Vadose Zone Journal

A multichannel ground‐penetrating radar unit, which consists of multiple fixed transmitters and receivers, allows one to obtain time‐lapse multi‐offset gathers (MOG) by fixing the unit in one place at the expense of spatial resolution. When applying common semblance analysis for dielectric constant estimation, it is important that MOG, such as common midpoint (CMP) data, are collected with a small antenna step size to obtain clear contrast in semblance values. Thus, a reliable computational technique for constructing spatially dense CMP data from sparse CMP data is required. An F‐K filter method based on the projection onto convex sets (POCS) algorithm was developed to interpolate missing traces. In our previous study, a novel method based on the F‐K filter was proposed by optimizing the filter zone to construct dense CMP data from sparse CMP data. The objective of this study was to formally compare the novel F‐K filter method with a method based on the POCS algorithm. The former method consists of a normal moveout correction and a fan‐shaped F‐K filter, the parameters of which are optimized by leave‐one‐out cross‐validation. The novel method was able to construct a dense reflected hyperbolic‐shaped wave signal well from the sparse CMP data collected from a sand box filled with uniform dry sand, and to improve the velocity estimation with semblance analysis. The novel method is considered more suitable for the construction of dense data from sparsely collected CMP data than the method based on the POCS algorithm.

High‐yield almond (Prunus amygdalus) production requires irrigation to support soil water availability throughout the summer. We aimed to develop tools that project almond trees’ water requirements and then integrate them into irrigation management. We postulated that deficit irrigation would limit root allocation and reduce irrigation efficiency. Thus, we monitored soil water availability (soil water content and potential evapotranspiration) and tree physiology (transpiration and trunk circumference) under 600, 900, or 1250 mm summer irrigations. A soil‐tree model was trained to predict trees’ transpiration as the soil dried between irrigation events. The model included an empirical function for tree transpiration at variable soil water potentials (50% loss by −1 MPa). Field records corroborated the soil‐tree model projections that 600 mm irrigation reduces soil water potential to −1 MPa and limits transpiration after winter soil water reserves deplete. Trees at deficit irrigation did grow deep roots to extract soil winter reserves, thus disputing our original notion. A 900 mm irrigation matched transpiration and maintained soil at −0.6 MPa. The 1250 mm irrigation exceeded transpiration and narrowed trees’ water uptake to the upper 50 cm. The reciprocity between soil water and transpiration dictates that predetermined irrigation would limit or exceed transpiration. The soil‐tree model could project transpiration responses to soil water variability, thereby supporting irrigation by trees’ transpiration. The model uses soil water dynamics, rather than insular water potential measures, to account for spatial discrepancies between water application and uptake depths. Hence, the soil‐tree model lays a computational framework for precision irrigation by automated sensory.

Soil hydraulic parameters (SHP) play a crucial role controlling the spatiotemporal distribution of water in the soil–plant continuum and thus affect water availability for crops. To provide reliable information on the SHP at different scales, measurement techniques with a good spatial resolution and low labor costs are required. In this study, we used crosshole ground penetrating radar (GPR)‐derived soil water contents (SWCs) measured along horizontal rhizotubes under a controlled experimental test site cropped with winter wheat to estimate the unimodal and dual‐porosity soil hydraulic characteristics with different soil layer setups. Therefore, sequential inversion of the GPR‐derived SWCs was performed using the hydrological model HYDRUS‐1D, whereby the SWC data were either averaged prior inversion or used in a spatially distributed way. To analyze if the time‐lapse gathered GPR data contain enough information to estimate the SHP, additional synthetic studies were performed increasing the data resolution to daily GPR measurements. The results showed that the time‐lapse data contained enough information to estimate the SHP accurately. Additionally, spatially distributed soil hydraulic characteristics differed from the one estimated based on averaged SWCs derived from spatially distributed GPR data. Finally, we derived spatially resolved SHP, which can be used for 3D process rhizosphere processes and root–soil interaction modeling.

