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.

Conditions of scarcity for a water supply system occur when the available resource are not able to satisfy the connected demands. They can arise both from a decreasing of the inflow to the exploited resources and/or from a increasing of the demand. Such conditions can be assessed by a water balance model able to simulate both the hydrological processes describing the relationships between the meteorological forcing (precipitation) and the inflows to the exploited reservoir, and the intra- and inter-annual time distribution of the connected demand and the reservoir management policies. We present a numerical modelling tool, developed for the management of the Maggiore Lake, that computes at daily scale the water budget of such reservoir taking into account 1) the monthly precipitation over the watershed basin and the related inflow; 2) the seasonal demand for irrigation and 3) the operative hydrometric levels constraints to the lake water withdrawal. The model represents precipitation over the basin through the space mean of the standardized precipitation indices computed at different aggregation scales using observed time series. The relationship between the precipitation regime and the inflow to the reservoir is obtained through a simple multilinear regression model, considering the SPI computed at 1, 3 and 6 months as independent variables: this allows to take hydrological processes into account featuring different characteristic times and to simulate both the historic inflow regime and the possible conditions forecast by climate scenarios. The regression model is validated on the precipitation and lake inflow observations in the period 1996-2013 using a leave-one-out cross validation. The seasonal irrigation demand is assigned based on the extensions of crops fed by the lake water and regardless of the climate conditions; the actual supply is limited by the operative hydrometric range of allowable water levels, which stop water distribution when the lake level is below a given threshold. This hydrometric condition defines the onset of water scarcity for the irrigation water supply system: such conditions and the associated possible non stationarity are analysed in terms of occurrence, duration and intensity considering both the mean and the extreme values of reliability, resiliency and vulnerability through an overall scarcity risk index. Results from the Maggiore lake case study for the period 1994-2013 are presented and discussed.

Management of water supply systems under shortage conditions due to drought requires computational tools able to relate the past precipitation regime over different time scales to future water resources availability. This work proposes a modelling framework to address the occurrence of shortage for water supply systems whose resource is constituted by natural or artificial reservoirs. The proposed methodology aims at identifying "management triggers" for possible mitigation measures. Emphasis is given on the use of standardized indices to promote information sharing. The implemented tool is structured into five modules: "hydrological" module; "scenarios" module; "reservoir" module; a module for the evaluation of "indices of shortage"; and a "support to early-warning" module. The whole procedure has been applied to three Italian reservoirs. For each water body, a case specific shortage early-warning system, based on standardized precipitation indices has been identified, allowing the implementation of efficient local mitigation measures.

The aim of this research is to assess a technique to retrieve actual evapotranspiration (ET) maps from remote sensing images by the combination of two different procedures. The first one, known as triangle method, computes the evapotranspiration fraction (EF) defined as the ratio LE/Rn, where Rn is the net radiation at the surface and LE is the latent heat flux. LE is directly proportional to ET. In order to retrieve LE, and consequently the surface evapotranspiration, a second procedure computes the net radiation of the investigated area. Therefore, by inversion, it is possible to obtain an estimate of the ET. The validation of these variables, net radiation and evapotranspiration derived from MODIS data, has been undertaken on the Capitanata area, Southern Italy, by comparison of model results with in-situ measurements provided by the Consorzio per la Bonifica della Capitanata di Foggia. © 2012 IEEE.

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.

The paper presents the preliminary results of a scientific initiative aiming at the definition and implementation of innovative management options to mitigate the environmental impacts of groundwater pumping in coastal aquifers. In fact seawater intrusion in such aquifers is very often caused by the over-exploitation of groundwater mainly due to the increasing water demand in the agricultural sector in the last decades in most semiarid or arid countries of the world. Consequently, the sustainable management of groundwater under the principles of transparent and efficient water use has highlighted the issue of measuring and accounting the water volumes withdrawn from the groundwater. The objective of the research activity is the design of an innovative monitoring system for sustainable groundwater exploitation. Such an ambitious target requires an accurate analysis of existent and potential stakeholders' conflicts. These conflicts are crucial for the implementation of strategies and activities by the different institutions that are involved in the management of water resources. Therefore, a central role in the development of the project is the stakeholder involvement, with particular emphasis on conflict assessment. In this work, conflicts analysis has concerned both the acceptability of groundwater protection measures and the feasibility of groundwater monitoring strategies.

