Groundwater Level Changes Research Articles

Spatial and temporal forecasting of groundwater anomalies in complex aquifer undergoing climate and land use change

Monitoring groundwater (GW) level variations, or anomalies in multiple wells, over long periods of time is essential to understanding changes in regional groundwater resource availability. However, it is challenging to predict these GW anomalies over the long term in agricultural areas due to complicated boundary conditions, heterogeneous hydrogeological characteristics, and groundwater extraction, as well as nonlinear interactions among these factors. To overcome this challenge, we developed an advanced modeling framework based on a recurrent neural network of long short-term memory (LSTM) as an alternative to complex and computationally expensive physical models. GW anomalies were forecast two months in advance (t + 2) based on the evaluation of drivers that influence GW dynamics in densely irrigated agricultural regions. An application and evaluation of this new approach was conducted in the Wisconsin Central Sands (WCS) region in the U.S., one of the most productive agricultural regions. The modeling framework was developed for the period 1958–2020 by utilizing easily accessible dynamic and static variables to represent hydrometeorological and geological characteristics. GW anomaly observations were acquired from 26 piezometers (wells) installed in the sandy aquifer in WCS over 10–60 years. The subset of GW observations from ∼ 10–60 years, not used in model training, can forecast GW anomalies two months out with a coefficient of determination R2 ∼ 0.8. Additionally, MAE was less than 0.34 m/month across the study region for both temporal and spatial modeling frameworks. Groundwater anomalies showed high spatiotemporal variability, and their responses are influenced differently by boundary conditions, catchment geology, climate, and topography across locations. Sites with higher autocorrelation with previous two-months GW anomalies reduced bias by increasing R2. Land use change and irrigation pumping have interactive effects on GW anomalies forecasting. The novelty of this study is identifying the regional drivers of GW fluxes. This case-specific information and location-related simplification, modification, and assumption of LSTM is a unique contribution to the existing literature. Our framework can be used as an alternative method of simulating water availability and groundwater level changes in areas where subsurface properties are unknown or difficult to determine.

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.

Groundwater Storage Variations across Climate Zones from Southern Poland to Arctic Sweden: Comparing GRACE-GLDAS Models with Well Data

The aim of this paper is to assess the correlation of groundwater level changes (or groundwater level anomalies (GWLA)) obtained from direct measurements in wells with groundwater storage anomalies (GWSA) calculated using Gravity Recovery and Climate Experiment (GRACE) products and Global Land Data Assimilation Systems (GLDAS) models across different climate zones, from temperate Poland to Arctic Sweden. We recognize that such validation studies are needed to increase the understanding of the spatio-temporal limits of remote sensing model applicability, not least in data-scarce sub-Arctic and Arctic environments where processes are complex due to the impacts of snow and (perma) frost. Results for temperate climates in Poland and southern Sweden show that, whereas one of the models (JPL_NOAH_GWSA) failed due to water balance term overestimation, the other model (CSR_CLM_GWSA) produced excellent results of monthly groundwater dynamics when compared with the observations in 387 groundwater wells in the region during 2003–2022 (cross-correlation coefficient of 0.8). However, for the sub-Arctic and Arctic northern Sweden, the model suitable for other regions failed to reproduce typical northern groundwater regimes (of the region’s 85 wells), where winter levels decrease due to the blocking effect of ground frost on groundwater recharge. This suggests, more generally, that conventional methods for deriving GWSA and its seasonality ceases to be reliable in the presence of considerably infiltration-blocking ground frost and permafrost (whereas snow storage modules perform well), which hence need further attention in future research. Regarding long-term groundwater level trends, remote sensing results for southern Sweden show increasing levels, in contrast with observed unchanged to decreasing (~10 mm/a) levels, which may not necessarily be due to errors in the remote sensing model but may rather emphasize impacts of anthropogenic pressures, which are higher near the observation wells that are often located in eskers used for water supply. For sub-Arctic and Arctic Sweden, the (relatively uncertain) trend of the remote sensing results nevertheless agrees reasonably well with the groundwater well observations that show increasing groundwater levels of up to ~14 mm/a, which, e.g., is consistent with reported trends of large Siberian river basins.

