Van Genuchten Mualem Parameters Research Articles

Stochastic analysis of plant available water estimates and soil water balance components simulated by a hydrological model

AbstractThe uncertainty in soil hydraulic parameters is often not taken into account in process‐based hydrological modeling. Performing runs with 104 stochastic parameter realizations, we evaluated the propagation of uncertainty in the Van Genuchten–Mualem (VGM) parameters into estimates of the threshold values of soil water content used to calculate the total and readily available water, and on the long‐term (30 years) simulations of evaporation, transpiration, bottom flux, and runoff by the SWAP hydrological model. The simulated scenarios included weather data from a location in southeast Brazil and seven soils from the same region cropped with maize, comprising a wide range of texture classes. The results showed that uncertainties in VGM parameters affect the estimates of total and readily available water. Water balance components obtained by a deterministic simulation with average VGM parameters did not always agree with the average or median of stochastic simulations, and stochastic simulations including parameter uncertainties should be preferred. Variations in yearly rainfall characteristics were more important for bottom flux and evaporation, while transpiration and runoff were more strongly influenced by the variations in soil hydraulic properties.

Reduce uncertainty in soil hydrological modeling: A comparison of soil hydraulic parameters generated by random sampling and pedotransfer function

Numerical simulation of unsaturated soil hydrology relies on calibrated soil hydraulic parameters, which are subject to uncertainty due to imperfect information during the inverse modelling. This study investigates the effectiveness of reducing parameter uncertainty using the recently developed Rosetta 3 pedotransfer function. The GLUE method was employed for numerical modeling using the Darcy-Richards equation under two strategies for sampling Mualem-van Genuchten (MvG) parameters: the first uses conventional random generation of MvG parameters (GLUE-random), while the second adopts Rosetta 3 to transfer soil particle composition to MvG parameter (GLUE-Rosetta). Both approaches were used for inverse modeling of 9 typical soils, each with a recommended parameter set defined as true values and associated soil moisture dynamics as observations. The posterior parameters selected with both GLUE-random and GLUE-Rosetta show an equifinality phenomenon. GLUE-random fails to provide well-constrained posterior parameters to recover the pre-defined true values, and its posterior results of soil water characteristic curve (SWCC) and soil hydraulic conductivity function (HCF) are poorly constrained. In contrast, GLUE-Rosetta significantly improves the accuracy of the inversely-estimated soil hydraulic parameters, and the ensemble of posterior SWCC and HCF also encompasses the predefined true curves. The results demonstrate the effectiveness of using Rosetta 3 to reduce the dimensionality of the optimization problem, which results in reliable estimation of soil hydraulic parameters and soil particle compositions. Moreover, GLUE-Rosetta outperforms GLUE-random in predicting soil moisture dynamics under different rainfall intensities. Overall, it is recommended to integrate Rosetta 3 with existing optimization tools to reduce the uncertainty of soil parameters and support more reliable modeling of unsaturated soil hydrology.

Estimation of hydraulic parameters in a heterogeneous low‐lying farmland near Venice

AbstractEstimating the hydraulic properties of the vadose zone is essential to understand soil‐water dynamics and achieving appropriate water management in agricultural lands. Inverse modelling methods are commonly used to estimate hydraulic properties from field observations. Unlike the extensively applied local search methodologies, data assimilation techniques can fully account for multiple uncertainties and are becoming a widely used tool for estimating hydraulic parameters. However, only few applications on real field tests are available. The main objective of this study was to estimate the van Genuchten‐Mualem (VGM) parameters and the saturated hydraulic conductivity () of a heterogeneous low‐lying farmland at the margin of the Venice Lagoon, Italy, characterized by high peat content, sandy drifts, and a very shallow water table. To this end, two methods were tested, that is, the Ensemble Smoother (ES) and the Levenberg–Marquardt (LM) algorithm associated with hydrological modelling performed with Hydrus‐1D. Volumetric water content (VWC) observations were collected at three monitoring sites from May to September 2011. Results on parameters highlighted that the ES technique effectively reduced the uncertainty of and , but it was less effective on and . The results on VWC showed that the ES efficiency decreased with the increasing non‐linearity of the system (e.g., higher sand content) and when the variability of the experimental data was lower (e.g., deepest soil layers where saturation remained permanently close to 1). Both LM and ES allowed to reproduce the VWC observations in the calibration and validation phases, with the former and the latter performing better in the case of sandy and peat soils, respectively. As concerns the method applicability, the ES was less time‐demanding as it efficiently updated all the parameters at once and was less dependent on the user choices. Finally, the paper points out the importance of previous knowledge of the VGM parameters (e.g., from lab hydraulic analyses) in defining the constraints for the optimization.

