Evaluation and development of hydraulic conductivity pedotransfer functions for Australian soil

Budiman Minasny; Alex. B. McBratney

doi:10.1071/sr99110

Evaluation and development of hydraulic conductivity pedotransfer functions for Australian soil

Soil Research ◽

10.1071/sr99110 ◽

2000 ◽

Vol 38 (4) ◽

pp. 905 ◽

Cited By ~ 37

Author(s):

Budiman Minasny ◽

Alex. B. McBratney

Keyword(s):

Particle Size ◽

Fractal Dimension ◽

Hydraulic Conductivity ◽

Clayey Soil ◽

Fractal Model ◽

Effective Porosity ◽

Pedotransfer Functions ◽

Soil Science Society ◽

Data Set ◽

Australian Soil

Pedotransfer functions (PTFs) for predicting saturated hydraulic conductivity (Ks) were evaluated using published Australian soil data sets. Eight published PTFs were evaluated. Generally, published PTFs provide a satisfactory estimation of Ks depending on the spatial scale and accuracy of prediction. Several PTFs were developed in this study, including the power function of effective porosity, multiple linear regression, fractal model, and artificial neural networks. Different methods for estimating the fractal dimension of particle-size distributions showed no significant differences in predicting Ks . The simplest model for estimating fractal dimension from the log–log plot of particle-size distribution is therefore recommended. The data set was also stratified into 3 broad classes of texture: sandy, loamy, and clayey. Stratification of PTFs based on textural class showed small improvements in estimation. The published PTF of Dane and Puckett (1994) Proc. Int. Workshop (Univ. of California: Riverside, CA) gives the best prediction for sandy soil; the PTF of Cosby et al. (1984) Water Resources Research 20, 682–90 gives the best production for loamy soil; and the PTF of Schaap et al. (1998) Soil Science Society of America Journal 62, 847–55 gives the best prediction for clayey soil. The data set used comprised different field and laboratory measurements over large areas, and limited predictive variables were available. The PTFs developed here may predict adequately in large areas (residuals = 10–20 mm/h), but for site-specific applications, local calibration is needed.

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Global Mapping of Soil Saturated Hydraulic Conductivity Combining Legacy Data, Spatial Covariates and Machine Learning

10.5194/ismc2021-90 ◽

2021 ◽

Author(s):

Surya Gupta ◽

Peter Lehmann ◽

Andreas Papritz ◽

Tomislav Hengl ◽

Sara Bonetti ◽

...

Keyword(s):

Machine Learning ◽

Hydraulic Conductivity ◽

Cross Validation ◽

Arable Land ◽

Remotely Sensed ◽

Saturated Hydraulic Conductivity ◽

Pedotransfer Functions ◽

Data Set ◽

Global Database ◽

Spatially Distributed

<p>Saturated soil hydraulic conductivity (Ksat) is a key parameter in many hydrological and climatic modeling applications, as it controls the partitioning between precipitation, infiltration and runoff. Values of Ksat are often deduced from Pedotransfer Functions (PTFs) using maps of soil attributes. To circumvent inherent limitations of present PTFs (heavy reliance of arable land measurements, ignoring soil structure, and geographic bias to temperate regions), we propose a new global Ksat map at 1&#8211;km resolution by harnessing technological advances in machine learning and availability of remotely sensed surrogate information (terrain, climate and vegetation). We compiled a comprehensive Ksat data set with 13,258 data geo-referenced points from literature and other sources. The data were standardized and quality-checked in order to provide a global database of soil saturated hydraulic conductivity (SoilKsatDB). The SoilKsatDB was then applied to develop a Covariate-based GeoTransfer Function (CoGTF) model for predicting spatially distributed Ksat values using remotely sensed information on various environmental covariates. The model accuracy assessment based on spatial cross-validation shows a concordance correlation coefficient (CCC) of 0.16 and a root meansquare error (RMSE) of 1.18 for log10 Ksat values in cm/day (CCC=0.79 and RMSE=0.72 for non spatial cross-validation). The generated maps of Ksat represent spatial patterns of soil formation processes more distinctly than previous global maps of Ksat based on soil texture information and bulk density. The validation indicates that Ksat could be modeled without bias using CoGTFs that harness spatially distributed surface and climate attributes, compared to soil information based PTFs. The relatively poor performance of all models in the validation (low CCC and high RMSE) highlights the need for the collection of additional Ksat values to train the model for regions with sparse data.</p>

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Using genetic programming to transform from Australian to USDA/FAO soil particle-size classification system

Soil Research ◽

10.1071/sr12139 ◽

2012 ◽

Vol 50 (6) ◽

pp. 443 ◽

Cited By ~ 9

Author(s):