Exploring groundwater depletion and land subsidence dynamics in Taiwan’s Choushui river alluvial fan: insights from integrated GNSS and hydrogeological data analysis

The Choushui River Alluvial Fan (CRAF) is a major agricultural area in Taiwan with heavy groundwater usage. The extraction of groundwater here has caused land subsidence, which is now a significant global environmental issue. This study analyzes land subsidence in the CRAF by integrating hydrogeological data from 233 groundwater monitoring stations across four aquifers (CRAF Groundwater_NET) and data from 50 continuous GNSS stations (CRAF GNSS_NET). We developed an automated processing flow for GNSS static surveying within CRAF GNSS_NET, and further employed a time-series fitting method to examine the long-term trends and annual changes for both GNSS and groundwater level data. Our analysis of the time-series data from the past decade identifies areas of significant groundwater level depletion and subsidence hotspots. We explore the relationship between groundwater level variations and surface displacements within CRAF, utilizing GNSS data to analyze horizontal and vertical displacement trends, as well as annual changes. We integrate these findings with hydrogeological data to understand regional subsidence patterns. Our results indicate that CRAF is characterized by distinct hydrogeological features. The study finds that the amplitudes of annual changes in both groundwater level and vertical displacement generally increase from northeast to southwest in the analyzed region. One particular area shows significant groundwater level decline, with the most severe rate recorded at 0.54 m/year. Similarly, GNSS analysis indicates pronounced subsidence trends in the same area, with rates ranging from 4.2 to 5.2 cm/year. These findings highlight the critical need for the development of effective groundwater management strategies to ensure sustainable use of groundwater resources and to implement mitigation measures against land subsidence in similar multiple-aquifer settings.

Application of the Data-Driven Method and Hydrochemistry Analysis to Predict Groundwater Level Change Induced by the Mining Activities: A Case Study of Yili Coalfield in Xinjiang, Norwest China

As the medium of geological information, groundwater provides an indirect method to solve the secondary disasters of mining activities. Identifying the groundwater regime of overburden aquifers induced by the mining disturbance is significant in mining safety and geological environment protection. This study proposes the novel data-driven algorithm based on the combination of machine learning methods and hydrochemical analyses to predict anomalous changes in groundwater levels within the mine and its neighboring areas induced after mining activities accurately. The hydrochemistry analysis reveals that the dissolution of carbonate and evaporite and the cation exchange function are the main hydrochemical process for controlling the groundwater environment. The anomalous change in the hydrochemistry characteristic in different aquifers reveals that the hydraulic connection between different aquifers is enhanced by mining activities. The continuous wavelet coherence is used to reveal the nonlinear relationship between the groundwater level change and external influencing factors. Based on the above analysis, the groundwater level, precipitation, mine water inflow, and unit goal area could be considered as the input variables of the hydrological model. Two different data-driven algorithms, the Decision Tree and the Long Short-Term Memory (LSTM) neural network, are introduced to construct the hydrological prediction model. Four error metrics (MAPE, RMSE, NSE and R2) are applied for evaluating the performance of hydrological model. For the NSE value, the predictive accuracy of the hydrological model constructed using LSTM is 8% higher than that of Decision Tree algorithm. Accurately predicting the anomalous change in groundwater level caused by the mining activities could ensure the safety of coal mining and prevent the secondary disaster of mining activities.

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.

