Effective management of Managed Aquifer Recharge (MAR) systems is strongly influenced by the interplay between geological reservoir features and kinetically controlled geochemical reactions. The former are often approximated by a simple geometrical distribution of hydrofacies, while the latter are approximated as unreactive systems or as systems that always reach geochemical equilibria. Fluid-rock interactions induce modifications of the physical-chemical characteristics of the mineral porous medium, the dissolved gases compositions, and the aqueous solution. This setup induces modifications in preferential flow paths, pollutant residence time, and pollutant persistence in the reservoir. The numerical simulation of such multiphase systems is thus challenging due to the combined nonlinear and time-dependent effects acting on them. The presented results focus on the evaluation of the multifaceted uncertainty through a sensitivity analysis, derived from the quantification of the individual impact of: (i) the adaptive spatial discretization resolution, (ii) the kinetically controlled processes, and (iii) the heterogeneity uncertainty in multiphase reactive transport simulations. A MAR system is modelled over a highly heterogeneous geological section, accounting for reactive processes associated with water-rock-gas interactions. These processes are evaluated through the Transition State Theory. The workflow involves (i) geometric spatial discretization of the reservoir in the form of an unstructured mesh, coupled with geostatistical generation of porosity and permeability fields (as continuous and categorical variables, respectively) using MUSE software (Miola 2025, PhD thesis & EGU25); (ii) segmentation of the domain into homogeneous regions via FSUM (Sorgente et al. 2026, Computers & Geosciences) and localized mesh refinement with an increase of details over hydraulic impedance surfaces; (iii) conversion of stochastic geological models into data formats, and (iv) execution of parallel multiphase reactive transport simulations with PFLOTRAN (Hammond et al. 2014, Water Resources Research). The entire process is managed through EWOPE (Miola et al. 2026, Computers & Geosciences), an open-source computational workflow tracker, which ensures full reproducibility and traceability by recording metadata, enabling the backward reconstruction of computational history and a deep analysis of the single multi-realization computations, the core of the uncertainty evaluation. Simulation results indicate that a MAR can be planned and managed by considering a multi-scenario involving variations in unsaturated medium properties, pollutant arrival times, and variations in the flow patterns, from a probabilistic point of view.

We present MUSE (Modeling Uncertainty as a Support for the Environment), a geostatistics framework for quantifying spatial uncertainty through mesh-based stochastic simulation, able to handle both continuous and categorical attributes. Grounded in geostatistical theory, MUSE treats environmental observations as regionalized variables and is fully scalable across two- and three-dimensional domains. Implemented as a C++ open-source tool, it adopts pragmatic fit-for-purposes computational solutions to support demanding geoscientific workflows. MUSE (i) employs a rich data format for encoding sampled data features and their relations; (ii) integrates geometry processing techniques to represent the morphology of computational domains through 2D/3D structured or unstructured meshes; (iii) provides an automatic and/or supervised 3D variography and anisotropy detection, taking into account stratigraphic coordinates; (iv) computes parallel stochastic simulations to generate multiple equiprobable realizations for uncertainty quantification on mesh-based supports. Workflow transparency and reproducibility are ensured through a companion tool that formalizes, tracks, and preserves modeling processes over time.MUSE has been validated across diverse operational settings, demonstrating generality, multi-dimensionality, flexibility, and computational efficiency, with applications spanning geological modeling, geophysics, geothermal resource evaluation, real-time environmental monitoring and related fields. Through its modular design, MUSE offers a unified, reproducible environment for spatial uncertainty-aware modeling, addressed to a broad audience of environmental professionals, agencies, research institutions, and policy- and decision-makers.

We present an algorithm for segmenting a (stochastic) scalar field defined on an unstructured mesh into a given number of parts. It can be applied to any type of mesh, such as triangular/tetrahedral meshes, 2D/3D grids, and generic polygonal/polyhedral meshes, inducing a classification of the mesh elements into regions with limited noise and smooth boundaries. The algorithm offers multiple output options, providing valuable information about the segmentation and the mesh regions in various file formats, thus making it suitable for practical applications. We show the algorithm at work in different application scenarios, ranging from environmental geochemistry to marine sciences and groundwater modeling, proving its efficacy and versatility.