Accurate estimation of effective saturated hydraulic conductivity (Ksat) of a vadose zone with an underlying shallow perched aquifer is challenging. Standard pumping tests and double‐ring infiltrometers are unsuitable for this purpose. Therefore, a bailout test (BOT) was conducted in nine soil pits to evaluate the hydraulic properties of the vadose zone and the subjacent shallow‐perched aquifer in a porous bed of a wadi in Oman. The analytical (Kirkham's type) and numerical (HYDRUS‐2D) models were also used. Upon instantaneous emptying, the water level rises in a pit, owing to seepage from the ambient shallow aquifer. The draw up was monitored and compared with modeling, which assumes a homogeneous or layered van Genuchten's soil. The soils of the excavated pits were characterized as sandy‐textured, gleyed, and containing calcareous and clay‐enriched layers. A good match between HYDRUS‐analytical results for layered soils and an idealized homogeneous one illustrates that BOT is a robust and quick technique for the estimating Ksat = 2–3 cm/h in the coarse‐textured wadi bed vadose zone overlaying a transient water table. BOT is better than classical auger hole tests because they test a larger soil volume through induced seepage. BOT is also more suitable for drainage trenches and other excavations, allowing for easier observation, soil sampling from the banks, and rapid dewatering.

To understand and explain soil moisture dynamics, the role of the vegetation is crucial. Hydrological processes in agricultural soils are strongly affected by rooting depth and root distribution. We present a new approach to monitor and model root dynamics and its influence on soil moisture using minirhizotrons combined with phenological observations. Field setups consist of a portable root scanner and acrylic glass tubes installed in the soil at the start of the growing season. 360° scans of soil and roots are taken regularly at different depths in the tubes. Root traits are identified automatically for each soil layer and complemented by observations of aboveground plant phenology. Results from minirhizotron data collected at an agrometeorological observatory in Germany show for both cereal and rapeseed (Brassica napus L.) crops a higher root density in deeper soil layers and a lower density near the soil surface when compared to literature data. An opposite picture emerged for maize (Zea mays L.) and potato (Solanum tuberosum L.), whereas vertical root distribution in sugar beet (Beta vulgaris subsp. vulgaris) had a different seasonal course than expected from literature. Applying the new root distribution data in calculations of the soil water balance resulted in differences of more than 3% in absolute volumetric soil water content. A comparison with in situ measurements of volumetric soil moisture at 20‐ and 50‐cm depth revealed a significant improvement of the model results due to the new parameterization. Thus, we argue that minirhizotrons constitute a useful supplement to hydrological observatories and can help understand and predict soil moisture dynamics in the critical zone.

Engineered surface barriers are expected to be used to cover sites containing buried waste radionuclides after site closure. Understanding the transport of contaminants in the soil below a surface barrier is of importance in order to protect the underlying groundwater. The travel velocity of these contaminants is dependent on numerous factors, such as the thickness, properties, the pre‐barrier hydrologic condition of the vadose zone, and the initial depths and properties of the contaminants. Although solute transport can be modeled with numerical modeling using site‐specific data, by nature a numerical method does not reveal the relationship between inputs and outputs. This work develops algebraic expressions of the velocities of convective solute transport in the vadose zone below a surface barrier, time lag, solute safe depth, zero‐protection depth of a surface barrier, and solute travel time to the groundwater. This development shows that solute convection in the soil with a surface barrier consists of up to three stages: constant high velocity, decreasing velocity, and constant low velocity. Solute transport based on the algebraic expressions was verified against numerical simulations. The algebraic expressions may be used to assess the expected impacts of a surface barrier to the convective transport of contaminants in the vadose zone.

