Estimation Of Soil Hydraulic Properties Research Articles

On the Information Content of Cosmic‐Ray Neutron Data in the Inverse Estimation of Soil Hydraulic Properties

Core Ideas Cosmic‐ray neutron data are used to inversely estimate soil hydraulic properties. The forward neutron operator COSMIC is coupled with the vadose zone model HYDRUS‐1D. Bayesian analyses confirm the information content of cosmic‐ray neutron data. Observations of soil moisture content from remote sensing platforms can be used in conjunction with hydrological models to inversely estimate soil hydraulic properties (SHPs). In recent years, cosmic‐ray neutron sensing (CRNS) has proven to be a reliable method for the estimation of area‐average soil moisture at field scales. However, its use in the inverse estimation of the effective SHPs is largely unexplored. Thus, the main objective of this study was to assess the information content of aboveground fast‐neutron counts to estimate SHPs using both a synthetic modeling study and actual experimental data from the Rollesbroich catchment in Germany. For this, the forward neutron operator COSMIC was externally coupled with the hydrological model HYDRUS‐1D. The coupled model was combined with the Affine Invariant Ensemble Sampler to calculate the posterior distributions of effective soil hydraulic parameters as well as the model‐predictive uncertainty for different synthetic and experimental scenarios. Measured water contents at different depths were used to assess estimated SHPs. The analysis of both synthetic and actual CRNS data from homogenous and heterogeneous soil profiles, respectively, led to confident estimations of the shape parameters α and n, while higher uncertainty was observed for the saturated hydraulic conductivity. Furthermore, results demonstrated that neutron data are less influenced by local sources of uncertainty compared with near‐surface point measurements. The simultaneous use of CRNS and water content data further reduced the overall uncertainty, opening up new perspectives for the combination of CRNS with other remote sensing techniques for the inverse estimation of the effective SHPs.

Infiltration from the Pedon to Global Grid Scales: An Overview and Outlook for Land Surface Modeling

Core Ideas Land surface models (LSMs) show a large variety in describing and upscaling infiltration. Soil structural effects on infiltration in LSMs are mostly neglected. New soil databases may help to parameterize infiltration processes in LSMs. Infiltration in soils is a key process that partitions precipitation at the land surface into surface runoff and water that enters the soil profile. We reviewed the basic principles of water infiltration in soils and we analyzed approaches commonly used in land surface models (LSMs) to quantify infiltration as well as its numerical implementation and sensitivity to model parameters. We reviewed methods to upscale infiltration from the point to the field, hillslope, and grid cell scales of LSMs. Despite the progress that has been made, upscaling of local‐scale infiltration processes to the grid scale used in LSMs is still far from being treated rigorously. We still lack a consistent theoretical framework to predict effective fluxes and parameters that control infiltration in LSMs. Our analysis shows that there is a large variety of approaches used to estimate soil hydraulic properties. Novel, highly resolved soil information at higher resolutions than the grid scale of LSMs may help in better quantifying subgrid variability of key infiltration parameters. Currently, only a few LSMs consider the impact of soil structure on soil hydraulic properties. Finally, we identified several processes not yet considered in LSMs that are known to strongly influence infiltration. Especially, the impact of soil structure on infiltration requires further research. To tackle these challenges and integrate current knowledge on soil processes affecting infiltration processes into LSMs, we advocate a stronger exchange and scientific interaction between the soil and the land surface modeling communities.

Functional Extremum Solution and Parameter Estimation for One‐Dimensional Vertical Infiltration using the Brooks–Corey Model

