Groundwater Level Fluctuations Research Articles

Exploring AI approaches for predicting groundwater levels in coastal agro-climatic zones: a case study in Cuttack District, Odisha

Groundwater level (GWL) prediction across various time scales is essential for efficient management and governance of water resources especially in regions characterized by arid and semi-arid climates, and it holds great significance. Within certain coastal regions, agro-climatic zones give rise to challenges like water scarcity in summer and waterlogging during the rainy season, resulting in reduced GWL during scarcity periods and saltwater intrusion that contaminates groundwater. This study emphasizes on application of diverse AI methodologies, encompassing Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Wavelet Transform-based ANN (W-ANN), ANFIS (W-ANFIS), SVR (W-SVR), and LSTM (W-LSTM) models for quantitative assessment of groundwater in Odisha's Cuttack District, aiming to comprehend GWL fluctuations across the region. The investigation leverages historical groundwater data from monitoring wells, incorporating monthly datasets of rainfall, temperature, relative humidity, and GWLs. Through comparative assessment using statistical methods namely Pearson’s R (R), co-efficient of determination (R2), Root Mean Squared Error (RMSE), and Sum of Squared Error (SSE), the most precise and robust AI approach for groundwater estimation in the area is identified. The W-LSTM (R2-0.78196, RMSE- 0.09254, R-0.88428 and SSE-2.66357) and W-ANFIS (R2-0.74068, RMSE-0.08229, R-0.86063 and SSE-2.10596) hybrid algorithms consistently achieved the most accurate predictions for GWLs followed by W-SVR, W-ANN hybrid models and LSTM and ANN for all stations. Overall, this study demonstrated promising outcomes, offering a dependable foundation for water resources planners to guide future investigations into groundwater resources.

The general patterns of water flow in loess slope system and implications for slope geological security

Loess regions face significant challenges in quantifying hydrological processes and assessing geological environmental risks due to the prevalent development of preferential pathways and the limitations of existing monitoring technologies. To advance this knowledge, this study presents an improved electrical resistivity tomography (ERT) device, specifically designed for loess moisture observations. By refining the testing principle, power supply mode, and data collection method within the existing ERT framework, the new device offers unmanned operation, automatic data acquisition, remote transmission, and cost efficiency. It effectively tracks water movement and groundwater level fluctuations across various hydrological conditions, supporting long-term online monitoring of hydrological processes of loess slopes. Through the analysis of monitoring data and classification of 12 observed preferential flow types, water movement in loess systems can be generalized into four general patterns: uniform infiltration, preferential infiltration, inflowing diffusion, and lateral flow. This generalized scheme provides a simplified modeling approach for other researchers to quantify slope hydrodynamics and to assess geological safety risks involving preferential flow. Based on these insights and field investigations, a conceptual framework is proposed to elucidate the seepage-structure synergistic initiating mechanism of loess landslides. This framework suggests that water entry and movement patterns within the slope depend on the slope geological structure related to preferential pathways and the prevailing hydrological scenarios. Landslide occurs as the result of the progressive failure and reciprocal evolution between the slope hydrological environments and geological structure, which may also pose potential eco-hydrological risks. The outcome advances the development of slope hydrological monitoring technology and enhances the understanding of water movement laws and the associated geological environmental risks in loess slope systems, which is of vital importance to the early warning methods of loess landslides that account for preferential flow and for theoretical modeling of preferential flow in related disciplines.

Decoding uranium variation over the Indian peninsula through leveraging standardized precipitation evapotranspiration indices and groundwater level fluctuations

