Gilberto Pambianchi

Groundwater resources in semi-arid regions are increasingly threatened by agricultural intensification, irrigation expansion, and climate variability. This study investigates the influence of irrigation practices on groundwater recharge and quality degradation in the semi-arid Regueb Basin, Central Tunisia, using an integrated framework combining hydrochemical analysis, irrigation water quality indices, GIS-based spatial modeling, and explainable machine learning (XAI). Thirty groundwater samples were analyzed for major physicochemical parameters and irrigation suitability indicators, including Electrical Conductivity (EC), Total Dissolved Solids (TDS), Sodium Adsorption Ratio (SAR), sodium percentage (%Na), and Irrigation Water Quality Index (IWQI). Hydrochemical facies are dominated by Ca–Mg–Cl, Na–Cl, and Ca–Mg–SO₄ water types, reflecting the combined effects of evaporite dissolution, water–rock interaction, evaporation, and irrigation return flow. Groundwater salinity is generally high, with EC values ranging from 1490 to 8710 µS/cm, while nitrate concentrations frequently exceed the World Health Organization guideline value of 50 mg/L in intensively cultivated zones, indicating significant anthropogenic contamination linked to fertilizer leaching and irrigation practices. GIS-based recharge assessment indicates that irrigation return flow may represent an important component of effective recharge in cultivated areas under semi-arid conditions while simultaneously contributing to salinization and nutrient accumulation within the aquifer system. However, quantitative partitioning of recharge sources requires further investigation using tracer-based approaches and numerical modeling. More than 40% of groundwater samples were classified as unsuitable for irrigation because of elevated salinity and sodicity hazards. To explore the relationships among irrigation water quality indicators, several machine-learning algorithms were evaluated for IWQI estimation and interpretation. Linear Regression achieved the highest performance for IWQI estimation (R² = 0.9839), reflecting the strong internal relationships among irrigation water quality indicators. SHapley Additive exPlanations (SHAP) analysis identified Residual Sodium Carbonate (RSC) as the most influential parameter controlling irrigation water quality. The results highlight the dual role of irrigation as both a recharge-enhancing mechanism and a driver of groundwater degradation. This study provides an integrated hydrochemical–GIS–XAI framework for identifying vulnerable zones and supporting sustainable groundwater management strategies in semi-arid agricultural regions.

Land-use and land cover (LULC) change is a key driver of environmental dynamics in semi-arid Mediterranean watersheds, strongly influencing hydrological processes, soil degradation, and ecosystem stability. In this context, understanding and predicting spatial–temporal land transformations is essential for sustainable watershed management. This study proposes a nonlinear spatial–temporal modeling framework integrating a hybrid Artificial Neural Network (ANN), Cellular Automata (CA), and Markov chain approach to simulate LULC dynamics in the Sebdou watershed, northwestern Algeria. Multi-temporal Landsat imagery (1985, 2005, and 2025), combined with topographic, socio-economic, and accessibility variables (slope, population density, distance to roads, and hydrographic network), was used to reconstruct historical land-use patterns and identify key driving forces of change. A supervised Maximum Likelihood classification achieved high accuracies, with overall accuracy ranging from 92.87% to 96.26% and Kappa coefficients between 0.85 and 0.91. The ANN model was trained to estimate nonlinear transition potentials, while the CA component incorporated spatial neighborhood effects to simulate land allocation processes. Markov chain analysis provided temporal transition probabilities, enabling the construction of a coupled ANN–CA–Markov framework for scenario-based prediction. Model validation against observed 2025 LULC maps indicated strong agreement in quantity distribution (Kappa histogram = 0.767), while spatial agreement (Kappa = 0.3566) reflected inherent spatial displacement typical of CA-based stochastic allocation. Simulation results for 2045 indicate continued urban expansion along major transport corridors, progressive decline of dense forest cover, and increasing bare soil areas, while agricultural land remains dominant but increasingly fragmented. These trends highlight the growing influence of anthropogenic pressure and accessibility factors on landscape restructuring in semi-arid environments. The proposed hybrid framework provides a robust decision-support tool for anticipating land-use dynamics and assessing future environmental pressures in Mediterranean drylands. Its integration with hydrological and erosion models can further support sustainable watershed planning under combined socio-economic and climatic changes.

