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The assessment of the response of slopes to precipitations is important for various applications, from water resources management to hazard assessment due to extreme rainfall events. It is well known that the underground conditions prior to the initiation of rainfall events control the hydrological processes that occur in slopes, affecting the water exchange through their boundaries. The present study aims at identifying hydrological variables to be monitored and modelled, suitable to improve the prediction of slope response to precipitations, for the case of a slope covered with loose pyroclastic coarse-grained soil overlaying a karstic bedrock, typical of southern Apennines (Italy). Field monitoring has been carried out for three years at the slope, including stream level recordings, meteorological recordings, and soil water content and suction measurements, which allowed setting up a physically based hydrological model of the slope, coupling the unsaturated flow in the soil cover with a perched aquifer developing in the fractured bedrock. To enlarge the field dataset, a synthetic dataset has been generated, linking a previously calibrated stochastic rainfall generator to the hydrological model. In this way, a synthetic dataset of 1000 years has been obtained, containing information on rainfall, aquifer water level and soil volumetric water content at different depths. Machine Learning techniques have been used to unwrap the relationships linking the studied variables, typically non-linear. The Random Forest technique has been used to assess the importance of each variable on the slope response, and the k-means clustering technique has been used to explore the geometrical disposition of data, so to identify seasonally recurrent different conditions controlling the slope response. The results indicate that the slope response, in terms of the fraction of rainwater remaining stored in the soil cover at the end of each rainfall event, can be predicted from the underground conditions prior to the rainfall initiation, weighting the role, on one hand, of the soil moisture excess above field capacity, controlling the ease of the water to flow in and out of the soil cover and, on the other hand, of the perched aquifer water level, that gives evidence of the activation of effective slope drainage.

Rainfall-triggered landslides are widespread geohazards, often characterized by shallow and fast movements. Their occurrence is reported in many mountainous areas, and its cumulative effects are sometimes comparable to great catastrophes (Banco Mundial, 2012). Particularly, southern Apennines of Campania (Italy), commonly covered by pyroclastic deposits laying upon karstic bedrock, are subjected to recurrent shallow landslides (Marino et al., 2021). Different triggering mechanisms have been hypothesized, and investigation on the hydrological processes predisposing slopes to failure is still needed. This study focuses on a slope where hydrometeorological monitoring has been carried out for several years, and landslides recently occurred. To assess the conditions leading to landslides, a 1000-year hourly synthetic dataset, mimicking the hydrological response of the slope to meteorological forcing, was generated. Specifically, a stochastic NSRP rainfall model was coupled with a hydrological model of the unsaturated flow through the soil cover, connected to a perched aquifer forming in the uppermost bedrock during the rainy season. Both the models had been previously calibrated based on field data (Greco et al, 2013, 2018; Marino et al, 2020).The synthetic dataset was analyzed with k-means clustering and Random Forest techniques, to identify the hydrologic conditions, before the onset of rainfall events, controlling the amount of rainwater remaining stored in the soil cover at the end of rainfall, thus affecting slope equilibrium. Stability was analyzed under the infinite slope hypothesis, considering the contribution of suction to unsaturated soil shear strength.The results show how the different hydrologic behaviors, related to slope underground water conditions before the onset of rainfall, as well as the total event rainfall, control slope stability. In fact, two different landslide triggering mechanisms are clearly identified. On one hand, when antecedent slope conditions hamper the fast drainage of infiltrating water out of the soil cover through the underlying fractured bedrock, typical of late autumn, slope failure is triggered by infiltration during the largest rainfall events, as almost all rainwater remains stored in the soil cover. On the other hand, when the bedrock is already filled with water previously drained from the soil cover, as at the end of very rainy autumns and winters, landslides can be triggered also by relatively small rainfall, as the bedrock cannot receive more water, and even exfiltration from the bedrock can occur.ReferencesBanco Mundial (2012). Análisis de la gestión del riesgo de desastres en Colombia: un aporte para la construcción de políticas públicas. https://doi.org/333.3109861/A56Greco R et al (2013). Hydrological modelling of a slope covered with shallow pyroclastic deposits from field monitoring data. https://doi.org/10.5194/hess-17-4001-2013Greco R et al (2021). Recurrent rainfall-induced landslides on the slopes with pyroclastic cover of Partenio Mountains (Campania, Italy): Comparison of 1999 and 2019 events. https://doi.org/10.1016/j.enggeo.2021.106160Greco et al (2018). Interaction between perched epikarst aquifer and unsaturated soil cover in the initiation of shallow landslides in pyroclastic soils. https://doi.org/10.3390/w10070948Marino et al (2021). Soil moisture information can improve shallow landslide forecasting using the hydrometeorological threshold approach. https://doi.org/ 10.1007/s10346-020-01420-8Marino et al (2021). Prediction of shallow landslides in pyroclastic-covered slopes by coupled modeling of unsaturated and saturated groundwater flow. https://doi.org/10.1007/s10346-020-01484