In many arid and semi-arid regions agriculture is the main user of GW, causing problems with the quantity and quality of water, but there are few institutional policies and regulations governing sustainable GW exploitation. The authors suggest an integrated methodology for enabling local GW management, capable of combining the need for GW protection with socio-economic and behavioural determinants of GW use. In the proposed tool, integration is reinforced by the inclusion of multiple stakeholders, and the use of Bayesian Belief Networks (BBN) to simulate and explore these stakeholders' attitude to GW exploitation and their responses to the introduction of new protection policies. BBNs and hydrological system properties are integrated in a GIS-based decision support system - GeSAP - which can elaborate and analyse scenarios concerning the pressure on GW due to exploitation for irrigation, and the effectiveness of protection policies, taking into account the level of consensus. In addition, the GIS interface makes it possible to spatialize the information and to investigate model results.The paper presents the results of an experimental application of the GeSAP tool to support GW planning and management in the Apulia Region (Southern Italy). To evaluate the actual usability of the GeSAP tool, case study applications were performed involving the main experts in GW protection and the regional decision-makers. Results showed that GeSAP can simulate farmers' behaviour concerning the selection of water sources for irrigation, allowing evaluation of the effectiveness of a wide range of strategies which impact water demand and consumption.

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.

The local downscaling of rainfall predictions provided by climate models is crucial for the assessment of climate change impacts on most ecological processes related to the land/water cycle, such as vegetation dynamics, soil-bacteria activity, and ecological response of water-bodies. In this study, we present a methodology to analyze the predictive performance of a Regional Climate Model (RCM) with regard to daily rainfall fields. A comparison between statistical properties of rainfall observations and model control simulations was performed through a robust and meaningful representation of the precipitation process. Our objectives were, first, to evaluate RCM bias data at basin-scale against daily rainfall records coming from a rain gauge network, and then to propose a simple framework to investigate possible alterations of the daily rainfall occurrence and intensity under climate change by way of a stochastic model suitable to investigate both ordinary regimes and extreme climate events.The RCM adopted for the the study region produced a general underestimation of mean storm intensity for all seasons in the control run. From the bias analysis at daily scale, the RCM has shown a good capability to simulate the occurrence of wet periods being able to reproduce the winter storm systems, but a poor capability to simulate the need to operate a correction of the climate model output to obtain more realistic input data to be used in impact studies at the local scale. A further result of the adopted bias correction was the significant reduction of the effects of climate change on daily rainfall statistics corresponding to rainfall features much closer to the historical data than in the raw RCM output data.

Various downscaling techniques have been developedto bridge the scale gap between global climate models(GCMs) and finer scales required to assess hydrological impactsof climate change. Such techniques may be groupedinto two downscaling approaches: the deterministic dynamicaldownscaling (DD) and the statistical downscaling (SD).Although SD has been traditionally seen as an alternative toDD, recent works on statistical downscaling have aimed tocombine the benefits of these two approaches. The overallobjective of this study is to assess whether a DD processingperformed before the SD permits to obtain more suitableclimate scenarios for basin scale hydrological applicationsstarting from GCM simulations. The case study presentedhere focuses on the Apulia region (South East of Italy,surface area about 20 000 km2), characterised by a typicalMediterranean climate; the monthly cumulated precipitationand monthly mean of daily minimum and maximum temperaturedistribution were examined for the period 1953-2000. The fifth-generation ECHAM model from the Max-Planck-Institute for Meteorology was adopted as GCM. TheDD was carried out with the Protheus system (ENEA), whilethe SD was performed through a monthly quantile-quantilecorrection. The SD resulted efficient in reducing the meanbias in the spatial distribution at both annual and seasonalscales, but it was not able to correct the miss-modelled nonstationarycomponents of the GCM dynamics. The DD provideda partial correction by enhancing the spatial heterogeneityof trends and the long-term time evolution predictedby the GCM. The best results were obtained through thecombination of both DD and SD approaches.

