Groundwater Level Data Research Articles

Processes controlling the extent of groundwater pollution with chromium from tanneries in the Hazaribagh area, Dhaka, Bangladesh

The Hazaribagh industrial area in Dhaka city, the capital of Bangladesh, is considered one of the hotspots of chromium (Cr) pollution, due to excessive discharge of Cr contaminated waste over decades by approximately 150 tanneries. In 2000, elevated Cr concentrations were observed in the underlying Dupi Tila Aquifer (DTA), which is heavily deployed for drinking water supply of Dhaka city's population. In the following years, Cr concentrations in the DTA have dropped and apparently stayed low. In 2010, elevated Cr concentrations were found again in the DTA. This study aims to evaluate the status of the total Cr contamination in the surface waters, groundwater, and soils in the area, to clarify the temporal evolution of the total Cr contamination pattern in the DTA. For this, we collected water and soil samples in 2012, 2013, 2014, and 2019 for (hydro)chemical characterization and analyzed new groundwater level data on the development of the cone of depression below the city.Our study indicates that the temporal evolution of the total Cr contamination in the DTA is closely coupled to the groundwater dynamics. The rapid growth of the cone of depression due to excessive pumping resulted (i) in a disconnection of the groundwater table from the heavily contaminated Hazaribagh soils, and (ii) in an increased gradient between the contaminated surface waters and the groundwater, increasing infiltration into the groundwater. Finally, (iii) the further growth of the cone of depression resulted in an inflow of fresh groundwater from the west of Dhaka city, causing a dilution effect. Although in 2017 tanneries were moved out of the Hazaribagh area, the contaminated soils still pose a threat to groundwater quality when groundwater levels would recover.

Multivariate data assimilation of GRACE, SMOS, SMAP measurements for improved regional soil moisture and groundwater storage estimates

Assimilating remote sensing observations into land surface models has become common practice to improve the accuracy of terrestrial water storage (TWS) estimates such as soil moisture and groundwater, for understanding the land surface interaction with the climate system, as well as assessing regional and global water resources. Such remote sensing observations include soil moisture information from the L-band Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions, and TWS information from the Gravity Recovery And Climate Experiment (GRACE). This study evaluates the benefit of assimilating them into the Community Atmosphere and Biosphere Land Exchange (CABLE) land surface model. The evaluation is conducted in the Goulburn River catchment, South-East Australia, where various in situ soil moisture and groundwater level data are available for validating data assimilation (DA) approaches. It is found that the performance of DA mainly depends on the type of observations that are assimilated. The SMOS/SMAP-only assimilation (SM DA) improves the top soil moisture but degrades the groundwater storage estimates, whereas the GRACE-only assimilation (GRACE DA) improves only the groundwater component. Assimilating both observations (multivariate DA) results in increased accuracy of both soil moisture and groundwater storage estimates. These findings demonstrate the added value of multivariate DA for simultaneously improving different model states, thus leading to a more robust DA system.

Land Subsidence Vulnerability Assessment of Rural Settlements in Fars Province

Purpose- Land subsidence is caused by natural factors and human activities around the world. Fars Province, located in the south of Iran, is subject to land subsidence due to the uncontrolled exploitation of groundwater, causing damages to the population and human settlements and also environmental, social and economic areas. Design/methodology/approach- The present research is descriptive in terms of describing land subsidence in the case study region, whereas it is also analytical as time series analysis techniques based on Radar Interferometry (InSAR) is applied to monitor temporal changes in subsidence in Darab and Fasa Plains, including 470 rural points. Using 8 ENVISAT ASAR images spanning between 2005 and 2010, nine Interferograms were processed. In the study area. Geographic Information System (GIS) is then used to study groundwater level decline at the well locations in a 24-year period (from 1991 to 2015). Findings- The results of the research confirm that there is a significant correlation between groundwater water level decline and land surface subsidence. Time series analysis of the processed Interferograms indicate the mean displacement velocity map, demonstrating the maximum subsidence rate of 25 cm/yr. The InSAR analysis reveal within the study area subsidence rate of 25 cm/year in 24 years period and locally exceeding 30 cm/yr in the last decade. This area of significant subsidence is limited in its spatial extent to the agricultural land and is partly influenced by the large-scale over-exploitation of groundwater resources in the region study. The temporal and areal relationships of subsidence and groundwater level data suggest that a significant part of the observed subsidence in the Darab region is caused by intense groundwater extraction which has led to widespread compaction within the upper parts of the up to 300m. Socioeconomic analysis and the subsidence hazard map show that 105523 people are generally at risk of subsidence, of 65068 who are at high risk. In addition, there are 2679 socioeconomic infrastructures such as public service at risk of damage by land subsidence. Research limitations/implications- Limitation in In SAR data access, especially for long-term data was one of the main limitations in land subsidence research and also in this research. Practical implications- Integrated water resource management and the observed extraction of groundwater could influence the subsidence rate in the regions exposed to land subsidence. Originality/value- This research will be important to provide vulnerability in regions with groundwater overexploitation.