This paper presents the integration of classical limit equilibrium methods for slope stability analysis, including the Bishop, Morgenstern–Price and Spencer formulations, into a dedicated QGIS plugin. The approach exploits digital terrain and bedrock models to perform slope stability assessments directly within a GIS environment, thereby enabling the definition of landslide susceptibility maps without resorting to the simplifying infinite slope assumption. An efficient and robust implementation of the method of slices is proposed to ensure accurate estimates of both the factor of safety and the associated critical slip surface while limiting computational cost. The critical slip surface is identified using a hybrid discrete–continuous optimisation strategy based on a physically based parametrization of the potential slip surfaces. The formulation allows soil heterogeneity, stratification and depth-dependent saturation conditions to be explicitly considered. The proposed approach is validated against complex natural hillslopes. The results show good agreement with available reference data in terms of predicted unstable areas and computed safety factors. The accuracy of the outcomes ultimately depends on the quality of the input data, including soil properties, spatial variability, rainfall history and groundwater levels.

Scientific workflows are essential in modern geoscientific research, where complex, multi-stage computational pipelines are used to analyze heterogeneous environmental data. Ensuring the reproducibility and traceability of these workflows is critical but often challenging due to their intricacy and evolving structure. We introduce EWoPe (Embeddable Workflow Persistence), a lightweight and embeddable methodology and C++ library designed to persist and reconstruct scientific workflows over time. Unlike existing workflow systems and provenance tools, our method adds minimal overhead to workflow execution: it does not automate or optimize processes, but instead ensures persistence through lightweight and structured metadata. EWoPe models workflows as directed acyclic graphs (DAGs), in which each data node is linked to computational tasks through metadata. The latter captures input-output dependencies, algorithm parameters, execution commands, and intermediate results, supporting full traceability and reproducibility of computational histories. EWoPe offers dual usability: as a standalone command-line tool or as an embeddable component within larger applications. We show its flexibility and applicability through a case study involving a complex workflow leading to subsurface reaction-transport modeling, starting from boreholes data. The EWoPe library is publicly available and designed to be extensible, making it suitable for a wide range of scientific domains, including geochemistry, geophysics, environmental engineering, and any other fields where transparency and data integrity are critical.

Decision-making in environmental monitoring and remediation is heavily influenced by technical and regulatory tools. However, these tools often fail to take full account of the spatial heterogeneity of geological matrices. This diffuse behavior results in an ineffective approach to managing local environmental characteristics. To address this issue, stochastic geochemical modelling allows for more informed management of remediation thresholds by quantifying the spatial uncertainty of a regionalized variable. In this context, our research aims to explore the concept of geochemical compatibility of a matrix using a stochastic approach. In particular, the spatial geochemical compatibility (SGC) of stream sediments will be investigated using a tailored processing workflow to ensure a priori data quality control. The proposed workflow formalises a multi-step computational pipeline that includes the definition of computational domains, the implementation of geostatistical techniques and the performance checking of the stochastic model. The application of the pipeline provides a digital stochastic-based representation of the uncertainty about stream sediments of a heterogeneous area for 34 elements. For each element, 8 layers summarize as many statistical indices as are extracted from the local cumulative probability density functions. The pipeline has been validated by testing it in the heterogeneous mountainous area of the Liguria region (Italy) and a multilayer high-resolution (200 m) geochemical numerical model (GNML) has been generated. This model is starting from 2021 publicly distributed and used as a decision support tool for SGC in routine regulatory and monitoring practice. It empowers the community with geochemical knowledge and helps local decision makers to understand and use the concept of environmental uncertainty. Deep control over each computational step and quality control of performance suggests how the pipeline can be generalized in complex areas, from single catchments to entire countries, and for different regionalized variables.

Over the past few decades, geoscientists have progressively exploited and integrated techniques and tools from Applied Mathematics, Statistics, and Computer Science to investigate and simulate natural phenomena. Depending on situation, the sequence of computational steps may vary, leading to intricate workflows that are difficult to reproduce and/or further revisit. To ensure that such workflows can be repeated for validation, peer review, or further investigation, it is necessary to implement strategies of workflow persistence, that is the ability to maintain the continuity, integrity, and reproducibility of a workflow over time.In this context, we propose an efficient strategy to support workflow persistence of Geoscience pipelines (i.e., Environmental Workflow Persistence methodology EWoPe). Our approach enables to document each workflow step, including details about data sources, processing algorithms, parameters, final and intermediate outputs. Documentation aids in understanding the workflow's methodology, promotes transparency, and ensures replicability. Our methodology views workflows as hierarchical tree data structures. In this representation, each node describes data, whether it's input data or the result of a computational step, and each arc is a computational step that uses its respective nodes as inputs to generate output nodes. The relationship between input and output can be described as either one-to-one or one-to-many, allowing the flexibility to support either singular or multiple outcomes from a single input.The approach ensures the persistence of workflows by employing JSON (JavaScript Object Notation) encoding. JSON is a lightweight data interchange format designed for human readability and ease of both machine parsing and generation. By this persistence workflow management, each node within a workflow consists of two elements. One encodes the raw data itself (in one or multiple files). The other is a JSON file that describes through metadata the computational step responsible for generating the raw data, including the reference to input data and parameters. Such a JSON file serves to trace and certify the source of the data, offering a starting point for retracing the workflow backward to its original input data or to an intermediate result of interest.Currently, EWoPe methodology has been implemented and integrated into MUSE (Modeling Uncertainty as a Support for Environment) (Miola et al., STAG2022), a computational infrastructure to evaluate spatial uncertainty in multi-scenario applications such as in environmental geochemistry, reservoir geology, or infrastructure engineering. MUSE allows running specific multi-stage workflows that involve spatial discretization algorithms, geostatistics, and stochastic simulations: the usage of EWoPe methodology in MUSE can be seen as an example of its deployment.By exploiting transparency of input-output relationships and thus ensuring the reproducibility of results, EWoPe methodology offers significant benefits to both scientists and downstream communities involved in utilizing environmental computational frameworks.Acknowledgements: Funded by the European Union - NextGenerationEU and by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), Mission 4, Component 2, Investment 1.5, project “RAISE - Robotics and AI for Socio-economic Empowerment” (ECS00000035) and by PON "Ricerca e Innovazione" 2014-2020, Asse IV "Istruzione e ricerca per il recupero", Azione IV.5 "Dottorati su tematiche green" DM 1061/2021.