Water distribution networks (WDNs) are an important critical infrastructure, but they are increasingly at risk from contamination (WHO, 2014). The causes can be several: chlorination equipment malfunctioning, low pressure, contaminant intrusion in water tank, accidental cross-connection between drinking-water and non-drinking-water, etc... To limit potential threat to public health, it is advisable to install a network of sensors that can monitor water quality in real time and provide information about potential contamination risks. With the proliferation of IoT technologies and low-cost sensors capable of monitoring water quality parameters, it is now possible to implement a real-time monitoring network by overcoming the difficulties associated with biochemical analyses of water samples in a laboratory. Despite the modern technologies, the placement of sensors in the water network is still an open task for researchers. The main sensor placement methodologies use optimization techniques to minimize or maximize either single- or multi-objective functions (Ostfeld et al., 2008), but they require a calibrated model of the network, which is not always available because the calibration process is expensive and time-consuming.Recently, a novel approach (Santonastaso et al., 2021) based on the use of the topological centrality metric, which does not require hydraulic information and simulations, has been proposed, showing good effectiveness and easy applicability by water utilities to define locations for quality sensors, owing to its simplicity compared to optimization-based approaches.In this work, different weights such as the length of pipes, the diameter and the water demand were used to improve the performance of the adopted topological approach, as well as to evaluate the impact of the weight, used to compute centrality metrics, in relation to the most used objective functions: number of people exposed to the contaminant; number of detected contamination events; length of contaminated pipes; amount of contaminant consumed by users; detection time of contamination. ReferencesWorld Health Organization. (‎2014)‎. Water safety in distribution systems. World Health Organization. https://apps.who.int/iris/handle/10665/204422Ostfeld A, Uber JG, Salomons E, Berry JW, Hart WE, Phillips CA, Watson JP, Dorini G, Jonkergouw P, Kapelan Z, di Pierro F, Khu ST, Savic D, Eliades D, Polycarpou M, Ghimire SR, Barkdoll BD, Gueli R, Huang JJ, McBean EA, James W, Krause A, Leskovec J, Isovitsch S, Xu J, Guestrin C, VanBriesen J, Small M, Fischbeck P, Preis A, Propato M, Piller O, Trachtman GB, Wu ZY, Walski T (2008) The battle of the water sensor networks (BWSN): a design challenge for engineers and algorithms. J Water Resour Plan Manag 134:556–568. https://doi.org/10.1061/(ASCE)0733-9496(2008)134:6(556)Santonastaso, G., Di Nardo, A., Creaco, E. et al. Comparison of topological, empirical and optimization-based approaches for locating quality detection points in water distribution networks. Environ Sci Pollut Res 28, 33844–33853 (2021). https://doi.org/10.1007/s11356-020-10519-3

Many mountainous areas of Campania, Southern Italy, are characterized by steep slopes covered by layered granular unsaturated pyroclastic deposits, mainly consisting of ashes and pumices, laying upon fractured limestone bedrock. The total thickness of the soil is quite variable with a few meters (1.0 m–1.5 m) in the steepest part of the slopes, and larger at the foot. Shallow landslides are often triggered after large and intense precipitations, turning into destructive debris flows that cause heavy damage and victims. The slope of Cervinara, located around 40 km Northeast of Naples (Campania, Italy), was involved in a catastrophic debris flow between 15-16 December 1999, triggered by a rainfall event of 325 mm in about 48 h. Since 2001, hydrological monitoring activities have been carried out at the slope, by measuring precipitation depth, soil volumetric water content and capillary tension. Moreover, in December 2017 an automatic hydro-meteorological station has been installed at the elevation of 575 m a.s.l., near the scarp of the 1999 landslide. It allows the assessment of slope hydrological balance, by identifying the major hydrological processes involving the cover and the perched aquifer, which develops in the upper part of the fractured bedrock during the rainy season. Lately, since 1 December 2022, new monitoring activities started. A remotely accessible low-cost network has been installed moving away from the landslide scarp of 1999, for expanding the area interested by soil moisture monitoring. The tested prototype network is based on the use of capacitive sensors placed at nodes located 20 m apart from each other with a communication system within the domain of Internet of Things (IoT) technology. Specifically, the low-cost sensors network allowed measurements of soil water content, communicating through short-range wireless IoT system (i.e., Wi-Fi) thanks to ESP32 boards. The field data can be visualized remotely on ThingSpeakTM IoT platform on laptops and smartphones.The tested IoT-based low-cost network shows the potential to enhance the amount of monitored hydrological data at affordable cost, so to improve risk management in landslide-prone areas. The same IoT network architecture with diffuse measurements can be replicated with long-distance radio communication between nodes, which allows extending the mutual distance up to few kilometers.

The assessment of the response of slopes to precipitations is important for several applications: from drought associated problems to the evaluation of the occurrence of threatening events such as floods and landslides (Bogaard & Greco, 2016). This study aims at identifying the most important variables, that can be monitored in the field, suitable to describe the initial conditions that control the capability of a slope to store infiltrating water at the end of precipitation events. The case study of the slopes near the town of Cervinara, southern Italy, is presented, where field observations and laboratory experiments allowed the understanding of the water processes at different scales (Marino et al., 2020). A synthetic dataset, simulating the major hydraulic processes observed in the field, was generated to enrich the available data. It was built by simulating the response of the slope to a 1000-year long synthetic rainfall series, generated with the NSRP model, with a physically based model coupling the unsaturated flow in the coarse granular soil cover with the shallow aquifer hosted by the uppermost part of the underlying fractured limestone bedrock (Marino et al., 2021). The hydraulic behavior of the soil cover is modelled with the 1D Richards’ equation, while the aquifer, connected to the soil cover through its lower boundary condition, is modelled as a simple linear reservoir. Two variables expressing underground antecedent conditions, one hour before any rainfall event, were analyzed: mean water content in the uppermost meter of the soil cover and aquifer water level. The slope response was quantified as the fraction of rainwater remaining stored in the soil cover at the end of any rainfall event. The non-linear relationships linking the three variables were studied with clustering and random forest techniques, allowing the identification of three major hydrological conditions. The first one is linked to dry seasons, when the lowest aquifer water level coincides with soil water content below field capacity: in this condition, rainwater tends to remain completely stored in the soil at the end of rain events. Once the soil cover overcomes the field capacity, two different conditions are found. When the aquifer water level is high, active drainage through the soil-bedrock interface limits the increase of water stored in the soil cover. Conversely, when the aquifer water level is low, it corresponds to impeded drainage, i.e., there is little leakage from the soil cover to the bedrock. In this condition, most rainwater tends to remain stored in the soil cover even when it is already wet at the beginning of the rain event. References Bogaard, T., & Greco, R. (2016). Landslide hydrology: from hydrology to pore pressure. Wiley Interdisciplinary Reviews: Water, 3(3), 439–459. https://doi.org/10.1002/wat2.1126 Marino, P., Comegna, L., Damiano, E., Olivares, L., & Greco, R. (2020). Monitoring the hydrological balance of a landslide-prone slope covered by pyroclastic deposits over limestone fractured bedrock. Water (Switzerland), 12(12). https://doi.org/10.3390/w12123309 Marino, P., Santonastaso, G. F., Fan, X., & Greco, R. (2021). Prediction of shallow landslides in pyroclastic-covered slopes by coupled modeling of unsaturated and saturated groundwater flow. Landslides, 18(1), 31–41. https://doi.org/10.1007/s10346-020-01484-6