Among different uses of freshwater, irrigation is the most impacting groundwater resource, leading to water table depletion and possible seawater intrusion. The unbalance between the availability of water resources and demand is currently exacerbated and could become worse in the near future in accordance with climate change observations and scenarios provided by Intergovernmental Panel on Climate Change (IPCC). In this context, Increasing Maximum Capacity of the surface reservoir (IMC) and Managed Aquifer Recharge (MAR) are adaptation measures that have the potential to enhance water supply systems resiliency. In this paper, a multiple-users and multiple-resources-Water Supply System (WSS) model is implemented to evaluate the effectiveness of these two adaptation strategies in a context of overexploited groundwater under the RCP 4.5 and the RCP 8.5 IPCC scenarios. The presented a case study that is located in the Puglia, a semi-arid region of South Italy characterized by a conspicuous water demand for irrigation. We observed that, although no significant long-term trend affects the proposed precipitation scenarios, the expected temperature increase highly impacts the WSS resources due to the associated increase of water demand for irrigation purposes. Under the RCP 4.5 the MAR scenario results are more effective than the IMC during long term wet periods (typically 5 years) and successfully compensates the impact on the groundwater resources. Differently, under RCP 8.5, due to more persistent dry periods, both adaptation scenarios fail and groundwater resource become exposed to massive sea water intrusion during the second half of the century. We conclude that the MAR scenario is a suitable adaptation strategy to face the expected future changes in climate, although mitigation actions to reduce greenhouse gases are strongly required.

Various downscaling techniques have been developed to bridge the scale gap between global climate models (GCMs) and finer scales required to assess hydrological impacts of climate change. Although statistical downscaling (SD) has been traditionally seen as an alternative to dynamical downscaling (DD), recent works on statistical downscaling have aimed to combine the benefits of these two approaches. The overall objective of this study is to assess whether a DD processing performed before the SD is able to provide more reliable climate forcing for crop water demand models. The case study presented here focuses on the Maggiore Lake (Alpine region), with a watershed of approximately 4750 km2 and whose waters are mainly used for irrigation purposes in the Lombardia and Piemonte regions. The fifth-generation ECHAM model from the Max-Planck-Institute for Meteorology was adopted as GCM. The DD was carried out with the Protheus system (ENEA), while the SD was performed through a monthly quantile-quantile correction of the precipitation data collected in the period 1950-2012 by the 19 rainfall gauges located in the watershed area (some of them operating not continuously during the study period). The relationship between the precipitation regime and the inflow to the reservoir is obtained through a simple multilinear regression model, validated using both precipitation data and inflow measurements to the lake in the period 1996-2012 then, the same relation has been applied to the control (20c) and scenario (a1b) simulations downscaled by means of the different downscaling approaches (DD, SD and combined DD-SD). The resulting forcing has been used as input to a daily water balance model taking into account the inflow to the lake, the demand for irrigation and the reservoir management policies. The impact of the different downscaling approaches on the water budget scenarios has been evaluated in terms of occurrence, duration and intensity of water scarcity periods.

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.

The Alimini water system, located in south eastern part of Italy, named Salento peninsula, is constituted from two connected coastal lakes, Alimini Piccolo and Alimini Grande. Specifically, Alimini Piccolo is a small freshwater body, directly fed by rainfall and by shallow porous aquifer through of several springs. From '50s Alimini Piccolo provides the surrounding area with water for agriculture and domestic use. In June 2013, IRSA-CNR started a study concerning the quali-quantitative characterization of the hydrogeological system feeding the Alimini Piccolo, in order to investigate the potential for additional exploiting of the lake as a resource for drinking water. For the purpose, a monitoring system has been set up for an entire hydrological year. Continuous measurements of water level, electrical conductivity and temperature, such as quantitative and qualitative monitoring monthly campaigns both in groundwater and in the lake have been carried out. In order to support the above mentioned surveys, Electrical Resistivity Tomography (ERT) has been carried out to identify geological structures and hydrogeological features, to better understand the system feeding the Alimini Piccolo and to set the boundary conditions of the hydrological model useful to represent the water balance of the lake.

