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Hydrogeology of the lower sector of the Basento and Cavone river basins (southern Italy), has been little studied so far, although this area plays a strategic role being an agricultural area, for the production of wheat and organic olive oil, and represents a historical economic pole, with the presence of an industrial area and several extraction sites of natural gas. In this paper, starting from official Italian geological maps, a hydrogeological conceptual model and geodatabase were developed through hydrogeological characterization of lithological formations, analysis of topographic cartography and satellite images, as well as an extensive hydrogeological survey. The results are represented by a hydrogeological map at the 1:50,000 scale, hydrostratigraphic cross-sections, groundwater flow schemes, and a groundwater database. These are valuable tools for knowledge and may be used as a reference for future hydrogeological studies, as well as planning and decision-making in groundwater management.

Groundwater, a vital resource for drinking water supply, must be managed sustainably to ensure its availability and quality. In France, the SISE-Eaux database on water intended for human consumption, archived by the Regional Health Agencies (ARS) since 1990, constitutes a rich source of information. This study focused on the groundwater of the Nouvelle-Aquitaine region, the largest administrative region in metropolitan France, covering 84,061 km2 with 6 million inhabitants. It is based on a 22-year data extraction, resulting in a matrix of 121,649 observations and 51 physico-chemical and bacteriological parameters. Following logarithmic transformation of the data and fitting of variograms using the mean value of each parameter for each sampling point, the spatial distribution of numerous parameters across the region is presented. From this initial sparse matrix, a dense matrix of 23,319 samples (rows) and 15 key parameters (columns) was selected for a multivariate approach. A Principal Component Analysis (PCA) was used to condense the information and create summary maps capturing over 68% of the information contained in the dense matrix. The combined results of the multivariate analysis (dense matrix) and the distribution of individual parameters (sparse matrix) highlight the diversity of sources contributing to the spatial variability of groundwater, such as the role of lithology, the origin and pathways of fecal contamination, and the influence of redox processes. Neither the large size of the study area nor the high number of parameters proved to be an obstacle to the analysis. The understanding of ongoing processes and the factorial axis distribution maps, which enable the spatial representation of these mechanisms, can be used to facilitate groundwater monitoring and protection.

This study highlights the importance of distinguishing the mechanisms driving spatial and temporal variances in groundwater database analyses, with a particular focus on bacteriological contamination processes. Groundwater quality data from the Bourgogne-Franche-Comté region of France forms the basis of this investigation. Specifically, the SISE-EAUX database includes 3569 groundwater samples collected over various dates from 989 monitoring points. The methodology involves structuring the data into three distinct sets: (1) A spatio-temporal dataset without any conditioning; (2) A spatial dataset that assigns the mean values of each parameter to each sampling point; (3) A temporal dataset that captures deviations from the mean for each sampling point and parameter. These datasets enable a separate analysis of spatial and temporal variances. Principal component analysis (PCA) and parameter hierarchical clustering were used to compare the results, yielding valuable insights into the underlying processes. This analysis helps distinguish between factors related to geological or pedological spatial distributions and those associated with climatic events, such as intense rainfall episodes exhibiting seasonal patterns. Such differentiation enhances the understanding of fecal contamination vectors and nitrate pollution, which are often linked to surface and subsurface runoff in vulnerable catchment areas. While conceptually clear, the practical separation of spatial and temporal variability presents challenges. For example, catchments sensitive to surface water inflows during rainfall events are unevenly distributed across the region, correlating with specific natural environments. As a result, areas of high temporal variability are also well-structured spatially, underscoring the interdependence of these two types of variability. This complexity is exemplified by the behavior of iron, which varies in association with surface and subsurface parameters depending on spatial and temporal contexts. Additionally, asynchronous sampling and varying frequencies across sites lead to discrepancies in the average temporal data acquisition between points. These disparities can influence spatial variability calculations, as temporal variability effects are not entirely removed. Despite these challenges, the distinction between spatial and temporal components is essential for a deeper understanding of groundwater quality mechanisms. This refined approach improves diagnostic precision and supports more targeted and effective water resource management strategies.

