Basin-scale hydrogeological, geophysical, geochemical and isotopic characterization: an essential tool for building a Decision Support System for the sustainable management of alluvial aquifer systems within the provinces of Milan and Monza-Brianza
Accepted: 24 September 2016
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CAP Group is a public company, supplying the municipalities within the provinces of Milan and Monza/Brianza (Northern Italy) with the integrated water service: 197 municipalities and more than 2 million users served, 887 wells, 154 wall-mounted tanks and hubs, a water supply network of over 7500 km, from which approximately 250 million cubic metres of water per year are withdrawn. The drinking water supply comes exclusively from groundwater resources, circulating in several overlapping aquifer systems. Basin-scale water resource management, as required by the European Water Framework Directive (2000/60/EC), is an extremely complex task. In view of this backdrop, CAP is currently developing a project called Infrastructural Aqueduct Plan that relies on a Decision Support System approach. The paper describes the preliminary steps concerning the design of a prototype Decision Support System aiming at the management of groundwater resources on a basin scale (Ticino and Adda rivers area). CAP Group Decision Support System is intended to be a package allowing for water resource assessment, identification of boundary conditions, climatic driving forces and demographic pressures, simulation and investigation of future forecasts and comparison of alternative policy measures. The project has been designed in steps including Geodatabase building, geographic information system (GIS) analysis (including multilayer analysis) and numerical modelling. The data collected in the geodatabase were analyzed to design GIS quantitative and qualitative thematic maps in order to perform the multilayer analysis of current and future state and impacts, for providing the decision maker with a comprehensive picture of the water system. The multilayer analysis relies on specific indicators based on some quantitative and qualitative data: hydrogeological, chemical, isotopic, soil use and hazards, climatic and demographic. Each parameter belonging to these macro areas were classified by 7-criticality classes scale and weights were assigned to each of them. For each macro area a synthetic index was calculated by multiplying class values with weights. These synthetic indexes were managed with a multilayer approach and compared with other models and tools (e.g. geological model, numerical groundwater model, distribution network model) in order to obtain criticality indexes. The assessment of these criticality indexes allow to evaluate alternative and strategic solutions to achieve a more efficient and sustainable water system management using a best choice approach. Currently the project team is working on multilayer analysis. The next task will be the implementation of groundwater numerical model.
Supporting AgenciesMarco Petitta, Sapienza University (Rome)
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