Groundwater Level Fluctuations Research Articles

Responses of microbial communities subjected to hydrodynamically induced disturbances in an organic contaminated site

Organic contaminated sites have gained significant attention as a prominent contributor to shallow groundwater contamination. However, limited knowledge exists regarding the impact of hydrodynamic effects on microbially mediated contaminant degradation at such sites. In this study, we investigated the distribution characteristics and community structure of prokaryotic microorganisms at the selected site during both wet and dry seasons, with a particular focus on their environmental adaptations. The results revealed significant seasonal variations (P < 0.05) in the α-diversity of prokaryotes within groundwater. The dry season showed more exclusive OTUs than the wet season. The response of prokaryotic metabolism to organic pollution pressure in different seasons was explored by PICRUSt2, and enzymes associated with the degradation of organic pollutants were identified based on the predicted functions. The results showed that hormesis was considered as an adaptive response of microbial communities under pollution stress. In addition, structural equation models demonstrated that groundwater level fluctuations can, directly and indirectly, affect the abundance and diversity of prokaryotes through other factors such as oxidation reduction potential (ORP), dissolved oxygen (DO), and naphthalene (Nap). Overall, our findings imply that the taxonomic composition and functional properties of prokaryotes in groundwater in organic contaminated sites is influenced by the interaction between seasonal variations and characteristics of organic pollution. The results provide new insights into microbiological processes in groundwater systems in organic contaminated sites.

Natural Sinkhole Monitoring and Characterization: The Case of Latera Sinkhole (Latium, Central Italy)

The occurrence of sinkhole phenomena in Italy is a prevalent and very uncertain class of geological hazards that pose a significant threat to human infrastructure and individuals. These events are characterized by their unpredictability and the challenges associated with their accurate forecasting. Both natural and anthropic factors influence the occurrence of these events; therefore, accurate identification of the above factors is critical for effective proactive and predictive efforts. The work presented in this paper refers to a collapse that occurred in a volcanic region in northern Latium (central Italy) on 31 January 2023. The area has been monitored using drones since the early stages of the sinkhole’s formation and has continued to date. Then, the collapse and the neighboring area were examined via geophysical and geochemical investigations to identify potential underlying factors. Geophysical and geochemical data were combined to provide a preliminary hypothesis on the collapse’s genesis. The obtained data indicate that the structural collapse can be attributable to the fluctuation in groundwater levels as well as the development of instabilities along its banks, leading to a growth in its dimensions.

Analyzing Coastal Groundwater Variability in Sindhudurg, Maharashtra: A Spatio-temporal GIS Approach+6

Due to a sharp increase in groundwater demands in coastal regions, groundwater levels are getting depleted day by day. The local groundwater table fluctuation eventually speeds up sea water intrusion. The present study investigates spatial and temporal groundwater level fluctuations to identify the area affected by sea water intrusion as well as for sustainable water resource management in coastal stretch of Sindhudurg district, Maharashtra, which is about 121 km. Using groundwater level data collected from Central Groundwater Board (CGWB), Sindhudurg for the period from 1991 to 2022, fluctuations of groundwater levels were examined. The collected levels were evaluated for monsoon, pre-monsoon &amp; post-monsoon seasons and all levels were interpolated over the study area using the Inverse Distance Weighted Method as a spatial interpolation technique in QGIS 3.22.1, in order to obtain the actual differences in groundwater levels. According to the results, the groundwater level varies from 1.88m to 13.45 m below ground level (bgl) in the pre-monsoon season, from 0.1m to 7.05 m (bgl) in the post-monsoon season and from 1m to 10.6m (bgl) in the monsoon season. Using statistical tests like Mann-Kendall test &amp; Sen's slope estimator, the pattern of variation of groundwater levels was anticipated. According to our findings, over 80% of the basin has a falling water table during the monsoon season. It was observed that the groundwater level in the pre-monsoon season was more as compared to those in the monsoon and the post-monsoon seasons in the entire study area. The overall outcome of the study demonstrates that the groundwater level fluctuations were non-uniform in the study area, which may cause an increase in the sea water intrusion, leading to a decrease in crop production. Keywords: GIS, Groundwater level, Sea water intrusion, Mann Kendall test, Spatial interpolation, Sen’s slope estimator.