Stochastic Merging of Soil Hydraulic Properties for Vadose Zone Hydrological Modeling

Soil hydraulic properties (SHPs) are commonly determined in soil samples with replicas. Whether these replicas are taken at a same location to represent a specific point or at several locations to represent a larger area, the results should be merged into a final data set to be used in modeling. For this data set to be representative, standard errors and a correlation matrix must be considered in the merging process. We present a method to perform this merging and give an example using stochastic realizations of van Genuchten-Mualem (VGM) parameters generated by Cholesky decomposition to merge the SHP and associated statistics into a merged parameter set. To do so, we used VGM parameters obtained at sample scale in three replicas from a Brazilian savanna soil through inverse modeling of laboratory evaporation experiments. The effectiveness and representativeness of the proposed methodology were evaluated by observing the frequency distribution of different levels of output, comparing individual and merged sample properties. The outputs include VGM parameters, retention and conductivity characteristics, and water balance components stochastically predicted by a hydrological model. The performed stochastic merging correctly represented the variability of the combined replicas, especially with respect to hydrological model outputs of soil water balance components. Using the mean hydraulic property parameter values to deterministically predict water balance components may yield values that are substantially different from the mean values of stochastic realizations. This suggests that the deterministic prediction using mean parameter values in vadose zone hydrological modeling may result in unrepresentative outputs.

In situ estimation of soil hydraulic and hydrodispersive properties by inversion of electromagnetic induction measurements and soil hydrological modeling

Abstract. Soil hydraulic and hydrodispersive properties are necessary for modeling water and solute fluxes in agricultural and environmental systems. Despite the major efforts in developing methods (e.g., laboratory-based, pedotransfer functions), their characterization at applicative scales remains an imperative requirement. Accordingly, this paper proposes a noninvasive in situ method integrating electromagnetic induction (EMI) and hydrological modeling to estimate soil hydraulic and transport properties at the plot scale. To this end, we carried out two sequential water infiltration and solute transport experiments and conducted time-lapse EMI surveys using a CMD Mini-Explorer to examine how well this methodology can be used to (i) monitor water content dynamic after irrigation and to estimate the soil hydraulic van Genuchten–Mualem parameters from the water infiltration experiment as well as (ii) to monitor solute concentration and to estimate solute dispersivity from the solute transport experiment. We then compared the results with those estimated by direct time domain reflectometry (TDR) and tensiometer probe measurements. The EMI significantly underestimated the water content distribution observed by TDR, but the water content evolved similarly over time. This introduced two main effects on soil hydraulic properties obtained by the two methods: (i) similar water retention curve shapes, but underestimated saturated water content from the EMI method, resulting in a scaled water retention curve when compared with the TDR method; the EMI-based water retention curve can be scaled by measuring the actual saturated water content at the end of the experiment with TDR probes or by weighing soil samples; (ii) almost overlapping hydraulic conductivity curves, as expected when considering that the shape of the hydraulic conductivity curve primarily reflects changes in water content over time. Nevertheless, EMI-based estimations of soil hydraulic properties and transport properties were found to be fairly accurate in comparison with those obtained from direct TDR measurements and tensiometer probe measurements.

Analyzing dual porosity in soil hydraulic properties using soil databases for pedotransfer function development

AbstractCurrent databases of soil hydraulic properties (SHPs) have typically been used to develop pedotransfer functions (PTFs) to estimate water retention [θ(h)] assuming a unimodal pore‐size distribution. However, natural soils often show the presence of bimodal to multimodal pore‐size distributions. Here, we used three widely spread databases for PTF development: UNsaturated SOil hydraulic DAtabase (UNSODA) 2.0, Vereecken, and European hydropedological data inventory (EU‐HYDI), to analyze the presence of structural effects in both θ(h) and hydraulic conductivity [K(h)]. Only undisturbed samples were included in the analysis that contained enough datapoints for both θ(h) and K(h) properties, especially in the wet range. One‐hundred ninety‐two samples were suitable for our analysis, which is only 1% of the total samples in the three databases. Results showed that 65% of the samples exhibited a bimodal pore‐size distribution, and bimodality was not limited to fine‐textured but also coarser‐textured soils. The Mualem–van Genuchten (MvG) expression for both unimodal and bimodal soils was not able to predict the observed unsaturated K. Only a joint fitting of measured θ(h) and K(h) functions provided parameter estimates that were able to describe unsaturated K for uni‐ and bimodal soils. In addition, we observed a negative relationship between α and n in the case of low sand content (<52%) for both unimodal and bimodal matrix domain properties, contradicting the classical notion. The ratio of α for the macropore and matrix domain was positively correlated with the fraction of macropores and sand content. We anticipate that the results will contribute to deriving PTF for structured soils and avoid unrealistic combinations of MvG parameters.