The occurrence of shortage events on a water supply system can be investigated through models that simulate hydrological processes by describing the atmosphere/surface water/soil/groundwater interfaces, water demand variability and management options for different uses. However, when the supply system is fed by several water resources and dynamics changes of demand, it is necessary to develop models able to simulate the cause-effect mechanisms that involve not only the water budget physical processes, but also the choices of the users in terms of distribution of the demand among each resource and the actions implemented by the managers. The proposed overall model merges: (i) a 1 km2 discrete monthly soil water mass balance model (G-MAT) to estimate recharge to the aquifer, soil water content and surface runoff; (ii) a stochastic model based on a multi linear regression of standard precipitation index (SPI-Q) to reproduce inflow to surface water storage; (iii) a simple monthly reservoir water balance model considering inflow, demands and storage volumes; (iiii) a simple groundwater lumped budget model that considers soil recharge and well extraction following the management rules of the water supply system and the available surface water storage. While we consider the only seasonal variability for domestically and industrial water demand, the agricultural demand is estimated on the base of the monthly soil water content. The developed overall model has been implemented for the case study of the Fortore water supply system (Apulia region, South Italy), managed by the Consorzio di Bonifica della Capitanata. It allows to simulate the conjunctive use of the water from the Occhito artificial reservoir (160 Mm3) and from groundwater. We successfully reproduce the Occhito dam level variability (both seasonal and inter-annual) as well as the observed groundwater depletion until the early 2000 and the following recover. The resulting model is able to monitor relative contribution of groundwater recharge non stationarity (mainly driven by precipitation variability) and associated agricultural water demand (driven by soil water content and thus by both evapotranspiration and precipitation non stationarity) to the aquifer stress. It also gives the opportunity to easily run impact scenarios on groundwater considering change in climate forcing, agricultural superficies, surface water storage or water supply management.

The increasing concentration of CO2 and other radiative activetrace gases in the atmosphere is causing tropospherictemperatures to rise. Changes in other climatic featuressuch as precipitation, cloudiness, humidity, and windinessare likely to follow changes in temperature. Such changescould have deep implications for hydrologic processes in generaland for water availability to sustain both rain-fed andirrigated agriculture, in particular.Evapotranspiration (ET) is the compound term describingthe physical processes of water transfer into the atmosphereby evaporation from soil and transpiration through vegetation;ET constitutes an important component of hydrologicalbalance particularly in semi-arid and sub-humid climates.Evapotranspiration is determined by various mathematicalmodels using climatic factors such as temperature, radiation,humidity, and wind speed, while its direct measurement isseldom available in experimental fields.A relatively new technology using on remote sensing to retrieveland surface parameters such as surface temperature,albedo and vegetation indices - which are indispensable to remotelysensed ET models - is adopted here to map regional,meso- and macro-scale patterns of ET at the earth's surfacein a globally consistent and economically feasible manner.The aim of our research is to assess a technique to retrieveET maps from remote sensing images by the combination oftwo different procedures. The first one, known as trianglemethod, computes the evapotranspiration fraction (EF) de-fined as the ratio LE/Rn, where Rn is the net radiation atthe surface and LE is the latent heat flux. LE is directly proportionalto ET. In order to retrieve LE, and consequentlythe surface evapotranspiration, a second procedure computesthe net radiation of the investigated area. Therefore, by inversion,it is possible to obtain an estimate of the ET.The validation of these variables, the net radiation and ofthe evapotranspiration derived from MODIS data, has beenundertaken on the Capitanata area, Southern Italy, by comparisonof model results with in-situ measurements providedby the Consorzio per la Bonifica della Capitanata di Foggia.

Water resources management is often characterized by conflicts in many arid and semi-arid regions, where agriculture is the main user of groundwater (GW). Conflicts could arise among different decision-makers and stakeholders. Moreover, different policies can interact each other hampering or facilitating their implementation and effectiveness. This contribution describes a new implementation of GeSAP, an integrated modelling tool for enabling local GW management by combining the need for GW protection with socio-economic and behavioural determinants of GW use. GeSAP is based on the involvement of multiple stakeholders and the use of Bayesian Belief Networks (BBN) to simulate and explore their attitude relative to GW exploitation and their responses to the introduction of new protection and agricultural policies. In this work, GeSAP was implemented in the area of the Capitanata Irrigation Users Organization, located in the Apulia region (southern Italy). It was used to simulate the reactions of the main stakeholders involved in GW protection policy implementation and to assess the policy's effectiveness in terms of actual reduction of GW exploitation. Furthermore, the interactions between the GW protection policy and the coming reform of the Common Agricultural Policy (CAP) was investigated. The results of the application proved the capability of the GeSAP tool to assess the actual effectiveness of GW protection policy by investigating how far this policy could be considered acceptable by farmers. In addition, this study demonstrates how the effectiveness of the GW protection policy could be affected by the interaction with the CAP reform. The latter could strongly impact the balance between water demand and availability with the effect of ifying the positive synergy between CAP and GW protection policy. Although water management issues are not explicitly mentioned among the main scopes of the CAP, this work clearly demonstrates the impact that such policy could have on farmers' decisions on water use

