Stories of a water-table: anomalous depressions, aquitard breaches and seasonal implications, Shelby County, Tennessee, USA.

Stories of a water-table: anomalous depressions, aquitard breaches and seasonal implications, Shelby County, Tennessee, USA. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Stories of a water-table: anomalous depressions, aquitard breaches and seasonal implications, Shelby County, Tennessee, USA. Daniela Lozano-Medina, Brian Waldron, Scott Schoefernacker, Anzhelika Antipova, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2507984/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jul, 2023 Read the published version in Environmental Monitoring and Assessment → Version 1 posted 4 You are reading this latest preprint version Abstract An extensive water level survey of the water-table aquifer (i.e., shallow aquifer) within Shelby County, Tennessee, was conducted in the dry (Fall 2020) and wet (Spring 2021) seasons. Water-table surfaces were generated using cokriging to observe seasonal differences to identify anomalous water-table depressions, indicative of an underlying aquitard breach. Seasonal differences were attributed to non-coincident control and timing between the surveys as well as when optimum dry (fall) and wet (spring) conditions existed, as observed through comparisons with continuous historical water levels from 12 shallow monitoring wells. Additionally, data from Fall 2020 were compared to previous studies in 2005 and 2015 to determine decadal changes in levels and shape of the water-table surface which were mostly attributed to changes in data control and potential climate variations. A prediction error map was generated from the 2020 dataset to identify areas of the county with high-prediction error (> 7.0 m) to offer guidance on where future well control would be optimal. Water-table surface water levels water level seasonal change cokriging aquitard inter-aquifer exchange Figures Figure 1 Figure 3 Figure 4 Figure 5 Figure 6 Figure 9 Figure 10 Figure 12 Figure 13 Figure 14 Figure 16 Introduction Groundwater is an important source of drinking water in many parts of the world and understanding its flow and fluctuations within a hydrogeological system is crucial to protecting this critical resource. Water-level monitoring allows a glimpse of where the water is and where it is moving to. A less common benefit is found in stressed aquifers that are impacted through inter-aquifer water exchange which is common and naturally occurring. This leakage can be exacerbated when preferential flow paths exist through natural breaches in an aquitard, allowing for modern water to infiltrate into an underlying aquifer causing water quality concerns. Water levels can show the areas where this preferential exchange occurs beneath the surface (Bradshaw, 2011 ; Konduro-Narsimha, 2007 ; Ogletree, 2016 ). An example can be found in the multi-layered aquifer system of the Mississippi embayment in Shelby County, Tennessee, where the presence of aquitard breaches has been investigated for decades (Brahana & Broshears, 2001 ; Carmichael et al., 2018 ; Criner et al., 1964 ; Graham & Parks, 1986 ; Kingsbury & Parks, 1993 ; Konduro-Narsimha, 2007 ; Larsen et al., 2003 , 2013 ; Ogletree, 2016 ; Parks, 1990 ; Schoefernacker, 2018 ; Torres-Uribe, 2020 ; Waldron et al., 2009 ). Significant withdrawals for municipal and industrial uses have caused substantial water-level declines (> 35 m) in the Memphis aquifer, the primary water source for this region since the late 1800s (Brahana & Broshears, 2001 ; Criner & Parks, 1976 ). This decline has resulted in a downward vertical gradient where water from the unstressed water-table aquifer finds preferential leakage paths through breaches in the intervening aquitard between these two aquifers (Brahana & Broshears, 2001 ; Criner et al., 1964 ; Criner & Parks, 1976 ; Graham, 1982 ; Kingsbury, 1996 ; Parks & Carmichael, 1990 ; Waldron & Larsen, 2015 ). Given that the water-table aquifer is more susceptible to contamination from anthropogenic sources due to its unconfined condition and is of lesser water quality than the Memphis aquifer, identifying aquitard breaches between these two aquifers is paramount. A valuable product of collecting water levels in the water-table aquifer (or shallow aquifer) is the development of a water surface where anomalous depressions can help identify these hidden breaches since pumping from the water-table aquifer is limited. Another valuable use is their incorporation into ongoing numerical modeling of the area’s groundwater resources (Clark & Hart, 2009 ; Torres-Uribe, 2020 ; Villalpando-Vizcaino et al., 2021 ). Hence, this investigation seeks to (1) map water levels in the water-table aquifer; (2) identify potential aquitard breaches; (3) address seasonal water-level fluctuations; and (4) provide data for the calibration of the Shelby County numerical groundwater model. In addition, this research aims to illustrate the importance of data control and appropriate data acquisition timing. Study area The Mississippi embayment is a collection of unconsolidated aquifers and aquitardsthat underlies portions of eight states in the south-central United States (Clark & Hart, 2009 ; Graham & Parks, 1986 ; Waldron et al., 2011 ) (Figs. 1 and 2 ). Located within the embayment is Shelby County, Tennessee, which solely relies on groundwater for public supply, with a total withdrawal of 696,000 m 3 /day in 2015 (Dieter et al., 2018 ). There are three primary freshwater aquifers in Shelby County: the water-table, Memphis and Fort Pillow aquifers (Fig. 2 ). The water-table aquifer ranges in thickness from 0 to 30 m and comprised of alluvial and fluvial deposits throughout the county (Brahana & Broshears, 2001 ; Graham & Parks, 1986 ; Konduro-Narsimha, 2007 ; Parks & Carmichael, 1990 ) and includes the Mississippi River valley alluvial (MRVA) aquifer on the westside of the bluff line (see Fig. 3 ) (Lloyd and Lyke, 1995 ); however, the MRVA aquifer is not investigated in this study. The water-table aquifer in the eastern portion of the county corresponds to the unconfined area of the Memphis aquifer (Parks, 1990 ; Urbano et al., 2006 ). The water-table aquifer supplies water to some domestic and farm wells (Parks, 1990 ; Waldron et al., 2011 ) although high-capacity pumping in the water-table aquifer is limited or non-existent. The water-table aquifer is underlaid by the upper Claiborne confining unit (UCCU), ranging in thickness from 1 to 61 m (Larsen et al., 2016 ). The UCCU is comprised of the Cockfield and Cook Mountain formations (Larsen et al., 2003 , 2016 ) and acts as an aquitard, limiting the downward vertical water exchange between the water-table and Memphis aquifers (Graham & Parks, 1986 ), except for those areas, termed “breaches”, where the UCCU is either absent or thinning, or has fault-related connections. Underlying the UCCU is the Memphis aquifer which is composed primarily of sand, with some clay and lignite and ranges from 122 to 274 m thick (Larsen et al., 2016 ). It is the most productive aquifer in the Memphis area providing approximately 95% of the groundwater used for domestic, industrial and agricultural uses (Graham & Parks, 1986 ; Kingsbury, 1996 ). The underlying Flour Island confining unit separates the Memphis and Fort Pillow aquifers, which is another important aquifer to the area. Only the water-table aquifer and, by proxy of suspected breach locations in the UCCU, are considered in this investigation. Methodology The development of a water-table map requires the identification of measurement locations, various measurement procedures, data processing, and interpolation of the final water levels. Three prior investigations were performed in 1987 (Parks 1990 ), 2005 (Konduro-Narsimha, 2007 ) and 2015 (Ogletree, 2016 ) which are compared with the Fall 2020 survey of this investigation. However, this effort builds upon prior measured locations from these studies and follows more closely the post-processing procedures developed by Ogletree ( 2016 ), that generated water-table maps using Empirical Bayesian Kriging incorporating ground elevation as a secondary variable. All prior investigations took water-level measurements during the dry season (September to early November) at available wells screened within the water-table aquifer. Surface water measurements along major rivers and tributaries were collected assuming aquifer connection and mostly gaining conditions as suggested by Parks ( 1990 ). Konduro-Narsimha ( 2007 ) and Ogletree ( 2016 ) took physical measurements of stream surface elevations while Parks ( 1990 ) relied on historical U.S. Geological Survey (USGS) 7.5-minute quadrangle elevation contours at stream crossings. These measurements represented a 40-year span, though Parks ( 1990 ) concluded that any physical changes over this 40-year span were insignificant. Data collection This investigation collected water-level measurements at water-table monitoring and private wells in addition to surface water levels at bridge crossings following Konduro-Narsimha ( 2007 ) and Ogletree ( 2016 ). A compilation of historical water levels from wells screened within the water-table aquifer at monitored sites (e.g., Divisions of Underground Storage Tanks or Remediation, termed LUST and DOR, repsectively) were obtained from the Tennessee Department of Environment and Conservation (TDEC) for both dry and wet seasons where available. Similar to Konduro-Narsimha ( 2007 ) and Ogletree ( 2016 ), these historical water levels were compiled and averaged over the 5-year period to be incorporated to the dataset. Unlike prior investigations, this study also performed a water-level survey during the wet season. The first water-level survey was conducted from mid-September through early October 2020. The second survey was conducted from late March through early April 2021. Following the USGS Groundwater Technical Procedures, depth to water was measured using Solinst electric water-level meters (e-tapes) calibrated through the USGS Hydrologic Instrumentation Facility (HIF) program prior to the surveys (Cunningham & Schalk, 2011 ). Water levels were obtained from 99 wells throughout the county, usually located proximal to utility wellfields (Fig. 3 ), with some exceptions of isolated wells scattered throughout the county. Given the scarcity of public monitoring wells in rural areas of unincorporated Shelby County (see Fig. 3 ), an assessment of privately owned wells was conducted. Approximately 60 private wells were identified from the Shelby County Health Department records as screened within the water-table aquifer, yet only nine were used for water-level measurements due to property access and well construction restrictions. Direct connection between surface water bodies (i.e., rivers and tributaries) and the water-table aquifer was assumed to exist based on Parks ( 1990 ) and Larsen et al. ( 2013 ); therefore, water levels were collected from three main rivers in the area: the Loosahatchie River, Wolf River, and Nonconnah Creek, as well as their tributaries. Following the methodologies described by Konduro-Narsimha ( 2007 ) and Ogletree ( 2016 ), water-level measurements were obtained at stream-bridge crossings using previously defined benchmarks (i.e., pre-installed bridge railing plates) as the point of measure. In some cases, there were no pre-installed plates so a different point-of-measure was used. Plate placement, which occurred during Konduro-Narsimha ( 2007 ), attempted to find minimal surface water displacement since bridge crossing can constrict flow and often have erosion control structures. The same was attempted when finding alternative measuring points. E-tapes were extended from the designated measuring points down to the water surface, watching for wind effects to ensure a vertical dropdown to the water surface. Though not ultimately used, water levels were also obtained from flowing springs in isolated parts of the county. Most of the springs, except for one, were in the Shelby Forest area (see Fig. 3 ). These measurements were later discarded from the final dataset as they are located within the MRVA aquifer west of the bluff line (Fig. 3 ), the western boundary for this study. To minimize spatial and measurement inaccuracies, all accessed features (e.g., wells and river benchmarks) were surveyed using a survey-grade R2 Trimble Global Positioning System (GPS) unit. Spatial precision (x,y) was less than 1 cm with a vertical precision (z) less than 5 cm. The GPS unit accuracy was regularly tested against a U.S. Army Corps of Engineers’ first-order, grade A survey marker prior to surveying. As mentioned, historical water levels from sites monitored by the TDEC were obtained for the dry and wet season periods for the five-year period, 2015 to 2020. Available data was averaged to a single value per site. A total of 22 leaking underground storage tank (LUST) and 111 Division of Remediation (DOR) sites were reviewed, resulting in four LUST and 18 DOR sites that met the criteria and were added to the dataset (see Fig. 3 ). Data processing Collected water level elevations (Appendix 1) were interpolated using cokriging. Kriging is a widely used interpolation method that estimates missing values based on the weighted average of the available data (Isaaks & Srivastava, 1991 ; Snyder, 2008 ). This interpolation scheme modifies the weights of clustered data so that grouped points with similar information are assigned a lower weight (Snyder, 2008 ). Additionally, and contrary to other interpolation methods, kriging estimates the standard interpolation error which can indicate where additional control is needed to reduce interpolation uncertainty (Olea & Davis, 1999 ; Theodossiou & Latinopoulos, 2006 ). There are several kriging variations with differing approaches, including ordinary kriging, universal kriging, Bayesian kriging, cokriging, etc. (Oyana & Margai, 2015 ). Given the sparsity and clustering of measurement points, cokriging was selected as the interpolation method for this study, similar to others (Ahmadi & Sedghamiz, 2008 ; Boezio et al., 2006 ; Chung & Rogers, 2012 ; Hoeksema et al., 1989 ; Ogletree, 2016 ; Ruopu Li & Lin Zhao, 2011). Cokriging minimizes the variance of the estimation error by using more than one variable to compute missing values (Isaaks & Srivastava, 1991 ). The primary variable used was water elevation and the secondary variable ground surface elevation (See Appendix 1), where it was assumed that the unconfined aquifer water levels generally conforms to the topography (Desbarats et al., 2002 ; Parks, 1990 ). Ground surface elevation data was obtained from a 1-m LiDAR Digital Elevation Model (DEM) of Shelby County generated in 2020 (CAESER, 2020 ). Prior to cokriging, it was necessary to determine if a strong correlation between groundwater elevations and topography existed. A Pearson correlation coefficient of 0.70 was obtained; close to the 0.73 coefficient obtained by Snyder ( 2008 ) in a similar investigation. This correlation coefficient used the 1-m LiDAR; however, to better relate to the smallest sampling spatial resolution, a sensitivity test was conducted. The sensitivity test assessed the advantage of resampling the highly detailed 1-meter LiDAR DEM surface to a larger resampled size using a bilinear resampling technique. Following Desbarats et al. ( 2002 ), increasing the cell size to 90 m did not notably change the correlation coefficient of 0.70. Therefore, this resampled surface was used in the cokriging processing which reduced the cokriging processing time significantly. The result of cokriging produces an interpolated surface, or raster with a grid size that followed the methodology described by (Hengl, 2006 ) using Eq. 1 . The square-grid size (P) is a function of an empirical constant, the study area (A) and the number of sampling points (N). $$P=0.0791\sqrt{\frac{A}{N}}$$ 1 Subsequent to creating rasters, contours were produced by first applying a smoothing algorithm to the rasters by averaging each cell with its surrounding neighbors within a 1 km radius so to capture four neighboring cells. Then, contours were generated on a 3-meter interval given the typical 10 ft interval used for the study area (Criner & Parks, 1976 ; Kingsbury, 1996 , 2018 ; Parks, 1990 ). Results And Discussion Data for fall and spring were not normally distributed; therefore, both datasets were log transformed to better approximate a normal distribution using skewness (0) and kurtosis (3.0) as the indicators of following a normal distribution. When mapping the data in a three-dimensional space and projecting the trend of data on the x- and y-axes, the water-table data had a quadratic trend and the ground surface a linear trend. Knowing this, it was possible to remove the trend during the cokriging process. Table 1 Skewness and Kurtosis of raw and log transformed data. Raw data Log transformed data Fall Spring Topography Fall Spring Topography Skewness 0.6137 0.6346 0.2985 0.2712 0.3138 -0.0258 Kurtosis 3.3778 3.2882 2.6189 3.0337 3.0733 2.5358 Through investigation of the semivariogram, many models were displayed against the data to determine which best followed the trend of the data bins. Given the s-shape of the semivariogram, the Gaussian model was the most appropriate choice. Choosing to optimize the model fit via autocorrelation (i.e., automated within ArcGIS Pro®), the resulting cokriging parameters are provided in the Table 2 . Table 2 Groundwater and ground elevation interpolation parameters Fall 2020 Spring 2021 Groundwater elevation Ground elevation Groundwater elevation Ground elevation Nugget 0.0003 0.001 0.0013 0.0019 Major Range (m) 3,981.72 5,561.43 Sill 0.008 0.01 0.007 0.0023 Lag Size (m) 497.71 695.18 Number of lags 12 12 Maximum neighbors 15 10 15 10 Minimum neighbors 8 5 8 5 Sector type 1 Sector 1 Sector Average standard error (m) 4.35 4.34 The resulting grid spacing for fall and spring surfaces obtained from Eq. 1 was 210 m. Since cokriging does not identify and respect hydrologic boundaries, there were some contour lines that were inaccurately crossing streams or forming depressions; therefore, contours were modified to follow a path that matched the assumption that the water-table is hydraulically connected to surface water. Although the existence of a breach under a stream is possible (Brunner et al., 2011 ; Sophocleous, 2002 ; Urbano et al., 2006 ), there is insufficient data to substantiate those artificial depressions. Figure 4 (Fall 2020) and Fig. 5 (Spring 2021) show the water-table maps for Fall 2020 and Spring 2021, respectively. Anomalous water-table depressions An important outcome of developing a water-table map in this area, is the indication of potential aquitard breaches reflected by anomalous depressions (Figs. 4 and 5 , dark red boxes) as there is no known high-capacity pumping that would cause such depressions in these areas. Most, if not all, of the anomalous depressions shown in previous figures have been identified in the past