Groundwater Throughflow and Seawater Intrusion in High Quality Coastal Aquifers

Climate change impact on groundwater resources in sandbar aquifers in southern Baltic coast

Shallow coastal aquifers are vulnerable hydrosystems controlled by many factors, related to climate, seawater-freshwater interactions and human activity. Given on-going climate change, sea level rise and increasing human impact, it is especially true for groundwater resources situated in sandbars. We developed numerical models of unsaturated zone water flow for two sandbars in northern Poland: the Vistula Spit and the Hel Spit using HYDRUS-1D. The simulations were performed for three types of land use: pine forest, grass cover and bare soil, for 2024-2100 based on weather data and sea level rise forecasts for two emissions scenarios (RCP 4.5 and RCP 8.5). The results present prognosis of groundwater recharge, water table level and water content changeability in near-term (2023-2040), mid-term (2041-2060), and long-term period (2081-2100). Expected sea level rise and decreasing hydraulic gradient of the sandbar aquifers will probably cause in-land movement of the freshwater-saltwater interface, leading to significant decrease or complete salinization of groundwater resources. The study shows that holistic monitoring including groundwater level and salinization, sea level rise, and metheorological data (precipitation amount and variability, temperature) is crucial for sustainable management of vulnerable aquifers located in sandbars.

Seawater intrusion pattern recognition supported by unsupervised learning: A systematic review and application

Seawater intrusion is among the world's leading causes of groundwater contamination, as salty water can affect potable water access, food production, and ecosystem functions. To explore such contamination sources, multivariate analysis supported by unsupervised learning tools has been used for decades to aid in water resource pattern recognition, clustering, and water quality data variability characterization. This study proposes a systematic review of these techniques applied for supporting seawater intrusion identification based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and subsequent bibliometric analysis of 102 coastal hydrogeological studies. The most relevant identified methods, including principal components analysis (PCA), hierarchical clustering analysis, K-means clustering, and self-organizing maps, are explained and applied to a case study. Although 74 % of the studies that applied dimensional reduction methods, such as PCA, associated most of the database variance with the salinization process, 77 % of the studies that applied clustering methods associated at least one water sample cluster with the influence of seawater intrusion. Based on the review and a practical demonstration using the open-source R software platform, recommendations are made regarding data preprocessing, research opportunities, and publishing information necessary to replicate and validate the studies.

Digital Twin Concept Utilizing Electrical Resistivity Tomography for Monitoring Seawater Intrusion

Electrical resistivity tomography (ERT) has been seen as an appropriate instrument in several works to monitor and aid in the control of seawater intrusion (SWI) in coastal groundwater systems. This study seeks to discuss the synthesis of a digital twin that couples information between the physical space through ERT as a monitoring sensor and the digital space using SWI simulations to accurately model the behavior of SWI in the present and future settings. To showcase the concept, a Python-based simulation was presented that shows (a) the joint forward modeling-simulation scheme for calculating expected ERT apparent resistivity values from simulated SWI and (b) the calibration of the digital coastal aquifer system through genetic algorithm to accurately match the outputs of the SWI simulations with the ERT measurements.

Advancing remote sensing and machine learning-driven frameworks for groundwater withdrawal estimation in Arizona: Linking land subsidence to groundwater withdrawals

Groundwater plays a crucial role in sustaining global food security but is being over-exploited in many basins of the world. Despite its importance and finite availability, local-scale monitoring of groundwater withdrawals required for sustainable water management practices is not carried out in most countries, including the United States. In this study, we combine publicly available datasets into a machine learning framework for estimating groundwater withdrawals over the state of Arizona. Here we include evapotranspiration, precipitation, crop coefficients, land use, annual discharge, well density, and watershed stress metrics for our predictions. We employ random forests to predict groundwater withdrawals from 2002 to 2020 at a 2 km spatial resolution using in situ groundwater withdrawal data available for Arizona Active Management Areas (AMA) and Irrigation Non-Expansion Areas (INA) from 2002 to 2009 for training and 2010-2020 for validating the model respectively. The results show high training ( R 2 ≈ 0.9 ) and good testing ( R 2 ≈ 0.7 ) scores with normalized mean absolute error (NMAE) ≈ 0.62 and normalized root mean square error (NRMSE) ≈ 2.34 for the AMA/INA region. Using this method, we spatially extrapolate the existing groundwater withdrawal estimates to the entire state and observe the co-occurrence of both groundwater withdrawals and land subsidence in South-Central and Southern Arizona. Our model predicts groundwater withdrawals in regions where production wells are present on agricultural lands and subsidence is observed from Interferometric Synthetic Aperture Radar (InSAR), but withdrawals are not monitored. By performing a comparative analysis over these regions using the predicted groundwater withdrawals and InSAR-based land subsidence estimates, we observe a varying degree of subsidence for similar volumes of withdrawals in different basins. The performance of our model on validation datasets and its favourable comparison with independent water use proxies such as InSAR demonstrate the effectiveness and extensibility of our combined remote sensing and machine learning-based approach.