Modeling seasonal soil moisture dynamics in gley soils in relation to groundwater table oscillations in eastern Croatia

This study aims to explain complex vadose zone hydrology of fine-textured (gley) agricultural soils influenced by a shallow and dynamic groundwater (GW) levels. The field site was located in the Biđ field (Eastern Croatia), where a detailed soil survey was performed. The simulations included a three-year period (2016–2018) at four locations. Soil hydraulic parameters (SHP) were estimated based on variables determined in the laboratory, while soil water flow was monitored using in-field zero-tension lysimeters. Piezometers were installed and used to monitor daily oscillations of groundwater levels (average depth to GW 2.2 m), while data from nearby Sava River was monitored. Unsaturated flow and water regime assessment was performed using HYDRUS-1D numerical modeling. Additional SHP optimization of van Genuchten-Mualem parameters (α and n) was performed using Shuffled Complex Evolution algorithm (SCE). The autocorrelation analysis was used to detect patterns in the precipitation, GW, and river level time series, while the Mutual Information (MI) was used to estimate the codependence of the processes in unsaturated zone and the main hydrological events. The model successfully (R2 0.72 – 0.94) reproduced measured lysimeters outflows. The outflows from lysimeters were connected to precipitation patterns, transpiration intensity, and soil moisture content influenced by the shallow water table. Comparable MI values obtained for precipitation, GW, and river level suggest a concurrent role of these parameters in the unsaturated flow dynamics. The relationship between upward flux/water storage change into the domain, and transpiration/growth stages, suggests a strong connection between the water fluxes and the root water uptake. Results confirm the importance of GW for the agricultural production due to the major influence on upper soil layer moisture.

Use of water retention data and soil physical quality index S to quantify hard-setting and degree of soil compactness indices of gypsiferous soils

Hard-setting gypsiferous soils become hard and compact during drying without the impact of soil compaction caused by agricultural field traffic. Thus, these soils are difficult or impossible to cultivate unless they are re-wetted. Moreover, it has previously been shown that Dexter soil physical quality index (S-index) negatively affected by compaction. This study was done to quantify the hard-setting as well as the soil compactness (expressed in terms of the degree of compactness, DC, which is relative density expressed as the ratio of bulk density to a reference density) using water retention data and soil physical quality index of fifteen soils with a wide range of gypsum content (30−301 g kg−1). Repacked soil cores were prepared with less disturbance of soil aggregates ≤ 4 mm). Water retention curves of the soils were measured in the matric suction range 10−15000 hPa. A single-porosity van Genuchten-Mualem model was fitted to water retention data. S-index and Dexter hard-setting (HDexter) were calculated using the van Genuchten-Mualem parameters. The relationships between S-index and both DC and HDexter were investigated for the 15 gypsiferous soils. A strong positive polynomial and power correlation were found between DC and HDexter and S (i.e. 1/S) respectively, and that are valid across different soil gypsum content. Significant negative relationships between HDexter and soil gypsum content were observed. Positive polynomial relations were derived between S and bulk density and polynomial relation was obtained between HDexter and bulk density. Positive relationships were observed between HDexter, DC, and relative bulk density. These findings confirmed that the new proposed HDexter index enables the prediction of soil hard-setting behavior to quantify the gypsiferous soil physical quality using only water retention data with no need to measure soil mechanical strength.