through general mapping of the UCCU thickness (Parks, 1990 ), water-table maps (Konduro-Narsimha, 2007 ; Ogletree, 2016 ; Parks, 1990 ) or groundwater modeling (Jazaei et al., 2018; Villalpando-Vizcaino et al., 2021 ), with some localized efforts in depressions (A) through (E) (Fig. 4 a and Fig. 5 a) that include groundwater tracers, detailed water levels, surface water leakage, and drilling. Within the MLGW Sheahan wellfield (Fig. 3 , Figs. 4 (A) and 5 (A)), depression (A) exists in the water table with a 9-meter drop three kilometers long from Nonconnah Creek north towards the center of the wellfield. This depression has been previously identified and characterized through surface water leakage observations (Larsen et al., 2013 ; Nyman, 1965 ), groundwater tracers (Graham & Parks, 1986 ; Larsen et al., 2003 , 2013 ), water-table depressions (Konduro-Narsimha, 2007 ; Ogletree, 2016 ; Parks, 1990 ), drilling (Hasan personal communication, 2021), and groundwater modeling (Torres-Uribe et al., 2021; Villalpando-Vizcaino et al., 2021 ). Depression B, east of the MLGW Allen wellfield, was also noted by Bradshaw ( 2011 ) using tracer data as two potential breaches near Cane Creek; however, the exact location was not defined. Additional, reports from the Memphis Defense Depot (Memphis Depot; Fig. 3 ), indicate a connection between the water-table and Memphis aquifers based on geologic cross-sections (HDR, 2017 ). Depression C has been previously identified near the MLGW Lichterman wellfield is characterized by thinning or absent UCCU, a downward hydraulic gradient between the water-table and Memphis, and areas of inter-aquifer water exchange aquifers (Graham & Parks, 1986 ; Nyman, 1965 ; Smith, 2018 ). Similarly, Depression E was observed by Konduro-Narsimha ( 2007 ), Gallo ( 2015 ) and Ogletree ( 2016 ) and near the confluence of two branches of Fletcher Creek, correlating with a suspected breach identified by Parks ( 1990 ). Depression D is located near the former Shelby County Landfill in Shelby Farms (Fig. 3 ), where Bradley ( 1991 ) identified and confirmed a breach directly north of the landfill. Additional studies to substantiate and delineate this breach include seismic reflection (Waldron et al., 2009 ), electrical resistivity and geochemical analysis (Schoefernacker, 2018 ), groundwater tracers (Mirecki & Parks, 1994 ), groundwater modeling and geophysical methods (Gentry et al., 2006 ), and water-table maps (Konduro-Narsimha, 2007 ; Ogletree, 2016 ; Parks, 1990 ). Previous water-table maps (Konduro-Narsimha, 2007 ; Ogletree, 2016 ; Parks, 1990 ) identified Depressions A through E; however, the shape and extent differs due to changes in data control and the methodology followed to generate groundwater contours. Although the extent of anomalous depressions are an indicator or a potential breach, they do not provide a detailed shape and orientation due to lack of borehole or well control. The general depressions observed in the water-table maps are somewhat circular as they tend to be centered around a single control point. To better characterize the shape and size of the breaches, the water-table map method should be complemented by additional methods such as detailed geologic mapping, additional boreholes and geophysical methods. When comparing differences in the water table between fall Fig. 4 and spring Fig. 5 ., the most significant differences are found where data control is inconsistent as also observed by Ogletree ( 2016 ). Generally, wells and surface water features were measured during both seasons but some historical sites had available data for only one of the two seasons, causing data inconsistencies. Figure 6 shows some areas where there is a significant change in the water-table due to data control issues. Each Pair (1–2) represents the same spatial footprint shown in Fig. 4 and Fig. 5 . In Fig. 6 , Pair 1, the depression elongates to the northeast as two additional historical points are available for spring. Also, an 81 masl additional peak is observed in spring, south of Nonconnah Creek due to the addition of a historical point. On the other hand, in Fig. 6 , Pair 2, the historical point added in spring produces a new peak that was not observed in the fall surface. This produces a water level rise of 12 meters. In areas where control is maintained between the seasons, the general structure of the contours remains very similar, only changing in level, but not in shape. Similarly, areas that heavily relied on topography data for interpolation due to lack of data control, remained unchanged in shape and level regardless of the season. Seasonal analysis It was anticipated that spring water levels would be higher than those in the fall since the rainy season is during the winter and spring months. According to the USGS National Water Information System (NWIS) surface water database, the Wolf and Loosahatchie River stages typically remain at baseflow conditions between the months of July and December and rise to 4.5 m on average reaching their maxima during April and May. The Nonconnah Creek’s stage remains more stable throughout the year only rising after precipitation events but returning to baseflow conditions shortly (couple of days) after. This behavior was observed in the surface water locations between fall and spring; however, groundwater showed an anomalous seasonal behavior in some locations throughout the county, differing from the expected higher levels in spring than in fall. Figure 7 and Fig. 8 show water level changes between Fall 2020 and Spring 2021 for surface water and groundwater measurements. Surface water locations near the Mississippi River are affected by backwater conditions that can reach as much as 9 km upstream in the three major rivers in Shelby County. Backwater conditions can cause a rise in water levels during the spring by as much as 7 meters near the confluence of these rivers with the Mississippi River as seen in Fig. 7 . Water level changes between seasons became less significant moving upstream. Generally, surface water levels throughout the county were higher during spring with some exceptions such as Fletcher Creek, a tributary to the Wolf River in the central portion of the county, where water levels were an average 4 cm lower during the spring. Water levels along the rest of the tributaries rose less than 30 cm from fall to spring. Groundwater levels showed unexpected behavior in some cases as seen in Fig. 8 . Out of 124 groundwater monitoring sites measured during both seasons, 35 had higher elevations during the fall when compared to the spring. This resulted in a negative seasonal change (i.e., spring minus fall), which was considered abnormal. The average negative seasonal water level change was 23.6 cm with a standard deviation of 15.7 cm. The remaining wells behaved as expected with a positive seasonal change with an average variation of 88.1 cm with a standard deviation of 88.7 cm. Although the negative seasonal variation was less significant, a preliminary analysis was conducted to relate abnormal water level variations to a physical cause, such as proximity to open water bodies or confirmed breaches. Land use was also considered assuming that water infiltration is greater among more vegetated areas rather than impervious, developed zones. Results indicated no apparent causality; therefore, other factors were considered. An analysis was performed using long-term data recorded by pressure transducers (Solinst Inc. Levelogger®) deployed in 12 water-table monitoring wells throughout the county (Fig. 3 ). These transducers have been collecting pressure data every 15 minutes from 2019 to 2021. The objective was to observe whether short-term or long-term behaviors of the water table provided any reasoning for the negative seasonal differences. Short-term variation was set over a two-week period, both with a rolling average and specific to the survey periods. As provided in Table 3 , rolling two-week averages were taken at each instrumented well to see head variations. Likewise, the average variation of water levels during the survey periods is also listed (i.e., 19 days for the fall survey and 12 days for the spring). Table 3 Rolling average water level two-week variation. Values are in cm. Total Fall (9/14/20– 10/2/2020) Spring (3/29/2021–4/9/2021) Well ID Average SD Average SD Average SD Sh:K-156 15.33 4.90 11.89 1.48 17.82 1.67 Sh:R-032 10.00 4.99 5.97 0.54 12.29 2.70 Sh:J-172 28.71 9.26 27.30 3.28 44.53 14.57 Sh:J-206 25.99 7.67 20.16 2.51 33.93 8.85 Sh:J-196 24.89 6.97 21.86 2.68 34.89 7.79 Sh:J-220 22.23 8.87 15.38 2.54 32.75 6.88 Sh:K-171 36.28 19.19 17.61 5.45 57.14 13.77 Sh:L-110 UR-25S 78.84 75.26 19.02 10.01 142.56 64.98 Sh:K-169 28.44 9.66 21.81 3.95 46.52 12.83 Sh:K-163 39.99 15.56 44.76 19.20 38.56 6.10 Sh:J-242 62.35 47.10 17.81 5.16 102.39 48.65 GG-MW1 27.77 27.94 5.81 2.27 110.30 14.99 Average 33.40 19.78 19.11 4.92 56.14 16.98 A total average of 33.40 cm with a standard deviation (SD) of 19.78 cm were obtained from averaging the complete data set (2019–2021) of collected data of all 12 wells. For fall and spring, 19.11 cm (SD of 4.92) and 56.14 cm (SD of 16.98) averages were obtained, respectively. This suggests water levels tend to be less variable during fall than during spring, likely due to recharge in the spring. During the data collection period, it was planned that all wells in clustered areas (e.g., MLGW wellfields, Shelby Farms) were surveyed on the same day to ensure better data consistency between neighboring wells. However, given the size of the county and distribution of monitoring points, each survey took between two to three weeks to complete. Within this period, it is apparent from Table 3 that water levels could have shifted ± 33.40 cm (e.g., using the total average) depending on the day when collected. Figure 9 Seasonal change in all wells measured for both seasons. The black dashed box represents the total average ± 33.40 variation attributed to short term fluctuations of the water-table aquifer. Bars falling within the box (80% of red, 30.5% of blue) are shown as a lighter color. Additional IDs and water elevations for each well are found in Appendix 1. Out of all the water levels that decreased from fall to spring (i.e., negative change or red bars), 80% fall within the black box. Conversely, only 30.5% of the blue bars (i.e., higher levels in the spring compared to fall) fall within the box. This further shows that seasonal change was more significant in wells with higher levels during spring, but still does not fully explain the lower readings beyond the − 0.33 m threshold for some wells during the spring as compared to fall. Following the seasonal change analysis for all wells, the long-term variations (July 2019 to October 2021) of the water-table aquifer were analyzed using the observed readings collected by pressure transducers. Wells Sh:J-172 and Sh:J-220 (Fig. 10 and Fig. 11 ) were selected to illustrate the range of water-level fluctuations seen in the wells listed in Table 3 . With longer periods of record, seasonal groundwater patterns become more apparent. In the case of well Sh:J-172, data was collected 94 days earlier than the lowest value it had for fall, and 30 days earlier than the highest level in spring. Similarly, for well Sh:J-220, data collection for fall occurred 77 days before the water reached its lowest point, while spring data was collected right as the water table approached its highest peak of the year. A similar analysis was conducted with the rest of the wells with transducer data (Table 4 ). Wells Sh:K-156, Sh:K-163 and Sh:L-110 UR-25S are excluded from the analysis given that Sh:K-156 and Sh:K-163 are located within the breach in Sheahan wellfield and Sh:L-110 UR-25S is strongly influenced by Nonconnah Creek; hence, resulting in behaviors differing notably from the ones seen in Fig. 10 and Fig. 11 . Wells GG-MW1 and Sh:J-242 are also located in close proximity to the Wolf River and Nonconnah Creek, respectively; however, these follow a similar pattern to the rest of the wells in the analysis and, therefore, are included in the analysis. The reason why some wells near the same stream (i.e., L-110 UR-25S and Sh:J-242) have differing water level behaviors requires further investigation and is not addressed in this study. Table 4 lists each transducer water level for the dry and wet seasons, in comparison to the water levels obtained during the water level surveys. Table 4 Dates of lowest and highest (i.e., fall and spring) water levels recorded during the data collection periods in comparison to the lowest and highest dates and levels of each season. Fall 2020 Spring 2021 Lowest Point Data collection period (9/14/20 − 10/2/20) Highest Point Data collection period (3/29/21 − 4/9/21) Well Date Level (masl) Lowest date Level (masl) Days diff. Level diff. Date Level (masl) Highest date Level (masl) Days diff. Level diff. R-032 12/28/2020 84.93 10/1/2020 84.97 88 0.04 5/27/2021 85.16 4/7/2021 85.13 50 0.03 J-172 1/4/2021 74.51 10/2/2020 74.88 94 0.37 5/9/2021 76.24 4/9/2021 76.16 30 0.08 J-206 1/28/2021 63.1 10/2/2020 63.29 118 0.19 5/28/2021 63.88 4/9/2021 63.73 49 0.15 J-196 12/29/2020 67.96 10/2/2020 68.22 88 0.26 5/28/2021 68.84 4/9/2021 68.75 49 0.09 J-220 12/18/2020 66.25 10/2/2020 66.33 77 0.08 3/31/2021 67.05 3/31/2021 67.05 0 0 K-171 12/13/2020 76.85 10/2/2020 76.94 72 0.09 4/2/2021 78.79 4/2/2021 78.79 0 0 K-169 11/17/2020 84.47 10/2/2020 84.67 46 0.2 5/9/2021 86.62 4/9/2021 86.56 30 0.06 SAA-1 10/26/2020 62.3 10/2/2020 62.36 24 0.06 3/31/2021 65.48 3/31/2021 65.48 0 0 GG-MW1 12/13/2020 74.04 10/2/2020 74.15 72 0.11 4/2/2021 75.98 4/2/2021 75.98 0 0 Average 75.44 0.16 23.11 0.05 Results indicate that for Fall 2020, the water level survey concluded approximately 75 days before the water table reached its lowest level. At the lowest level, water levels were on average 16 cm lower than they were during the last day of the water level survey on October 2nd. Considering this 75-day difference, the ideal date to obtain the lowest water levels for fall is around the third week of December. During Spring 2021, four wells were measured when the water table was at its highest level; however, the remainder were surveyed approximately 23 days too early. It is not a simple case of shifting future spring survey dates as pushing the date forward would capture higher levels in some wells (e.g., Sh:R-032, Sh:J-172, Sh:J-206, Sh:J-196, Sh:K-169) while resulting in lower levels in others (e.g., Sh:J-220, Sh:K-171, Sh:J-242, GG-MW1). Nevertheless, with only an average 5-cm water level difference if shifted by 23 days, data collection any time during April is appropriate. Considering that water levels fell an average 16 cm from the last day of the fall survey and rose an average of 5 cm after the last day of the spring surveying, if the water-level surveys had been conducted at the right time for both seasons (i.e., lowest and highest levels of the year), water level variations would have been an average 21 cm higher (sum of both seasons shift averages). Recalling the average 23.6-cm seasonal variation within the wells that had higher levels in fall, the abnormal negative seasonal behavior observed may be attributed to the incorrect timing of the water-level surveys. Given that these ideal dates for data collection are based on data from the past two years, an extended period of data was required to identify dates of minimum and maximum levels from the water-table aquifer. There are several wells in Shelby County, screened within the water-table aquifer, with long-term monitoring data. However, most have measurements that are sporadic throughout the year, so it is difficult to estimate the actual dates of lowest and highest levels from this data. For this reason, an analysis was conducted using well Sh:P-099, which has been monitored daily by the USGS since 1994. Table 5 shows the dates of the highest and lowest level in the aquifer each year since 2015. Table 5 Highest and lowest levels per year recorded in well P-099 from 2015 to 2020 Year Maximum level Minimum level 2015 April 26th November 14th 2016 May 4th November 19th 2017 May 4th December 16th 2018 April 28th December 12th 2019 April 18th October 7th 2020 April 3rd November 17th Based on the dates from Tables 4 and 5 , it is observed that the approximate months to capture lowest and highest levels in the water table for future monitoring efforts are November-December, and April-May, respectively. Decadal analysis Data collected during the 2005 (Konduro-Narsimha, 2007 ) and 2015 (Ogletree, 2016 ) water-level surveys were reprocessed following the same methodology employed for this study to compare water tables over the past 15 years (2005 to 2020; see Figs. 12 and 13 ). Parks ( 1990 ) was not included because his data collection methods differed significantly from later surveys. Data collection for 2005 and 2015 were conducted during the fall months; thus, only the Fall 2020 surface was used for comparison purposes. There is significant variability in control for each year, especially with historical data and private wells. Historical sites are problematic since they are temporal and can change as new sites are added and some are closed over time, while private wells are impacted by factors such as: well destruction, upgrades that limit port accessibility, owner changes and denied access. Impacts to these sites affect the overall county control as these locations help filling out the data gaps outside wellfield clusters. Table 6 shows the amount of control points by category and by year. Table 6 Data control for survey years 2005 (Konduro-Narsimha, 2007 ), 2015 (Ogletree,2016), and 2020. Numbers in parentheses represent points that match with those measured in 2020. 