José Padarian ◽

Budiman Minasny ◽

Alex McBratney

Keyword(s):

Particle Size ◽

Genetic Programming ◽

Classification Systems ◽

Soil Survey ◽

Pedotransfer Functions ◽

Programming Technique ◽

Size Classification ◽

The Difference ◽

Prior Models ◽

Australian Soil

The difference between the International (adopted by Australia) and the USDA/FAO particle-size classification systems is the limit between silt and sand fractions (20 μm for the International and 50 µm for the USDA/FAO). In order to work with pedotransfer functions generated under the USDA/FAO system with Australian soil survey data, a conversion should be attempted. The aim of this work is to improve prior models using larger datasets and a genetic programming technique, in the form of a symbolic regression. The 2–50 µm fraction was predicted using a USDA dataset which included both particle-size classification systems. The presented model reduced the root mean square error (%) by 14.96 and 23.62% (IGBP-DIS dataset and Australian dataset, respectively), compared with the previous model.

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Relationships between field texture and particle-size distribution in Australia and their implications

Soil Research ◽

10.1071/sr07051 ◽

2007 ◽

Vol 45 (6) ◽

pp. 428 ◽

Cited By ~ 6

Author(s):

Budiman Minasny ◽

Alex B. McBratney ◽

Damien J. Field ◽

Grant Tranter ◽

Neil J. McKenzie ◽

...

Keyword(s):

Particle Size ◽

Particle Size Distribution ◽

Size Distribution ◽

Classification System ◽

Soil Classification ◽

Particle Size Analysis ◽

Pedotransfer Functions ◽

Size Analysis ◽

Texture Contrast ◽

Australian Soil

This paper aims to establish the means and ranges of clay, silt, and sand contents from field texture classes, and to investigate the differences in the field texture classes and texture determined from particle-size analysis. The results of this paper have 2 practical applications: (1) to estimate the particle size distribution and its uncertainty from field texture as input to pedotransfer functions, and (2) to examine the criteria of texture contrast soils in the Australian Soil Classification system. Estimates of clay, silt, and sand content for each field texture class are given and this allows the field texture classes to be plotted in the texture triangle. There are considerable differences between field texture classes and particle-size classes. Based on the uncertainties in determining the clay content from field texture, we establish the probability of the occurrence of a texture contrast soil according to the Australian Soil Classification system, given the texture of the B2 horizon and its overlying A horizon. I enjoy doing the soil-texture feel test with my fingers or kneading a clay soil, which is a short step from ceramics or sculpture. Hans Jenny (1984)

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Developing Pseudo Continuous Pedotransfer Functions for International Soils Measured with the Evaporation Method and the HYPROP System: II. The Soil Hydraulic Conductivity Curve

Water ◽

10.3390/w13060878 ◽

2021 ◽

Vol 13 (6) ◽

pp. 878

Author(s):

Amninder Singh ◽

Amir Haghverdi ◽

Hasan Sabri Öztürk ◽

Wolfgang Durner

Keyword(s):

Hydraulic Conductivity ◽

Soil Texture ◽

Measured Data ◽

Pedotransfer Functions ◽

Data Sets ◽

Data Set ◽

Soil Hydraulic Conductivity ◽

Evaporation Method ◽

International Data ◽

Soil Hydraulic

Direct measurement of unsaturated hydraulic parameters is costly and time-consuming. Pedotransfer functions (PTFs) are typically developed to estimate soil hydraulic properties from readily available soil attributes. For the first time, in this study, we developed PTFs to estimate the soil hydraulic conductivity (log(K)) directly from measured data. We adopted the pseudo continuous neural network PTF (PCNN-PTF) approach and assessed its accuracy and reliability using two independent data sets with hydraulic conductivity measured via the evaporation method. The primary data set contained 150 international soils (6963 measured data pairs), and the second dataset consisted of 79 repacked Turkish soil samples (1340 measured data pairs). Four models with different combinations of the input attributes, including soil texture (sand, silt, clay), bulk density (BD), and organic matter content (SOM), were developed. The best performing international (root mean square error, RMSE = 0.520) and local (RMSE = 0.317) PTFs only had soil texture information as inputs when developed and tested using the same data set to estimate log(K). However, adding BD and SOM as input parameters increased the reliability of the international PCNN-PTFs when the Turkish data set was used as the test data set. We observed an overall improvement in the performance of PTFs with the increasing number of data points per soil textural class. The PCNN-PTFs consistently performed high across tension ranges when developed and tested using the international data set. Incorporating the Turkish data set into PTF development substantially improved the accuracy of the PTFs (on average close to 60% reduction in RMSE). Consequently, we recommend integrating local HYPROPTM (Hydraulic Property Analyzer, Meter Group Inc., USA) data sets into the international data set used in this study and retraining the PCNN-PTFs to enhance their performance for that specific region.