The Impact of Beaver Dams on the Dynamic of Groundwater Levels at Łąki Soleckie

Areas excluded from agricultural production are susceptible to the presence of beaver families. The most significant changes occur during the initial period, when agricultural utilization is abandoned and beavers establish their presence on the land. During this period, some parcels remain uncultivated, while agricultural activities persist in neighboring areas. This situation is accompanied by the destruction of beaver dams, especially during periods of abundant water resources, and notably during intensive fieldwork. The article presents field studies aimed at determining the extent to which constructed and operational beaver dams contribute to changes in groundwater levels in drained peatland areas. In order to protect and sustainably use peat soils, it is necessary to maintain their high moisture content by ensuring a high groundwater level elevation. This can be achieved through the use of existing damming structures in the area (levees, weirs). Beaver dams can also serve a similar function, blocking the outflow of water from peat lands by raising the water level and consequently retaining it naturally. The specific objective was to develop principles for verifying factors influencing the effects of beaver dam construction on groundwater levels in fields within their range of influence. The water table levels within the study area during rainless periods were influenced by water levels in ditches, dependent on beaver activity in the nearby river. Beaver activities, manifested through dam construction, were influenced by periodic water resources in the river, defined by the cumulative monthly precipitation. Factors affecting groundwater levels in rainless periods on the plots also included the distance from the river cross-section and the permeability of soils expressed by the filtration coefficient of the active layer. Beaver dams had the greatest impact on stabilizing the water table in the soil profile closest to the river.

Research on land subsidence-rebound affected by dualistic water cycle driven by climate change and human activities in Dezhou City, China

Over-exploitation of groundwater is the principal anthropogenic trigger for land subsidence in the North China Plain (NCP). With the promulgation of policies on groundwater extraction restrictions and bans, seasonal fluctuations and uplift have begun to occur in some typical subsidence areas in the NCP. In order to explore the land subsidence-rebound drivers, this study takes Dezhou City, a typical subsidence area in the NCP, as the study area and selects the main drivers in climate change: precipitation and evapotranspiration and the main driver in human activities: groundwater extraction, which together forms the background of the regional dualistic water cycle. In this background, this study selects the corresponding datasets and uses the improved Water Balance constraints Recurrent Neural Network (WB-RNN) method to establish the link between precipitation data, evapotranspiration data, groundwater extraction and groundwater level. Then uses the Gate Recurrent Unit-Convolutional Neural Network (GRU-CNN) method to establish the link between groundwater level and Interferometric Synthetic Aperture Radar (InSAR) deformation, and then obtains the link between the drivers of the dualistic water cycle and land subsidence. Comparative analysis and changing model inputs found that 1) Seasonal fluctuations in land subsidence-rebound in Dezhou City are mainly controlled by the groundwater levels in Aquifer Groups III and IV, with amplitudes up to 15 mm per year. 2) Groundwater level changes in different layers lag precipitation at different times. Excluding the influence of human extraction factors, the maximum ground surface deformation fluctuation caused by monthly 260 mm precipitation can be up to 21.44 mm. 3) The impact of short-term heavy precipitation caused by Typhoon Lekima and Doksuri in 2019 and 2023 can cause deformation fluctuations to a certain extent.

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.

Variation of groundwater level due to land use, precipitation, and earthquake in Yogyakarta City from 2005 to 2020

Yogyakarta City not only experiences rapid land use conversion and precipitation decline but also experiences frequent earthquake events. These factors can have a significant impact on groundwater variation dynamics in this city. Therefore, the aim of this research is to analyze the factors that can influence groundwater oscillation based on precipitation, land use, earthquake, and river discharge changes. The method used to analyze spatio-temporal groundwater level and precipitation are by applying space-time Bayesian statistics since Bayesian has lower root mean square error compared to the space-time variogram and inverse distance methods. Land use is classified with false color composite for Landsat ETM and infrared color composite for Landsat 8 since these color composites well classify built-up and non-built-up areas. River discharge is analyzed as a primary indication of earthquake event impact on groundwater level variation.There is no significant variation of groundwater level from 2005 to 2020. Groundwater fluctuation is classified as stable since the magnitude of change is approximately less than 1.5 m from dry to wet period. It is also probably because there is abundances of groundwater resources in Yogyakarta City that shallow aquifer have very high hydraulic conductivity. The conversion speed from non-built-up area into built-up area is 26% from 2005 to 2015, while 8% from 2015 to 2020. At least 78% of Yogyakarta City is built-up areas in 2020. Abrupt changes in groundwater level is due to: a) declining trends of precipitation on May 2009 to July 2016 with average accumulative monthly residual rainfall ranging from 500 mm month−1 to 1300 mm month−1, and b) high magnitude earthquake occurrences on May 2006, November 2010 and 2015 when groundwater patterns and levels including discharge rivers showed distinct behavior. This study shows that precipitation and earthquake are the major contribution to groundwater level variation compared to land use.

