Groundwater Anomalies Research Articles

Hydrogeochemical and rare earth element properties of geothermal waters and the use of abandoned oil wells in Diyarbakır

Türkiye is one of the most tectonically active regions in the world. Diyarbakır, which was chosen as the study area, is located in the south-eastern Anatolian region of Türkiye and also in the south of the Bitlis-Zagros Suture Zone (BZSZ). Some of the geothermal resources in Diyarbakır are actively exploited. The temperature of Çermik geothermal waters was recorded at 40–51 °C by MTA (General Directorate of Mineral Research and Exploration) between 2010 and 2014. However, subsequent measurements conducted following the Kahramanmaraş earthquake (Mw: 7.7 on 6 February 2023) revealed that the surface temperature of geothermal fluids had increased to between 52 and 56 °C. Previous studies have indicated that there are also many abandoned oil wells in the region where the wellhead temperatures are around 107 °C. As a result of hydrogeochemical analyses, deep circulating geothermal waters such as Çermik have Na-HCO3 composition, while shallow circulating and mixed cold surface waters such as Çınar, Bismil and Sur have Na-Ca-Cl-HCO3 composition. According to the chalcedony geothermometer results, the reservoir temperatures in the study area vary between 90 °C and 173 °C. REY (Rare Earth Elements and Yttrium) results show that geothermal fluids in the study area have negative cerium (Ce), europium (Eu) and yttrium (Y) anomalies. The negative Ce anomaly indicates that the colder aquifers are close to oxygen-rich surface waters and thermal fluids interact with Karacadağ volcanic rocks. Eu anomalies in groundwater are controlled by preferential mobilisation of Eu2+ during water-rock interaction compared to Eu3+, and negative Eu anomalies indicate leakage of geothermal fluids from granites. The Y/Ho values of the thermal fluids in the study area are between 30 and 40. This value is due to the active contribution of fluorine during the mobilisation of REY as a fluoride or fluorocarbonate ligand complex, causing Y to behave as a heavy pseudolanthanide. δ18O and δ2H isotope values show that the geothermal waters in the study area are of meteoric origin. The obtained results reveal that Diyarbakir province has a geothermal potential and usage potential can be increased by using the abandoned oil wells.

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.

Co-occurring characteristics and mechanisms of geogenic ammonium and phosphorus in Quaternary alluvial-lacustrine aquifer systems

High loads of nitrogen (N) and phosphorus (P) in groundwater are a pressing global environmental concern. Geogenic ammonium (NH4+) and P anomalies in groundwater as unique forms of contamination, have been extensively reported. However, despite significant efforts on the enrichment of geogenic NH4+ or P, little is known about their co-occurring characteristics and mechanisms in groundwater. Therefore, focusing on the Dongting Plain (DTP) within the central Yangtze River basin, a typical alluvial-lacustrine plain, this study analyzed hydrogeochemistry, stable carbon isotopes, and dissolved organic matter (DOM) fluorescence. The results revealed a clear correlation between the groundwater DOM humification degree and the redox processes. In different redox processes, four distinct groundwater types emerged sequentially: low-NH4+ & low-P, low-NH4+ & high-P, high-NH4+ & high-P, and high-NH4+ & low-P. In organic matter (OM) degradation process, when dissolved oxygen and nitrate acted as electron acceptors, the low-NH4+ & low-P groundwater dominated. The low-NH4+ & high-P groundwater dominated when Mn(IV) and amorphous Fe(III) (oxyhydr)oxides served as the electron acceptors, where P enrichment was primarily attributed to the reductive dissolution of amorphous Fe(III) (oxyhydr)oxides. The high-NH4+ & high-P groundwater dominated when SO42-, crystalline Fe(III) (oxyhydr)oxides, and CO2 served as the electron acceptors. During this phase, OM mineralization resulted in the enrichment of both NH4+ and P. Furthermore, the reductive dissolution of crystalline Fe(III) (oxyhydr)oxides further increased P concentrations in groundwater. During the strong degradation of OM to the methanogenic stage, two processes occurred simultaneously: anaerobic oxidation of methane and reductive dissolution of Fe(III) (oxyhydr)oxides. These processes led to an increase in the concentration of HCO3– and Fe(II) in groundwater. During this phase, large amounts of HCO3– and Fe(II) combine to form siderite precipitates. These precipitates adsorb P from the groundwater, eventually leading to a decrease in P concentration and the formation of high-NH4+ & low-P groundwater. This study offers novel insights into the simultaneous cycling of N and P in Quaternary alluvial-lacustrine aquifer systems.

