U.S. Department of the Interior

U.S. Geological Survey Scientific Investigations Map 3265

Estimation of Reservoir Storage Capacity Using Multibeam Sonar and Terrestrial Lidar, Randy Poynter Lake, Rockdale County, Georgia, 2012

By

K.G. Lee

2013 

Abstract

The U.S. Geological Survey, in cooperation with the Rockdale County Department of Water Resources, conducted a bathymetric and 

topographic survey of Randy Poynter Lake in northern Georgia in 2012. The Randy Poynter Lake watershed drains surface area from Rockdale, 

Gwinnett, and Walton Counties. The reservoir serves as the water supply for the Conyers-Rockdale Big Haynes Impoundment Authority.

The Randy Poynter reservoir was surveyed to prepare a current bathymetric map and determine storage capacities at specified water-surface 

elevations. Topographic and bathymetric data were collected using a marine-based mobile mapping unit to estimate storage capacity. The marinebased mobile mapping unit operates with several components: multibeam echosounder, singlebeam echosounder, light detection and ranging 

system, navigation and motion-sensing system, and data acquisition computer. All data were processed and combined to develop a triangulated 

irregular network, a reservoir capacity table, and a bathymetric contour map. 

Introduction

Randy Poynter Lake is an approximately 640-acre reservoir that was constructed to store water to meet the drinking water needs of 

Rockdale County, Georgia. The lake was formed following the impoundment of Big Haynes Creek with the construction of Jack Turner 

Dam. Randy Poynter Lake is located in Black Shoals Park, which is about 6 miles north of Conyers, Georgia. The lake is also used for 

recreational activities in northwestern Georgia. Protection and monitoring of this water supply is a concern to the Rockdale County 

Department of Water Resources. Changes in land use, such as timber clear-cutting and residential development in basins that drain into 

the lake, have raised concern about possible changes in the rate of sedimentation in the reservoir. Sedimentation impacts a reservoir 

if the decreased storage capacity prevents supplying the full service for which the reservoir was designed. Sufficient capacity must be 

maintained in domestic water-supply reservoirs to assure continuity of supply during periods of prolonged drought and to meet expected 

increases in water demand. 

The U.S. Geological Survey (USGS), in cooperation with the Rockdale County Department of Water Resources, collected 

bathymetric and topographic data at Randy Poynter Lake using a marine-based mobile mapping unit. The results of the survey of Randy 

Poynter Lake will be used to help understand the ecosystem and develop a component that is essential to a water census for the Rockdale 

County water supply. The results can also be used as a baseline survey to document temporal changes in lakebed elevation and storage 

capacities. 

Description of the Study Area

Randy Poynter Lake is located in north Rockdale County, Georgia (fig. 1). The reservoir was created by the impoundment of Big 

Haynes Creek approximately 6.5 miles upstream from its confluence with the Yellow River. Following the construction of Jack Turner 

Dam, the lake was dedicated in April 1998 and opened to the public in April 2000. The reservoir extends from Jack Turner Dam on the 

south (fig. 2) to the Haralson Mill covered bridge on the north (fig. 3). The Randy Poynter Lake watershed drains surface area from 

Rockdale, Gwinnett, and Walton Counties. The reservoir serves as the water supply for the Conyers-Rockdale Big Haynes Impoundment 

Authority. Due to rapid population growth beginning in the 1960s, and associated water demands, water resources are limited. The 2010 

census reported a 22-percent increase in the population of Rockdale County from 2000 to 2010 (Census Viewer, 2013). The Randy 

Poynter reservoir is designed to meet Rockdale County’s estimated water-supply needs through 2030 (Rockdale County, 2013). The 

reservoir and nearby Black Shoals Park also serve as a recreational attraction.

The area of study has a subtropical climate characterized by warm, humid weather. According to long-term climatological records 

compiled by the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) Climate Group, the mean annual air temperature is 62.7 °F (degrees Fahrenheit). Generally, July is the hottest month with a mean temperature of 90.3 °F, and January is the coldest 

with a mean temperature of 32.7 °F (PRISM Climate Group, 2012). Average annual precipitation, based on records for the 1971–2000 

period at precipitation stations, is approximately 51 inches (in.) (PRISM Climate Group, 2012). The drainage basin is located in the 

Piedmont Plateau province. The Piedmont Plateau province encompasses nearly one-third of the State and lies between the Blue Ridge 

Mountains and the Upper Coastal Plain. The soil in the study area, as described by a countywide soil survey, is predominately PacoletWedowee-Ashlar (Lathem, 2000). This soil type is typically well drained to excessively drained and has a loamy surface layer and a 

loamy or clayey subsoil. This area is characterized by steep hillslopes, which increases susceptibility to erosion and thus sedimentation. 