The present study aims to predict the concentration of uranium (U) in groundwater in India by utilizing water quantity indicators, such as Standardised Precipitation Index (SPI), Standardised Precipitation Evapotranspiration Index (SPEI), and Gravity Recovery and Climate Experiment (GRACE) based groundwater levels. The study adopts a multi-scale approach, ranging from state-level to agroclimatic zones. The findings indicate that the highest levels of U (101–500 μg.L−1), which surpass the World Health Organization (WHO) prescribed limit of 30 μg.L−1 for drinking water, are primarily concentrated in India's plateau and hills regions. The GRACE data map portrays a downward trend in groundwater levels throughout India, with the mid-Gangetic plains experiencing the most significant decline. The meteorological aspect of the study, as indicated by SPI and SPEI, reveals that the plateau and hills region is experiencing a decline in rainfall. The SPEI further underscores the grim picture of decreasing precipitation in northern India. Additionally, the study employs cluster analysis to cluster states according to the division of agro-climatic zones. Lastly, the study employs a random forest algorithm to assess the relative importance of each predictor and predict U concentration under the trinity of precipitation, extraction, and evaporation. The most significant contribution of this work is to identify the hotspots in India that require the most attention in terms of U toxicity owing to groundwater decline. Overall, this study highlights the need for immediate attention to mitigate the adverse impacts of U contamination and aims at sensitizing the stakeholders towards the compelling need to fulfil SDG-3 (health aspects due to U hazard) and SDG-6 (groundwater over-exploitation and deteriorating water quality).

Deformation Monitoring Based on SBAS-InSAR and Leveling Measurement: A Case Study of the Jing-Mi Diversion Canal in China.

The Jing-Mi Diversion Canal is a large-scale water diversion project in Beijing. Routine monitoring is crucial for the reliability and stability of urban water supply. Compared with traditional monitoring methods, interferometric synthetic aperture radar (InSAR) has the advantages of large scale and high accuracy. Based on the small baseline subset InSAR, 187 ascending and 102 descending SAR images obtained from Sentinel-1 were used to detect the deformation along the diversion canal from 2017 to 2023. The results show that there was a sinking trend along the diversion canal. The subsidence was serious in the first half of the canal, and continued to sink from 2019 to 2020. The subsidence was alleviated in 2023. Combined with leveling measurements, the InSAR deformation monitoring results of important pumping station buildings were verified. The measurement accuracy of InSAR can reach the millimeter level. We extracted the groundwater level time series and subsidence for risky canal segments. Through pixel-by-pixel comparison, it was found that fluctuations in groundwater level would have some impact on surface deformation. Severe local subsidence or uplift deformation occasionally occurred. To ensure the safety of water diversion, the monitoring and maintenance of relevant pump station buildings in risky areas should be increased in the future.

Influence of ecological water diversion on the spatiotemporal distribution of groundwater in the Minqin Basin

This study examines the dynamic changes in groundwater levels and their spatial distribution in the Minqin Basin after the diversion of water from the ecosystem. It selected monitoring wells in different regions of the basin and analyzed the annual and seasonal fluctuations in groundwater levels before and after the water diversion. According to the findings, there is a notable shift in the rate of change in groundwater levels of the irrigated areas of the Minqin Basin: from an average of 0.62 m per year between 2001 and 2007 to a mere 0.01 m per year between 2008 and 2022. Since 2010, the groundwater level in the Qingtu Lake has increased by 1.01 m. The decline of Minqin Basin’s groundwater levels slowed down, and some places even witnessed rises in the groundwater level. The water levels in the region are generally stable.

Regional activity of landslide movements affecting the bank of meandering river: Insights using tree-ring based methods

Landslides on the banks of meander bends of lower rivers pose a potential risk to the navigability of rivers, but also to their potentially dangerous damming. To properly understand the relationship of river action on the stability of adjacent landslides, it is necessary to analyse the historical evolution and spatial extent of landslide activity. In this study, the results of a regional reconstruction of landslide movements on the lower reaches of the Váh River (central Slovakia) are presented, which allows for a more comprehensive disclosure of the river-slide relationship compared to classical case studies. Using data from a 408 tree-ring series of 204 disturbed trees, 43 reactivations of five landslides were dated over the last ca. 45 years. Analysis of the relationship between the spatial extent of landslide activity and distance from the river revealed that in two cases there was a direct relationship where landslide activity gradually decreased with increasing distance from the river (these were the landslides closest to the river). Given the absence of erosional forms on the riverbank, it is likely that the landslides are not being destabilized by lateral river erosion and the relief of their faces, but rather by the loading of their bodies due to rising groundwater during floods. On the other hand, one landslide (furthest from the river) showed increasing landslide activity towards the main scarp. This pattern is consistent with the retrogressive landslide development generally expected for other landslides in the wider region. For the remaining two landslides, no significant relationship between spatial landslide activity and distance from the river was found, which, however, cannot rule it out, but rather suggests that the influences on the initiation of landslide movements in this case are very complex and probably combine both the influence of groundwater level fluctuations and the occurrence of critical rainfall events.