Colloid‐facilitated transport has been widely acknowledged as a significant contributor to surface water and groundwater pollution. Compared to water‐dissolved solutes, colloids are subject to a size exclusion effect (SEE) due to their inherent size when transported through porous media. However, only a few studies have quantitatively assessed SEE on colloid transport in porous media. In this study, we conducted dynamic observation experiments (in a quartz sand micro‐model) using a high‐speed camera and directly observed the size exclusion phenomenon of colloid transport in pores. Moreover, we further performed column experiments to quantitatively characterize the breakthrough curves of silica colloids with diameters ranging from 0.1 to 5 µm through different‐size sands. Silica colloids displayed a distinct SEE, as demonstrated by their earlier breakthroughs compared to the NaNO3 tracer. This observation served as a quantitative indicator of the SEE. The breakthrough curves were fitted using the advection‐dispersion equation to quantify this phenomenon, allowing for calculating the water saturation that is inaccessible to mobile colloids (γ). It was determined that γ exhibited a significant negative linear correlation (p < 0.01) with the average grain size (d50) of porous media. Additionally, power–law correlations were identified between γ and the colloid diameter (dp) and dp/d50. When the grain size of porous media remained below 1031 µm, an increase in the colloid diameter (from 0.1 to 5 µm) resulted in a more noticeable SEE, leading to the proportion of inaccessible pore regions to all pores increasing from 0.017 to 0.098, with this change following a power–law trend. These findings provide valuable insights into quantifying the SEE on silica colloids during transport in porous media. Furthermore, they provide theoretical support for future quantitative research on the mechanisms underlying colloid‐facilitated transport.

Cultivated peatlands are increasingly regarded as hot spots due to climate change and other environmental concerns. Flexible water management, such as controlled drainage, is proposed to optimize cultivation and reduce environmental risks in peatland fields. The hydrological and environmental implications of controlled drainage depend on site‐specific variables, and it is unclear how controlled drainage should be implemented in various conditions. Simulation models are a promising approach to systemically study the field hydrology, as models can capture the complete water balance, which is difficult through experimental studies alone. We calibrated and validated the spatially distributed model FLUSH to describe the hydrology of a field block with a shallow peat cover and controlled drainage in central western Finland. The objectives were to analyze the hydrological effects of controlled drainage and detect hydrological connections between the field and an adjacent upslope forest area. The results showed how inflow from the forest can induce high observed drain discharge but impacted the block groundwater tables only in the proximity of the forest (distance <25 m). The effect of controlled drainage on groundwater tables was on average 0.15 m and seasonally varying. Controlled drainage reduced drain discharge, and the reduction was larger with the forest area included in the model. While controlled drainage effects on groundwater levels and soil moisture were insensitive to groundwater influxes from adjacent areas, the water balance impact highlights the role of hydrological connections in the hydrology of cultivated peatlands under controlled drainage.

Soil moisture (SM) information is invaluable for a wide range of applications, including weather forecasting, hydrological and land surface modeling, and agricultural production. However, there is still a lack of sensing information that adequately represents root‐zone SM for longer periods and larger spatial scales. One option for root‐zone SM observation is terrestrial gamma radiation (TGR), as it is inversely related to SM. Hence, the near real‐time data of more than 5000 environmental gamma radiation (EGR) monitoring stations archived by the EUropean Radiological Data Exchange Platform (EURDEP) is a potential source to develop a root‐zone SM product for Europe without extra investments in SM sensors. This study aims to investigate to what extent the EURDEP data can be used for SM estimation. For this, two EGR monitoring stations were equipped with in situ SM sensors to measure reference SM. The terrestrial component of EGR was extracted after eliminating the contributions of rain washout and secondary cosmic radiation, and used to obtain a functional relationship with SM. We predicted the weekly volumetric SM with a root mean square error of 7%–9% from TGR measurements. Nevertheless, we believe that this technique, due to its greater penetration depth and long data legacy, can provide useful data complementary to satellite‐based remote sensing techniques to estimate root‐zone SM at the continental scale.

A practical approach for modeling field‐scale moisture flow in a highly heterogeneous unsaturated medium is described in this study. The validity of this approach is demonstrated through comparison of the numerical simulations with field observations at a semiarid site located in southcentral Washington State. The methodology is based on upscaling the core scale hydraulic properties and combining power‐law and tensorial connectivity‐tortuosity (PA‐TCT) approaches to derive macroscopic anisotropy parameters for each hydrostratigraphic unit (HSU) identified in the field. Each heterogeneous HSU is approximated by an equivalent homogeneous medium (EHM) model for which PA‐TCT parameters are used in the flow simulations. The available field data on moisture content and matric potential are compared with steady‐state flow simulations based on the mean form of Richards' equation. While the homogenization or averaging of heterogeneities, embedded in the EHM modeling approximation, cannot capture all of the field‐scale variability, the simulated steady‐state moisture and matric potential profiles capture well the central tendency of the field data. This approach is deemed practical for assessing the fate and transport of contaminants in highly heterogeneous unsaturated media at the transport scale of hundreds of meters.