In general, land use within the Randy Poynter drainage basin, as defined by the National Land Cover Database (NLCD) 2006 

(Fry and others, 2011), can be classified as urban. Low-, medium-, and high-intensity residential and developed open-space land coverages were combined to compute total urban land use. Urban land use was the predominant basinwide land use in 2006 at 42 percent. 

Inspection of aerial photography indicates further urban development since the completion of the 2006 NLCD. Forested land (cumulative 

total of mixed, deciduous, and evergreen) in 2006 accounted for 35 percent of the basin. Inspection of land use trends indicates that 

the primary change in the Randy Poynter drainage basin has been a decrease in forested areas and an increase in urbanization. Three 

datasets were used for comparison: NLCD 1992–2001 Retrofit Change Product (Fry and others, 2009), NLCD 2001 (Homer and others, 

2004), and NLCD 2006 (Fry and others, 2011). The results indicated an 11-percent reduction in forested land and a 5-percent increase in 

urbanization from 1992 to 2001. Similarly, comparison of the 2001 and 2006 NLCD showed a 4-percent decrease in forested land and 

an 11-percent increase in urbanization.

Methods and Data Collection

In 2012, the USGS, in cooperation with the Rockdale County Department of Water Resources, collected bathymetric and topographic data at Randy Poynter Lake using a marine-based mobile mapping unit (fig. 4) to estimate storage capacity. The marine-based 

mobile mapping unit operates with several components: multibeam echosounder (MBES), singlebeam echosounder (SBES), light 

detection and ranging (lidar) system, navigation and motion-sensing system, and data acquisition computer. The marine-based mobile 

mapping unit was deployed July 16–21, 2012, and August 13–16, 2012. 

Bathymetric data were collected using the multibeam and singlebeam echosounders. The MBES used in this study was an R2Sonic 

Sonic 2024 multibeam echosounder (http://r2sonic.com). The Sonic 2024 collects a wide swath of high-resolution bathymetric data 

by recording the intensity of sound reflected off the lake bottom (acoustic backscatter). The Sonic 2024 was operated at a frequency of 

300 kilohertz (kHz), and data were collected at a 120-degree swath sector with a 50-percent overlap between swaths. The water-surface 

elevation at the time of data collections was 727 feet (ft) above North American Vertical Datum of 1988 (NAVD 88). The MBES was 

used to provide full coverage of the lakebed at elevations below 725 ft NAVD 88. Additional data were collected in shallow areas using 

an Odom Hydrotrac SBES (http://www.odomhydrographic.com). The data collected with the SBES were used to fill data gaps and 

validate the data collected with the MBES. The MBES data were collected in longitudinal transects to provide a complete swath of the 

reservoir. The SBES data were collected by driving along the shoreline and surveying cross sections in desired locations. The resulting 

bathymetric datasets were processed using filters in the HYPACK/HYSWEEP software (http://www.hypack.com) to remove data spikes 

or erroneous points. The data collected by the MBES and the SBES were compared in areas of overlap data collection. The average 

difference in lakebed elevation was 3 centimeters (cm) (1.2 in.).

Thirteen miles of topographic data were collected along the shoreline using lidar. The lidar system used was an Optech ILRISHD-ER-MC laser scanner (http://www.optech.ca). The laser scanner has an 80-percent reflectivity range of 1,700 meters (m) (5,577 ft), 

a laser repetition rate of 10 kHz, an average raw-range accuracy of 3 to 4 millimeters (mm) (0.1 to 0.16 in.) at 100 m (328 ft), and a 

maximum sample density (point-to-point spacing) of 1.3 mm at 100 m (0.05 in. at 328 ft). The lidar data were collected at an equal 

distance from the shoreline to reduce error resulting from reference frame misalignments. Additional topographic data points were collected during June 18–20, July 16–21, and August 13–16, 2012, to evaluate lidar data, fill data gaps, and define the full pool elevation of 

the reservoir. These points were collected using a Real-Time Network Global Navigation Satellite System (RTN-GNSS) established and 

maintained by eGPS Solutions (http://egps.net). RTN-GNSS elevation data were collected at 70 points within the georeferenced lidar 

point cloud. The average vertical difference and standard deviation of these points were 7 mm and 7 cm (0.28 to 2.8 in.), respectively. 

