Drinking water security is a life safety issue as an adequate supply of safe water is essential for economic, social and sanitary reasons. Damage to any element of a water system, as well as corruption of resource quality, may have significant effects on the population it serves and on all other dependent resources and activities. As well as an analysis of the reliability of water distribution systems in ordinary conditions, it is also crucial to assess system vulnerability in the event of natural disasters and of malicious or accidental anthropogenic acts. The present work summarizes the initial results of research activities that are underway with the intention of developing a vulnerability assessment methodology for drinking water infrastructures subject to hazardous events. The main aim of the work was therefore to provide decision makers with an effective operational tool which could support them mainly to increase risk awareness and preparedness and, possibly, to ease emergency management. The proposed tool is based on Bayesian Belief Networks (BBN), a probabilistic methodology which has demonstrated outstanding potential to integrate a range of sources of knowledge, a great flexibility and the ability to handle in a mathematically sound way uncertainty due to data scarcity and/or limited knowledge of the system to be managed. The tool was implemented to analyze the vulnerability of two of the most important water supply systems in the Apulia region (southern Italy) which have been damaged in the past by natural hazards. As well as being useful for testing and improving the predictive capabilities of the methodology and for possibly modifying its structure and features, the case studies have also helped to underline its strengths and weaknesses. Particularly, the experiences carried out demonstrated how the

The safety of drinking water infrastructures is fundamental for economic, social and sanitary reasons, andshould be guaranteed both during ordinary service and in case of emergencies. Besides verifying and preventingdeterioration and ageing, the response of the system to extreme events should be also carefully analyzed. As a matterof fact, depending on the level of preparedness that water system authorities have adopted, the restoration of systemfunctionality may require days, weeks, or even months.Water supply systems are vulnerable towards several hazardous events, which can be mainly classified asnatural (such as earthquakes, hurricanes, volcanic eruptions, landslides, fires...) or anthropic (both intentional andaccidental, such as pollution, operational mistakes, black-outs etc.). Referring to the potential consequences of suchevents on the system, physical damages consisting in breakage or malfunctioning of one or more elements of thenetwork, should be distinguished from water contamination.A research activity is being developed by the Water Research Institute of the National Research Council(Istituto di Ricerca sulle Acque del Consiglio Nazionale delle Ricerche IRSA-CNR), supported by the ItalianDepartment of Civil Protection (Dipartimento della Protezione Civile - DPC), with the aim of defining a strategicDecision Support System (DSS) for efficient and coherent decision-making in case of threats involving drinkingwater infrastructures. The DSS is based on Bayesian Belief Networks (BBNs), a semi-quantitative probabilistic toolparticularly useful for managing emergency situations, characterized by time shortness and information uncertainty.It should be mainly used for detecting potential shortcomings of the system during emergencies, but also for helpingwater authorities in defining priorities of action, even with reference to ordinary management procedures.In the following, the methodological approach adopted is firstly presented, with specific reference to the mainfeatures of BBNs. Then, the structure of the methodology is described in synthesis. At last, the applicability of thetool is discussed, referring to a couple of real case studies developed with the cooperation of Acquedotto PuglieseS.p.A., an Italian water authority.

Water management is a controversial environmental policy issue, due to the heterogeneity of interests associated with a shared resource and the increasing level of conflict among water uses and users. Nowadays, there is a cumulative interest in enhancing multi-stakeholder decision-making processes, overtaking binding mercantile business, in water management domain. This requires the development of dynamic decision-aiding tools able to integrate the different problem frames held by the decision makers, to clarify the differences, to support the creation of collaborative decision-making processes and to provide shared platforms of interactions. In literature, these issues are faced by concepts such as Ostrom's action arena and Ostanello-Tsoukiàs' interaction space (IS). The analysis of the interactions structure and of the different problem framing involved are fundamental premises for a successful debate for the management of a common-pool resource. Specifically, the present paper suggests a dynamic evolution of the IS, highlighting its criticalities. It develops an alternative perspective on the problem, using a System Dynamics Model (SDM), exploring how different actions can influence the decision-making processes of various stakeholders involved in the IS. The SDM has been implemented in a multi-stakeholders decision-making situation in order to support water management and groundwater protection in the agricultural systems in the Capitanata area (Apulia region, Southern Italy).

