Hydraulic Conductivity Model Research Articles

Enhancing aquifer protective capacity prediction over Ibeator and environ, Southeastern Nigeria using artificial neural networks and multivariate linear regression analysis

Aquifer protection is essential for securing a sustainable supply of clean water. This study integrates an artificial neural network (ANN) model, identifying non-linear connections, with multivariate linear regression (MLR) analysis to improve predictions of aquifer protective capacity and assess vulnerability. Twelve vertical electrical soundings (VES) were conducted with a maximum electrode spacing of 250 m. Aquifer parameters derived from the VES dataset were analyzed using ANN to capture complex patterns. The ANN model, trained on historical data, learned the relationship between input variables and protective capacity. MLR analysis identified influential factors affecting vulnerability. Results reveal varying aquifer depths, with Umudime being the deepest and western parts having the shallowest depths. The resistivity map shows high values around Okorobi and Uhuala and low values in eastern to northeastern parts. Hydraulic conductivity and 3D subsurface models exhibit an inverse relationship with resistivity. Transmissivity and storativity maps exhibit similar patterns. MLR outperforms ANN in predicting resistivity, transmissivity, and storability, indicating high forecasting accuracy for aquifer protective capacity. Input parameters' contribution levels follow a specific order for different aquifer properties. R2 Value 0.0869, indicating a weak correlation between the predicted and actual values in ANN model while R2 Value 0.9775 in MLR model shows a strong correlation and much better performance than the ANN model. The results of the modeling suggest that both the ANN and MLR models have shown promising effectiveness and accuracy in predicting aquifer parameters, aiding decision-makers in implementing targeted protection measures, predicting aquifer parameters, providing insights for effective management strategies.

A generalized van Genuchten model for unsaturated soil hydraulic conductivity

AbstractThe hydrodynamics of variably saturated soils or porous media in general are described via nonlinear functions of water retention and hydraulic conductivity, which facilitate the simulation of various mass and energy transport processes (e.g., water, heat, contaminants, colloids) within the porous medium. We set out to derive improved functions for more accurate estimations of soil hydraulic functions to advance the simulation of porous medium hydrodynamics. A new model is proposed for estimating the unsaturated hydraulic conductivity (UHC) from a soil water retention (SWR) function that is parameterized via nonlinear regression of measured data. The function can be viewed as a generalized van Genuchten (1980) model (GVG). We tested the new SWR and UHC expressions for numerous data sets from literature that cover a wide range of soil textures. Our comparisons reveal more accurate estimations using the GVG model by comparison with the original van Genuchten model.

Three‐Dimensional Probabilistic Hydrofacies Modeling Using Machine Learning

AbstractCharacterizing the 3D distribution of hydraulic properties in glacial sediments is challenging due to fine‐scale heterogeneity and complexity. Borehole lithological data provide high vertical resolution but low horizontal resolution. Geophysical methods can fill gaps between boreholes, providing improved horizontal resolution but low vertical resolution. Machine learning can combine borehole and geophysical data to overcome these challenges. However, few studies have compared multiple machine learning methods for predicting hydrofacies in glacial aquifer systems. This study uses colocated airborne electromagnetic resistivity and borehole lithology data to train multiple machine learning models and predict the 3D distribution of hydrofacies in glacial deposits of eastern Nebraska, USA. Random Forest, Gradient Boosting Classifier, Extreme Gradient Boosting, Multilayer Perceptron, and Stacking Classifier were used to model 3D probabilistic distributions of hydrofacies (sand and clay) at a grid size of 200 m × 200 m × 3 m. Comparison of the predicted 3D hydrofacies models shows that the probability distributions and the contrasts between hydrofacies vary. The classification metrics show that the Stacking Classifier model performed better than other machine learning models in predicting hydrofacies. Multi‐Layer Perceptron and Stacking Classifier models show sharp vertical transitions between the low and high sand probability while other machine learning models show gradual transitions. K‐means clustering was used to translate the Stacking Classifier model into a 4‐class hydraulic conductivity model. This study shows that machine learning methods advance our understanding of glacial hydrogeology by improving the vertical and horizontal resolution of hydrofacies distribution and resolving aquifer‐aquifer and stream‐aquifer connections.