Revealing mechanisms regulating diurnal groundwater level fluctuation in a temperate fen peatland: A spatiotemporal exploration

Diurnal groundwater level fluctuations (DGLF), closely tied to the metabolic rhythm of wetland vegetation, offer insights into direct groundwater consumption. This study is centered on exploring the spatial and temporal patterns of DGLF within the riparian aquifer of the Upper Biebrza region, motivated by the need to understand those sub-daily ecohydrological dynamics at play in this ecosystem, on a process-oriented scale. Through integrated spatiotemporal multivariate analysis, we aim to identify the key potential factors driving variations in these fluctuations across the landscape gradient, as well as, at different time scales. The study employed a comprehensive approach to gather data on groundwater heads and direct groundwater fluxes, utilizing high-temporal-resolution wells within a monitoring network. Meteorological variables from a dedicated station and high-resolution remote sensing maps were also integrated into the dataset. Analysis revealed distinct seasonal patterns and correlations in diurnal groundwater fluctuations, closely correlated with air temperature, solar radiation and subsequently vegetation phenology. Soil moisture content and summer rainfall events also influenced the intensity of these diurnal fluctuations. Dry periods intensified fluctuations, indicating an elevated reliance on groundwater by phreatophytes, while fluctuations decreased after rainfall events, signaling a shift in vegetation’s water source preference to soil moisture. Based on integrated data interpretation, a couple of potential mechanisms, reasonably forming the spatial variation pattern of DGLF, were formulated. Notably, the influence of local hydraulic gradients at transitional forest landscape edges, where higher amplitude fluctuations occurred, compared to lower fluctuations at midpoints. The proximity to the peat-mineral interface influences diurnal fluctuations, with wells closer to the interface showing sustained high-amplitude fluctuations driven by higher rates of recharge. Additionally, the influence of river proximity was explored, revealing dampened fluctuations in wells closer to the river due to rapidly changing hydraulic gradients. Further systematic experimentation, including numerical and data-driven modeling, is needed to validate these hypotheses. The study findings provide perspectives into (DGLF) patterns and distributions, enabling more accurate groundwater evapotranspiration (ETg) estimation and opening new avenues for considering ecohydrological feedback regarding carbon–water interaction, in relation to the daily-scale water table position.

Spatio-temporal trends in long-term seasonal groundwater level of South-western Punjab using non-parametric statistical tests.

To manage groundwater resources and develop an action plan, it is crucial to understand the long-term behavior of groundwater level (GWL) fluctuations. In this study, Geographic Information System (GIS) and non-parametric statistical tests were applied for detecting long-term (1973 to 2020) spatio-temporal variations and trends in GWL from 137 observation wells evenly distributed across the south-western part of Punjab. This region has experienced significant changes in GWL over the decades. The non-parametric statistical tests included Mann-Kendall (MK), Sens's SlopeEstimator (SSE), and Innovative Trend Analysis (ITA). The study observed significant trends in GWL fluctuations before and after monsoon. The MK and SSE tests showed a statistically increasing trend in observation wells with about 65.7% and 67.2% increase before and after monsoon, respectively. The innovative trend analysis (ITA) also revealed a statistically increasing trend in observation wells with an increase of about 63.5% and 65.7% pre and post-monsoon season, respectively. The results indicate lowering of GWL in the northern districts of southwestern Punjab, while the southern districts experience rising GWLs. This discrepancy can be attributed to diverse agricultural activities and reduced over-exploitation of groundwater in the southern district due to soil salinity and the presence of brackish groundwater. These findings provide valuable insights into the dynamics of GWL in the studied region, highlighting notable trends associated with seasonal variations.