Core Ideas Least action and variational principles were used to simulate unsaturated vertical infiltration. A functional extremum explicit solution to Richards' equation was obtained. A new method describes nonlinear relationships between cumulative infiltration, infiltration rate and wetting‐front depth. The method could be used to accurately predict soil water content and estimate soil hydraulic parameters. A simple analytical solution of one‐dimensional vertical infiltration of soil water is of great importance for the estimation of soil hydraulic properties and for precision irrigation. We use a new approximate analytical method based on the principle of least action and the variational principle for simulating the vertical infiltration of water in unsaturated soil for a constant‐saturation upper boundary and for estimating infiltration parameters. The method proposes the functional extremum problem of one‐dimensional vertical infiltration of water in unsaturated soil using the Brooks–Corey model. A functional extremum solution (FES) to Richards' equation was obtained using the Euler–Lagrange equation and the integral mean‐value theorem. A modified functional extremum solution (MFES) was also proposed empirically to improve precision based on the FES of horizontal absorption and numerical data simulated by Hydrus‐1D. The relationships between a parameter derived by the value theorem of the integral (α), the air‐entry suction (hd), and the wetting front depth (zf) were analyzed by comparing MFES with the numerical distribution of soil water content. The MFES was used to describe the nonlinear relationship between cumulative infiltration and zf, with the relative error (Re) of < 4.1% and the coefficient of determination (R2) > 0.986. The MFES was also used to describe the nonlinear relationship between the infiltration rate (i) and the reciprocal of zf for soils with hd ≥10 cm, with Re < 6.0% and R2 > 0.8453. A simple method for estimating soil saturated hydraulic conductivity (Ks) was proposed from the nonlinear relationship between i and zf, and the sensitivities of parameters in the Brooks–Corey model were also analyzed.

Estimation of soil water retention curve in semi-arid areas using fractal dimension

The soil water retention curve (SWRC) is one of the important hydraulic functions in water flow modeling and solute transport in the porous medium. Direct measurement of SWRC is time consuming and expensive, therefore different models have been developed to describe it. In this study, a model based on fractal theory was derived to estimate water retention curve. The fractal dimension of SWRC (D SWRC ) for 130 soil samples (with a spread range of soil texture) were determined and tried to find out a simple relation between this parameter and easily available soil properties such as clay, silt and sand contents, lime percent and bulk density by applying multiple linear regression analysis. The measured D SWRC for 110 soil samples used for regression analysis and 20 soil samples was used for model validation. The regression analysis showed a linear relationship between D SWRC , with clay, silt contents and soil bulk density with the goodness of fit, R 2 = 0.909, but lime content did not show any significant effect on SWRC prediction improvement. Therefore, it can be concluded that estimating SWRC in calcareous soil using D SWRC obtained from soil easily measured properties will be a good, rapid and reliable alternative for reliable estimation of soil hydraulic properties of these areas. Keywords: Fractal model; lime percent; Regression analysis; Soil water retention curve

Approaches for Estimating Soil Water Retention Curves at Various Bulk Densities With the Extended Van Genuchten Model

AbstractSoil bulk density (ρb) variations influence soil hydraulic properties, such as the water retention curve (WRC), but they are usually ignored in soil water simulation models. We extend the van Genuchten WRC model parameters to account for ρb variations using a series of empirical expressions. WRC measurements made on eight soils with various ρb, and textures are used to calibrate these ρb‐related empirical equations. Accordingly, two approaches are developed to estimate WRCs of soils at various ρb. Another eight soils with a wide range of ρb and textures are used to evaluate the accuracy of the new approaches. Approach 1 estimates WRCs for each soil at various ρb using a WRC measurement made at a reference ρb and the soil texture fractions. This approach gives reasonable WRC estimates for the eight validation soils, with an average root‐mean‐square error (RMSE) of 0.025 m3/m3 and an average determination coefficient (R2) of 0.94. For Approach 2, a WRC measurement made at a reference ρb and one additional water content‐matric potential value measured at a different ρb value are used, which produces WRC estimates with an average RMSE of 0.017 m3/m3 and an average R2 of 0.97. The methodology used in Approach 2 is also applied to the Brooks and Corey WRC model to obtain accurate and precise WRC estimates. The proposed approaches have the potential to be incorporated into simulation models for estimating soil hydraulic properties that are affected by transient and variable ρb.