Impact of Hydrogeological Characteristics on the Development of Underground Space Resources in Valley Cities

The development of urban underground space is a crucial aspect of current urban development. However, various factors pose challenges during the development process. In particular, in the valley cities of western China, groundwater has a significant impact on the utilization of underground space. This study focuses on understanding the influence of hydrogeological characteristics on the development of underground space in Ledu District, Haidong. Firstly, the hydrogeological characteristics of the aquifer in the study area are analyzed to determine the recharge and discharge characteristics. The dynamic changes in the groundwater table over time are then examined. Subsequently, the hydrochemical characteristics of groundwater are investigated to expound on the influence mechanism of groundwater on the development of underground space resources in Ledu District. The study reveals that the hydrodynamic effect of groundwater in the study area is substantial and has a significant physical scouring effect on underground structures. The fluctuation of groundwater levels affects the stability of buildings by increasing groundwater buoyancy. Additionally, this fluctuation also leads to engineering and environmental problems such as differential settlement and land salinization. The chemical characteristics of groundwater primarily impact the stability of building foundations through the corrosive effects of groundwater composition. This research clarifies the influence mechanism of groundwater on the development of underground space in typical valley cities of western China. The findings have practical significance for the evaluation, planning, development, and utilization of urban underground space resources in Haidong.

Spatiotemporal Variation and Long-Range Correlation of Groundwater Levels in Odessa, Ukraine

Increasing groundwater levels (GWLs) may become one of the most serious issues for the city of Odessa, Ukraine. This study investigated the spatial distribution characteristics and multifractal scaling behaviour of the groundwater-level/-depth fluctuations for a Quaternary aquifer in the city of Odessa using a geostatistical approach and multifractal detrended fluctuation analysis (MF-DFA). These two methods were applied to monthly GWL fluctuation time series from 1970 to 2020 to monitor 72 hydrogeological wells situated in different parts of the city of Odessa. The spatial distribution of the GWLs revealed an overall trend of decline and recovery from 1970 to 2020 in the study area, except for most of the southern region, where a persistent recovery of the groundwater depth was observed. The MF-DFA results suggest that the dynamics of the GWL fluctuations have multifractal characteristics in the Odessa area. In addition, both long-range correlations and fat-tail probability distribution contribute to the multifractality. However, long-range correlations among the fluctuations made a major contribution to the observed multifractality of the GWL fluctuation time series. The generalised Hurst exponents show a wide range of change (0.20 &lt; h(q) &lt; 2.85), indicating the sensitivity of the GWL fluctuations to changes in small-scale factors and large-scale factors. Regarding the long-range correlations of the GWL depths, the Hurst exponents (q = 2) demonstrated the positive persistence of groundwater-depth recovery in the southern region and the persistence of groundwater-depth variation in the other regions of the study area. The dynamic changes in the GWL depths in the Odessa area may be influenced by both natural and anthropogenic factors.

Monitoring of groundwater level fluctuations at flooded area of lowland forests of the Sava River (Serbia)

Lowland pedunculate oak forests in the alluvium of the river Sava are of a great economic and ecological importance. Their growth and development features, and their survival, as well, mostly depend on soil moisture and available water. The paper deals with the influence of groundwater level on pedunculate oak forests condition in flooded area of Donji Srem. During four-year long research, the average (reference) groundwater level was determined, and extremes, the wettest and the driest year, were determined based on climate factors such as temperature and rainfalls. Deviation of the average from the reference groundwater level in the vegetation period during some years indicates possibility of risk zones presence. These zones are defined according to geostatistical analysis – ArcGIS, Kriging model and they are very significant for forestry practice, because they can predict some drought events and warn forestry experts to react in time in order to prevent big damages. Bearing in mind that investigated area is flooded, and that groundwater level is close to the soil surface, these anaerobic site conditions are obviously more suitable for another hygrophilous species than for pedunculate oak.