In the area of Casamicciola, on the island of Ischia, in the Gulf of Naples, on November 26, 2022, heavy rain triggered landslides that killed people and caused great damage to buildings and roads. Rain gauges on the island recorded heavy rainfall starting at midnight on November 25. The 6-hour cumulative rainfall (between 00:00 on 25/11 and 06:00 on 26/11) resulted 126 mm. The peak hourly rainfall at the two nearest rain gauges was 51.6 mm in Forio and 50.4 mm in Monte Epomeo, attained just before the triggering of the major landslide. The attainment of critical rainfall depth was so sudden, that rain gauges recordings did not allow deploying timely risk mitigation measures. In this context, an effective Landslide Early Warning System (LEWS), based not only on rain gauges, would be an important tool to mitigate the impact of landslides. The goal of a LEWS is to provide timely information to individuals and organizations, so that they can take appropriate actions to reduce the risk. These systems typically use a combination of monitoring networks and modeling techniques, to issue real-time warnings when the probability of a landslide becomes high. A well-designed LEWS can save lives, reduce property damage, and minimize the economic impact of the events. In this paper, a novel approach to LEWS, based on machine learning and radar data, is proposed. Specifically, a random forest model is trained to define pre-alarm thresholds based on radar measurements available on the portal MISTRAL (Mistral portal Meteo Italian SupercompuTing poRtAL), and on rainfall measurements from four rain gauges on the island of Ischia. Two concentric monitoring areas around the island of Ischia are divided into 16 sectors, and the model evaluates every five minutes the percentage of nodes in each sector where the rainfall height detected by the radar exceeds assigned thresholds, corresponding to pre-alarm stages. Preliminary results show the prospects of using machine learning in LEWS.

The assessment of the response of slopes to precipitation is important for various applications, from water supply management to hazard assessment due to extreme rainfall events. It is well known that the underground conditions prior to the initiation of rainfall events control the hydrological processes that occur in slopes, affecting the water exchange through their boundaries. The present study aims at identifying suitable variables to be monitored and modelled to predict the response of the slope to precipitations. A case study consisting of a loose pyroclastic coarse grained soil cover overlaying a karstic bedrock located in the southern Apennines (Italy) is described, where field monitoring has been carried out, comprising stream level recordings, meteorological recordings, and soil water content among others. Nevertheless, to enhance the field dataset, the slope hydraulic behaviour of the case study has been simulated with a physically based model linked to a synthetic rainfall time series, getting a consistent hourly timeseries dataset of 1000 years, containing information on rainfall, aquifer water level and soil volumetric water content at different depths. Machine Learning techniques have been used to unwrap the relationships amongst the studied variables, which relations are commonly non-linear. The Random Forest technique has been used to assess the way the slope response could be addressed and the importance of each variable on the slope response and the k-means clustering technique has been used to explore the geometrical disposition of data, and so the identification of seasonally recurrent different scenarios linked to the slope response. It has been shown that the slope response in terms of the rainwater being stored in the soil cover is naturally highly dependent on the rainfall amount, but water drainage and storage processes can be identified by normalizing the change in water storage with the rainfall depth. Indeed, with the methodology presented here, different hydrometeorological scenarios controlling major hydrological processes have been identified not only from the meteorological and seasonal behaviour but also from the underground conditions prior to the rainfall initiation, weighting the role, on one hand, of the field capacity value on the ease of the water to flow in and out of the soil cover and, on the other hand, of the ground water level, the increase of which gives evidence of the activation of slope drainage even during relatively intense rainfall events.

Slopes covered with unsaturated shallow pyroclastic deposits, lying upon fractured limestone bedrock, are widespread in the mountains around Naples (southern Italy). Rainfall infiltration, reducing soil suction, eventually triggers shallow landslides. While drastic reduction of suction is unanimously recognized as the triggering mechanism, there is still debate about the hydrological processes controlling slope drainage and causing the establishment of landslide predisposing conditions. Field observations at the slope of Cervinara suggested that temporary storage of water in a perched aquifer, in the upper part of the fractured bedrock, may affect the leakage through the soil-bedrock interface. Hence, a physically based model, coupling flows in the unsaturated soil cover and in the perched aquifer, has been applied to three large rainfall events which occurred in December 1999 (when a landslide was triggered), January 2009, and November 2012. The results highlight that the different responses of soil and aquifer to precipitations, related not only to rainfall event characteristics (i.e., duration and mean intensity) but also to the initial conditions of the slope, determined by antecedent precipitations, can play a prominent role in the triggering of landslides. In fact, further simulations with synthetic rainfall events and different initial conditions provide a possible interpretation of the triggering of the landslide of December 1999, indicating that a soil profile with dry conditions at the base and a low level in the perched aquifer, typical of late autumn, can impede the drainage of infiltrating water through the soil-bedrock interface, thus favoring the build-up of pore pressure within the soil cover.

Permanent partitioning represents one of the more effective approaches for an innovative management of water distribution networks (WDNs) because it allows simplification of the network control defining small subsystem defined as a district meter area (DMA) in which it is simpler to compute water balance, to achieve pressure management and to check water quality. The design of permanent water network partitioning (WNP) is very difficult to apply to large water networks using traditional approaches based on empirical considerations and trial and errors techniques. Recently, the authors developed the software SWANP that allows definition automatically of the number and dimension and shape of a DMA using different innovative heuristic optimization procedures. In this paper a novel release 3.5 of SWANP is presented that implements novel algorithms, based on social network theory for the clustering phase and a genetic multiobjective for the dividing phase, to automatically design DMAs of a real, large water network in Alcalá de Henares, Spain.