Determination of Ground Water Stressed Regions of Visakhapatnam District using ArcMap

Water is essential for human life, which is vastly used for basic needs like drinking and other domestic, irrigational and industrial purposes etc. The main sources of water are classified as surface and sub-surface sources. The surface water resources are varied like rivers, ponds, lakes etc. but the sub-surface resources are only water bearing aquifers. Day by day the sub surface water is degrading due to many reasons like pollution, climate change etc. Therefore, studies on conservation of ground water is important. For the present study, data of ground water levels for eleven years (2007-2017) at 69 locations of various mandals (zones) in Visakhapatnam district (Andhra Pradesh, India) was obtained from the A.P. Ground Water and Water Audit Department, Visakhapatnam. By using ArcMap of ArcGIS and the spatial and temporal variations were mapped onto the base map of Visakhapatnam. The results indicated ‘water stressed areas’ in the study region. It was observed that the ground water levels dwindled drastically in the years 2016 and 2017. The results of the study highlighted the immediate necessity to scale up the ground water conservation measures and efficient design of the ground water resource systems in the area.

Quantification of Neural Network Uncertainties on the Hydrogeological Predictions by Probability Density Functions

The risk of drought impacting the drinking water and agricultural production is worrying in the developed countries, especially in a changing climate context. To manage and prevent this phenomenon, real-time monitoring and predictive systems are emerging as the key solutions. In the field of artificial intelligence, neural networks are one of these predictive systems. This family of parameterized models is a composition of neuronal functions, which apply a non-linear transformation from their inputs to their outputs. These networks are able to learn a hydro(geo)logical system behaviour using a database composed of observed inputs (rainfall, evapotranspiration, etc.) and outputs (groundwater level, discharge, etc.), thanks to an algorithm minimizing a cost function between observed and simulated outputs. However, it remains difficult to assess the uncertainty generated by these models, possibly leading to misinterpretations by the end users. These uncertainties are mainly of three types. The first is related to the input data. Indeed, hydrosystems are surface elements whereas meteorological inputs are punctual elements. The interpolation error can, therefore, be significant because of the lack of knowledge between gauging stations. The second is the neural network model architecture itself. It is possible to deal with this source of uncertainty using regularization methods. Finally, the neural networks are submitted to uncertainties related to parameter initialization, before the training step. The initial parameters may have an important impact on the results. In this paper, we address the prediction of the Blavet groundwater level (Bretagne, France). In order to assess uncertainties, we will first focus on the parameters initialization of the model. Neuronal models are optimized using cross-validation and early stopping. Then, an ensemble model is realized, in which each member is the result of a unique set of parameters initialization. The purpose of the study is to define how many initializations are necessary to obtain a reasonable confidence interval for forecasts, with the smallest interval and the higher rate of observed points inside this interval. The best model will be determined using cross-validation scores thereby ensuring optimal robustness. We show that, in this case study, an ensemble model of 20 different initializations is sufficient to estimate uncertainty while preserving quality. In the second part, the resulting ensemble model will be used to estimate the global model uncertainty using probability density functions (pdf) applied to the distribution of groundwater level data and cross-validation scores of forecasts. It reveals that the groundwater level predictions are composed of two mixed distributions. Therefore, we will use the expectation-maximization algorithm (EM) to obtain parameters of mixed models. Mixed normal and mixed Gumbel laws, among five mixed distributions assessed, give the best groundwater distribution and are able to generate an abacus drawing uncertainty of model.