Detecting groundwater level changes related to the 2016 Kumamoto Earthquake

This study presented the first attempt to detect precursory changes in groundwater level before the 2016 Kumamoto Earthquake. This detection was achieved by accurately determining the relationship between long-term groundwater level fluctuation and crustal deformation over 16 years through analysis of groundwater level time-series data acquired at 17 sites within the study area. Here, we show that the observed groundwater levels were lower than the modelled levels in aquifers composed of porous strata (Togawa lava and part of the pre-Aso volcanic rocks), and that there were larger differences until 2014, which diminished until the occurrence of the Kumamoto Earthquake. The initial reduction in the modelled groundwater level and the latter recovery were most likely caused by crustal strain relaxation associated with the large 2011 earthquake off the Pacific coast of Tohoku (Mw 9.0) and the strain accumulation prior to the 2016 Kumamoto Earthquake.

PFAS release from the subsurface and capillary fringe during managed aquifer recharge

Managed aquifer recharge (MAR) is a sustainable way of harvesting groundwater in water-stressed urbanized areas, where reclaimed wastewater or stormwater is applied on a large basin to infiltrate water into the groundwater aquifer naturally. This process could rapidly fluctuate the water table and move the capillary fringe boundary, and the change in flow dynamic and associated geochemical changes could trigger the release of sequestered pollutants, including per- and polyfluoroalkyl substances (PFAS), also known as ‘forever chemicals’, from the subsurface and capillary fringe. Yet, the potential of PFAS release from the subsurface and capillary zone during recharge events when the water table rapidly fluctuates has not been evaluated. This study uses laboratory column experiments to simulate PFAS release from pre-contaminated subsurface and capillary fringe during groundwater table fluctuation. The results reveal that the groundwater level fluctuations during MAR increased the release of perfluorobutanesulfonic acid (PFBS) and perfluorooctanesulfonic acid (PFOS) from the capillary fringe, but the fraction released depended on PFAS type and their association with soil colloids. A higher proportion of PFOS in column effluent was found to be associated with particles, while a greater portion of released PFBS was in a free or dissolved state. The direction of water table fluctuation did not affect the release of PFAS in this study. A lack of change in the concentration of bromide, a conservative tracer, during flow interruption, indicates that diffusion of PFAS through reconnected pores during water table rise had an insignificant effect on PFAS release. Overall, this study provides insights into how PFAS can be released from the subsurface and capillary fringe during managed aquifer recharge when the groundwater level is expected to fluctuate quickly.

A novel committee-based framework for modeling groundwater level fluctuations: A combination of mathematical and machine learning models using the weighted multi-model ensemble mean algorithm

water level (GWL) fluctuations simulation can be divided into three general categories: Analytical solution, conceptual or physical-based models, and data-based or data-driven models. The goal of this research is to develop the MODFLOW conceptual model and the GMDH data-driven model as single models (SMs) for GWL modeling, and then to combine these models to develop a novel mathematical-machine learning model based on a weighted multi-model ensemble mean approach (MMWEM) for a more accurate GWL simulation. For this purpose, the Miqan aquifer, located in northeastern Iran, was selected as a case study. Hydrological and hydrogeological data of the Miqan wetland for ten years (September 2013 to August 2023) with a monthly time step were used to compile models. First, the MODFLOW model was developed for one month (steady-state), then calibrated for nine years (unsteady-state), and finally tested for one year. In the second step, the GMDH model was developed. The grasshopper optimization algorithm (GOA) and the singular value decomposition (SVD) algorithms were used to determine the Ivakhnenko polynomial coefficients of the GMDH (SVD-GMDH and GOA-GMDH). 80% of the data was used to develop SVD-GMDH and GOA-GMDH, and other data was used to test models. Consequently, the sequential forward floating selection (SFFS) method was used to select the best input variables for the SVD-GMDH and GOA-GMDH. The coefficient of determination (R2), root mean squared error (RMSE), and Nash-Sutcliffe Efficiency coefficient (NSE) were used to evaluate the performance of the models. Based on the results of this study, using each of the SMs, GWL can be simulated with acceptable accuracy. However, a closer examination of the results of SMs showed that the MODFLOW has the worst performance among SMs, while the GOA-GMDH has the best function for simulating GWL. In the third step, SMs was used to develop the MMWEM. The GWL of 13 of the 34 piezometers studied in this study was better simulated with the MODFLOW and GOA-GMDH than the SVD-GMDH. Therefore, these models were used to develop the MMWEM for these piezometers. For GWL simulation in 21 piezometers, the SVD-GMDH and GOA-GMDH perform better than the MODFLOW, so these models were used to develop the MMWEM in these piezometers. Finally, the results of the SMs Moreover, the MMWEM were compared with each other. Based on the results of this study, the MMWEM performs better than SMs. In many studies, only the performance of each numerical model and machine learning to simulate groundwater level (GWL) fluctuations is evaluated. Then, their results are compared with each other. However, according to this study, the combination of the results of these models is a big step in improving the accuracy of the groundwater level simulation process. In addition to simulating the groundwater level, this method can simulate other quantitative and qualitative parameters of water resources.

