Water Loss (WL) is a global issue. In Italy, for instance, WL reached 36.2% of the total fresh water conveyed in 2020. The maintenance of a water supply system is a strategic task that requires a huge amount of investment every year. In this work, we focused on the use of Distributed Fiber Optic Sensors (DFOS) based on Stimulated Brillouin Scattering (SBS) technology for monitoring water pipeline networks. We worked on High-Density Polyethylene (HDPE) pipes, today the most widely used for creating water pipelines. By winding and fixing the optic fiber cable on the pipe’s external surface, we verified the ability to detect strain related to pressure anomalies along a pipeline, e.g., those caused by water leakage. We performed two experimental phases. In the first one, we assessed the sensibility of sensor layout on an HDPE pipeline solicited with static pressure. We investigated the viscoelastic rheology of the material by calibrating and validating the parameters of a Burger model, in which Maxwell and Kelvin-Voigt models are connected in series. In the second experimental phase, instead, we focused on the detection of the pressure anomaly produced by leakage in a pipeline circuit set up with running water moved by a pump. The theoretical and experimental studies performed returned overall positive feedback on the use of DFOS for the monitoring of HDPE water pipelines. Future developments will be focused on more detailed studies of this monitoring solution and on the industrial production of “natively smart” HDPE pipes in which DFOS cables are integrated into the pipeline surface during the extrusion process.

We present a case study of a Structural Health Monitoring (SHM) hybrid system based on Brillouin Distributed Fiber Optic Sensors (D-FOS), Vibrating Wire (VW) extensometers and temperature probes for an existing historical water penstock bridge positioned in a mountain valley in Valle d’Aosta Region, Northwestern Italy. We assessed Brillouin D-FOS performances for this kind of infrastructure, characterized by a complex structural layout and located in a harsh environment. A comparison with the more traditional strain monitoring technology offered by VW strain gauges was performed. The D-FOS strain cable has been bonded to the concrete members using a polyurethane-base adhesive, ensuring a rigid strain transfer. The raw data from all sensors are interpolated on a unique general timestamp with hourly resolution. Strain data from D-FOS and VW strain gauges are then corrected from temperature effects and compared. Considering the inherent differences between the two monitoring technologies, results show a good overall matching between strain time series collected by D-FOS and VW sensors. Brillouin D-FOS proves to be a good solution in terms of performance and economic investment for SHM systems on complex infrastructures such as hydropower plants, which involve extensive geometry combined with the need for detailed and continuous strain monitoring.

Present and prospective climate change will likely affect the hydrological cycle in sensitive areas, such as the Alps, thus impacting water-based activities. A most representative example is hydropower production, i.e., exploitation of water to produce energy. In the Italian Alps hydropower is strictly dependent upon water from snow and ice melt, and both are decreasing in response to global warming. Here, we study the effects of potential climate change scenarios at 2100 upon hydropower production from the Chavonne plant, in Valle d’Aosta region of Italy, a run-of-the-river (ROR) plant taking water from two high altitude glacierized catchments of Val di Cogne, and Valsavarenche. We use Poli-Hydro, a state-of-the-art hydrological model to mimic the hydrological budget of the area, including ice and snow melt share. Projections of the hydrological budget were built until 2100 by means of selected climate change scenarios, under proper downscaling. We used runs of three General Circulation Models (GCMs), EC-Earth, CCSM4, and ECHAM6.0 under three Representative Concentration Pathways RCP 2.6, RCP 4.5, and RCP 8.5 from AR5 of IPCC, and of their updated version under four Shared Socio-Economic Pathways SSP1 2.6, SSP2 4.5, SSP3 7.0, and SSP5 8.5 from AR6. We then assessed hydropower production changes against a recent control run CR period (2005–2015). Mean annual flow is estimated at 14.33 m³ s⁻¹ during CR, with ice melt contribution ca. 2%, and snow melt contribution ca. 44%. Ice cover in 2005 was estimated as 19.2 km², reaching in 2015, 9.93 km². Mean hydropower production was estimated at 153.72 GWh during the CR. Temperature would largely increase throughout the century (+0.93 °C on average at the half century, +2.45 °C at the end of the century). The ice covered area would be largely depleted (ca. −86%, −94% respectively), with reduced contribution of ice melt (0.23%, <0.1%, respectively) and snow melt (ca. 37%, 33%, respectively). Precipitation would show uncertain patterns, and hence incoming discharge at the plant would erratically vary (−29% to +24% half century, −27% to +59% end of century). Hydropower production displays a large dependence upon monthly discharge patterns, with mostly positive variations (+2.90% on average at half century, +6.95% on average at end of century), with its change driven by exceedance of plant’s capacity.