Incorporating Spatio-Temporal Changes of Well Irrigation into a Distributed Hydrologic Model to Improve Groundwater Anomaly Estimations for Basins with Expanding Agricultural Lands

This paper seeks to address the deficiency of utilizing satellite-based GRACE observations and model-based GLDAS water budget components in estimating the changes in the groundwater storage in Konya Endorheic Basin (KEB), a basin experiencing considerable land use land cover (LULC) change, primarily agricultural expansion. Cereal cultivation in the basin has a slight decreasing trend, however, the cultivation of crops with high water consumption, such as maize and sunflower, is increasing substantially. And total agricultural areas are increasing. GRACE-GLDAS approach does not accurately give the long-term groundwater decline in the basin, mainly because the land surface models employed in GLDAS cannot realistically simulate variations in water budget components as they do not consider the changes in LULC and do not possess an elaborated irrigation scheme. Here, we used a fully-distributed mesoscale hydrologic model, mHM, that can handle multiple LULC maps from different years. The model was modified to incorporate the spatio-temporal changes of agricultural fields in KEB and an explicit irrigation scheme since we hypothesized that the groundwater depletion is mainly caused by well irrigation. mHM was calibrated against streamflow observations for the period 2004–2019. The simulations show that the use of mHM with the incorporated features gives groundwater storage changes that are more consistent with the well-based observations than those obtained from the GRACE-GLDAS approach. On the other hand, the mHM simulation with a static LULC map, as in GLDAS models but with a better representation of irrigated fields, provides groundwater anomaly changes that are more consistent with the GRACE-GLDAS results, a further justification of insufficiency of the GLDAS-based approach in estimating groundwater variations for basins with considerable landscape change.

Characterization of groundwater storage changes in the Amazon River Basin based on downscaling of GRACE/GRACE-FO data with machine learning models

Groundwater storage changes in the Amazon River Basin (ARB) play an important role in the hydrological behavior of the region, with significant influence on climate variability and rainforest ecosystems. The GRACE and GRACE-FO satellite missions provide gravity anomalies from which it is possible to monitor changes in terrestrial water storage, albeit at low spatial resolution. This study downscaled GRACE and GRACE-FO data from machine learning models from 1° (110 km approx) to 0.25° (27.5 km approx). It estimated the spatiotemporal variability of terrestrial and groundwater storage anomalies between 2002 and 2021 for the Amazon River Basin. In parallel, the Random Forest and AdaBoost algorithms were compared and analyzed. The results reflected a good fit of the models with a very low error and a slight superiority in the predictions obtained by AdaBoost. On the predictions at 0.25°, spatial patterns associated with the strong influence on storage changes of some rivers and snow-capped mountains were identified, as well as an increase in the accuracy of the scaled data of the original ones. Positive long-term behavior was also obtained in terrestrial and groundwater storage of 14.26 ± 1.18 km3/yr and + 22.24 ± 1.18 km3/yr, respectively. Validation of the time series of groundwater anomalies to water levels in the monitoring wells obtained maximum correlation coefficients of 0.85 with confidence levels of 0.01. These results are promising for satellite information in water management, especially in regional monitoring of unconfined aquifers. The obtained data is stored in a dedicated repository (Satizábal-Alarcón et al., 2023).

Ecosystem groundwater use enhances carbon assimilation and tree growth in a semi-arid Oak Savanna