Hybrid machine learning models for groundwater level prediction in a snow‐dominated region: An evaluation of EEMD, VMD and EWT decomposition techniques

Hybrid machine learning models for groundwater level prediction in a <scp>snow‐dominated</scp> region: An evaluation of <scp>EEMD</scp>, <scp>VMD</scp> and <scp>EWT</scp> decomposition techniques

Key technologies for improving resilience of super-large diameter shield tunnel affected by large variable loads underneath the Yellow River

Environmental conditions may have a significant impact on the construction and operation of underground works, and sudden changes in environmental conditions may lead to underground engineering damage. Considering the Yellow River crossing tunnel on Huanggang Road in Jinan as an example, this paper analyzes the mechanical performance of the lining of the shield tunnel with an external diameter of 16.8 m, which faces the challenge of large variable loads in operation stage, such as deep riverbed erosion and deposition, large-scale heightening of river embankment, seasonal changes in groundwater level, and key technologies for improving the resilience of tunnel structure are proposed accordingly. The results can not only assist in the construction of the super-large-diameter shield tunnel project in Jinan City but also provide technical support for other shield tunnel projects crossing rivers, lakes, and seas.

Quantitative Assessment and Impact Analysis of Land Surface Deformation in Wuxi Based on PS-InSAR and GARCH Model

Land surface deformation, including subsidence and uplift, has significant impacts on human life and the natural environment. In recent years, the city of Wuxi, China has experienced large-scale surface deformation following the implementation of a groundwater abstraction ban policy in 2005. To accurately measure the regional impacts and understand the underlying mechanisms, we investigated the spatiotemporal characteristics of surface deformation in Wuxi from 2015 to 2023 using 100 Sentinel-1A SAR images and the Persistent Scatterer InSAR (PS-InSAR) technique. The results revealed that surface deformation in Wuxi exhibited significant spatial and temporal variations, with some areas experiencing alternating trends of subsidence and uplift rather than consistent unidirectional change. To uncover the factors influencing this volatility, we conducted a comprehensive analysis focusing on groundwater, precipitation, and soil geology. This study found strong correlations between the groundwater level changes and surface deformation, with the soft soil geology of the area, characterized by alternating layers of sand and clay, further increasing the surface volatility. Moreover, we innovatively applied the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, typically used in financial analyses, to analyze the subsidence displacement time series in Wuxi. Based on this model, we propose a new “Amplitude Factor” index to evaluate overall surface deformation volatility in the city. Our qualitative assessment of surface stability based on the Amplitude Factor was consistent with research findings, demonstrating the accuracy and effectiveness of the proposed model. These results provide valuable insights for urban planning, construction, and safety control, highlighting the importance of continuous monitoring and analysis of surface deformation volatility for the city’s future development and safety.

Groundwater Sustainability and Land Subsidence in California’s Central Valley

The Central Valley of California is one of the most prolific agricultural regions in the world. Agriculture is reliant on the conjunctive use of surface-water and groundwater. The lack of available surface-water and land-use changes have led to pumping-induced groundwater-level and storage declines, land subsidence, changes to streamflow and the environment, and the degradation of water quality. As a result, in part, the Sustainable Groundwater Management Act (SGMA) was developed. An examination of the components of SGMA and contextualizing regional model applications within the SGMA framework was undertaken to better understand and quantify many of the components of SGMA. Specifically, the U.S. Geological Survey (USGS) updated the Central Valley Hydrologic Model (CVHM) to assess hydrologic system responses to climatic variation, surface-water availability, land-use changes, and groundwater pumping. MODFLOW-OWHM has been enhanced to simulate the timing of land subsidence and attribute its inelastic and elastic portions. In addition to extending CVHM through 2019, the new version, CVHM2, includes several enhancements as follows: managed aquifer recharge (MAR), pumping with multi-aquifer wells, inflows from ungauged watersheds, and more detailed water-balance subregions, streamflow network, diversions, tile drains, land use, aquifer properties, and groundwater level and land subsidence observations. Combined with historical approximations, CVHM2 estimates approximately 158 km3 of storage loss in the Central Valley from pre-development to 2019. About 15% of the total storage loss is permanent loss of storage from subsidence that has caused damage to infrastructure. Climate extremes will likely complicate the efforts of water managers to store more water in the ground. CVHM2 can provide data in the form of aggregated input datasets, simulate climatic variations and changes, land-use changes or water management scenarios, and resulting changes in groundwater levels, storage, and land subsidence to assist decision-makers in the conjunctive management of water supplies.