Flood damage assessment is an essential asset in flood risk management, and the literature brings several models, most based on damage curves. This kind of approach is essential and well established but not suitable for being applied in the immediate aftermaths of a flood. Herein, a new application of a zero-order model for the Po-Venetian plain area, which covers more than 20% of the Italian territory, is conceived and implemented. At first, a calibration of the model with damage data coming from past events in the region is carried out, showing that the calibration is site-specific. In particular, it is shown how the exponent in the power-law form of the model is the same as obtained in a previous work on another territory, while the intercept, defined as specific damage, varies. Then the algorithm is applied to the area affected during the flood that hit the Emilia-Romagna region (Northern Italy), in December 2020, for validation. Knowledge of the affected area comes from the quick assessment procedure commonly performed by the National Fire Corps in any significant flood, employing low-altitude all-weather helicopter GPS surveys. The paper shows that combining the model with such a rapid mapping technique leads to a near real-time estimation of damages in the aftermath of floods. The results are aimed at obtaining a first approximation figure. The estimation error shows this figure to be not far from those obtained with much more detailed approaches, requiring a much longer time for their setting. For that reason, the model results can contribute, among other things, by directing the immediate post-emergency management operations and promptly allocating financial resources for recovery.

Quantitative data on observed flood ground effects are precious information to assess current risk levels and to improve our capability to forecast future flood damage, with the final aim of defining effective prevention policies and checking their success. This paper presents the first collection and analysis of flood damage claims produced in Italy in the past 7 years since a homogeneous national procedure for damage recognition became available. The database currently contains more than 70,000 claims referring to significant events and shows good homogeneity on the intensity of the related phenomena. We then propose an empirical model, based on observed data, to allow for a quick estimation of direct damage to private assets (i.e., residential buildings), based only on the knowledge of the perimeter of the flooded area. Single model calibration was performed at the multi-regional scale, focused on southern Italy. Model validation shows encouraging performances, considering the considerable natural uncertainty that characterizes this type of estimate. The procedure is of great interest when there is a need to evaluate, however roughly, flood damage in the immediacy of the event to assess the extent of the flood effects and to plan support actions for the affected communities.

The problem of levee embankment control during high flows is crucial for flood risk management in floodplains. Levee defense lines are often hundreds of kilometers long and surveys during emergencies are not easy tasks. For these reasons, levees monitored with in situ sensors and a suitable Information Technology (IT) real-time data communication and integration infrastructure, so-called “smart levees”, are gaining increasing interest as a crucial protection technology in floodplains. The paper presents the conceptualization of a prototype of a levee smart revetment, based on the integration of an optical fiber (OF) cable into a steel double-twisted wire mesh. In this paper the feasibility of this kind of revetment is firstly assessed. The flow pattern of overtopping water on the embankment is discussed, thus producing a raw estimation of the shear stress acting on the revetment in the field. A sample case is then analyzed in both numerical and laboratory tests. For this purpose, a numerical Finite Element Model (FEM) to describe the mechanical behavior of a double-twisted wire mesh when loaded along its own plane is presented. Numerical results indicate that the related strain, relatively low as compared to the steel wire yield stress, can be fully detected by the optical fiber continuous Brillouin sensor. This has been validated by the experimental activity performed and a digital twin of the prototype of the smart revetment, suitable for virtually testing the product under any load and constraint conditions and tailoring the production process, has been created.