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Different Soil Particle-Size Classification Systems for Calculating Volume Fractal Dimension—A Case Study of Pinus sylvestris var. Mongolica in Mu Us Sandy Land, China

Applied Sciences ◽

10.3390/app8101872 ◽

2018 ◽

Vol 8 (10) ◽

pp. 1872 ◽

Cited By ~ 4

Author(s):

Jifeng Deng ◽

Chengzhong Ma ◽

Hongzhou Yu

Keyword(s):

Particle Size ◽

Fractal Dimension ◽

Pinus Sylvestris ◽

Fractal Model ◽

Soil Particle ◽

Environmental Research ◽

Sandy Land ◽

Mu Us Sandy Land ◽

Size Classification ◽

Soil Particle Size

Characterizing changes in the soil particle-size distributions (PSD) are a major issue in environmental research because it has a great impact on soil properties, soil management, and desertification. To date, the use of soil volume fractal dimension (D) is a feasible approach to describe PSD, and its calculation is mainly dependent on subdivisions of clay, silt, sand fractions as well as different soil particle-size classification (PSC) systems. But few studies have developed appropriate research works on how PSC systems affect the calculations of D. Therefore, in this study, topsoil (0–5 cm) across nine forest density gradients of Pinus sylvestris var. mongolica plantations (MPPs) ranging from 900–2700 trees ha–1 were selected in the Mu Us sandy land, China. The D of soil was calculated by measuring soil PSD through fractal model and laser diffraction technique. The experimental results showed that: (1) The predominant PSD was distributed within the sand classification followed by clay and silt particle contents, which were far less prevalent in the study area. The general order of D values (Ds) was USDA (1993) > ISO14688 (2002) > ISSS (1929) > Katschinski (1957) > China (1987) > Blott & Pye (2012) PSC systems. (2) Ds were significantly positively related to the contents of clay and silt, and Ds were significantly negatively to the sand content. Ds were susceptible to the MPPs establishment and forest densities. (3) Ds of six PSC systems were significantly positive correlated, which indicated that they not only have difference, but also have close connection. (4) According to the fractal model and descriptions of soil fractions under different PSC systems, refining scales of clay and sand fractions could increase Ds, while the refining scale of silt fraction could decrease Ds. From the conclusions above, it is highly recommended that USDA (1993) and Blott & Pye (2012) PSC systems be used as reliable and practical PSC systems for describing and calculating D of soil PSD.

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A FRACTAL MODEL OF PERMEABILITY FOR THE LIQUID HELIUM FLOW IN CABLE-IN-CONDUIT CONDUCTORS

Fractals ◽

10.1142/s0218348x19500646 ◽

2019 ◽

Vol 27 (04) ◽

pp. 1950064

Author(s):

ZHICAI MA ◽

WURUI TA ◽

YUANWEN GAO

Keyword(s):

Fractal Dimension ◽

Liquid Helium ◽

Cross Section ◽

Heat Removal ◽

Approximate Expression ◽

Average Diameter ◽

Fractal Model ◽

Effective Porosity ◽

Helium Flow ◽

Permeability Model

The heat removal capability and the coolant pumping costs in the design of cable-in-conduit conductors are depend on the thermo-hydraulics of the liquid helium flow. Therefore, the accurate knowledge of the thermo-hydraulics of the flow is significant for the design of the cables, especially for permeability. In this paper, the fractal method is proposed to describe the cable cross-section and an approximate expression is derived. Then, a fractal permeability model for helium flow in CICCs is presented based on a porous medium analogy. The feasibility and validity of this model is verified by the comparison of the predicted values and the experimental values. The fractal model indicates that permeability of cables is determined by the cable geometric parameters, such as the effective porosity, the average cabling angle, the average diameter of strands and the pore area fractal dimension of the cable cross-section. This model does not contain any empirical constants or fitting constants and can be used to explain the mechanism and to predict the permeability of the helium flow in CICCs. Furthermore, the effects of cable geometric characteristics on the presented fractal permeability model are also analyzed and simulated. The results imply that permeability of cables decreases with increasing the cabling angle, increases with the effective porosity, the pore fractal dimension and the average diameter of the strands increase. These results are consistent with the physical situations.

