The phenomenon of seawater intrusion is becoming increasingly problematic, particularly in low-lying coastal regions and areas that rely heavily on aquifers for their freshwater supply. It is, therefore, vital to address the causes and consequences of this phenomenon in order to ensure the security of water resources and the sustainable use of water. The objective of this paper was twofold: firstly, to delineate zones with different salinization levels over time; secondly, to investigate the factors controlling seawater intrusion of the Nile Delta aquifer. Aquifer data were collected in Sharkia governorate, Egypt, over three historical periods of years: 1996, 2007, and 2018. The dataset used to create the linear model of coregionalization consisted of hydrogeological (water level), hydrodynamic (pH, EC, Na, Mg, K, Ca, HCO3, SO4), and auxiliary (distances from salt and freshwater sources) variables. Cokriging was applied to produce spatial thematic maps of the studied variables for the three years of the survey. In addition, factorial cokriging was applied to understand the processes beyond the change in the aquifer water quality and map the zones with similar characteristics. Results of mapping the first factor at long range over the three years indicated that there was an increase in seawater intrusion, especially in the northeastern part of the study area. The main cause of aquifer salinization over time was the depletion of the groundwater resource due to overexploitation.

The work aims to estimate natural greenhouse gas emissions from soils in the Sibari Coastal Plain (Southern Italy), to understand (i) the contribution in terms of the total amount of CO₂ and CH₄ emitted in non-volcanic areas, (ii) the relationship among emitted gas, land use, organic matter and tectonic structures, and (iii) their potential environmental implications. Data were elaborated with statistical and geostatistical methods to separate the different populations and obtain prediction and probability maps. Methane fluxes had values consistently below the detection limit (0.032 g ∙ m⁻² ∙ d⁻¹) except for three measurement points randomly distributed along the plain. Statistical and geostatistical methods allowed to discriminate three main CO₂ flux populations: (i) high-flux population (Pop. B - mean value of 63.65 g ∙ m⁻² ∙ d⁻¹), located near the mouth of the Crati River and related to the massive presence of buried organic matter in the form of peat; (ii) medium-flux population (Pop. A2 - mean value of 8.37 g ∙ m⁻² ∙ d⁻¹) which is the result of soil respiration, and (iii) low-flux population (Pop. A1 - mean value of 1.85 g ∙ m⁻² ∙ d⁻¹) due to areas where low permeability or increases in saturated aquifer thickness may control the overall flux. In the study area, a total CO₂ emission of about 2671 t ∙ d⁻¹ was calculated, which, if compared to the average total flux expected for simple soil respiration (1284 t ∙ d⁻¹), represents a non-negligible value in the total Carbon balance. Finally, the comparison with representative normalized fluxes from volcanic and non-volcanic areas confirms the critical role of coastal plains in total atmospheric CO₂ emissions. The proposed approach can be applied to areas with comparable or different geological and climatic settings to trace their contribution in terms of greenhouse gas release to the atmosphere.

Estimating the spatial distribution of hydromechanical properties in the investigated subsoil by defining an Engineering Geological Model (EGM) is crucial in urban planning, geotechnical designing and mining activities. The EGM is always affected by (i) the spatial variability of the measured properties of soils and rocks, (ii) the uncertainties related to measurement and spatial estimation, as well as (iii) the propagated uncertainty related to the analytical formulation of the transformation equation. The latter is highly impactful on the overall uncertainty when design/target variables cannot be measured directly (e.g., in the case of piezocone Cone Penetration Test–CPTu measurements). This paper focuses on assessing the Propagated Uncertainty (PU) when defining 3D EGMs of three CPTu-derived design/target variables: the undrained shear resistance (s_u), the friction angle (φ^′), and the hydraulic conductivity (k). We applied the Sequential Gaussian Co-Simulation method (SGCS) to the measured profiles of tip (q_c) and shaft resistance (f_s), and the pore pressure (u₂), measured through CPTus in a portion of Bologna district (Italy). First, we calculated 1000 realizations of the measured variables using SGCS; then, we used the available transformation equations to obtain the same number of realizations of s_u, φ^′, and k. The results showed that PU is larger when the transformation equation used to obtain the design/target variable is very complex and dependent on more than one input variable, such as in the case of k. Instead, linear (i.e., for s_u) or logarithmic (i.e., for φ^′) transformation functions do not contribute to the overall uncertainty of results considerably.