Ecosystem reliance on groundwater, defined here as water stored in the saturated zone deeper than one meter beneath the surface, has been documented in many semi-arid, arid, and seasonally-dry regions around the world. In California, groundwater sustains ecosystems and mitigates mortality during drought. However, the effect of groundwater on carbon cycling still remains largely unresolved. Here we use 20 years of eddy covariance, groundwater, and tree growth measurements to isolate the impact of groundwater on carbon cycling in a semi-arid Mediterranean system in California during the summer dry season. We show that daily ecosystem groundwater use increases under positive groundwater anomalies and is associated with increased carbon assimilation and evapotranspiration rates. Negative groundwater anomalies result in significantly reduced ecosystem groundwater uptake, gross primary productivity, and evapotranspiration, with a simultaneous increase in water use efficiency. Three machine learning algorithms better predict gross primary productivity and tree growth anomalies when trained using groundwater data. These models suggest that groundwater has a unique effect on carbon assimilation and allocation to woody growth. After controlling for the effect of soil moisture, which is often decoupled from groundwater dynamics at the site, wet groundwater anomalies increase canopy carbon assimilation by 179.4 ± 25.7 g C m−2 (17 % of annual gross primary productivity) over the course of the summer season relative to dry groundwater anomalies. Similarly, annual tree growth increases by 0.175 ± 0.035 mm (17.7 % of annual growth) between dry and wet groundwater anomalies, independent of soil moisture dynamics. Our results demonstrate the importance of deep subsurface water resources to carbon assimilation and woody growth in dryland systems, as well as the benefits of collocated, long-term eddy covariance and ancillary datasets to improve understanding of complex ecosystem dynamics.

Decoupling of Ecological and Hydrological Drought Conditions in the Limpopo River Basin Inferred from Groundwater Storage and NDVI Anomalies

Droughts are projected to increase in intensity and frequency with the rise of global mean temperatures. However, not all drought indices equally capture the variety of influences that each hydrologic component has on the duration and magnitude of a period of water deficit. While such indices often agree with one another due to precipitation being the major input, heterogeneous responses caused by groundwater recharge, soil moisture memory, and vegetation dynamics may lead to a decoupling of identifiable drought conditions. As a semi-arid basin, the Limpopo River Basin (LRB) is a severely water-stressed region associated with unique climate patterns that regularly affect hydrological extremes. In this study, we find that vegetation indices show no significant long-term trends (S-statistic 9; p-value 0.779), opposing that of the modeled groundwater anomalies (S-statistic -57; p-value 0.05) in the growing season for a period of 18 years (2004–2022). Although the Mann-Kendall time series statistics for NDVI and drought indices are non-significant when basin-averaged, spatial heterogeneity further reveals that such a decoupling trend between vegetation and groundwater anomalies is indeed significant (p-value < 0.05) in colluvial, low-land aquifers to the southeast, while they remain more coupled in the central-west LRB, where more bedrock aquifers dominate. The conclusions of this study highlight the importance of ecological conditions with respect to water availability and suggest that water management must be informed by local vegetation species, especially in the face of depleting groundwater resources.

Optimizing the spatial arrangement of groundwater heat pumps and their well locations

Increasing numbers of groundwater heat pump systems in urban areas can lead to negative thermal interference between neighboring systems. These systems and their associated wells can be optimally positioned so as to minimize such interactions and maximize the use of the shallow geothermal potential. This paper presents a new method that determines the optimal positioning of groundwater heat pumps and their wells by concurrently considering relevant technical and regulatory constraints. The method is based on an improved analytical model for estimating thermal anomalies in groundwater, i.e. thermal plumes, and integer linear programming optimization. The method is tested using a neighborhood with 56 parcels, i.e. potential systems, in two optimization cases: steady and dynamic. In both cases, the method successfully determines both the systems to be installed and the optimal locations of the wells such that relevant spatial regulations are satisfied and the energy extracted from the aquifer is maximized. The dynamic case is somewhat more restrictive, as 43 of 56 potential systems are installed, compared to 44 in the steady case. This method represents a significant improvement over existing ones, since it is able to efficiently optimize the locations and well layouts of a large number of systems simultaneously.

Forecasting groundwater anomaly in the future using satellite information and machine learning