Common parametrizations of soil hydraulic properties rely on unimodal curves, which cannot accurately represent the properties of many macroporous, aggregated, mixed, or compacted soils. Multimodal hydraulic curves are increasingly used to represent these structured soils in eco‐hydrological models, but the dynamics of the processes that shape soil structure—and the resulting dynamics of soil hydraulic properties—are often neglected. In cases such as compaction recovery, where the structure‐shaping process can be modeled, coupling the evolving pore volumes to soil hydraulic properties in a physically based way remains challenging. Here, we show how modeled or estimated soil structure evolution, when expressed as a time series of porosities in a few pore size classes, can be assimilated into established models of soil hydraulic properties. Our method relies on the division of retention models into smooth segments, whose water contents can be independently adjusted. We apply the approach to examples of modeled soil structure evolution from the published literature: one describing soil structure recovery after compaction and one describing structure formation as a result of organic amendment. In the cases considered, the estimated soil hydraulic conductivity varies more strongly than the modeled porosity which drives it. This shows that transport‐related soil functions can be impacted longer (after compaction) or sooner (after amendment) than suggested by the evolution of structural metrics such as porosity. In general, modeling the evolution of soil hydraulic properties in cases such as these paves the way for holistic, process‐based modeling of land management practices and their impact on soil functioning.

Soil water content (SWC) estimation is important for many areas including hydrology, agriculture, soil science, and environmental science. Ground penetrating radar (GPR) is a promising geophysical method for SWC estimation. However, at present, most of the studies are based on partial information of GPR, like travel time or amplitude information, to invert the SWC. Full waveform inversion (FWI) can use the information of the entire waveform, which can improve the accuracy of parameter estimation. This study proposes a novel SWC estimation scheme by using the FWI of GPR, optimized by the grey wolf optimizer (GWO) algorithm. The proposed scheme includes a petrophysical relationship to link the SWC with the relative dielectric permittivity, 1D GPR forward modeling, and a GWO optimization algorithm. First, numerical modeling was carried out, and the proposed scheme was applied to both noise‐free and noisy data to verify its applicability. Then, the proposed method was applied to data collected from a field experimental site. These results, derived from both synthetic and real datasets, show that the proposed inversion scheme resulted in a good match between the observed and calculated GPR data. In the numerical modeling, it was observed that the SWC could be inverted accurately, even when noise was present in the data. These demonstrate that the GWO method can be applied for the quantitative interpretation of GPR data. The proposed scheme shows potential for SWC estimation by using GPR full waveform data.

Understanding the dynamics of the soil–vegetation–atmosphere (SVA) system, particularly in arid and semiarid regions, remains challenging due to its intricate and interdependent nature. This system creates problems for rangeland administration, such as insurance and risk management. This paper focuses on the complex interactions within the SVA system, particularly on rangeland ecosystems in Spain's semiarid and arid regions. By employing multifractal detrended cross‐correlation analysis (MFCCA), we explore the joint behavior of key variables, including precipitation (PCP), evapotranspiration (ETP), aridity index (Arid. I.), soil water availability (SWA), biomass (Bio), and normalized difference vegetation index (NDVI). Analyzing a 20‐year data series from Madrid and Almeria provinces, we reveal distinct patterns in the studied variables’ persistence, multifractality, and asymmetry. Notably, the differences in the generalized Hurst exponents (hxy${{{\mathrm{h}}}_{xy}}$(q)) between Madrid and Almeria for SWA with NDVI, SWA with Bio, and NDVI with Bio underscore distinct interactions in these regions. Moreover, multifractal analyses unveil differences in the complexity of joint variables’ behaviors in the two regions. Almeria exhibits higher multifractality across variables, indicating more complex and variable environmental interactions, likely due to its more arid conditions. These findings suggest that Almeria has more sensitivity to changes, requiring adaptive management strategies, while in Madrid, water availability and related variables play a more dominant role in driving vegetation dynamics. These findings shed light through MFCCA on the nuanced dynamics of rangeland ecosystems in semiarid and arid regions, emphasizing the importance of considering complexity‐based approaches to understand the intricate interplay among key variables in the SVA system.