Large areas of Campania (southern Italy) are characterized by steep slopes covered with shallow deposits of loose pyroclastic materials, laying upon bedrocks with different characteristics (i.e., limestones, dolomites, volcanic tuff). The pyroclastic covers, usually in unsaturated conditions, are frequently affected by rainfall-induced shallow landslides, which cause heavy damage to property and infrastructures and sometimes casualties. Owing to the brittle behavior of the involved soils, hardly exhibiting any deformation before failure, the occurrence of such landslides is not easily predictable, so that operational early warning systems for rainfall-induced landslides (LEWS) usually rely only on empirical thresholds based on precipitation information (i.e., intensity and duration of triggering rainfall event). Anyway, the reliability of landslide prediction would benefit from the inclusion of hydrological information about the condition of the slope cover before the onset of the triggering rainfall (e.g., Marino et al., 2020a).Three years of continuous field monitoring carried out at the slope of Cervinara, located around 40 km north-east of the city of Naples, where a destructive flowslide occurred in December 1999, have provided insight of the hydrological processes controlling the water balance of the pyroclastic deposits, laying upon a densely fractured limestone bedrock, where a temporary perched aquifer develops during the rainy season (Marino et al., 2020b). This knowledge allowed setting up a physically based model capable of identifying the seasonality of the predisposing conditions leading to slope failure (Greco et al., 2018; Marino et al., 2021). Aiming at identifying the hydrological processes mostly affecting landslide triggering, the model is coupled with a stochastic rainfall generator (i.e., the Neyman-Scott rectangular pulse model), previously calibrated based on 20 years hourly rainfall data, obtaining a 1000 years long synthetic series of the slope cover response to precipitations (in terms of soil suction, water content, perched aquifer water level, and leakage through the soil-bedrock interface). The obtained synthetic dataset of rainfall and hydrological variables have been analyzed with machine-learning techniques, so to identify the most effective combination of variables for landslide predictions.The analysis of the synthetic data allows identifying the most suitable variables to be monitored, for assessing the hydrologic conditions predisposing the slopes to failure. In fact, the obtained results are confirmed by the analysis of the available field monitoring data, indicating that coupling rainfall measurements with field and remote hydrological monitoring significantly improves landslide prediction.ReferencesGreco R, Marino P, Santonastaso GF, Damiano E (2018). Interaction between perched epikarst aquifer and unsaturated soil cover in the initiation of shallow landslides in pyroclastic soils. Water 10:948.Marino P, Peres DJ, Cancelliere A, Greco R, Bogaard TA (2020a). Soil moisture information can improve shallow landslide forecasting using the hydrometeorological threshold approach. Landslides 17(9): 2041-2054.Marino P, Comegna L, Damiano E, Olivares L, Greco R (2020b). Monitoring the Hydrological Balance of a Landslide-Prone Slope Covered by Pyroclastic Deposits over Limestone Fractured Bedrock. Water 12(12): 3309.Marino P, Santonastaso GF, Fan X, Greco R (2021). Prediction of shallow landslides in pyroclastic-covered slopes by coupled modeling of unsaturated and saturated groundwater flow. Landslides 18(1): 31-41.

The reliability of water distribution networks (WDN) can be defined as the ability of the system to meet water demands under both normal and abnormal conditions (Bao and Mays, 1990). The measure of reliability is influenced by many factors: possible failure of one or more components, unusual high demand, connectivity of the network, poor water quality, etc. Although reliability of WDN was originally defined by Goulter (1987) and Walters (1988), measuring reliability is an open challenge for researchers. Consequently, there is no established measure of WDN reliability.In recent years, many studies have attempted to mathematically define the reliability of water distribution networks and several synthetic indices are provided as proxy of reliability measures. The objective of this paper is to investigate the suitability of two of these metrics: one based on surplus of power head and other on Shannon’s informational entropy for the assessment of reliability of partitioned water distribution networks (WDNs). The creation of permanent DMAs involves permanently an alteration of the WDN by closing multiple lines at the same time, and, therefore, it is a more severe test than those commonly used in the scientific literature where a little or no disruption occurs to the operation of the WDN (e.g., segment isolation or demand amplification). In addition, the two metrics were compared with other known indicators of hydraulic performance to determine which of them is better suited to evaluate the reliability of water network partitioning. For this purpose, a medium-sized water distribution network in South of Italy was used as case study and the hydraulic simulations were performed with a pressure-driven approach using EPANET2.2 software. ReferencesBao, Y., Mays, L.W. (1990). "Model for water distribution system reliability", J. Hydrual. Eng., 116, 1119-1137Greco, R., Di Nardo, A., Santonastaso, G.F (2012). "Resilience and entropy as indices of robustness of water distribution networks", J. Hydroinformatics, 14 (3), 761–771.Gouher, I.C. (1987). "Current and future use of system analysis in water distribution network design", Civ. Engrg. Sys., 4(4), 175-184.Walters, G. (1988). "Optimal design of pipe networks: a review." Proc.,1st Int. Conf. on Compo and Water Resour., Vol. 2, Computational Mechanics Publications, Southampton, U.K., 21-31.

Operational early warning systems for rainfall-induced landslides (LEWS) usually rely on simple empirical thresholds based on the statistical analysis of either triggering rainfall characteristics, e.g. intensity and duration (Guzzetti et al., 2007). The main pro of this simplified approach is that it requires only rainfall records, at the desired spatial and temporal resolution, and a database of landslides with known time and location. The effect of the hydrologic conditions of the slopes prior the onset of the triggering rainfall is usually neglected, limiting the performance of the LEWS, which often give rise to false and missing alarms. To address this issue, antecedent precipitation is sometimes included in the definition of the threshold, but the identification of the antecedent precipitation duration is doubtful, as this approach neglects non-linear hydrological processes affecting slope response. Hydro-meteorological thresholds, linking a variable accounting for the antecedent hydrologic conditions with a characteristic of the triggering rainfall, have been recently proposed (Bogaard and Greco, 2018).In this study, hydro-meteorological thresholds for landslide prediction are identified for a slope in southern Italy, characterized by an unsaturated pyroclastic soil cover laying upon fractured limestone bedrock and subject to rainfall-induced shallow landslides. To this aim, a synthetic 1000 years long hourly point rainfall record is generated with the Neyman-Scott rectangular pulse stochastic model, calibrated thanks to available measured rainfall. The response of the slope to the synthetic rainfall record is simulated by means of a physically-based model, which couples unsaturated flow in the soil cover with a temporary perched aquifer in the limestone bedrock, and allows estimating all the terms of slope water balance (Greco et al., 2018). The stability of the slope is eventually assessed under the infinite slope hypothesis, allowing the identification of the occurrence of landslides.The obtained synthetic dataset of rainfall and hydrologic variables has been exploited for the definition of hydro-meteorological thresholds. All the combinations of hydrologic variables with triggering rainfall height have been analyzed with several clustering techniques, so to identify the most effective combinations for landslide predictions. References:Bogaard TA, Greco R (2018). Invited perspectives: Hydrological perspectives on precipitation intensity-duration thresholds for landslide initiation: proposing hydro-meteorological thresholds, Nat Hazards Earth Syst Sci, 18: 31–39.Greco R, Marino P, Santonastaso GF, Damiano E (2018). Interaction between Perched Epikarst Aquifer and Unsaturated Soil Cover in the Initiation of Shallow Landslides in Pyroclastic Soils, Water, 10: 948.Guzzetti F, Peruccacci S, Rossi M, Stark CP (2007). Rainfall thresholds for the initiation of landslides in central and southern Europe, Meteorol Atmos Phys, 98: 239–267.