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TWO-DIMENSIONAL FRACTAL MODEL FOR ULTIMATE CRUSHING STATE OF COARSE AGGREGATES

Fractals ◽

10.1142/s0218348x19501093 ◽

2019 ◽

Vol 27 (07) ◽

pp. 1950109

Author(s):

QIANMI YU ◽

JIANKUN LIU ◽

UJWALKUMAR D. PATIL ◽

SURYA S. C. CONGRESS ◽

ANAND J. PUPPALA

Keyword(s):

Particle Size ◽

Fractal Dimension ◽

Fractal Geometry ◽

Fractal Theory ◽

Size Fraction ◽

Size Variation ◽

Fractal Model ◽

Fragmentation Model ◽

Two Dimensional ◽

Coarse Aggregates

The research on the ultimate crushing state of coarse aggregates is beneficial to analyze and predict the evolutionary process of crushing. The Growing Path method uses the two-dimensional fractal geometry structure to simulate the size variation of particle size fraction during the particle breakage of coarse aggregates and it serves to investigate the ultimate fractal dimension corresponding to the ultimate crushing state of coarse aggregates. This method manifests the self-growing characteristics of particle size distribution in the process of particle crushing. This study found that the two-dimensional image of ultimate fractal model was precisely similar to that of the Sierpinski gasket of fractal theory when the ultimate crushing state was reached. The results from the model analysis show that the theoretically ultimate fractal dimension is about 2.585, which is consistent with the existing results calculated from the three-dimensional ultimate fragmentation model of cataclastic rock located in the fault zones. The relationship between two fractal models was analyzed. Furthermore, the application of fractal geometry presented in this study will also serve as a reference for the analysis of the other chaos phenomena observed in geotechnical engineering.

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Prediction of hydraulic conductivity for some Australian soils

Soil Research ◽

10.1071/sr02120 ◽

2003 ◽

Vol 41 (6) ◽

pp. 1077 ◽

Cited By ~ 6

Author(s):

Zahra Paydar ◽

Anthony J. Ringrose-Voase

Keyword(s):

Hydraulic Conductivity ◽

Water Retention ◽

Management Practices ◽

Measurement Techniques ◽

Effective Porosity ◽

Saturated Hydraulic Conductivity ◽

Pedotransfer Functions ◽

Water Retention Curve ◽

Soil Water Retention Curve ◽

Soil Water Retention

Pedotransfer functions and their use in simulation modelling have attracted much attention during recent years. In the absence of measured hydraulic conductivity data, prediction from other soil properties would be most useful. A functional form relating near-saturated hydraulic conductivity to the soil water retention curve based on the Kozeny–Carman equation was investigated on Australian soils. For a dataset comprising a range of soil textures and structural conditions (107 samples with bulk density >1.2 Mg/m3) a power-law relationship between near-saturated hydraulic conductivity, effective porosity, and pore size distribution index was obtained. The function was tested on 2 different datasets for independent evaluation. The results showed poor predictions for most soils in this study. While the reasons for poor predictions might be the difference in the measurement techniques or potentials, it is thought that the proposed function mostly fails predictions on soils with high organic matter and management practices affecting macropores and soil structure (e.g. crust). The proposed function did not show much improvement over the more general form of the Kozeny–Carman equation with empirical coefficients. In the absence of other data, the modified Kozeny–Carman equation (with or without water retention parameters) can be used, with caution, on similar soils and larger scale applications. More data are needed to test the reliabilty of these functions for use in specific locations.

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The Australian soil texture boomerang: a comparison of the Australian and USDA/FAO soil particle-size classification systems

Soil Research ◽

10.1071/sr00065 ◽

2001 ◽

Vol 39 (6) ◽

pp. 1443 ◽

Cited By ~ 38

Author(s):

Budiman Minasny ◽

Alex. B. McBratney

Keyword(s):

Particle Size ◽

International System ◽

Classification Systems ◽

Pedotransfer Functions ◽

Particle Size Fractions ◽

Particle Size Data ◽

Size Limits ◽

Australian Soil ◽

Size Data

The different classification of particle-size fractions used in Australia compared with other countries presents a problem for the immediate adoption of the exotic pedotransfer functions. Australia adopted the international system which defined silt as particles with diameters in the range 2–20 m, while the USDA/FAO define it as 2–50 m. We present empirical equations to convert between the two systems. The USDA/FAO textural classes were also plotted in the International system’s coordinate. The USDA/FAO classes in the International system had a ‘boomerang’ shape and only occupy 60% of the triangle. Particle-size data showed that the data are evenly distributed in the USDA/FAO triangle, while most data are concentrated in the boomerang in the International system. We therefore suggest that it would seem wise for most countries to consider adopting the particle-size limits and texture classes of the USDA/FAO system.

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