• A machine learning model based remote sensing and terrestrial data was developed. • Fill the data gap in GRACE and GFO satellites data by machine learning. • Soil Moisture, temperature and evapotranspiration was estimated by the CanESM2. • Terrestrial water storage and groundwater anomaly were predicted for the future. By applying Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO) satellites, this study estimates the value of Terrestrial Water Storage Anomaly (TWSA) and GroundWater Anomaly (GWA) in Lake Urmia Basin (LUB), Iran. TWSA changes are first calculated using GRACE/GFO satellites where the best Gaussian Filter Radius in Kilometer (GFRK) with the least error is obtained. To provide a model and a practical convenient equation for modeling the value of TWSA in the study area, six parameters affected the value of TWSA is used. These data are obtained from two data centers: Global Land Data Assimilation System (GLDAS) and fifth generation (V5) European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5). This information is obtained in the Google Earth Engine (GEE) environment by developing JavaScript code for the study area and on a monthly basis for 107 points that have a suitable distribution in the area. The TWSA value is also calculated using GRACE/GFO data satellites for 10 pixels that completely cover the area. Next, Machine Learning (ML) is used to model the TWSA value. The best model is obtained for 2–6 inputs in calculating the TWSA value. The results indicate that the use of three inputs of Soil Moisture in 10–40 cm (SM 2 ), Average Temperature (AT), and Evapotranspiration (ET) present the best performance compared to other models. Finally, GRACE/GFO missing data are obtained using this equation. To calculate the TWSA value in the future, three input parameters are downscaled using the second-generation Canadian Earth System Model (CanESM2) to predict the TWSA value. The results of climate change show that the value of TWSA in the two periods 2021–2040 and 2041–2060 has a reduction compared to the average measurement period. Also, due to TWSA changes, the value of GWA would be drastically reduced in the future.

Coupling of multi-hydrochemical and statistical methods for identifying apparent background levels of major components and anthropogenic anomalous activities in shallow groundwater of the Liujiang Basin, China.

Natural background levels (NBLs) is a prerequisite for distinguishing anthropogenic groundwater pollution and judging the evolution of groundwater quality. However, due to regional differences of hydrogeochemitry and water-rock interaction, coupled with long-term anthropogenic activities, it is no longer accurate to assess NBLs with only statistical methods or without considering human impact. Herein, multi-hydrochemical and statistical methods were examined to identify apparent background levels and anthropogenic anomalous activities of shallow groundwater by selecting Liujiang Basin as a study area. The results showed that the differences in hydrochemical characteristics among each hydrogeological unit (HU) fully illustrated the necessity of rationally dividing HU for background value identification. The application of the concept of apparent background levels (ABLs), that is, incorporating normal human activities into the background levels, efficiently solved the problem of being unable to obtain pristine NBLs due to long-term human activities. The coupling of Hydrochemistry and Grubbs' test (Hydro-Grubbs) was confirmed as the optimal method in identifying and eliminating anthropogenic groundwater anomalies, performing sufficiently superiority when compared with purely statistical methods. It is mainly because the Hydro-Grubbs method not only considers the discreteness of the data itself, but also considers the internal connection and evolution process of the hydrochemical compositions. For the eliminated abnormal points, 91.0-93.6% of which have been effectively explained by pollution percentage index and the impact of coal mining, industrial activities, residents, agricultural activities, and septic tanks leakage, proving the rationality and reliability of Hydro-Grubbs method and ABLs evaluation result. This finding will assist in accurately identifying anthropogenic pollution on a regional scale and guiding future efforts to protect groundwater resources.

Exploring groundwater drought responsiveness in lowland post-glacial environments

Groundwater drought response to meteorological forcing depends on initial hydrological conditions. This makes it difficult to characterise groundwater droughts and identify the drought vulnerability of aquifers. The objective is to increase the understanding of groundwater memory and response to meteorological forcing in lowland post-glacial environments. Eighty-one groundwater hydrographs are analysed, using the standardised groundwater level index (SGI) and the precipitation index. Memory and response times are assessed using auto- and cross-correlation functions. Response time is estimated by comparing two approaches: (1) the traditional use of the maximum cross-correlation, and (2) the alternative use of the cross-correlation slope. Results are interpreted for different hydrogeological settings. The analysis showed that sand aquifers have the longest memory and response times, particularly in confined settings where the memory could be over 4 years. Silts and tills have relatively short memories and response times, at less than 1 year, though median values for silt are higher than for unconfined sand aquifers. In this study, estimating response time using the correlation slope is superior at capturing the initial response time of groundwater to precipitation. However, the results showed that groundwater anomalies in lowland post-glacial environments are sometimes more influenced by climate teleconnections than concurrent forcing. This emphasises the need for a holistic approach for the characterisation and projection of groundwater drought, as it develops in simultaneous response to meteorological forcing at different timescales.