In the era of Smart Cities, in which the paradigms of smart water and smart grid are keywords of technological progress, advancements in metering systems allow for water consumption data collection at the end-use level, which is necessary to profile users' behaviors and to promote sustainable use of water resources. In this paper, a real case study of residential water end-use consumption monitoring shows how data collected at a high-resolution rate allow for the evaluation of consumption profiles. The analysis was carried out by calculating consumption statistics, hourly consumption patterns, daily use frequency, and weekly use frequency. Then, the comparison of two consumption profiles, computed before and after the COVID-19 lockdown, allows us to understand how a change in social and economic factors can affect users' behavior. Finally, new perspectives for water demand modeling and management, based on data with high temporal frequency, are presented. HIGHLIGHTS Real water consumption data gathered at the fixture level contribute to understand users’ behaviors and identify their impact on demand peak.; Statistical evaluations used to analyze data sampled with a high temporal frequency show how disaggregated data facilitate the identification of water consumption patterns.; The COVID-19 pandemic was used to explore how water end-use data allow for understanding changes of user's behavior.; Data collected at the end-use level are possibly eligible as numerical benchmarks to investigate disaggregation algorithms.

Rainfall-induced shallow landslides occurring along slopes of mountains around the city of Naples (southern Italy), characterized by pyroclastic covers laying upon fractured limestone bedrock, pose serious threat to settlements and towns located at their foot. Although the triggering mechanism, related to suction reduction in the initially unsaturated soil cover is quite clear, there is still debate about the hydrological processes which predispose the slopes to failure. In this paper, the predisposing factors are analyzed with reference to two intense storms, both with about 300 mm rainfall, occurred in the same period of the year in 1999 and 2019. The two storms triggered several shallow landslides in an area of few km2 of the northeast-facing slopes of Partenio Mountains. The two events are compared analyzing both the characteristics of antecedent and event precipitations, as well as simulating the response of the slopes to rainfall with the help of a mathematical model. Specifically, two slopes are analyzed: one, near the town of Cervinara, where landslides were triggered only in 1999; another, near the town of San Martino Valle Caudina, where landslides occurred during both the events. The results of the model simulations indicate that rainstorm duration (shorter in 1999) and antecedent precipitation during the weeks before the event (smaller in 1999) are the main reasons of the different responses of the slopes to the two rainfall events. In fact, both drier initial conditions at the base of the soil cover and shorter storm duration limit the possibility of drainage of infiltrating water out of the soil cover through the soil-bedrock interface, thus favoring the suction reduction required for slope failure. The obtained results provide a key for the interpretation of landslides, often occurring in late autumn-early winter in the area.

Water network partitioning (WNP) represents an efficient strategy to improve management of water distribution networks, reduce water losses and monitor water quality. It consists in physically dividing of a water distribution network (WDN) into districted metered areas (DMAs) through the placement of flow meters and isolation valves on boundary pipes between DMAs. In this paper, a novel methodology for designing DMAs is proposed that provides districts with quite similar node elevations and minimizes the number of boundary pipes in order to simplify pressure management and reduce the number of devices to place into the network.

The positioning of quality detection points as well as the frequency of sampling is a crucial aspect for the implementation of Water Safety Plans (WSPs), which have been proposed worldwide to ensure water quality and to minimize the risk from contamination in water distribution networks (WDNs). In this regard, some international legislations and best practices about quality of drinking water suggest very fine sampling frequencies, but they do not specify where the detection points should be located in a WDN. In this paper, three different approaches, based on empiricism, optimization and topology, respectively, were applied to locate detection quality points in a WDN. The comparison highlighted that empirical approach commonly adopted by water utility practitioners is unsatisfactory. The optimization-based approach, although performing significantly better, is difficult to apply, since it requires a calibrated hydraulic model. The topological approach, based on the use of the betweenness centrality and not requiring any hydraulic information and simulation, proves to be effective, and it can be easily adopted by water utilities to identify the location for quality detection points, due to its simplicity compared with the optimization-based approach.

Pump-and-treat technology is among the most used technologies for groundwater remediation. While conventional, vertical wells (VRWs) are well-known and used from long time, horizontal wells (HRWs) have been explored for remediation technologies only in last few decades. HRWs have shown to outperform vertical wells in terms of versatility, productivity and clean-up times under certain conditions. In this paper, the efficacy of an innovative pump-and-treat (P&T) configuration for groundwater remediation obtained by adopting either VRWs or HRWs technology is comparatively tested. A 3D transient finite element model of an unconfined aquifer containing a hexavalent chromium (Cr(VI)) contamination plume is considered to compare a single horizontal well configuration vs a range of spatially-optimised arrays containing vertical wells. A sensitivity analysis aimed at finding the best configuration to minimise the remediation time and the related cost is carried out by comparing different well diameters, D, pumping rates, Q, and position of wells. A comparative cost analysis demonstrates that, for the examined case-study, a single HRW achieves the clean-up goals in the same time span as for a greater number of vertical wells, but at higher price due to the excavation costs.

Disaggregation techniques are useful to separate data aggregated into single end-use categories, and they are becoming of great interest in the water sector due to the technological innovation in metering systems that has made water consumption data available. However, in order to apply disaggregation methods, high-resolution data at the end-use level are needed. To face this problem, the paper presents an Internet of Things water end-use monitoring system that is able to read real-time end-use consumption in a residential apartment equipped as a pilot site. Moreover, the paper describes preliminary consideration of the dataset and the potentiality of end-use consumption measures to apply disaggregation techniques and profile users’ behaviors.