A Review on Applications of Time-Lapse Electrical Resistivity Tomography Over the Last 30 Years : Perspectives for Mining Waste Monitoring

Mining operations generate large amounts of wastes which are usually stored into large-scale storage facilities which pose major environmental concerns and must be properly monitored to manage the risk of catastrophic failures and also to control the generation of contaminated mine drainage. In this context, non-invasive monitoring techniques such as time-lapse electrical resistivity tomography (TL-ERT) are promising since they provide large-scale subsurface information that complements surface observations (walkover, aerial photogrammetry or remote sensing) and traditional monitoring tools, which often sample a tiny proportion of the mining waste storage facilities. The purposes of this review are as follows: (i) to understand the current state of research on TL-ERT for various applications; (ii) to create a reference library for future research on TL-ERT and geoelectrical monitoring mining waste; and (iii) to identify promising areas of development and future research needs on this issue according to our experience. This review describes the theoretical basis of geoelectrical monitoring and provides an overview of TL-ERT applications and developments over the last 30 years from a database of over 650 case studies, not limited to mining operations (e.g., landslide, permafrost). In particular, the review focuses on the applications of ERT for mining waste characterization and monitoring and a database of 150 case studies is used to identify promising applications for long-term autonomous geoelectrical monitoring of the geotechnical and geochemical stability of mining wastes. Potential challenges that could emerge from a broader adoption of TL-ERT monitoring for mining wastes are discussed. The review also considers recent advances in instrumentation, data acquisition, processing and interpretation for long-term monitoring and draws future research perspectives and promising avenues which could help improve the design and accuracy of future geoelectric monitoring programs in mining wastes.

Delineating the origins and processes of groundwater salinization and quality degradation in a coastal irrigated plain, Korba (Northeastern Tunisia)

From 2006 to 2020, groundwater investigations were conducted in the Korba coastal aquifer in northern Tunisia along two flow paths (transects S1 and S2), perpendicular to the shoreline. Groundwater sampling, hydrodynamic monitoring, and electrical tomography imaging were performed in situ. Geochemical analysis (Ionic ratios, ionic deltas, conventional diagrams, and stable isotopes) and modelling using PHREEQC, and multivariate statistical analysis were applied. The objective was to identify the potential origin of groundwater salinization (i.e., high TDS and NO₃) and to study associated processes. The groundwater flow inversion was corroborated by the piezometric survey in transect S1, where a piezometric depression of 5 m was detected at 4000 m from the seashore. Seawater intrusion and agricultural contamination, mainly through N-fertilizers, both contribute to groundwater mineralization and consequently salinization, according to PCA analysis. The impacted geochemical area of seawater intrusion was estimated to be 4000 and 1500 m, respectively, along transect S1 and transect S2. Inversely, agricultural contamination acts in internal areas beginning at 2000 m and 1500 m from the shoreline for S1 and S2, respectively. Results of different scenarios of inverse geochemical modelling along flow paths indicated that mixing, ion exchange, dissolution of gypsum, and precipitation of dolomite and calcite are the main processes controlling the groundwater composition in the coastal study area.

Characterizing sediment bacterial community and identifying the biological indicators in a seawater-freshwater transition zone during the wet and dry seasons

Seawater intrusion has a detrimental effect on agriculture, industry, and human health. One question of particular interest is how the microbial community responds to and reflects seawater intrusion with seasonal variation. The current study explored the seasonal changes in bacterial community composition and interaction in the vicinity of Pearl River Estuary in dry season (January) and wet season (September). Results indicated that the salinity of sediment samples obtained in dry season was higher than that in wet season. The salt stress induced a declined alpha diversity but resulted in a loosely connected and unstable biotic interaction network in the bacterial communities. Random forest prediction and redundancy analysis of bacterial community indicated that salinity substantially affected the bacterial communities. Multiple lines of evidence, including the enrichment of bacterial taxa in the high-salinity location, microbe-microbe interactions, environment-microbe interactions, and machine learning approach, demonstrated that the families Moraxellaceae and Planococcaceae were the keystone taxa and were resistant to salt stress, which suggested that both of them can be used as potential biological indicators of monitoring and controlling seawater intrusion in coastal zone areas.