The accuracy of local downscaling of rainfall predictions provided by climate models is crucial for the assessment of climate change impacts on hydrological processes because the presence of bias in downscaled precipitation may produce large bias in the assessment of soil moisture dynamics, river flows, and groundwater recharge. In this study, the output of a regional climate model (RCM) is downscaled using a stochastic modelling of the point rainfall process able to adequately reproduce the daily rainfall intermittency which is one of the crucial aspects for the hydrological processes characterizing Mediterranean environments. The historical time-series from a dense raingauge network were used for the analysis of the RCM biasin terms of dry and wet daily period and then to investigate the predicted alteration in the local rainfall regime. A Poisson Rectangular Pulse (PRP) model (Rodriguez-Iturbe et al., 1987) was finally adopted for the stochastic generation of local daily rainfall as a continuous-time point process with forcing parameters resulting from the bias correction of the RCM scenario.

L'utilizzo di dati telerilevati nell'ambito dell'agricoltura di precisione e dell'analisi di modelli idrologici ha assunto, negli ultimi anni, crescenteimportanza. In particolare, al fine di esaminare le condizioni della vegetazione da un punto di vista qualitativo e quantitativo, sono state sviluppate svariate metodologie che hanno portato alla formulazione di diversi indici di vegetazione. Tra di essi, l'indice di area fogliare (Leaf Area Index - LAI) rappresenta uno dei più importanti parametri per quantificare i processi fisici e biologici relativi alla copertura vegetale ed al consumo di acqua del suolo. In questo studio si presenta un confronto tra i modelli maggiormente adoperati per il calcolo del LAI applicati in un'area di studio localizzata nella zona della Capitanata (Sud Italia), nella quale sono state isolate ed analizzate, nella loro variabilità spazio-temporale, alcune classi distintive degli usi del suolo ivi predominanti. L'analisi si è concentrata sulla maniera in cui i modelli hanno risposto alla variabilità dell'uso del suolo, valutando l'idoneità di ogni dato modello nella caratterizzazione di ciascuna particolare area.In questo modo sono stati identificatigli algoritmi più adatti a ciascun tipo di zona nei casi in cui non si disponga di misure dirette raccolte in situ.

The aim of this paper is to explore the effects and linkages between snow cover areas, distribution, probability and measured water discharge along east Mediterranean coastal watershed using moderate-resolution satellite images (MODIS-Terra). The Nahr Ibrahim River is a typical Lebanese watershed with an area of 326 km2 stretching between the sea and mountainous terrain to the east. The largest snow cover often exists in January-February with snow-free conditions between June and November. Image analysis enabled to analyze the temporal variability of the mean and maximum monthly areas of snow cover between 2000 and 2013. Snow cover dynamics were compared with the discharge from main springs (Afqa and Rouaiss) feeding the river and the probability of snow cover was estimated. The mean monthly snow cover, snow melting rates and springs discharge were found to be in direct relationship. In addition, the measured water discharge at the river mouth was found to be higher than the discharge of the two main feeding springs. This indicates a contribution of groundwater to the stream flow, which is again in direct connection with snow melting at the upper bordering slopes and probably from neighboring watersheds.Considering the characteristics of the mountainous rocks (i.e. Sinkholes, fissured and karstified limestone), the pedo-climatic and land cover conditions affect the hydrological regime which is directly responding to the area and temporal distribution of snow cover, which appears after two months from snowing events. This is reflected on water productivity and related disciplines (Agricultural yield, floods). This study highlights the potential of satellite snow detection over thewatershed to estimate snow cover duration curve, forecast the stream flow regime and volume for better water management and flood risk preparedness.

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.

The Leaf Area Index is an important vegetation biophysical parameter, defined as a ratio of leaf area to unit ground surface area (Watson, 1947). This index is related to several vegetation exchange processes, providing information on changes in productivity or climate impacts on ecosystems. In literature it is possible to find many algorithms to its retrieval (Viña, 2011; Ganguly, 2012). The choice of the model to use become thus crucial for any kind of application. The following research aims to compare different models of LAI, undergoing the following steps: first, through the USGS archive we selected a sample of images acquired by Landsat-8 satellite, from 2013 to 2016, with 30m of spatial resolution. Subsequently, we carried out a classification of soil based on different uses, which led to the identification of five land use classes. Then, images were preprocessed through Envi and Matlab softwares, in order to isolate a particular sub-region and apply correction of cloudiness and radiometric calibration. Therefore data processing consisted of vegetation indices calculation: NDVI (Normalized Difference Vegetation Index) (Rouse et Al., 1974), WDVI (Weighted Difference Vegetation Index) (Clevers, 1988), and EVI (Enhanced Vegetation Index) (Liu and Huete, 1995). Then, LAI algorithms were chosen and applied. Finally, multi-temporal statistical analysis was carried out to evaluate the most performing models for every land cover category, according to existing experimental data.