Degraded groundwater quality, characterized by elevated salinity and nitrate concentrations, poses significant public health concerns, particularly for vulnerable populations such as children. High content of nitrate in drinking water may lead to non-carcinogenic health risks, highlighting the urgent need for sustainable groundwater management strategies to protect both human health and environmental integrity. This study assesses the suitability of groundwater resources in the Regueb Basin for irrigation and drinking purposes, with particular attention paid to nitrate contamination. The Irrigation Water Quality Index (IWQI) indicates considerable spatial variability in groundwater quality, with values varying between 15.86 and 89.55 and a median of 41.69, reflecting differing levels of suitability for irrigation across the basin. Similarly, the Drinking Water Quality Index (DWQI) ranges from 149.16 to 982.42, with a median value of 445.71, suggesting significant concerns regarding groundwater suitability for drinking purposes. The health risk assessment (HHRA) based on the Nitrate Pollution Index (NPI) and the nitrate hazard quotient (HQ__nitrate) reveal substantial risks to human health. NPI values vary between 0.45 and 5.5, with a median of 1.65 indicating varying levels of nitrate pollution. The HQ__nitrate results show that all groundwater samples (100%) pose health risks for children (HQ > 1). For women, 75.61% of HQ values exceed the safe threshold, affecting approximately 80% of the study area, whereas for men, 48.48% of HQ values exceed 1, impacting about 36.67% of the area. Overall, these findings highlight the urgent need for effective groundwater management strategies to mitigate nitrate contamination and ensure the safe and sustainable use of the groundwater resources in the Regueb Basin.

The increasing frequency of disasters caused by landslides, mainly due to climate change leading to more intense extreme events, requires reliable predictive models for risk mitigation. Italy, in particular, is a country at high risk of landslides, but the lack of an updated catalogue of landslide activation dates poses a significant challenge for defining reliable activation thresholds. This study develops a methodology for mapping landslide susceptibility based on events in a pilot area of central Italy, integrating a database of landslides with known activation dates with predisposing and triggering parameters. Two statistical techniques were compared to assess their predictive performance in discriminating landslide from non-landslide conditions during extreme precipitation events. A comparison between binary logistic regression (BLR) and decision trees (QUEST) revealed the clear superiority of the BLR model, which achieved excellent predictive accuracy (AUC = 0.913). The model identified clay-rich lithology, gentle slopes (0–16°) and maximum daily precipitation as the most significant controlling factors. This result led to the generation of three derivative products: a susceptibility map, a hazard map for an extreme precipitation scenario with a 100-year return period, and a spatially distributed map of activation thresholds. This threshold map quantifies the intensity of precipitation required to exceed a critical probability of landslide initiation (p > 0.7) at any point in the territory. The susceptibility map highlights critical areas within the study area, while the hazard map also includes the return period of the event. The threshold map is a direct and operational tool for early warning systems, transforming a statistical model into a guide for real-time risk management. The study area serves as a pilot area that could allow this methodology to be replicated. With the integration of real-time meteorological data, it could function as a real-time warning system. The proposed framework therefore provides a directly actionable tool for civil protection agencies, land-use planning authorities, and emergency managers, enabling location-specific rainfall alert thresholds to be issued rather than a single regional value, with the potential to reduce both false alarms and missed warnings.

The concept of Landscape Sensitivity is now more topical than ever, given the intense stresses associated with increasing hydrogeological instability because of strong anthropic pressures and extreme climatic events. One of the environments most affected by anthropogenic stresses and catastrophic weather events is undoubtedly the river environment. Studies conducted in the river plains of the Marche region in Italy, starting from the second half of the last century till today, have shown that the deepening of the riverbed is of a maximum of 10 m in the main river channels. Subsequently, these incisions were amplified by the massive impermeabilization of the floodplains and by works in the riverbeds, built to prevent erosion. In the basins of Chienti and Potenza Rivers, the downcutting was about 4–8 m, compatible with the averages of the Marche region rivers for the same period. These deepenings were subsequently amplified, starting in the mid-1980s, with the construction of sewage treatment plants near the main watercourses. In many cases, erosion phenomena, both lateral and vertical, have been observed, sometimes triggering landslide movements in the proximal banks. The analyses made it possible to highlight that this imbalance in river dynamics is due to the convergence of physical processes, such as the increase in discharge, and chemical processes that produce alteration of clay minerals related to surfactants and other chemical pollutants. This study represents a first attempt to highlight a little-known issue that could influence river sustainability of areas subject to significant anthropic pressures.