Pedotransfer Function for the Brunswick Soil Hydraulic Property Model and Comparison to the van Genuchten‐Mualem Model

AbstractModeling soil hydraulic properties requires an effective representation of capillary and noncapillary storage and conductivity. This is made possible by using physically comprehensive yet flexible soil hydraulic property models. Such a model (Brunswick [BW] model) was introduced by Weber et al. (2019, https://doi.org/10.1029/2018WR024584), and it overcomes some core deficiencies present in the widely used van Genuchten‐Mualem (VGM) model. We first compared the performance of the BW model to that of the VGM model in its ability to describe water retention and hydraulic conductivity data on a set of measurements of 402 soil samples with textures covering the entire range of classes. Second, we developed a simple transfer function to predict BW parameters based on VGM parameters. Combined with our new function, any existing pedotransfer function for the prediction of the VGM parameters can be extended to predict BW model parameters. Based on information criteria, the smaller variance of the residuals, and a 40% reduction in mean absolute error in the hydraulic conductivity over all samples, the BW model clearly outperforms VGM. This is possible as the BW model explicitly accounts for hydraulic properties of dry soils. With the new pedotransfer function developed in this study, better descriptions of water retention and hydraulic conductivities are possible. We are convinced that this will strengthen the utility of the new model and enable improved field‐scale simulations, climate change impact assessments on water, energy and nutrient fluxes, as well as crop productivity in agroecosystems by soil‐crop and land‐surface modeling. The models and the pedotransfer function are included in an R package spsh (https://cran.r‐project.org/package=spsh).

Effect of Tillage Practices and Irrigation Schedule on Soil Quality and Yield of Some Rice (Oryza sativa L.) in Sokoto Rima Flood Plain

Sokoto Rima Flood Plain (SRFP) is located in Sudan Savanna agro-ecology of Nigeria, and is faced with the common challenge of flooding towards the end of the rainy season and widespread drought with high potential for irrigated rice production during dry season. Two year experiment was conducted in the dry season of 2018 and 2019 in a farmer`s field, near the Usmanu Danfodiyo University Teaching and Research Farm, Kwalkwalawa in SRFP, Sokoto State. The treatments consisted of factorial combination of two tillage practices; conventional and reduced tillage, three irrigation schedules; one, two and three days schedule of irrigation and three rice varieties (FARO 44, 60 and 61). Treatments were laid in a split plot design replicated three times, where tillage practices and irrigation schedules were allocated to the main plots while rice varieties to the sub-plots. Data on particle size distribution, bulk density and water retention were simulated by RETC model for Mualem-van Genuchten parameters and for estimating the soil physical quality index (S) and the grain yield (kgh-1) of rice varieties were extrapolated from the net plots. Statistical analysis software was used to analyze all data generated, where significant means were compared using Duncan Multiple Range Test. The results revealed that RETC output for van Genutchen parameters demonstrated significant difference between the two tillage practices, irrigation schedule and depth on the value of curve fitting parameters (n). Soil physical quality index (S) increased with increase in soil depth, although not statistically different. From the finding, the S-index in the study area was in the range of 0.023-0.025, which by grouping was considered moderately suitable for optimal root growth. Also, FARO 44 performed better than other varieties in both 2018 (540.05 kgh-1) and 2019 (5835.85 kgh-1), respectively with one day irrigation schedule and convectional tillage practice. Forage legume incorporation during rainy season should be encouraged to improve the soil physical quality over time.

Parameter and State Estimation of One-Dimensional Infiltration Processes: A Simultaneous Approach

The Richards equation plays an important role in the study of agro-hydrological systems. It models the water movement in soil in the vadose zone, which is driven by capillary and gravitational forces. Its states (capillary potential) and parameters (hydraulic conductivity, saturated and residual soil moistures and van Genuchten-Mualem parameters) are essential for the accuracy of mathematical modeling, yet difficult to obtain experimentally. In this work, an estimation approach is developed to estimate the parameters and states of Richards equation simultaneously. In the proposed approach, parameter identifiability and sensitivity analysis are used to determine the most important parameters for estimation purpose. Three common estimation schemes (extended Kalman filter, ensemble Kalman filter and moving horizon estimation) are investigated. The estimation performance is compared and analyzed based on extensive simulations.

Simultaneous Parameter and State Estimation of Agro-Hydrological Systems

The Richards equation plays an important role in the study of agro-hydrological systems. It models the water movement in soil in the vadose zone, which is driven by capillary and gravitational forces. Its states (capillary potential) and parameters (hydraulic conductivity, saturated and residual soil moistures and van Genuchten-Mualem parameters) are essential for the accuracy of mathematical modeling, yet difficult to obtain experimentally. In this work, an estimation approach is developed to estimate the parameters and states of the Richards equation simultaneously. Parameter identifiability and sensitivity analysis are used to determine the most important parameters for estimation purpose. Three common estimation schemes (extended Kalman filter, ensemble Kalman filter and moving horizon estimation) are investigated. The estimation performance is compared and analyzed based on extensive simulations.