Modelling objects at a large resolution or scale brings challenges in the storage and processing of data and requires efficient structures. In the context of modelling urban environments, we face both issues: 3D data from acquisition extends at geographic scale, and digitization of buildings of historical value can be particularly dense. Therefore, it is crucial to exploit the point cloud derived from acquisition as much as possible, before (or alongside) deriving other representations (e.g., surface or volume meshes) for further needs (e.g., visualization, simulation). In this paper, we present our work in processing 3D data of urban areas towards the generation of a semantic model for a city digital twin. Specifically, we focus on the recognition of shape primitives (e.g., planes, cylinders, spheres) in point clouds representing urban scenes, with the main application being the semantic segmentation into walls, roofs, streets, domes, vaults, arches, and so on.Here, we extend the conference contribution in Romanengo et al. (2023a), where we presented our preliminary results on single buildings. In this extended version, we generalize the approach to manage whole cities by preliminarily splitting the point cloud building-wise and streamlining the pipeline. We added a thorough experimentation with a benchmark dataset from the city of Tallinn (47,000 buildings), a portion of Vaihingen (170 building) and our case studies in Catania and Matera, Italy (4 high-resolution buildings). Results show that our approach successfully deals with point clouds of considerable size, either surveyed at high resolution or covering wide areas. In both cases, it proves robust to input noise and outliers but sensitive to uneven sampling density.

The digital twin (DT) paradigm provides a purpose-built digital representation of a physical system. DTs are often composed by several interconnected models, which need to be specifically tailored to fit the DT purposes. The objective of this work is the development of an urban digital twin (UDT) for the city of Matera, following urban intelligence (UI) paradigm. The latter leverages the multi-disciplinary integration and optimization of the city systems and subsystems to develop purpose-driven DTs and support the decision-making process. The UDT is intended to support governance, stakeholders, and citizens, integrating morphological data for the city representation, reliable simulation tools and data-driven methods for the city state prediction (e.g., traffic, solar irradiation), optimization algorithms for planning and emergency response, sensors for vehicle and pedestrian traffic volumes and environmental monitoring (e.g., pollutant distributions), and a participatory data collection process from the citizens. A Data Lake is used to make available to the UDT modules the data produced by the morphological representation, simulations/data-driven methods, sensors, and the participatory data. The Data Lake provides a standardized approach for the aggregation and extraction of information. Finally, an Urban Sensing Engine supports the decision-making process with artificial intelligence forms of reasoning, to effectively combine the information produced by the UDT components.

Intermediate-depth seismicity is common in subducting slabs and the seismicity rate shows some statistically significant yet enigmatic global positive correlation with the maximal throw of outer-rise normal faults. Here, we have simulated the formation and subduction of outer-rise faults, using 2D thermomechanical numerical models of intra-oceanic subduction with coupled brittle-ductile damage of bending plates. We observed that outer-rise faults are formed episodically during slab segmentation and their maximal throw grows with time. When been subducted to intermediate depth, these faults are locally reactivated either by i) slab unbending/bending, simultaneous to the formation of new outer-rise faults or ii) episodic interplate coupling related to the rugged morphology of the faulted downgoing plate. Faults reactivation is concurrent with a local, transient deviatoric stress increase in intraslab domains among these structures. We suggest that slab domains affected by stress increase could be the appropriate location where potential brittle deformation can occur, generating intermediate-depth intraslab earthquakes, that are predominantly localized in heterogeneous regions of dense faulting formed within slab-segments boundaries. The temporal coincidence of stress growth at intermediate depths and throw-growth of, newly-formed, outer-rise faults at the surface may possibly explain the observed global positive correlation of intermediate-depth seismicity rate with maximal fault throw. Intermediate-depth intra-slab earthquakes can be generated in the regions of subducted slabs affected by transient stress increases, according to 2-D thermomechanical modeling of bending plates at subduction zones.

