Data-Driven Approach Supporting Groundwater Resource Understanding, Protection and Management

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 17833

Special Issue Editors


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Guest Editor
Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milano, Italy
Interests: hydrogeochemical modelling; trace elements; groundwater quality; groundwater/surface water interactions
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Guest Editor
Department of Earth and Environmental Sciences, University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milan, Italy
Interests: hydrogeology; groundwater; climate change effects on groundwater resources; water safety plans; groundwater natural contamination; natural background levels (NBL)

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Guest Editor
TU Delft, Delft University of Technology, Delft, The Netherlands
Interests: reactive transport modeling; groundwater contamination; hydro(bio)geochemical processes modeling; uncertainty quantification; geostatistics for hydrogeology and environmental sciences; time series analysis
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Bioresource Engineering, McGill University, Montreal, QC, Canada
Interests: hydro(geo)logy; machine/deep learning and data-driven modeling; hydrological forecasting; time series; water quality; groundwater vulnerability and contamination risk; groundwater resources management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Groundwater is a vital resource for human and ecosystem needs worldwide. In a scenario of climate change and increasing anthropogenic impacts on groundwater, understanding the main drivers affecting groundwater resource quality and availability is the main challenge in the scope of more sustainable groundwater management. In the last few decades, monitoring networks' expansion and persistence over time have led to increased data availability: environmental datasets are growing in size, complexity, and resolution. Extensive monitoring data require proper techniques and tools to be managed, elaborated, interpreted, and integrated on a spatial or temporal scale to obtain reliable results.  The scientific community is constantly working on understanding the best and most up-to-date techniques to investigate and exploit these valuable data. This Special Issue aims to expand the knowledge on data-driven applications on groundwater data. We welcome the submission of papers concerning data analysis and modelling of groundwater quality or quantity datasets; examples can be: a) Data mining, spatial, temporal, or multivariate analysis of groundwater quality data or b) time series analysis and forecasting of groundwater head and springs discharge. We also encourage new insights on overcoming the most widely known problems such as missing or non-detected data or sensors and analytical uncertainty management. The final goal of this Special Issue is collecting up-to-date applications of data-driven techniques in the scope of groundwater resource understanding, protection, and management.  

Dr. Marco Rotiroti
Dr. Chiara Zanotti
Dr. Diego Di Curzio
Dr. Rahim Barzegar
Guest Editors

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Keywords

  • groundwater
  • data mining
  • multivariate analysis
  • time series analysis
  • machine learning
  • geostatistics
  • neural networks
  • groundwater forecasting
  • data management and preprocessing

Published Papers (8 papers)

