test, overall MLR and SUR were somewhat better than CFN in prediction of the point PTFs, whereas CFN performed better than the other two methods in prediction of the parametric PTFs. The F.F values of FC and θr for CFN, MLR, and SUR methods were 0.705. 0.805, 0.795 and 0.356, −0.290, −0.290, respectively, which refer to the best and worst predictions. Properties (Ks, θr, α) having some difficulty in prediction were better predicted by CFN and SUR methods, where these methods predict all hydraulic properties from basic soil properties simultaneously rather than individually as in MLR. This suggests that multivariate analysis using such functional relationships between hydraulic properties and basic soil properties can be utilized in developing more accurate point and parametric PTFs with less time and effort.

Modeling Soil Water Movement under Low Head Ponding and Gravity Infiltration Using Data Determined with Different Methods

The soil water retention curve (SWRC) is one of the basic characteristics used in determining soil hydraulic properties, including unsaturated hydraulic conductivity. As its measurement is time consuming and difficult, much effort has been expended to develop indirect methods, such as pedotransfer functions and empirical relationships, to estimate SWRC. In this study, three methods were evaluated based on estimation of retention models parameters and, consequently, the soil water retention curve. For this purpose, soil data collected from three data bases, totaling 72 soil samples with 11 different textures, were used in this study. The statistical parameters such as: MR (mean of residual), RE (relative error), RMSE (root mean square error), AIC (Akaike’s information criterion) and GMER (geometric mean error ratio) showed that the Saxton et al. (1986) method estimates the soil water retention curve better than the other methods.Key words: Pedotransfer function, soil texture, soil water retention curve