Increasing pressure affects water resources, especially in the agricultural sector, with cascading impacts on energy consumption. This is particularly relevant in the Mediterranean area, showing significant water scarcity problems, further exacerbated by the crucial economic role of agricultural production. Assessing the sustainability of water resource use is thus essential to preserving ecosystems and maintaining high levels of agricultural productivity. This paper proposes an integrated methodology based on the Water-Energy-Food Nexus to evaluate the multi-dimensional implications of irrigation practices. Three different indices are introduced, based on an analysis of the most influential factors. The methodology is then implemented in a catchment located in Puglia (Italy) and a comparative analysis of the three indices is presented. The results mainly highlight that economic land productivity is a key driver of irrigated agriculture, and that groundwater is highly affordable compared to surface water, thus being often dangerously perceived as freely available.

A water allocation model at farm-scale was developed to interpret water allocation patterns in an intensive agriculturaldistrict of Southern Italy, supplied by groundwater and surface waters (from reservoir) with variable costs and distinctmanagement regimes. The model aims at evaluating the impact of farm-scale water costs on water resourcesmanagement and groundwater conservation at district scale. Semi-structured interviews were carried out involvinglocal stakeholders to define (i) the relationship between irrigation source selection and water tariff applied by theirrigation district, and (ii) the conjunctive use of groundwater based on water cost convenience. It was demonstratedthat farmers' choice depends on the ratio between volumetric water tariff and the groundwater pumping cost at farmscale.The results also demonstrated that a restrictive water tariff policy applied during drought periods produced anincrease in the groundwater use instead of reducing the water consumption. The model allowed to analyze the driversinfluencing farmers' behaviour, thus assessing the effectiveness of water protection policies, specifically those relatedto water tariff.

Evidences from flood risk management demonstrated that a deep understanding of the main physical phenomena to be addressed is often not enough but should be also integrated with stakeholders' knowledge and risk perception. Particularly, the effectiveness of flood risk management strategies is highly dependent on stakeholders' perception and attitudes, which play a critical role on how individuals and institutions act to mitigate risks. Furthermore, practitioners and policy-makers realized that grey infrastructures may not be the most suitable solution to reduce flood risk, and that a shift from grey solutions to Nature Based Solutions is required. Within this framework, the present work describes a methodology to enhance the Nature Based Solutions implementation by facilitating the generation, acquisition and diffusion of different stakeholders' risk perceptions. It is based on the combination of Problem Structuring Methods for the elicitation of stakeholders' risk perceptions through individual Fuzzy Cognitive Maps, and Ambiguity Analysis for the investigation of differences in risk perceptions and problem framing. The outputs of the Ambiguity Analysis, used during a participatory workshop, facilitated a dialogue aligning the divergences and promoting the social acceptance of Nature Based Solutions. These results of the implementation of this multi-step methodology in the Glin??ica river basin (Slovenia) are discussed.

The availability and the quality of drinking water are key requirements for the well-being and the safety of a community, both in ordinary conditions and in case of disasters. Providing safe drinking water in emergency contributes to limit the intensity and the duration of crises, and is thus one of the main concerns for decision-makers, who operate under significant uncertainty. The present work proposes a Decision Support System for the emergency management of drinking water supply systems, integrating: i) a vulnerability assessment model based on Bayesian Belief Networks with the related uncertainty assessment model; ii) a model for impact, and related uncertainty assessment, based on Bayesian Belief Networks. The results of these models are jointly analyzed, providing decision-makers with a ranking of the priority of intervention. A GIS interface (G-Net) is developed to manage both input spatial information and results. The methodology is implemented in L'Aquila case study, discussing the potentialities associated to the use of the tool dealing with information and data uncertainty.