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Research

19 pages, 4240 KiB  
Article
Towards Groundwater-Level Prediction Using Prophet Forecasting Method by Exploiting a High-Resolution Hydrogeological Monitoring System
by Davide Fronzi, Gagan Narang, Alessandro Galdelli, Alessandro Pepi, Adriano Mancini and Alberto Tazioli
Water 2024, 16(1), 152; https://doi.org/10.3390/w16010152 - 30 Dec 2023
Cited by 2 | Viewed by 2766
Abstract
Forecasting of water availability has become of increasing interest in recent decades, especially due to growing human pressure and climate change, affecting groundwater resources towards a perceivable depletion. Numerous research papers developed at various spatial scales successfully investigated daily or seasonal groundwater level [...] Read more.
Forecasting of water availability has become of increasing interest in recent decades, especially due to growing human pressure and climate change, affecting groundwater resources towards a perceivable depletion. Numerous research papers developed at various spatial scales successfully investigated daily or seasonal groundwater level prediction starting from measured meteorological data (i.e., precipitation and temperature) and observed groundwater levels, by exploiting data-driven approaches. Barely a few research combine the meteorological variables and groundwater level data with unsaturated zone monitored variables (i.e., soil water content, soil temperature, and bulk electric conductivity), and—in most of these—the vadose zone is monitored only at a single depth. Our approach exploits a high spatial-temporal resolution hydrogeological monitoring system developed in the Conero Mt. Regional Park (central Italy) to predict groundwater level trends of a shallow aquifer exploited for drinking purposes. The field equipment consists of a thermo-pluviometric station, three volumetric water content, electric conductivity, and soil temperature probes in the vadose zone at 0.6 m, 0.9 m, and 1.7 m, respectively, and a piezometer instrumented with a permanent water-level probe. The monitored period started in January 2022, and the variables were recorded every fifteen minutes for more than one hydrologic year, except the groundwater level which was recorded on a daily scale. The developed model consists of three “virtual boxes” (i.e., atmosphere, unsaturated zone, and saturated zone) for which the hydrological variables characterizing each box were integrated into a time series forecasting model based on Prophet developed in the Python environment. Each measured parameter was tested for its influence on groundwater level prediction. The model was fine-tuned to an acceptable prediction (roughly 20% ahead of the monitored period). The quantitative analysis reveals that optimal results are achieved by expoiting the hydrological variables collected in the vadose zone at a depth of 1.7 m below ground level, with a Mean Absolute Error (MAE) of 0.189, a Mean Absolute Percentage Error (MAPE) of 0.062, a Root Mean Square Error (RMSE) of 0.244, and a Correlation coefficient of 0.923. This study stresses the importance of calibrating groundwater level prediction methods by exploring the hydrologic variables of the vadose zone in conjunction with those of the saturated zone and meteorological data, thus emphasizing the role of hydrologic time series forecasting as a challenging but vital aspect of optimizing groundwater management. Full article
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16 pages, 6393 KiB  
Article
Estimating a Reliable Water Budget at a Basin Scale: A Comparison between the Geostatistical and Traditional Methods (Foro River Basin, Central Italy)
by Alessia Di Giovanni, Diego Di Curzio, Davide Pantanella, Cristiana Picchi and Sergio Rusi
Water 2023, 15(23), 4083; https://doi.org/10.3390/w15234083 - 24 Nov 2023
Viewed by 870
Abstract
Recently, new numerical methods have been applied to weather data for the estimation of water budget, especially when the lack of measured data is considerable. Geostatistics is one of the most powerful approaches when it comes to studying spatially relevant natural phenomena, as [...] Read more.
Recently, new numerical methods have been applied to weather data for the estimation of water budget, especially when the lack of measured data is considerable. Geostatistics is one of the most powerful approaches when it comes to studying spatially relevant natural phenomena, as it considers the spatial correlation among measurements over a specific study area and provides the associate uncertainty. In this study, we tested the feasibility of using a geostatistical method to provide a reliable estimation of the water budget of the Foro river basin (Central Italy) by comparing the obtained results with those of a traditional yet robust method. The results obtained with the geostatistical approach proved to be in line with the ones from the traditional method. Additionally, it was possible to quantify the uncertainty associated with the discharge values, making the estimates more reliable than the ones obtained with the traditional approach. However, the yearly distribution of river discharge obtained using both methods appeared to be dissimilar to the measured ones. The surface water uses, as well as the regulatory effect of the carbonate and alluvial aquifer regime, may affect the river discharge variability over the year and then can account for similar discrepancies between the inflow and outflow water volumes. Full article