 The data collected with the MBES, SBES and lidar are accurately represented in three-dimensional space by use of the navigation and 

motion-sensing system. The Applanix (http://www.applanix.com) Position Orientation System for Marine Vessels (POS MV) was used to 

measure the pitch, roll, and heading of the boat, accurate within ±0.02 degree, and heave within 5 percent of the heave amplitude, or 5 cm (2 in.), 

whichever is greater (Applanix Corporation, 2006). The POS MV utilizes an inertial measurement unit (IMU) coupled with Global Positioning 

System (GPS) antennas to produce highly detailed and accurate georeferenced point clouds. A temporary benchmark was established and a GPS 

base station was used to aid in the accuracy of the survey. The navigation information from the survey was post-processed using the POS-Pac 

Mobile Mapping Suite (MMS) software (Applanix Corporation, 2010). This was done by combining the POS MV-recorded real-time data and 

GPS base station data to generate a blended navigation solution (standard best-estimate of travel or SBET file). The post-processed navigation 

data (SBET file) was applied to the MBES, SBES, and lidar data. In addition to the SBET file, POS-Pac MMS generates a file describing the 

accuracy of the post-processed solution. The horizontal accuracy of the post-processed file has a minimum and maximum root mean square 

(RMS) error of 0.4 and 2 cm (0.2 and 0.8 in.), respectively. The RMS error of the vertical accuracy ranged from 0.7 to 2.9 cm (0.3 to 1.1 in.). 

Stated accuracies are based on peak-to-peak errors, and the majority of the points have higher accuracies and lower associated errors.

The errors associated with the collection of bathymetric data can be classified as systematic or random. Systematic errors are those that can 

be measured or modeled through calibration (Byrnes and others, 2002). Random errors are a result of the limitations of the measuring device 

and an inability to perfectly model the systematic errors. Errors associated with the SBET file would be applicable to the positional accuracy of 

the bathymetric data. The bathymetric point accuracy is represented by an error ellipsoid of which one of the vertical components is the depth 

measurement error. Because the lake floor is not visible, random errors associated with the limitations of the MBES are more difficult to quantify. 

To minimize these errors, quality assurance assessments were performed in real time during the survey. Additional field data were also collected 

to accurately calibrate the MBES. A sound velocity cast was taken for each day of data collection to account for variations of sound in the water 

column. Patch tests were performed to check for variations in the orientation of the sonar head and timing. The latency test was performed to 

measure the timing offset between the MBES and GPS component of the POS MV. The angular offsets of the transducer head were measured 

for their respective axes—longitudinal axis (roll test), lateral axis (pitch test), and rotation about vertical axis (yaw test). All timing (latency) and 

angular offsets (roll, pitch, and yaw) were accounted for in the HYPACK/HYSWEEP software. 

Storage Capacities

The four datasets (MBES, SBES, lidar, and RTN-GNSS) were processed and combined to provide a three-dimensional model of the Randy 

Poynter reservoir. The three-dimensional model was used to calculate reservoir storage volumes using HYPACK bathymetric software. This 

software produces volume data by performing computations between two specified surfaces (for example, a lakebed and a water-surface elevation). Erroneous soundings that resulted from instrument error during data collection in the field were removed by using a 0.5-ft grid overlay and 

averaging all points that fall within each square. A sensitivity analysis was performed to determine the optimal grid size used to estimate capacity. 

A triangulated irregular network (TIN) model of the bathymetric surface was then created in HYPACK from the subset data. The TIN model 

was used as the lower bounding surface for computation of storage capacity, and specified water-surface elevations were input as upper surface 

boundaries. Volumes were then computed at various water-surface elevations (table 1; fig. 5). The relation of water-surface elevation to storage 

capacity is rated to an elevation of 735 ft (NAVD 88), which is approximately the crest of the Jack Turner Dam spillway. 

Bathymetric Contours

Bathymetric contours were created using HYPACK bathymetric software in conjunction with ArcGIS (Environmental Systems Research 

Institute, 2010) geographic information system (GIS) software. The field bathymetric sounding data were reduced in HYPACK by overlaying a 

5-ft grid and averaging all the data points that fall within each square. Subsetting the original sounding data with the 5-ft grid removes unnecessary spikes that may result from instrument error in the data-collection process. A coarser grid was used for the generation of the contours as 

opposed to the estimation of capacity. The generation of contours does not require that all minor features on the lakebed be defined. The subset 

data were then imported into ArcGIS, and a TIN model of the bathymetric surface was numerically rendered (fig. 6). Elevation contours of the 

bathymetric surface were created from the TIN surface model in ArcGIS. Because contours generated from a TIN surface model are produced 

from triangles, their jagged edges do not fully represent the smooth curvatures of the true bathymetric surface of Randy Poynter Lake (Wilson 

and Richards, 2006). Thus, the elevation contours generated by the software were subsequently edited and smoothed using the Polynomial 