2005 2015 2020 Public monitoring wells 114 (70) 104 (74) 99 Surface water crossings 56 (50) 52 (51) 69 Private wells 37 (9) 11 (7) 12 Historical sites 42 (0) 99 (5) 19 Total 249 (129) 266 (137) 199 As seen in Table 6 , data control generally decreased over time. Public monitoring wells, historical sites and private well measurements decreased from 2005 to 2020 with only surface water crossings increasing with time. It can be seen that data control increased from 2005 to 2015, but decreased from 2015 to 2020, with 2020 having the lowest data control between the three water-level surveys. The most notable differences between the 2005 and 2015 water-table surfaces and 2020 are found in areas with significant changes in data control. Ogletree ( 2016 ) analyzed on the differences in water-table elevations in relation to data control changes between 2005 and 2015 and concluded that considerable changes between water surfaces are not the result of physical changes in water-level elevations, but differences related to data control. Similar conditions are observed when comparing with 2020 data. Figure 14 shows Section 1 (Figs. 12 and 13 ), a clear example where significant changes in contour shape and levels can be attributed to differences in data control. For Section 1, the size of the observed elevated water level was significantly reduced from 2005 and 2015 to 2020. Both in 2005 and 2015, there was a historical site control point slightly offset from each other (yellow and blue triangles) that created a higher water level to the northeast of Section 1. In 2020, these historical points were absent, centralizing the peak around a single point towards the center of the map. The highest contour elevation was also reduced from 108 m in 2005 to 99 m in 2020. Areas of the county with less significant changes in data control have less variation in the shape of contours, particularly within the MLGW wellfields where data control tends to be more clustered and consistent. An additional analysis was conducted to observe the long-term water-level variation from 2005 to 2015 and 2020 (Fig. 15 ). Results indicate that in 2015, water levels were generally lower than 2005 while water levels in 2020 were overall higher than 2005, with some exceptions in both cases. For illustrative purposes only, the black dashed box represents the average ± 33.40 cm variation that could be attributed to short-term water-level fluctuations (Table 3 ) rather than a long-term change – recall that the short-term variation was over a shorter period, 2019–2021. On average, water levels were 0.27 m lower in 2015 than in 2005, while water levels in 2020 were 1.33 m higher than in 2005. To further examine water-table fluctuation trends observed for each year, a comparison with long-term monitoring data from well Sh:P-099 was conducted. This well is near the Memphis Zoo (see Fig. 3 ), has several water features that may leak and provide artificial recharge to the water-table aquifer. However, assuming the leakage is near constant, the general trend of the water table is relatively affected and simply shifts up. According to this site, groundwater levels in the water-table aquifer have been rising since monitoring began in 1994. Although in 2015 water levels fell below 2005 and 2020 levels (Fig. 16 ), matching the behavior shown in Fig. 15 for Number ID 48. According to the National Oceanic and Atmospheric Administration (NOAA), precipitation in Shelby County has increased on average 190 mm over the last 30 years (NOAA, 2021 ), which might account for the rising trend in water levels observed in well P-099. However, records show that 2005 was a drier year with an annual precipitation of 1084 mm, lower than 2015 and 2020 with 1403 and 1441 mm, respectively. This suggests that water levels in the water-table aquifer are influenced by more than just recharge from precipitation, or by differences in time scales between rain events and aquifer fluctuations. Probability surface The last analysis performed using the water level survey data was to ascertain areas of high prediction error in the derived surfaces which would direct future efforts to fill those gaps before the next survey. A map showing the areas with highest prediction error was generated (Fig. 17 ) based on survey location and the standard deviation of the values obtained from the interpolation. A total of 179 permanent monitoring locations from the monitoring network, which includes public and private wells, and stream crossings were used for this analysis. Due to the changing nature of historical sites, they were removed from this analysis. Significant errors (data gaps) ranging from 5.88 to 7.35 m are shown between the major rivers (i.e., Loosahatchie River, Wolf River, and Nonconnah Creek) in eastern Shelby County, where the highest error area of 7.35 m is found as it is away from utility wellfields, accessible private wells, and between river crossings. Additionally, there are pockets of high error along the periphery of the county as well as along the Loosahatchie River and northward where the majority of control is only surface water. Northern Shelby County monitoring control relies almost solely on stream crossings, and if the analysis was done exclusively considering monitoring wells, this area would have a significantly higher error than the one observed in Fig. 17 . Conclusion Mapping water levels of a water-table aquifer is useful for identifying areas of preferential leakage to the underlying confined aquifer through potential breaches in the intervening aquitard. Water-table maps for Fall 2020 and Spring 2021 show previously observed groundwater depressions within Sheahan wellfield, Shelby Farms, in areas west of Lichterman wellfield, east of Allen wellfield and east of McCord wellfield. The most significant differences in contour shape and water levels between both water tables are found in areas with non-coincident control between the two seasons. Outside of these historical depressions, no new depressions were observed with the current monitoring network. Typical seasonal behavior is observed in surface water bodies (i.e., rivers and tributaries) as water levels were generally higher during spring than fall, more significantly near the confluence of the three major rivers in the county with the Mississippi River where values were as high as 7 m. Seasonal differences decreased as locations moved upstream with some exceptions attributed to river geomorphology. Anomalous seasonal behavior (i.e., higher water levels during fall than spring) is seen in 35 out of the 124 wells surveyed for both seasons, with an average seasonal variation of 23.6 cm. The remaining wells showed an average 88.1 cm rise in water levels from fall to spring. A short-term analysis of the water table based on pressure transducer data showed an average ± 33.40 cm two-week variation in the water-table aquifer. Additional pressure transducer observations showed that the fall water-level survey concluded approximately 75 days before the water table approached its lowest level in fall (16 cm lower) while the spring survey occurred 23 days before it reached its highest peak (5 cm higher) in spring. Hence, abnormal seasonal change is attributed more to the timing of the water-level surveys rather than a physical phenomenon of the hydrogeology. From decadal data, it was observed that groundwater levels were higher in 2020 than in 2015 and 2005, while levels were lower in 2015 than 2005. Differences in the water-table maps for each survey are attributed to significant differences in data control. To identify areas to potentially improve the monitoring network in the future, a standard error map was generated and showed a prediction error of up to 7.35 m in areas with no control, more significantly in eastern Shelby County and between the major rivers, with the northern part of the county relying mostly on river crossing data. Declarations The authors declare no conflict of interest. Acknowledgements This research was funded by Memphis Light Gas and Water (MLGW) and supported by the Center for Applied Earth Science and Engineering Research (CAESER). The authors are grateful for the support provided by these sponsors and by the people that helped with this project. Data availability The datasets generated during and/or analyzed during the current study are available from the corresponding authors upon reasonable request. Funding This research was funded by Memphis Light Gas and Water (MLGW) and supported by the Center for Applied Earth Science and Engineering Research (CAESER). Author Contributions Daniela Lozano-Medina collected, processed and analyzed all data, wrote the main manuscript and prepared all figures and tables except for Figure 2. Dr. Brian Waldron also contributed to writing the main manuscript, and to the latter review of it. He also led the research. Dr. Scott Schoefernacker contributed to data collection logistics, handcontouring of the water-table maps, and latter review of the manuscript. Dr. Anzhelika Antipova supported all the statistical analysis. Rodrigo Villalpando-Vizcaino contributed to data collection, processing and analysing and latter review of the manuscript. References Ahmadi, S. H., & Sedghamiz, A. (2008). Application and evaluation of kriging and cokriging methods on groundwater depth mapping. Environmental Monitoring and Assessment , 138 (1), 357–368. https://doi.org/10.1007/s10661-007-9803-2 Boezio, M. N. M., Costa, J. F. C. L., & Koppe, J. C. (2006). Kriging with an external drift versus collocated cokriging for water table mapping. Applied Earth Science , 115 (3), 103–112. https://doi.org/10.1179/174327506X138896 Bradley, M. W. (1991). Ground-water hydrology and the effects of vertical leakage and leachate migration on ground-water quality near the Shelby County landfill, Memphis, Tennessee (Report No. 90–4075; Water-Resources Investigations Report). USGS Publications Warehouse. https://doi.org/10.3133/wri904075 Bradshaw, E. A. (2011). Assessment of Groundwater leakage through the upper claiborne confining unit to the Memphis aquifer in the Allen wellfield, Memphis, Tennessee . The University of Memphis. Brahana, J. V., & Broshears, R. E. (2001). Hydrogeology and ground-water flow in the Memphis and Fort Pillow aquifers in the Memphis area, Tennessee (Report No. 89–4131; Water-Resources Investigations Report, Issues 89–4131). USGS Publications Warehouse. https://doi.org/10.3133/wri894131 Brunner, P., Cook, P., & Simmons, C. (2011). Disconnected Surface Water and Groundwater: From Theory to Practice. Ground Water , 49 , 460–467. https://doi.org/10.1111/j.1745-6584.2010.00752.x CAESER. (2020). Shelby County LiDAR surface 2018 . Carmichael, J. K., Kingsbury, J. A., Larsen, D., & Schoefernacker, S. (2018). Preliminary evaluation of the hydrogeology and groundwater quality of the Mississippi River Valley Alluvial Aquifer and Memphis Aquifer at the Tennessee Valley Authority Allen Power Plants, Memphis, Shelby County, Tennessee (Report No. 2018–1097; Open-File Report, Issues 2018–1097, p. 78). USGS Publications Warehouse. https://doi.org/10.3133/ofr20181097 Chung, J., & Rogers, J. D. (2012). Interpolations of Groundwater Table Elevation in Dissected Uplands. Groundwater , 50 (4), 598–607. https://doi.org/10.1111/j.1745-6584.2011.00889.x Clark, B. R., & Hart, R. M. (2009). The Mississippi Embayment Regional Aquifer Study (MERAS): Documentation of a groundwater-flow model constructed to assess water availability in the Mississippi embayment (Report No. 2009–5172; Scientific Investigations Report). USGS Publications Warehouse. https://doi.org/10.3133/sir20095172 Criner, J. H., & Parks, W. S. (1976). Historic water-level changes and pumpage from the principal aquifers of the Memphis area, Tennessee: 1886–1975 (Report No. 76–67; Water-Resources Investigations Report, Issues 76–67, p. 54). USGS Publications Warehouse. https://doi.org/10.3133/wri7667 Criner, J. H., Sun, P.-C. P., & Nyman, D. J. (1964). Hydrology of aquifer systems in the Memphis area, Tennessee (Report No. 1779O; Water Supply Paper, Issue 1779O). USGS Publications Warehouse. https://doi.org/10.3133/wsp1779O Cunningham, W. L., & Schalk, C. W. (2011). Groundwater Technical Procedures of the U.S. Geological Survey (Techniques and Methods) [Techniques and Methods]. United States Geological Survey. Desbarats, A. J., Logan, C. E., Hinton, M. J., & Sharpe, D. R. (2002). On the kriging of water table elevations using collateral information from a digital elevation model. Journal of Hydrology , 14. Dieter, C. A., Maupin, M. A., Caldwell, R. R., Harris, M. A., Ivahnenko, T. I., Lovelace, J. K., Barber, N. L., & Linsey, K. S. (2018). Estimated use of water in the United States in 2015 (Report No. 1441; Circular, p. 76). USGS Publications Warehouse. https://doi.org/10.3133/cir1441 Gallo, H. (2015). Hydrologic and geochemical investigation of modern leakage near the McCord wellfield, Memphis, Tennessee . The University of Memphis. Gentry, R., Mckay, L., Thonnard, N., Anerson, J., & Larsen, D. (2006). Novel Techniques for Investigating Recharge to the Memphis Aquifer . Graham, D. D. (1982). Effects of urban development on the aquifers in the Memphis area, Tennessee (Report No. 82–4024; Water-Resources Investigations Report, Issues 82–4024). USGS Publications Warehouse. https://doi.org/10.3133/wri824024 Graham, D. D., & Parks, W. S. (1986). Potential for leakage among principal aquifers in the Memphis area, Tennessee (Report No. 85–4295; Water-Resources Investigations Report, Issues 85–4295). USGS Publications Warehouse. https://doi.org/10.3133/wri854295 HDR. (2017). April 2017 Lont-Term Monitoring Summart Report: Defense Depot Memphis, Tennessee . Hengl, T. (2006). Finding the right pixel size. Computers & Geosciences , 32 , 1283–1298. https://doi.org/10.1016/j.cageo.2005.11.008 Hoeksema, R. J., Clapp, R. B., Thomas, A. L., Hunley, A. E., Farrow, N. D., & Dearstone, K. C. (1989). Cokriging model for estimation of water table elevation. Water Resources Research , 25 (3), 429–438. https://doi.org/10.1029/WR025i003p00429 Isaaks, E. H., & Srivastava, R. M. (1991). An Introduction to Applied Geostatistics. Computers & Geosciences , 17 (3), 471–473. https://doi.org/10.1016/0098-3004(91)90055-I Kingsbury, J. A. (1996). Altitude of the potentiometric surfaces, September 1995, and historical water-level changes in the Memphis and Fort Pillow aquifers in the Memphis area, Tennessee (Report No. 96–4278; Water-Resources Investigations Report, Issues 96–4278). USGS Publications Warehouse. https://doi.org/10.3133/wri964278 Kingsbury, J. A. (2018). Altitude of the potentiometric surface, 2000–15, and historical water-level changes in the Memphis aquifer in the Memphis area, Tennessee (Report No. 3415; Scientific Investigations Map). USGS Publications Warehouse. https://doi.org/10.3133/sim3415 Kingsbury, J. A., & Parks, W. S. (1993). Hydrogeology of the principal aquifers and relation of faults to interaquifer leakage in the Memphis area, Tennessee (Report No. 93–4075; Water-Resources Investigations Report, Issues 93–4075). USGS Publications Warehouse. https://doi.org/10.3133/wri934075 Konduro-Narsimha, V. K. (2007). Altitudes of Water Levels 2005, and Historic Water Level Change in Surficial and Memphis Aquifer . University of Memphis. Larsen, D., Gentry, R. W., & Solomon, D. K. (2003). The geochemistry and mixing of leakage in a semi-confined aquifer at a municipal well field, Memphis, Tennessee, USA. Applied Geochemistry , 21. Larsen, D., Morat, J., Waldron, B., Ivey, S., & Anderson, J. (2013). Stream Loss Contributions to a Municipal Water Supply Aquifer in Memphis, Tennessee. Environmental and Engineering Geoscience , 19 , 265–287. https://doi.org/10.2113/gseegeosci.19.3.265 Larsen, D., Waldron, B., Schoefernacker, S., Gallo, H., Koban, J., & Bradshaw, E. (2016). Application of Environmental Tracers in the Memphis Aquifer and Implication for Sustainability of Groundwater Resources in the Memphis Metropolitan Area , Tennessee. Journal of Contemporary Water Research & Education, 159(1), 78–104. https://doi.org/10.1111/j.1936-704X.2016.03231.x Lloyd, O.B.; Lyke, W.L. (1995). Ground Water Atlas of the United States: Segment 10, Illinois, Indiana, Kentucky, Ohio, Tennessee. U.S. Geological Survey Hydrologic Atlas, 730-K; 30p. Mirecki, J. E., & Parks, W. S. (1994). Leachate geochemistry at a municipal landfill, Memphis, Tennessee. Ground Water , 32 (3), 390–398. USGS Publications Warehouse. NOAA, N. C. for E. information. (2021). Climate at a Glance: County Time Series . https://www.ncdc.noaa.gov/cag/ Nyman, D. J. (1965). Predicted hydrologic effects of pumping from the Lichterman Well Field in the Memphis Area, Tennessee (Report No. 1819B; Water Supply Paper). USGS Publications Warehouse. https://doi.org/10.3133/wsp1819B Ogletree, B. T. (2016). Geostatistical Analysis of the Water Table Aquifer in Shelby County, Tennessee . University of Memphis. Olea, R. A., & Davis, J. C. (1999). Optimizing the High Plains Aquifer Water-level Observation Network (Open-File No. 1999–15). Kansas Geological Survey. https://www.kgs.ku.edu/Hydro/Levels/OFR99_15/index.html Oyana, T. J., & Margai, F. (2015). Spatial Analysis: Statistics, Visualization, and Computational Methods . CRC Press. https://books.google.com/books?id=h2FECgAAQBAJ Parks, W. S. (1990). Assessment of the potential for contamination of the Memphis Aquifer, in the Memphis area, Tennessee (p. 44). United States Geological Survey. Parks, W. S., & Carmichael, J. K. (1990). Geology and ground-water resources of the Memphis Sand in western Tennessee (Report No. 88–4182; Water-Resources Investigations Report, Issues 88–4182). USGS Publications Warehouse. https://doi.org/10.3133/wri884182 Ruopu Li & Lin Zhao. (2011). Vadose zone mapping using geographic information systems and geostatistics a case study in the Elkhorn River Basin, Nebraska, USA. 2011 International Symposium on Water Resource and Environmental Protection , 3177–3179. https://doi.org/10.1109/ISWREP.2011.5893555 Schoefernacker, S. (2018). Evaluation and evolution of a groundwater contaminant plume at the former Shelby County Landfill, Memphis, Tennessee . The University of Memphis. Smith, M. R. (2018). Evaluating modern recharge to the Memphis aquifer at the Lichterman wellfield, Memphis, Tennessee . The University of Memphis. Snyder, D. T. (2008). Estimated depth to ground water and configuration of the water table in the Portland, Oregon area (Report 2008–5059; p. 40). U.S. Geological Survey. https://pubs.usgs.gov/sir/2008/5059/ Sophocleous, M. (2002). Interactions between groundwater and surface water: The state of the science. Hydrogeology Journal , 10 (1), 52–67. https://doi.org/10.1007/s10040-001-0170-8 Theodossiou, N., & Latinopoulos, P. (2006). Evaluation and optimisation of groundwater observation networks using the Kriging methodology. Environmental Modelling & Software , 21 (7), 991–1000. https://doi.org/10.1016/j.envsoft.2005.05.001 Torres-Uribe, H. E. (2020). Application of a numerical grounwater model to determine the spatial configuration of breaches near a municipal well field in Memphis, Tennessee . The University of Memphis. Torres-Uribe Hugo E., Waldron Brian, Larsen Daniel, & Schoefernacker Scott. (2021). Application of Numerical Groundwater Model to Determine Spatial Configuration of Confining Unit Breaches near a Municipal Well Field in Memphis, Tennessee. Journal of Hydrologic Engineering , 26 (9), 05021021. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002117 Urbano, L., Waldron, B., Larsen, D., & Shook, H. (2006). Groundwater–surfacewater interactions at the transition of an aquifer from unconfined to confined. Journal of Hydrology , 321 , 200–212. https://doi.org/10.1016/j.jhydrol.2005.08.001 Villalpando-Vizcaino, R., Waldron, B., Larsen, D., & Schoefernacker, S. (2021). Development of a Numerical Multi-Layered Groundwater Model to Simulate Inter-Aquifer Water Exchange in Shelby County, Tennessee. Water , 13 (18). https://doi.org/10.3390/w13182583 Waldron, B., Harris, J. B., Larsen, D., & Pell, A. (2009). Mapping an aquitard breach using shear-wave seismic reflection. Hydrogeology Journal , 17 (3), 505–517. https://doi.org/10.1007/s10040-008-0400-4 Waldron, B., & Larsen, D. (2015). Pre-Development Groundwater Conditions Surrounding Memphis, Tennessee: Controversy and Unexpected Outcomes. JAWRA Journal of the American Water Resources Association , 51 (1), 133–153. https://doi.org/10.1111/jawr.12240 Waldron, B., Larsen, D., Hannigan, R., Csontos, R., Anderson, J., Dowling, C., & Bouldin, J. (2011). Mississippi Embayment Regional Ground Water Study (EPA/600/R-10/130; Issue EPA/600/R-10/130, p. 192). U.S. Environmental Protection Agency. Additional Declarations No competing interests reported. Supplementary Files Appendix1.docx Cite Share Download PDF Status: Published Journal Publication published 14 Jul, 2023 Read the published version in Environmental Monitoring and Assessment → Version 1 posted Editorial decision: Major revision 15 Feb, 2023 Editor assigned by journal 15 Feb, 2023 Submission checks completed at journal 15 Feb, 2023 First submitted to journal 23 Jan, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2507984","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":176290428,"identity":"83e1a6d0-a6c5-48a0-ab2e-9b4507a3ecff","order_by":0,"name":"Daniela Lozano-Medina","email":"data:image/png;base64,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","orcid":"","institution":"The University of Memphis","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Daniela","middleName":"","lastName":"Lozano-Medina","suffix":""},{"id":176290429,"identity":"6ab4e3e0-6862-4a0e-b2b9-04344fa2671a","order_by":1,"name":"Brian Waldron","email":"","orcid":"","institution":"The University of Memphis","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Brian","middleName":"","lastName":"Waldron","suffix":""},{"id":176290430,"identity":"fbf31f85-4e5c-46aa-8b7c-831732738eca","order_by":2,"name":"Scott Schoefernacker","email":"","orcid":"","institution":"The University of Memphis","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Scott","middleName":"","lastName":"Schoefernacker","suffix":""},{"id":176290431,"identity":"24219a1b-f296-49e6-a2ff-7f129002a41d","order_by":3,"name":"Anzhelika Antipova","email":"","orcid":"","institution":"The University of Memphis","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Anzhelika","middleName":"","lastName":"Antipova","suffix":""},{"id":176290432,"identity":"7041264b-16ce-4827-b67e-73a92bed3e6b","order_by":4,"name":"Rodrigo Villalpando-Vizcaino","email":"","orcid":"","institution":"The University of Memphis","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rodrigo","middleName":"","lastName":"Villalpando-Vizcaino","suffix":""}],"badges":[],"createdAt":"2023-01-23 18:29:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2507984/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2507984/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10661-023-11531-z","type":"published","date":"2023-07-15T01:08:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":33055605,"identity":"10bb91f7-04f1-4339-aff4-6e32ae07ada3","added_by":"auto","created_at":"2023-02-16 22:13:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":7647403,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of Shelby County within the Mississippi embayment aquifer system (Clark \u0026amp; Hart, 2009)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2507984/v1/cce247546e39eca97cf3df72.png"},{"id":33056301,"identity":"a1669d89-f218-48e1-9f34-a072be445015","added_by":"auto","created_at":"2023-02-16 22:29:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":440121,"visible":true,"origin":"","legend":"\u003cp\u003eWater-level network locations with public and private wells, surface water features, TDEC sites, and flowing springs.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-2507984/v1/9490cf71079a8dfb770f6980.png"},{"id":33056308,"identity":"ee629a55-80ef-437e-872a-e178830deefb","added_by":"auto","created_at":"2023-02-16 22:29:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":813730,"visible":true,"origin":"","legend":"\u003cp\u003eFall 2020 water-table map. Gray lines represent original contours from co-kriging where dashed-colored contours represent manual adjustments proximal to streams. Dotted lines represent approximate locations. Dashed lines were manually modified. Hatched lines represent depressions. Dashed red boxes A-E represent water-table depressions shown in insets A-E; Pairs 1-2 blue dashed boxes are compared to Fig. 5 (Spring 2021 water-table map); Section 1 (black dotted line) is compares to water-table maps from 2005 and 2015.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-2507984/v1/12d739eafce16ad65b172e74.png"},{"id":33056160,"identity":"9f5179e2-d7c1-44e7-8ff6-4a42e27959a5","added_by":"auto","created_at":"2023-02-16 22:21:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":769379,"visible":true,"origin":"","legend":"\u003cp\u003eSpring 2021 water-table map. Gray lines represent original contours from co-kriging where dashed-colored contours represent manual adjustments proximal to streams. Dotted lines represent approximate locations. Dashed lines were manually modified. Hatched lines represent depressions. Dashed red boxes A-E represent water-table depressions; Pair 1-2 in blue dashed boxes are compared to Fig. 4 (Fall 2020 water-table map). b) Insets of water-table depressions, same as boxes A-E.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-2507984/v1/3400c57bbfa6d025968d1dce.png"},{"id":33056714,"identity":"86f6b8b8-b8de-4c79-9bf9-9b9b285199ee","added_by":"auto","created_at":"2023-02-16 22:37:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":8187069,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant change in contour shape and dimensions from fall to spring following the addition of a single control point in the latter season in each case, indicated in a red dashed box. Each pair (1-2) is obtained from the same spatial footprint, all indicated in dark blue dashed boxes in Fig. 4 and Fig. 5. Gray lines represent original contours from co-kriging where dashed-colored contours represent manual adjustments proximal to streams. Dotted lines represent approximate locations. Dashed lines were manually modified. Hatched lines represent depressions\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-2507984/v1/0ea4c4cba32644898050df4c.png"},{"id":33057422,"identity":"16072058-e5ad-4365-8c0a-2ad8aa92253c","added_by":"auto","created_at":"2023-02-16 22:45:01","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":63805,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal change in all wells measured for both seasons. The black dashed box represents the total average ±33.40 variation attributed to short term fluctuations of the water-table aquifer. Bars falling within the box (80% of red, 30.5% of blue) are shown as a lighter color. Additional IDs and water elevations for each well are found in Appendix 1.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-2507984/v1/c4bb8f025a190c12b8588a07.png"},{"id":33056704,"identity":"f66028e6-c75f-4848-8ba4-87124239fb60","added_by":"auto","created_at":"2023-02-16 22:37:01","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":2616692,"visible":true,"origin":"","legend":"\u003cp\u003eContinuous groundwater elevation for well Sh:J-172. Data collection periods for fall (orange shade) and spring (green shade) are also shown, along with the lowest and highest (i.e., dry season and wet season) water levels recorded for each season, displayed as gray dashed lines.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-2507984/v1/551ecba7da8a08936488d040.png"},{"id":33056711,"identity":"7aa2ddf2-f0a8-4da6-a5d3-9857bd0616d2","added_by":"auto","created_at":"2023-02-16 22:37:01","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":778843,"visible":true,"origin":"","legend":"\u003cp\u003e2005 water-table map produced following the same methodology as Figs. 4 and 5, using data collected during Fall 2005. Gray lines represent original contours from co-kriging where dashed-colored contours represent manual adjustments proximal to streams. Dotted lines represent approximate locations. Dashed lines were manually modified. Hatched lines represent depressions. Black dotted box represents the section used for decadal comparison and are also seen in Fig. 4 and Fig. 13.\u003c/p\u003e","description":"","filename":"Figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-2507984/v1/353aff655b6409b146721111.png"},{"id":33056172,"identity":"2a3e0ed1-aa9e-4516-a660-b997c1cff7fa","added_by":"auto","created_at":"2023-02-16 22:21:01","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":889667,"visible":true,"origin":"","legend":"\u003cp\u003e2015 water-table map produced following the same methodology as Figs.4 and 5, using data collected during Fall 2015. Gray lines represent original contours from co-kriging where dashed-colored contours represent manual adjustments proximal to streams. Dotted lines represent approximate locations. Dashed lines were manually modified. Hatched lines represent depressions. Black dotted box represents the section used for decadal comparison and are also seen in Fig. 4 and Fig. 12.\u003c/p\u003e","description":"","filename":"Figure13.png","url":"https://assets-eu.researchsquare.com/files/rs-2507984/v1/90592640ada758345f34488b.png"},{"id":33055621,"identity":"1ce33053-5211-41ab-87a1-a5bb6ad67330","added_by":"auto","created_at":"2023-02-16 22:13:02","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":5804511,"visible":true,"origin":"","legend":"\u003cp\u003eDecadal comparison between water-table maps. Sections have coincident footprints in each year’s map. Contour intervals remain consistent for comparison purposes, except for the highest and lowest value in each case. Surveyed features unique to a single survey are shown in primary-colored triangles; features found in any two survey combination are shown in secondary color squares; and features for found in all three surveys are displayed as gray circles.\u003c/p\u003e","description":"","filename":"Figure14.png","url":"https://assets-eu.researchsquare.com/files/rs-2507984/v1/46d2bad2d3bd402a7e926142.png"},{"id":33057428,"identity":"1ee888c9-e8d8-470e-86a0-9038f81ad494","added_by":"auto","created_at":"2023-02-16 22:45:01","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":2570416,"visible":true,"origin":"","legend":"\u003cp\u003eWell Sh:P-099 historical groundwater level (MASL) obtained from long-term USGS monitoring records. Water-level survey years are indicated with arrows and the highest level for each survey is represented in gray dashed lines for comparison purposes.\u003c/p\u003e","description":"","filename":"Figure16.png","url":"https://assets-eu.researchsquare.com/files/rs-2507984/v1/59ad03cf6804ef7dd264f688.png"},{"id":41737022,"identity":"b4ec1862-38cb-44ab-a0ac-b1b6cd1bedcc","added_by":"auto","created_at":"2023-08-18 04:14:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5939234,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2507984/v1/4ec5109c-d7e4-4afe-9a39-503bc710aa1f.pdf"},{"id":33055604,"identity":"c57e7c97-3f0a-474d-8014-8a394e23d53e","added_by":"auto","created_at":"2023-02-16 22:13:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":81869,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-2507984/v1/489daeb0c16a7a05b9b4c38d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Stories of a water-table: anomalous depressions, aquitard breaches and seasonal implications, Shelby County, Tennessee, USA.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGroundwater is an important source of drinking water in many parts of the world and understanding its flow and fluctuations within a hydrogeological system is crucial to protecting this critical resource. Water-level monitoring allows a glimpse of where the water is and where it is moving to. A less common benefit is found in stressed aquifers that are impacted through inter-aquifer water exchange which is common and naturally occurring. This leakage can be exacerbated when preferential flow paths exist through natural breaches in an aquitard, allowing for modern water to infiltrate into an underlying aquifer causing water quality concerns. Water levels can show the areas where this preferential exchange occurs beneath the surface (Bradshaw, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Konduro-Narsimha, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Ogletree, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). An example can be found in the multi-layered aquifer system of the Mississippi embayment in Shelby County, Tennessee, where the presence of aquitard breaches has been investigated for decades (Brahana \u0026amp; Broshears, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Carmichael et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Criner et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1964\u003c/span\u003e; Graham \u0026amp; Parks, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Kingsbury \u0026amp; Parks, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Konduro-Narsimha, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Larsen et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ogletree, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Parks, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Schoefernacker, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Torres-Uribe, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Waldron et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSignificant withdrawals for municipal and industrial uses have caused substantial water-level declines (\u0026gt;\u0026thinsp;35 m) in the Memphis aquifer, the primary water source for this region since the late 1800s (Brahana \u0026amp; Broshears, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Criner \u0026amp; Parks, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1976\u003c/span\u003e). This decline has resulted in a downward vertical gradient where water from the unstressed water-table aquifer finds preferential leakage paths through breaches in the intervening aquitard between these two aquifers (Brahana \u0026amp; Broshears, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Criner et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1964\u003c/span\u003e; Criner \u0026amp; Parks, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1976\u003c/span\u003e; Graham, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Kingsbury, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Parks \u0026amp; Carmichael, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Waldron \u0026amp; Larsen, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Given that the water-table aquifer is more susceptible to contamination from anthropogenic sources due to its unconfined condition and is of lesser water quality than the Memphis aquifer, identifying aquitard breaches between these two aquifers is paramount.\u003c/p\u003e \u003cp\u003eA valuable product of collecting water levels in the water-table aquifer (or shallow aquifer) is the development of a water surface where anomalous depressions can help identify these hidden breaches since pumping from the water-table aquifer is limited. Another valuable use is their incorporation into ongoing numerical modeling of the area\u0026rsquo;s groundwater resources (Clark \u0026amp; Hart, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Torres-Uribe, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Villalpando-Vizcaino et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Hence, this investigation seeks to (1) map water levels in the water-table aquifer; (2) identify potential aquitard breaches; (3) address seasonal water-level fluctuations; and (4) provide data for the calibration of the Shelby County numerical groundwater model. In addition, this research aims to illustrate the importance of data control and appropriate data acquisition timing.\u003c/p\u003e \u003cp\u003eStudy area\u003c/p\u003e \u003cp\u003eThe Mississippi embayment is a collection of unconsolidated aquifers and aquitardsthat underlies portions of eight states in the south-central United States (Clark \u0026amp; Hart, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Graham \u0026amp; Parks, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Waldron et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Located within the embayment is Shelby County, Tennessee, which solely relies on groundwater for public supply, with a total withdrawal of 696,000 m\u003csup\u003e3\u003c/sup\u003e/day in 2015 (Dieter et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere are three primary freshwater aquifers in Shelby County: the water-table, Memphis and Fort Pillow aquifers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The water-table aquifer ranges in thickness from 0 to 30 m and comprised of alluvial and fluvial deposits throughout the county (Brahana \u0026amp; Broshears, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Graham \u0026amp; Parks, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Konduro-Narsimha, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Parks \u0026amp; Carmichael, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) and includes the Mississippi River valley alluvial (MRVA) aquifer on the westside of the bluff line (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) (Lloyd and Lyke, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1995\u003c/span\u003e); however, the MRVA aquifer is not investigated in this study. The water-table aquifer in the eastern portion of the county corresponds to the unconfined area of the Memphis aquifer (Parks, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Urbano et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The water-table aquifer supplies water to some domestic and farm wells (Parks, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Waldron et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) although high-capacity pumping in the water-table aquifer is limited or non-existent.\u003c/p\u003e \u003cp\u003eThe water-table aquifer is underlaid by the upper Claiborne confining unit (UCCU), ranging in thickness from 1 to 61 m (Larsen et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The UCCU is comprised of the Cockfield and Cook Mountain formations (Larsen et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and acts as an aquitard, limiting the downward vertical water exchange between the water-table and Memphis aquifers (Graham \u0026amp; Parks, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), except for those areas, termed \u0026ldquo;breaches\u0026rdquo;, where the UCCU is either absent or thinning, or has fault-related connections.\u003c/p\u003e \u003cp\u003eUnderlying the UCCU is the Memphis aquifer which is composed primarily of sand, with some clay and lignite and ranges from 122 to 274 m thick (Larsen et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). It is the most productive aquifer in the Memphis area providing approximately 95% of the groundwater used for domestic, industrial and agricultural uses (Graham \u0026amp; Parks, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Kingsbury, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The underlying Flour Island confining unit separates the Memphis and Fort Pillow aquifers, which is another important aquifer to the area. Only the water-table aquifer and, by proxy of suspected breach locations in the UCCU, are considered in this investigation.