As other mountainous areas of Campania (Italy), mount Partenio consists of carbonate rocks covered with layered air-fall deposits originated by eruptions of the two volcanic complexes of the area (Somma Vesuvius and Phlegrean Fields). The deposits are alternated layers of ashes (loamy sands) and pumices (sands with gravel), both materials characterized by negligible effective cohesion. The thickness of the deposit ranges between few centimeters along the steepest slopes (up to 50°) to some meters at the foot of the slopes, with gentle inclination. The equilibrium of the covers along the steepest slopes is guaranteed by the contribution of suction to soil shear strength. After intense and prolonged rain, this contribution is reduced by infiltrating water being stored within the cover, sometimes leading to shallow landslide triggering.The two most recent landslide events in the area occurred on 16.12.1999 and 21.12.2019. In the first case, several landslides were triggered along slopes with inclination larger than 40°, in an area of about 10 km2, some of which evolved in the form of fast debris flows which caused damages to buildings and some victims in the town of Cervinara. In the second case, two major landslides were reported, one of which, along a slope with inclination between 42° and 45°, very close to two of the landslides of 1999, damaged roads and buildings in the town of San Martino Valle Caudina.After the event of 1999, a hydro-meteorological monitoring station was installed near the scarp of the major landslide. Thanks to the monitoring data and laboratory investigation on the hydraulic properties of the involved soils, a mathematical model of the response of the slope to precipitation was developed (Greco et al., 2013). The model couples unsaturated flows in the pyroclastic cover with the groundwater system developing in the underlying fractured limestone bedrock, and it allows satisfactorily reproducing the seasonal trends of the terms of the hydrological balance of the slope (Greco et al., 2018).In this study, the two events of 1999 and 2019 are compared, in terms of pre-event and event rainfall characteristics, as well as by simulating the response of the slopes by means of the mathematical model during the entire year until the day of the landslides. The obtained results show the importance of the interplay between predisposing conditions, related to the rainfall history during the months before the event, and the characteristics of the triggering event. The model simulations indicate that, while in 1999 failure conditions are predicted along slopes with inclination larger than 40°, regardless cover thickness, in 2019 landslide triggering is predicted only on slopes mantled by a cover thinner than 1.5 meters with inclination larger than 42°.ReferencesR. Greco, L. Comegna, E. Damiano, A. Guida, L. Olivares, L. Picarelli (2013). Hydrological modelling of a slope covered with shallow pyroclastic deposits from field monitoring data. Hydrology and Earth System Sciences, 17: 4001-4013.R. Greco, P. Marino, G.F. Santonastaso, E. Damiano (2018). Interaction between perched epikarst aquifer and unsaturated soil cover in the initiation of shallow landslides in pyroclastic soils. Water, 10(7): 948.

International audience; The paper presents the results of the Battle of Postdisaster Response and Restoration (BPDRR) presented in a special session at the first International water distribution systems analysis & computing and control in the water industry (WDSA/CCWI) Joint Conference, held in Kingston, Ontario, Canada, in July 2018. The BPDRR problem focused on how to respond and restore water service after the occurrence of five earthquake scenarios that cause structural damage in a water distribution system. Participants were required to propose a prioritization schedule to fix the damages of each scenario while following restrictions on visibility/nonvisibility of damages. Each team/approach was evaluated against six performance criteria: (1)time without supply for hospital/firefighting, (2)rapidity of recovery, (3)resilience loss, (4)average time of no user service, (5)number of users without service for eight consecutive hours, and (6)water loss. Three main types of approaches were identified from the submissions: (1)general-purpose metaheuristic algorithms, (2)greedy algorithms, and (3)ranking-based prioritizations. All three approaches showed potential to solve the challenge efficiently. The results of the participants showed that for this network, the impact of a large-diameter pipe failure on the network is more significant than several smaller pipes failures. The location of isolation valves and the size of hydraulic segments influenced the resilience of the system during emergencies. On average, the interruptions to water supply (hospitals and firefighting) varied considerably among solutions and emergency scenarios, highlighting the importance of private water storage for emergencies. The effects of damages and repair work were more noticeable during the peak demand periods (morning and noontime) than during the low-flow periods; and tank storage helped to preserve functionality of the network in the first few hours after a simulated event.

The paper presents a hardware/software platform that integrates IoT technologies for deploying a smart water network in a domestic environment. Low cost sensors and edge nodes have been deployed in a real pilot for monitoring water fixture use. A data collection platform, which can run in the Fog or in Cloud, allows for processing of raw data and monitoring. It makes available raw and intermediate data to offload the computation of complex Big Data analytic. The paper presents preliminary results according to the data available after two months of operation.

This paper proposes a combined management strategy for monitoring water distribution networks (WDNs). This strategy is based on the application of water network partitioning (WNP) for the creation of district metered areas (DMAs) and on the installation of sensors for water quality monitoring. The proposed methodology was tested on a real WDN, showing that boundary pipes, at which flowmeters are installed to monitor flow, are good candidate locations for sensor installation, when considered along with few other nodes detected through topological criteria on the partitioned WDN. The option of considering only these potential locations, instead of all WDN nodes, inside a multi-objective optimization process, helps in reducing the search space of possible solutions and, ultimately, the computational burden. The solutions obtained with the optimization are effective in reducing affected population and detection time in contamination scenarios, and in increasing detection likelihood and redundancy of the monitoring system. Last but most importantly, these solutions offer benefits in terms of management and costs. In fact, installing a sensor alongside the flowmeter present between two adjacent DMAs yields managerial advantages associated with the closeness of the two devices. Furthermore, economic benefits due to the possibility of sharing some electronical components for data acquisition, saving, and transmission are derived.