Geostatistical Analysis of Groundwater Data

Water resources are stressed because of the country's increasing population and increased water requirements. Even though a good understanding of both surface and groundwater hydrological systems make it possible to manage these resources properly. To study the main characteristics of formation of clusters of groundwater levels, statistical analysis has been used. Geostatistics is a class of statistics used to analyze and predict the values associated with spatial or spatiotemporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. The Statistical analysis is applied to monthly groundwater levels fluctuation data over a period of 2004-2017 in Mysuru, Mandya, Chamarajanagara and Hassan districts of Southern Karnataka in India. The groundwater levels data is collected from 197 Observation Wells from the districts. The Statistical methods like K-Means Clustering and Agglomerative Hierarchical Cluster Analysis is used to perform the datasets. Grouping is made using AHC method, during this process results are obtained by graph called Dendrogram. The obtained results are compared with the LULC maps of all 4 districts. Different grouping (cluster) is made for groundwater level fluctuations for proper conclusion to arrive.

Reconstruction of groundwater levels to impute missing values using singular and multichannel spectrum analysis: application to the Ardabil Plain, Iran

ABSTRACT Groundwater-level time series often have a substantial number of missing values which should be taken into consideration before using them for further analysis, particularly for numerical groundwater flow modelling applications. This study aims to comprehensively compare two data-driven models, singular spectrum analysis (SSA) and multichannel spectrum analysis (MSSA), to reconstruct groundwater-level time series and impute the missing values for 25 piezometric stations in Ardabil Plain, northwest Iran. The reconstructed groundwater-level time series are assessed against the complete observed groundwater time series, while the imputed values are appraised against the artificially created gap values. The results show that both SSA and MSSA demonstrate a solid competency in imputation and reconstruction of groundwater-level data. However, depending on the spatial correlation between the piezometers, and the most suitable probability distribution function (pdf) fitted to the time series of each piezometer, the performance may vary from piezometer to piezometer.

Countermeasures of water and mud inrush disaster in completely weathered granite tunnels: a case study

This paper presents a case study of the countermeasures of water and mud inrush disasters in the Junchang Tunnel in Guangxi, China. This tunnel was constructed in a weathered granite formation, and four serious water and mud inrush disasters occurred during construction. The geological and hydrological conditions are first reviewed to understand their effects on the water and mud inrush disasters. Four potential causes of the serious water and mud inrush disasters in the Junchang Tunnel were proposed, including the poor stability of the completely weathered granite, the high groundwater level and large groundwater flow rate, insufficient pre-reinforcement of the surrounding rock and the disturbances caused by tunnel construction. Water-sealing and ground reinforcement measures were suggested before excavation to improve the stability of the surrounding rock and plug the water outflow channels, and full-face curtain grouting was considered as the first choice. Furthermore, several improvements, including the combined utilization of various grouting materials, supplementary grouting with steel pipes in collapsed boreholes and dynamically optimized grouting methods, were implemented to solve the serious water inflow and collapse problems experienced during the drilling and grouting work. A detailed curtain grouting scheme was proposed and applied in practice. A comprehensive evaluation based on inspection borehole investigations and P–Q–t analysis indicated that the curtain grouting method yielded satisfactory performance in water sealing and ground reinforcement. In addition, the groundwater level and settlement data collected in the field validated the effectiveness of the proposed countermeasures.

Application of GALDIT in Assessing the Seawater Intrusion Vulnerability of Jeju Island, South Korea

Seawater intrusion (SWI) is a major environmental threat to groundwater resources in coastal regions. GALDIT is an index-based SWI vulnerability model that is increasingly being used in many parts of the world to identify regions that are vulnerable to various types of SWI based on six major parameters. In this study, we conducted a vulnerability assessment of Jeju Island to SWI based on several years of collected groundwater level data and hydrogeological values where the objectives of the study were to visualize the distribution of recent SWI, to increase the reliability of the GALDIT assessment method by improving current GALDIT techniques, and to respond effectively to diagnoses of SWI on Jeju. To improve the GALDIT assessment method to fit the Jeju model, the possibility of electrical conductivity was explored instead of standard GALDIT parameters that represented the existing impact of SWI. Improvements to the GALDIT vulnerability assessment method made it clear that groundwater became increasingly vulnerable to SWI in the existing high-vulnerability group. The results of this research may be used to develop a quantitative index for rational decision-making on policies and suggest the need for further improvements in groundwater management, with a stronger focus on easing groundwater use.

Towards flexible groundwater-level prediction for adaptive water management: using Facebook’s Prophet forecasting approach

ABSTRACT There is an increasing need for accurate groundwater-level (GWL) prediction to support effective seasonal water management. It is desirable for forecasting tools to be not only accurate but also accessible for decision-makers. We test the Prophet forecasting procedure, an open-source code released by Facebook, to address these challenges. It is based on an additive model considering non-periodic changes and periodic components in a Bayesian framework with easily-interpretable parameters. Predictions of daily GWL data in an area affected by pumping near a tourist complex in the Ramsar wetland area of Doñana (Spain) are compared to other forecasting methods. Prophet outperforms most methods in predicting GWL making it a fast and flexible forecasting tool for hydrologists and water managers. Furthermore, it allows gaining insight into the influence of each component of the forecast separately, helping to assess the hydrodynamic response to external drivers such as groundwater pumping.