Groundwater, widely used for supplying drinking water to populations, is a vital resource that must be managed sustainably, which requires a thorough understanding of its diverse physico-chemical and bacteriological characteristics. This study, based on a 27-year extraction from the Sise-Eaux database (1993–2020), focused on the island of Corsica (72,000 km2), which is diverse in terms of altitude and slopes and features a strong lithological contrast between crystalline Corsica and metamorphic and sedimentary Corsica. Following logarithmic conditioning of the data (662 water catchments, 2830 samples, and 15 parameters) and distinguishing between spatial and spatiotemporal variances, a principal component analysis was conducted to achieve dimensionality reduction and to identify the processes driving water diversity. In addition, the spatial structure of the parameters was studied. The analysis notably distinguishes a seasonal determinism for bacterial contamination (rain, runoff, bacterial transport, and contamination of catchments) and a more strictly spatial determinism (geographic, lithological, and land use factors). The behavior of each parameter allowed for their classification into seven distinct groups based on their average coordinates on the factorial axes, accounting for 95% of the dataset’s total variance. Several strategies can be considered for the inventory and mapping of groundwater, namely, (1) establishing quality parameter distribution maps, (2) dimensionality reduction through principal component analysis followed by two sub-options: (2a) mapping factorial axes or (2b) establishing a typology of parameters based on their behavior and mapping a representative for each group. The advantages and disadvantages of each of these strategies are discussed.

At the Department of Earth and Environmental Sciences of the University of Milano-Bicocca (DISAT-UNIMIB), a hydrogeological well database, called TANGRAM©, has been developed and published on line at www.TANGRAM.samit.unimib.it, developing an earlier 1989 DOS version. This package can be used to store, display, and process all data related to water wells, including administrative information, well characteristics, stratigraphic logs, water levels, pumping rates, and other hydrogeological information. Currently, the database contains more than 39.200 wells located in the Italian region of Lombardy (90%), Piedmont (9%) and Valle d’Aosta (1%). TANGRAM© has been created both as a tool for researches and for public administration’s administrators who have projects in common with DISAT-UNIMIB. Indeed, transferring wells data from paper into TANGRAM© offers both an easier and more robust way to correlate hydrogeological data and a more organized management of the administrative information. Some Administrations use TANGRAM© regularly as a tool for wells data management (Brescia Province, ARPA Valle Aosta). An innovative aspect of the database is the quantitative extraction of stratigraphic data. In the part of the software intended for research purposes, all well logs are translated into 8-digit alphanumeric codes and the user composes the code interpreting the description at each stratigraphic level. So the stratigraphic well data can be coded, then quantified and processed. This is made possible by attributing a weight to the digits of the code for textures. The program calculates the weighted percentage of the chosen lithology, as related to each individual layer. These extractions are the starting point for subsequent hydrogeological studies: well head protection area, reconstruction of the dynamics of flow, realization of the quarry plans and flux and transport hydrogeological models. The results of a two-dimensional distribution of coarse, medium and fine sized material in the first 80 meters of depth are presented here for a study area located within the Province of Brescia (Italy).

This work provides a groundwater flow and transport model of trichlorethylene and tetrachlorethylene contamination in the Cecina's coastal aquifer. The contamination analysis, with source located in the Poggio Gagliardo area (Montescudaio, Pisa), was necessary to optimize the groundwater monitoring and remediation design. The work was carried out in two phases: • design of a conceptual model of the aquifer using GIS analysis of many stratigraphic, chemical and hydrogeological data, collected from 2004 to 2012 in six aqueduct wells; • implementation of a groundwater flow and transport numerical model using the MODFLOW 88/96 and MT3D code and the graphical user interface GroundWaterVistas 5. The conceptual model hypothesizes a multilayer aquifer in the coastal plain extended to the sandy-clay hills, recharged by rainfall and by the Cecina River. The aquifer shows important hydrodynamic features affecting both the contamination spreading, due to the presence of a perched and heavily polluted layer separate from the underlying productive aquifer, and the hydrological balance, due to a thick separation layer that limits exchanges between the river and the second groundwater aquifer. The numerical model, built using increasingly complex versions of the initial conceptual model, has been calibrated using monitoring surveys conducted by the Environmental Protection Agency of Regione Toscana (ARPAT), in order to obtain possible forecast scenarios based on the minimum and maximum flow periods, and it is currently used as a tool for decision support regarding the reclamation and/or protection of the aquifer. Future developments will regard the implementation of the multilayer transport model, based on a new survey, and the final coupling with the regional hydrological model named MOBIDIC.