Estimates of the van Genuchten (1980, abbreviated as VG) parameters and saturated hydraulic conductivity (Ks) were made for the contiguous United States at a resolution of 100 m and seven soil depths by combining the SoilGrids+ (SG+) soil property maps of Ramcharan et al. with the R3H3 member of the Rosetta3 hierarchical pedotransfer functions (PTFs) of Zhang et al. To this end, we developed multi‐threaded code that significantly speeds up computation (up to a factor 25) depending on the level of parallelism. We verified estimates first by calculating simple summary statistics of estimated basic properties of SG+ with actual measured soil properties for 14,113 pedons in the National Cooperative Soil Survey (NCSS) (2023) labsample database. Next, we computed summary statistics of PTF‐estimated moisture contents for NCSS and SG+ data. The results show estimation errors are dominated by intrinsic errors of the PTF, and that (potentially correctable) systematic errors are present in SG+ soil properties and PTF estimates. The resulting hydraulic property maps contain well over 750 million points for each of the seven layers and show considerable horizontal and depth variation for each VG parameter and Ks, except the VG “n” parameter, which is dominated by values between 1.25 and 1.6. The hydraulic property maps are 99.9% complete, and we demonstrate that plausible profiles and uncertainty information can be generated for virtually each point. The maps are available as two multi‐channel GeoTIFF maps per SG+ layer: one with the five hydraulic parameters and one with the corresponding covariances.

Information about the spatial distribution of soil hydraulic parameters is necessary for the accurate prediction of soil water flow and the coupled movement of chemicals and heat at the field scale using a process‐based model. Physics‐informed neural networks (PINNs), which can provide physical constraints in deep learning to obtain a mesh‐free solution, can be used to inversely estimate soil hydraulic parameters from less and noisy training data. Previous studies using PINNs have successfully estimated soil hydraulic parameters for homogeneous soil but estimating such parameters of layered soil profiles where the interface depth and the parameters are unknown still has some difficulties. The objective of this study was to develop PINNs to inversely estimate the distribution of soil hydraulic parameters, such as saturated hydraulic conductivity and α and n of the Mualem–van Genuchten model directly within layered soil profiles by predicting changes in the pressure head from training data based on simulation results at given depths during infiltration. The impact of factors affecting PINNs performance, such as the weights assigned to each component of the loss function, time range used in error computations, and number of samples used to assess the physical constraint, was investigated. By assigning a larger weight to the physical constraint and excluding the earlier stage of infiltration in the loss function, the changes in the pressure head and the three soil hydraulic parameter distributions within the layered soil were successfully estimated. The developed PINNs can be further applied to more complex soils and can be improved.

Advancing and receding water contact angles, often denoted as the maximum and minimum apparent water contact angles, are crucial parameters reflecting a soil's water holding capacity. These parameters play an important role in establishing theoretical soil–water characteristic curves (SWCCs) for unsaturated soils. However, pre‐assuming constant advancing and receding contact angles during soil wetting and drying processes may be erroneous due to their close correlations with the water content and void ratio. To address this research gap, systematic laboratory measurements were conducted on a loess with different void ratios and water contents. Apparent water contact angles were acquired using an axisymmetric drop shape analyzer, enabling a comprehensive dataset. Analysis of variance was employed to assess the statistically significant differences between void ratios and water contents. The results reveal a significant increase in the observed water contact angle as the void ratio decreases and a decrease with increasing water content. Although both the void ratio and water content influence the water contact angle, the latter has a more pronounced effect. The relationship between the receding water contact angle and water content/void ratio is observed to be linear. The identification of this linear relationship offers insights into the fitting of the SWCC for loess across varying void ratios. This study serves to enhance theoretical methodologies, particularly in the adaptation of contact angles, thus facilitating the development of more precise SWCC models.