Prediction model of hydraulic conductivity for sedimentary residual soil mixed bentonite as compacted clay liner

Prediction model of hydraulic conductivity for sedimentary residual soil mixed bentonite as compacted clay liner

A percolation model of unsaturated hydraulic conductivity using three-parameter Weibull distribution

Accurate estimation of unsaturated hydraulic conductivity, K(Sw), is crucial for modeling water and solute transport in variably-saturated soils. In this study, a new model for the estimation of K(Sw) based on percolation theory and critical path analysis is presented, in which the pore-throat size distribution is described by the three-parameter Weibull probability density function. To evaluate the performance of the proposed K(Sw) model, two datasets including more than three hundred samples are analyzed. The first dataset includes 240 pore-network simulations, while the second dataset consists of 101 soil samples from the UNSODA database. For the pore-network simulations, for which both pore-throat size distributions and capillary pressure curves are available, we estimate K(Sw) from the capillary pressure curve with RMSLE = 0.54 slightly better than the estimations obtained from the pore-throat size distribution with RMSLE = 0.6. For the soil samples, since only the capillary pressure curve is measured, we estimate K(Sw) from the capillary pressure curve and evaluate three previously proposed approaches for determining the exponent, α, in Poiseuille's law. We find that RMSLE = 0.98 for α = 3, RMSLE = 0.87 for α = 2(4 − D) − (3 − D)/(2D − 3), and RMSLE = 1.06 for α = 4 − 0.74D, in which D is the fractal dimension characterizing the pore space. Comparing the results with previous studies in which the power-law probability density function was used to characterize capillary pressure curve and pore sizes shows that the proposed three-parameter Weibull distribution, in conjunction with the CPA analysis, may more accurately estimate K(Sw) for a wide variety of soils ranging from very fine to very coarse textures.

Groundwater pollution vulnerability assessment using a modified DRASTIC model in Ho Chi Minh City, Vietnam

AbstractThe Pleistocene aquifer serves as a vital water source for various activities in Ho Chi Minh City, Vietnam, including concentrated and individual exploitation. In the present study, the DRASTIC (D, depth of water; R, net recharge; A, aquifer media; S, soil media; T, topography; I, impact of vadose zone; C, hydraulic conductivity) model was used to evaluate the groundwater sensitivity of the study area. To analyse Ho Chi Minh City's Upper‐Middle Pleistocene aquifer vulnerability, the analytic hierarchy process (AHP) method was used to optimize the DRASTIC score and include land use (LU) characteristics. Four distinct weights were used: DRASTIC, modified DRASTIC‐LU, AHP‐DRASTIC and modified AHP‐DRASTIC‐LU. This study identified low, moderate and high vulnerability for 12%, 55% and 33% of the DRASTIC‐LU index values, respectively. The AHP‐DRASTIC index classifies 61%, 26% and 13% of sites as low, moderate and highly vulnerable, respectively. The study reveals that 52%, 30% and 18% of the area are vulnerable to the modified AHP‐DRASTIC‐LU index classes. The most sensitive factors are shallow aquifer roofs, recharge and LU. The real‐world accuracy of the DRASTIC models was tested using 106 groundwater nitrate concentrations. The modified AHP‐DRASTIC‐LU is the most accurate and appropriate model for the current research region.

Modeling hydraulic conductivity function of frozen soil

Hydraulic conductivity function for frozen soils (HCFF) is crucial for accurately simulating the water transfer process in cold regions, impacting hydrological states and frost heave diseases. However, the HCFF model capturing the relation between hydraulic conductivity and unfrozen water content (θ) or temperature (T) remains an open question. This study delves into HCFF encompassing both empirical and prediction models. This study begins with introducing θ-form and innovative T-form empirical HCFF models, showcasing their solid performance in fitting measured hydraulic conductivity data. Furthermore, the extension of the T-form empirical model to incorporate vapor flow and bimodal soils, through the introduction of maximum vapor conductivity and weighting factors, demonstrates a closer alignment with physical mechanisms and observed trends. In terms of predicting HCFF, the study provides a theoretical foundation through statistical hydraulic conductivity models and thermodynamic equilibrium in frozen soils. Additionally, it clarifies three routes for HCFF prediction—single Soil Freeze Characteristic Curve (SFCC), single Soil Water Characteristic Curve (SWCC), and a combination of the two—and validates their effectiveness through test results and comparison of HCFF prediction outcomes using SWCC and SFCC data from specific soil samples. In practice, the empirical and prediction models are recommended for available hydraulic conductivity data and available SFCC or SWCC data, respectively. Overall, this study not only lays the theoretical groundwork for most prediction models but also presents a valid empirical equation for modeling hydraulic conductivity function tailored specifically for frozen soils.

