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Soil water storage is controlled by topography, soil texture, vegetation, water routing processes, and the depth to the water table. Interactions among these factors may give rise to scale-dependent nonstationary and nonlinear patterns in soil water storage. The objectives of this study were to identify the dominant scales of variation of nonstationary and nonlinear soil water storage and delineate the dominant controls at those scales in a hummocky landscape using the Hilbert-Huang transform (HHT). Soil water storage (up to 140 cm) was measured along a 128-point transect established at St. Denis National Wildlife Area, Saskatchewan, Canada, using time domain reflectometry and a neutron probe. Empirical mode decomposition was used to decompose the measured soil water storage series into six different intrinsic mode functions (IMFs) according on their characteristic scales. The first IMF represented the variations at small scales, the second IMF might characterize the variations associated with microtopography and the landform elements. The IMF 3 was highly correlated with elevation and had the largest variance contribution toward the total variance among all the IMFs. The fourth IMF was correlated to organic C (OC), showing the long-term history of water availability, which may be a reflection of topographic setting or the elevation. The fifth and sixth IMFs were associated with elevation, soil texture, and OC but they contributed a small fraction of the total variance. Therefore, decomposition made through HHT was physically meaningful and provided improved prediction of soil water storage from topography, soil texture, and OC. [ABSTRACT FROM AUTHOR]

The heat puise probe (HPP) method can be used to measure specific heat capacity (c), thermal conductivity (λ), and water content of soil; however, many factors are believed to contribute to measurement errors in both 1 and c from the HPP method. The objective of this study was to examine if contact resistance, heat-driven flow, temperature-dependent thermal properties, or epoxy fillings affect the estimated soil thermal properties and water content from the HPP method. Heat pulse experiments were conducted on oven-dry sand, water-saturated sand, sand saturated with agar-stabilized water, and coarse snow (large grain size). The results showed that for the sand and coarse snow, the thermal contact resistance did not contribute to the overestimation of heat capacity. There was little difference in measured water contents between sand saturated with water and sand saturated with agar- stabilized water, suggesting no obvious heat-driven flow under saturated conditions. Compared with an 8-s heat pulse with a power strength (q′) >80 W m-1, a long heat pulse of duration 60 s and q′ < 6 W m-1 reduced the overestimation of soil c and water content (θw) on oven-dry sands. Both temperature-dependent soil thermal properties and epoxy filling inside the heater needle contributed to the overestimation of c and θw. [ABSTRACT FROM AUTHOR]

Saturated hydraulic conductivity (Ks) is one of the key parameters in modeling solute and water movement in the vadose zone. Field and laboratory measurement of is time consuming, and hence is not practical for characterizing the large spatial and temporal variability of Ks. As an alternative to direct measurements, pedotransfer functions (PTFs), which estimate Ks from readily available soil data, are being widely adopted. This study explores the utility of a promising data-driven method, namely, genetic programming (GP), to develop PTFs for estimating Ks from sand, silt, and clay contents and bulk density (Db). A data set from the Unsaturated Soil Hydraulic Database (UNSODA) was considered in this study. The performance of the GP models were compared with the neural networks (NNs) model, as it is the most widely adopted method for developing PTFs. The uncertainty of the PTFs was evaluated by combining the GP and the NN models, using the nonparametric bootstrap method. Results from the study indicate that GP appears to be a promising tool for developing PTFs for estimating Ks. The better performance of the GP model may be attributed to the ability of GP to optimize both the model structure and its parameters in unison. For the PTFs developed using GP, the uncertainty due to model structure is shown to be more than the uncertainty due to model parameters. Moreover, the results indicate that it is difficult, if not impossible, to achieve better prediction and less uncertainty simultaneously. [ABSTRACT FROM AUTHOR]

Modeling contaminant and water flow through soil requires accurate estimates of soil hydraulic properties in field scale. Although artificial neural networks (ANNs) based pedotransfer functions (PTFs) have been successfully adopted in modeling soil hydraulic properties at larger scales (national, continental, and intercontinental), the utility of ANNs in modeling saturated hydraulic conductivity (Ks) at a smaller (field) scale has rarely been reported. Hence, the objectives of this study are (i) to investigate the applicability of neural networks in estimating Ks at field scales, (ii) to compare the performance of the field-scale PTFs with the published neural networks program Rosetta, and (iii) to compare the performance of two different ensemble methods, namely Bagging and Boosting in estimating Ks. Datasets from two distinct sites are considered in the study. The performances of the models were evaluated when only sand, silt, and clay content (SSC) were used as inputs, and when SSC and bulk density Pb (SSC + Pb) were used as inputs. For both datasets, the field scale models performed better than Rosetta. The comparison of field-scale ANN models employing bagging and boosting algorithms indicates that the neural network model employing the boosting algorithm results in better generalization by reducing both the bias and variance of the neural network models. [ABSTRACT FROM AUTHOR]

Tension infiltrometer measurements have been used to measure steady-state infiltration rates at applied tensions. The measurements can be used to determined soil hydraulic properties through linear or nonlinear regression. Such regression methods are often based on a few imprecise field measurements, and the traditional regression analysis may not yield valid estimates and reliable predictions. The objective of this study is to introduce fuzzy linear regression as an alternative to statistical regression analysis in determining hydraulic properties from tension infiltrometer measurements. Using a tension infiltrometer, in situ steady-state infiltration rates [q-(h)] were measured at six different tensions (h) between 3 and 22 cm of water on silty loam and clay loam soils. Hydraulic properties (i.e., field saturated hydraulic conductivity, Kfs, and inverse macroscopic capillary length scale, αG) and their confidence intervals were estimated following the fuzzy least-square linear regression procedure with the minimum fuzziness criterion and linear least-squares method (traditional statistical method). Both calculation procedures yielded the same mean hydraulic properties. A comparison between fuzzy and statistical ln q-(h) relationship indicated that the confidence bands resulting from both procedures enveloped all the measurement points, but fuzzy regression estimates offered a tighter fit around the midpoint values (least-square estimates). Fuzzy linear regression is more reliable and may be used as a complement or an alternative to statistical linear regression analysis for determining hydraulic properties from tension infiltrometer measurements. [ABSTRACT FROM AUTHOR]