Understanding soil–water dynamics in cultivated organic soils (histosols) is crucial for sustainable agriculture, ecosystem preservation, and climate change mitigation. Data on the soil water retention curves (SWRCs) of these histosols in Canada are not readily available in the literature. The Hydrus‐1D model was used to predict SWRCs for a cultivated organic soil in Quebec. The model was validated with matric potential measured in a soil column at 10, 26, and 48 cm. The optimized hydraulic parameters in the Hydrus model resulted in field capacity moisture contents of 0.6, 0.696, and 0.49 cm³ cm⁻³ at the three depths, respectively. Wilting point moisture contents of 0.165, 0.107, and 0.14 cm³ cm⁻³ were obtained at the same depths. Model accuracy was confirmed with a Nash–Sutcliffe efficiency of 0.76, 0.91, and 0.78 for the three depths, respectively. These findings can inform decisions regarding water management practices such as irrigation and drainage requirements for intensively cultivated organic soils.

Solutions of the linearized Richardson–Richards Equation (RRE) for one‐dimensional soil water flow in layered soils with sinusoidal flux in the frequency domain are derived. We evaluate the accuracy of our analytical and other analytical solutions by comparing them with results from a standard numerical model. Our analytical solution agrees with the numerical solution under multi‐layered heterogeneous soil, while others disagree. We also demonstrate the capability of the proposed solution to simulate soil moisture dynamics under a realistic, multi‐frequency flux case. The procedure described in the paper is valid for any series of arbitrary periodic flux superpositions for layered heterogeneous Ks${{K}_s}$. Moreover, our solution is efficient in the calculation compared with numerical solutions, especially when dealing with long‐time series soil moisture, which can provide a validation of numerical models.

The 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.

Stable water isotopologs can add valuable information to the understanding of evaporation processes. The identification of the evaporation front from isotopolog concentration depth profiles under very dry soil conditions is of particular interest. We compared two different models that describe isotopolog transport in a drying unsaturated porous medium: SiSPAT‐Isotope and DuMux. In DuMux, the medium can dry out completely whereas in SiSPAT‐Isotope, drying is limited to the residual water saturation. We evaluated the impact of residual water saturation on simulated isotopic concentration. For a low residual water saturation, both models simulated similar isotopolog concentrations. For high residual water saturation, SiSPAT‐Isotope simulated considerably lower concentrations than DuMux. This is attributed to the buffering of changes in isotopolog concentrations by the residual water in SiSPAT‐Isotope and an additional enrichment due to evaporation of residual water in DuMux. Additionally, we present a comparison between high‐frequency experimental data and model simulations. We found that diffusive transport processes in the laminar boundary layer and in the dried‐out surface soil layer need to be represented correctly to reproduce the observed downward movement of the evaporation front and the associated peak of isotopolog enrichment. Artificially increasing the boundary layer thickness to reproduce a decrease in evaporation rate leads to incorrect simulation of the location of the evaporation front and isotopolog concentration profile.

Shear strength equation is a basic theory for solving many geotechnical engineering problems. Although the shear strength equation has received widespread attention, shear strength of clay under wide suction range and different initial void ratio ee conditions cannot be well predicted. This study aims to establish a new strength equation applicable to soils within a wide suction range. Considering the capillary and adsorptive parts of soil–water interactions, a cohesion expression related to the degree of adsorbed water saturation Sra and the effective stress related to the degree of capillary water saturation Src are proposed. After that, based on the Mohr–Coulomb theory, a shear strength equation of unsaturated soils in a wide range of suction under various ee is proposed. Five parameters are included in the equation. It is easy to calibrate them through shear tests on saturated and the fully dried soils. It is verified that not only the sandy clay till and clayed silt but also the expansive soil's shear strength in wide ranges of suction under various ee can be well predicted.

