REPORT OF INVESTIGATIONS NO. 73 ANALYSISAND …John H. Talley, State Geologist REPORT OF INVESTIGATIONS NO. 73 ANALYSISAND SUMMARY OF WATER-TABLE MAPS FORTHEDELAWARECOASTALPLAIN By

State of Delaware

DELAWARE GEOLOGICAL SURVEY

John H. Talley, State Geologist

REPORT OF INVESTIGATIONS NO. 73

ANALYSIS AND SUMMARY OF WATER-TABLE MAPSFOR THE DELAWARE COASTAL PLAIN

By

Matthew J. Martin1 and A. Scott Andres2

University of Delaware

Newark, Delaware

2008

1Delta Development Group, Inc.2Delaware Geological Survey

RESEARCH

DELAWARE

GEOLOGICALSURVEY

EXPL

ORA

TIO

N

SERVICE

Dry Conditions Normal Conditions Wet Conditions

State of Delaware

DELAWARE GEOLOGICAL SURVEY

John H. Talley, State Geologist

REPORT OF INVESTIGATIONS NO. 73

ANALYSIS AND SUMMARY OFWATER-TABLE MAPS

FOR THE DELAWARE COASTAL PLAIN

By

Matthew J. Martin1 and A. Scott Andres2

University of Delaware

Newark, Delaware

2008

1Delta Development Group, Inc.2Delaware Geological Survey

RESEARCH

DELAWARE

GEOLOGICALSURVEY

EXPL

ORA

TIO

N

SERVICE

Use of trade, product, or firm names in this report is for descriptive pur-poses only and does not imply endorsement by the Delaware GeologicalSurvey.

Page

ABSTRACT ..............................................................................................................................................................................1

INTRODUCTION ....................................................................................................................................................................1

Purpose and Scope ..............................................................................................................................................................2

Acknowledgments ..............................................................................................................................................................2

METHODS ................................................................................................................................................................................2

Data Compilation and Statistical Evaluation ......................................................................................................................3Depth-to-water data......................................................................................................................................................3Surface-water features..................................................................................................................................................3

Model Development ............................................................................................................................................................3Hydrologic conditions ..................................................................................................................................................3Multiple linear regression and the initial water table ..................................................................................................4

Estimation of Water-Table Elevation ..................................................................................................................................5

Potential improvements to Water-Table DEM from LIDAR-Derived DEM......................................................................5

RESULTS AND DISCUSSION ..............................................................................................................................................6

Sussex County Water-Table Elevation ................................................................................................................................7

Kent County Water-Table Elevation ..................................................................................................................................8

New Castle County Water-Table Elevation ........................................................................................................................8

Depth to Water ....................................................................................................................................................................8

Comparison of Existing DLG Hydrography to LIDAR-Derived DEM ............................................................................9

CONCLUSIONS ......................................................................................................................................................................9

REFERENCES CITED..........................................................................................................................................................10

Page

1. Depth to water under normal conditions for the Inland Bays watershed in Sussex County, Delaware. ...........................2

2. Hydrograph for well Qe44-01 showing monthly depth to water below the land surface. ................................................4

3. Illustration of the initial water table including graphical representation of water-table terms and vertical datums. ........5

(Figures 4A through 6C are located on Plate 1)........................................................................................................In Pocket

4A. Water-table elevation under dry conditions for Sussex County, Delaware.

4B. Water-table elevation under normal conditions for Sussex County, Delaware.

4C. Water-table elevation under wet conditions for Sussex County, Delaware.

5A Water-table elevation under dry conditions for Kent County, Delaware.

5B. Water-table elevation under normal conditions for Kent County, Delaware.

5C. Water-table elevation under wet conditions for Kent County, Delaware.

6A. Water-table elevation under dry conditions for New Castle County, Delaware

6B. Water-table elevation under normal conditions for New Castle County, Delaware.

6C. Water-table elevation under wet conditions for New Castle County, Delaware.

TABLE OF CONTENTS

ILLUSTRATIONS

Page

(Figures 7A through 9C are located on Plate 2 ) ........................................................................................................In Pocket

7A. Depth to water under dry conditions for Sussex County, Delaware.

7B. Depth to water under normal conditions for Sussex County, Delaware.

7C. Depth to water under wet conditions for Sussex County, Delaware.

8A. Depth to water under dry conditions for Kent County, Delaware.

8B. Depth to water under normal conditions for Kent County, Delaware.

8C. Depth to water under wet conditions for Kent County, Delaware.

9A. Depth to water under dry conditions for New Castle County, Delaware.

9B. Depth to water under normal conditions for New Castle County, Delaware.

9C. Depth to water under wet conditions for New Castle County, Delaware.

10. Illustration showing stream segment elevation artifacts caused by misalignment of 1:24,000 hydrography DLG with

LIDAR DEM and road crossings. ....................................................................................................................................10

TABLES

Page

Table 1. Long-period observation wells used for each county ..................................................................................................4

Table 2. Grid dimensions and numbers of ground-water points used to estimate water-table elevation and depthto water table. ...............................................................................................................................................................6

Table 3. Coefficients for MLR and LR used to calculate the water-table elevation for each county and/orcounty section...............................................................................................................................................................7

Table 4. Statistical comparison of residuals from water-table elevation estimations for normal conditions. ..........................8

Table 5. Comparisons of depth to water and percentage of land area.......................................................................................9

ILLUSTRATIONS CONTINUED

INTRODUCTION

The water table is defined as the surface on which thewater pressure in the pores of a porous medium is exactlyatmospheric (Freeze and Cherry, 1979). In practice, the posi-tion of the water table is measured in wells constructed withopenings along their lengths and penetrating just deep enoughto encounter standing water. Water located at or beneath thewater table is ground water. Given the climate and relativelypermeable subsurface materials in Delaware, the water tableoften occurs at depths less than 10 ft below land surface(Andres and Martin, 2005; Martin and Andres, 2005a, b, c).