Using CPTu profiles for subsoil characterisation, transformation equations must be used to obtain the hydro-mechanical properties for structures and infrastructure designing. Additionally, the uncertainty and the spatial variability of measured parameters must be taken into account for a reliable geotechnical design. In this work, we used a Stochastic Simulation approach to define reliable 3D models of two geotechnical designing variables for granular soils (friction angle–ϕ’ and the Darcy permeability coefficient–k) from tip resistance (qc), sleeve friction (fs), and pore pressure (u2) profiles. The selected method – the Sequential Gaussian Co-Simulation (SGCS) – provided reliable optimized 3D models of the spatial distribution of the variables of interest and allowed quantifying the propagation of the estimation uncertainty associated with the raw measurement models through the transformation equations. Overestimation (OE) and Underestimation (UE) percentages for a confidence interval of 68% were calculated throughout the 3D model: granular soils showed a larger uncertainty than fine soils concerning the measured variables (qc, fs, and u2). In granular soils, the measured variable uncertainty varies up to 100% but the derived variables show different behavior: ϕ’ shows UE and OE less than 25% while k reaches 100%. These differences in the propagated uncertainties depend on the transformation equations and the measured variable dependence.

In the past, soil-layer delineation methods can usually only take a single type of input data, e.g., soil-type data at boreholes. However, this does not fit in the geotechnical engineering practice where multiple types of data are usually available during site investigation (e.g., borehole data and cone penetration test data are both available). This paper adopts a novel data-driven method for soil-layer delineation that accommodates multiple types of site investigation data. The basic idea is to include liquid limit (LL), plasticity index (PI), and fines content (FC) into the soil parameters of analysis. According to the Unified Soil Classification System (USCS), the information of (LL, PI, FC) can be used to determine whether the soil is sand, silt, or clay. As a result, the conditional random field simulation results for (LL, PI, FC) can be used to delineate sand, silt, and clay layers. If extra soil parameters (such as cone penetration test results) are incorporated, the novel method can accommodate multiple types of site investigation data. A real example of the Fucino Basin in Italy is adopted to demonstrate the application of the novel data-driven soil-delineation method.

Biomarkers such as functional gene mRNA (transcripts) and proteins (enzymes) provide direct proof of metabolic regulation during the reductive dechlorination (RD) of chlorinated ethenes (CEs). Yet, current models to simulate their spatiotemporal variability are not flexible enough to mimic the homologous behavior of RDase functional genes. To this end, we developed new enzyme-based kinetics to model the concentrations of CEs together with the transcript and enzyme levels during RD. First, the model was calibrated to existing microcosm data on RD of cis-DCE. The model mirrored the tceA and vcrA gene expression and the production of their enzymes in Dehalococcoides spp. Considering tceA and vcrA as homologous instead of nonhomologous improved fitting of the mRNA time series. Second, CEs and biomarker patterns were explored as a proof of concept under groundwater flow conditions, considering degraders occurring in immobile and mobile states. Under both microcosm and flow conditions, biomarker-rate relationships were nonlinear hysteretic because tceA and vcrA acted as homologous genes. The mobile biomarkers additionally undergo advective-dispersive transport, which increases the nonlinearity and makes the observed patterns even more challenging to interpret. The model offers a thorough mechanistic description of RD while also allowing simulation of spatiotemporal dynamic patterns of various key biomarkers in aquifers.

The Gran Sasso carbonate aquifer is the largest and most productive in the Apennines. Its hydrogeological structure has been studied since the middle of the last century for the springs’ characterization for drinking purposes and for a motorway tunnel. Meanwhile, its hydrodynamic parametrization is less developed and has been limited to monitoring the discharge and chemical and isotopic parameters. Secondary porosity characterizes the aquifer, and an underlying impermeable marly complex represents the basal aquiclude. It might appear inappropriate to characterize the hydraulic properties via pumping tests, as their reliability has been proven in homogeneous and isotropic media. However, the high extent of the aquifer, the wells’ location, the scarcity of information available and the lack of alternatives has forced the estimation of hydrodynamic parameters as in porous aquifers and the experimental testing of the aquifer, especially in maximum pumping conditions, for a possible exploitation increase. Since aquifer testing was performed during the normal well field’s activities, it was not possible to perform typical tests. Therefore, the step-drawdown test was conducted by turning on an increasing number of wells over time and keeping the observation points fixed. As results, a mean hydraulic conductivity of 5 × 10−3 m/s and a mean transmissivity of 0.3 m2/s were established without interrupting the water supply; meanwhile, the influence radius and flow directions were also estimated.