\u003c/p\u003e "},{"header":"Methodology","content":"\u003cp\u003eThe development of a water-table map requires the identification of measurement locations, various measurement procedures, data processing, and interpolation of the final water levels. Three prior investigations were performed in 1987 (Parks \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), 2005 (Konduro-Narsimha, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and 2015 (Ogletree, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) which are compared with the Fall 2020 survey of this investigation. However, this effort builds upon prior measured locations from these studies and follows more closely the post-processing procedures developed by Ogletree (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), that generated water-table maps using Empirical Bayesian Kriging incorporating ground elevation as a secondary variable.\u003c/p\u003e \u003cp\u003eAll prior investigations took water-level measurements during the dry season (September to early November) at available wells screened within the water-table aquifer. Surface water measurements along major rivers and tributaries were collected assuming aquifer connection and mostly gaining conditions as suggested by Parks (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Konduro-Narsimha (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and Ogletree (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) took physical measurements of stream surface elevations while Parks (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) relied on historical U.S. Geological Survey (USGS) 7.5-minute quadrangle elevation contours at stream crossings. These measurements represented a 40-year span, though Parks (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) concluded that any physical changes over this 40-year span were insignificant.\u003c/p\u003e \u003cp\u003eData collection\u003c/p\u003e \u003cp\u003eThis investigation collected water-level measurements at water-table monitoring and private wells in addition to surface water levels at bridge crossings following Konduro-Narsimha (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and Ogletree (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A compilation of historical water levels from wells screened within the water-table aquifer at monitored sites (e.g., Divisions of Underground Storage Tanks or Remediation, termed LUST and DOR, repsectively) were obtained from the Tennessee Department of Environment and Conservation (TDEC) for both dry and wet seasons where available. Similar to Konduro-Narsimha (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and Ogletree (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), these historical water levels were compiled and averaged over the 5-year period to be incorporated to the dataset.\u003c/p\u003e \u003cp\u003eUnlike prior investigations, this study also performed a water-level survey during the wet season. The first water-level survey was conducted from mid-September through early October 2020. The second survey was conducted from late March through early April 2021. Following the USGS Groundwater Technical Procedures, depth to water was measured using Solinst electric water-level meters (e-tapes) calibrated through the USGS Hydrologic Instrumentation Facility (HIF) program prior to the surveys (Cunningham \u0026amp; Schalk, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Water levels were obtained from 99 wells throughout the county, usually located proximal to utility wellfields (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), with some exceptions of isolated wells scattered throughout the county. Given the scarcity of public monitoring wells in rural areas of unincorporated Shelby County (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), an assessment of privately owned wells was conducted. Approximately 60 private wells were identified from the Shelby County Health Department records as screened within the water-table aquifer, yet only nine were used for water-level measurements due to property access and well construction restrictions.\u003c/p\u003e \u003cp\u003eDirect connection between surface water bodies (i.e., rivers and tributaries) and the water-table aquifer was assumed to exist based on Parks (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) and Larsen et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); therefore, water levels were collected from three main rivers in the area: the Loosahatchie River, Wolf River, and Nonconnah Creek, as well as their tributaries. Following the methodologies described by Konduro-Narsimha (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and Ogletree (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), water-level measurements were obtained at stream-bridge crossings using previously defined benchmarks (i.e., pre-installed bridge railing plates) as the point of measure. In some cases, there were no pre-installed plates so a different point-of-measure was used.\u003c/p\u003e \u003cp\u003ePlate placement, which occurred during Konduro-Narsimha (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), attempted to find minimal surface water displacement since bridge crossing can constrict flow and often have erosion control structures. The same was attempted when finding alternative measuring points. E-tapes were extended from the designated measuring points down to the water surface, watching for wind effects to ensure a vertical dropdown to the water surface. Though not ultimately used, water levels were also obtained from flowing springs in isolated parts of the county. Most of the springs, except for one, were in the Shelby Forest area (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These measurements were later discarded from the final dataset as they are located within the MRVA aquifer west of the bluff line (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the western boundary for this study.\u003c/p\u003e \u003cp\u003eTo minimize spatial and measurement inaccuracies, all accessed features (e.g., wells and river benchmarks) were surveyed using a survey-grade R2 Trimble Global Positioning System (GPS) unit. Spatial precision (x,y) was less than 1 cm with a vertical precision (z) less than 5 cm. The GPS unit accuracy was regularly tested against a U.S. Army Corps of Engineers\u0026rsquo; first-order, grade A survey marker prior to surveying. As mentioned, historical water levels from sites monitored by the TDEC were obtained for the dry and wet season periods for the five-year period, 2015 to 2020. Available data was averaged to a single value per site. A total of 22 leaking underground storage tank (LUST) and 111 Division of Remediation (DOR) sites were reviewed, resulting in four LUST and 18 DOR sites that met the criteria and were added to the dataset (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eData processing\u003c/p\u003e \u003cp\u003eCollected water level elevations (Appendix 1) were interpolated using cokriging. Kriging is a widely used interpolation method that estimates missing values based on the weighted average of the available data (Isaaks \u0026amp; Srivastava, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Snyder, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This interpolation scheme modifies the weights of clustered data so that grouped points with similar information are assigned a lower weight (Snyder, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Additionally, and contrary to other interpolation methods, kriging estimates the standard interpolation error which can indicate where additional control is needed to reduce interpolation uncertainty (Olea \u0026amp; Davis, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Theodossiou \u0026amp; Latinopoulos, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere are several kriging variations with differing approaches, including ordinary kriging, universal kriging, Bayesian kriging, cokriging, etc. (Oyana \u0026amp; Margai, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Given the sparsity and clustering of measurement points, cokriging was selected as the interpolation method for this study, similar to others (Ahmadi \u0026amp; Sedghamiz, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Boezio et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Chung \u0026amp; Rogers, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hoeksema et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Ogletree, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ruopu Li \u0026amp; Lin Zhao, 2011). Cokriging minimizes the variance of the estimation error by using more than one variable to compute missing values (Isaaks \u0026amp; Srivastava, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). The primary variable used was water elevation and the secondary variable ground surface elevation (See Appendix 1), where it was assumed that the unconfined aquifer water levels generally conforms to the topography (Desbarats et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Parks, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Ground surface elevation data was obtained from a 1-m LiDAR Digital Elevation Model (DEM) of Shelby County generated in 2020 (CAESER, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrior to cokriging, it was necessary to determine if a strong correlation between groundwater elevations and topography existed. A Pearson correlation coefficient of 0.70 was obtained; close to the 0.73 coefficient obtained by Snyder (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) in a similar investigation. This correlation coefficient used the 1-m LiDAR; however, to better relate to the smallest sampling spatial resolution, a sensitivity test was conducted. The sensitivity test assessed the advantage of resampling the highly detailed 1-meter LiDAR DEM surface to a larger resampled size using a bilinear resampling technique. Following Desbarats et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), increasing the cell size to 90 m did not notably change the correlation coefficient of 0.70. Therefore, this resampled surface was used in the cokriging processing which reduced the cokriging processing time significantly.\u003c/p\u003e \u003cp\u003eThe result of cokriging produces an interpolated surface, or raster with a grid size that followed the methodology described by (Hengl, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) using Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The square-grid size (P) is a function of an empirical constant, the study area (A) and the number of sampling points (N).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$P=0.0791\\sqrt{\\frac{A}{N}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSubsequent to creating rasters, contours were produced by first applying a smoothing algorithm to the rasters by averaging each cell with its surrounding neighbors within a 1 km radius so to capture four neighboring cells. Then, contours were generated on a 3-meter interval given the typical 10 ft interval used for the study area (Criner \u0026amp; Parks, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1976\u003c/span\u003e; Kingsbury, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Parks, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results And Discussion","content":"\u003cp\u003eData for fall and spring were not normally distributed; therefore, both datasets were log transformed to better approximate a normal distribution using skewness (0) and kurtosis (3.0) as the indicators of following a normal distribution. When mapping the data in a three-dimensional space and projecting the trend of data on the x- and y-axes, the water-table data had a quadratic trend and the ground surface a linear trend. Knowing this, it was possible to remove the trend during the cokriging process.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSkewness and Kurtosis of raw and log transformed data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eRaw data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eLog transformed data\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTopography\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTopography\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSkewness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKurtosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.3778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.6189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.0337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.0733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.5358\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThrough investigation of the semivariogram, many models were displayed against the data to determine which best followed the trend of the data bins. Given the s-shape of the semivariogram, the Gaussian model was the most appropriate choice. Choosing to optimize the model fit via autocorrelation (i.e., automated within ArcGIS Pro\u0026reg;), the resulting cokriging parameters are provided in the Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGroundwater and ground elevation interpolation parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFall 2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eSpring 2021\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroundwater elevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGround elevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroundwater elevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGround elevation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNugget\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor Range (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3,981.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e5,561.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSill\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLag Size (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e497.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e695.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of lags\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum neighbors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum neighbors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 Sector\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1 Sector\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage standard error (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe resulting grid spacing for fall and spring surfaces obtained from Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e was 210 m. Since cokriging does not identify and respect hydrologic boundaries, there were some contour lines that were inaccurately crossing streams or forming depressions; therefore, contours were modified to follow a path that matched the assumption that the water-table is hydraulically connected to surface water. Although the existence of a breach under a stream is possible (Brunner et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sophocleous, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Urbano et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), there is insufficient data to substantiate those artificial depressions. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (Fall 2020) and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (Spring 2021) show the water-table maps for Fall 2020 and Spring 2021, respectively.\u003c/p\u003e \u003cp\u003eAnomalous water-table depressions\u003c/p\u003e \u003cp\u003eAn important outcome of developing a water-table map in this area, is the indication of potential aquitard breaches reflected by anomalous depressions (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, dark red boxes) as there is no known high-capacity pumping that would cause such depressions in these areas. Most, if not all, of the anomalous depressions shown in previous figures have been identified in the past through general mapping of the UCCU thickness (Parks, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), water-table maps (Konduro-Narsimha, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Ogletree, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Parks, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) or groundwater modeling (Jazaei et al., 2018; Villalpando-Vizcaino et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), with some localized efforts in depressions (A) through (E) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea) that include groundwater tracers, detailed water levels, surface water leakage, and drilling.