An efficient analytical model for horizontal infiltration in soils

An efficient method for nonlinear approximate solutions of the one-dimensional horizontal absorption equation is presented. The method pertains to a semi-infinite soil with uniform initial condition and constant soil water content at the beginning of a soil column. It does not make any prior assumptions about the form of the diffusivity function. The nonlinear solution is of simple combination of power and exponential functions and expresses in explicit form the distance as function of time and soil water content. It involves all the physical parameters and four additional fitting parameters which can be obtained by simple fitting procedure. Algebraic expressions that quantify the main infiltration process are derived from the basic equation. These include the sorptivity, the infiltration rate, the cumulative infiltration rate, and the wetting front. A comparison with exact, numerical and other analytical solutions validates the accuracy of the approximation and shows its advantage to other approximate solutions. The analytical expressions are used to predict experimental soil moisture profiles from simple measurements such as sorptivity, wetting front, and initial and final soil water contents. The method is applied to estimate soil hydraulic properties using horizontal water absorption experiments. The results of the parameter estimation procedure are compared to other existing results and show good agreements. The solution method is also tested to various diffusivity functions and its limitation is discussed in detail.

Spatial prediction of near surface soil water retention functions using hydrogeophysics and empirical orthogonal functions

The hydrological community often turns to widely available spatial datasets such as the NRCS Soil Survey Geographic database (SSURGO) to characterize the spatial variability of soil properties. When used to spatially characterize and parameterize watershed models, this has served as a reasonable first approximation when lacking localized or incomplete soil data. Within agriculture, soil data has been left relatively coarse when compared to numerous other data sources measured. This is because localized soil sampling is both expensive and time intense, thus a need exists in better connecting spatial datasets with ground observations. Given that hydrogeophysics is data-dense, rapid, non-invasive, and relatively easy to adopt, it is a promising technique to help dovetail localized soil sampling with spatially exhaustive datasets. In this work, we utilize two common near surface geophysical methods, cosmic-ray neutron probe and electromagnetic induction, to identify temporally stable spatial patterns of measured geophysical properties in three 65 ha agricultural fields in western Nebraska. This is achieved by repeat geophysical observations of the same study area across a range of wet to dry field conditions in order to evaluate with an empirical orthogonal function. Shallow cores were then extracted within each identified zone and water retention functions were generated in the laboratory. Using EOF patterns as a covariate, we quantify the predictive skill of estimating soil hydraulic properties in areas without measurement using a bootstrap validation analysis. Results indicate that sampling locations informed via repeat hydrogeophysical surveys, required only five cores to reduce the cross-validation root mean squared error by an average of 64% as compared to soil parameters predicted by a commonly used benchmark, SSURGO and ROSETTA. The reduction to five strategically located samples within the 65 ha fields reduces sampling efforts by up to ∼90% as compared to the common practice of soil grid sampling every 1 ha.

Inverse estimation of soil hydraulic properties and water repellency following artificially induced drought stress

Abstract Global climate change is projected to continue and result in prolonged and more intense droughts, which can increase soil water repellency (SWR). To be able to estimate the consequences of SWR on vadose zone hydrology, it is important to determine soil hydraulic properties (SHP). Sequential modeling using HYDRUS (2D/3D) was performed on an experimental field site with artificially imposed drought scenarios (moderately M and severely S stressed) and a control plot. First, inverse modeling was performed for SHP estimation based on water and ethanol infiltration experimental data, followed by model validation on one selected irrigation event. Finally, hillslope modeling was performed to assess water balance for 2014. Results suggest that prolonged dry periods can increase soil water repellency. Inverse modeling was successfully performed for infiltrating liquids, water and ethanol, with R2 and model efficiency (E) values both > 0.9. SHP derived from the ethanol measurements showed large differences in van Genuchten-Mualem (VGM) parameters for the M and S plots compared to water infiltration experiments. SWR resulted in large saturated hydraulic conductivity (Ks ) decrease on the M and S scenarios. After validation of SHP on water content measurements during a selected irrigation event, one year simulations (2014) showed that water repellency increases surface runoff in non-structured soils at hillslopes.