The first efforts to map the water-table for the state ofDelaware were undertaken in the 1950s and were a coopera-tive effort between the United States Geological Survey(USGS), the Delaware Division of Highways, and theDelaware Geological Survey (DGS). Maps from this projectwere published as paper maps in the Hydrologic Atlas seriesat a scale of 1:24,000 and depicted the water table with con-tour lines at a 10-ft interval. These water-table maps havebeen widely used by both the public and private sectors(Andres and Martin, 2005). Despite the usefulness of thesepaper maps, more data are now available and recent advancesin computer technology and the expanding use of GeographicInformation Systems (GIS) have made it necessary to updatewater-table maps into a digital format.

The configuration of the water table is one of the majorfactors that controls regional ground-water flow patterns(Freeze and Witherspoon, 1967). Ground water moves slow-ly underground in the down-gradient direction and eventual-ly discharges into streams, lakes, and oceans (Perlman, 2005).Because ground water is such an integral part of the watercycle, planners and developers often need to have a strategicplan when dealing with water resources. Excess pumping ofwells over extended periods of time can result in lowering the

water table leading to an increase in the cost of pumping fromgreater depths, depletion of the amount of water available forimportant wetland habitats, and salt-water intrusion intodomestic water supplies (Dunne and Leopold, 1998). In addi-tion, the practice of well drilling to extract ground water isdependent upon an understanding of the depth to the watertable. Wells must be finished below the water table, and thedepth of the water table determines the final specifications ofthe well.

Obtaining an accurate representation of the water table isalso crucial to the success of many hydrologic modelingefforts (Williams and Williamson, 1989). Estimated water-table elevation can be used to specify heads in the surficialaquifer for a ground-water flow model, to estimate depths toareas of potential ground-water contamination, or to simulaterecharge and discharge rates of the surficial aquifer(Sepulveda, 2003).

In many areas throughout Delaware, the depth to thewater table has a direct effect on how people utilize the land.For instance, based on depth to the water table, it can bedetermined whether or not a site is suitable for a standard sub-surface wastewater-disposal system. Water-table depth is akey facet in many engineering, hydrogeologic, environmentalmanagement, and regulatory decisions. Depth to water is animportant factor in risk assessments, site assessments, evalu-ation of permit compliance data, registration of pesticides anddetermining acceptable application rates. Shallow depth toground water has been the principal motive for constructingthe extensive ditch networks that can be found in manywatersheds in Delaware. In many areas, the water table is alsothe top of the aquifer that provides water for potable, agricul-tural, commercial, and industrial uses. The thickness of thisaquifer is one factor that controls the amount of water that isavailable to wells (Andres and Martin, 2005).

Delaware Geological Survey • Report of Investigations No. 73 1

ANALYSIS AND SUMMARY OFWATER-TABLE MAPS

FOR THE DELAWARE COASTAL PLAIN

ABSTRACT

A multiple linear regression method was used to estimate water-table elevations under dry, normal, and wet conditionsfor the Coastal Plain of Delaware. The variables used in the regression are elevation of an initial water table and depth to theinitial water table from land surface. The initial water table is computed from a local polynomial regression of elevations ofsurface-water features. Correlation coefficients from the multiple linear regression estimation account for more than 90 per-cent of the variability observed in ground-water level data. The estimated water table is presented in raster format as GIS-ready grids with 30-m horizontal (~98 ft) and 0.305-m (1 ft) vertical resolutions.

Water-table elevation and depth are key facets in many engineering, hydrogeologic, and environmental management andregulatory decisions. Depth to water is an important factor in risk assessments, site assessments, evaluation of permit com-pliance data, registration of pesticides, and determining acceptable pesticide application rates. Water-table elevations are usedto compute ground-water flow directions and, along with information about aquifer properties (e.g., hydraulic conductivityand porosity), are used to compute ground-water flow velocities. Therefore, obtaining an accurate representation of the watertable is also crucial to the success of many hydrologic modeling efforts.

Water-table elevations can also be estimated from simple linear regression on elevations of either land surface or initialwater table. The goodness-of-fits of elevations estimated from these surfaces are similar to that of multiple linear regression.Visual analysis of the distributions of the differences between observed and estimated water elevations (residuals) shows thatthe multiple linear regression-derived surfaces better fit observations than do surfaces estimated by simple linear regression.

Depth to the water table is a prevailing factor in deter-mining the ecological function of a landscape. For example,many wetlands are found where the water table is at or nearland surface for portions of the year. The duration of stand-ing water in large part prescribes the plant and animal com-munities that can live at that site. Under fair or “normal”weather conditions, the surfaces of Coastal Plain streams andponds represent the intersection of the water table with landsurface (Winter, 1999; Andres and Martin, 2005).

Purpose and Scope

The purpose of this report is to provide both a briefreview of the pilot project, the Inland Bays Watershed Water-Table Mapping Project, and a detailed summary and analysisof the results of mapping the water table for the DelawareCoastal Plain. The goals of the Delaware Coastal Plain pro-ject were to use the methodology and procedures establishedduring the pilot project to map the water table for the remain-der of Sussex County, as well as Kent County and NewCastle County.