The evaluation of landslide hazards in seismic areas is based on a deterministic analysis, which is unable to account for various uncertainties in the analysis process. This paper focuses on the probabilistic local seismic hazard analysis and extends the results to the landslide hazard analysis to consider both the uncertainties of the ground deformations and the strengths. The work studies the areas between Nibbiano and Sant’Erasmo hamlets in the Camerino municipality located in central Italy, where all constructions present evidence of damage caused by both the seismic sequence of 2016–2017 and the slope instability. An exhaustive geological and geophysical investigation has clarified the geological, geomorphological, and hydrogeological characteristics of the area, enabling a new characterization of material stress-strain behaviour. The study reveals that the low stiffness of the debris covers, and their fair degree of permeability contribute to potential instability scenarios triggered by both intense rainfall and the effects of strong earthquakes. The goal was to utilize the results to support local urban planning because in-depth knowledge of the possible evolutionary scenarios of the slopes is fundamental to the management of the degree of danger for structures, especially for people. Moreover, it was shown once again how a multi-source approach, with different investigation techniques, cannot be ignored for the study of the evolution of complex landslides.

The present study aims to assess the tectonic activity in the South Setifian allochthonous complex, providing insights into the evolution of the landscape. A morphometric analysis of Jebel Youcef Mountain (JYM) in Eastern Algeria was conducted to assess neotectonic activity. Six quantitative parameters were analyzed: stream length-gradient index, asymmetric factor, hypsometric integral, valley floor width-to-valley height ratio, index of drainage basin shape, and index of mountain front sinuosity across the 16 river basins in the region. The geomorphic indices are combined into a single index of relative tectonic activity (IRTA), categorized into four classes: very high, high, moderate, and low. The results identified two major lineament sets. The NE-SW lineament set is the dominant structural feature, playing a key role in driving recent geological processes and deformation in the study area. In contrast, the E-W and NW-SE lineament sets exert a more localized influence, primarily affecting the Jurassic formations at Kef El Ahmar’s central peak in Jebel Youcef, though they exhibit relatively lower tectonic activity compared to the NE-SW lineament set. Based on the relative active tectonic classes, significant neotectonic activity is evident in the study area, as shown by distinctive basement fracturing. The findings contribute to understanding the structural processes in the study area. Furthermore, the study establishes a systematic framework for analyzing tectonic activity and landscape morphology evolution, enhancing our perception of the convergence between the North African Alpine zones and the Atlas range.

The evaluation of landslide hazard in seismic areas is based on a deterministic analysis to define the stability of the sites. However, deterministic analysis is not able to consider various uncertainties in the analysis process. This paper focuses on the results established by the probabilistic seismic hazard analysis and extends the probabilistic landslide hazard analysis to consider the uncertainties of opposing ground deformations and strength. The work studies the areas between Nibbiano and Sant'Erasmo inhabited centres in the Camerino municipality located in central Italy. The current state of the areas shows dangerous conditions for the building structures. In fact, all constructions present evidence of damage caused by both the 2016 earthquake and slope instability. The studies, carried out through careful geological survey and detailed geophysical investigations, are performed to define the geological, geomorphological and hydrogeological characteristics of the areas and also to run a new analysis aimed to define the material stress-strain behavior. Both inhabited Centers are located on debris deposits with highly variable thicknesses. The key point regards the definition of the depth of unstable mass, its position and its extension. Therefore, this contribution aims to summarize the knowledge of the landslide phenomenon that affected the area, trying to interpret its evolution with new field investigations. Indeed, during the first step, related to the geological and geomorphological detailed surveys, a remarkable geological model was obtained to better interpret the slope dynamics in relation to the structures of this section of the ridge. This model was also confirmed by the geophysical investigations. The study involves a more detailed definition of the landslide perimeter. Indeed, to analyze the phenomenon, it was necessary to study both the local seismic amplification with finite element 2D simulations and the slope stability conditions with finite 3D difference simulation.