Arsenic Mineral in Volcanic Tuff, a Source of Arsenic Anomaly in Groundwater: City of Chihuahua, Mexico

Arsenic is a naturally occurring trace element that causes many health effects when present in drinking water. Elevated arsenic concentrations in water are often attributed to nearby felsic volcanic sequences; however, the specific rock units to which the groundwater anomalies can be accredited are rarely identified. The groundwater from wells around the city of Chihuahua, Mexico, contains high arsenic content. Arsenic in groundwater increases toward the base rock containing Tertiary volcanic rocks. Through detailed scanning electron microscope (SEM) and electron microprobe (EMP) work, arsenic minerals are identified in the cavities of the Tertiary volcanic tuff from the northeast part of the Tabalaopa Basin, city of Chihuahua. Arsenic minerals, the As–Sr–Al phase (a possible arsenogoyazite–arsenoflorencite group mineral) crystallized in the vesicles of the tuff and the As–Y bearing phase included in biotite, prevail in the studied Tertiary volcanic outcrops. Based on the current study, the arsenic anomaly in the Tabalaopa–Aldama aquifer corresponds to these arsenic phases in the Tertiary volcanic rocks.

Economic and environmental impact assessment of sustainable future irrigation practices in the Indus Basin of Pakistan

Pakistan’s agriculture is characterized by insecure water supply and poor irrigation practices. We investigate the economic and environmental feasibility of alternative improved irrigation technologies (IIT) by estimating the site-specific irrigation costs, groundwater anomalies, and CO2 emissions. IIT consider different energy sources including solar power in combination with changes in the irrigation method. The status quo irrigation costs are estimated to 1301 million US$ year−1, its groundwater depletion to 6.3 mm year−1 and CO2 emissions to 4.12 million t year−1, of which 96% originate from energy consumption and 4% via bicarbonate extraction from groundwater. Irrigation costs of IIT increase with all energy sources compared to the status quo, which is mainly based on diesel engine. This is because of additional variable and fixed costs for system’s operation. Of these, subsidized electricity induces lowest costs for farmers with 63% extra costs followed by solar energy with 77%. However, groundwater depletion can even be reversed with 35% rise in groundwater levels via IIT. Solar powered irrigation can break down CO2 emissions by 81% whilst other energy sources boost emissions by up to 410%. Results suggest that there is an extremely opposing development between economic and ecological preferences, requiring stakeholders to negotiate viable trade-offs.

An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

The lack of high-quality continental-scale groundwater table depth observations necessitates developing an indirect method to produce reliable estimation for water table depth anomalies (wtda) over Europe to facilitate European groundwater management under drought conditions. Long Short-Term Memory (LSTM) networks are a deep learning technology to exploit long-short-term dependencies in the input-output relationship, which have been observed in the response of groundwater dynamics to atmospheric and land surface processes. Here, we introduced different input variables including precipitation anomalies (pra), which is the most common proxy of wtda, for the networks to arrive at improved wtda estimates at individual pixels over Europe in various experiments. All input and target data involved in this study were obtained from the simulated TSMP-G2A data set. We performed wavelet coherence analysis to gain a comprehensive understanding of the contributions of different input variable combinations to wtda estimates. Based on the different experiments, we derived an indirect method utilizing LSTM networks with pra and soil moisture anomaly (θa) as input, which achieved the optimal network performance. The regional medians of test R2 scores and RMSEs obtained by the method in the areas with wtd ≤ 3.0 m were 76–95% and 0.17–0.30, respectively, constituting a 20–66% increase in median R2 and a 0.19–0.30 decrease in median RMSEs compared to the LSTM networks only with pra as input. Our results show that introducing θa significantly improved the performance of the trained networks to predict wtda, indicating the substantial contribution of θa to explain groundwater anomalies. Also, the European wtda map reproduced by the method had good agreement with that derived from the TSMP-G2A data set with respect to drought severity, successfully detecting ~41% of strong drought events (wtda ≥ 1.5) and ~29% of extreme drought events (wtda ≥ 2) in August 2015. The study emphasizes the importance to combine soil moisture information with precipitation information in quantifying or predicting groundwater anomalies. In the future, the indirect method derived in this study can be transferred to real-time monitoring of groundwater drought at the continental scale using remotely sensed soil moisture and precipitation observations or respective information from weather prediction models.