Approximation with Exponential Kernel (PAEK) algorithm within ArcGIS. The PAEK algorithm removes vertices along the contour lines based 

on a tolerance specified by the user. The tolerance is the length of a “moving” path used in calculating the new vertices (Environmental Systems 

Research Institute, 2010). The smoothed contours were compared with the original edited elevation data to ensure that the true topography of the 

lakebed was represented. Contours are labeled at 5-ft elevation intervals (fig. 7).

The Randy Poynter reservoir is relatively young and few spatial datasets exist. Pre-impoundment contours created through photogrammetry 

were available and compared to the 2012 dataset. A TIN surface model was created from the pre-impoundment contours and directly compared 

to the TIN created from the 2012 dataset. A grid was generated showing the difference between the two datasets. This gave a general overview 

of the scour and deposition that has occurred over the life of the reservoir. The grid indicated that the greatest area of deposition is the upstream 

inflow point of Big Haynes Creek (fig. 8). This could be attributed to sediment inflows, difference in accuracy of the two surveys, or a 

combination of both.

References Cited

Applanix Corporation, 2006, POS-MVTM V4 user guide, Rev. 0: Richmond Hill, Ontario, Canada, PUBS–MAN–000513, 369 p.

Applanix Corporation, 2010, POS-PacTM MMSTM GNSS-inertial tools user manual, Rev. 5: Richmond Hill, Ontario, Canada, PUBSMAN-001768, 248 p.

Byrnes, M.R., Baker, J.L., and Li, Feng, 2002, Quantifying potential measurement errors associated with bathymetric change analysis, 

ERDC/CHL CHETN-IV-50; U.S. Army Engineer Research and Development Center, Vicksburg, Miss., http://chl.erdc.usace.army.mil/library/

publications/chetn/pdf/chetn-iv-50.pdf.

Census Viewer, 2013, Population of Rockdale County, Georgia: Census 2010 and 2000 interactive map, demographics, statistics, graphs, quick 

facts, accessed March 2013 at http://censusviewer.com/county/GA/Rockdale.

Environmental Systems Research Institute, 2010, ArcGIS–A complete integrated system, accessed February 2011 at 

http://www.esri.com/software/arcgis/.

Fry, J.A., Coan, M.J., Homer, C.G., Meyer, D.K., and Wickham, J.D., 2009, Completion of the National Land Cover Database (NLCD) 1992–2001 

Land Cover Change Retrofit product: U.S. Geological Survey Open-File Report 2008–1379, 18 p., http://pubs.usgs.gov/of/2008/1379/.

Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., Barnes, C., Herold, N., and Wickham, J., 2011, Completion of the 2006 National Land 

Cover Database for the conterminous United States: Photogrammetric Engineering and Remote Sensing, v. 77, no. 9, p. 858–864.

Homer, Collin, Huang, Chengquan, Yang, Limin, Wylie, Bruce, and Coan, Michael, 2004, Development of a 2001 National Landcover Database 

for the United States: Photogrammetric Engineering and Remote Sensing, v. 70, no. 7, p. 829–840.

Lathem, J.R., 2000, Soil survey of Newton and Rockdale Counties, Georgia: United States Department of Agriculture, Natural Resources 

Conservation Service, accessed February 2003 at http://soildatamart.nrcs.usda.gov/Manuscripts/GA649/0/newton_rockdale.pdf.

PRISM Climate Group, 2012, Oregon State University, accessed February 2012 at http://prism.oregonstate.edu.

Rockdale County, 2013, Randy Poynter Lake and Jack Turner Dam, accessed May 2012 at http://www.rockdalecounty.org/main.cfm?id=2761.

Wilson, G.L., and Richards, J.M., 2006, Procedural documentation and accuracy assessment of bathymetric maps and area/capacity tables for small 

reservoirs: U.S. Geological Survey Scientific Investigations Report 2006–5208, 24 p. plus oversize figs., http://pubs.usgs.gov/sir/2006/5208/.