Both abiotic and biotic natural spheres benefit from the high reactivity of natural waters, which are ubiquitous on planet Earth. The use of speciation-solubility codes like PHREEQC can provide a deeper understanding of aqueous equilibria and water-rock interactions. A significant number of newly derived variables are produced by these computations, which may significantly increase compared to input data. It is crucial to process vast amounts of data efficiently, particularly when dealing with datasets that contain thousands of water analyses.To tackle this problem, we present PHREESQL, a software package designed to efficiently store and manage extensive data generated by geochemical speciation computations performed by PHREEQC. High efficiency in data extraction and filtering of the entire output from a single run can be achieved through a well-designed relational SQL database structure. The PHREESQL can be used as a stand-alone package or embedded in third-party applications. Thanks to the SQL structure, it is possible to create links with unstructured meshes by developers and experts in reaction-transport problems. Real-time data management from multiparameter devices in field and laboratory settings is made possible and efficient by parallel computation options and software integration. The toolkit encompasses both a C++ library and a command-line interface, facilitating its use by geochemists with limited programming skills.

This paper proposes two algorithms to impose seepage boundary conditions in the context of Richards’ equation for groundwater flows in unsaturated media. Seepage conditions are non-linear boundary conditions, that can be formulated as a set of unilateral constraints on both the pressure head and the water flux at the ground surface, together with a complementarity condition: these conditions in practice require switching between Neumann and Dirichlet boundary conditions on unknown portions on the boundary. Upon realizing the similarities of these conditions with unilateral contact problems in mechanics, we take inspiration from that literature to propose two approaches: the first method relies on a strongly consistent penalization term, whereas the second one is obtained by an hybridization approach, in which the value of the pressure on the surface is treated as a separate set of unknowns. The flow problem is discretized in mixed form with div-conforming elements so that the water mass is preserved. Numerical experiments show the validity of the proposed strategy in handling the seepage boundary conditions on geometries with increasing complexity.

This paper presents an optimised algorithm implementing the method of slices for slope stability analysis. The algorithm features a novel physically based parameterisation of slip surfaces, defined by their geometric characteristics at the endpoints, capturing all and only valid failure mechanisms. The algorithm also employs a hybrid discrete–continuous search strategy to identify the critical slip surface, beginning with a discrete computation of the factor of safety over a coarse grid spanning the entire parameter space, followed by a focused continuous exploration of the most promising region via a simplex optimisation. This methodology reduces computational time up to 92

This paper tackles the volumetric representation of geophysical and geotechnical data, gathered during exploration surveys of the subsoil; in particular, we focus on the modeling and analysis of underwater deposits. The creation of a 3D model as support to geological interpretation has to take into account the heterogeneity of the input data, coming from offshore acquisition campaigns. Some data are massive, but cover the domain unevenly, e.g., along dense differently spaced lines, while others are very sparse, e.g., borehole locations with soil sampling and CPTU (Piezocone Penetration Test) locations. A automatic process is presented to generate the subsurfaces and volume defining a sub-seabed deposit, starting from the identification of relevant morphological features in seismic data. In particular, simplification and refinement based on geostatistics have been applied to generate regular 2D meshes from strongly anisotropic data, in order to improve the quality of the final 3D tetrahedral mesh. Furthermore, we also use geostatistics to predict geotechnical parameters from local surveys and estimate their distribution on the whole domain: in this way the 3D model will include relevant geological features of the deposit and allow extrapolating different geotechnical information with associated uncertainty. The volume characterization and its 3D inspection will support geological analysis and planning of future engineering activities. The developed methodology has been tested on two real case studies. • We implemented a complete pipeline for the 3D modeling of stratified deposits • Our research action follows the surface-based approach • We combine computer graphics methodologies with geophysics and geostatistics

The creation of 3D models of heritage and architectural sites requires proper technologies able to capture a wide area at fine geometric and appearance detail. In this paper we address the acquisition and digitization of three challenging Points of Interest in Matera, Italy. The sites, both outdoor and indoor, are characterised by limited accessibility, complex morphology and poor lighting conditions. We describe our experience with a portable, lightweight laser scanner, describing the planning, acquisition and post-processing phases, and providing some lessons learnt in order to achieve good results in terms of quality and resolution.