High‐Resolution Shortwave Infrared Imaging of Water Infiltration into Dry Soil

Core Ideas SWIR imaging for quantification of soil moisture dynamics is introduced. High spatiotemporal resolution images yield accurate soil moisture profile. Flux density profiles during water infiltration were quantified with this method. The new technique opens potential avenues for soil hydraulic properties estimation. A novel proximal sensing framework for high‐resolution soil water content profile retrieval under laboratory conditions has been developed. Constant‐head upward‐flow experiments were conducted for a number of soils that cover a wide textural range. The soils were packed into quartz Hele–Shaw cells, and the profile was imaged at high temporal frequency with a shortwave infrared (SWIR) camera in the 900‐ to 1700‐nm electromagnetic domain during upward infiltration of water. The SWIR reflectance recorded for each spatial pixel was converted to soil water content with a recently developed linear physical model. Because of the linearity of the model, its parameters were assumed to be identical at both the pixel and column scales, and this allowed simple self‐calibration during the experiment. The obtained water content profiles were in good agreement with soil water content data measured independently with a recently developed time domain reflectometry array with 1‐cm depth resolution. In addition, the accuracy of the soil water content profiles was verified based on the water mass balance. The high‐spatiotemporal‐resolution SWIR reflectance‐derived water content profiles allow calculation of water flux densities, which provides a potential new avenue for the rapid estimation of soil hydraulic properties and processes via inverse numerical or analytical modeling.

Estimating Soil Hydraulic Parameters by Inverse Modeling with PEST

Core Ideas Coupling SWAP with PEST, soil moisture and ET are included in inverse modeling of soil water flow. Multi‐objective optimization could improve model predictions and reduce parameter uncertainty. Observation errors and frequency and spatial arrangement impact predictions and uncertainty. The accurate estimation of soil hydraulic properties is an important part of the application of hydrological models for quantifying water transport in the vadose zone. Various inverse models have been developed to solve hydraulic properties optimization problems, and the accuracy of the results obtained by different algorithms is still debated because of the inherent ill‐posedness of such problems. In this study, we coupled an agrohydrological Soil–Water–Atmosphere–Plant (SWAP) model with an independent parameter estimation program (PEST) to calibrate the soil hydraulic parameters and investigate the uncertainty in the parameter estimations. The objectives of this study were to assess to what extent the SWAP model can be calibrated from a single observation (only soil moisture, θ) and further to investigate whether involving additional evapotranspiration fluxes including actual evapotranspiration (ETa), actual evaporation (Ea), or actual transpiration (Ta) can lead to an improvement in model predictions and a reduction in parameter uncertainty. Extensive synthetic experiments were conducted to achieve the objectives. Both double (winter wheat [Triticum aestivum L.]–summer maize [Zea mays L.]) and single (only winter wheat or only summer maize) cropping systems were considered in the multi‐objective optimizations. The results indicate that although the addition of evapotranspiration fluxes does not necessarily improve the accuracy of the soil moisture prediction, it can reduce the parameter uncertainty for both single‐ and double‐cropping systems. Moreover, the parameter estimation of the topsoil layer could greatly benefit from the addition of Ea, whereas the addition of Ta could help reduce the parameter uncertainty of the lower layers.

Analytical Method to Determine Soil Hydraulic Properties from Vertical Infiltration Experiments

Core Ideas A new method was proposed to determine Brooks‐Corey (BC) model parameters. Only a vertical infiltration experiment and simple measurements were required. The new method has great potentials to be applied in situ on various scales. Soil hydraulic properties are basic inputs for simulating water and solute transport in the vadose zone. Many methods have been developed to directly measure or indirectly estimate soil hydraulic properties. However, low‐cost, simple and robust methods are still scarce even in a laboratory. Here we present a new analytical method, based on an approximate solution to the one‐dimensional vertical infiltration problem that can be used to determine Brooks–Corey model parameters. Measurements include cumulative infiltration, wetting front advance with time, and initial and final upper boundary water contents, during a one‐dimensional vertical infiltration experiment. Infiltration experiments with twelve disturbed soils were performed to test the new method. The results indicated that the estimated saturated hydraulic conductivities from transient infiltration processes were consistent with those measured with a constant‐head method. The estimated soil water retention curves (SWRCs) by the analytical method for the twelve soils were in agreement with those measured by a direct method in which soil water potentials were measured through tensiometers. The root mean square error (RMSE) of the estimated soil water contents by the new method were less than 0.045 cm3 cm–3 for the twelve soils. Compared to most direct methods, the new method is inexpensive, rapid and easy to use, and thus can be a substitute for direct methods in a laboratory. Although how to monitor representative wetting front remains unsolved, the new method has a potential for use in the future in determining in situ soil hydraulic properties where lateral flows are negligible.