Groundwater Recharge from Below under Changing Hydro-Meteorological Conditions in a Forested and Grassland Site of the Great Hungarian Plain

The process of groundwater evapotranspiration and its subsequent recharge are fundamental aspects of the Earth’s natural water cycle and have significant implications for the preservation and functionality of various forested ecosystems. This study presents a case analysis examining the recent fluctuations in groundwater levels and their replenishment in two wells situated at a designated forested experimental area and a control site. The magnitude and temporal fluctuations of groundwater recharge were examined through the utilisation of a novel adaptation of the traditional White method, which was specifically tailored to the local context. We also tested the sensitivity of the White method as an indicator of the system’s behaviour because the signal has changed in relation to the access of the forests to groundwater under the conditions of regionally declining groundwater resources and a warming climate. The novelty of this approach is found in the examination of the temporal fluctuations in groundwater recharge, which are influenced by both a decrease in groundwater levels caused by forest evaporation in response to climate change and a regional reduction in groundwater supplies. As a result, the ongoing decrease in groundwater levels may have significant adverse effects on local forests.

Groundwater level forecasting using ensemble coactive neuro-fuzzy inference system

A modeling framework utilizing the coactive neuro-fuzzy inference system (CANFIS) has been developed for multi-lead time groundwater level (GWL) forecasting in four different wells located in Texas and Florida, USA. Various model input combinations, including GWL, precipitation, temperature, and surface water level variables, have been derived based on proposed correlation analysis using singular spectrum analysis (SSA) remainders. The models have been trained on data subsets of varying lengths to identify the optimal training data duration. Additionally, we have introduced the bagging ensemble learning method to enhance the performance of the CANFIS model. As part of a comprehensive model evaluation process, the best-performing CANFIS model for each forecasting scenario has undergone uncertainty analysis using bootstrap sampling. Our results reveal that the CANFIS model performs satisfactorily for daily forecasting but leaves room for improvement in monthly forecasting, particularly for two-month and three-month ahead forecasts. Moreover, we have identified several optimal input combinations, highlighting the significance of the temperature variable in monthly forecasting. Furthermore, our findings indicate that additional training data does not necessarily lead to improved performance. The ensemble CANFIS model has demonstrated significant performance enhancement, particularly for monthly forecasting. Finally, the CANFIS model uncertainty analysis has shown satisfactory results for daily forecasting scenarios, while monthly forecasting models exhibit higher uncertainties, particularly during periods with distinctly different GWL fluctuation patterns.

Groundwater level forecasting in a data-scarce region through remote sensing data downscaling, hydrological modeling, and machine learning: A case study from Morocco

Study regionThe upstream part of the Essaouira basin, a data-scare region in Morocco, Northwestern Africa. Study focusThe scarcity of hydro-climate data is a significant challenge found in several regions worldwide, where qualitative and quantitative water resource information remains limited. Estimating and predicting groundwater levels (GWL) in such areas is a significant challenge in producing knowledge for effective water resource management. To address this issue, the present study aimed to use the Soil and Water Assessment Tool (SWAT) model in conjunction with downscaled total water storage (TWS) data (9 km) obtained from Gravity Recovery And Climate Experiment (GRACE) and machine learning techniques, specifically random forest (RF) and support vector machine (SVM), to estimate and predict the variation in GWL. New hydrological insights for the regionThis study constitutes a first of its kind in the study area; the SWAT model was set up for 10 years, with a warm-up period from 2000 to 2001, calibration from 2002 to 2007, and validation from 2008 to 2010. The statistical indices (Coefficient of Determination (R²) ≥ 0.73, R² ≥ 0.78, Nash–Sutcliffe model efficiency coefficient (NSE) ≥ 0.67, NSE ≥ 0.80 respectively for calibration and validation) highlight a significant correlation, implying the model's capability to faithfully reproduce the streamflow. The downscaled TWS demonstrates an impressive ability to identify and monitor fluctuations in GWL. Using machine learning algorithms (RF and SVR), the prediction of GWL yielded satisfactory results, NSE = 0.78 and root mean square error (RMSE) = 0.33, NSE = 0.51 and RMSE = 0.49 for the RF and SVR, respectively. Despite some limitations, our approach provided promising results in GWL prediction, with the possibility of expanding to other data-scarce regions.