Disasters impacts on urban environment are the result of interactions among natural and human systems, which are intimately linked each other. What is more, cities are directly dependent on infrastructures providing essential services (Lifeline Systems, LS). The operation of LS in ordinary conditions as well as after disasters is crucial. Among the LS, drinking water supply deserves a critical role for citizens.The present work summarizes some preliminary activities related to an ongoing EU funded research project. The main aim of the paper is to define a System Dynamic Model (SDM) to assess the evolution of resilience of a drinking water supply system in case of natural disasters, with particular attention to the role of both 'structural' and 'non-structural' parameters. Reflections are carried out on L'Aquila (Italy) case study, since drinking water infrastructures were significantly stressed during the 2009 earthquake, causing a limited functionality in the aftermath of the event. Furthermore, the reallocation of citizens in temporary shelters determined a change in the demand pattern, requiring a dynamic adaptation of the infrastructure. Based on an innovative approach to resilience, the model was developed also to simulate different emergency management scenarios, corresponding to different disaster management strategies.

The present work summarizes the theoretical development process and the preliminary results of a research activity oriented to the definition of a Decision Support System (DSS) to be used for managing drinking water systems exposed to different hazard classes. The core of such DSS is a probabilistic vulnerability assessment tool based on Bayesian Belief Networks, mainly developed integrating expert knowledge and literature information. This vulnerability assessment tool proved able to define a reliable map of vulnerability levels for complex and interconnected infrastructures, thus helping decision-makers in the selection of the optimal strategies to respond to emergencies. The DSS is based also on the implementation of hydraulic models, both for gravity and pressurized water mains, which should provide information regarding the changes in the hydraulic behavior of the network due to a specific event or an action. A case study is described, confirming the potentialities of the proposed tool.

L'IRSA-CNR ha svolto attività di ricerca per la definizione di una metodologia di stima della vulnerabilità delle infrastrutture di approvvigionamento idropotabile, esposti a eventi calamitosi, nell'ambito di un Accordo quadro con il Dipartimento Nazionale della Protezione Civile. Tale stima viene effettuata in maniera probabilistica con una modellistica basata sulle reti Bayesiane (BBNs), in funzione delle caratteristiche strutturali, ambientali ed operative degli elementi. La metodologia si traduce in un sistema di supporto alle decisioni (DSS) capace di integrare e gestire le informazioni disponibili, per fornire ai decisori indicazioni sulle procedure da adottare nella gestione di reti acquedottistiche in emergenza. Nell'ambito del DSS è stato realizzato il tool G-Net, che aggiunge la componente GIS con la duplice funzione di elaborare i dati di input per il modello di vulnerabilità (gestito dal sw Netica(TM)) e di visualizzare i risultati in termini di cartografia. È stato sviluppato in Python in modalità loosely-coupled con Netica, per fornire una dimensione spaziale al DSS e migliorarne l'efficacia. La preparazione dei dati di input del modello prevede una caratterizzazione completa degli elementi dell'infrastruttura, ottenuta mediante analisi spaziale utilizzando interfacce personalizzate che ne automatizzano la procedura. G-Net è stato testato in vari casi di studio esposti a calamità (ad es. L'Aquila).

Irrigated agriculture plays a vital role for the socio-economic development of the Mediterranean area, although it has significant impacts on both water and energy resources. Therefore, in a context in which water resources are also experiencing increasing pressures, there is an urgent need for supporting their sustainable management. This may be an extremely challenging task, especially at the local scale, due to the several interconnected dynamics affecting the state of a complex irrigation system. In fact, multiple actors are involved in decision-making processes, and the use of natural resources (and their mutual interactions) strongly depends on their behaviors, which affect the system as a whole. In this context, the present study proposes an integrated methodology, based on the Water Energy Food Nexus (WEFN), specifically focused on the sustainable management of water resources for irrigation. Firstly, a model based on Causal Loop Diagrams (CLD) is developed in order to get a deep insight into the key dynamics behind a complex irrigation system. Secondly, three indices based on the "footprint" concept are identified, in order to synthesize such dynamics. The integration of these two approaches support investigating the whole system and, particularly, understanding the influence of multiple decisional actors on it, as well as the role of a set of key drivers and constraints. This might also allow drawing some relevant conclusions, useful for supporting effective decisions oriented to a sustainable water resources management. Specific reference is made to a case study, the Capitanata irrigation system, located in the Southern Italy.

