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Soil structural traits provide links between soil structure and ecosystem functioning. The size and stability of soil aggregates are assumed to provide information on aggregate formation and turnover. A standard method to analyse these traits is to determine the mass distribution on sieves. The major drawback of this method is the small size resolution because of a small number of size classes. A promising, yet still unexplored, method for size distribution analysis in soil science, is dynamic image analysis, which foremost allows a much larger diameter resolution and the assessment of both size and shape distributions. The aim of our study was to validate the applicability of dynamic digital image analysis in combination with sonication to characterize the size and shape distribution and the stability of aggregates. We used two different heterogeneous reference materials and three different soil samples with different aggregate stabilities to test this method. The soil samples were chosen based on increasing clay, humus and calcium carbonate contents. The method yielded reproducible results for diameter and shape distributions for both reference materials and soil samples. The most important improvement compared to well‐established methods was the extremely large size resolution. This allows specification of the pattern of diameter‐dependent breakup curves by relating them to specific soil properties. The information on sphericity adds supplementary information on the aggregates released. We found much lower sphericity of 1‐mm aggregates mobilized from topsoil samples formed from the activity of living organisms than aggregates mobilized from subsoil samples formed mainly by physicochemical processes. Highlights: Our aim was to validate dynamic digital image analysis to characterize soil aggregates.Dynamic image analysis allows high resolution and shape analysis compared to established methods.The method yielded reproducible results for diameter and shape distributions.We established high‐resolution disruption patterns of aggregates enabling new approaches in future research. [ABSTRACT FROM AUTHOR]

We address the problem of stochastic simulation of soil particle-size curves (PSCs) in heterogeneous aquifer systems. Unlike traditional approaches that focus solely on a few selected features of PSCs (e.g., selected quantiles), our approach considers the entire particle-size curves and can optionally include conditioning on available data. We rely on our prior work to model PSCs as cumulative distribution functions and interpret their density functions as functional compositions. We thus approximate the latter through an expansion over an appropriate basis of functions. This enables us to (a) effectively deal with the data dimensionality and constraints and (b) to develop a simulation method for PSCs based upon a suitable and well defined projection procedure. The new theoretical framework allows representing and reproducing the complete information content embedded in PSC data. As a first field application, we demonstrate the quality of unconditional and conditional simulations obtained with our methodology by considering a set of particle-size curves collected within a shallow alluvial aquifer in the Neckar river valley, Germany. [ABSTRACT FROM AUTHOR]