Evaluation of Different Predictor Models for Detailed Soil Particle-Size Distribution

An accurate mathematical representation of soil particle-size distribution (PSD) is required to estimate soil hydraulic properties or to compare texture measurements using different classification systems. However, many databases do not contain full PSD data, but instead contain only the clay, silt, and sand mass fractions. The objective of this study was to evaluate the abilities of four PSD models (the Skaggs model, the Fooladmand model, the modified Gray model GM (1,1), and the Fredlund model) to predict detailed PSD using limited soil textural data and to determine the effects of soil texture on the performance of the individual PSD model. The mean absolute error (MAE) and root mean square error (RMSE) were used to measure the goodness-of-fit of the models, and the Akaike's information criterion (AIC) was used to compare the quality of model fits. The performance of all PSD models except the GM (1,1) improved with increasing clay content in soils. This result showed that the GM (1,1) was less dependent on soil texture. The Fredlund model was the best for describing the PSDs of all soil textures except in the sand textural class. However, the GM (1,1) showed better performance as the sand content increased. These results indicated that the Fredlund model showed the best performance and the least values of all evaluation criteria, and can be used using limited soil textural data for detailed PSD.

Upward infiltration–evaporation method to estimate soil hydraulic properties

ABSTRACTDetermination of saturated hydraulic conductivity, Ks, and the van Genuchten water retention curve θ(h) parameters is crucial in evaluating unsaturated soil water flow. The aim of this work is to present a method to estimate Ks, α and n from numerical analysis of an upward infiltration process at saturation (Cap0), with (Cap0 + h) and without (Cap0) an overpressure step (h) at the end of the wetting phase, followed by an evaporation process (Evap). The HYDRUS model as well as a brute-force search method were used for theoretical loam soil parameter estimation. The uniqueness and the accuracy of solutions from the response surfaces, Ks–n, α–n and Ks–α, were evaluated for different scenarios. Numerical experiments showed that only the Cap0 + Evap and Cap0 + h + Evap scenarios were univocally able to estimate the hydraulic properties. The method gave reliable results in sand, loam and clay-loam soils.

Comparison of Mathematical Models for the layout of Granulometric Curves of Brazilian Soils

The particle size distribution of solid particles (PSD) is critical in determining the potential for compaction, the availability and the redistribution of water in the soil, especially in the areas of building material and soil mechanics, among others. However, many banks of soil data does not contain detailed PSD data, but only some fractions. A mathematical accurate representation of the PSD is required to estimate soil hydraulic properties and texture comparing measurements of different classification systems. The objective of this study was to compare the performance of 12 models, with 2 and 3 tuning parameters proposed in the literature to represent the PSD, and, predict the water retention curve in the soil, from a wide range of Brazilian soils textures. The statistical parameters (NDEs, RD, MS CRM) showed three models tuning parameters proposed by Lima & Silva, Weibull and Fredlund got the best performance, with lower NDE, RD, MS very close to one and CRM values very low. The models Lima & Silva, Weibul, Fredlund and Skaggs, with three tuning parameters, and the models Skaggs end Lima & Silva, with two parameters, proved to be suitable for estimate the water retention curve in the soil, for soils with coarse and fine texture. Keywords: Granulometry; Soil texture; Curve Adjustments.

Combining global sensitivity analysis and multiobjective optimisation to estimate soil hydraulic properties and representations of various sole and mixed crops for the agro-hydrological SWAP model