Analytic network process based approach for delineation of groundwater potential zones in Korba district, Central India using remote sensing and GIS

The need for augmentation of groundwater resources is attributed to the rapid urbanization and manifold increase in the population in the Indian context. To circumvent the problem the present study is aimed at delineating the groundwater potential zones (GWPZ’s) using Analytic Network Process (ANP) model integrated with Remote Sensing (RS) and Geographic Information System (GIS) in Korba district, Chhattisgarh, Central India. Key parameters selected to achieve the targeted approach include aquifer characteristics, geomorphological features, soil types, slope, drainage density, elevation, lineament density, land use land cover, depth to groundwater level and rainfall data are selected to prepare the GWPZ maps. The final GWPZ map is classified into four categories, viz., poor, moderate, high and very high. Results obtained using these parameters indicate that the groundwater potential zones can be classified from study areas as high to very high (62.86%), moderate (17.95%) and poor (19.19%) category of the total area. In total, 96% model accuracy measurement has been achieved, which is in agreement with the available well yield data. Hence, an ANP based GIS model may be an effective and reliable method for delineating groundwater potential zones in any region that is hitherto unused to its full potential and this would pave way for sustainable groundwater exploration.

The effect of seasonal rainfall on nutrient input and biological productivity in the Yax Chen cave system (Ox Bel Ha), Mexico, and implications for μXRF core studies of paleohydrology

Lakes and speleothems from Mexico's Yucatan Peninsula have been used extensively over the past decades for paleoclimate studies, however aquifer condition and its response to climate change has received little attention. Cenotes (sinkholes) and coastal caves have been shown to record the paleohydrology of the aquifer, but there is little information on sedimentation in these cave systems and its response to climate change. Newly developed μXRF instrumentation for the analysis of cores can achieve subannual resolution due to small measurement increments, but short period studies examining weathering inputs and rainfall have not yet been undertaken, hindering paleoenvironmental interpretations of lake and cave sediment records. This study examines the spatial and temporal relationship of cave sediment geochemistry in the anchialine cave system of Yax Chen (Quintana Roo, Mexico). Sediment traps (n = 51) were placed at seventeen stations along the 2.7 km flooded cave system, which transitions from mangrove to upland forest terrain, with cenotes of variable size and frequency along its length. Sediment traps were collected every ~6 months from May 2013–May 2017 along with rainfall and groundwater level data. There are distinct responses of lithogenic (Fe, Ti, Sr - limestone weathering) and biogenic influenced (Si, K, S - phytoplankton and mangrove sediment) elements in the sediment samples associated with seasonal rainfall and hurricanes. While lithogenic elements (Ti/K) show a direct relationship seasonal rainfall, the sedimentation of biologically influenced (Si/Ti) elements exhibit a 6–12 month lagged response with large rainfalls such as Hurricane Ingrid in 2013 and other tropical storms throughout the study period.

Impact of land use change dynamics on sustainability of groundwater resources using earth observation data

The aim of the present work was to determine the effects of long-term changes in land use/land cover (LULC) on surface and groundwater resources of quaternary aquifers in the Lucknow area of the Ganga plain, based on the analysis of multi-temporal satellite and field survey data. Changes in LULC for the period of 2008 and 2016 were mapped using Landsat-7 and Landsat-8 satellite data by applying hybrid image classification techniques. Hydro-geomorphic feature mapping for 2008 and 2016 was also carried out to monitor changes in important surface and subsurface hydro-geomorphic features of the area such as paleochannels, cut-off meanders, oxbow lakes, meander scars, flood plains and younger alluvial plain, and observation found that a decreasing trend in most features. Groundwater level data of selected wells for 2008 and 2016 were used to analyze the trend in groundwater level depletion and its relation with land use change and hydro-geomorphic features. In general, the observations from the present study clearly indicate that large-scale changes in groundwater reservoirs has been taken place due to changes in LULC, hydro-geomorphic features and extensive groundwater exploration practices over the past decade. The results show 4.09% increase in built-up land, 5.49% increase in open area, 8.80% decrease in vegetation cover, 2.31% decrease in agricultural land and 0.35% decrease in surface water bodies. The results observed through monitoring of LULC change, along with water level dynamics of the area, provide scientific data base for the protection, governance and decision-making for water resources management.