The removal of groundwater contamination is a complex process due to the hydro-geochemical characteristics of the specific site, related maintenance and the possible presence of several types of pollutants, both organic and inorganic. In recent decades, there has been an increasing drive towards more sustainable treatment for contaminated groundwater as opposed to “intensive” treatments, i.e. with high requirements for onsite infrastructure, energy and resource use. In this study, a new remediation technology is proposed, combining the use of advanced drainage systems with adsorption processes, termed “In-situ reactive DRAINage system for groundwater TREATment” (In-DRAIN-TREAT). By taking advantage of the groundwater natural gradient, In-DRAIN-TREAT collects the contaminated groundwater via a drainage system and treats the polluted water directly into an active cell located downstream, avoiding external energy inputs. Preliminary results indicate the applicability and high efficiency of In-DRAIN-TREAT when compared with a permeable reactive barrier (PRB). In-DRAIN-TREAT is applied to remediate a theoretical aquifer with low permeability, contaminated by a 13 m wide hexavalent chromium (CrVI) plume. This is achieved in less than a year, via a drain DN500, 32 m long, a 30 m3 treatment cell filled with activated carbon and no energy consumption. A comparison with permeable barriers also shows a preliminary 63% volume reduction, with a related 10% decrease of remediation costs.

This paper proposes a general framework to adjust water distribution network (WDN) partitioning algorithms to account for the real positions of isolation valves. It is based on changing the reference WDN topology, on which partitioning algorithms typically operate. Specifically, the WDN topological description based on nodes and pipes must be replaced with a dual topology based on WDN segments and isolation valves. A generic partitioning algorithm can then be applied to the dual topology. As a result, DMAs are obtained by merging adjacent segments while inter-DMAboundary pipes are forced to be selected among the valve-fittedpipes that separate segments. This framework enables obtaining feasible WDN partitioning solutions, which do not need additional isolation valves to be installed. The applications of the novel framework to three WDN partitioning algorithms prove its effective applicability in one explicatory WDN and two WDNs of the real world.

Adsorptive wells arrays are an innovative outline of Permeable Reactive Barrier (PRBs) made of a definite number of passive deep wells opportunely distributed in the aquifer, known as PAB-D (Discontinuous Permeable Adsorptive Barrier). They are generally located downstream the contaminated groundwater flow and perpendicularly to the groundwater flow direction. Being PAB-D wells filled with adsorbing media, whose hydraulic permeability is higher than the surrounding media, the array will create a targeted capture zone, which will force the contaminated water to pass through the whole PAB-D, allowing for both the interception of the contaminant plume and its treatment. In this work, an optimized configuration of PAB-D is presented, for the in situ-remediation of an aquifer simultaneously contaminated by benzene and toluene. The design optimization of the PAB-D was performed by using COMSOL Multiphysics®, in which numerical simulations reproduced the transport and the adsorptive phenomena occurring inside the aquifer and the barrier itself. The proposed technique was applied to the remediation of an aquifer located in an urban area in the north of Naples (Italy), in proximity of numerous landfills, where the contamination was spread over an area of 0.10 km2. Simulation results confirm the effectiveness of the PAB-D, being both pollutant plumes intercepted and their concentrations reduced below their correspondent Italian regulatory threshold values. The best array configuration of PAB-D resulted made of 741 wells, each having a diameter of 0.6 m, which was also compared with a continuous barrier (PAB C) showing a reduction of about 49% of the volume and 35% of the overall remediation cost.

Effective landfill management and operation require an accurate evaluation of the occurrence and extent of odour emission events, which are among the main causes of resident complaints and concerns, in particular in densely urbanised areas. This paper proposes a fuzzy optimal protection system (FOPS) based on fuzzy logic to manage odour production from a municipal solid waste (MSW) landfill. The case study is a MSW landfill in an old quarry site located 6 km north-west of Naples city centre (Italy). The aim is to reduce the odour nuisance in the area surrounding the landfill where there are several sensitive receptors. FOPS is based on logical relationships between local atmospheric dynamics, number and intensity of odour pollution events detected by certain fixed receptors and odour emission rate via an optimal fuzzy approach. Such system allows to start, in real time, established mitigation actions required to further reduce the odorous emissions from the landfill itself (e.g. spraying of perfumed substances and activation of extraction wells), especially when weather conditions might not be favourable and cause by causing a higher odour perception. The fuzzy system was coupled with the air pollutant transport software CALPUFF to simulate the odour dispersion in the considered area taking into account both different odour emission rates and local weather conditions. FOPS results show that this approach can be very useful as, by measuring the odour concentrations, a significant reduction of the odour exceedances over the thresholds fixed, to minimise the olfactory harassment, was observed in the whole area studied.

Installing an efficient monitoring and control sensor system provides the possibility to carry out main tasks on Water Distribution Networks (WDNs) management and protection. Given the WDNs complexity, efficient numerical techniques are needed to support optimal monitoring system design. Generally, it is appropriate to locate sensors at highly connected places in the WDN with water flow reaching several parts of the network. This paper introduces a general method to support water utilities on the decision making process for an efficient water system monitoring. The proposal is based on graph spectral techniques that take advantage on spectrum properties of the adjacency matrix of the water distribution network graph. It is consequently created a novel tool-set of graph spectral techniques adapted to improve the water monitoring tasks and consequently simplify further sensor placement. This is approached with no need of hydraulic simulation, as data availability is often limited or not suitable to face anomaly events changing assets and distribution performance. A real water distribution network serving a town near to Naples is used to analyze the proposed graph spectral methodology. In order to test the proposed procedure, a comparison was made with a sensor layout obtained through a bi-objective optimization, through some performance indicators. The results confirm the effectiveness of the proposed spectral procedure.

This paper aims to explore the suitability of compact resilience metrics for application to partitioned water distribution networks (WDNs). WDN partitioning represents a different test from the usual reliability tests performed in the scientific literature, in which the operation of the WDN is unperturbed, or marginally perturbed (e.g., by segment isolation or demand amplification). The creation of permanent district metering areas (DMAs), which is carried out through the simultaneous closure of numerous links, represents, instead, a larger and permanent perturbation that deserves special attention. In this analysis, two metrics, namely the Global Resilience Failure (GRF) and the energy efficiency indices, were compared in pressure-driven approach with WDN performance indicators. The results in a real WDN, which is partitioned in a growing number of DMAs, proved that both the GRF is more sensitive to the weaknesses arisen in the partitioning processes.

The recent development and applications of social network theory in many fields of engineering (electricity, gas, transport, water, etc.) allows both the understanding of networks and to improve their management. Social network theory coupled to the availability of real time data and big data analysis techniques can change drastically the traditional approaches to manage civil networks. Recently, some authors are working to apply this novel approach, based on social network theory, on the water distribution networks using: a) graph partitioning algorithms to define optimal district meter areas both for water losses identification and for water network protection, b) innovative topological, energy and hydraulic indices to analyze performance; and c) GIS (Geographical Information System) to provide a more effective display of results and to improve network behavior in specific operational conditions. In this paper, a novel release 3.5 of SWANP software, that implements all these features, was tested on a real large water network in Alcalá de Henares, Spain.