Deep learning framework with Bayesian data imputation for modelling and forecasting groundwater levels

Although traditional physical models have been used to analyse groundwater systems, the emergence of novel machine learning models can improve the accuracy of the predictions. Deep learning has been prominent in environmental and climate change problems. In this paper, we present a framework for utilising deep learning models to predict groundwater levels based on nearby streamflow and rainfall data. We address the missing data problem using a Bayesian linear regression model within the deep learning framework. Our deep learning framework utilises models such as long-short term memory (LSTM) networks and convolutional neural networks (CNN) for multi-step ahead time series prediction. We examine the fluctuations in groundwater levels at various boreholes located near Middle Creek in New South Wales, Australia. We use the National Collaborative Research Infrastructure Strategy (NCRIS) groundwater database and utilise Bayesian linear regression to impute missing data. We investigate the accuracy of the selected models for individual and regional basins and univariate and multivariate strategies. Our results show that the LSTM-based regional model with multivariate strategy using rainfall data provided the best accuracy.

Active anaerobic methane oxidation in the groundwater table fluctuation zone of rice paddies

Rice paddies are globally important sources of methane emissions and also active regions for methane consumption. However, the impact of fluctuating groundwater levels on methane cycling has received limited attention. In this study, we delved into the activity and microbial mechanisms underlying anaerobic oxidation of methane (AOM) in paddy fields. A comprehensive approach was employed, including 13C stable isotope assays, inhibition experiments, real-time quantitative reverse transcription PCR, metagenomic sequencing, and binning technology. Geochemical profiles revealed the abundant coexistence of both methane and electron acceptors in the groundwater table fluctuation (GTF) zone, at a depth of 40–60 cm. Notably, the GTF zone exhibited the highest rate of AOM, potentially linked to the reduction of iron oxides and nitrate. Within this zone, Candidatus Methanoperedens (belonging to the ANME-2d group) dominated the Archaea population, accounting for a remarkable 85.4 %. Furthermore, our results from inhibition experiments, RT-qPCR, and metagenome-assembled genome (MAG) analysis highlighted the active role of Ca. Methanoperedens GTF50 in the GTF zone. This microorganism could independently mediate AOM process through the intriguing “reverse methanogenesis” pathway. Considering the similarity in geochemical conditions across different paddy fields, it is likely that Ca. Methanoperedens-mediated AOM is prevalent in the GTF zones.

Viscoelastic–plastic rheological model and its application to tunnels

Tunnels exhibit obvious continuous deformation during excavation and operation. This behaviour is closely associated with the time-dependent behaviour of rocks, which is induced by groundwater-level fluctuation and prolonged periodic rainfall infiltration. This paper proposes a rheological model consisting of a Hooke elastomer, Kelvin body and novel plastic element in series (called the HKP model) to describe the creep response of rocks considering the characteristics of dry–wet cycles. First, dry–wet cycle creep tests were carried out to investigate the time-dependent behaviour: that is, the creep behaviour of sandstone. Then the creep equation of the viscoelastic–plastic model was derived, and the damage coefficients under the effect of dry–wet cycles and time were obtained. Finally, the HKP model was established to investigate continuous deformation during tunnel excavation. The results reveal that dry–wet cycles have obvious effects on the physical properties and creep behaviour of sandstone. The creep behaviour of sandstone undergoes three stages – namely, the decaying, steady and accelerated stages – that can be reasonably described by the proposed HKP model. In practice, the proposed model can accurately predict the creep behaviour of rocks in tunnels owing to excavation. Thus, the HKP model can help in establishing tunnel maintenance strategies to ensure long-term safety.

Evaluating tidal impact on ground source heat exchanger performance under fluctuating groundwater levels