Analysis of earthquake-induced groundwater level change using self-organizing maps

For a better understanding of possible physical links between geophysical observables and earthquake characteristics, it is important to analyze statistical spatiotemporal patterns in nature related to such events. For this purpose, characteristic changes in groundwater level (GWL) were observed before and after the 2016 Kumamoto earthquake in Japan. Previous research has shown that self-organizing maps (SOM) can be used to classify complex patterns of GWL-change during different parts of the earthquake sequence. In this study, we used before and after earthquake GWL data as input vectors to SOM. In total, 64 observed GWLs were classified into 12 different clusters. Most shallow wells displayed GWL difference that was small during the foreshock (first earthquake) and large during the main-shock (second earthquake). Upstream deep wells showed relatively large difference in water level from 1 to 2 days after the earthquakes. The GWL rapidly increased just after the earthquake, then tended to gradually decrease from September. Most of the shallow wells in the unconfined aquifer rapidly recovered to initial GWLs within several hours to several days, because of hydrostatic pressure. However, most of the deep wells in the confined aquifer needed longer time to recover, in some cases several weeks to several months. These findings are important for the physical understanding of earthquake effects on the groundwater environment, disaster prevention, and possibility for development of earthquake precursors.

The effectiveness of a drainage tunnel in increasing the rainfall threshold of a deep-seated landslide

A rising level of groundwater is a critical trigger for deep-seated landslides. Accordingly, an effective measure to improve the stability of a landslide is to reduce the groundwater level of a slope by using a drainage system. This study investigates the effectiveness of drainage tunnels in increasing the rainfall threshold of a deep-seated landslide. Monitoring results show that the movement of the landslide is highly sensitive to the prevailing groundwater level (GL), and the value of GL has a direct connection with the movement of a slope. Based on continuous monitoring of data of groundwater level (GL) and precipitation, the Particle Swarm Optimization Support Vector Machine (PSO-SVM) model was developed to predict GL based on antecedent rainfall. The calculated results show that the performance of the PSO-SVM model is acceptable. Using intensity-duration-frequency (IDF) analysis and the PSO-SVM model, the rainfall threshold of the landslide in this study was estimated to range from 63 to 78 mm before the drainage tunnel was completed. This contrasted with a rainfall threshold ranging from 144 to 162 mm after the drainage tunnel was completed. This shows that the construction of a drainage tunnel increased the rainfall threshold of the landslide significantly, nearly doubling it.

Parameterisation of physical models to configure subsurface characteristics of groundwater basins

Owing to its simple procedures, the Head-guided Zonation (HGZ) method can be applied in parameterisation step of groundwater modelling to configure subsurface characteristics of areas where spatial subsurface data is scarce. This method principally employs accessible groundwater level data to divide the area of interest into several zones, for which values of subsurface properties, i.e. hydraulic conductivity and specific yield are assigned. However, current studies provide no guideline on how to effectively divide the zones using head contours to achieve robust model performance. This paper presents development and performance assessments of four HGZ models using four different contour spacings, 30 m, 40 m, 50 m and 60 m on an eogenetic karst catchment in Rote Island, Indonesia. For the first time, three user-friendly ratios, ‘area – head contour interval (A/ci)’, ‘x grid length – head contour interval (xgrid/ci)’ and ‘y grid length – head contour interval (ygrid/ci)’, are introduced as a basis to effectively divide model domain into zones. This study demonstrates that through proper selection of the three ratios in HGZ method a robust parameterisation of subsurface characteristics in groundwater modelling can be achieved.

Monitoring urban land surface deformation (2004–2010) from InSAR, groundwater and levelling data: A case study of Changzhou city, China

Land deformation caused by the overexploitation of groundwater has become a potential geological hazard in Changzhou, China. To reduce the potential damage from deformation disasters, it is quite necessary to monitor them. In this study, C-band ENVISAT ASAR synthetic aperture radar (SAR) datasets acquired from 2004 to 2010 were processed with the small baseline subset (SBAS) interferometric SAR (InSAR) method to investigate the spatial–temporal distribution of land deformation. Levelling survey data were used to assess the deformation accuracy measured by the InSAR method, and groundwater level data were used to analyse the incentives for deformation. The SBAS InSAR results found several subsidence regions in the Wujin district and the central part of the Zhonglou district. The annual deformation rate calculated from the C-band interferograms ranged from −65 to +35 mm/yr in the line of sight (LOS) direction, and the cumulative subsidence ranged from −0.35 to 0.13 m in the vertical direction. The levelling survey data show that the two measurements are generally consistent. Correlation analysis between the groundwater table and InSAR measurements at six groundwater well stations shows that groundwater recharges can cause land rebound within the Changzhou urban region. However, subsidence was also observed simultaneously with a rising water table, which might have been caused by the status of consolidation of the strata. Our study provides scientific evidence on the management of groundwater extraction and the assessment of land-subsidence hazard.

