As groundwater quality monitoring networks have been expanded over the last decades, significant time series are now available. Therefore, a scientific effort is needed to explore innovative techniques for groundwater quality time series exploitation. In this work, time series exploratory analysis and time series cluster analysis are applied to groundwater contamination data with the aim of developing data-driven monitoring strategies. The study area is an urban area characterized by several superimposing historical contamination sources and a complex hydrogeological setting. A multivariate time series cluster analysis was performed on PCE and TCE concentrations data over a 10 years time span. The time series clustering was performed based on the Dynamic Time Warping method. The results of the clustering identified 3 clusters associated with diffuse background contamination and 7 clusters associated with local hotspots, characterized by specific time profiles. Similarly, a univariate time series cluster analysis was applied to Cr(VI) data, identifying 3 background clusters and 7 hotspots, including 4 singletons. The clustering outputs provided the basis for the implementation of data-driven monitoring strategies and early warning systems. For the clusters associated with diffuse background contaminations and those with constant trends, trigger levels were calculated with the 95° percentile, constituting future threshold values for early warnings. For the clusters with pluriannual trends, either oscillatory or monotonous, specific monitoring strategies were proposed based on trends’ directions. Results show that the spatio-temporal overview of the data variability obtained from the time series cluster analysis helped to extract relevant information from the data while neglecting measurements noise and uncertainty, supporting the implementation of a more efficient groundwater quality monitoring.
The town of Mantua is a good example of an urban area with an intricate surface water system leading to complex groundwater/surface-water interactions. In this context, the Site of National Interest (SIN) “Laghi di Mantova e Polo Chimico”, is characterized by intense pumping activity by means of industrial wells and hydraulic barriers. In order to establish the interactions between groundwater and the surface water system, evaluating their relation with the pumping activities, a transient groundwater numerical model was developed (January 2016 - December 2018) using MODFLOW-2005 and the Streamflow-Routing (SFR2) package, following a participatory approach. Results show how, depending on the minimum/ maximum groundwater conditions and the discharge values of the surface channels, the relation between groundwater/surface-waters can vary during the year, also affecting the operation of the hydraulic barriers. The stakeholders could use the calibrated model in the future to ensure optimal management of the pumping activities within the SIN.
Urbanization is a worldwide process that recently has culminated in wider use of the subsurface, determining a significant interaction between groundwater and underground infrastructures. This can result in infiltrations, corrosion, and stability issues for the subsurface elements. Numerical models are the most applied tools to manage these situations. Using MODFLOW-USG and combining the use of Wall (HFB) and DRN packages, this study aimed at simulating underground infrastructures (i.e., subway lines and public car parks) and quantifying their infiltrations. This issue has been deeply investigated to evaluate water inrush during tunnel construction, but problems also occur with regard to the operation of tunnels. The methodology has involved developing a steady-state groundwater flow model, calibrated against a maximum groundwater condition, for the western portion of Milan city (Northern Italy, Lombardy Region). Overall findings pointed out that the most impacted areas are sections of subway tunnels already identified as submerged. This spatial coherence with historical information could act both as validation of the model and a step forward, as infiltrations resulting from an interaction with the water table were quantified. The methodology allowed for the improvement of the urban conceptual model and could support the stakeholders in adopting proper measures to manage the interactions between groundwater and the underground infrastructures.
<p><span>Groundwater systems are going to play an increasingly important role in facing climate change, representing one of the most significant worldwide water sources. At the same time, climate change may inevitabily lead to considerable direct and indirect impacts on groundwater systems. </span></p><p><span>The aim of this work is the development of a knowledge framework for groundwater bodies in relation to water availability and its vulnerability to possible climate change scenarios, identifying the mitigation action that can be adopted to resiliently respond to changes. The study area is the province of Brescia, in northern Italy, including 100 municipalities served by 183 wells and 98 springs. This area includes a higher plain, hosting a unconfined acquifer, a lower plain with several layered confined acquifers and two morainic amphitheaters. </span></p><p><span>To define the evolutionary scenarios of groundwater resources at basin and sub-basin scale, hydrodynamic conditions and temporal trends, over a time span from 2009 to 2021, have been evaluated.</span></p><p><span>Groundwater availability data have been analysed in relation to hydro-nivo-meteorological data collected from the meteorological stations distributed in the area. Mann-Kendall and Sen Slope estimator have been applied for trend identification and changing point analysis to explore groundwater time series.</span></p><p><span>Regarding precipitation, a first analysis aimed at the identification of extreme phenomena through the yearly distributions of dry and rainy days and through the calculation of specific indices such as SPI (Standard Precipitation Index) and PCI (Precipitation Concentration Index).</span></p><p><span>The piezometric and precipitation series have been subjected to time series decomposition, a mathematical procedure that splits the original series into three sub-components: seasonal, trend, and random. Successively, a comparative analysis has been performed between the three components of groundwater levels and the three components of the neighboring rain stations data. </span></p><p><span>This methodology allowed to investigate the actual effect of precipitation on groundwater level variability with respect to the other components that contribute to the total water budget: it emerged that in the higher plain the effects of irrigation return flow contributes to the summer groundwater table rise more than precipitation, and that in the lower plain groundwater table depth is more related to human abstraction than local precipitation. These results provide the basis for implementing future sustainable water management plans.</span></p>
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