Coastal regions undergo dynamic changes due to tidal forces, posing distinct challenges for groundwater management and utilization of ground source heat pump (GSHP) systems. This study examines the impact of tidal fluctuations on groundwater levels and the performance of ground heat exchangers (GHEs), specifically focusing on the suitability of horizontal directional drilling (HDD) technology in high-tidal zones. Itoman City in Okinawa Prefecture, Southern Japan, serves as an ideal location for this study due to its complex coastal hydrogeological conditions and increasing interest in renewable energy solutions. Employing a comprehensive approach involving field experiments, FEFLOW software simulations, and model validation, we aim to elucidate the intricate relationship between tidal dynamics, groundwater behavior, and GHE performance. Furthermore, our investigation assesses the effectiveness of HDD-GHEs installed in highly tidal-affected zones to determine the optimal installation depth for this type of GHE in coastal regions. According to our research, the layer at a depth of 30 m is most affected by tidal influence and fluctuations in groundwater levels in coastal regions. This layer is the most suitable for installing horizontal HDD-GHEs, with an average heat exchange rate of about 89 W/m. This rate is approximately four and two times higher than the depths above and below this layer, respectively, due to groundwater saturation and flow in the formation. Furthermore, we noticed that the heat exchange rate increases from the top to the bottom of this layer, but the extent of this increase diminishes as the depth increases. This is because deeper depths are less affected by tidal fluctuations in groundwater levels.

Impacts of High-Concentration Turbid Water on the Groundwater Environment of the Tedori River Alluvial Fan in Japan

The occurrence of high-concentration turbid water due to a large landslide in the upper reaches of the Tedori River Basin in Japan in May 2015 led to a rapid decline in the groundwater levels within the alluvial fan. However, factors other than turbid water, such as changes in precipitation patterns, can have a significant impact on groundwater levels but have not been thoroughly investigated. By analyzing the relationship between river water and groundwater levels, we found that by 2018, conditions had returned to those observed prior to the turbidity events. Regarding seepage, we found that approximately 24% of the Tedori River’s discharge contributed to seepage before the turbidity event. In contrast, during the post-turbidity years, seepage decreased between 2015 and 2017 and returned to the pre-turbidity levels by 2018. Furthermore, by constructing a hydrological model and examining the contributions of turbidity and precipitation, we found that in 2015, turbidity contributed to 76% of the groundwater level changes, whereas precipitation accounted for 24%. In contrast, in 2016, turbidity contributed to 67%, while precipitation contributed to 33%. In essence, the first year was characterized by a significant contribution from turbidity, while precipitation also played a significant role in groundwater level fluctuations in the second year.

Groundwater Recharge Assessment in Central Benin: The Case of the Collines Region (West Africa)

The objective of this study was to assess groundwater recharge in the hard-rock central region of Benin so as to compare it with the water needs of the local population. To reach this objective, we applied the Water Table Fluctuation (WTF) method, which requires long-term monitoring of groundwater level fluctuations. Groundwater level time series were used in combination with other data (including time series of surface water discharge and rainfall) to estimate groundwater recharge but also to shed further light on the relationship between surface water and groundwater. The results demonstrated that the minimum inter-annual groundwater recharge amount is about 1.09 × 109 m3, which is enough to cover the basic water needs of the local population. It should be highlighted that in sub-regions where the density of the population is high, water shortage can still occur with the above estimated groundwater recharge amount. This study has also illustrated that when applying the WTF method, sites with a highly uncertain specific yield can be detected.

A system dynamics approach for sustainable groundwater management considering climate change, inter-organizational relationships, and domestic water saving

This article uses system dynamic modeling to examine the relationship between water allocation from the Ilam dam and groundwater fluctuation, as well as the effects of climate change, changes in inter-organizational relationships, and water demand management on water allocation and groundwater level fluctuations. The results showed that agricultural water allocation has a direct effect on groundwater level fluctuations. In the first scenario, which examined the effects of climate change, the comparison of three climate change scenarios (SSP126, SSP245, and SSP585) was carried out. The results illustrated that with temperature increasing and precipitation decreasing occurred in SSP585, the groundwater drawdown increases about 0.8 m more than SSP126. The second scenario examined the effects of changes in inter-organizational relationships. Based on the results of centrality measures in the social network analysis method, it was found that the organizational power of the stakeholder organization in the agriculture sector is greater than the ones in the environment sector. This causes the allocation to the agriculture sector to be prioritized over the environment. This scenario was analyzed by assuming a change in organizational power and giving priority to the water allocation of the environmental sector. The results showed that environmental needs (maximum 0.3 MCM) were almost entirely met in the second scenario, compared to the basic scenario. Also, the decrease in agricultural water allocation has led to an increase in groundwater usage by farmers, resulting in a 20 cm drop in groundwater levels. Therefore, regarding the sustainable management of water resources, directing attention towards alternative livelihoods for farmers can contribute to environmental water rights and the remediation of aquifers. The third scenario examined the effects of water demand management. The results showed that saving 20–25% of water in the domestic sector can reduce 40–50 cm maximum groundwater drawdown which align with sustainable groundwater management.