Appropriate methodologies and procedures for calculat-ing the water table for the Coastal Plain of Delaware wereestablished in the Inland Bays Watershed Water-TableMapping Project. Water-table elevation maps were producedfor dry, normal, and wet conditions using a variety of esti-mation methods, making qualitative comparisons betweenthe different methods and pre-existing water-table maps, anddetermining which of the estimation methods could be usedto map the Coastal Plain of Delaware in a cost-effective andtimely manner. One crucial constraint in choosing a suitableestimation method for mapping the entire state was that ithad to rely on existing data because available funding wasnot sufficient to construct new wells or to support collectionof additional water-level measurements.

The Inland Bays watershed (Fig. 1) was selected as thepilot project by the Delaware Geological Survey and theDelaware Department of Natural Resources andEnvironmental Control (DNREC) Water Supply Section(WSS) because of the readily available pre-existing water-level data from previous hydrologic studies conducted in thisregion. In addition, the watershed was identified as a highpriority area for a number of regulatory and environmentalrestoration efforts that can use the resultant information.

After creating and analyzing water-table maps for dry,normal, and wet conditions using various statistical estima-tors, it was determined that the method which produced themost desirable results was an algorithm based on a multiplelinear regression (MLR) equation to estimate the water table.Water-table elevation and depth-to-water maps for theremainder of Sussex County, Kent County, and New CastleCounty were then produced using this algorithm (Martin andAndres, 2005a, b, c).

The map products created by this work are being uti-lized to support various public environmental programs andprivate site reviews that require hydrologic assessment.These map products will be an important tool in the assess-ment process; however, they depict estimates of water-tableelevation and are, therefore, not intended to supplant on-sitedata collection efforts. The water-table maps will not

be published paper maps; however, they are publishedas GIS-ready products and are available fordownload from the Delaware Geological Survey’s website(http://www.udel.edu/dgs).

Acknowledgments

This work was funded by the DNREC through grantsfrom the U.S. Environmental Protection Agency Ground-Water Protection and Source Water Protection Programs.Evan M. Costas, Cheryl A. Duffy, Bailey L. Dugan, Scott V.Lynch, and Tamika K. Odrick assisted with the work. RonaldGraeber, John Barndt, Blair Venables, Joshua Kasper, andScott Strohmeier of the DNREC are thanked for makingmonitoring data from numerous wastewater disposalfacilities available. Stacey Chirnside of the University ofDelaware Department of Bioresources Engineering isthanked for providing access to ground-water data fromseveral research projects. Thomas E. McKenna (DGS), JohnT. Barndt (DNREC), and Geoffrey C. Bohling (KansasGeological Survey) critically reviewed the manuscript.

METHODS

This work has three primary components: data compila-tion, statistical evaluation and model development, andestimation of the water-table elevation. Water level and welldata were extracted from a DGS, Oracle-based database.Spatial data management and processing were done withdesktop and workstation components of ArcGIS v9.0 (ESRI,2003), ArcGIS v9.1 (ESRI, 2005) and Surfer v8 (GoldenSoftware, 2002) software. Horizontal coordinates of all dataare in meters, using the Universal Transverse Mercator

2 Delaware Geological Survey • Report of Investigations No. 73

Figure 1. Depth to water under normal conditions for the InlandBays watershed in Sussex County, Delaware.

http://www.udel.edu/dgs


projection and North American Datum of 1983 (NAD83).Elevations are reported relative to the NorthAmerican VerticalDatum of 1988 (NAVD88). Statistics were computed withfunctions and procedures contained in Oracle, ArcGIS, andMicrosoft Excel.

Data Compilation and Statistical Evaluation

Land-surface elevation (LSE) data that were usedthroughout the estimation process are from a 30-m digitalelevation model (DEM) created by John Mackenzie of theUniversity of Delaware’s Spatial Analysis Lab, and fromUSGS 1:24,000-scale topographic maps.

Depth-to-water data

Depth-to-water (DTW) and well data were acquired fromthe files and electronic databases of the DGS, DNREC,USGS, and the University of Delaware Department ofBioresources Engineering. DNREC data were extracted fromthe files and electronic databases of the Site Investigation andRestoration Branch, Water Supply Section, Ground-WaterDischarges Section, Spray Irrigation Program, and TankManagement Branch of the DNREC. Additional monitoring-well and water-level data were obtained for New CastleCounty from the USGS (1996). The data gathered were of twotypes: one type consisting of time-series depth-to-water mea-surements from monitoring wells, the other type from singlestatic depth-to-water measurements reported by well drillerson well completion reports. Prior to analysis, all depth-to-water data were converted to depth relative to ground-surfacedatum. Depth-to-water data from monitoring wells typicallyare reported to the nearest 0.01 ft; data from well completionreports usually are reported to the nearest foot. The accuracyof measurements from individual wells was evaluated bycomparison to measurements in nearby wells and by convert-ing depth-to-water to water-table elevation. Because the ele-vation of the water table is above 0 ft under static conditions,water elevations less than 0 ft and greater than LSE are gener-ally considered to be non-representative of local conditionsor inaccurate and were removed from the dataset.