Sensitivity analysis and multiobjective optimisation are established diagnostic instruments for the identification of uncertainty factors and structural deficits in environmental simulation models. Although the application of both techniques provides a comprehensive understanding of model behaviour, they are seldom practised in combination. In this study, the Sobol global sensitivity method and the multiobjective algorithm AMALGAM are combined to assess the agro-hydrological SWAP model for simulating the soil water balance of different sole and mixed crops based on hydrological and phenological field observations. Fifteen unknown model parameters are subjected to the sensitivity analysis (GSA) with the aim of finding their importance to model performance of matric potential (F 1) and soil water content (F 2). Subsequently, sensitive parameters are calibrated by optimising F 1 and F 2 simultaneously. The GSA showed that the description of the rooting density and potential evapotranspiration is of crucial important to F 1, and that soil properties are most relevant for F 2. Parameter interactions played a primary role in the response of matric potential, being irrelevant for F 2. Structural model deficiencies in reproducing both objectives simultaneously were found in the multiobjective analysis, meaning that deterioration in the fit to one of the objectives is in favour of the other. However, solutions exist that produce satisfying fits to both observational types, suggesting that the SWAP model has the capability of simulating the soil water balance of the crops considered. The results of the evaluation period revealed model deficiencies in simulating the process under an environmental regime significantly different from that of the calibration period, indicating the necessity of acquiring a broader spectrum of environmental regimes for parameter calibration. Overall, this study demonstrates how complex and variable the relationship between parameters and model outputs can be in environmental models and highlights the value of combining global sensitivity analysis and multiobjective optimisation in order to improve model performances.

Derivation and Validation of a New Soil Pore‐Structure‐Dependent Flux–Saturation Relationship

Core Ideas A soil pore‐structure‐dependent soil water flux–saturation relationship F(Θ) was derived. This F(Θ) depends on the shape coefficient of soil water retention curve and initial saturation. This F(Θ) is simple and accurate for constant‐saturation infiltration and absorption. Recently, a method based on the soil water flux (F)–saturation (Θ) relationship, F(Θ), assumed to be independent of time and soil properties, has become an important technique to simplify solving Richards' equation so as to develop a simple, rapid, and low‐cost approach to estimate soil hydraulic properties. However, the actual F(Θ) is soil pore‐structure dependent. So far, there exists no theory that can provide a specific functional form of soil pore‐structure‐dependent F(Θ). In this research, we derived a general soil moisture profile function and then a general expression of F(Θ): F = Θ[1 + a2(1 −)]. The only parameter in F(Θ), a2, is a function of the initial soil water saturation and the soil pore structure index that reflects the shape of the soil water retention curve. Infiltration experiments with three test soils and four actual soils were conducted to test the proposed relationships. The results verified the independence of F(Θ) on time at small time scales. The results also indicated that the derived formula predicted F(Θ) in good agreement with what was measured for all test soils under different initial soil moisture conditions. In addition, the upper and lower limit curves of F(Θ) calculated by the proposed formula were consistent with the theoretical curves. Compared with other empirical relationships, the new formula was the best for describing the theoretical upper limit curve of F(Θ). Furthermore, the new theoretical relationship was also found to be appropriate for horizontal absorption. Generally, the developed relationship is more accurate and helpful to solve Richards' equation.

A Low-Cost Automated Test Column to Estimate Soil Hydraulic Characteristics in Unsaturated Porous Media

The estimation of soil hydraulic properties in the vadose zone has some issues, such as accuracy, acquisition time, and cost. In this study, an inexpensive automated test column (ATC) was developed to characterize water flow in a homogeneous unsaturated porous medium by the simultaneous estimation of three hydraulic state variables: water content, matric potential, and water flow rates. The ATC includes five electrical resistance probes, two minitensiometers, and a drop counter, which were tested with infiltration tests using the Hydrus-1D model. The results show that calibrations of electrical resistance probes reasonably match with similar studies, and the maximum error of calibration of the tensiometers was 4.6% with respect to the full range. Data measured by the drop counter installed in the ATC exhibited a high consistency with the electrical resistance probes, which provides an independent verification of the model and indicates an evaluation of the water mass balance. The study results show good performance of the model against the infiltration tests, which suggests a robustness of the methodology developed in this study. An extension to the applicability of this system could be successfully used in low-budget projects in large-scale field experiments, which may be correlated with resistivity changes.