Comparison of soil hydraulic properties estimated from steady-state experiments and transient field observations through simulating soil moisture in regenerated Sphagnum moss

Growing Sphagnum moss for peatland restoration and fibre farming requires the proper moisture regime be maintained; thus, there is a desire to optimize growth by creating ideal hydrological conditions. However, it is uncertain which parameterization method is most suitable to describe field-scale processes and which soil water retention model and hydraulic conductivity curve (approach) is the most acceptable to use. Parameterizations of the van Genuchten – Mualem (VGM) equation were done using RETC, curve fitting to direct measurements of the water retention and unsaturated hydraulic conductivity curves; and Hydrus-1D, inverse modelling to field observations of soil moisture. The acceptability of each parameterization was tested by comparing soil moisture estimates based on forward simulations to observed soil moisture in two regenerated moss profiles, established in 1970 and 2006 cases, respectively. The transient field model simulated soil moisture well, and had an RMSE of 0.05 and 0.06 for 1970 and 2006, respectively. The most error occurred during the wettest and driest periods of the simulations. Simulated soil moisture was consistently drier than the observed soil moisture, in the steady-state laboratory simulation, and had markedly higher RMSE, 0.14 and 0.27 for the 1970 and 2006 profiles, respectively. The estimate of the VGM α parameter, an approximately the inverse of the air-entry pressure, fit to direct measurements of the retention and unsaturated hydraulic conductivity curves was an order of magnitude higher than that fit to field observation. The results of the simulation suggest that inverse modelling to field soil moisture should be used to estimate VGM parameters to more accurately represent field-scale soil moisture dynamics.

Prediction of soil hard-setting and physical quality using water retention data

Hard-setting soils become hard and compact during drying, and are therefore difficult or impossible to cultivate unless they are re-wetted. This study was done to quantify the hard-setting behavior on nine soils with a wide range of texture (from sandy loam to silty clay). Repacked soil cores were prepared with least disturbance of soil aggregates (05‒4 mm). Water retention curves of the soils were measured in the matric suction range 10–15,000 hPa in different bulk densities (1.20–1.70 Mg m−3). Single-porosity van Genuchten-Mualem and dual-porosity Dexter models were fitted to the water retention data. Dexter's indices of soil physical quality (S) and hard-setting (HDexter) were calculated using the van Genuchten-Mualem parameters. Soil physical quality indices S1 and S2 (slopes of water retention curve at first and second inflection points, related to matrix and structural pore spaces, respectively) and new hard-setting indices (H1 and H2 related to matrix and structural pore spaces, respectively) were also calculated from the parameters of dual-porosity model. Tensile strength (Y) of the soil samples prepared at different bulk densities was also measured from near-saturation to oven-dry. Slopes of relationships between Y and water content were used as soil strength-based hard-setting indices. A larger slope means that the soil is more susceptible to hard-setting. The HDexter, H1 and H2 increased with an increment in the bulk density, whereas the S, S1 and S2 decreased. Significant positive relationships between hard-setting indices (HDexter and H1) and calcium carbonate content were observed. Negative linear relations were derived between S, S1 and S2, and bulk density and positive linear relations were obtained between HDexter, H1 and H2, and bulk density. Soil hard-setting and physical quality indices were significantly correlated with relative bulk density. Positive relationships were observed between soil strength-based hard-setting indices and relative bulk density, and the correlations between these indices and H2 and S1 were significantly positive and negative, respectively. These findings confirmed that the new proposed indices enable prediction of soil hard-setting behavior using only water retention data with no need to measure soil mechanical strength.