Depth-to-water and well data were managed and ana-lyzed in a relational database format. Structured QueryLanguage (SQL) queries were assembled to create tables inOracle that contained well locations, land surface elevations,water-level observations, and computed statistics (mean, min-imum, maximum, standard deviation, and number of observa-tions) of observations made in the months and years of nor-mal, dry, and wet conditions. Each of Delaware’s counties(Sussex, Kent and New Castle) had its own well-informationdataset.

Surface-water features

In the Coastal Plain of Delaware, topographic relief issmall and aquifers consist of unconsolidated sediments. In thistype of hydrogeologic setting, the surfaces of streams, ponds,and swamps can be assumed to be the water table under fairweather conditions (Freeze and Cherry, 1979). This assump-tion also was used in the production of the 1960s hydrologicatlases and other regional evaluations of the water table inDelaware (Johnston, 1973, 1976).

Streams, ponds, and swamps have a direct correspon-dence to the water table; therefore, acquiring the elevationsand maintaining the spatial configuration of these surface-water features is an important part of modeling the water table.Locations of surface-water features are from the 1992 USGS1:24,000 hydrography digital line graph (DLG) datasetobtained from DataMIL (datamil.delaware.gov). These dataare stored in an ArcGIS personal geodatabase. DLG hydro-graphic data were converted into 30-m gridded raster datasetswith each grid node set to a value of zero. The grid geometrieswere set to correspond to the 30-m land surface DEM. Two30-m grids were created: one for shorelines and fringing tidalmarshes, and one for fresh-water streams. Two 90-m gridswere created from DLG hydrographic polygons: one for fresh-water ponds and swamps, and one for tidal marshes and theocean (Andres and Martin, 2005) For the grids representingfresh-water features, the elevation of each grid node was setequal to the elevation from the corresponding land-surfaceDEM. The raster calculator was also used to set elevations ofnodes representing salt-water marshes to 1 ft, and to set theelevations of nodes representing the shorelines to 0 ft. Thesegrids were converted to point datasets and merged (Andresand Martin, 2005).

Surface-water feature point data were modified toreduce noise in the dataset. Areas of steep land slope nearstreams produced some data points with anomalous eleva-tion values. The number of these anomalous values was min-imized by removing points occurring more than 15 m fromsurface-water features.

Model Development

Hydrologic conditions

For this work, dry, normal, and wet conditions weredetermined from time-series measurements of depth-to-water.Depth-to-water measurements have been collected at approx-imately monthly intervals for more than 30 years in a numberof observation wells located throughout the state. A set ofobservation wells was chosen for each county to define thehydrologic conditions for that particular area (Table 1). Amulti-step procedure was used to identify dry, normal, and wetconditions from observations made in those wells.

Ideally, comparison of long-term water-level observa-tions made at different locations should use data measured onthe same days and at regular intervals (e.g., monthly measure-ments should be made on the same day of the month in thewells being compared). To correct for the fact that this did notoccur, the observed water levels were used to interpolate waterlevels on the 15τη of each month for each month that a waterlevel was measured. For some months when water levels werenot measured, levels were interpolated from measurementsmade within 25 days of the 15τη day of the unmeasured month.Interpolation was done by on-screen digitizing of hydro-graphs. No estimates were made if water levels were notobserved within 25 days of the 15τη day of the unmeasuredmonth

Statistical measures of the water-level observations werecomputed and the corresponding dates that those water levelsoccurred were identified. From these statistics (Table 1), dry,

datamil.delaware.gov


normal, and wet hydrologic conditions were defined.Normal conditions were defined as the months with DTWlevels falling between the 40τη and 60τη percentiles (Fig. 2) inthe wells that were compared. Dry conditions (lowest waterlevels) were defined as months where the DTW levels fellbetween the 75τη and 95τη percentiles (Fig. 2) and wet condi-tions (highest water levels) were defined as months wherethe DTW levels fell between the 5τη and 25τη percentiles(Fig. 2). These percentile values were chosen as a balancebetween having an adequate number of dates to identifywells for estimating the water table and minimizing thedifferences in water levels within a particular groupcompared to differences between dry, normal, and wetgroups. Extreme values (< 5τη and > 95τη percentiles) wereexcluded from the analysis.

Multiple linear regression and the initial water table

Sepulveda (2003) reported that estimation of the water-table elevation by linear regression (LR) on LSE could beimproved by a multiple linear regression (MLR) procedure

that used a “minimum water table” along with land surfaceelevation to estimate the water-table elevation. A keyassumption used in many water-table estimation projects isthat streams, ponds, and swamps represent the intersection ofthe water table with land surface (Winter, 1999) (Fig. 3).Thus, the land-surface DEM was used to assign elevations tothe surface-water features.

The minimum water table was then estimated by com-puting a grid from the elevations of surface-water features(Sepulveda, 2003). In this process, the minimum and maxi-mum elevations of the minimum water table are 0 ft (e.g.,tidal-water elevation) and land-surface elevation, respective-ly. For clarity, Sepulveda’s term “minimum water table” isreplaced by “initial water table” (INITWT) for application toDelaware. For Sussex County and Kent County, estimates ofthe INITWT were created by a 5th-order local polynomialregression method.A kriging algorithm was used to calculatethe INITWT for New Castle County. Three separate initialwater-table grids were created for Sussex, three for Kent,and two for New Castle Counties.

