Michele Vurro

A GIS tool for hydrogeological water balance evaluation on a regional scale in semi-arid environments

A GIS tool to evaluate hydrogeological water balance based on a mass-balance model applied to surface and subsurface systems is discussed. The tool is designed for managers responsible for groundwater resource planning during conditions of water shortage. In developing the tool, the natural groundwater recharge was evaluated through the application of a soil water balance equation, and defined as the difference between the inflows (rainfall, irrigation) and the outflows (plant evapotranspiration, surface run-off). A distributed approach was used in the soil water balance equation to account for the spatial variability of climate and landscape features. Conversely the groundwater balance was calculated on a watershed or aquifer scale, using a lumped water balance equation, in which withdrawals for different uses were estimated together with inflows from other water bodies and coastal outflows. The model was implemented on a GIS platform with an automatic routine that manages all the data sets required and allows for the forecasting of groundwater storage volumes. Furthermore, the model was able to evaluate agricultural water demands under different climatic and management scenarios. A tool which provides a summary of the results and performs a statistical analysis for any portion of the study area was also implemented. The model was applied to a coastal region of Southern Italy. The averaged groundwater balance calculated by the model was in agreement with the piezometric head and chlorine concentration trends measured in selected monitoring wells.

Integrating local and technical knowledge to support soil salinity monitoring in the Amudarya river basin

The role of monitoring is changing due to the increasing awareness of complexity and uncertainty in environmental resources management. Monitoring systems are required to support critical reflection about the effectiveness of actions toward the achievement of management objectives. To this aim, monitoring should be based on a strong integrated and multi-scale approach. Monitoring costs could be prohibitive if the monitoring is only based on traditional scientific methods of measurements. To deal with these issues, the design of an innovative monitoring system should be based on the integration between different sources of knowledge and information. In this work the usability of local knowledge to support environmental monitoring is investigated. A multi-step participatory monitoring design process has been implemented aiming to design a program for soil salinity monitoring in the lower Amudarya river basin in Uzbekistan. Although there is an increasing awareness of the importance of stakeholders being involved in decision processes, the current socio-cultural and institutional context is not favourable to the participatory approach. The choice of method to be implemented in this work was influenced by such conditions. The analysis of the lessons learned from the experiences gained in this project revealed some important clues concerning the development of a locally-based monitoring program. These lessons can be subdivided according to three fundamental issues: the long term involvement of local community members in monitoring activities, the acceptance of locally-based monitoring systems by decision makers, and the reliability of monitoring information.

A Bayesian vulnerability assessment tool for drinking water mains under extreme events

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 use of BBN was consistent with the lack of data reliability, quality and accessibility which are typical of complex infrastructures, such as the water distribution networks. The potential applications and future developments of the proposed tool have been also discussed accordingly.

Flexible delivery schedules to improve farm irrigation and reduce pressure on groundwater: a case study in southern Italy

This study was conducted on an irrigated area of southern Italy to analyze the current operation of a large-scale irrigation delivery system and the effects of the operation procedures on crop irrigation management and aquifer salinity increase. The area is characterized by relatively high levels of groundwater salinity in the summer that are probably due to intensive groundwater pumping by farmers during periods of peak irrigation demand, with the resulting seawater intrusion. Two alternative delivery schedules, namely the rotation delivery schedule and the flexible delivery schedule, referred to as RDS and FDS, respectively, were simulated using a soil-water balance model under different combinations of crop, soil and climatic conditions. The first set of simulations concerned the farm irrigation management constrained by the rotational delivery used by the local water management organization. The second scenario simulated the farm irrigation schedule most commonly used by growers in the area for maximizing crop yields. Based on crop irrigation management under RDS and FDS, two alternative operational scenarios were also developed at the scheme level and then compared for evaluation. Winter and summer salinity maps of the aquifer were developed by interpolating salinity measurements of the groundwater samples collected during the 2006 irrigation season. From these maps, a close relationship can be inferred among delivery schedule, aquifer exploitation and salinity increase, which justifies the need for implementing FDS that might reduce the groundwater demand for irrigation.

MSANOS: Data-Driven, Multi-Approach Software for Optimal Redesign of Environmental Monitoring Networks

Within the recent EU Water Framework Directive and the modification introduced into national water-related legislation, monitoring assumes great importance in the frame of territorial managerial activities. Recently, a number of public environmental agencies have invested resources in planning improvements to existing monitoring networks. In effect, many reasons justify having a monitoring network that is optimally arranged in the territory of interest. In fact, modest or sparse coverage of the monitored area or redundancies and clustering of monitoring locations often make it impossible to provide the manager with sufficient knowledge for decision-making processes. The above mentioned are typical cases requiring optimal redesign of the whole network; fortunately, using appropriate stochastic or deterministic methods, it is possible to rearrange the existing network by eliminating, adding, or moving monitoring locations and producing the optimal arrangement with regard to specific managerial objectives. This paper describes a new software application, MSANOS, containing some spatial optimization methods selected as the most effective among those reported in literature. In the following, it is shown that MSANOS is actually able to carry out a complete redesign of an existing monitoring network in either the addition or the reduction sense. Both model-based and design-based objective functions have been embedded in the software with the option of choosing, case by case, the most suitable with regard to the available information and the managerial optimization objectives. Finally, two applications for testing the goodness of an existing monitoring network and the optimal reduction of an existing groundwater-level monitoring network of the aquifer of Tavoliere located in Apulia (South Italy), constrained to limit the information loss, are presented.