Development and Application of a New Exponential Model for Hydraulic Conductivity with Depth of Rock Mass

Hydraulic conductivity generally decreases with depth in the Earth’s crust. The hydraulic conductivity–depth relationship has been assessed through mathematical models, enabling predictions of hydraulic conductivity in depths beyond the reach of direct measurements. However, it is observed that beyond a certain depth, hydraulic conductivity tends to stabilize; this phenomenon cannot be effectively characterized by the previous models. Thus, these models may make inaccurate predictions at deeper depths. In this work, we introduce an innovative exponential model to effectively assess the conductivity–depth relationship, particularly addressing the stabilization at greater depths. This model, in comparison with an earlier power-like model, has been applied to a globally sourced dataset encompassing a range of lithologies and geological structures. Results reveal that the proposed exponential model outperforms the power-like model in correctly representing the stabilized conductivity, and it well captures the fast stabilization effect of multiple datasets. Further, the proposed model has been utilized to analyze three distinct groups of datasets, revealing how lithology, geological stabilization, and faults impact the conductivity–depth relationship. The hydraulic conductivity decays to the residual hydraulic conductivity in the order (fast to slow): metamorphic rocks, sandstones, igneous rock, mudstones. The mean hydraulic conductivity in stable regions is roughly an order of magnitude lower than unstable regions. The faults showcase a dual role in both promoting and inhibiting hydraulic conductivity. The new exponential model has been successfully applied to a dataset from a specific engineering site to make predictions, demonstrating its practical usage. In the future, this model may serve as a potential tool for groundwater management, geothermal energy collection, pollutant transport, and other engineering projects.

ERT data assimilation to characterize aquifer hydraulic conductivity heterogeneity through a heat‐tracing experiment

AbstractGeothermal energy systems, such as heat pumps relying on aquifers, use renewable sources of energy that are accessible in urban areas. It is necessary to characterize the subsurface hydraulic properties prior to the installation of such systems. In this context, a heat‐tracing experiment is a typical field test that can help with the characterization of the subsurface. During a heat‐tracing experiment, monitoring with downhole temperature sensors, water‐level pressure transducers and electrical resistivity tomography (ERT) can be used to help characterize the hydrogeological properties. Previous monitoring tools have shortcomings, such as low‐resolution data and over‐smoothing; thus, they fail to reproduce the heterogeneity of hydrogeological properties. Ensemble Kalman filter (EnKF) is a promising tool that can overcome the over‐smoothing problem to replicate the hydrogeological property heterogeneity. In this work, we proposed a new procedure to assimilate time‐lapse cross‐borehole ERT data into a numerical model of groundwater flow and heat transfer, where the groundwater is extracted and heated water is reinjected into an unconfined sandy‐gravel aquifer. The finite element model (FEFLOW 7.3) of groundwater flow and heat transfer is integrated with petrophysical relationship and electrical forward modelling (ResIPy) to estimate cross‐borehole ERT measurements. Then, the estimated apparent resistivity is assimilated to update the hydraulic conductivity model using EnKF. The results of the application of the proposed approach to an experimental site located in Quebec City (Canada) demonstrate that the heterogeneity of K is correctly reproduced as the updated K model is reasonably consistent with the lithological log. In addition, the proposed approach was able to replicate the cross‐borehole ERT field and temperature measurements. The comparison between prior and posterior distribution of K with slug test results shows that the EnKF made the final (assimilated) distribution of K move towards K values inferred with slug tests.

Establishment and application of propped hydraulic fracture conductivity theoretical model based on fracturing efficiency index