The second variable in the MLR equation is a depth tothe initial water table, which was calculated by subtractingthe initial water-table elevation from the land surface eleva-tion DEM. Thus, the general form of the multiple linearregression equation is:

Est WTi = β1 * INITWTi + β2 * (LSEi-INITWTi) (1)

where:

Est WTi = estimated water-table elevation at point iβ1 = regression coefficient 1INITWTi = initial water-table at point iβ2 = regression coefficient 2LSEi = land-surface elevation at point i(LSEi-INITWTi) = depth to the initial water table atpoint i

The regression coefficients, β1 and β2, were calculatedfrom the depth-to-water and well datasets. INITWT anddepth to INITWT were converted into point feature class for-mat in ArcGIS and then exported into Microsoft Excel forthe regression analysis. The dry, normal, and wet welldatasets for each county produce their own unique sets ofregression coefficients. The effectiveness of MLR was com-pared to simple LRs on LSE and the INITWT by comparing

Table 1. Long-period observation wells used for each county. Values are depth to water measured in feet below land surface.

Figure 2. Hydrograph for well Qe44-01 showing monthly depth towater (DTW) below the land surface. Data points are estimated forthe 15th of each month. Statistics derived from estimated data. Linesrepresent 25τη, 40τη, 60τη, and 75τη percentiles of data distribution.


statistical measures of observed and predicted WTEs.

Estimation of Water-Table Elevation

The elevation of the water table is the distance of thewater-table surface from a vertical datum, in this case theNAVD88, which is approximately sea level. The two variables(the initial water table and the depth to the initial water table)and the two coefficients (coefficient β1 and coefficient β2)were computed and applied to the multiple linear regressionequation. The resultant water-table elevation maps for eachcounty are continuous surfaces; however, observations of thesurface exist at irregularly spaced locations. The multiple lin-ear regression equation interpolates water-table elevationsbetween these surface observations to produce the continuoussurface. The water-table elevation grids are in the form of GISgrids with 30-m horizontal and 1-ft vertical resolution.

The water-table grids were completed as a series ofsub-grids that were subsequently merged into single county-wide grids. For example, a water-table DEM for normalconditions for eastern Sussex County was merged with awater-table DEM for normal conditions for western SussexCounty.

There are several different methods that can be used tomosaic raster datasets, and because the grids overlapped insome areas, a weighted average algorithm was used followedby a filtering step. The weight-based algorithm is dependenton the distance from the pixel to the edge within the over-lapping area. As an example of a filter, the different sectionsof Sussex County were separated based upon hydrography,so the merged Sussex County WTE grids were put through a3x3 Gaussian low-pass filter that removes higher frequencyvariations in grid values and smooths the artifacts along theseams.

Merging the county DEMs into a statewide grid wasexplored; however, the large size of the resultant grid severe-ly taxes the performance of even high-end PC workstations.In addition, the merge process resulted in unwanted grid arti-facts because the DEMs are regular grids and the county

boundaries are in part formed by meandering streams. Theseartifacts are a problem because when the statewide grid iscut into county grids, there are no-value nodes located in theinterior of the resultant county grids. To work around theseissues, the grids are completed by county and include anoverlap of 200 m into the adjacent county. If a user needsa simple map covering more than one county for displaypurposes, then the county grids are adequate. Any analyticalwork (i.e., slope and aspect, hillshade, etc.) that requiresa seamless grid across county boundaries will require theuser to merge the grids and develop the appropriate smooth-ing procedures most suited to the location and scale ofinvestigation.

Potential Improvements to Water-Table DEM from

LIDAR-Derived DEM

DEMs of land surface produced from aircraft-borneLIDAR (Light Detection And Ranging) data offer the poten-tial for increasing the horizontal and vertical resolutions ofthe elevations of surface-water features and water-tableDEMs. Compared to the 30-m horizontal and 1-ft verticalresolution DEMs derived from 1:24,000 DLG data, experi-mental DEMs produced from LIDAR data collected by air-craft-borne sensors in the past few years typically result inDEMs with 2-m horizontal and approximately 0.328-ftvertical resolutions.

A simple experiment was conducted with experimentalLIDAR-derived DEMs produced by the USGS for two ran-domly selected small watersheds located in Sussex County. Inthe same way that the elevations of surface-water featureswere determined from existing DLG hydrography data(USGS, 1992) and 30-m DEMs (Mackenzie, 1999), LIDAR-derived DEMs and the USGS (1992) hydrography DLG datawere used to determine elevations of surface-water features.The resulting point elevation data were visually compared tothe LIDAR-derived DEM to determine if the DLG-derivedpoints were aligned with the local elevation minima on the

Figure 3. Illustration showing the initial water table including graphical representation of water-table terms and vertical datums.Illustration modified from Sepulveda (2003).

baxterTypewritten Text

baxterText BoxLIDAR-derived DEM.

RESULTS AND DISCUSSION

Water-table elevations were estimated as a series ofoverlapping grids (Table 2). For each grid area, a set of long-term observation wells was used to define dry, normal, andwet periods (Table 1). This enabled collection of water-levelobservations made in additional wells during those periods.Separate INITWT and MLR estimations (Tables 3 and 4)were run for each grid area.

