The social, economic, and ecological importance of the aquifer system within the Bacchiglione basin (Veneto, IT) is noteworthy, and there is considerable disagreement among previous studies over its sustainable use. Investigating the long-term quantitative sustainability of the groundwater system, this study presents a statistical methodology that can be applied to similar cases. Using a combination of robust and widely used techniques, we apply the seasonal Mann–Kendall test and the Sen’s slope estimator to the recorded groundwater level timeseries. The analysis is carried out on a large and heterogeneous proprietary dataset gathering hourly groundwater level timeseries at 79 control points, acquired during the period 2005–2019. The test identifies significant decreasing trends for most of the available records, unlike previous studies on the quantitative status of the same resource which covered the domain investigated here for a slightly different period: 2000–2014. The present study questions the reason for such diverging results by focusing on the method’s accuracy. After carrying out a Fourier analysis on the longest available timeseries, for studies of groundwater status assessment this work suggests applying the Mann–Kendall test to timeseries longer than 20 years (because otherwise the analysis would be affected by interannual periodicities of the water cycle). A further analysis of two 60-year-long monthly timeseries between 1960 and 2020 supports the actual sustainable use of the groundwater resource, the past deployment of the groundwater resources notwithstanding. Results thus prove more reliable, and meaningful inferences on the longterm sustainability of the groundwater system are possible.

This study compares three imputation methods applied to the field observations of hydraulic head in subsurface hydrology. Hydrogeological studies that analyze the timeseries of groundwater elevations often face issues with missing data that may mislead both the interpretation of the relevant processes and the accuracy of the analyses. The imputation methods adopted for this comparative study are relatively simple to be implemented and thus are easily applicable to large datasets. They are: (i) the spline interpolation, (ii) the autoregressive linear model, and (iii) the patched kriging. The average of their results is also analyzed. By artificially generating gaps in timeseries, the results of the various imputation methods are tested. The spline interpolation is shown to be the poorest performing one. The patched kriging method usually proves to be the best option, exploiting the spatial correlations of the groundwater elevations, even though spurious trends due to the the activation of neighboring sensors at times affect their reconstructions. The autoregressive linear model proves to be a reasonable choice; however, it lacks hydrogeological controls. The ensemble average of all methods is a reasonable compromise. Additionally, by interpolating a large dataset of 53 timeseries observing the variabilities of statistical measures, the study finds that the specific choice of the imputation method only marginally affects the overarching statistics.

In hydrogeology, it is often difficult to fully understand the hydraulic factors affecting the recharge of groundwater systems. Particularly, at a regional scale, the groundwater system can have different drivers depending on the considered area, i.e., soil permeability, paleochannels, and precipitation. Chemicalphysical (i.e. temperature) or hydrogeochemical data can help such understanding. However, this type of information is usually sparse at the regional scale, whereas extended groundwater piezometric head monitoring is more common. This study aims at exploiting these longitudinal observations of the hydraulic head to validate (and possibly bring more insights into) the geological structural model of aquifer systems. Clustering control points based on the piezometric head average annual variations can help the system conceptualization in two ways: (i) clusters can geographically identify areas with similar hydrogeological behavior; and (ii) the typical cluster annual variation with its ups and downs can bring insights on the recharge component of an aquifer system. Nevertheless, visual clustering can be a long and subjective procedure, thus this study suggests the use of the Principal Component Analysis to cluster the control points with a similar average annual variation of their recorded time series. This study supports the proposed analysis by applying it to the monitoring data of the Bacchiglione basin resulting in (i) clusters identified based on the number, moment, and lengths of groundwater level peaks and minima, (ii) wellgathered clusters in space, underpinning the groundwater hydrograph dependence on local driving factors. Furthermore, the investigation of clustering anomalies highlighted the relevance of the presence of time series with different recording periods pinpointing, however, the method’s capacity to spot a change in the hydrogeological cycle over the years.

Hydrogeological timeseries of hydraulic head contain important information for modelling the groundwater resource. Calibrating in transient conditions allows to define both conductivity and specific storage fields plus, in case, other flow boundary conditions (BCs) that fit at best the observations. Moreover, by having at least one year of records, different hydrological conditions are considered and fitted.The major problem encountered by hydrogeologists is that hydrological records often have missing values. Then, different choices on observation sampling time are possible: for example, using daily data with missing values or monthly data that fastens also the model. These choices can alter the calibration process and affect the parameters estimation.This study aims at understanding if and how optimal estimated parameter sets are different and, therefore, if the different choice on the time interval can preclude a proper calibration of the groundwater model. This analysis was performed by calibrating: (i) with all daily data, (ii) with different percentages of missing values on daily data, (iii) with weekly data, (iv) with monthly data and (v) with stationary conditions.The estimated parameter sets of the different models obtained by using part of the data available (to simulate the loss of information) are compared to a base model, which is the best fit achieved by using all available daily observations. The flow model and calibration setup are constant for all models, only timeseries‘ observation vary.The analysis is carried out on a real case of study: a flow model is built using the software FEFLOW for an area of the Bacchiglione Basin (Veneto, Italy). This area has been selected in a way to facilitate the calibration process. It is located on the plain close to the Leogra river where the aquifer is unconfined. The domain has both upstream and downstream borders roughly perpendicular to the regional groundwater flow direction and passing by sensors recording continuously the hydraulic head. In this way, the following BCs can be assigned: the Dirichlet BCs with transient values of the corresponding recording sensor for the boarders upstream and downstream and no-flow conditions for the lateral borders. Furthermore, inside the study area, there are sensors monitoring the hydraulic head, i.e. transient observations. Two borderline and four central sensors are recording daily values of hydraulic head. The year 2016 was chosen as calibration period, since no data is missing.The comparison of resulting conductivity and specific storage fields is carried out by visual inspection of fields heterogeneity and statistical distributions. Moreover, models’ uncertainty is quantified with a calibration-constrained Monte-Carlo analysis.The main understanding of this analysis is the anomalous result estimated by the monthly data model respect to other models: both conductivity and specific storage field are different in their heterogeneity and magnitude, reaching unlikely values.This comprehension is important because the choice of monthly data is usually done for data scarcity or model fastening, but the effects on estimated fields are evident and important to consider. The analysis shows how different observations types, meaning daily to monthly data, affect the calibration process.

In order to investigate hydraulic connections between the Brenta river and its groundwater aquifers, this work performs a stochastic analysis of measured groundwater temperature timeseries. The aim is to compare their trends to verify their mutual influence in stream seepage pathways. Additionally, the study wants to assess whether a large check dam, built across the river to increase aquifer recharge, is working properly. Field data were obtained by a water gauge placed directly into the stream, by two riverbank control points and by a monitoring well representing the regional groundwater thermal trend. Timeseries were evaluated by applying three different analyses - Fourier analysis, autocorrelation and sliding time-window Pearson correlation. Control points results were compared to the inflows to sort whether the aquifer is locally more affected by river dispersion or by regional groundwater flow. The three considered methods substantially agree in showing an increase of the seepage area upstream of the check dam and, at the same time, a marked reduction of groundwater recharge after few years of operation.