Hydraulic fracturing technology is a key technology for the development of unconventional oil and gas reservoirs, and the conductivity of hydraulic fractures has a great impact on their production. The paper introduces the "fracturing efficiency index" as means of characterising the dynamic relationship between hydraulic fracture expansion and proppant filling. It also suggests a way to figure out the proppant fracture inflow capacity. This approach addresses issues such as uneven fracturing fluid dosage, a wide range of injection discharges, and inconsistent sand spreading concentration encountered during fracturing operations. Based on this model, the influencing factors of the flow-conducting capacity such as proppant particle size, sand spreading concentration, liquid injection displacement, and proppant crushing rate, were analyzed, and the calculated results of this model are closer to the experimental results of the API flow-conducting capacity test compared with the calculated results of the reference model. Finally, this model was used to optimize the fluid volume, sand volume, and displacement of hydraulic fracturing in the Jiping 1H shale gas field. The research results show that: (1) the fracturing efficiency index is negatively correlated with the conductivity of hydraulic fractures, whereas is positively correlated with the enhancement of conductivity; (2) our model is applicable to the calculation of supported fracture inflow capacity for high sand concentration, large particle size, intermediate fracture flow and low crushing rate, and the results are close to more than 98.58%, 98.29%, 95.68% and 95.08% of the experimental results, respectively; (3) On-site hydraulic fracturing does not result in higher production capacity with more fracturing fluid dosage, higher displacement and more sand addition; there are optimal fluid, optimal displacement and optimal sand intervals, which are optimized to increase production by 26.92%. It can be seen that the hydraulic fracture conductivity model can optimize various parameters of on-site fracturing construction.

Model of Hydraulic Conductivity, Infiltration Rate, and Permeability at Gold Mine Waste Dump in North Sulawesi, Indonesia

The research area is a gold mine operating in North Sulawesi. The aim of the study was to analyze and calculate hydrological parameters, namely: hydraulic conductivity, infiltration rate, and permeability to find out how strong the soil cover is at one level of waste disposal. The method used is the Measurement of hydraulic conductivity, infiltration rate, and permeability in the field, analysis, and calculation of hydraulic conductivity, infiltration rate, and permeability based on field data. In the designated regions of the waste dump, specifically areas 1a, b, and c, we observed certain hydrological patterns that are worth noting. Firstly, the hydraulic conductivity in these areas, which is a crucial determinant of the rate at which water can move through the soil, consistently showcased low average values. This is further supported by the similarly slow infiltration rate identified in the same zones. The ability of the soil to transmit water, i.e., its permeability, also followed this trend, with values leaning towards the lower end of the scale, indicating very slow permeability. One major contributory factor to these patterns appears to be the soil's composition. Predominantly made up of sandy loam, the soil in these areas exhibits high water retention capabilities. Sandy loam, by its nature, binds and retains water effectively, which could potentially explain the observed hydrological behaviors in waste dump areas 1a, b, and c.

Bimodal unsaturated hydraulic conductivity derived from water retention parameters by accounting for clay-water interactions: Deriving a plausible set of hydraulic parameters

We developed a novel, lognormal, pore-scale, unsaturated hydraulic conductivity model, K(ψ)Model, which does not require saturated hydraulic conductivity, Ks, as an input parameter. K(ψ)Model is derived solely from hydraulic parameters describing a bimodal, lognormal, pore-scale, soil water retention curve θ(ψ).The K(ψ)Model is based on the Hagen-Poiseuille equation, which represents the soil as a bundle of parallel, non-intersecting capillary tubes. To improve the modelling of fine-textured soils we introduced a novel model to consider the clay-water interaction. This model assumes that clay-water interaction occurs for soils having more than 30% of clay and an effective matrix porosity greater than 35%.Compared to previously developed models, the K(ψ)Model does not require the use of integrals and can be computed from a spreadsheet and distinguishes between macropore (non-equilibrium) and matrix (equilibrium) flows.The K(ψ)Model gives improved results when the hydraulic parameters are dynamically constrained and when θ(ψ) describes a bimodal, lognormal distribution.

An Unsaturated Hydraulic Conductivity Model Based on the Capillary Bundle Model, the Brooks‐Corey Model and Waxman‐Smits Model

AbstractSoil unsaturated hydraulic conductivity (K), which depends on water content (θ) and matric potential (ψ), exhibits a high degree of variability at the field scale. Here we first develop a theoretical hydraulic‐electrical conductivity (σ) relationship under low and high salinity cases based on the capillary bundle model and Waxman and Smits model which can account for the non‐linear behavior of σ at low salinities. Then the K‐σ relationship is converted into a K(θ, ψ) model using the Brooks‐Corey model. The model includes two parameters c and γ. Parameter c accounts for the variation of the term (λ + 2)/(λ + 4) where λ is the pore size distribution parameter in the Brooks‐Corey model, and the term m‐n where m and n are Archie's saturation and cementation exponents, respectively. Parameter γ is the sum of the tortuosity factor accounting for the differences between hydraulic and electrical tortuosity and Archie's saturation exponent. Based on a calibration data set of 150 soils selected from the UNSODA database, the best fitting log(c) and γ values were determined as −2.53 and 1.92, −4.39 and −0.14, −5.01 and −1.34, and −5.79 and −2.27 for four textural groups. The estimated log10(K) values with the new K(θ, ψ) model compared well to the measured values from an independent data set of 49 soils selected from the UNSODA database, with mean error (ME), relative error (RE), root mean square error (RMSE) and coefficient of determination (R2) values of 0.02, 8.8%, 0.80 and 0.73, respectively. A second test of the new K(θ, ψ) model using a data set representing 23 soils reported in the literature also showed good agreement between estimated and measured log10(K) values with ME of −0.01, RE of 9.5%, RMSE of 0.77 and R2 of 0.85. The new K(θ, ψ) model outperformed the Mualem‐van Genuchten model and two recently published pedo‐transfer functions. The new K(θ, ψ) model can be applied for estimating K under field conditions and for hydrologic modeling without need for soil water retention curve data fitting to derive a K function.