Support vector machine and data assimilation framework for Groundwater Level Forecasting using GRACE satellite data

Groundwater (GW) is a vital resource for human consumption, agriculture, environmental sustainability, and socio-economic development in different parts of the world. Besides, GW plays a crucial role in minimizing the impacts during extreme drought events. However, there is a fast decline in groundwater resources due to increased sectoral water demand compounded by reduced rainfall and rising temperature. Therefore, it is essential to develop appropriate tools to study spatio-temporal dynamics and predict groundwater levels to improve water resource management, especially during drought events. In this study, we applied machine learning algorithms based on support vector machines (SVMs), combined with data assimilation (DA) technique to predict the change in groundwater levels (CGWLs) at 1 to 3-month time scales for 46 GW wells located at the northeast United States. The in-situ climate variables and the Gravity Recovery and Climate Experiment (GRACE) mission-informed groundwater anomalies data (GWA) are used to develop the models. The results suggest that SVMs (SVM-DA) models forced with limited climate variables (i.e., precipitation, solar radiation, air temperature, infrared surface temperature) can forecast the CGWLs up to 3-month lead times at most of the locations. The addition of GRACE data as a forcing variable can improve the performance of SVMs at most of the stations, where a strong relationship exists between the CGWLs and the GWA. The SVM-DA model comparatively performed better than SVMs at most of the stations.

REY Patterns and Their Natural Anomalies in Waters and Brines: The Correlation of Gd and Y Anomalies

Rare earths and yttrium (REY) distribution patterns of the hydrosphere reveal systematic correlations of Gd and Y anomalies besides the non-correlated redox-dependent Ce and Eu anomalies. Eu anomalies are inherited by dissolution of feldspars in igneous rocks, whereas Ce, Gd and Y anomalies develop in aqueous systems in contact with minerals and amorphous matter. Natural, positive Gd and Y anomalies in REY patterns characterize high-salinity fluids from the Dead Sea, Israel/Jordan, the Great Salt Lake, USA, the Aral Sea, Kazakhstan/Uzbekistan, ground- and surface water worldwide. Extreme Gd anomalies mostly originate from anthropogenic sources. The correlation of Gd and Y anomalies at low temperature in water bodies differ from geothermal ones. In nature, dynamic systems prevail in which either solids settle in water columns or water moves through permeable sediments or sedimentary rocks. In both cases, the anomalies in water develop due to repeated equilibration with solid matter. Thus, these anomalies provide information about the hydrological history of seawater, fresh groundwater and continental brines. When migrating, the interaction of aqueous phases with mineral surfaces leads to increasing anomalies because the more hydrophillic Gd and Y preferentially remain in the aqueous phase compared to their nearest neighbors. The correlation coefficients between Gd and Y anomalies in groundwater is 0.5–0.9. In lakes and oceans, it is about 0.1–0.8, under anomalous conditions it can increase to 1.

Drought Variability over the Conterminous United States for the Past Century

AbstractWe examine the drought variability over the conterminous United States (CONUS) for 1915–2018 using the Noah-MP land surface model. We examine different model options on drought reconstruction, including optional representation of groundwater and dynamic vegetation phenology. Over our 104-yr reconstruction period, we identify 12 great droughts that each covered at least 36% of CONUS and lasted for at least 5 months. The great droughts tend to have smaller areas when groundwater and/or dynamic vegetation are included in the model configuration. We detect a small decreasing trend in dry area coverage over CONUS in all configurations. We identify 45 major droughts in the baseline (with a dry area coverage greater than 23.6% of CONUS) that are, on average, somewhat less severe than great droughts. We find that representation of groundwater tends to increase drought duration for both great and major droughts, primarily by leading to earlier drought onset (some due to short-lived recovery from a previous drought) or later demise (groundwater anomalies lag precipitation anomalies). In contrast, representation of dynamic vegetation tends to shorten major droughts duration, primarily due to earlier drought demise (closed stoma or dead vegetation reduces ET loss during droughts). On a regional basis, the U.S. Southwest (Southeast) has the longest (shortest) major drought durations. Consistent with earlier work, dry area coverage in all subregions except the Southwest has decreased. The effects of groundwater and dynamic vegetation vary regionally due to differences in groundwater depths (hence connectivity with the surface) and vegetation types.