Two interesting observations can be made regarding thecoefficients of the MLR equations and of the coefficient forthe regression on INITWT. First, β1 values (weighting factorfor INITWT), except for one grid, are slightly less than 1. InSepulveda’s analysis of the water-table elevation in Florida(Sepulveda, 2003), the regression coefficients of the initialwater table were all ≥ 1 in all but one of his study groups.This indicates that the data (i.e., elevations of surface-waterfeatures) and methods (polynomial surface fit) used to esti-mate the INITWT in Delaware slightly overestimateobserved water-table elevation (WTE) rather than underesti-mate WTE as in Florida. Spatially, the INITWT elevations,regardless of hydrologic condition, are less than the estimat-ed WTEs in low-lying areas along the shorelines and aroundthe streams and bays. In addition, INITWT elevations foreastern Sussex County are less than the estimated WTEs inall areas under wet conditions. These conditions also can bepartially due to artifacts from estimating elevations of sur-face-water features from DLGs and DEMs. Second, themagnitude of β2 (weighting factor for depth to INITWT) islargest in New Castle County, and lowest in Sussex County.This is likely due to deeper incision of streams and greatertopographic relief in New Castle and Kent counties, andresultant greater depth to the INITWT.

Simple linear regressions were performed with the nor-mal condition water-level data on both LSE and the INITWTfor statistical comparison to the multiple linear regressionmethod (Tables 3 and 4). The coefficients of determination(R2), which show the proportions of sample variancesaccounted for by the regression equations, are very similar

between the MLR and both LR models. Root mean squareerror (RMS), a statistical measure of the magnitude of thetotal estimation error, were also calculated for the MLR andboth LR methods. The RMS value for the MLR method issmaller than the RMS values produced from the LSE LR andthe INITWT LR analyses. These statistical measures indicatethat the MLR method is a slightly more accurate predictor ofwater-table elevation than a simple LR on LSE or INITWT.It is important to note that a statewide analysis by simple LRon LSE fairly accurately predicts the normal WTE, and thatWTE is approximately 80 percent of LSE (Table 4).

A second way of assessing the goodness of fit betweenthe different estimation methods is to evaluate the individualresiduals, or observed minus predicted WTE values. In gen-eral, differences between the 2νδ and 3ρδ quartiles and 5τη and95τη percentiles of residuals from the MLR method are lessthan similar differences from both of the LR methods. Thisindicates that the MLR method better estimates 90 percent ofwater-level observations than do the LR methods. However,the MLR method did produce a higher range (maximum-minimum) of residuals in Kent County and New CastleCounty than did the LSE LR; this is likely a result of increas-ing LSE values and ranges in these two counties.

On closer inspection, many of the largest residuals arelocated near areas of steepest topography, and some arelocated near bodies of tidal surface water. In the cases ofsteep topography, errors in coordinates of measurement loca-tion can result in significant changes in LSE and observedWTE. In cases of measurements made in northern NewCastle County, where topographic contours have 10 ft inter-vals, an error in horizontal position of just 60 m can easilyresult in a change in LSE and of the observed WTE of 10 to20 ft. Larger residuals associated with measurement pointslocated near bodies of tidal surface water indicate that thosemeasurement points may not be indicative of WTE of thewater-table aquifer. It is also possible that some of the largeresiduals are artifacts from estimating the elevations of sur-face-water features from DLGs and DEMs.

Table 2. Grid dimensions and numbers of ground-water points used to estimate water-table elevation and depth to water table. Surfacewater points were used to estimate the initial water table. Ground-water points were used in the multiple linear regression estimationprocedure.


Sussex County Water-Table Elevation

Creating the water-table elevation maps for SussexCounty, Delaware, was a multi-step process that involveddividing the county into three separate geographic sections(east, west, and north). In large part, the geographic sectionsof Sussex County were delineated based on watershedboundaries and DLG lines representing hydrography (e.g.,streams) in the area. Each geographic region of SussexCounty has its own unique well data set, and thus its ownunique set of regression coefficients for dry, normal, and wetconditions. Water-table elevation grids for each hydrologiccondition were created for eastern, western, and northernSussex County and were then merged to create a unified,county-wide water-table elevation grid for dry (Fig. 4A),normal (Fig. 4B), and wet (Fig. 4C) conditions (Martin andAndres, 2005a).

Hydrologic conditions for the area identified as easternSussex County were determined by comparing the long-termwater-level measurements in monitoring wells Ng11-01 andQe44-01. The observation well dataset used to compute theregression coefficients in this area included water-levelsfrom 1,320 wells (Table 2). Locations of these measurementsare not spread evenly across the study area.

Long-term water-level measurements from monitoringwells Nc45-01 and Qe44-01 were compared in order todefine the time periods for dry, normal, and wet conditionsfor western Sussex County. The water-level observationdatasets for this area included data from 728 water-levelobservation points (Table 2) that were unevenly distributedacross the study area.

Dry, normal, and wet conditions for the area defined asnorthern Sussex County were determined from the compari-son of long-term water-level measurements in monitoringwells Nc45-01 and Ng11-01. A total of 1,114 wells wasincluded in the water-level observation data set (Table 2).

In all three areas, MLR equations and weighing factors(Table 3) were used to calculate water-table elevation grids.The final water-table elevation maps for Sussex County hadelevations ranging from 0 to 66 ft. Because land-surface ele-vation was a component of the multiple linear regression, thewater-table elevation maps resemble the land surface eleva-tion DEM maps. Water-table elevations, in general, increasewith increasing land surface elevations. This is true for Kentand New Castle counties as well.


Table 3. Coefficients for MLR and LR used to calculate the water-table elevation for each county and/or county section. The first regres-sion coefficient (β1) is multiplied by the initial water table (INITWT). The second regression coefficient (β2) is multiplied by the regressor(LSE-MINWT).

Table 4. Statistical comparison of residuals from water-table elevation (WTE) estimation residuals for normal conditions. Residuals werecalculated using multiple linear regression (MLR), linear regression on land surface elevation (LSE), and linear regression on the initialwater table (INITWT).