The Association between Meteorological Drought and the State of the Groundwater Level in Bursa, Turkey

This study addressed the intricate interplay between meteorological droughts and groundwater level fluctuations in the vicinity of Mount Uludag in Bursa, Turkey. To achieve this, an exhaustive analysis encompassing monthly precipitation records and groundwater level data sourced from three meteorological stations and eight groundwater observation points spanning the period from 2007 to 2018 was performed. Subsequently, this study employed the Standard Precipitation Index (SPI) and Standard Groundwater Level (SGL) metrics, meticulously calculating the temporal extents of drought events for each respective time series. Following this, a judicious application of both the Thiessen and Support Vector Machine (SVM) methodologies was undertaken to ascertain the optimal groundwater observation wells and their corresponding SGL durations, aligning them with SPI durations tied to the selected meteorological stations. The SVM technique, in particular, excelled in the identification of the most pertinent observation wells. Additionally, the Elman Neural Network (ENN) and its optimized version through the Firefly Algorithm (ENN-FA), demonstrated their prowess in accurately predicting SPI durations based on SGL durations. The results were favorable, as evidenced by the commendable performance metrics of the Normalized Root Mean Square Error (NRMSE), the Nash–Sutcliffe Efficiency (NSE), the product of the coefficient of determination and the slope of the regression line (bR2), and the Kling–Gupta Efficiency (KGE). Consequently, the favorable simulation results were construed as evidence supporting the presence of a discernible association between SGL and the duration of the SPI. As we substantiate the concordance between the temporal extent of meteorological droughts and the perturbations in groundwater levels, this unmistakably underscores the fact that the historical fluctuations in groundwater levels within the region were predominantly attributable to climatic influences, rather than being instigated by anthropogenic activities. Nevertheless, it is imperative to underscore that this revelation should not be misconstrued as an endorsement of future heedless exploitation of groundwater resources.

Modeling groundwater-level responses to multiple stresses using transfer-function models and wavelet analysis in a coastal aquifer system

In coastal aquifers, dynamic stresses such as climate forcings, groundwater withdrawals, and ocean tidal fluctuations cause nonlinear responses to groundwater levels. Such responses to the stresses impact groundwater resources and related flooding and infrastructure risks at multiple scales. We used time-series models such as transfer-function models and wavelet analysis to quantify the relative contribution of these stresses to groundwater-level fluctuation in wells from the unconfined and confined aquifers in an Atlantic coastal aquifer. Climate forcings, such as precipitation and temperature, explained most of the groundwater-level variation for wells in the unconfined aquifer, whereas groundwater withdrawals were the dominant driver of groundwater levels for wells in the confined aquifer. The impact of groundwater withdrawals also was detected in several wells in the unconfined aquifer. Although the influence of ocean tides on groundwater levels commonly is observed in coastal aquifers, we found that daily groundwater withdrawals can obscure the semi-diurnal coherence signal of the two series. The magnitude of groundwater-level fluctuation that could be explained solely by tides was minor compared to that explained by climate or withdrawal stresses. Transfer-function modeling showed seasonal withdrawals from wells in confined aquifers had a significant, yet heterogeneous influence on groundwater levels in coastal aquifers, which highlights climate and withdrawals as key compounding stresses in coastal hydrology. This study demonstrates the value of time-series approaches to advance characterization of groundwater systems in areas with limited hydrogeologic parameter information.