\u003c/p\u003e \u003cp\u003eWithin the MLGW Sheahan wellfield (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (A) and 5 (A)), depression (A) exists in the water table with a 9-meter drop three kilometers long from Nonconnah Creek north towards the center of the wellfield. This depression has been previously identified and characterized through surface water leakage observations (Larsen et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Nyman, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1965\u003c/span\u003e), groundwater tracers (Graham \u0026amp; Parks, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Larsen et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), water-table depressions (Konduro-Narsimha, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Ogletree, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Parks, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), drilling (Hasan personal communication, 2021), and groundwater modeling (Torres-Uribe et al., 2021; Villalpando-Vizcaino et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDepression B, east of the MLGW Allen wellfield, was also noted by Bradshaw (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) using tracer data as two potential breaches near Cane Creek; however, the exact location was not defined. Additional, reports from the Memphis Defense Depot (Memphis Depot; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), indicate a connection between the water-table and Memphis aquifers based on geologic cross-sections (HDR, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Depression C has been previously identified near the MLGW Lichterman wellfield is characterized by thinning or absent UCCU, a downward hydraulic gradient between the water-table and Memphis, and areas of inter-aquifer water exchange aquifers (Graham \u0026amp; Parks, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Nyman, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1965\u003c/span\u003e; Smith, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, Depression E was observed by Konduro-Narsimha (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), Gallo (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Ogletree (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and near the confluence of two branches of Fletcher Creek, correlating with a suspected breach identified by Parks (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDepression D is located near the former Shelby County Landfill in Shelby Farms (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), where Bradley (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) identified and confirmed a breach directly north of the landfill. Additional studies to substantiate and delineate this breach include seismic reflection (Waldron et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), electrical resistivity and geochemical analysis (Schoefernacker, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), groundwater tracers (Mirecki \u0026amp; Parks, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), groundwater modeling and geophysical methods (Gentry et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and water-table maps (Konduro-Narsimha, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Ogletree, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Parks, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious water-table maps (Konduro-Narsimha, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Ogletree, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Parks, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) identified Depressions A through E; however, the shape and extent differs due to changes in data control and the methodology followed to generate groundwater contours. Although the extent of anomalous depressions are an indicator or a potential breach, they do not provide a detailed shape and orientation due to lack of borehole or well control. The general depressions observed in the water-table maps are somewhat circular as they tend to be centered around a single control point. To better characterize the shape and size of the breaches, the water-table map method should be complemented by additional methods such as detailed geologic mapping, additional boreholes and geophysical methods.\u003c/p\u003e \u003cp\u003eWhen comparing differences in the water table between fall Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and spring Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e., the most significant differences are found where data control is inconsistent as also observed by Ogletree (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Generally, wells and surface water features were measured during both seasons but some historical sites had available data for only one of the two seasons, causing data inconsistencies. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows some areas where there is a significant change in the water-table due to data control issues. Each Pair (1\u0026ndash;2) represents the same spatial footprint shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Pair 1, the depression elongates to the northeast as two additional historical points are available for spring. Also, an 81 masl additional peak is observed in spring, south of Nonconnah Creek due to the addition of a historical point. On the other hand, in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Pair 2, the historical point added in spring produces a new peak that was not observed in the fall surface. This produces a water level rise of 12 meters. In areas where control is maintained between the seasons, the general structure of the contours remains very similar, only changing in level, but not in shape. Similarly, areas that heavily relied on topography data for interpolation due to lack of data control, remained unchanged in shape and level regardless of the season.\u003c/p\u003e \u003cp\u003eSeasonal analysis\u003c/p\u003e \u003cp\u003eIt was anticipated that spring water levels would be higher than those in the fall since the rainy season is during the winter and spring months. According to the USGS National Water Information System (NWIS) surface water database, the Wolf and Loosahatchie River stages typically remain at baseflow conditions between the months of July and December and rise to 4.5 m on average reaching their maxima during April and May. The Nonconnah Creek\u0026rsquo;s stage remains more stable throughout the year only rising after precipitation events but returning to baseflow conditions shortly (couple of days) after. This behavior was observed in the surface water locations between fall and spring; however, groundwater showed an anomalous seasonal behavior in some locations throughout the county, differing from the expected higher levels in spring than in fall. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e show water level changes between Fall 2020 and Spring 2021 for surface water and groundwater measurements.\u003c/p\u003e\u003cp\u003eSurface water locations near the Mississippi River are affected by backwater conditions that can reach as much as 9 km upstream in the three major rivers in Shelby County. Backwater conditions can cause a rise in water levels during the spring by as much as 7 meters near the confluence of these rivers with the Mississippi River as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Water level changes between seasons became less significant moving upstream. Generally, surface water levels throughout the county were higher during spring with some exceptions such as Fletcher Creek, a tributary to the Wolf River in the central portion of the county, where water levels were an average 4 cm lower during the spring. Water levels along the rest of the tributaries rose less than 30 cm from fall to spring.\u003c/p\u003e \u003cp\u003eGroundwater levels showed unexpected behavior in some cases as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. Out of 124 groundwater monitoring sites measured during both seasons, 35 had higher elevations during the fall when compared to the spring. This resulted in a negative seasonal change (i.e., spring minus fall), which was considered abnormal. The average negative seasonal water level change was 23.6 cm with a standard deviation of 15.7 cm. The remaining wells behaved as expected with a positive seasonal change with an average variation of 88.1 cm with a standard deviation of 88.7 cm.\u003c/p\u003e \u003cp\u003eAlthough the negative seasonal variation was less significant, a preliminary analysis was conducted to relate abnormal water level variations to a physical cause, such as proximity to open water bodies or confirmed breaches. Land use was also considered assuming that water infiltration is greater among more vegetated areas rather than impervious, developed zones. Results indicated no apparent causality; therefore, other factors were considered.\u003c/p\u003e \u003cp\u003eAn analysis was performed using long-term data recorded by pressure transducers (Solinst Inc. Levelogger\u0026reg;) deployed in 12 water-table monitoring wells throughout the county (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These transducers have been collecting pressure data every 15 minutes from 2019 to 2021. The objective was to observe whether short-term or long-term behaviors of the water table provided any reasoning for the negative seasonal differences. Short-term variation was set over a two-week period, both with a rolling average and specific to the survey periods. As provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, rolling two-week averages were taken at each instrumented well to see head variations. Likewise, the average variation of water levels during the survey periods is also listed (i.e., 19 days for the fall survey and 12 days for the spring).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRolling average water level two-week variation. Values are in cm.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eFall\u003c/p\u003e \u003cp\u003e(9/14/20\u0026ndash; 10/2/2020)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003cp\u003e(3/29/2021\u0026ndash;4/9/2021)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSh:K-156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSh:R-032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSh:J-172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSh:J-206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSh:J-196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSh:J-220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSh:K-171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSh:L-110 UR-25S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e142.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSh:K-169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSh:K-163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSh:J-242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e102.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e48.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG-MW1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAverage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA total average of 33.40 cm with a standard deviation (SD) of 19.78 cm were obtained from averaging the complete data set (2019\u0026ndash;2021) of collected data of all 12 wells. For fall and spring, 19.11 cm (SD of 4.92) and 56.14 cm (SD of 16.98) averages were obtained, respectively. This suggests water levels tend to be less variable during fall than during spring, likely due to recharge in the spring.\u003c/p\u003e \u003cp\u003eDuring the data collection period, it was planned that all wells in clustered areas (e.g., MLGW wellfields, Shelby Farms) were surveyed on the same day to ensure better data consistency between neighboring wells. However, given the size of the county and distribution of monitoring points, each survey took between two to three weeks to complete. Within this period, it is apparent from Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e that water levels could have shifted\u0026thinsp;\u0026plusmn;\u0026thinsp;33.40 cm (e.g., using the total average) depending on the day when collected.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e Seasonal change in all wells measured for both seasons. The black dashed box represents the total average\u0026thinsp;\u0026plusmn;\u0026thinsp;33.40 variation attributed to short term fluctuations of the water-table aquifer. Bars falling within the box (80% of red, 30.5% of blue) are shown as a lighter color. Additional IDs and water elevations for each well are found in Appendix 1.\u003c/p\u003e \u003cp\u003eOut of all the water levels that decreased from fall to spring (i.e., negative change or red bars), 80% fall within the black box. Conversely, only 30.5% of the blue bars (i.e., higher levels in the spring compared to fall) fall within the box. This further shows that seasonal change was more significant in wells with higher levels during spring, but still does not fully explain the lower readings beyond the \u0026minus;\u0026thinsp;0.33 m threshold for some wells during the spring as compared to fall.\u003c/p\u003e \u003cp\u003eFollowing the seasonal change analysis for all wells, the long-term variations (July 2019 to October 2021) of the water-table aquifer were analyzed using the observed readings collected by pressure transducers. Wells Sh:J-172 and Sh:J-220 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e) were selected to illustrate the range of water-level fluctuations seen in the wells listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eWith longer periods of record, seasonal groundwater patterns become more apparent. In the case of well Sh:J-172, data was collected 94 days earlier than the lowest value it had for fall, and 30 days earlier than the highest level in spring. Similarly, for well Sh:J-220, data collection for fall occurred 77 days before the water reached its lowest point, while spring data was collected right as the water table approached its highest peak of the year. A similar analysis was conducted with the rest of the wells with transducer data (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Wells Sh:K-156, Sh:K-163 and Sh:L-110 UR-25S are excluded from the analysis given that Sh:K-156 and Sh:K-163 are located within the breach in Sheahan wellfield and Sh:L-110 UR-25S is strongly influenced by Nonconnah Creek; hence, resulting in behaviors differing notably from the ones seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e. Wells GG-MW1 and Sh:J-242 are also located in close proximity to the Wolf River and Nonconnah Creek, respectively; however, these follow a similar pattern to the rest of the wells in the analysis and, therefore, are included in the analysis. The reason why some wells near the same stream (i.e., L-110 UR-25S and Sh:J-242) have differing water level behaviors requires further investigation and is not addressed in this study.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e lists each transducer water level for the dry and wet seasons, in comparison to the water levels obtained during the water level surveys.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDates of lowest and highest (i.e., fall and spring) water levels recorded during the data collection periods in comparison to the lowest and highest dates and levels of each season.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eFall 2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003eSpring 2021\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLowest Point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eData collection period\u003c/p\u003e \u003cp\u003e(9/14/20\u0026thinsp;\u0026minus;\u0026thinsp;10/2/20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eHighest Point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003eData collection period\u003c/p\u003e \u003cp\u003e(3/29/21\u0026thinsp;\u0026minus;\u0026thinsp;4/9/21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLevel (masl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLowest date\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLevel (masl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDays diff.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLevel diff.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLevel (masl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHighest date\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eLevel (masl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eDays diff.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eLevel diff.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12/28/2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10/1/2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5/27/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e85.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4/7/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e85.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJ-172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/4/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10/2/2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5/9/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e76.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4/9/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e76.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJ-206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/28/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10/2/2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5/28/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e63.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4/9/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e63.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJ-196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12/29/2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10/2/2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5/28/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e68.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4/9/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e68.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJ-220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12/18/2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10/2/2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3/31/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e67.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3/31/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e67.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK-171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12/13/2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10/2/2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4/2/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e78.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4/2/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e78.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK-169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11/17/2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10/2/2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5/9/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e86.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4/9/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e86.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAA-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10/26/2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10/2/2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3/31/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e65.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3/31/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e65.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG-MW1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12/13/2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10/2/2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4/2/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e75.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4/2/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e75.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e75.44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e23.11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eResults indicate that for Fall 2020, the water level survey concluded approximately 75 days before the water table reached its lowest level. At the lowest level, water levels were on average 16 cm lower than they were during the last day of the water level survey on October 2nd. Considering this 75-day difference, the ideal date to obtain the lowest water levels for fall is around the third week of December. During Spring 2021, four wells were measured when the water table was at its highest level; however, the remainder were surveyed approximately 23 days too early. It is not a simple case of shifting future spring survey dates as pushing the date forward would capture higher levels in some wells (e.g., Sh:R-032, Sh:J-172, Sh:J-206, Sh:J-196, Sh:K-169) while resulting in lower levels in others (e.g., Sh:J-220, Sh:K-171, Sh:J-242, GG-MW1). Nevertheless, with only an average 5-cm water level difference if shifted by 23 days, data collection any time during April is appropriate. Considering that water levels fell an average 16 cm from the last day of the fall survey and rose an average of 5 cm after the last day of the spring surveying, if the water-level surveys had been conducted at the right time for both seasons (i.e., lowest and highest levels of the year), water level variations would have been an average 21 cm higher (sum of both seasons shift averages). Recalling the average 23.6-cm seasonal variation within the wells that had higher levels in fall, the abnormal negative seasonal behavior observed may be attributed to the incorrect timing of the water-level surveys.