Estimation of available water capacity components of two-layered soils using crop model inversion: Effect of crop type and water regime

Characterization of the soil water reservoir is critical for understanding the interactions between crops and their environment and the impacts of land use and environmental changes on the hydrology of agricultural catchments especially in tropical context. Recent studies have shown that inversion of crop models is a powerful tool for retrieving information on root zone properties. Increasing availability of remotely sensed soil and vegetation observations makes it well suited for large scale applications. The potential of this methodology has however never been properly evaluated on extensive experimental datasets and previous studies suggested that the quality of estimation of soil hydraulic properties may vary depending on agro-environmental situations. The objective of this study was to evaluate this approach on an extensive field experiment. The dataset covered four crops (sunflower, sorghum, turmeric, maize) grown on different soils and several years in South India. The components of AWC (available water capacity) namely soil water content at field capacity and wilting point, and soil depth of two-layered soils were estimated by inversion of the crop model STICS with the GLUE (generalized likelihood uncertainty estimation) approach using observations of surface soil moisture (SSM; typically from 0 to 10cm deep) and leaf area index (LAI), which are attainable from radar remote sensing in tropical regions with frequent cloudy conditions. The results showed that the quality of parameter estimation largely depends on the hydric regime and its interaction with crop type. A mean relative absolute error of 5% for field capacity of surface layer, 10% for field capacity of root zone, 15% for wilting point of surface layer and root zone, and 20% for soil depth can be obtained in favorable conditions. A few observations of SSM (during wet and dry soil moisture periods) and LAI (within water stress periods) were sufficient to significantly improve the estimation of AWC components. These results show the potential of crop model inversion for estimating the AWC components of two-layered soils and may guide the sampling of representative years and fields to use this technique for mapping soil properties that are relevant for distributed hydrological modelling.

Pedotransfer Functions for Estimating Soil Hydraulic Properties: A Review

Characterization of soil hydraulic properties is important to environment management; however, it is well recognized that it is laborious, time-consuming and expensive to directly measure soil hydraulic properties. This paper reviews the development of pedotransfer functions (PTFs) used as an alternative tool to estimate soil hydraulic properties during the last two decades. Modern soil survey techniques like satellite imagery/remote sensing has been used in developing PTFs. Compared to mechanistic approaches, empirical relationships between physical properties and hydraulic properties have received wide preference for predicting soil hydraulic properties. Many PTFs based on different parametric functions can be found in the literature. A number of researchers have pursued a universal function that can describe water retention characteristics of all types of soils, but no single function can be termed generic though van Genuchten (VG) function has been the most widely adopted. Most of the reported parametric PTFs focus on estimation of VG parameters to obtain water retention curve (WRC). A number of physical, morphological and chemical properties have been used as predictor variables in PTFs. Conventionally, regression algorithms/techniques (statistical/neural regression) have been used for calibrating PTFs. However, there are reports of utilizing data mining techniques, e.g., pattern recognition and genetic algorithm. It is inferred that it is critical to refine the data used for calibration to improve the accuracy and reliability of the PTFs. Many statistical indices, including root mean square error (RMSE), index of agreement (d), maximum absolute error (ME), mean absolute error (MAE), coefficient of determination (r2) and correlation coefficient (r), have been used by different researchers to evaluate and validate PTFs. It is argued that being location specific, research interest in PTFs will continue till generic PTFs are developed and validated. In future studies, improved methods will be required to extract information from the existing database.

Estimating soil moisture and soil thermal and hydraulic properties by assimilating soil temperatures using a particle batch smoother

This study investigates the potential of estimating the soil moisture profile and the soil thermal and hydraulic properties by assimilating soil temperature at shallow depths using a particle batch smoother (PBS) using synthetic tests. Soil hydraulic properties influence the redistribution of soil moisture within the soil profile. Soil moisture, in turn, influences the soil thermal properties and surface energy balance through evaporation, and hence the soil heat transfer. Synthetic experiments were used to test the hypothesis that assimilating soil temperature observations could lead to improved estimates of soil hydraulic properties. We also compared different data assimilation strategies to investigate the added value of jointly estimating soil thermal and hydraulic properties in soil moisture profile estimation. Results show that both soil thermal and hydraulic properties can be estimated using shallow soil temperatures. Jointly updating soil hydraulic properties and soil states yields robust and accurate soil moisture estimates. Further improvement is observed when soil thermal properties were also estimated together with the soil hydraulic properties and soil states. Finally, we show that the inclusion of a tuning factor to prevent rapid fluctuations of parameter estimation, yields improved soil moisture, temperature, and thermal and hydraulic properties.