Numerical modeling of the groundwater flow in the fractured and karst aquifer of the Salento peninsula (Southern Italy)

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Giovanna De Filippis *
Mauro Giudici
Stefano Margiotta
Fiorella Mazzone
Sergio Negrini
Chiara Vassena
(*) Corresponding Author:
Giovanna De Filippis | giovanna.defilippis@unimi.it

Abstract

Water resources represented by coastal aquifers are very important for regions characterized by a relevant request of freshwater, but limited rainfall, lack of surface water bodies and intrusion of the seawater through the sediments which host groundwater. Therefore some coastal areas, like the Salento peninsula (southern Italy), are subjected to the risk of desertification and a proper management of groundwater resources requires tools to analyze and predict the water balance and the evolution of the physical system in response to human activities (e.g., ground water withdrawals) and climatic factors. The Salento peninsula is a typical Mediterranean basin, where the main water resource is the aquifer hosted in Cretaceous carbonatic rocks (Calcare di Altamura, Altamura limestone): this is a fractured and karst aquifer, with a poor recharge and complex relationships with the sea. In order to develop a tool to assess the water balance at regional scale for the considered aquifer system, a groundwater flow model was developed by Giudici et al. (2012a); it is based on a conceptual model obtained from a reconstruction of the hydrostratigraphic architecture of the region, which includes the main aquifer and the overlaying rocks characterized by low permeability which can host local and relatively thin aquifers. In this paper that work is updated, by improving the reconstruction of the hydraulic head and of the conceptual model, above all in those areas that the previous model evidenced as critical for the absence of fresh water along the whole aquifer thickness. Moreover, since the estimate of some model’s input parameters is affected by high uncertainty, a sensitivity analysis is performed to evaluate the effects of this uncertainty on the model’s results.

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