Groundwater Level Forecasting Using Machine Learning: A Case Study of the Baekje Weir in Four Major Rivers Project, South Korea

AbstractUnderstanding the impact of human‐made structures on groundwater levels is essential, with structures like dams or weirs presenting unique challenges and opportunities for study. The Baekje weir in South Korea presents an interesting case as the weir has undergone full gate opening, which is generally not the case for weirs and reservoirs, providing valuable opportunity for simulating weir removal conditions. The main objectives are investigation of groundwater level fluctuations under various weir operations, distances from the weir, and seasonal variations. The study utilizes observed data that simulates conditions with and without the weir, including scenarios of full gate opening. Multiple machine learning algorithms—Random Forest (RF), Artificial Neural Network, Support Vector Regression (SVR), Gradient Boosting, and Extreme Gradient Boosting (XGBoost)—are used to develop accurate groundwater level prediction models. The models' performance is assessed using coefficient of determination, Root mean square error (RMSE), Mean Absolute Error (MAE) indices, and visualized through Taylor diagrams. Results indicate that XGBoost outperforms other models in all three groups during both training and testing phases. Specifically, XGBoost surpasses RF by 2.09% (R2), 5.66% (RMSE), and 10.1% (MAE) in training, and outperforms SVR by 11.2% (R2), 42.0% (RMSE), and 129.2% (MAE) in testing. Additionally, the study generates groundwater level maps, providing a practical tool for managing groundwater systems and informing decision‐making in weir operations. This study not only sheds light on the dynamic relationship between weir operations and groundwater levels but also provides actionable insights for effective water management in similar hydrological settings.

Impacts of vegetation and tidal conditions on porewater and salt transport in coastal wetlands: Numerical simulations and field evidence

Coastal wetlands are crucial ecosystems with high fragility to environmental alterations. Despite early efforts, interactions of water and salt with vegetation in coastal wetlands have rarely been investigated especially with increasing anthropogenic disturbances. Here, field data and numerical simulations were utilized to understand the influences of vegetation, evapotranspiration (ETa), tide, and inundation events on porewater and salt transport in highly regulated tidal riparian wetlands. At the site, the presence of Phragmites australis significantly enhanced soil hydraulic conductivity (KS) and facilitated hydraulic connections between groundwater and surface soil, resulting in the rapid response of soil salinity to groundwater level fluctuations. Simulation results further demonstrated the combined effects of tides and ETa on the salinity distributions, revealing the formation of a high salinity zone in the middle region due to the inflow of water from the tides and ETa-driven upward movements of water and salt; however, as salinity levels increased within the high salinity zone, the growth of Phragmites australis was constrained, leading to reduced transpiration (Ta). Tidal parameters such as amplitudes and mean water levels could affect salt accumulation processes in soils and further the high-salinity zone. Finally, inundation events from extreme upstream discharge had a flushing and diluting effect on accumulated salts, alleviating salinity stress and promoting vegetation Ta and growth; however, further increases in ETa led to salt re-accumulation, potentially reinstating the salinity distribution pattern after inundation. These findings enhance our understanding of ecohydrological processes in coastal wetlands and have important implications for wetland management and restoration.