Evaluating seawater intrusion forecast uncertainty under climate change in the Pajaro Valley, California

Climate change and climate variability impacts such as rising sea levels have the potential to exacerbate seawater intrusion and the strain on coastal freshwater resources in already stressed groundwater basins such as those in the Pajaro Valley groundwater basin, California. Regional hydrologic models are often coupled with climate projections to forecast future hydrologic conditions and inform adaptive resources management strategies. However, there is high uncertainty in the future projections of water resources due to uncertainties from downscaling global general circulation models (GCMs) to local scale climate change projections, future land use changes, and the inherent uncertainty of developed hydrologic models. Future climate projections and the magnitude of their influence on modeled hydrologic drivers are highly variable. Therefore, to develop a forecast model, an ensemble of different projections can be used to capture a wider range of basin responses and the associated uncertainties in the modeled forecasts. Understanding the reliability and uncertainty of forecasts is important for developing climate adaptation strategies such as developing protective thresholds, particularly at the basin scale where the impacts are felt, and adaptation is implemented. To demonstrate this, an uncertainty analysis of groundwater level and seawater intrusion forecasts for the Pajaro Valley groundwater basin was performed using an ensemble of three future climate projections with the Pajaro Valley Integrated Hydrologic Model (PVIHM) and the first-order second moment (FOSM) method. FOSM uncertainty analysis of hydrologic forecasts across a multi-GCM climate ensemble provides an upper and lower bound of potential impacts of climate change on sustainability targets related to mitigating seawater intrusion. The groundwater level forecasts’ narrow range of variability can help policymakers in adaptation planning by constraining possible outcomes to a focused range for risk-management decisions. However, less than one-third of groundwater level forecasts met the current protection thresholds to prevent chronic lowering of groundwater. Therefore, sustainability targets may need to be reassessed. Relative to groundwater level changes, the seawater intrusion forecasts had larger uncertainty due to the GCM climate projections and the simulated hydrologic response that were compounded by the propagation of scaling and bias from the GCMs and model simplifications in simulating the coastal boundary.

Prediction of groundwater level changes based on machine learning technique in highly groundwater irrigated alluvial aquifers of south-central Punjab, India

Groundwater serves as a vital resource for all living organisms. In regions extensively reliant on groundwater irrigation, hydro-climatic factors, groundwater extraction, and the flow of surface water exhibit an indirect interdependence. This study primarily aims to anticipate GWL in such highly irrigated zones using the Machine Learning (ML) approach. To achieve this, the widely employed Random Forest (RF), Bagging-Reduce Error Pruning Tree (Bagging-REPTree), and Bagging-Decision Stump Tree (Bagging-DSTree) models have been employed for the accurate forecasting of groundwater levels. The long-term pre-monsoon and post-monsoon (fourteen locations) data set of South-Central Punjab state has been applied for the model calibration/training and validation/testing. Seven statistical indices were used such as percent bias (PBIAS), root mean square error (RMSE), normalized root mean square error (nRMSE), RMSE-observation standard deviation ratio (RSR), mean absolute error (MAE), Nash Sutcliffe efficiency (NSE) and correlation coefficient (CC) for the model performance analysis. The results revealed that the RF model outperformed in pre-monsoon (testing phase) (RMSE = 0.682, NSE = 0.958) as well as the post-monsoon (testing phase) (RMSE = 0.150, NSE = 0.997) compared to the other two models in the station Ahmadapur and the similar trend is observed in all the stations. Overall, the RF model demonstrates superior performance in predicting groundwater levels during both pre-monsoon and post-monsoon seasons, particularly in highly groundwater-irrigated alluvial aquifers of the southern region.