An integrated field scale computational model for hydraulic conductivity of high energy explosive driven fracturing

An integrated field scale computational model for hydraulic conductivity of high energy explosive driven fracturing

Theoretical and numerical study of contaminant transport in clayey barriers using a revised numerical model considering the dependency of membrane efficiency and hydraulic conductivity on solute concentration

Solid waste is often buried in landfills isolated with a bentonite-based clay barrier to guarantee the high quality of groundwater. As the efficiency of clay barriers is highly dependent on solute concentration, this study aims to modify membrane efficiency, effective diffusion, and hydraulic conductivity of bentonite-based clayey barriers exposed to saline environments for numerical investigation of solute transport in such barriers. Therefore, the theoretical equations were modified as a function of solute concentration instead of constant values. First, a model was extended for membrane efficiency as a function of void ratio and solute concentration. Second, an apparent tortuosity model was developed as a function of porosity and membrane efficiency to adjust the effective diffusion coefficient. Moreover, a recently developed semi-empirical solute-dependent hydraulic conductivity model was employed, which is dependent on solute concentration, liquid limit, and void ratio of the clayey barrier. Afterward, four approaches for applying these coefficients were defined as either “variable” or “constant” functions in ten numerical scenarios using COMSOL Multiphysics. The results reveal that “variable” membrane efficiency affects the outcomes in lower concentrations, while “variable” hydraulic conductivity is more influential in the domain of higher concentrations. Although all approaches converge to the same ultimate distribution of solute concentration using the Neumann exit boundary condition, the choice of different approaches clearly affects the ultimate state for the Dirichlet exit boundary condition. As the thickness of the barrier increases, the ultimate state is reached later, and choosing the approach to apply coefficients is more influential. Decreasing the hydraulic gradient postpones the solute breakthrough in the barrier, and picking the variable coefficients is more crucial in higher hydraulic gradients.

Study on Multiscale Fluid–Solid Coupling Theoretical Model and Productivity Analysis of Horizontal Well in Shale Gas Reservoirs

In order to study the productivity dynamic evolution laws of shale gas horizontal well, a multiscale seepage theoretical model considering the fluid–solid coupling effect is proposed first in this paper, which introduces an anisotropic matrix permeability model that comprehensively considers multiple flow regimes and an artificial hydraulic fracture conductivity model that considers proppant deformation and embedment. Then the reliability of the theoretical model is verified by the field production data obtained from the Marcellus shale. Based on the established productivity model, the evolution laws of pore pressure and permeability in different areas of the reservoir with different production periods are studied first, and then the shale gas production rate and cumulative production under different physical mechanisms, different shale gas flow regimes, and different reservoir parameters are quantified and analyzed. The simulation results show that the pore pressure in the hydraulic fracture area decreases rapidly at the initial stage of production, resulting in large deformation, and the fracture conductivity tends to be stable gradually at the later stage of production. The desorption effect will increase shale gas production, while the stress sensitivity is just the opposite. When only real gas flow is considered, the gas cumulative production is the highest. The fluid–solid coupling of hydraulic fractures has a great impact on the production, while the fluid–solid coupling in the matrix area has a small impact on the production, which can be ignored if appropriate. During the sensitivity analysis of reservoir parameters, it is observed that the influence of parameters of hydraulic fractures on production is much higher than that of matrix regional parameters. During the production process, these influencing factors can be adjusted according to the change of production data to achieve the optimization of shale gas production. In a word, the new model proposed in this paper provides a more accurate method to estimate the impact of multiple physical mechanisms, especially the fluid–solid coupling effect, on shale gas production compared with the existing model. The research results of this study can provide some reference and guidance for the dynamic prediction of shale gas production and optimization of production parameters.