Kent County Water-Table Elevation

Initially, the process for calculating the water-table ele-vation maps for Kent County was going to be consistent withthat for Sussex County. Long-term water-level measure-ments from monitoring wells Mc51-01, Md22-01, and Jd42-03 were compared in order to define the time periods for dry,normal, and wet hydrologic conditions. Kent County wasdivided into three geographical sections (south, central, andnorth) based on watershed boundaries and hydrography.However, the water-level observation point data sets for cen-tral and northern Kent County were not adequate calculateaccurate water-table elevation grids. The regression analysisproduced poor R2 values for each condition due to an insuf-ficient number of water-level observation points. Therefore,the water-level points were incorporated together to form asingle data set and the water table was estimated for theentire county as a whole entity. As a result, the initial water-table grids for southern, central, and northern Kent Countywere also merged together to form a unified Kent Countyinitial water-table grid. The water-level observation pointdata set for Kent County consisted of 2,176 wells (Table 2).The regression analysis performed on these data sets yieldedthe regression equations (Table 3) for each hydrologic con-dition and the resulting water-table elevation grids for dry(Fig. 5A), normal (Fig. 5B), and wet (Fig. 5C) conditions inKent County (Martin and Andres, 2005b).

New Castle County Water-Table Elevation

Calculating the water-table elevation maps for NewCastle County also involved applying the same concepts thatwere established in Sussex County by dividing New CastleCounty into two separate sections (north and south) with theC&D Canal acting as the hydrologic boundary. Long-term

water-level measurements from monitoring wells Jd42-03,Hb14-01, and Db24-10 were compared in order to define thetime periods for dry, normal, and wet conditions. However,as was the case in Kent County, the regression analysis per-formed on the water-level point data sets for these areasfailed to produce useable correlation coefficients; therefore,the water-level points for the north and south sections werejoined to produce a single data set for the entire county.INITWT grids for New Castle County were created with anordinary kriging algorithm because grid elevations comput-ed by local polynomial regression were too high at low LSEand too low at high LSE. It is likely that local polynomialregression could not adequately reproduce the greater reliefof land surface and the water table in New Castle County.The INITWT grids for southern, and northern New CastleCounty were also merged together to form a unified NewCastle County INITWT grid. The water-level point data setfor New Castle County contained an uneven distribution of812 wells (Table 2). The regression analysis performed onthese wells produced the regression equations (Table 3) thatwere used to create the water-table elevation maps for dry(Fig. 6A), normal (Fig. 6B), and wet (Fig. 6C) conditions inthe Coastal Plain of New Castle County (Martin and Andres,2005c). When using the water-table elevation and subse-quent depth-to-water maps for New Castle County it isimportant to note that the Piedmont region of Delaware wasexcluded from this work due to the sparse availability andinaccuracy of water-level data for this area.

Depth to Water

The water-table DEMs for dry, normal, and wet condi-tions for Sussex (Figs. 7A, B, and C), Kent (Figs. 8A, B, andC), and New Castle (Figs. 9A, B, and C) counties were


subtracted from the land surface DEM to produce depth-to-water grids. When comparing depth to water to the percentageof land area (Table 5) it becomes apparent that a significantportion of the Coastal Plain of Delaware can be classified ashaving a shallow water table. Under normal conditions, 71percent of the land area has a depth to water of less than 10 ftand 21 percent of the land area has a depth to water of less than5 ft, with these percentages being significantly higher inSussex and Kent counties.

When dealing with depths to water of less than 10 ft therewill likely be significant environmental issues with largerwastewater disposal facilities such as rapid infiltration basinsand community disposal systems (USEPA, 1999, 2003).When dealing with depths to water of less than 5 ft, sitesbecome high risk for individual standard domestic subsurfacewastewater disposal systems (DNREC, 2005) and for anyexcavations, building foundations, and basements.

Comparison of Existing DLG Hydrography toLIDAR-Derived DEM

Utilizing existing DEMs, DLGs, water level data, andGIS tools to estimate the water-table elevation was cost effi-cient and effective; however, the potential still exists forgreater precision and accuracy through the use of LIDAR-derived DEMs of land surface and elevations of surface-waterfeatures. It is not unreasonable to expect that water-table gridresolutions could be increased to the 2-m horizontal and0.328-ft levels of the LIDAR-derived DEMs. However,improvements to the water-table DEMs from LIDAR DEMswill require significant additional efforts as visual comparisonindicate that there are inaccuracies in the DLG locations ofsurface-water features. There also are data processing artifacts

that result from the procedures used to estimate elevations ofsurface features from land surface DEMs.

Locational inaccuracies in the 1:24,000 hydrographyDLGs were evident where DLG locations of surface-waterfeatures did not align with the local minimum land surface ele-vations on the 30-m DEMs; data processing artifacts are evi-dent where the elevations of stream features do not decrease inthe downstream direction (Andres and Martin, 2005). Theseissues become even more apparent when comparing the1:24,000 hydrography DLGs with LIDAR-derived DEMS(Fig. 10). Reducing the effects of these problems will requirework to locate the streams within the areas of local topo-graphic minima and to mitigate any other artifacts in theLIDAR DEMs that result from bridges, culverts, channelobstructions, and/or data processing problems.

CONCLUSIONS

Water-table depth is a key facet in many engineering,hydrogeologic, and environmental management and regulato-ry decisions. Depth to water is an important factor in riskassessments, site assessments, evaluation of permit compli-ance data, and registration of pesticides and determiningacceptable application rates. Obtaining an accurate represen-tation of the water table is also crucial to the success of manyhydrologic modeling efforts.