Saturated hydraulic conductivity (Ks) is an important soil hydraulic property that affects water flow and the transport of dissolved solutes. Obtaining sufficient and reliable Ks data for large-scale process modeling is always a challenge due to the extremely high spatial variability. The objectives of this study were (i) to determine if a monofractal or multifractal approach is needed to describe the variability in Ks and its soil surrogates, and (ii) to identify which soil property best reflects the spatial distribution of Ks across a wider range of scales. Saturated hydraulic conductivity and soil physical property data were collected from a 384-m transect, located at Smeaton, 5K, Canada. Observation scale variability and relationships were examined using statistical and geostatistical methods. Statistical scale-invariance was evaluated through the Hurst scaling parameter (H). Multiple scale variability and relationships were studied using multifractal and joint multifractal techniques. Results indicate that for all the studied vari- ables 0.80 < H < 0.90, suggesting a certain degree of statistical scale-invariance and long-range dependency. At the observation scale, the variability in Ks was significantly related to sand (SA) and silt (SI) distribution (R = 0.40 for SA and -0.39 for SI, P < 0.01; n = 128), whereas, across a wider range of scales, the variability in Ks was related only to clay (CL) and organic C (OC). The result indicates scale dependent relationships between Ks and soil physical properties, which implies that the success of predictive models such as pedotransfer functions (PTFs) and Ks aggregation techniques depends largely on the correspondence between observation and implementation scales. [ABSTRACT FROM AUTHOR]

Characterization of water-conducting porosity at and near saturation is required in understanding rainfall and snowmelt infiltration and runoff as well as chemical transport in soil. There are methods available to quantify water-conducting porosity in situ, but with serious limitations. The objective of this paper was to present a general equation for water-conducting porosity based on ponded- and tension-infiltration measurements. Some analytical solutions are developed for specific unsaturated hydraulic conductivity functions such as the Gardner's exponential and rational power models, Brooks and Corey model, and van Genuchten-Mualem model. Tension infiltrometer measurements were taken at six different pressure heads between -0.3 to -2.2 kPa and double-ring infiltrometer measurements at a pressure head of 0.35 kPa. The analytical solutions were compared with numerical solutions and existing methods for calculation of waterconducting porosity. Both the analytical and numerical solutions can reliably determine the water-conducting porosity of surface soils in situ within the practical pressure head range of the tension infiltrometer. Our method gave consistent water-conducting porosity, regardless of the width of pressure head ranges. The existing methods overestimated water-conducting macroporosity by a factor of greater than two and overestimated total water-conducting porosity by a factor of >10 for measurements taken at large pressure head intervals compared with that of our method. Combining with hydraulic parameter estimation from tension infiltrometer measurements, our method may reduce the number of tension infiltration measurements required to calculate water-conducting porosity. [ABSTRACT FROM AUTHOR]

Accurate measurement of local soil water flux, at the same location as solute concentration, remains a challenge, and the lack of these measurements limits our understanding of field solute transport. The objective of this paper is to develop and test methods for measuring local soil water flux under steady-state constant flux infiltration, typical of field solute transport experiments. The methods use vertically installed time domain reflectometry (TDR) probes to measure local solute mass flux from the change in TDR measured impedance after either a step increase or decrease (flushing) in electrolyte (tracer) concentration in the applied water. The local soil water flux was calculated directly from 1he local solute mass flux. The methods assume that water flow and solute transport are one-dimensional and that TDR estimates of bulk electrical conductivity are not sensitive to the vertical distribution of the applied tracer in the pore water. Field experiments indicate estimates of local soil water flux from step increase and step decrease of tracer concentration were very similar (R² ≥ 0.63) and average water flux was similar to the application rate. There was moderate correlation between local water fluxes measured using these steady-state methods, and a previously reported method based on early time measurements of water storage during the initial (transient) states of water infiltration. The methods can be used to estimate field average hydraulic conductivity as a function of soil water content. [ABSTRACT FROM AUTHOR]

Presents a quasi-analytical solution for water storage to a fixed depth for constant flux infiltration. Use of the solution for predicting water flow and chemical transport processes taking place in surface soils.

Matric potential is a measure of the combined capillary and adsorptive forces of soil particles and is imperative for determining both the direction and magnitude of water flow in unsaturated soils. Measuring soil matric potential in semiarid and arid environments is a challenge. Current methods cannot measure the entire range of values, from 0 to -1.5 MPa. The objective of this research was to develop a new sensor capable of measuring the relationship between the dielectric constant of a ceramic core and the range of field soil matric potential values. This instrument uses two Cu TDR rods wrapped around a dual-threaded Plexiglas core, which is inserted into a porous ceramic block salvaged from damaged Campbell Scientific 229 heat dissipation sensors. The relationship of the ceramic's measured dielectric constant to soil matric potential is determined through a laboratory calibration curve. Calibration of the probe involved the use of a tension table and pressure plate apparatus. The dielectric constant of the probe was measured at a range of points between 0 and -1.5 MPa, and a modified van Genuchten water retention model was used to fit the data (R2 = 0.84). The proposed instrument provided an effective method of measuring soil matric potentials from 0 to -1.5 MPa. [ABSTRACT FROM AUTHOR]