The assessment of the impact of long-term climate variability on water supply systems depends not only on possible variations of the resources availability, but also on the variation of the demand. In this framework, a robust estimation of direct (climate induced) and indirect (anthropogenically induced) effects of climate change is mandatory to design mitigation measures, especially in those regions of the planet where the groundwater equilibrium is strongly perturbed by exploitations for irrigation purposes. The main goal of this contribution is to propose a comprehensive model that integrates distributed crop water requirements with surface and groundwater mass balance, able to consider management rules of the water supply system. The proposed overall model, implemented, calibrated and validated for the case study of the Fortore water supply system (Apulia region, South Italy), permits to simulate the conjunctive use of the water from a surface artificial reservoir and from groundwater. The relative contributions of groundwater recharges and withdrawals to the aquifer stress have been evaluated under different climate perturbations, with emphasis on irrigation practices. Results point out that irrigated agriculture primarily affects groundwater discharge, indicating that ecosystem services connected to river base flow are particularly exposed to climate variation in irrigated areas. Moreover, findings show that the recharge both to surface and to groundwater is mainly affected by drier climate conditions, while hotter conditions have a major impact on the water demand. The non-linearity arising from combined drier and hotter conditions may exacerbate the aquifer stress by exposing it to massive sea-water intrusion.

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.

Water, a precious and valuable natural resource in the Middle East, is a limiting factor for its development. The possible climate alterations likely to impact the Region with increasing global air temperatures and changing in precipitation patterns would lead to a sensible modification of hydrological processes at regional and local scales. Analyzing the potential impacts of climatic changes is an important step toward adaptation and mitigation especially for countries with vulnerable water resources. The objective of this study is to develop basin scale climate change scenarios (CCS) for a coastal watershed in Lebanon, Nahr Ibrahim watershed (NIW), which is representative of the snow-melt dominated watersheds located in Mount Lebanon (the so called water tower of the Middle East). This is the main objective of the biennial research programme 2012-2013 for "Modelling Water Balance Using Remotely Sensed Data" funded under the Scientific Cooperation between the National Research Council of Italy (CNR) and the National Council for Scientific Research of Lebanon (CNRS-L). To this end the regional climate model PRECIS (Providing Regional Climates for Impact Studies) developed by Hadley Centre of U.K. was adopted as a dynamic downscaling of a global climate model (GCM) thus providing spatially detailed projections and scenarios of future climate over the area of interest at a resolution of about 25x25 km. Daily simulations for precipitation (P), maximum and minimum temperature (Tmax, Tmin) from PRECIS were adopted to evaluate the impact of climate change on the water balance of the NIW by taking into account the time series for the recent past (1980-2000), the present (2001-2011), the near future (2012-2032) and the distant future (2080-2098) and comparing them with the available climate observations in order to assess the possible future variation in precipitation and temperature. The available stations' observations were also used to derive monthly regressions between climate and topographic elevations to be adopted as a simple interpolation tool to estimate the spatial distribution of T and P in NIW. Changes in climate variables resulted significant for the near future only for T while in the distant future, both P and T showed respectively remarkable decrease and increase. These alterations will correspond to a decrease in snow-covered area and anticipated snow-melt thus altering the water balance of NIW both in terms of mean values and variability. To simulate the impacts on stream flow regimes, basin scale CCS were coupled with a conceptual water balance model named NIWaB that was previously developed and calibrated. A consistent module of the model was developed to capture the space-time dynamics of the snow cover and snowmelt contribution. Such computations allowed to assess the possible climate change impacts on the hydrological signature of NIW which resulted strongly influenced by a shorter snowy period and a consequent enhanced seasonality

Agriculture and farming worldwide are responsible for numerous environmental threats, including degradation of land and water resource depletion. Underlining the dynamic interaction between bio-physical and socio-economic drivers is the key towards a more sustainable land and water management. With regard to a highly-developed agricultural area in Southern Italy, multi-regression models were developed to provide an ex-post interpretation of the observed inter-annual variability of cropped land. The main drivers related to Common Agricultural Policy support, product market prices, crop yield, and irrigation water availability were investigated. The adopted models revealed the different weights of each driver. The findings reported the role that direct payments played in supporting the extension of irrigated crops, such as processing tomato. Likewise, the models pointed out the decoupled payment scheme as the most important driver of change in the crop pattern over the last years.

