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Calibrating and verifying 2-D and 3-D vadose zone flow and transport models requires detailed information on water and solute redistribution. Among the different water flow and mass transfer determination methods, staining tracers have the best spatial resolution allowing visualization and quantification of fluid flow including preferential flow paths. Staining techniques have been used successfully for several decades; however, the hydrological community is still searching for an “ideal” vadose zone tracer regarding flow path visualization. To date, most research using staining dyes is carried out with Brilliant Blue FCF. Fluorescent dyes such as Uranine, however, have significant advantages over nonfluorescents which makes them a promising alternative. This paper presents the first analysis of key properties any fluorescent substance must possess to qualify as a staining fluorescent tracer in vadose zone hydrological applications. First, we summarize the main physico-chemical properties of Uranine and evaluate its staining tracer potential with conventional suitability indicators and visibility testing in a soil profile. Based on numerical analysis using the theory of fluorescence, we show that a low molar absorption coefficient is a crucial parameter to quantify concentration accurately. In addition, excitation of a tracer on wavelengths different from the maximum excitation wavelength can extend the linear range of the concentration-fluorescence relationship significantly. Finally, we develop criteria for evaluating the suitability of any potential fluorescent soil staining compound for soil hydrological applications: 1) high quantum yield, 2) low molar absorption coefficient, 3) fluorescence independent of temperature, 4) low photodecomposition rates, and 5) fluorescence stable across a wide range of pH values.

Classic soil physics relies heavily on the concept of representative elementary volume (REV), which is necessary to perform upscaling from the studied soil samples and parameterize continuum scale hydrological models (e.g., based on Richards or Darcy equations). In this paper, we explore the boundaries of the classic REV concept and conventional representativity studies that claim REV for any given physical property, and that its values converge to a steady value with increasing sample volume. We chose two undisturbed soil samples of standard size from Ah and B horizons, subcropped two subvolumes within each of them and performed pore‐scale flow simulations using binarized X‐ray microtomography scans as input data. The volume of the simulation domains was 9003 voxels, with a physical volume within two orders of magnitude of the whole soil core. Based on the 3D pore geometry images and the resulting flow velocity and pressure fields, we performed REV analysis for the saturated hydraulic conductivity and porosity. Although in general subvolumes showed classical REV behaviour (convergence of porosity and saturated hydraulic conductance (Ksat)to plateau‐like behaviour), their flow properties converged to different REV values. We also evaluated the stationarity of pore structures by computing the directional correlation functions to explain the observed non‐unique behaviour. We concluded that neither of the studied samples can be considered to be representative due to their structural non‐stationarity. We extensively discussed the implications of these results for the upscaling and parameterization of continuum‐scale flow models. We argued that the plateau in Ksat (or any other physical property) is a necessary, but insufficient, condition for the REV, which requires (at least) pore structure stationarity as an additional criterion. REV as a concept is much broader than previously anticipated and the possibility of establishing REVs in structured soils will require an analysis of tensorial flow properties with correct boundary conditions, multiscale soil structure imaging with stationarity analysis, and pore‐scale simulations on fused multiscale images. Highlights: Ksat analysis within subvolumes showed classical REV behaviour but resulted in non‐unique REVs for the soil samples studiedBased on the stationarity analysis we argued that the conventional soil core samples studied were not REVsPlateau‐like behaviour in physical properties is a necessary but insufficient condition for REVSoil structure information is necessary to properly establish REV or upscale flow properties [ABSTRACT FROM AUTHOR]

Soil structure is highly interconnected to all of its properties and functions. The structure for most soils is very complex and hierarchical in nature. Considering the fact that a truly multiscale digital 3D soil structure model for a single genetic horizon, even with the resolution not finer than 1 μm, will contain an enormous amount (approx. up to 1015 voxels or even more) of data, it is an appealing idea to compress this structural information. Effective management and pore‐scale simulations based on such datasets do not seem feasible at the moment. Another approach would be to reduce the complexity to a limited but meaningful set of characteristics/parameters, for example using universal correlation functions (CFs). In this study, we successfully compressed the soil structural information in the form of 3D binary images into a set of correlation functions, each of which is described using only six parameters. We used four different correlation functions (two‐point probability, lineal, cluster and surface‐surface functions) computed in three orthogonal directions for the pores. The methodology was applied to 16 different soil 3D images obtained using X‐ray microtomography (XCT) and segmented into pores and solids. All computed CFs were fitted using a superposition of three basis functions. In other words, we reduced 900–13003 voxel images into sets of 72 parameters. Fitting of computed correlation functions and reducing them to a number of parameters is a powerful way of compressing soil structural information. However, the analysis based on parameters alone is different from the one where correlation functions are used. This problem can be negated by uncompressing the correlation functions back from these parameters before any application. This way, correlation functions are not only a way to compress the soil structural information with minimal loss, but also may be used to solve a number of additional problems, including the comparison and differentiation of soil samples, location of elementary volumes, effective physical property prediction using machine learning, and fusion of hierarchical soil structures. Highlights: The 900–13003 voxels soil XCT scans were compressed into sets of 72 parametersThe use of fitted parameters alone may result in the inconsistent analysis of the soil structuresEach soil structure was uniquely described by a set of directional correlation functionsCorrelation functions were found to be sensitive to the structural difference of all the studied soils [ABSTRACT FROM AUTHOR]