Trend Analysis of Precipitation, Groundwater Level and Flow Rate Data by using Mann-Kendall and Sen's Slope Estimator Statistical Tests in the Petorca Communer

This article analyses one of the most complex socio-environmental conflicts in Chile: The Petorca water crisis. Petorca has been experiencing a shortage of water for several years. The cause of these variations has not yet been completely analyzed and the problem has remained unchanged. This paper evaluates hydrology variables to identify, quantify and demonstrate the cause of water shortages in the study area. Mann-Kendall and Sen’s slope test statistically are applied to evaluate records of rainfall monitoring stations, water level and flow rate. To determine the existence of trends over time and quantify its magnitude. A total of 760 years of data were used by analyzing 4974 precipitation events, 788 water level and 1384 flow rate records. The results indicated that (1) There is no positive or negative trend in precipitation data over the years; (2) four well-monitoring stations have a positive trend but with an insignificant magnitude; (3) all flow-monitoring stations have a negative trend. Likewise, it was evidenced that water levels and flow rates have been affecting abruptly since 2014, obtaining null records and remain constant over time. For the same period, there is no evidence of precipitation anomalies. As a result, water resources face a host of serious threats, all of which are caused primarily by human activity. Finally, the community of Petorca is experiencing a water shortage event, which is evidenced for the first time with this scientific article.

Characterizing groundwater and surface-water interaction using hydrograph-separation techniques and groundwater-level data throughout the Mississippi Delta, USA

The Mississippi Delta, a portion of the Mississippi Alluvial Plain (MAP) located in northwest Mississippi (USA), is an area dense with industrial-level agriculture sustained by groundwater-dependent irrigation supplied by the Mississippi River Valley alluvial aquifer. Observed declines in groundwater-level elevations and streamflow, contemporaneous with increases in irrigation, have raised concerns about future groundwater availability and the effects of groundwater withdrawals on streamflow. To quantify the impacts of groundwater withdrawals on streamflow and increase understanding of groundwater and surface-water interaction in the MAP, hydrograph-separation techniques were used to estimate baseflow and identify statistical streamflow trends. The analysis was conducted using the US Geological Survey Groundwater Toolbox open-source software and daily hydrologic data provided by a spatially distributed network of paired groundwater wells and streamgage sites. This study found that statistically significant reductions in stream baseflow occurred in areas with substantial groundwater-level declines. The use of hydrograph-separation and trend analyses to quantify the impacts of groundwater withdrawals and the use of streamflow as a proxy for changes in groundwater availability may be applicable in other altered environments. Characterizing and defining hydrologic relations between groundwater and surface water will help scientists and water-resource managers refine a regional groundwater-flow model that includes the Mississippi Delta, which will be used to aid water-resource managers in future decisions concerning the alluvial aquifer.

Spatiotemporal geostatistical modeling of groundwater levels under a Bayesian framework using means of physical background

The joint spatiotemporal modeling of aquifer level fluctuations provides a significant tool in the prediction of groundwater levels at unvisited locations. Two types of variogram functions, appropriately modified to incorporate physical information, are presented and compared under Space-time Residual kriging methodology using the Bayesian Bootstrap idea to quantify uncertainty. The spatiotemporal trend is approximated using a component of physical meaning. A recently developed Spartan type non-separable variogram function employs tools of physical meaning to enhance the efficiency and reliability of spatiotemporal geostatistical modeling in groundwater applications. In addition, the product-sum space-time variogram is applied and involves in the separable variogram functions a scale factor of hydraulic conductivity directional ratio. The proposed variogram functions involve a non-Euclidean distance metric and are mathematically valid (i.e., constitutes permissible models). Herein, the efficiency of the proposed tools is tested using groundwater level data from an alluvial unconfined aquifer. Both functions provide improved results compared to those of the Space-time Ordinary kriging. Between the two, the non-separable one has the most efficient performance.