Table 1. Reservoir storage volumes for Randy Poynter Lake at specified 

water-surface elevations in 2012

[Capacity at specified water-surface elevations were calculated from the bathymetric TIN 

model created in HYPACK and not from smoothed bathymetric contours. Elevation, as 

used in this report, refers to distance above the North American Vertical Datum of 1988 

(NAVD 88)]

Water-surface 

elevation, in feet 

above NAVD 88

Storage capacity, 

in acre-feet

Storage capacity, 

in billion gallons

Storage capacity, 

in cubic kilometers

690 380 0.12 7.7

691 480 0.16 9.8

692 580 0.19 12

693 690 0.22 14

694 820 0.27 17

695 960 0.31 20

696 1,110 0.36 23

697 1,280 0.42 26

698 1,460 0.48 30

699 1,650 0.54 34

700 1,860 0.61 38

701 2,070 0.68 42

702 2,300 0.75 47

703 2,540 0.83 52

704 2,800 0.91 57

705 3,060 1.0 62

706 3,340 1.1 68

707 3,630 1.2 74

708 3,920 1.3 80

709 4,230 1.4 86

710 4,550 1.5 93

711 4,880 1.6 99

712 5,220 1.7 106

713 5,580 1.8 114

714 5,940 1.9 121

715 6,320 2.1 129

716 6,710 2.2 137

717 7,120 2.3 145

718 7,530 2.4 153

719 7,960 2.6 162

720 8,390 2.7 171

721 8,840 2.9 180

722 9,300 3.0 189

723 9,780 3.2 199

724 10,300 3.4 210

725 10,800 3.5 220

726 11,300 3.7 230

727 11,800 3.8 240

728 12,300 4.0 250

729 12,900 4.2 263

730 13,400 4.4 273

731 14,000 4.6 285

732 14,600 4.8 297

733 15,200 5.0 309

734 15,800 5.1 322

735 16,500 5.4 336

!

Conyers

Base from U.S. Geological Survey digital data, 1:100,000

Hydrography from National Hydrography Dataset Plus, 1:100,000

0 1 2 3 4 5 MILES

0 1 2 3 4 5 KILOMETERS

N

Haynes

Creek

Haralson Mill covered bridge

Black Shoals Park

ROCKDALE

COUNTY

GWINNETT

COUNTY

WALTON

COUNTY

NEWTON

COUNTY

HENRY

COUNTY

DEKALB

COUNTY

EXPLANATION

138

120

20

138

120 20

Randy

Poynter

Lake

Jack

Turner

Dam

Big

Location of Rockdale County,

Georgia

N

Figure 1. Location of Randy Poynter Lake, Rockdale County, Georgia.

Figure 2. Randy Poynter Lake looking upstream from Jack Turner Dam, July 2012.

Figure 3. The upstream extent of Randy Poynter Lake showing the Haralson Mill covered bridge, 

July 2012. Figure 5. The relation between water-surface elevation and storage capacity for Randy Poynter Lake, Georgia, 2012.

Figure 4. A U.S. Geological Survey boat equipped with a marine-based mobile mapping unit.

Figure 6. Bathymetric surface of Randy Poynter Lake, Rockdale County, Georgia, 2012.

Figure 7. Bathymetric contours of Randy Poynter Lake, Rockdale County, Georgia, 2012.

Figure 8. Increase in elevation of Randy Poynter Lake as indicated by the comparison of pre-impoundment contours and the current 

(2012) bathymetric survey.

Prepared in cooperation with the Rockdale County Department of Water Resources

(Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.)

pstream extent of Randy Poynter Lake showing Haralson Mill Covered Bridge, Rockdale County, Georgis, July 2012. .

740

730

720

710

700

690

680

670

Water-surface elevation, in feet above NAVD 88

0 2,000 4,000 6,000 8,000

Storage capacity, in acre-feet

10,000 12,000 14,000 16,000 18,000

731–735

726–730

721–725

716–720

711–715

706–710

701–705

696–700

691–695

686–690

681–685

676–680

Areal extent of survey

0 0.2 0.4 MILE

0 0.25 0.5 KILOMETER

EXPLANATION

Triangulated irregular network

elevation, in feet above NAVD 88

33°44'

83°56'

83°56'30"

83°57'

33°45'30"

33°45'

33°44'30"

680

685

690

695

700

705

710

715

720

725

730

Areal extent of survey

0 0.2 0.4 MILE

0 0.25 0.5 KILOMETER

EXPLANATION

Bathymetric contours–Shows altitude of reservoir bottom.

Contour interval 5 feet. Datum is NAVD 88

33°44'

83°56'

83°56'30"

83°57'

33°45'30"

33°45'

33°44'30"

Elevation difference, in feet

EXPLANATION

15

14

13

12

11

10

9

8

Areal extent

N Haralson Mill

covered bridge Haralson

Mill

Road