Integration of Magnetic Data and Geo-Spatial Analysis for Characterising Aquiferious Yields in New Estate, Covenant University, Ota, Ogun State

This study was conducted to understand the possible causes of abortive boreholes along the New Estate in Covenant University. The conceptual model predicted the two groundwater characteristics of weathered and the unweathered zones. The V-shape curve revealed two groundwater anomalies with high and low curves. The peak anomaly indicates the higher resistivity, presumed to be the geological structures of unsaturated features in the area. The sink V-shape curve indicate an anomaly with groundwater features, presenting low resistivity values attributed to be water bearing formation that will be viable for groundwater potential. The Iso-resistivity contour map identified possible groundwater features. The first Iso-resistivity column is a region of aquitard with a semipervious in nature that could transmit water in a slower rate. The second groundwater feature in column 2 is a perch aquifer that could enhance the main aquifer due to interconnectivity and pore pressure of downward-upward migration in confined hydrogeological aquifer setting. Column 3 depicts a complete impermeable feature of aquifuge or aquiclude of subsurface structure with rich in clay sediments or stratum of sediments found at the depth range of 30 m to 270 m. The forth groundwater column indicates a promising water bearing formation, probably sandy in nature with a depth range of 210 to 300 m and thickness of about 90 m. The spatial analysis identified the two locations along the survey areas to be suitable for productive boreholes as well as the trending of the groundwater in the area to be Northwestern Southeast direction. Consequently, the model could be a guide to organize site data, understand site processes and screen for applicability of drilling. These multiple methods of geophysical, spatial analysis and conceptual model could be used a tool for mapping hydrocarbon bearing formation.

Groundwater Anomaly Related to CCS-CO2 Injection and the 2018 Hokkaido Eastern Iburi Earthquake in Japan

Carbon capture and storage (CCS) is considered a key technology for reducing CO2emissions into the atmosphere. Nonetheless, there are concerns that if injected CO2migrates in the crust, it may trigger slip of pre-existing faults. In order to test if this is the case, covariations of carbon, hydrogen, and oxygen isotopes of groundwater measured from Uenae well, southern Hokkaido, Japan are reported. This well is located 13 km away from the injection point of the Tomakomai CCS project and 21 km from the epicenter of September 6th, 2018 Hokkaido Eastern Iburi earthquake (M 6.7). Carbon isotope composition was constant from June 2015 to February 2018, and decreased significantly from April 2018 to November 2019, while total dissolved inorganic carbon (TDIC) content showed a corresponding increase. A decrease in radiocarbon andδ13C values suggests aquifer contamination by anthropogenic carbon, which could possibly be attributable to CCS-injected CO2. If such is the case, the CO2enriched fluid may have initially migrated through permeable channels, blocking the fluid flow from the source region, increasing pore pressure in the focal region and triggering the natural earthquake where the brittle crust is already critically stressed.

Effects of vegetation restoration on groundwater drought in the Loess Plateau, China

The Loess Plateau (LP) is a typical water-limited area. Since the revegetation plan started in 1999, the rapid growth of vegetation has not only significantly changed the local water cycle, but also probably affected regional groundwater drought. However, the effect of revegetation on groundwater drought remains largely unexplored. To this end, we isolated the groundwater anomalies from terrestrial water storage of the Gravity Recovery and Climate Experiment satellites and soil moisture of the Global Land Data Assimilation System, and further used the drought severity index to characterize groundwater drought. The evolution characteristics of groundwater drought in the LP were analyzed, and the effect of revegetation plan on groundwater drought were explored. Results indicated that: (1) Normalized Difference Vegetation Index (NDVI) during 2003 ~ 2015 in the LP was growing rapidly, meanwhile, groundwater storage significantly decreased (p < 0.01) and groundwater drought intensified in terms of its area and intensity; (2) compared with meteorological factors, NDVI is more strikingly correlated (p < 0.05) with groundwater drought on annual, seasonal and monthly scales, especially near the key areas of vegetation restoration; (3) the growth rate of vegetation is a dominant factor affecting groundwater drought in the LP, in which the groundwater consumption rate caused by vegetation dynamics in the significant area is higher than non-significant area. Our research results provide guidance for formulation scientific and sustainable ecological restoration policies in the LP, and also offer new ideas for the study of the relationship between vegetation and groundwater drought.