Assessment of mining impact on groundwater is one of critical considerations for longwall extension and sustainability, however usually constrained by limited data availability, hydrogeological variation, and the complex coupled hydro-mechanical behaviour. This paper aims to determine the factors and mechanism of groundwater depressurisation and identify knowledge gaps and methodological limitations for improving groundwater impact assessment. Analysis of dewatering cases in Australian, Chinese, and US coalfields demonstrates that piezometric drawdown can further lead to surface hydrology degradation, while the hydraulic responses vary with longwall parameters and geological conditions. Statistical interpretation of 422 height of fracturing datasets indicates that the groundwater impact positively correlates to panel geometry and depth of cover, and more pronounced in panel interaction and top coal caving cases. In situ stress, rock competency, clay mineral infillings, fault, valley topography, and surface–subsurface water interaction are geological and hydrogeological factors influencing groundwater hydraulics and long-term recovery. The dewatering mechanism involves permeability enhancement and extensive flow through fracture networks, where interconnected fractures provide steep hydraulic gradients and smooth flow pathways draining the overlying water to goaf of lower heads. Future research should improve fracture network identification and interconnectivity quantification, accompanied by description of fluid flow dynamics in the high fracture frequency and large fracture aperture context. The paper recommends a research framework to address the knowledge gaps with continuous data collection and field-scale numerical modelling as key technical support. The paper consolidates the understanding of longwall mining impacting mine hydrology and provides viewpoints that facilitate an improved assessment of groundwater depressurisation.

The coefficient of permeability ( k ) is an important soil property for various flow-related problems and site characterization. In this study, a new method for estimating k is proposed based on the time response of groundwater level (GWL) to river stage (RS) with changes in loosing and gaining streams during normal and rainy periods, respectively. Focus was given on establishing a method that does not require costly experimental testing procedure while utilizing locally available hydrology and geologic database. For this purpose, the GWL–RS correlation model was introduced and adopted to develop the k correlation model based on the time response of GWL to RS. The proposed method was calibrated using measured datasets of GWL and RS collected from 17 different regions. Based on the compared results from the measured datasets, a modification factor ( M f ) was established and adopted to further enhance the k correlation model. When the M f was adopted, the R 2 value of the prediction was 0.8756, and the RMSE decreased to 0.00643. To check the applicability of the proposed method, additional datasets were collected and adopted into the comparison. It was confirmed that measured and predicted soil permeabilities were in close agreement.

Groundwater, Earth’s largest nonfrozen freshwater reservoir, is vital for water supply security. Groundwater models help to manage complex domestic, agricultural, and industrial water demands while preserving ecosystem health under climate change. The community-driven groundwater model portal (GroMoPo) hosts groundwater model metadata to analyse biases and distribution of groundwater models. Over 450 models are currently featured on GroMoPo, with most models from high-GDP countries at local-to-regional scales. The GroMoPo initiative addresses current knowledge gaps and facilitates future collaboration and data sharing.

When studying large multiparametric databases with very heterogeneous parameters (microbiological, chemical, and physicochemical), covering a wide and heterogeneous area, the probability of observing extreme values (Z-score > 2.5) is high. The information carried by these few samples monopolizes a large part of the information conveyed by the entire database. The study of the spatial structure of the data and the identification of the mechanisms responsible for the water quality are then strongly degraded. Data transformation can be proposed to overcome these problems. This study deals with a database of 8110 groundwater analyses (Occitanie region, France), on which the bacteriological load was measured in Escherichia coli and Enterococci, in addition to electrical conductivity, major ions, Mn, Fe, As and pH. Three modes of data conditioning were tested and compared to the treatment with raw data. The results show that log transformation is the best option, revealing a relationship between E. coli content and all the other parameters. By reducing the impact of extreme values without eliminating them, it allowed a concentration of information on the first factorial axes of the PCA, and consequently a better definition of the associated processes. The spatial structure of the principal components and their cartographic representation is improved. The conditioning of the data with the square root function led to an intermediate improvement between the logarithmic transformation and the absence of conditioning. The application of these results should allow a targeted, more efficient, and therefore, less expensive monitoring of water quality by Regional Health Agencies.