Unraveling aquifer dynamics: Time series evaluation for informed groundwater management

Optimizing groundwater monitoring networks is essential for the sustainable management of this critical resource, which is vulnerable to both anthropogenic and natural changes. Focusing on the Hashtgerd aquifer in Iran, this research aims to enhance the efficiency of groundwater level monitoring systems by integrating statistical methods, artificial intelligence (AI), and hydrogeological studies. The study's core objective is to pinpoint and comprehend the factors that cause certain observation wells to become isolated from the main aquifer body, leading to parts of the aquifer being cut off from the central hydrogeological system. Employing clustering techniques on data from 29 observation wells, based on parameters like groundwater level (GWL), groundwater level change (GWLC), groundwater depth (GWD), air temperature (AT), distance (D), ground level (GL), and temperature (T) through Hierarchical Cluster Analysis (HCA), the research categorizes the groundwater information into four distinct groups. Significantly, Cluster 4 encompasses wells with similar characteristics. The analysis reveals that two wells, categorized in Clusters 1 and 2, exhibit unique behaviors, likely due to their connection to local karstic aquifers, as corroborated by geological evidence. Cluster 3's separation is determined through geophysical investigations, identifying confined conditions near specific wells, notably Wells 4 and 8. AI techniques further distinguish differences in meteorological influences and water discharges among the clusters, noting minimal weather impact on Clusters 1 and 2, considerable shallow groundwater influence on Cluster 3, and a pronounced effect of groundwater discharge on Cluster 4. This nuanced identification of aquifer segments disconnected from the main system underscores the need to refine monitoring networks for accurate assessment of groundwater resources. By leveraging a detailed analysis of aquifer complexities through state-of-the-art methodologies, the study guides towards informed, sustainable groundwater management strategies.

Using stable isotopes in deciphering climate changes from travertine deposits: the case of the Lapis Tiburtinus succession (Acque Albule Basin, Tivoli, Central Italy)

Quaternary stable isotope records of marine and lacustrine carbonate deposits as well as speleothems were extensively studied to reconstruct global and regional climatic evolution. This study demonstrates how stable isotope records of travertine provide fundamental information about climate and the consequences of its evolution on groundwater level fluctuations. The deposition of the Lapis Tiburtinus travertine succession occurred during the Late Pleistocene (150–30 ka), coeval with the last activity of the Colli Albani volcanic complex. Two boreholes (Sn1 and Sn2) were drilled into the Acque Albule Basin (23 km E of Rome), crossing the entire Lapis Tiburtinus succession. The Sn1 borehole in the central part of the basin crosscuts a travertine succession of 62.1 m in thickness, while the Sn2 borehole in the southern part of the basin is characterized by a travertine succession 36.3 m in thickness. Carbon and oxygen stable isotope ratios were analysed on 118 samples (59 samples both for Sn1 and Sn2 boreholes) representative of the entire Lapis Tiburtinus travertine succession crossed by the boreholes. Values, measured and correlated in the two drilled boreholes, permitted determination of the sensitivity of the travertine depositional system to glacial and interglacial cycles, unravelling the complex oxygen and carbon cycle dynamic recorded in such sedimentary succession. Moreover, the results obtained correlated with available pollen curves of the Mediterranean area (from the Castiglione crater, 25 km E of Rome). Regional and global oxygen isotope continental and marine curves, calibrated with the stratigraphy of the Acque Albule Basin, and available U/Th dating allow the identification of at least three phases of the last interglacial (Marine Isotope Stage 5-MIS5). The carbon isotope record, compared with CO2 flux reconstructed and associated with the volcanic activity of the Colli Albani volcanic complex, instead shows an influence from groundwater level changes. In particular, positive shifts that occurred during arid phases are associated with a lower groundwater level and increased CO2 degassing, inducing a major fractionation effect on carbon isotopes. Instead, the negative shifts occurring during more humid periods indicate the inhibition of CO2 degassing and increase in pressure, attesting to a rise in groundwater level. In this view, travertine deposits, frequently studied to define the tectonic setting and activity of the area where they develop, can thus also be used as a tool to understand climate changes and groundwater variations apparent in their stable oxygen and carbon isotope signature.