Recently, new numerical methods have been applied to weather data for the estimation of water budget, especially when the lack of measured data is considerable. Geostatistics is one of the most powerful approaches when it comes to studying spatially relevant natural phenomena, as it considers the spatial correlation among measurements over a specific study area and provides the associate uncertainty. In this study, we tested the feasibility of using a geostatistical method to provide a reliable estimation of the water budget of the Foro river basin (Central Italy) by comparing the obtained results with those of a traditional yet robust method. The results obtained with the geostatistical approach proved to be in line with the ones from the traditional method. Additionally, it was possible to quantify the uncertainty associated with the discharge values, making the estimates more reliable than the ones obtained with the traditional approach. However, the yearly distribution of river discharge obtained using both methods appeared to be dissimilar to the measured ones. The surface water uses, as well as the regulatory effect of the carbonate and alluvial aquifer regime, may affect the river discharge variability over the year and then can account for similar discrepancies between the inflow and outflow water volumes.

To perform a geotechnical reliable design, the spatial variability and the uncertainties related to the adopted engineering geological model (EGM) must be taken into account. However, any conceived EGM is characterized by uncertainties covering (1) the bias in the mathematical expression that transforms the measured parameters into design ones; and (2) the uncertainty associated with the variability of the soil and rock parameters in the prediction equations. Hereinafter, the sequential Gaussian co-simulation method (SGCS) has been applied to propagate the uncertainty in the calculation of the undrained shear resistance su from measured CPTu profiles (i.e., qc, fs, u2) through a linear model of co-regionalization. The studied area is located in the Po River alluvial plain (Bologna Province, Italy), where the mixture of silts, sands, and clays gets thicknesses of hundreds of meters. These heterogeneous deposits have been mechanically characterized through a 3D EGM to be used in reliability-based designing.

Accurate knowledge of the rain amount is a crucial driver in several hydrometeorological applications. This is especially true in complex orography territories, which are typically impervious, thus, leaving most mountain areas ungauged. Due to their spatial and temporal coverage, weather radars can potentially overcome such an issue. However, weather radar, if not accurately processed, can suffer from several limitations (e.g., beam blocking, altitude of the observation, path attenuation, and indirectness of the measurement) that can hamper the reliability of the rain estimates performed. In this study, a comparison between rain gauge and weather radar retrievals is performed in the target area of the Abruzzo region in Italy, which is characterized by a heterogeneous orography ranging from the seaside to Apennine ridge. Consequently, the Abruzzo region has an inhomogeneous distribution of the rain gauges, with station density decreasing with the altitude reaching approximately 1500 m a.s.l. Notwithstanding, pluviometric inflow spatial distribution shows a subregional dependency as a function of four climatic and altimetric factors: coastal, hilly, mountain, and inner plain areas (i.e., Marsica). Such areas are used in this analysis to characterize the radar retrieval vs. rain gauge amounts in each of those zones. Compared to previous studies on the topic, the analysis presented the importance of an accurate selection of the climatic and altimetric subregional areas where the radar vs. rain gauge comparison is undertaken. This aspect is not only of great importance to correct biases in radar retrieval in a more selective way, but it also paves the way for more accurate hydrometeorological applications (e.g., hydrological model initialization and quantification of aquifer recharge), which, in general, require the accurate knowledge of rain amounts upstream of a basin. To fill the gap caused by the uneven rain gauge distribution, ordinary Kriging (OK) was applied on a regional scale to obtain 2D maps of rainfall data, which were cumulated on a monthly and yearly basis. Weather radar data from the Italian mosaic were also considered, in terms of rain rate retrievals and cumulations performed on the same time frame used for rain gauges. The period considered for the analysis was two continuous years: 2017 and 2018. The output of the elaborations included raster maps for both radar and interpolated rain gauges, where each pixel contained a rainfall quantity. Although the results showed a general underestimation of the weather radar data, especially in mountain and Marsica areas, they were within the 95% confidence interval of the OK estimation. Our analysis highlighted that the average bias between radar and rain gauges, in terms of precipitation amounts, was a function of altitude and was almost constant in each of the selected areas. This achievement suggests that after a proper selection of homogeneous target areas, radar retrieval can be corrected using the denser network of rain gauges typically distributed at lower altitudes, and such correction can be extended at higher altitudes without loss of generality.