Iterative filter based estimation of fully 3D heterogeneous fields of permeability and Mualem-van Genuchten parameters

The accurate modeling of flow and transport in the vadose zone for agricultural and environmental applications requires knowledge about soil parameters. Soil parameters vary in space depending on soil texture and structure. In the present synthetic study we considered spatial variation of permeability (k), inverse of capillary entry pressure head (αVG) and exponent (n) of the Mualem-van Genuchten model. The iterative Ensemble Kalman filter (IEnKF) can estimate the spatially variable soil parameters if measurements of water saturation at different locations and times are available. We used as input daily precipitation data from the Berambadi catchment (southern India). We first considered that the parameters vary horizontally but are constant in the vertical direction. In this case log (k) and log (αVG) can be estimated satisfactorily with 30%–40% reduction of RMSE (compared to open loop runs), if the initial guess of the spatial correlation lengths of the heterogeneous fields is equal to or larger than the unknown, true values. The estimation of exponent n is poorer as the reduction of RMSE is just 20%. If vertical heterogeneity of the parameters is considered the estimation of log (k) and log (αVG) is only improved for the upper 1.5 m and estimation of n is not improved. We also demonstrate that the estimation problem can be simplified when flow in the unsaturated zone is predominantly vertical. If in this case soil hydraulic parameters are estimated with IEnKF at measurement locations and afterwards interpolated with kriging, results are produced with a similar quality as with 3D-IEnKF.

Evaluating pedotransfer functions of the Splintex model

The soil water retention curve (SWRC) is an essential hydraulic function for the understanding and modelling of soil hydraulic processes. Its direct determination is time consuming and sometimes expensive because it requires extensive sampling, especially when spatial and temporal variation of soil hydraulic properties are an issue. The use of pedotransfer functions (PTFs) is a viable alternative to predict the parameters of SWRC from more easily determined soil attributes. Splintex is a physically based engineering model containing two PTFs to estimate SWRC parameters and saturated hydraulic conductivity, but its performance and functionality have not yet been fully evaluated and disseminated for soil science. We examined the functionality of the PTFs available in Splintex to estimate parameters of the Van Genuchten–Mualem (VGM) SWRC equation with a laboratory‐measured dataset containing information on particle size, bulk and particle density, saturated water content and parameters of the SWRCs. In addition, we tested the performance of both PTFs for deriving some soil physical–structural variables calculated from estimated SWRC parameters. Compared with VGM parameters fitted to the laboratory dataset, the model performed well. Its predictive performance for soil air capacity, relative field capacity and soil physical–structural quality indices was also evaluated. The performance of Splintex for estimating the VGM parameters depended on the combined effect of all input variables rather than on isolated correlations between an input variable and a model parameter.Highlights Splintex provides two PTFs to estimate the Van Genuchten–Mualem parameters of the SWRC. In addition to PTF1 input, PTF2 needs an additional soil water content–retention observation. Performance of Splintex‐PTF1 against measured data and ‐PTF2 prediction is presented. PTF2 provided better results than PTF1 for estimating SPQ indices.

Constraining Unsaturated Hydraulic Parameters Using the Latin Hypercube Sampling Method and Coupled Hydrogeophysical Approach

The coupled hydrogeophysical approach has proved to be a valuable tool for improving the use of geoelectrical data for hydrological model parameterization. In the coupled approach, hydrological parameters are directly inferred from geoelectrical measurements in a forward manner to eliminate the uncertainty connected to the independent inversion of electrical resistivity data. Several numerical studies have been conducted to demonstrate the advantages of a coupled approach; however, only a few attempts have been made to apply the coupled approach to actual field data. In this study, we developed a 1D coupled hydrogeophysical code to estimate the van Genuchten–Mualem model parameters, K s, n, θ r and α, from time-lapse vertical electrical sounding data collected during a constant inflow infiltration experiment. van Genuchten–Mualem parameters were sampled using the Latin hypercube sampling method to provide a full coverage of the range of each parameter from their distributions. By applying the coupled approach, vertical electrical sounding data were coupled to hydrological models inferred from van Genuchten–Mualem parameter samples to investigate the feasibility of constraining the hydrological model. The key approaches taken in the study are to (1) integrate electrical resistivity and hydrological data and avoiding data inversion, (2) estimate the total water mass recovery of electrical resistivity data and consider it in van Genuchten–Mualem parameters evaluation and (3) correct the influence of subsurface temperature fluctuations during the infiltration experiment on electrical resistivity data. The results of the study revealed that the coupled hydrogeophysical approach can improve the value of geophysical measurements in hydrological model parameterization. However, the approach cannot overcome the technical limitations of the geoelectrical method associated with resolution and of water mass recovery.