Linkages between the micro‐scale of soil water and landscape scale of hydrological data could be improved with the analysis of soil factors in preferential flow rates. This rearrangement of the terminology on soil pore size from published literature focused on the relationship between aggregate and pore size. In the range of pore size relevant to water flow (>0.005 mm), a 2:1 ratio of aggregate to pore diameter approximated the mean of proposed pore size categories. Major functional change points in soil pore size were identified where water becomes mobile in soil (0.005 mm), where preferential flow among aggregate surfaces begins (0.3 mm), and where water flows without soil interaction (bypass flow ∼1.0 mm). A number of published equations supported the application of soil pore size in permeability estimation for modeling hydraulic conductivity. Common understanding of soil pore terminology would support water flow estimation from soil to landscape scales.

Preferential flow (PF) processes are governed by subsurface soil structures at various scales. Still, model validation and mechanistic understanding of PF are very lacking. We hypothesize that PF at hillslope and larger scales cannot be described and quantified when neglecting small‐scaled spatially variable processes and simplifying the model dimensionality. The objective was to learn from comparing simulation results of multidimensional (1D, 2D, and 3D) and multiscale (pedon, catena, and catchment) modeling approaches with comprehensive datasets, and so as to evaluate PF simulations based on the Richards’ equation (solved by the HYDRUS software). Results showed limited alignment between 1D simulations and soil moisture data, mainly affected by vertical changes in porosity, permeability, and precipitation features. 2D and 3D simulations outperformed 1D models. 3D simulations provided satisfactory description of PF dynamics at the pedon scale, considering accurate representations of soil and bedrock structures for three dimensions (vertical, horizontal, and surrounding area). In 2D simulations at the pedon scale, models incorporating dual‐porosity and anisotropy of soils yielded more accurate predictions of water dynamics than single‐porosity and isotropic models. Furthermore, the application of 2D simulation at the catena scale identify PF pathways owing to the enhanced representation of the hydraulic connectivity between different locations along the slope. The results confirmed the significance of multidimensional and multiscale modeling approaches for PF simulations in hillslope hydrology. Considering the complexity and parameterization of 2D and 3D “bottom‐up” physically based models in representing spatial variability within and between soil profiles and/or underlying bedrock geology, the results contribute to creating a modeling framework applicable to identify the PF processes and thus their implications in managing water resources.

Accurate information about soil water retention curves (SWRCs) of sands is essential for evaluating groundwater recharge and vulnerability to contamination in many shallow sandy aquifers which are widespread on post glacial areas in Northern Europe and North America. Pedotransfer functions (PTFs) allow to estimate SWRC from basic physical characteristics of soils, such as textural composition. However, in the case of clean sands which are dominated by a single textural fraction, PTFs should be based on more detailed information given by the particle size distribution. In this study we evaluated three parametric PTFs, which estimate parameters of the van Genuchten SWRC based on empirical correlations to the parameters of soil particle size distribution, and five semi‐physical PTFs, which derive the pore size distribution from particle size distribution. PTFs were compared to SWRCs fitted to the results of drainage experiments on sandy soil samples from six locations in Gdańsk region (northern Poland). Although in all samples the content of silt and clay fractions was low (<3.5%), the differences in actual content of fines strongly influenced the shape of SWRC. In contrast, the amount of gravel fraction (varying from 1% to 35%) did not have significant effect on SWRC. Semi‐physical PTFs were found to be more accurate than parametric PTFs. The best overall performance was shown by the semi‐physical Chang and Cheng PTF. Among the parametric PTFs the best accuracy was obtained with the Schaap and Bouten method. However, all considered functions showed limited accuracy in higher suction range. Additionally, infiltration experiments were performed on four sites. SWRCs were obtained from ring infiltrometer tests using the Beerkan estimation of soil transfer parameters (BEST) method and from the tension infiltrometer (TI) tests using numerical solution of the inverse problem based on the Richards equation. In almost all cases the wetting SWRCs were characterized by higher values of the pressure scaling parameter α compared to SWRCs measured in drainage experiments, which is consistent with the well‐known phenomenon of hysteresis in soils. However, the BEST method resulted in significantly higher α and hydraulic conductivity Ks than TI, probably due to activation of the largest soil pores during ponded infiltration.