LNAPL migration processes based on time-lapse electrical resistivity tomography

Contamination from light non-aqueous phase liquids (LNAPLs) and their derivatives, arising from exploration, production, and transportation, has become a prevalent pollution source. This poses direct threats to human health. However, conventional investigative methods face limitations when applied to studying the extent and migration process of LNAPL contamination, as well as the redistribution of LNAPL during groundwater level fluctuations. Conventional methods lack the ability to rapidly, efficiently, and in real-time acquire information about contaminated areas. Therefore, this study utilizes time-lapse electrical resistivity tomography to investigate the migration mechanism of LNAPL under unsaturated conditions, constant groundwater levels, and groundwater level reductions. A relationship between resistivity and water and oil contents was established and used for inverse calculation of LNAPL content via resistivity inversion. Time-lapse electrical resistivity tomography revealed LNAPL migration in a “concave” shape across three conditions. Groundwater presence notably slowed migration, hindering downward movement and leading to a floating oil band. A robust mathematical model was established to derive the relationship between resistivity and water and oil contents. Finally, LNAPL distribution under unsaturated conditions was inversely obtained from resistivity data, showing highest content at the top leak point, obstructed area, and bottom of soil column. Consequently, time-lapse electrical resistivity tomography demonstrates a notable capacity to characterize the LNAPL migration process. This technique constitutes an effective geophysical method for monitoring and describing the characteristics of LNAPL migration. Its significance lies in enhancing our understanding of remediation for LNAPL-induced groundwater and land contamination.

Monitoring hydrological changes with satellite data: Spring thaw's effect on soil moisture and groundwater in seasonal Freezing-Thawing zones

Due to the dense vegetation coverage in mountainous areas and difficulties in field monitoring in mid-high-latitude freezing-thawing zones, satellite and remote sensing technologies can be used to monitor long-term global hydrological changes. This study uses multiple satellite data of snow water equivalent (SWE), soil moisture (SM), and groundwater storage anomaly (GWSA) to analyse their spatial–temporal changes in the seasonal freezing-thawing Tumen River Basin. Water transformation during the thawing period is determined through groundwater level fluctuations and correlation analysis methods; thus, the recharge effects of snowmelt and seasonally frozen soil thawing on soil moisture and groundwater are identified. The snow begins to melt in February, and snow water equivalent decreases gradually from upstream to downstream after reaching the peak. Furthermore, seasonally frozen soil melts gradually from March. Soil moisture downstream is higher than that in the upper and middle reaches. Additionally, the GWSA increases gradually in the thawing period, which is high in mountainous areas and low in plains. The Spearman correlation coefficient among ΔSWE, ΔSM, and ΔGWSA is more significant than 0.5 and dependent on altitude. With snow and frozen soil melting, vertical infiltration and meltwater percolation increase soil moisture and GWSA. This phenomenon is predominant at low altitudes (<600 m) and pore phreatic groundwater in the middle and lower reaches. However, snowmelt is the dominant factor in groundwater recharge at high-altitude upper reaches. For seasonal freezing-thawing zones, a close water transformation was observed between snow melting, soil water thawing, and groundwater in spring. Thus, the environmental and ecological impact of spring snowmelt and floods in freezing-thawing zones can be monitored using multi-source data.

Groundwater-level fluctuation effects on petroleum hydrocarbons in vadose zones and their potential risks: Laboratory studies

Despite the role of the vadose zone protecting groundwater from contamination, the non-stationarity in this zone makes it difficult to predict the behavior of petroleum hydrocarbons (PH) therein. In laboratory soil columns with sandy and sandy loam soils, we simulated a vadose zone subjected to repeated groundwater-level fluctuation (GLF) to evaluate the behavior of PH under hydrodynamic conditions. The GLF vertically redistributed the PH, the extent of which was pronounced in the sandy soil with a high initial concentration due to the enhanced transport of the immiscible PH through the larger pores. The frequency of GLF did not show a substantial effect on the extent of PH redistribution but largely affected their attenuation. The greater GLF hindered PH volatilization by maintaining a high degree of water saturation, while the subsequent development of a local anaerobic regime inhibited biodegradation, which was more apparent in the sandy loam. Finally, a specific potential risk index was introduced to quantitatively compare the potential risk of PH contamination in different vadose zones exposed to GLF. Overall, the sandy soil contaminated with the higher total PH (TPH) concentration showed markedly higher potential risk indices (i.e., 18.4–29.0%), while the ones comprised of the sandy loam showed 0.6–4.9%, which increased under the greater number of GLF cycles.