Analysis of the Hysteretic Hydraulic Properties of Unsaturated Soil

Core Ideas We devised a setup to measure hydraulic properties using the multistep flux method. Results showed good agreement with the data obtained with the hanging water column. However, they deviated slightly from independent data obtained with the HYPROP. Knowledge of the unsaturated soil hydraulic properties is essential for modeling water flow and solute transport processes in variably saturated subsurface systems. Various experimental setups have been developed over the years to measure the hydraulic properties. We devised a relatively simple setup for simultaneous measurement of the water retention and unsaturated hydraulic conductivity curves using multistep flux (MSF) and hanging water column (HWC) experiments. Our focus was especially on medium‐ and coarse‐textured sands having relatively narrow particle‐size distributions as reflected by the van Genuchten–Mualem (VGM) hydraulic parameter n. Values of the VGM parameter n for some of the sands we used were as high as 15. We also measured the hydraulic properties using the HYPROP evaporation method. The MSF and HWC results showed excellent agreement, but deviated slightly from independent data obtained with the HYPROP measurement system.

High resolution modelling of soil moisture patterns with TerrSysMP: A comparison with sensor network data

The prediction of the spatial and temporal variability of land surface states and fluxes with land surface models at high spatial resolution is still a challenge. This study compares simulation results using TerrSysMP including a 3D variably saturated groundwater flow model (ParFlow) coupled to the Community Land Model (CLM) of a 38ha managed grassland head-water catchment in the Eifel (Germany), with soil water content (SWC) measurements from a wireless sensor network, actual evapotranspiration recorded by lysimeters and eddy covariance stations and discharge observations. TerrSysMP was discretized with a 10×10m lateral resolution, variable vertical resolution (0.025–0.575m), and the following parameterization strategies of the subsurface soil hydraulic parameters: (i) completely homogeneous, (ii) homogeneous parameters for different soil horizons, (iii) different parameters for each soil unit and soil horizon and (iv) heterogeneous stochastic realizations. Hydraulic conductivity and Mualem-Van Genuchten parameters in these simulations were sampled from probability density functions, constructed from either (i) soil texture measurements and Rosetta pedotransfer functions (ROS), or (ii) estimated soil hydraulic parameters by 1D inverse modelling using shuffle complex evolution (SCE).The results indicate that the spatial variability of SWC at the scale of a small headwater catchment is dominated by topography and spatially heterogeneous soil hydraulic parameters. The spatial variability of the soil water content thereby increases as a function of heterogeneity of soil hydraulic parameters. For lower levels of complexity, spatial variability of the SWC was underrepresented in particular for the ROS-simulations. Whereas all model simulations were able to reproduce the seasonal evapotranspiration variability, the poor discharge simulations with high model bias are likely related to short-term ET dynamics and the lack of information about bedrock characteristics and an on-site drainage system in the uncalibrated model. In general, simulation performance was better for the SCE setups. The SCE-simulations had a higher inverse air entry parameter resulting in SWC dynamics in better correspondence with data than the ROS simulations during dry periods. This illustrates that small scale measurements of soil hydraulic parameters cannot be transferred to the larger scale and that interpolated 1D inverse parameter estimates result in an acceptable performance for the catchment.

Development of one-dimensional solutions for water infiltration. Analysis and parameters estimation

Summary The design of an irrigation system requires proper understanding of the infiltration process. Two original approximate solutions were developed in this study in order to determine one-dimensional vertical infiltration and profile water distribution. The Boltzmann transformation was used to solve the Richards equation. The accuracy of the solutions was verified by comparing results obtained with Hydrus-1D simulations. The solutions were also compared with the US Department of Agriculture-Natural Resources and Conservation Service (USDA-NRCS) intake families for infiltrated depths between 2 and 16 cm, using soils from Carsel and Parrish database. Simulations results of both solutions were in close agreement with Hydrus-1D. For all the cases studied, the corresponding root mean square error (RMSE) values were up to 0.56 cm, with coefficients of correlation (R2) larger than 0.99. Therefore, The comparison shows inexact matching, with acceptable correspondence, between simulated soils and NRCS curve numbers, The procedure developed to fit the USDA–NRCS intake families with the proposed solutions leads to characterize each family by the van Genuchten–Mualem parameters, and an initial soil water content. Furthermore, results suggests to consider initial water content in fine-textured soils. In the case of intake families, initial water content began to affect in Family 0.5 and became very decisive in the Family 0.05. Additionally, the proposed method, which are applicable to any descriptor of unsaturated hydraulic properties, have practical application, including accurate determination of the sorptivity of a soil at various water contents, and requires low computational effort.