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27 pages, 6434 KiB  
Article
Assessing Groundwater Potential in a Mid-Mountain Dryland Area of North-Central Chile through Geospatial Mapping
by José Miguel Deformes, Jorge Núñez, Jerry P. Fairley, José Luis Arumí and Ricardo Oyarzún
Water 2023, 15(16), 3005; https://doi.org/10.3390/w15163005 - 20 Aug 2023
Viewed by 1415
Abstract
This study utilized the Random Forest (RF) algorithm to assess groundwater potential (GWP) in the mid-mountain region of the Coquimbo region, north-central Chile. A comprehensive evaluation of twenty-one factors, primarily derived from Digital Elevation Models (DEM) and satellite data, was conducted against a [...] Read more.
This study utilized the Random Forest (RF) algorithm to assess groundwater potential (GWP) in the mid-mountain region of the Coquimbo region, north-central Chile. A comprehensive evaluation of twenty-one factors, primarily derived from Digital Elevation Models (DEM) and satellite data, was conducted against a database of 3822 groundwater discharge points. The majority of them consisted of shallow wells with relatively low yields. The main objective was to develop a groundwater potential (GWP) map for the study area. Among the factors considered, six variables, including two anthropogenic factors (distance to roads and presence of agricultural communities) and four natural factors (slope, elevation, concavity, and ruggedness index), were identified as the most influential indicators of GWP. The RF approach demonstrated excellent performance, achieving an Area Under the Curve (AUC) value of 0.95, sensitivity of 0.88, specificity of 0.86, and kappa coefficient of 0.74 in the test set. The majority of the study area exhibited low GWP, while only 14% of the area demonstrated high or very high GWP. In addition to providing valuable guidance for future hydrogeological investigations in the region, the GWP map serves as a valuable tool for identifying the areas that are most vulnerable to water shortages. This is particularly significant, as the region has been severely affected by extended drought, making water supply a critical concern. Full article
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28 pages, 11296 KiB  
Article
The Assessment of the Groundwater Quality in the Coastal Aquifers of the Essaouira Basin, Southwestern Morocco, Using Hydrogeochemistry and Isotopic Signatures
by Otman El Mountassir and Mohammed Bahir
Water 2023, 15(9), 1769; https://doi.org/10.3390/w15091769 - 5 May 2023
Cited by 3 | Viewed by 2899
Abstract
Because of anthropogenic activity and seawater intrusion, coastal aquifers worldwide frequently face a threat to their water supply due to salinization. This paper investigates the assessment of the groundwater quality in coastal aquifers of the Hauturivien aquifer in the Essaouira basin. In this [...] Read more.
Because of anthropogenic activity and seawater intrusion, coastal aquifers worldwide frequently face a threat to their water supply due to salinization. This paper investigates the assessment of the groundwater quality in coastal aquifers of the Hauturivien aquifer in the Essaouira basin. In this study, 56 groundwater samples collected from the Hauturivian aquifer across four campaigns in 2017, 2018, 2019, and 2020 were subjected to multivariate analyses involving principal component analysis (PCA) and cluster analysis (CA) using SPSS software. Among the three main water types, the mixed Ca-Mg-Cl classification was predominant in the investigated aquifer. In addition to the natural processes (such as the water–rock interaction, ion exchange, dissolution/precipitation dynamics, and evaporation) that govern groundwater quality, current land use practices have increased salinization in this poorly drained semi-arid area. Based on assessments using Water Quality Index (WQI) and Irrigation Water Quality Index (IWQI), the water quality is suitable for human consumption, but its use for irrigation is limited to crops that can tolerate high salt levels. The stable isotopes (δ2H and δ18O) of groundwater demonstrated that local precipitation is the primary recharge source. Nonetheless, the evaporation process, influenced by various geological conditions, affects groundwater recharge, regardless of the topographical differences in the study area. Full article
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23 pages, 63784 KiB  
Article
Comparison of Three Imputation Methods for Groundwater Level Timeseries
by Mara Meggiorin, Giulia Passadore, Silvia Bertoldo, Andrea Sottani and Andrea Rinaldo
Water 2023, 15(4), 801; https://doi.org/10.3390/w15040801 - 17 Feb 2023
Cited by 3 | Viewed by 2635
Abstract
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 [...] Read more.
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. Full article
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16 pages, 6992 KiB  
Article
Wavelet Analysis on Groundwater, Surface-Water Levels and Water Temperature in Doñana National Park (Coastal Aquifer in Southwestern Spain)
by Jennifer Treviño, Miguel Rodríguez-Rodríguez, María José Montes-Vega, Héctor Aguilera, Ana Fernández-Ayuso and Nuria Fernández-Naranjo
Water 2023, 15(4), 796; https://doi.org/10.3390/w15040796 - 17 Feb 2023
Cited by 1 | Viewed by 2263
Abstract
The Doñana National Park (DNP) is a protected area with water resources drastically diminishing due to the unsustainable extraction of groundwater for agricultural irrigation and human consumption of a nearby coastal city. In this study, we explore the potential of wavelet analysis applied [...] Read more.