\u003c/p\u003e \u003cp\u003eGiven that these ideal dates for data collection are based on data from the past two years, an extended period of data was required to identify dates of minimum and maximum levels from the water-table aquifer. There are several wells in Shelby County, screened within the water-table aquifer, with long-term monitoring data. However, most have measurements that are sporadic throughout the year, so it is difficult to estimate the actual dates of lowest and highest levels from this data. For this reason, an analysis was conducted using well Sh:P-099, which has been monitored daily by the USGS since 1994. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the dates of the highest and lowest level in the aquifer each year since 2015.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHighest and lowest levels per year recorded in well P-099 from 2015 to 2020\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaximum level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimum level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApril 26th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNovember 14th\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMay 4th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNovember 19th\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMay 4th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDecember 16th\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApril 28th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDecember 12th\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApril 18th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOctober 7th\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApril 3rd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNovember 17th\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the dates from Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, it is observed that the approximate months to capture lowest and highest levels in the water table for future monitoring efforts are November-December, and April-May, respectively.\u003c/p\u003e \u003cp\u003eDecadal analysis\u003c/p\u003e \u003cp\u003eData collected during the 2005 (Konduro-Narsimha, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and 2015 (Ogletree, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) water-level surveys were reprocessed following the same methodology employed for this study to compare water tables over the past 15 years (2005 to 2020; see Figs.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e and \u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e). Parks (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) was not included because his data collection methods differed significantly from later surveys. Data collection for 2005 and 2015 were conducted during the fall months; thus, only the Fall 2020 surface was used for comparison purposes. There is significant variability in control for each year, especially with historical data and private wells. Historical sites are problematic since they are temporal and can change as new sites are added and some are closed over time, while private wells are impacted by factors such as: well destruction, upgrades that limit port accessibility, owner changes and denied access. Impacts to these sites affect the overall county control as these locations help filling out the data gaps outside wellfield clusters. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the amount of control points by category and by year.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData control for survey years 2005 (Konduro-Narsimha, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), 2015 (Ogletree,2016), and 2020. Numbers in parentheses represent points that match with those measured in 2020.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic monitoring wells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114 (70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurface water crossings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate wells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistorical sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e249 (129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e266 (137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs seen in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, data control generally decreased over time. Public monitoring wells, historical sites and private well measurements decreased from 2005 to 2020 with only surface water crossings increasing with time. It can be seen that data control increased from 2005 to 2015, but decreased from 2015 to 2020, with 2020 having the lowest data control between the three water-level surveys.\u003c/p\u003e \u003cp\u003eThe most notable differences between the 2005 and 2015 water-table surfaces and 2020 are found in areas with significant changes in data control. Ogletree (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) analyzed on the differences in water-table elevations in relation to data control changes between 2005 and 2015 and concluded that considerable changes between water surfaces are not the result of physical changes in water-level elevations, but differences related to data control. Similar conditions are observed when comparing with 2020 data. Figure\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e shows Section 1 (Figs.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e and \u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e), a clear example where significant changes in contour shape and levels can be attributed to differences in data control.\u003c/p\u003e \u003cp\u003eFor Section 1, the size of the observed elevated water level was significantly reduced from 2005 and 2015 to 2020. Both in 2005 and 2015, there was a historical site control point slightly offset from each other (yellow and blue triangles) that created a higher water level to the northeast of Section 1. In 2020, these historical points were absent, centralizing the peak around a single point towards the center of the map. The highest contour elevation was also reduced from 108 m in 2005 to 99 m in 2020. Areas of the county with less significant changes in data control have less variation in the shape of contours, particularly within the MLGW wellfields where data control tends to be more clustered and consistent.\u003c/p\u003e \u003cp\u003eAn additional analysis was conducted to observe the long-term water-level variation from 2005 to 2015 and 2020 (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResults indicate that in 2015, water levels were generally lower than 2005 while water levels in 2020 were overall higher than 2005, with some exceptions in both cases. For illustrative purposes only, the black dashed box represents the average\u0026thinsp;\u0026plusmn;\u0026thinsp;33.40 cm variation that could be attributed to short-term water-level fluctuations (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) rather than a long-term change \u0026ndash; recall that the short-term variation was over a shorter period, 2019\u0026ndash;2021. On average, water levels were 0.27 m lower in 2015 than in 2005, while water levels in 2020 were 1.33 m higher than in 2005.\u003c/p\u003e \u003cp\u003eTo further examine water-table fluctuation trends observed for each year, a comparison with long-term monitoring data from well Sh:P-099 was conducted. This well is near the Memphis Zoo (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), has several water features that may leak and provide artificial recharge to the water-table aquifer. However, assuming the leakage is near constant, the general trend of the water table is relatively affected and simply shifts up. According to this site, groundwater levels in the water-table aquifer have been rising since monitoring began in 1994. Although in 2015 water levels fell below 2005 and 2020 levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003e), matching the behavior shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e for Number ID 48.\u003c/p\u003e\u003cp\u003eAccording to the National Oceanic and Atmospheric Administration (NOAA), precipitation in Shelby County has increased on average 190 mm over the last 30 years (NOAA, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which might account for the rising trend in water levels observed in well P-099. However, records show that 2005 was a drier year with an annual precipitation of 1084 mm, lower than 2015 and 2020 with 1403 and 1441 mm, respectively. This suggests that water levels in the water-table aquifer are influenced by more than just recharge from precipitation, or by differences in time scales between rain events and aquifer fluctuations.\u003c/p\u003e \u003cp\u003eProbability surface\u003c/p\u003e \u003cp\u003eThe last analysis performed using the water level survey data was to ascertain areas of high prediction error in the derived surfaces which would direct future efforts to fill those gaps before the next survey. A map showing the areas with highest prediction error was generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e17\u003c/span\u003e) based on survey location and the standard deviation of the values obtained from the interpolation.\u003c/p\u003e \u003cp\u003eA total of 179 permanent monitoring locations from the monitoring network, which includes public and private wells, and stream crossings were used for this analysis. Due to the changing nature of historical sites, they were removed from this analysis. Significant errors (data gaps) ranging from 5.88 to 7.35 m are shown between the major rivers (i.e., Loosahatchie River, Wolf River, and Nonconnah Creek) in eastern Shelby County, where the highest error area of 7.35 m is found as it is away from utility wellfields, accessible private wells, and between river crossings. Additionally, there are pockets of high error along the periphery of the county as well as along the Loosahatchie River and northward where the majority of control is only surface water. Northern Shelby County monitoring control relies almost solely on stream crossings, and if the analysis was done exclusively considering monitoring wells, this area would have a significantly higher error than the one observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e17\u003c/span\u003e.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMapping water levels of a water-table aquifer is useful for identifying areas of preferential leakage to the underlying confined aquifer through potential breaches in the intervening aquitard. Water-table maps for Fall 2020 and Spring 2021 show previously observed groundwater depressions within Sheahan wellfield, Shelby Farms, in areas west of Lichterman wellfield, east of Allen wellfield and east of McCord wellfield. The most significant differences in contour shape and water levels between both water tables are found in areas with non-coincident control between the two seasons. Outside of these historical depressions, no new depressions were observed with the current monitoring network.\u003c/p\u003e \u003cp\u003eTypical seasonal behavior is observed in surface water bodies (i.e., rivers and tributaries) as water levels were generally higher during spring than fall, more significantly near the confluence of the three major rivers in the county with the Mississippi River where values were as high as 7 m. Seasonal differences decreased as locations moved upstream with some exceptions attributed to river geomorphology. Anomalous seasonal behavior (i.e., higher water levels during fall than spring) is seen in 35 out of the 124 wells surveyed for both seasons, with an average seasonal variation of 23.6 cm. The remaining wells showed an average 88.1 cm rise in water levels from fall to spring. A short-term analysis of the water table based on pressure transducer data showed an average\u0026thinsp;\u0026plusmn;\u0026thinsp;33.40 cm two-week variation in the water-table aquifer. Additional pressure transducer observations showed that the fall water-level survey concluded approximately 75 days before the water table approached its lowest level in fall (16 cm lower) while the spring survey occurred 23 days before it reached its highest peak (5 cm higher) in spring. Hence, abnormal seasonal change is attributed more to the timing of the water-level surveys rather than a physical phenomenon of the hydrogeology.\u003c/p\u003e \u003cp\u003eFrom decadal data, it was observed that groundwater levels were higher in 2020 than in 2015 and 2005, while levels were lower in 2015 than 2005. Differences in the water-table maps for each survey are attributed to significant differences in data control. To identify areas to potentially improve the monitoring network in the future, a standard error map was generated and showed a prediction error of up to 7.35 m in areas with no control, more significantly in eastern Shelby County and between the major rivers, with the northern part of the county relying mostly on river crossing data.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Memphis Light Gas and Water (MLGW) and supported by the Center for Applied Earth Science and Engineering Research (CAESER). The authors are grateful for the support provided by these sponsors and by the people that helped with this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Memphis Light Gas and Water (MLGW) and supported by the Center for Applied Earth Science and Engineering Research (CAESER).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDaniela Lozano-Medina collected, processed and analyzed all data, wrote the main manuscript and prepared all figures and tables except for Figure 2.\u003c/p\u003e\n\u003cp\u003eDr. Brian Waldron also contributed to writing the main manuscript, and to the latter review of it. He also led the research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDr. Scott Schoefernacker contributed to data collection logistics, handcontouring of the water-table maps, and latter review of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDr. Anzhelika Antipova supported all the statistical analysis.\u003c/p\u003e\n\u003cp\u003eRodrigo Villalpando-Vizcaino contributed to data collection, processing and analysing and latter review of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmadi, S. H., \u0026amp; Sedghamiz, A. (2008). Application and evaluation of kriging and cokriging methods on groundwater depth mapping. \u003cem\u003eEnvironmental Monitoring and Assessment\u003c/em\u003e, \u003cem\u003e138\u003c/em\u003e(1), 357\u0026ndash;368. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10661-007-9803-2\u003c/span\u003e\u003cspan address=\"10.1007/s10661-007-9803-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoezio, M. N. M., Costa, J. F. C. L., \u0026amp; Koppe, J. C. (2006). Kriging with an external drift versus collocated cokriging for water table mapping. \u003cem\u003eApplied Earth Science\u003c/em\u003e, \u003cem\u003e115\u003c/em\u003e(3), 103\u0026ndash;112. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1179/174327506X138896\u003c/span\u003e\u003cspan address=\"10.1179/174327506X138896\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBradley, M. W. (1991). \u003cem\u003eGround-water hydrology and the effects of vertical leakage and leachate migration on ground-water quality near the Shelby County landfill, Memphis, Tennessee\u003c/em\u003e (Report No. 90\u0026ndash;4075; Water-Resources Investigations Report). USGS Publications Warehouse. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3133/wri904075\u003c/span\u003e\u003cspan address=\"10.3133/wri904075\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBradshaw, E. A. (2011). \u003cem\u003eAssessment of Groundwater leakage through the upper claiborne confining unit to the Memphis aquifer in the Allen wellfield, Memphis, Tennessee\u003c/em\u003e. The University of Memphis.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrahana, J. V., \u0026amp; Broshears, R. E. (2001). \u003cem\u003eHydrogeology and ground-water flow in the Memphis and Fort Pillow aquifers in the Memphis area, Tennessee\u003c/em\u003e (Report No. 89\u0026ndash;4131; Water-Resources Investigations Report, Issues 89\u0026ndash;4131). USGS Publications Warehouse. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3133/wri894131\u003c/span\u003e\u003cspan address=\"10.3133/wri894131\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrunner, P., Cook, P., \u0026amp; Simmons, C. (2011). Disconnected Surface Water and Groundwater: From Theory to Practice. \u003cem\u003eGround Water\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e, 460\u0026ndash;467. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1745-6584.2010.00752.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1745-6584.2010.00752.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCAESER. (2020). \u003cem\u003eShelby County LiDAR surface 2018\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarmichael, J. K., Kingsbury, J. A., Larsen, D., \u0026amp; Schoefernacker, S. (2018). \u003cem\u003ePreliminary evaluation of the hydrogeology and groundwater quality of the Mississippi River Valley Alluvial Aquifer and Memphis Aquifer at the Tennessee Valley Authority Allen Power Plants, Memphis, Shelby County, Tennessee\u003c/em\u003e (Report No. 2018\u0026ndash;1097; Open-File Report, Issues 2018\u0026ndash;1097, p.\u0026nbsp;78). USGS Publications Warehouse. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3133/ofr20181097\u003c/span\u003e\u003cspan address=\"10.3133/ofr20181097\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChung, J., \u0026amp; Rogers, J. D. (2012). Interpolations of Groundwater Table Elevation in Dissected Uplands. \u003cem\u003eGroundwater\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e(4), 598\u0026ndash;607. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1745-6584.2011.00889.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1745-6584.2011.00889.