Upgraded closed‐form soil hydraulic functions from oven dryness to saturation

AbstractIn the past decades, remarkable progresses have been made in developing soil water retention (SWR) functions and relative hydraulic conductivity (kr) models for the full range of matric suction. However, such an existing SWR function and the corresponding kr model were found not to have a consistent prediction performance, that is, for a given soil, the SWR function could perform very well but the kr model might perform poorly or even fail. Given that both water retention and hydraulic conductivity need to be accurately predicted to simulate land‐atmosphere interactions, vadose‐zone soil–water dynamics, and shallow groundwater recharges, this paper proposed an upgraded SWR function and derived a new closed‐form kr model by combining the function with the well‐known Mualem model. The upgradation was done by introducing an exponential function for the pendular (i.e., dry and very dry) range of matric suction. The SWR function and kr model keep the conventional simplicity, reflect the capillary versus adsorptive soil–water mechanisms, eliminate fictitious parameters, and are continuously differentiable with closed forms. The assessment using the experimental data for 35 soils with nine different textures indicated that the upgraded SWR function and new kr model reproduced the data more accurately than the existing functions and models, as measured by visualization plots and four statistics, namely coefficient of determination, sum squared error, root mean squared error, and Nash–Sutcliffe efficiency. The contribution of this paper is to overcome the inconsistent‐performance issue mentioned above.

A Family of Soil Water Retention Models Based on Sigmoid Functions

AbstractThe soil water retention curve is the fundamental soil hydraulic property to characterize soil water movement and solute transport. Many efforts have been devoted in the past decades to developing models to describe soil water retention curves. However, most of them are empirical equations or assume that soil pore size distributions conform to a lognormal distribution. Yet, few effects have been undertaken to systematically propose and compare a series of possible alternative probability density functions to describe the sigmoid retention curves with parameters physically explainable. Here, we proposed a family of five soil water retention models based on sigmoid functions with parameters of clear physical implications coinciding with the statistical measures of soil pore size distribution. Compared with the widely used models (i.e., Brooks & Corey, 1964; Kosugi, 1996; van Genuchten, 1980), the proposed models have somewhat improved performances to characterize water retention data for a wide range of soil textures without introducing additional model parameters. Two of the proposed models are capable of characterizing the observed two local extrema in the moisture capacity curves. The associated unsaturated hydraulic conductivity models of the proposed soil water retention models are also derived, which show superior performance in characterizing the observed hydraulic conductivities compared with competing models, especially in macropore regimes. Additionally, we analyzed the parameter‐equivalent conversion between the proposed and the existing models, and a simple linear regression equation can be used to derive the parameters of the proposed models from the existing and other alternative different proposed models.

Selection of spatial prediction models of saturated hydraulic conductivity in soils containing rock fragments in an Andean micro-basin

Selection of spatial prediction models of saturated hydraulic conductivity in soils containing rock fragments in an Andean micro-basin

Rock fragments influence the water retention and hydraulic conductivity of soils

AbstractRock fragments (RFs) influence soil hydraulic properties (SHPs), and knowledge about the SHPs of stony soils is important in vadose zone hydrology. However, experimental evidence on effective SHPs of stony soils is still scarce and mostly restricted to water‐saturated conditions and low volumetric contents of RFs. We examined the influence of RFs on SHPs through a series of measurements. Stony soils were prepared by packing 250‐cm3 cylinders with soils of two textures (sandy loam and silt loam) and with different volumes of RFs (up to 50% v/v) with a diameter of 8–16 mm. Samples were prepared in a way that the background soils (diameter smaller than 2 mm) had identical bulk density. The simplified evaporation method was used to determine the effective SHPs of stony soils. We used the obtained SHP data to evaluate the performance of models, which predict the effective SHPs of stony soils from SHPs of the background soil. The results highlight the systematic dependency of SHPs on volumetric content of RFs. The difference between modeled and measured SHPs was substantial for cases in which the soil contained a high amount of RFs. Accounting for the moisture content of RFs improved the prediction of the effective water retention curve of stony soils compared with a simple scaling that used only the content of RFs. Among the evaluated models for the effective hydraulic conductivity, the model based on the general effective medium theory showed the best performance, particularly for low RF contents.