Impacts of Climate Change on Freshwater Bodies: Quantitative Aspects

In this chapter we present the results of the impact assessment on freshwater bodies in the Mediterranean region. Starting from the characterization of the general features of Mediterranean hydrology, main focus is given on large river basins discharging into the Mediterranean sea as well as to small and medium scale catchments representing almost half of the entire discharging basin. Groundwater representing a fundamental water resource for Mediterranean countries was also considered. Climate change impacts on the hydrological behavior of large river basins is investigated through the IRIS computational tool which was proved to be a versatile instrument for both climate studies and the assessment of model ability to simulate the hydrological cycle at catchment scale, taking advantage of the available observed discharge series to evaluate the reliability of future discharge projections. The results regarding some representative Mediterranean rivers using multiple climate models developed inside Circe have highlighted an open spread among twenty-first century projections. The problem of the effective information content of climate model simulations with respect to small scale impact studies is developed at the scale of medium and small catchments. Particularly at the space-time scales needed to describe the terrestrial water cycle in Mediterranean environments this is recognized among the most difficult problems facing both science and society. Therefore downscaling and bias-correction requirements have been treated in this chapter through specific methodologies which integrate dynamical downscaling with statistical downscaling always adopting ground based observation of climate variables as a powerful means to obtain more robust climate forcing for hydrological models. The assessment of climate change impacts on small and medium size catchments is developed through some representative case studies in which downscaling methodologies have been applied thanks to the availability of dense climate measurement networks. The impact assessment of water resources in the Apulia region (southern Italy) revealed a marked increase in the variability of hydrologic regimes as consequence of the increased rainfall variability predicted for the twenty-first century. Conversely only slight decreasing trends were detected in the annual water balance components. Similar results were found on a carbonate aquifer in Southern Italy in which a large Apennine spring have been selected as a significant hydrogeological systems with minimal anthropogenic pressures in the recharge areas. Finally a specific session is dedicated to the role of artificial dams in reducing the possible impacts of climate change. In particular, methodologies for the assessment of optimal dam dimensioning under climate change are presented as well as a reliability assessment based on water supply and demand imbalances.

A Suitable Tool for Sustainable Groundwater Management

Artificial recharge is used to increase the availability of groundwater storage and reduce saltwater intrusion in coastal aquifers, where pumping and droughts have severely impaired groundwater quality. The implementation of optimal recharge methods requires knowledge of physical, chemical, and biological phenomena involving water and wastewater filtration in the subsoil, together with engineering aspects related to plant design and maintenance operations. This study uses a novel Decision Support System (DSS), which includes soil aquifer treatment (SAT) evaluation, to design an artificial recharge plant. The DSS helps users make strategic decisions on selecting the most appropriate recharge methods and water treatment technologies at specific sites. This will enable the recovery of safe water using managed aquifer recharge (MAR) practices, and result in reduced recharge costs. The DSS was built using an artificial intelligence technique and knowledge-based technology, related to both quantitative and qualitative aspects of water supply for artificial recharge. The DSS software was implemented using rules based on the cumulative experience of wastewater treatment plant engineers and groundwater modeling. Appropriate model flow simulations were performed in porous and fractured coastal aquifers to evaluate the suitability of this technique for enhancing the integrated water resources management approach. Results obtained from the AQUASTRESS integrated project and DRINKADRIA IPA CBC suggest the most effective strategies for wastewater treatments prior to recharge at specific sites.

Detecting soil organic carbon by CASI hyperspectral images

Soil organic carbon (SOC) plays an important role in soil quality definition. In fact, soil organic matter (SOM) decline is one of the most relevant land degradation processes [1]. Therefore, an innovative methodology able to monitoring this soil property, collecting data more rapidly and economically, is needed. In this regard, remote sensing technique can open new scenarios of research. In particular, few studies have shown the capability to accurately determine SOC contents from airborne-hyperspectral sensors [2], [3], [4]. With this work we demonstrate that is possible to evaluate the Soil Organic Carbon in a test site in Apulia Region, Italy, through hyperspectral measurements by the airborne sensor CASI 1500, achieving very promising results.

Emergency Management of Drinking Water Infrastructures Based on a Bayesian Decision Support System

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.

Dealing with Uncertainty in Decision-Making for Drinking Water Supply Systems Exposed to Extreme Events

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.