This research investigates a set of data-driven methods for characterising coastal aquifers under data scarcity using available short hydrogeological time series and chemical surveys. Correlation between groundwater levels and climate indexes and time series analyses, with particular attention to the autocorrelation and cross-correlation functions, wavelet analyses and seasonal and trend decomposition, are extensively analysed using precipitation and groundwater level recordings to define the hydrodynamic mechanism of an aquifer system in response to climate factors. These methods rely on the hypothesis that the aquifer system is considered as a filter that modifies, retains, or attenuates the input signal, i.e., precipitation, into an output signal, such as spring discharge, groundwater level, river flow rate or other physical or chemical parameters. They can provide valuable insights into various aspects, encompassing the nature of the aquifer, the influence of climatic conditions, and significant abstractions. Multivariate statistical analysis is instead a valuable approach handling multiple geochemical and physical parameters to reveal spatial and temporal variations in groundwater quality, identify key hydrochemical processes, and assess how they change over time. Together with the Hydrogeochemical Facies Evolution-Diagram, these techniques allow to explore the salinisation process at the case study. Through Geostatistic, instead, the salinisation process and nitrate pollution are investigated in space and compared in time, allowing the identification of the areas more vulnerable. A part of the Thesis is dedicated to a comprehensive assessment of climate change projections and bias-correction techniques, employing historical and regional climate data, to discuss the potential impacts of weather projections on a coastal aquifer. The Thesis focuses on the complex coastal karst aquifer of Salento in Southern Italy, which presents numerous challenges, including geomorphological complexity, regional size, limited surface water resources, and significant water withdrawals for various human activities. The primary objective of this research is to investigate the hydrodynamic mechanism of such aquifer and discuss the potential problematics to which groundwater resources are exposed due to climate change and human pressure. The scope is to raise awareness among water utilities and political stakeholders in actuating mitigative actions and restrictions on the use of groundwater and exploring alternative measures to supply water demand. The study encourages for the establishment and consistent implementation of a comprehensive and strategic monitoring plan encompassing groundwater levels, water quality parameters, and other relevant variables aimed at ensuring the long-term sustainability and availability of groundwater resources for current and future generations.
Data-driven methods for qualitative and quantitative characterisation of coastal aquifers / Alfio, Maria Rosaria. - ELETTRONICO. - (2024). [10.60576/poliba/iris/alfio-maria-rosaria_phd2024]
Data-driven methods for qualitative and quantitative characterisation of coastal aquifers
Alfio, Maria Rosaria
2024-01-01
Abstract
This research investigates a set of data-driven methods for characterising coastal aquifers under data scarcity using available short hydrogeological time series and chemical surveys. Correlation between groundwater levels and climate indexes and time series analyses, with particular attention to the autocorrelation and cross-correlation functions, wavelet analyses and seasonal and trend decomposition, are extensively analysed using precipitation and groundwater level recordings to define the hydrodynamic mechanism of an aquifer system in response to climate factors. These methods rely on the hypothesis that the aquifer system is considered as a filter that modifies, retains, or attenuates the input signal, i.e., precipitation, into an output signal, such as spring discharge, groundwater level, river flow rate or other physical or chemical parameters. They can provide valuable insights into various aspects, encompassing the nature of the aquifer, the influence of climatic conditions, and significant abstractions. Multivariate statistical analysis is instead a valuable approach handling multiple geochemical and physical parameters to reveal spatial and temporal variations in groundwater quality, identify key hydrochemical processes, and assess how they change over time. Together with the Hydrogeochemical Facies Evolution-Diagram, these techniques allow to explore the salinisation process at the case study. Through Geostatistic, instead, the salinisation process and nitrate pollution are investigated in space and compared in time, allowing the identification of the areas more vulnerable. A part of the Thesis is dedicated to a comprehensive assessment of climate change projections and bias-correction techniques, employing historical and regional climate data, to discuss the potential impacts of weather projections on a coastal aquifer. The Thesis focuses on the complex coastal karst aquifer of Salento in Southern Italy, which presents numerous challenges, including geomorphological complexity, regional size, limited surface water resources, and significant water withdrawals for various human activities. The primary objective of this research is to investigate the hydrodynamic mechanism of such aquifer and discuss the potential problematics to which groundwater resources are exposed due to climate change and human pressure. The scope is to raise awareness among water utilities and political stakeholders in actuating mitigative actions and restrictions on the use of groundwater and exploring alternative measures to supply water demand. The study encourages for the establishment and consistent implementation of a comprehensive and strategic monitoring plan encompassing groundwater levels, water quality parameters, and other relevant variables aimed at ensuring the long-term sustainability and availability of groundwater resources for current and future generations.File | Dimensione | Formato | |
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