The interaction between fluids and tectonic structures such as fault systems is a much-discussed issue. Many scientific works are aimed at understanding what the role of fault systems in the displacement of deep fluids is, by investigating the interaction between the upper mantle, the lower crustal portion and the upraising of gasses carried by liquids. Many other scientific works try to explore the interaction between the recharge processes, i.e., precipitation, and the fault zones, aiming to recognize the function of the abovementioned structures and their capability to direct groundwater flow towards preferential drainage areas. Understanding the role of faults in the recharge processes of punctual and linear springs, meant as gaining streams, is a key point in hydrogeology, as it is known that faults can act either as flow barriers or as preferential flow paths. In this work an investigation of a fault system located in the Nera River catchment (Italy), based on geo-structural investigations, tracer tests, geochemical and isotopic recharge modelling, allows to identify the role of the normal fault system before and after the 2016–2017 central Italy seismic sequence (Mmax = 6.5). The outcome was achieved by an integrated approach consisting of a structural geology field work, combined with GIS-based analysis, and of a hydrogeological investigation based on artificial tracer tests and geochemical and isotopic analyses.

Nowadays, the balance between incoming precipitation and stream or spring discharge is a challenging aspect in many scientific disciplines related to water management. In this regard, although advances in the methodologies for water balance calculation concerning each component of the water cycle have been achieved, the Thornthwaite–Mather method remains one of the most used, especially for hydrogeological purposes. In fact, in contrast to physical-based models, which require many input parameters, the Thornthwaite–Mather method is a simple, empirical, data-driven procedure in which the error associated with its use is smaller than that associated with the measurement of input data. The disadvantage of this method is that elaboration times can be excessively long if a classical MS Excel file is used for a large amount of data. Although many authors have attempted to automatize the procedure using simple algorithms or graphical user interfaces, some bugs have been detected. For these reasons, we propose a WebApp for monthly water balance calculation, called WaterbalANce. WaterbalANce was written in Python and is driven by a serverless computing approach. Two respective European watersheds are selected and presented to demonstrate the application of this method.

Understanding the groundwater flow in carbonate aquifers represents a challenging aspect in hydrogeology, especially when they have been struck by strong seismic events. It has been proved that large earthquakes change springs hydrodynamic behaviour showing transitory or long-lasting variations and making their management much more difficult. This is the case of Sibillini Massif (central Italy), which has been hit by the well-known 2016–2017 seismic period. This work aims to improve the knowledge of carbonate aquifers groundwater circulation and their possible changes in the hydrodynamic behaviour, during and after a series of strong seismic events. The goal has been achieved by comparing long-time tracer tests and transient time-series analysis, based on a sliding-window approach. This approach allowed investigating transient variations in the carbonate aquifers recharge system, highlighting the changes of relationships between the inflow contributions to the spring discharge in the area. As a result, the seismically triggered pore pressure distribution, and the hydraulic conductivity variations, because of the ground shaking and the fault systems activation, account for all the mid- and long-term modifications in the recharge system of Sibillini aquifers, respectively. These outcomes provide valuable insights to the knowledge of aquifer response under similar hydrogeological conditions, that are vital for water management.

This study wants to give a contribution to the semi-automatic evaluation of rock mass discontinuities, orientation and spacing, as important parameters used in Engineering. In complex and inaccessible study areas, a traditional geological survey is hard to conduct, therefore, remote sensing techniques have proven to be a very useful tool for discontinuity analysis. However, critical expert judgment is necessary to make reliable analyses. For this reason, the open-source Python tool named DCS (Discontinuities Classification and Spacing) was developed to manage point cloud data. The tool is written in Python and is based on semi-supervised clustering. By this approach the users can: (a) estimate the number of discontinuity sets (here referred to as “clusters”) using the Error Sum of Squares (SSE) method and the K-means algorithm; (b) evaluate step by step the quality of the classification visualizing the stereonet and the scatterplot of dip vs. dip direction from the clustering; (c) supervise the clustering procedure through a manual initialization of centroids; (d) calculate the normal spacing. In contrast to other algorithms available in the literature, the DCS method does not require complex parameters as inputs for the classification and permits the users to supervise the procedure at each step. The DCS approach was tested on the steep coastal cliff of Ancona town (Italy), called the Cardeto–Passetto cliff, which is characterized by a complex fracturing and is largely affected by rockfall phenomena. The results of discontinuity orientation were validated with the field survey and compared with the ones of the FACETS plug-in of CloudCompare. In addition, the algorithm was tested and validated on regular surfaces of an anthropic wall located at the bottom of the cliff. Eventually, a kinematic analysis of rock slope stability was performed, discussing the advantages and limitations of the methods considered and making fundamental considerations on their use.

Forecasting of water availability has become of increasing interest in recent decades, especially due to growing human pressure and climate change, affecting groundwater resources towards a perceivable depletion. Numerous research papers developed at various spatial scales successfully investigated daily or seasonal groundwater level prediction starting from measured meteorological data (i.e., precipitation and temperature) and observed groundwater levels, by exploiting data-driven approaches. Barely a few research combine the meteorological variables and groundwater level data with unsaturated zone monitored variables (i.e., soil water content, soil temperature, and bulk electric conductivity), and—in most of these—the vadose zone is monitored only at a single depth. Our approach exploits a high spatial-temporal resolution hydrogeological monitoring system developed in the Conero Mt. Regional Park (central Italy) to predict groundwater level trends of a shallow aquifer exploited for drinking purposes. The field equipment consists of a thermo-pluviometric station, three volumetric water content, electric conductivity, and soil temperature probes in the vadose zone at 0.6 m, 0.9 m, and 1.7 m, respectively, and a piezometer instrumented with a permanent water-level probe. The monitored period started in January 2022, and the variables were recorded every fifteen minutes for more than one hydrologic year, except the groundwater level which was recorded on a daily scale. The developed model consists of three “virtual boxes” (i.e., atmosphere, unsaturated zone, and saturated zone) for which the hydrological variables characterizing each box were integrated into a time series forecasting model based on Prophet developed in the Python environment. Each measured parameter was tested for its influence on groundwater level prediction. The model was fine-tuned to an acceptable prediction (roughly 20% ahead of the monitored period). The quantitative analysis reveals that optimal results are achieved by expoiting the hydrological variables collected in the vadose zone at a depth of 1.7 m below ground level, with a Mean Absolute Error (MAE) of 0.189, a Mean Absolute Percentage Error (MAPE) of 0.062, a Root Mean Square Error (RMSE) of 0.244, and a Correlation coefficient of 0.923. This study stresses the importance of calibrating groundwater level prediction methods by exploring the hydrologic variables of the vadose zone in conjunction with those of the saturated zone and meteorological data, thus emphasizing the role of hydrologic time series forecasting as a challenging but vital aspect of optimizing groundwater management.