Water network protection from accidental and intentional contamination is one of the most critical issues for preserving the citizen health. Recently, some techniques have been proposed in the literature to define the optimal sensor placement. On the other hand, through the definition of permanent DMAs (District Meter Areas), water network partitioning allows significant reduction in the number of exposed users through the full isolation of DMA. In this paper, the optimal sensor placement is coupled with water network partitioning in order to define the best location of isolation valves and control stations, to be closed and installed respectively. The proposed procedure is based on different procedures, and it was tested on a real water network, showing that it is possible both to mitigate the impact of a water contamination and simplify the sensor placement through the water network partitioning.

Permeable Adsorptive Barriers (PABs), filled with a commercial activated carbon, are tested as a technique for the remediation of a thallium (I)-contaminated aquifer located in the south of Italy. Thallium adsorption capacity of the activated carbon is experimentally determined through dedicated laboratory tests, allowing to obtain the main modelling parameters to describe the adsorption phenomena within the barrier. A 2D numerical model, solved by using a finite element approach via COMSOL Multi-physics®, is used to simulate the contaminant transport within the aquifer and for the PAB design. Investigations are carried out on an innovative barrier configuration, called Discontinuous Permeable Adsorptive Barrier (PAB-D). In addition, an optimization procedure is followed to determine the optimum PAB-D parameters, and to evaluate the total costs of the intervention. A PAB-D made by an array of wells having a diameter of 1.5 m and spaced at a distance of 4 m from each other, is shown to be the most cost-effective of those tested, and ensures the aquifer restoration within 80 years. The simulation outcomes demonstrate that the designed PAB-D is an effective tool for the remediation of the aquifer under analysis, since the contaminant concentration downstream of the barrier is below the thallium regulatory limit for groundwater, also accounting for possible desorption phenomena. Finally, the best PAB-D configuration is compared with a continuous barrier (PAB-C), resulting in a 32% saving of adsorbing material volume, and 36% of the overall costs for the PAB-D.

The traditional approach to the design of water distribution systems (WDS) is based on the concept of topological and energy redundancy, resulting in systems with many loops and with nodal pressure higher than design requirements. In the early days of water supply networks, this oversizing was not seen as a problem. Currently, however, with aging water networks and pressure on utility finances, the cost of infrastructure maintenance and renewal constitutes a significant proportion of utility budgets. This paper proposes a novel approach that enables a water utility to better invest limited budgets by dividing the water system into a primary and a secondary group of pipes, with the former being the focus of the field investigation, maintenance, and renewal activities. The primary network is identified such that it guarantees the minimum hydraulic performance while achieving maximum topological redundancy in the overall system. The methodology is based on the use of local and global topological and energy metrics in conjunction with a heuristic optimization technique. A case study confirms that minimum nodal pressure can be substantially preserved by focusing activities on less than 70% of the pipes in the network, which means a reduction of over 30% of the total length of infrastructure and, consequently, of related field investigation and maintenance costs. The analysis of the local and global performance indexes also provides various insights and useful information for utility management.

Water Network Partitioning (WNP) improves water network management, simplifying the computation of water budgets and, consequently, allowing the identification and reduction of water loss. It is achieved by inserting flow meters and gate valves in the network, previously clustered in subsystems. The clustering and partitioning phases are carried out with different procedures. The first one requires clustering algorithms that assign network nodes to each district (or cluster). The second one chooses the boundary pipes where flow meters or gate valves are to be inserted. In this paper, SWANP software is employed to achieve a network clustering through two different algorithms based on a multilevel-recursive bisection and community-structure procedures. After that, a novel multi-objective function is introduced and applied to a large Mexican network integrating both cost and energy performance, thus providing a smart Decision Support System (DSS) based on qualitative and quantitative measures, and diagrams for evaluating the optimal layout in terms of the number of districts, cost, and hydraulic performance.

Water network partitioning (WNP) concerns the implementation of the paradigm of ‘divide and conquer’ in the water supply systems consisting in dividing a water network into k smaller subsystems by gate valves and flow meters. WNP allows improving the management of water distribution systems by transforming into Smart WAter Networks (SWANs) that represent key subsystems of Smart Cities. WNP is possible if water utilities start to use isolating valves (also with control devices) and flow meters. These instruments open new perspectives for the management of water supply systems both in terms of water saving and water protection. Recently, starting from the advantages offered by computing power and network analysis tools, some techniques based on graph partitioning algorithms have been developed by the authors to improve water management and protection, implementing the paradigm of ‘divide and conquer’. This paper presents the second release of SWANP 2.0 (Smart WAter Network Partitioning and Protection) software that integrates two different algorithms, based on multilevel and multi-agent techniques, for WNP and a novel algorithm, based on a multi-objective function, for water network protection from intentional contamination. The SWANP 2.0 software, developed in Python v2.7.6 language, provides the decision-maker different solutions comparing network layouts with some hydraulic and protection performance indices.

Water distribution networks are complex infrastructures characterized by very large dimensions (meshed networks with thousands of nodes) and reduced accessibility (generally they are buried underground). These peculiarities make it particularly difficult to manage and show numerous criticalities; at the same time, water distribution networks represent one of the main subsystems of smart cities. The paradigm “divide et impera” is a valid technique to improve the management and facilitate the maintenance, the water balance estimation, the water leakage detection and the pressures control. On this side, Water network District Metering (WDM), a method developed in England, and already implemented in many countries, represents a modern technique to organize urban water systems, offering new perspectives for design, management and maintenance. The WDM is recommended as one of the best practice to limit the huge amount of dispersed water in urban networks, simplifying water leakage detection, carrying out pressure management, improving water system management and monitoring of district hydraulic data. On this subject, the authors developed a specific software (SWANP 3.0) for water network partitioning that integrates the main optimization algorithms. This paper shows the main aspects of water network partitioning summarizing the principal recently proposed techniques in the literature aimed to find the best partitioning layout from an economic and performance point of view.