An extensive cooperative effort to produce readily avail-able water-table elevation maps for the state of Delaware wasundertaken in the 1950s. However, despite the usefulness ofthese paper contour maps, contemporary advances in comput-er technology and the expanding utilization of GIS has madeit necessary to update these maps and has brought about thedemand to have them published in a suitable digital format.

Table 5. Comparisons of depth to water (∆ΤΩ) and percentage of land area.

Delaware Geological Survey • Report of Investigations No. 73. 9

Mapping the water-table elevation of the DelawareCoastal Plain was accomplished by using pre-existing datasuch as long-term hydrographs to determine dry, normal, andwet hydrologic conditions, a 30-m DEM to assign elevationsto surface-water features, and well completion reports used toobtain static water levels of shallow domestic wells to producethe regression coefficients that were inserted into the multiplelinear regression equation. The resultant products are GISready grids with a horizontal spacing of 30 m and a verticalresolution of 1 ft.

Existing DEMs, DLGs, water-level data, and GIS toolsprovided a cost efficient and relatively accurate means to esti-mate the water-table elevation; however newer technologyoffers potential for greater precision and accuracy. LIDARmeasured DEMS offer the potential for increasing the hori-zontal and vertical resolutions of the water-table grids. Use ofLIDAR DEM data to estimate higher resolution grids ofwater-table elevation will require more accurate locationaldata for surface-water features as well as more powerful andefficient computers and software.

REFERENCES CITED

Andres, A. S., and Martin, M. J., 2005, Estimation of thewater table for the Inland Bays watershed, Delaware:Delaware Geological Survey Report of Investigations No.68, 20p.

Delaware Department of Natural Resources andEnvironmental Control (DNREC), 2005, Regulationsgoverning the design, installation and operation of on-sitewastewater treatment and disposal systems: DNRECDocument No. 40-08-05/04/07/01, 75p.

Dunne, T., and Leopold, L. B., 1998, Water in environmentalplanning: New York, New York, W. H. Freeman andCompany, 818 p.

ESRI, Inc., 2003, ARCGIS software v. 9.0: Redlands,California.

____ 2005, ARCGIS software v. 9.1: Redlands, California.Freeze, A. R., and Cherry, J. A., 1979, Groundwater:

Englewood Cliffs, New Jersey, Prentice Hall, Inc., 604 p.Freeze, A. R., and Witherspoon, P. A., 1967, Theoretical

analysis of regional ground-water flow: Water ResourcesResearch, vol. 3, p. 623-634.

Johnston, R.H., 1973, Hydrology of the Columbia(Pleistocene) deposits of Delaware: An appraisal of aregional water-table aquifer: Delaware Geological SurveyBulletin No. 14, 78p.

____ 1976, Relation of ground water to surface water in foursmall basins of the Delaware coastal plain: DelawareGeological Survey Report of Investigations No. 24, 56p.

Golden Software, Inc., 2002, Surfer software v. 8, Golden,Colorado.

Mackenzie, J., 1999, Watershed delineation project, alphadata release: www.udel.edu/FREC/spatlab/basins.

Martin, M. J., and Andres, A. S., 2005a, Digital water-tabledata for Sussex County, Delaware: Delaware GeologicalSurvey Digital Product 05-01, ESRI grid format.

____ 2005b, Digital water-table data for Kent County,Delaware: Delaware Geological Survey Digital Product05-01, ESRI grid format.

____ 2005c, Digital water-table data for New Castle County,Delaware: Delaware Geological Survey Digital Product05-01, ESRI grid format.

Perlman, H., 2005, Earth’s water: Ground water, UnitedStates Geological Survey, United States Department ofthe Interior, http://ga.water.usgs.gov/edu/earthgw.html.

Sepulveda, N., 2003, A statistical estimator of the spatial dis-tribution of the water-table altitude: Ground Water, vol.41, p. 66-71.

U.S. Environmental Protection Agency, 1999, The Class Vunderground injection control study, large-capacity septicsystems: U.S. Environmental Protection Agency,EPA/816-R-99-014e, 120p.

____ 2003, Rapid infiltration land treatment, U.S.Environmental Protection Agency wastewater technologyfact sheet: EPA 832-F-03-025, 6p.

U.S. Geological Survey 1992, Hydrography digital line graphfor Delaware: U.S. Geological Survey.

____ 1996, Water-level data for the industrial area northwestof Delaware City, Delaware, 1993-94, USGS Open FileReport 96-125: U.S. Environmental Protection Agency,Towson, Maryland, 23 p.

Williams, T. A., and Williamson, A. K., 1989, Estimatingwater-table altitudes for regional ground-water flowmodeling, U.S. Gulf Coast: Ground Water, vol. 27, p.333-340.

Winter, T. C., 1999, Relation of streams, lakes, and wetlandsto groundwater flow systems: Hydrogeology Journalvol. 7, no. 1, p. 28-45.


Figure 10. Illustration showing stream segment elevation artifactscaused by misalignment of 1:24,000 hydrography DLG withLIDAR DEM and road crossings.

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REPORT OF INVESTIGATIONS NO. 73 ANALYSISAND …John H. Talley, State Geologist REPORT OF INVESTIGATIONS NO. 73 ANALYSISAND SUMMARY OF WATER-TABLE MAPS FORTHEDELAWARECOASTALPLAIN By