Constraining the response of continental-scale groundwater flow to climate change

Numerical models of groundwater flow play a critical role for water management scenarios under climate extremes. Large-scale models play a key role in determining long range flow pathways from continental interiors to the oceans, yet struggle to simulate the local flow patterns offered by small-scale models. We have developed a highly scalable numerical framework to model continental groundwater flow which capture the intricate flow pathways between deep aquifers and the near-surface. The coupled thermal-hydraulic basin structure is inferred from hydraulic head measurements, recharge estimates from geochemical proxies, and borehole temperature data using a Bayesian framework. We use it to model the deep groundwater flow beneath the Sydney-Gunnedah-Bowen Basin, part of Australia's largest aquifer system. Coastal aquifers have flow rates of up to 0.3 m/day, and a corresponding groundwater residence time of just 2,000 years. In contrast, our model predicts slow flow rates of 0.005 m/day for inland aquifers, resulting in a groundwater residence time of [Formula: see text] 400,000 years. Perturbing the model to account for a drop in borehole water levels since 2000, we find that lengthened inland flow pathways depart significantly from pre-2000 streamlines as groundwater is drawn further from recharge zones in a drying climate. Our results illustrate that progressively increasing water extraction from inland aquifers may permanently alter long-range flow pathways. Our open-source modelling approach can be extended to any basin and may help inform policies on the sustainable management of groundwater.

Unveiling the extent of salinization to delineate the potential submarine groundwater discharge zones along the North-western coast of India

The North-West coast of India was notable for the urbanization and semiarid climate, particularly the Gujarat coastal region which was facing water crises and aquifer salinization issue. Under the light of these critical issues, the present study investigates the sources of aquifer salinization and probable location of submarine groundwater discharge (SGD) using an integrated approach of major ion chemistry, statistical techniques, and isotopic signature of groundwater (GW). The evolution of GW reveals that water facies changes from Ca²⁺-Mg²⁺-Cl^- to Na⁺-Cl type from the south Gujarat towards the Gulf of Khambhat. Log-normal distribution of Cl^- and NO₃^- divulges that different pollution sources influence the GW quality. Statistical findings supplemented with Isotopic signatures, ionic ratios and cross plots identified four classes of GW, which varies with degree of anthropogenic and seawater influences. Results suggested that seawater intrusion heavily influences 42% of the total GW samples, whereas 58% samples showed the probability of SGD. The study recommends the feasible locations of check dams as a remedial measure for controlling the salinization of coastal aquifer.

Application of Support Vector Regression and Metaheuristic Optimization Algorithms for Groundwater Potential Mapping in Gangneung-si, South Korea

The availability of groundwater is of concern. The demand for groundwater in Korea increased by more than 100% during the period 1994–2014. This problem will increase with population growth. Thus, a reliable groundwater analysis model for regional scale studies is needed. This study used the geographical information system (GIS) data and machine learning to map groundwater potential in Gangneung-si, South Korea. A spatial correlation performed using the frequency ratio was applied to determine the relationships between groundwater productivity (transmissivity data from 285 wells) and various factors. This study used four topography factors, four hydrological factors, and three geological factors, along with the normalized difference wetness index and land use and soil type. Support vector regression (SVR) and metaheuristic optimization algorithms—namely, grey wolf optimization (GWO), and particle swarm optimization (PSO), were used in the construction of the groundwater potential map. Model validation based on the area under the receiver operating curve (AUC) was used to determine model accuracy. The AUC values of groundwater potential maps made using the SVR, SVR_GWO, and SVR_PSO algorithms were 0.803, 0.878, and 0.814, respectively. Thus, the application of optimization algorithms increased model accuracy compared to the standard SVR algorithm. The findings of this study improve our understanding of groundwater potential in a given area and could be useful for policymakers aiming to manage water resources in the future.

Review of Seawater Intrusion in Western Coastal Regions of South Korea

Groundwater salinization in coastal aquifers because of seawater intrusion has raised serious concerns worldwide since it deteriorates the quality of drinking water and thereby threatens sustainable economic development. In particular, this problem has been a cause of growing concern in the western coastal regions of South Korea. In this paper, we review studies of seawater intrusion in western coastal regions of South Korea conducted over the past 20 years, particularly focusing on studies reported in international journals. We summarize the study locations, methods used, and major findings from individual and regional-scale studies. General methods used to identify and interpret seawater intrusion and subsequent geochemical processes are also presented. On the basis of insights gleaned from the previous studies, future research needs are discussed.