Conditions of shortage in a water supply system occur when available resources are not able to satisfy the related demands. Concerning surface reservoirs, three main elements should be taken into account in transient conditions: 1) the inflow; 2) the actual amount of water stored in the reservoir; 3) and the water demand. To assess the risk of water shortage (i.e. reliability, resiliency and vulnerability), a simple model able to translate the randomness of climate into reliable scenarios of inflow to the reservoir is extremely useful. In this contest physically-based water balance models (i.e. models based on hydrological processes) often present several limitations due to lack of observations for the calibration/validation procedure and to an over-parameterization. In this paper a simple statistical method to simulate the inflow to a surface reservoir based on Standardized Precipitation Indices is proposed. It is based on some basic assumptions: a) for management purposes the inflow to the reservoir and the connected water demand, can be assessed at monthly time scale; b) the monthly inflow is determined by the climatic forcing averaged in space over the watershed; c) as a first approximation the discharge is mainly dependent on precipitation taken into account at different time scales and with different "weights"; d) the parameters linking the precipitation regime to the inflow are considered constant over time. On the base of such assumptions, to seek for relationships between the precipitation regime and the inflow a multilinear regression model (called SPI-Q) is calibrated and validated at monthly scale using the least-square method: Q(m,i) = a_SPI1(m)SPI1(m,i)+ a_SPI3(m)SPI3(m,i) + a_SPI6(m)SPI6(m,i) + a0(m), where Q(m,i) is the inflow for the month m, year i; SPI1(m,i), SPI3(m,i) SPI6(m,i) are the Standardized Precipitation Indices computed for the month m, year i on the precipitations cumulated over 1, 3 and 6 months; a_SPI1(m), a_SPI3(m), a_SPI6(m) and a0(m) are the coefficients from the multilinear regression of SPI1, SPI3, SPI6 for the month m. It is worth to note that to suitably calibrate the SPI-Q model a statistically significant dataset (both for inflow and precipitation) is mandatory. The SPI-Q model has been applied to three basins in Italy, quite different in terms of climate conditions and hydrological features: the Lake Maggiore basin (Switzerland and North Italy), the Ridracoli basin (Central Italy) and the Occhito Basin (South Italy). Simulations resulted in good agreement with observations, mostly for low inflow regime; moreover, the values of the multilinear regression coefficients appeared to be representative of the different hydrological processes that affect the total monthly discharge to the reservoirs: for example, for the case study of the Lake Maggiore, during spring the inflow is mostly affected by the SPI6 that takes into account the snow melting of the cumulated winter precipitations, whereas the inflow to the

The main goal of this study is to evaluate the reliability of the Mise-a-la-Masse (MALM) technique associated with saline tracer tests for the characterization of groundwater flow direction and velocity. The experimental site is located in the upper part of the Alento River alluvial plain (Campania Region, Southern Italy). In this paper we present the hydrogeological setting, the experimental setup and the relevant field results. Subsequently, we compare those data against the simulated results obtained with a 3D resistivity model of the test area, coupled with a model describing the Advection - Dispersion equation for continuous tracer injection. In particular, we calculate a series of 3D forward solutions starting from a reference model, all derived from electrical tomography results, but taking into consideration different values of mean flow velocity and directions. Each electrical resistivity 3D model is used to produce synthetic voltage maps for MALM surveys. Finally, the synthetic MALM voltage maps are compared with the ones measured in the field in order to assess the information content of the MALM dataset with respect to the groundwater field characteristics. The results demonstrate that the information content of the MALM data is sufficient to define important characteristics of the aquifer geometry and properties. This work shows how a combination of three-dimensional time-lapse modeling of flow, tracer transport and electrical current can substantially contribute towards a quantitative interpretation of MALM measurements during a saline tracer test. This approach can thus revive the use of MALM as a practical, low cost field technique for tracer test monitoring and aquifer hydrodynamic characterization. (C) 2017 Elsevier B.V. All rights reserved.