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There are increasing evidences that probability distributions and associated statistical moments of a variety of hydrogeological and soil science variables and their spatial increments display distinctive scale-dependent features that are not captured by a typical Gaussian model. A Generalized Sub-Gaussian (GSG) model is able to capture key aspects of this pattern. We present the results of a suite of computational analyses set in a Monte Carlo framework and aimed at assessing the impact of a GSG structure of log hydraulic conductivity (Y) on transport of a conservative solute through a three-dimensional bounded porous medium under steady-state saturated Darcy flow. Our results indicate that the longitudinal spreading of a plume is on average significantly smaller for Sub-Gaussian than for Gaussian Y fields. Otherwise, the velocity field arising from a Sub-Gaussian Y field induces enhanced plume stretching with respect to what can be observed in a Gaussian Y setting, this aspect potentially influencing the strength of solute mixing within these two types of conductivity domains. We also find that, in some cases, it may be difficult to identify the nature of the underlying conductivity field relying solely on observations of solute concentrations migrating within the system. In this regard, we show that the action of local dispersion tends to mask the influence of Sub-Gaussianity on major transport metrics.

European rivers are increasingly impacted by frequent and lasting dry periods, with consequences on jeopardized ecosystems and local economies. Tools for monitoring the evolution of such impacts may be profitable exploited by public administration to assess environmental conditions and draw safeguard policies. This work presents the evolution of a methodology which integrates optical and radar imagery, by Copernicus Sentinel constellations, to map river water surfaces. Despite the base methodology being developed as a man-supervised classification, with necessity for the user to manually define training polygons, the proposed advancements will allow the system to automate training sample extraction. The process is based on the realization of binary masks, originated by processing optical and radar imagery with a BMax Otsu algorithm for image segmentation. The masks are then furtherly refined to obtain a reliable set of classified pixels, from which the training samples are extracted. A sensitivity analysis is performed for assessing the optimal amount of pixels to be considered, with respect to the total area of interest. Furthermore, the performances of several Machine Learning supervised classification algorithms are compared, leading to the selection of the best algorithm to be considered for future developments of the methodology. [ABSTRACT FROM AUTHOR]

We introduce a comprehensive and robust theoretical framework and operational workflow that can be employed to enhance our understanding, modeling and management capability of complex heterogeneous large-scale groundwater systems. Our framework encapsulates key components such as the three-dimensional nature of groundwater flows, river–aquifer interactions, probabilistic reconstruction of three-dimensional spatial distributions of geomaterials and associated properties across the subsurface, multi-objective optimization for model parameter estimation through stochastic calibration, and informed global sensitivity analysis (GSA). By integrating these components, we effectively consider the inherent uncertainty associated with subsurface system characterizations as well as their interactions with surface waterbodies. The approach enables us to identify parameters impacting diverse system responses. By employing a coevolutionary optimization algorithm, we ensure efficient model parameterization, facilitating simultaneous and informed optimization of the defined objective functions. Additionally, estimation of parameter uncertainty naturally leads to quantification of uncertainty in system responses. The methodology is designed to increase our knowledge of the dynamics of large-scale groundwater systems. It also has the potential to guide future data acquisition campaigns through an informed global sensitivity analysis. We demonstrate the effectiveness of our proposed methodology by applying it to the largest groundwater system in Italy. We address the challenges posed by the characterization of the heterogeneous spatial distribution of subsurface attributes across large-scale three-dimensional domains upon incorporating a recent probabilistic hydrogeological reconstruction specific to the study case. The system considered faces multiple challenges, including groundwater contamination, seawater intrusion, and water scarcity. Our study offers a promising modeling strategy applicable to large-scale subsurface systems and valuable insights into groundwater flow patterns that can then inform effective system management. [ABSTRACT FROM AUTHOR]

Chemical weathering associated with dissolution/precipitation at interfaces between minerals and flowing fluids is key for the evolution of geologic systems, including groundwater contamination and storage capacity. Relying on Atomic Force Microscopy (AFM) yields reaction rates at nanoscale resolutions. Challenges limiting our ability to quantify heterogeneity associated with these processes include establishing reliable platforms allowing AFM imaging of real‐time and in situ absolute material fluxes across mineral surfaces under continuous flow conditions to complement typically acquired surface topography images. We provide an experimental workflow and heterogeneous absolute rates at the nanoscale across the surface of a calcite crystal under dissolution. These high‐quality experimental observations are then interpreted through a stochastic approach. The latter is geared to embed diverse kinetic modes driving the degree of spatial heterogeneity of the reaction and corresponding to different mechanistic processes documented across the crystal surface. Plain Language Summary: Quantification of basic processes underpinning precipitation/dissolution at mineral/fluid interfaces is key for realistic assessment of chemical weathering rates driving rock morphology, subsurface storage capacity and contamination. We provide direct observation of the complex mechanistic processes acting at nanoscales through an original experimental platform relying on Atomic Force Microscopy imaging to evaluate absolute material fluxes associated with dissolution of a mineral subject to reaction under continuous flow conditions. Dissolution is characterized at very high spatial resolutions (∼10 nm). This enables observing in real‐time and in situ mechanistic processes driving system evolution. The ensuing rich data set of absolute reaction rates displays a marked degree of spatial heterogeneity. The latter is then interpreted within a stochastic framework to yield a detailed mechanistic appraisal of mineral dissolution. Key Points: A platform to evaluate absolute nanoscale topographic measurements of a crystal sample subject to dissolution/precipitation is designedThe associated spatially heterogeneous fields of absolute material fluxes across the surface are evaluatedReaction rates are described through a stochastic framework encapsulating behaviors of surface features driving dissolution processes [ABSTRACT FROM AUTHOR]

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