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClark, B. R., \u0026amp; Hart, R. M. (2009). \u003cem\u003eThe Mississippi Embayment Regional Aquifer Study (MERAS): Documentation of a groundwater-flow model constructed to assess water availability in the Mississippi embayment\u003c/em\u003e (Report No. 2009\u0026ndash;5172; Scientific Investigations Report). USGS Publications Warehouse. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3133/sir20095172\u003c/span\u003e\u003cspan address=\"10.3133/sir20095172\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCriner, J. H., \u0026amp; Parks, W. S. (1976). \u003cem\u003eHistoric water-level changes and pumpage from the principal aquifers of the Memphis area, Tennessee: 1886\u0026ndash;1975\u003c/em\u003e (Report No. 76\u0026ndash;67; Water-Resources Investigations Report, Issues 76\u0026ndash;67, p.\u0026nbsp;54). USGS Publications Warehouse. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3133/wri7667\u003c/span\u003e\u003cspan address=\"10.3133/wri7667\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCriner, J. H., Sun, P.-C. P., \u0026amp; Nyman, D. J. (1964). \u003cem\u003eHydrology of aquifer systems in the Memphis area, Tennessee\u003c/em\u003e (Report No. 1779O; Water Supply Paper, Issue 1779O). USGS Publications Warehouse. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3133/wsp1779O\u003c/span\u003e\u003cspan address=\"10.3133/wsp1779O\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCunningham, W. L., \u0026amp; Schalk, C. W. (2011). \u003cem\u003eGroundwater Technical Procedures of the U.S. Geological Survey\u003c/em\u003e (Techniques and Methods) [Techniques and Methods]. United States Geological Survey.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesbarats, A. J., Logan, C. E., Hinton, M. J., \u0026amp; Sharpe, D. R. (2002). On the kriging of water table elevations using collateral information from a digital elevation model. \u003cem\u003eJournal of Hydrology\u003c/em\u003e, 14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDieter, C. A., Maupin, M. A., Caldwell, R. R., Harris, M. A., Ivahnenko, T. I., Lovelace, J. K., Barber, N. L., \u0026amp; Linsey, K. S. (2018). \u003cem\u003eEstimated use of water in the United States in 2015\u003c/em\u003e (Report No. 1441; Circular, p.\u0026nbsp;76). USGS Publications Warehouse. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3133/cir1441\u003c/span\u003e\u003cspan address=\"10.3133/cir1441\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGallo, H. (2015). \u003cem\u003eHydrologic and geochemical investigation of modern leakage near the McCord wellfield, Memphis, Tennessee\u003c/em\u003e. The University of Memphis.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGentry, R., Mckay, L., Thonnard, N., Anerson, J., \u0026amp; Larsen, D. (2006). \u003cem\u003eNovel Techniques for Investigating Recharge to the Memphis Aquifer\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGraham, D. D. (1982). \u003cem\u003eEffects of urban development on the aquifers in the Memphis area, Tennessee\u003c/em\u003e (Report No. 82\u0026ndash;4024; Water-Resources Investigations Report, Issues 82\u0026ndash;4024). USGS Publications Warehouse. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3133/wri824024\u003c/span\u003e\u003cspan address=\"10.3133/wri824024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGraham, D. D., \u0026amp; Parks, W. S. (1986). \u003cem\u003ePotential for leakage among principal aquifers in the Memphis area, Tennessee\u003c/em\u003e (Report No. 85\u0026ndash;4295; Water-Resources Investigations Report, Issues 85\u0026ndash;4295). USGS Publications Warehouse. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3133/wri854295\u003c/span\u003e\u003cspan address=\"10.3133/wri854295\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHDR. (2017). \u003cem\u003eApril 2017 Lont-Term Monitoring Summart Report: Defense Depot Memphis, Tennessee\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHengl, T. (2006). Finding the right pixel size. \u003cem\u003eComputers\u003c/em\u003e \u0026amp; \u003cem\u003eGeosciences\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e, 1283\u0026ndash;1298. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cageo.2005.11.008\u003c/span\u003e\u003cspan address=\"10.1016/j.cageo.2005.11.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoeksema, R. J., Clapp, R. B., Thomas, A. L., Hunley, A. E., Farrow, N. D., \u0026amp; Dearstone, K. C. (1989). Cokriging model for estimation of water table elevation. \u003cem\u003eWater Resources Research\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(3), 429\u0026ndash;438. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/WR025i003p00429\u003c/span\u003e\u003cspan address=\"10.1029/WR025i003p00429\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsaaks, E. H., \u0026amp; Srivastava, R. M. (1991). An Introduction to Applied Geostatistics. \u003cem\u003eComputers\u003c/em\u003e \u0026amp; \u003cem\u003eGeosciences\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(3), 471\u0026ndash;473. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0098-3004(91)90055-I\u003c/span\u003e\u003cspan address=\"10.1016/0098-3004(91)90055-I\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKingsbury, J. A. (1996). \u003cem\u003eAltitude of the potentiometric surfaces, September 1995, and historical water-level changes in the Memphis and Fort Pillow aquifers in the Memphis area, Tennessee\u003c/em\u003e (Report No. 96\u0026ndash;4278; Water-Resources Investigations Report, Issues 96\u0026ndash;4278). USGS Publications Warehouse. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3133/wri964278\u003c/span\u003e\u003cspan address=\"10.3133/wri964278\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKingsbury, J. A. (2018). \u003cem\u003eAltitude of the potentiometric surface, 2000\u0026ndash;15, and historical water-level changes in the Memphis aquifer in the Memphis area, Tennessee\u003c/em\u003e (Report No. 3415; Scientific Investigations Map). USGS Publications Warehouse. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3133/sim3415\u003c/span\u003e\u003cspan address=\"10.3133/sim3415\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKingsbury, J. A., \u0026amp; Parks, W. S. (1993). \u003cem\u003eHydrogeology of the principal aquifers and relation of faults to interaquifer leakage in the Memphis area, Tennessee\u003c/em\u003e (Report No. 93\u0026ndash;4075; Water-Resources Investigations Report, Issues 93\u0026ndash;4075). USGS Publications Warehouse. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3133/wri934075\u003c/span\u003e\u003cspan address=\"10.3133/wri934075\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKonduro-Narsimha, V. K. (2007). \u003cem\u003eAltitudes of Water Levels 2005, and Historic Water Level Change in Surficial and Memphis Aquifer\u003c/em\u003e. University of Memphis.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLarsen, D., Gentry, R. W., \u0026amp; Solomon, D. K. (2003). The geochemistry and mixing of leakage in a semi-confined aquifer at a municipal well field, Memphis, Tennessee, USA. \u003cem\u003eApplied Geochemistry\u003c/em\u003e, 21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLarsen, D., Morat, J., Waldron, B., Ivey, S., \u0026amp; Anderson, J. (2013). Stream Loss Contributions to a Municipal Water Supply Aquifer in Memphis, Tennessee. \u003cem\u003eEnvironmental and Engineering Geoscience\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e, 265\u0026ndash;287. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2113/gseegeosci.19.3.265\u003c/span\u003e\u003cspan address=\"10.2113/gseegeosci.19.3.265\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLarsen, D., Waldron, B., Schoefernacker, S., Gallo, H., Koban, J., \u0026amp; Bradshaw, E. (2016). \u003cem\u003eApplication of Environmental Tracers in the Memphis Aquifer and Implication for Sustainability of Groundwater Resources in the Memphis Metropolitan Area\u003c/em\u003e, Tennessee. Journal of Contemporary Water Research \u0026amp; Education, 159(1), 78\u0026ndash;104. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1936-704X.2016.03231.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1936-704X.2016.03231.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLloyd, O.B.; Lyke, W.L. (1995). Ground Water Atlas of the United States: Segment 10, Illinois, Indiana, Kentucky, Ohio, Tennessee. U.S. Geological Survey Hydrologic Atlas, 730-K; 30p.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMirecki, J. E., \u0026amp; Parks, W. S. (1994). Leachate geochemistry at a municipal landfill, Memphis, Tennessee. \u003cem\u003eGround Water\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(3), 390\u0026ndash;398. USGS Publications Warehouse.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNOAA, N. C. for E. information. (2021). \u003cem\u003eClimate at a Glance: County Time Series\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncdc.noaa.gov/cag/\u003c/span\u003e\u003cspan address=\"https://www.ncdc.noaa.gov/cag/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNyman, D. J. (1965). \u003cem\u003ePredicted hydrologic effects of pumping from the Lichterman Well Field in the Memphis Area, Tennessee\u003c/em\u003e (Report No. 1819B; Water Supply Paper). USGS Publications Warehouse. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3133/wsp1819B\u003c/span\u003e\u003cspan address=\"10.3133/wsp1819B\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgletree, B. T. (2016). \u003cem\u003eGeostatistical Analysis of the Water Table Aquifer in Shelby County, Tennessee\u003c/em\u003e. University of Memphis.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlea, R. A., \u0026amp; Davis, J. C. (1999). \u003cem\u003eOptimizing the High Plains Aquifer Water-level Observation Network\u003c/em\u003e (Open-File No. 1999\u0026ndash;15). Kansas Geological Survey. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kgs.ku.edu/Hydro/Levels/OFR99_15/index.html\u003c/span\u003e\u003cspan address=\"https://www.kgs.ku.edu/Hydro/Levels/OFR99_15/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOyana, T. J., \u0026amp; Margai, F. (2015). \u003cem\u003eSpatial Analysis: Statistics, Visualization, and Computational Methods\u003c/em\u003e. CRC Press. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://books.google.com/books?id=h2FECgAAQBAJ\u003c/span\u003e\u003cspan address=\"https://books.google.com/books?id=h2FECgAAQBAJ\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParks, W. S. (1990). \u003cem\u003eAssessment of the potential for contamination of the Memphis Aquifer, in the Memphis area, Tennessee\u003c/em\u003e (p.\u0026nbsp;44). United States Geological Survey.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParks, W. S., \u0026amp; Carmichael, J. K. (1990). \u003cem\u003eGeology and ground-water resources of the Memphis Sand in western Tennessee\u003c/em\u003e (Report No. 88\u0026ndash;4182; Water-Resources Investigations Report, Issues 88\u0026ndash;4182). USGS Publications Warehouse. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3133/wri884182\u003c/span\u003e\u003cspan address=\"10.3133/wri884182\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuopu Li \u0026amp; Lin Zhao. (2011). Vadose zone mapping using geographic information systems and geostatistics a case study in the Elkhorn River Basin, Nebraska, USA. \u003cem\u003e2011 International Symposium on Water Resource and Environmental Protection\u003c/em\u003e, 3177\u0026ndash;3179. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/ISWREP.2011.5893555\u003c/span\u003e\u003cspan address=\"10.1109/ISWREP.2011.5893555\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchoefernacker, S. (2018). \u003cem\u003eEvaluation and evolution of a groundwater contaminant plume at the former Shelby County Landfill, Memphis, Tennessee\u003c/em\u003e. The University of Memphis.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith, M. R. (2018). \u003cem\u003eEvaluating modern recharge to the Memphis aquifer at the Lichterman wellfield, Memphis, Tennessee\u003c/em\u003e. The University of Memphis.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnyder, D. T. (2008). \u003cem\u003eEstimated depth to ground water and configuration of the water table in the Portland, Oregon area\u003c/em\u003e (Report 2008\u0026ndash;5059; p.\u0026nbsp;40). U.S. Geological Survey. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubs.usgs.gov/sir/2008/5059/\u003c/span\u003e\u003cspan address=\"https://pubs.usgs.gov/sir/2008/5059/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSophocleous, M. (2002). Interactions between groundwater and surface water: The state of the science. \u003cem\u003eHydrogeology Journal\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), 52\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10040-001-0170-8\u003c/span\u003e\u003cspan address=\"10.1007/s10040-001-0170-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTheodossiou, N., \u0026amp; Latinopoulos, P. (2006). Evaluation and optimisation of groundwater observation networks using the Kriging methodology. \u003cem\u003eEnvironmental Modelling\u003c/em\u003e \u0026amp; \u003cem\u003eSoftware\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(7), 991\u0026ndash;1000. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envsoft.2005.05.001\u003c/span\u003e\u003cspan address=\"10.1016/j.envsoft.2005.05.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorres-Uribe, H. E. (2020). \u003cem\u003eApplication of a numerical grounwater model to determine the spatial configuration of breaches near a municipal well field in Memphis, Tennessee\u003c/em\u003e. The University of Memphis.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorres-Uribe Hugo E., Waldron Brian, Larsen Daniel, \u0026amp; Schoefernacker Scott. (2021). Application of Numerical Groundwater Model to Determine Spatial Configuration of Confining Unit Breaches near a Municipal Well Field in Memphis, Tennessee. \u003cem\u003eJournal of Hydrologic Engineering\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(9), 05021021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1061/(ASCE)HE.1943-5584.0002117\u003c/span\u003e\u003cspan address=\"10.1061/(ASCE)HE.1943-5584.0002117\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUrbano, L., Waldron, B., Larsen, D., \u0026amp; Shook, H. (2006). Groundwater\u0026ndash;surfacewater interactions at the transition of an aquifer from unconfined to confined. \u003cem\u003eJournal of Hydrology\u003c/em\u003e, \u003cem\u003e321\u003c/em\u003e, 200\u0026ndash;212. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhydrol.2005.08.001\u003c/span\u003e\u003cspan address=\"10.1016/j.jhydrol.2005.08.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVillalpando-Vizcaino, R., Waldron, B., Larsen, D., \u0026amp; Schoefernacker, S. (2021). Development of a Numerical Multi-Layered Groundwater Model to Simulate Inter-Aquifer Water Exchange in Shelby County, Tennessee. \u003cem\u003eWater\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(18). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/w13182583\u003c/span\u003e\u003cspan address=\"10.3390/w13182583\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaldron, B., Harris, J. B., Larsen, D., \u0026amp; Pell, A. (2009). Mapping an aquitard breach using shear-wave seismic reflection. \u003cem\u003eHydrogeology Journal\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(3), 505\u0026ndash;517. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10040-008-0400-4\u003c/span\u003e\u003cspan address=\"10.1007/s10040-008-0400-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaldron, B., \u0026amp; Larsen, D. (2015). Pre-Development Groundwater Conditions Surrounding Memphis, Tennessee: Controversy and Unexpected Outcomes. \u003cem\u003eJAWRA Journal of the American Water Resources Association\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(1), 133\u0026ndash;153. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jawr.12240\u003c/span\u003e\u003cspan address=\"10.1111/jawr.12240\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaldron, B., Larsen, D., Hannigan, R., Csontos, R., Anderson, J., Dowling, C., \u0026amp; Bouldin, J. (2011). \u003cem\u003eMississippi Embayment Regional Ground Water Study\u003c/em\u003e (EPA/600/R-10/130; Issue EPA/600/R-10/130, p.\u0026nbsp;192). U.S. Environmental Protection Agency.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Water-table surface, water levels, water level seasonal change, cokriging, aquitard, inter-aquifer exchange ","lastPublishedDoi":"10.21203/rs.3.rs-2507984/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2507984/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAn extensive water level survey of the water-table aquifer (i.e., shallow aquifer) within Shelby County, Tennessee, was conducted in the dry (Fall 2020) and wet (Spring 2021) seasons. Water-table surfaces were generated using cokriging to observe seasonal differences to identify anomalous water-table depressions, indicative of an underlying aquitard breach. Seasonal differences were attributed to non-coincident control and timing between the surveys as well as when optimum dry (fall) and wet (spring) conditions existed, as observed through comparisons with continuous historical water levels from 12 shallow monitoring wells. Additionally, data from Fall 2020 were compared to previous studies in 2005 and 2015 to determine decadal changes in levels and shape of the water-table surface which were mostly attributed to changes in data control and potential climate variations. A prediction error map was generated from the 2020 dataset to identify areas of the county with high-prediction error (\u0026gt;\u0026thinsp;7.0 m) to offer guidance on where future well control would be optimal.\u003c/p\u003e","manuscriptTitle":"Stories of a water-table: anomalous depressions, aquitard breaches and seasonal implications, Shelby County, Tennessee, USA.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-02-16 22:12:55","doi":"10.21203/rs.3.rs-2507984/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-02-16T02:11:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-02-15T10:44:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-02-15T10:44:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2023-01-23T18:28:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"89faa483-2d19-434a-ab7f-47a1bd65c9b7","owner":[],"postedDate":"February 16th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-08-18T04:06:24+00:00","versionOfRecord":{"articleIdentity":"rs-2507984","link":"https://doi.org/10.1007/s10661-023-11531-z","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2023-07-15 01:08:23","publishedOnDateReadable":"July 15th, 2023"},"versionCreatedAt":"2023-02-16 22:12:55","video":"","vorDoi":"10.1007/s10661-023-11531-z","vorDoiUrl":"https://doi.org/10.1007/s10661-023-11531-z","workflowStages":[]},"version":"v1","identity":"rs-2507984","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2507984","identity":"rs-2507984","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}