Groundwater sustainability in the face of urban expansion: A case study on Kolkata's ongoing challenge

This study investigates the impact of urbanization on groundwater sustainability in the megacity of Kolkata, India, amidst the global trend of increasing urban populations. Utilizing a site-specific approach and multidisciplinary framework, the research analyses three decades (1990–2020) of urban development, correlating the expansion of built-up areas with changes in groundwater levels. Remote sensing and Geographic Information System (GIS) techniques, specifically the Normalized Difference Built-up Index (NDBI), are employed to assess built-up area dynamics. Groundwater data from governmental sources, combined with spatial analysis using Geographically Weighted Regression (GWR), reveal a complex relationship. Descriptive analysis indicates a significant rise in average built-up areas over the decades, with a notable increase in 2020. Correlation coefficients shift from negative in 1990 to strongly positive in 2020, signifying a growing dependence of groundwater levels on built-up areas. Spatial autocorrelation showing a strong clustered pattern in case of groundwater scenario over the years. Geographical analysis highlights varying impacts across different wards, especially in the southern part of Kolkata. Time series analysis forecasts a continued increase in built-up areas, potentially leading to a decline in groundwater levels. The study emphasizes the urgent need for site-specific interventions to ensure the sustainability of groundwater resources in Kolkata, as urbanization continues unabated. The methodology and findings offer valuable insights for policymakers and city planners to address the challenges posed by urban development and safeguard groundwater resources in the pursuit of a sustainable future.

Characterizing soil hydrology in the Indo-Gangetic plain of Bihar, India: Methods and preliminary results

In the Eastern Gangetic Plain (EGP) soil hydrology is a major determinant of land use and also governs the ecosystem services derived from cropping systems, particularly greenhouse gas (GHG) emissions from rice fields. To characterize patterns of soil hydrology in these, daily field monitoring of water levels was conducted during the monsoon (kharif) season in a comparatively wet (2021) and dry (2022) year with flooding depth and drainage tracked with field water tubes across 47 (2021) and 183 (2022) locations. Fields were clustered into hydrologic response types (HRT) which can then be used for land surface modelling, land use recommendations, and to target agronomic interventions that contribute to sustainable development outcomes. Clusters based on two methods of summarizing a single information source were compared. The information source was a time-series of field water-level observations, and the two methods were (1) the original time-series and their first differences and (2) a set of derived hydrologic descriptors that are conceptually related to greenhouse gas (GHG) emissions. Clustering was (1) by k-means with an optimization of cluster numbers and (2) by hierarchical clustering with the same number of clusters as identified by k-means. Hydrologic behaviour shifted dramatically between growing seasons, and it was not possible to identify consistent HRT's across years. The clusters had only a weak relation with soil properties, almost no relation with farmer perception of relative landscape position, and no relation with rice establishment method. Clusters based on time-series were moderately well predicted in the dry year 2022 by optimized random forest models, with the most important predictors being the number of irrigations, seasonal precipitation, pre-monsoon groundwater levels, seasonal groundwater level change, and pH, this latter as a surrogate for landscape position and other soil properties. In the wet year 2021 clusters were (poorly) predicted by just seasonal precipitation and pre-monsoon groundwater levels. This shows the complex relation of soil hydrology with landscape position and land management, as well as synoptic climate. By contrast, clusters based on the descriptors were not well-matched with those from the time-series, and could not be well predicted by random forest models. This shows that different clustering criteria may result in different interpretations of the landscape hydrology and thus different heuristics for anticipating the hydrology of a given field under different management choices.