Cities are highly dynamic systems, whose resilience is affected by the interconnectedness between "hard" and "soft" infrastructures. "Hard infrastructures" are the functional networks with physical elements providing goods or services. "Soft infrastructures" (culture, governance, and social patterns) encompass the social networks, make the hard infrastructures work, and are vital for understanding the consequences of disasters and the effectiveness of emergency management. Although the dynamic interactions between such infrastructures are highly complex in the case of the occurrence of hazardous events, it is fundamental to analyze them. The reliability of hard infrastructures during emergency management contributes to keep alive the social capital, while the community, its networks, and its own resilience influence the service provided by infrastructural systems. Resilience-thinking frameworks overcome the limits of the traditional engineering-oriented approaches, accounting for complexity of socio-technical-organizational networks, bridging the static and dynamic components of disasters across pre- and postevent contexts. The present work develops an integrated approach to operatively assess resilience for the hard and soft infrastructural systems, aiming at modeling the complexity of their interaction by adopting a graph theory-based approach and social network analysis. The developed approach has been experimentally implemented for assessing the integrated resilience of the hard/soft infrastructures during the L'Aquila 2009 earthquake.

The present work describes a model developed to interpret water allocation patterns in an intensive agricultural district of Southern Italy, supplied both by groundwater (at farm-scale) and surface water (managed by a local authority) with variable costs and specific operation. The model aims at evaluating the impact of some drivers (mainly the water cost) on water resources management and groundwater conservation at the district scale. The model is part of a Decision Support System (DSS) developed to investigate the main dynamics in an agricultural district, integrating in a model based on System Dynamics specific sub-modules (e.g. Crop Water Demand, Surface Reservoir Balance, Groundwater Balance and Farmers' Behavioural Model). Semi-structured interviews were carried out with local stakeholders in order to define (i) the relationship between the irrigation source selection and the water tariff applied in the irrigation district, and (ii) the selection of groundwater, based on cost, to fulfil the irrigation needs. The volumes from surface water were evaluated during the model calibration phase according to the expected irrigation needs, and found to be significantly correlated to the water stock in the reservoir well before the start of the irrigation season. The validation phase showed a good agreement between measured and simulated reservoir irrigation uptakes in the period 2000-2012. It was mainly shown that the preference for a water source depends mainly on the ratio between the surface water tariff and the groundwater pumping cost at farm-scale. The results also demonstrated that a restrictive water tariff policy applied during drought periods produced a marked increase in the groundwater use instead of reducing the water-irrigation consumption. Globally the model allows to better describe the drivers influencing farmers' behaviour and, thus, supports assessing the impacts of water policies, such as those related to water tariff.

There is growing awareness that fast response to emergency situation requires effective coordination among several institutional and non-institutional actors. The most common approaches, based on innovating technologies for information collection and management, are not sufficient to cope with the increasing complexity of emergency management. This work demonstrates that effective cooperation claims for a shift from information management to interaction management. Therefore, methods and tools are required in order to better understand the complexity of the interactions taking place during an emergency, and to analyse the actual roles and responsibilities of the different actors. This paper details the design and implementation of an integrated approach aiming to unravel the complexity of the interaction network based on Storytelling, the Problem Structuring Method, and Social Network Analysis. The potential of the integrated approach has been investigated in the Lorca (Spain) flood risk management case study. (C) 2017 The Authors. Published by Elsevier Ltd.