Impacts of Tidal Oscillations on Coastal Groundwater System in Reclaimed Land

The impact of tidal oscillations on groundwater in coastal reclamation land demonstrates the complex hydrodynamic interaction between seawater and coastal hydrological aquifer systems. The tidal action not only affects the temporal variability of groundwater levels but also exerts a significant influence on the groundwater gradients of salinity within the subsurface aquifers. This study takes the Songmu Area as an example to investigate this ocean–groundwater interaction. Songmu Area is located on a peninsula with coastal land reclamation in Dalian, China. Field campaigns were conducted in this area to measure the tidal action and groundwater parameters in a coastal reclaimed area at artificial backfill layers with pressure and salinity sensors, where the tidal signal can influence groundwater levels and salinity up to a one-kilometer range of inland. Tidal changes in the surface of the sea can be broken down into a number of simple, regular harmonic vibrations, each of which is called a tidal split. The tide and groundwater data were extracted using an enhanced harmonic analysis method. The fluctuations of groundwater levels and salinity were decomposed in response to the periodic tidal oscillation. Various constituents of tide attenuation in the coastal groundwater system were investigated. Our research shows that there is an exponential reduction in the fluctuating amplitude of groundwater levels and the groundwater salinity as distance further inland from the coast. The constituents of tide M2 (the period is 12.42 h of semi-diurnal tides, S2 (the period is 12.00 h of semi-diurnal tides), K1 (the period is 23.93 h of full-diurnal tides), and O1 (the period is 25.82 h of full-diurnal tides) behave differently for the tidal wave propagation and salinity variability in the coastal aquifer of reclaimed land. Among those constituents, M2 and S2 exhibit a higher degree of attenuation compared with K1 and O1. Understanding the relationship between groundwater levels and tidal fluctuations in coastal backfill areas is crucial for effective groundwater management strategies and mitigating the adverse impacts of seawater intrusion. This study can serve as a good understanding for assessing the impacts of various mitigation strategies.

Improving Forecasting Accuracy of Multi-Scale Groundwater Level Fluctuations Using a Heterogeneous Ensemble of Machine Learning Algorithms

Accurate groundwater level (GWL) forecasts are crucial for the efficient utilization, strategic long-term planning, and sustainable management of finite groundwater resources. These resources have a substantial impact on decisions related to irrigation planning, crop selection, and water supply. This study evaluates data-driven models using different machine learning algorithms to forecast GWL fluctuations for one, two, and three weeks ahead in Bangladesh’s Godagari upazila. To address the accuracy limitations inherent in individual forecasting models, a Bayesian model averaging (BMA)-based heterogeneous ensemble of forecasting models was proposed. The dataset encompasses 1807 weekly GWL readings (February 1984 to September 2018) from four wells, divided into training (70%), validation (15%), and testing (15%) subsets. Both standalone models and ensembles employed a Minimum Redundancy Maximum Relevance (MRMR) algorithm to select the most influential lag times among candidate GWL lags up to 15 weeks. Statistical metrics and visual aids were used to evaluate the standalone and ensemble GWL forecasts. The results consistently favor the heterogeneous BMA ensemble, excelling over standalone models for multi-step ahead forecasts across time horizons. For instance, at GT8134017, the BMA approach yielded values like R (0.93), NRMSE (0.09), MAE (0.50 m), IOA (0.96), NS (0.87), and a-20 index (0.94) for one-week-ahead forecasts. Despite a slight decline in performance with an increasing forecast horizon, evaluation indices confirmed the superior BMA ensemble performance. This ensemble also outperformed standalone models for other observation wells. Thus, the BMA-based heterogeneous ensemble emerges as a promising strategy to bolster multi-step ahead GWL forecasts within this area and beyond.

A Retrospective Analysis of Long-Term Groundwater Level Fluctuations in the Dokuchaev Pit in the Kamennaya Steppe Nature Reserve

A Retrospective Analysis of Long-Term Groundwater Level Fluctuations in the Dokuchaev Pit in the Kamennaya Steppe Nature Reserve