Organic soil greenhouse gas flux rates in hemiboreal old-growth Scots pine forests at different groundwater levels

Tree biomass and soils (especially organic soils) are significant carbon pools in forest ecosystems, therefore forest management practices, in order to ensure carbon storage in these pools and to mitigate climate change, are essential in reaching climate neutrality goals set by the European Union. Overall studies have focused on diverse aspects of forest carbon storage and greenhouse gas (GHG) fluxes from mineral soils, and recently also from organic soils. However, the information about old-growth forests and the long-term effects of drainage on GHG fluxes of organic soils is missing. Additionally, a large proportion of Scots pine (Pinus sylvestris L.) forests on organic soils in the hemiboreal region are drained to regulate groundwater level and to improve above-ground carbon storage. The study aims to assess the intra-annual dynamics of soil carbon dioxide (CO2) and methane (CH4) fluxes in hemiboreal old-growth Scots pine stands on organic soils with diverse groundwater levels. Six old-growth stands (130–180 years old) were evaluated. In old-growth forests, the main source of soil CO2 emissions is ground vegetation and tree roots (autotrophic respiration), while heterotrophic respiration contributes to almost half (41%) of the total forest floor ecosystem (soil) respiration. The total forest floor respiration and soil heterotrophic respiration are mainly affected by soil temperature, with minor but statistically significant contribution of groundwater level (model R2 = 0.78 and R2 = 0.56, respectively). The CO2 fluxes have a significant, yet weak positive relationship with groundwater level (RtCO2 R2 = 0.06 RhCO2 R2 = 0.08). In contrast, total soil CH4 uptake or release depends primarily on groundwater level fluctuations, with a minor but significant contribution of soil temperature (model R2 = 0.67). CH4 flux has high variability between stands.

Hydrological and biogeochemical processes controlling riparian groundwater quantity and quality during riverbank filtration

Groundwater exploitation in a riparian zone causes water infiltration from the river into the aquifer. Owing to adsorption and redox reactions along the flow path, the quality of water flowing from the river to groundwater wells is variably altered. The riverbed composition often involves spatiotemporal differences due to frequent changes in hydrological conditions. These changes create uncertainties in the transport and removal of solutes in the river water. In this study, the hydrodynamic field associated with riparian groundwater, changes in the structure of riverbed sediments caused by erosion and deposition, fluctuations in surface water and groundwater levels, and the removal efficiency of pollutants from groundwater through pumping were investigated. This involved in situ monitoring and sample testing of the composition of the river water, riverbed sediments, riverbed pore water, and groundwater during dry and wet seasons. Implementation of field in situ column experiments and molecular biology evidences were conducive to identifying the main biogeochemical processes occurring in the riverbed. The findings indicated that riparian groundwater exploitation alters the natural groundwater flow field, while fine sand deposition and microbial adsorption can reduce river recharge to aquifers by diminishing riverbed hydraulic conductivity. Shallow sediments within 1 m depth mainly involve NO3− reduction and E. coli adsorption. Reductive dissolution of Mn dominates in the deeper sediments. Additionally, reductive dissolution of Fe and dissimilatory nitrate reduction to ammonium (DNRA) drive high Fe2+ and NH4+ concentrations in groundwater. The findings can improve the management of riparian groundwater and aid in the optimization of a plan for its exploitation.

Spatial analysis of dynamic groundwater potential maps of Nagpur district of Maharashtra using the AHP and RS-GIS tools

ABSTRACT Identification of groundwater potential (GWP) is essential for groundwater management. Recently some areas of Nagpur district have faced water scarcity with severe groundwater level fluctuation (GWLF). The study incorporates the dynamic behaviour of rainfall, land use/cover, fractional impervious surface (FIS), and GWLF from 2017 to 2022 along with topographical wetness index (TWI), topographical ruggedness index (TRI), lineament density, drainage density, slope, soil, and geomorphology. The study employs the geographical information system (GIS)-based multi-criteria decision-making approach, analytical hierarchy process, and remote sensing for spatiotemporal GWP mapping. The weighted overlay tool of ArcGIS 10.5 was used to derive final GWP maps. Critically, the northwestern part of the study area experienced major shifts in GWP, 448 km2 area has increased under the poor GWP category representing a decline in recharge probability. The safe GWP category diminished by nearly 531 km2 which exacerbates the problem whereas, high GWP showed very less changes. The most sensitive parameters are identified using an area-sensitivity approach, which reveals that TRI, slope, soil, geomorphology, rainfall, and TWI cause 35, 29, 20, 19, 11, and 11% area changes, respectively, while maximizing their weights. The validation of GWP maps shows good agreement with pre- and post-monsoon well data. The methodology and results may serve for GWP appraisal of similar regions.