The Doñana National Park (DNP) is a protected area with water resources drastically diminishing due to the unsustainable extraction of groundwater for agricultural irrigation and human consumption of a nearby coastal city. In this study, we explore the potential of wavelet analysis applied to high-temporal-resolution groundwater-and-surface-water time series of temporary coastal ponds in the DNP. Wavelet analysis was used to measure the frequency of changes in water levels and water temperature, both crucial to our understanding of complex hydrodynamic patterns. Results show that the temporary ponds are groundwater-dependent ecosystems of a through-flow type and are still connected to the sand-dune aquifer, regardless of their hydrological affection, due to groundwater withdrawal. These ponds, even those most affected by pumping in nearby drills, are not perched over the saturated zone. This was proven by the evidence of a semi-diurnal (i.e., 6 h) signal in the surface-level time series of the shallow temporary ponds. This signal is, at the same time, related to the influence of the tides affecting the coastal sand-dune aquifer. Finally, we detected other hydrological processes that affect the ponds, such as evaporation and evapotranspiration, with a clear diurnal (12 h) signal. The maintenance of the ecological values and services to the society of this emblematic wetland is currently in jeopardy, due to the effect of the groundwater abstraction for irrigation. The results of this study contribute to the understanding of the behavior of these fragile ecosystems of DNP, and will also contribute to sound-integrated water-resource management. Full article
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14 pages, 3426 KiB  
Article
Assessing Groundwater Evolution with a Combined Approach of Hydrogeochemical Modelling and Data Analysis: Application to the Rhodope Coastal Aquifer (NE Greece)
by Evangelos Tziritis, Ekaterini Sachsamanoglou and Vassilis Aschonitis
Water 2023, 15(2), 230; https://doi.org/10.3390/w15020230 - 5 Jan 2023
Cited by 5 | Viewed by 1625
Abstract
Assessing the hydrogeochemical evolution of groundwater is a challenging task, which is further exacerbated when considering the multiple geogenic and anthropogenic impacts that affect its quality and the hydraulic interactions between different aquifer bodies. This study combined hydrogeochemical modelling and data analysis to [...] Read more.
Assessing the hydrogeochemical evolution of groundwater is a challenging task, which is further exacerbated when considering the multiple geogenic and anthropogenic impacts that affect its quality and the hydraulic interactions between different aquifer bodies. This study combined hydrogeochemical modelling and data analysis to assess this complex hydrogeological regime. Before modelling, the groundwater samples were clustered using a multivariate statistical method (hierarchical cluster analysis (HCA)). Then, the Geochemist Workbench (GWB) software was applied to model the hydrogeochemical groundwater evolution, including the dominant ion exchange process, and to explain the changes in groundwater chemistry towards its flow. The input data consisted of five key parameters from seventy-seven sampling points collected in two periods (accounting for the start and the end of the irrigation period). A data analytical approach based on the optimal mixing ratios between the interacting groundwater systems and recharge inputs was also performed as part of the methodological approach. It revealed a progressively temporal-dependent behaviour of the aquifer system during the irrigation period, resulting in seasonal changes in the hydrodynamic conditions and depletion of the upper aquifer layers. Specifically, the aquifer system was confirmed to undergo cation exchange as the dominant geochemical process that increases calcium concentrations. The complex hydrogeological regime was further evaluated by assessing the mixing ratios of the different aquifer layers. Hence, the aquifer system (bulk samples) was mixed with the irrigation water by 71% and 97% and with the lateral recharge by 76% and 29% for the beginning and at the end of the irrigation period, respectively. Overall, the joint assessments were confirmed by the hydrogeochemical status of the end-members and the modelling approach and explained the sequential changes in groundwater chemistry due to the dominant ion-exchange process and the mixing of different water bodies. The proposed methodological approach proved that it could be used as an exploratory and preliminary method for capturing the temporal dynamics in complex groundwater systems and supporting groundwater resource management. Full article
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19 pages, 9845 KiB  
Article
Multivariate Time Series Clustering of Groundwater Quality Data to Develop Data-Driven Monitoring Strategies in a Historically Contaminated Urban Area
by Chiara Zanotti, Marco Rotiroti, Agnese Redaelli, Mariachiara Caschetto, Letizia Fumagalli, Camilla Stano, Davide Sartirana and Tullia Bonomi
Water 2023, 15(1), 148; https://doi.org/10.3390/w15010148 - 30 Dec 2022
Cited by 3 | Viewed by 1795
Abstract
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 [...] Read more.
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. Full article
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