North Carolina Floodplain Mapping Program

LiDAR-Derived Flood-Inundation Maps for

Real-Time Flood-Mapping Applications,

Tar River Basin, North Carolina

Scientific Investigations Report 2007–5032
Cover.  Flooded subdivision in the Tar River basin, Greenville, North Carolina, October 1999 (photograph was contributed by the Federal 

Emergency Management Agency).
LiDAR-Derived Flood-Inundation Maps 

for Real-Time Flood-Mapping Applications, 

Tar River Basin, North Carolina

By Jerad D. Bales, Chad R. Wagner, Kirsten C. Tighe, and Silvia Terziotti

North Carolina Floodplain Mapping Program

Scientific Investigations Report 2007–5032

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark D. Myers, Director

U.S. Geological Survey, Reston, Virginia: 2007

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Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Suggested citation:

Bales, J.D., Wagner, C.R., Tighe, K.C., and Terziotti, Silvia, 2007, LiDAR-derived flood-inundation maps for real-time 

flood-mapping applications, Tar River basin, North Carolina: U.S. Geological Survey Scientific Investigations Report 

2007–5032, 42 p.
iii

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................2

Purpose and Scope ..............................................................................................................................3

Study Area..............................................................................................................................................3

Data .............................................................................................................................................................7

Hydrologic Data.....................................................................................................................................7

Topographic Data Derived from Light Detection and Ranging (LiDAR) Systems.......................7

Sources of LiDAR Data ...............................................................................................................7

Accuracy of Original LiDAR Data..............................................................................................8

Conversion of LiDAR Data to Digital Elevation Model ...........................................................8

Hydroconditioning of LiDAR Surfaces......................................................................................9

Quality Assurance......................................................................................................................11

Development of Flood-Inundation Maps .................................................................................................11

Hydraulic Modeling ............................................................................................................................11

Geometry data............................................................................................................................13

Downstream Boundary Conditions—Nontidal Sites ...........................................................15

Downstream Boundary Conditions—Tidal Sites..................................................................15

Upstream Boundary Conditions—Gaged Sites ....................................................................15

Upstream Boundary Conditions—Ungaged Sites................................................................16

Calibration and Performance...................................................................................................17

Flood-Inundation Maps......................................................................................................................19

Uncertainty Associated with Inundation Maps.............................................................................24

Data for Model Development and Calibration ......................................................................26

Steady-Flow Versus Unsteady-Flow Modeling.....................................................................27

One-Dimensional Versus Two-Dimensional Modeling........................................................29

Summary and Conclusions.........................................................................................................................30

References....................................................................................................................................................31

Appendix........................................................................................................................................................35

Figures

1. Selected study sites in the Tar-Pamlico River basin, North Carolina ..................................3

2. Annual flood peaks for the Tar River at Tarboro, North Carolina, 1897–2000 

and 1906–2005 ...............................................................................................................................4

3. Annual flood peaks, in percent by month, for Tar River at Tarboro and Little Fishing 

Creek near White Oak, North Carolina, 1960–2005 .................................................................5

4. Annual flood peaks, by month, and recurrence intervals at the Tar River at Tarboro, 

North Carolina, 1897–1900 and 1906–2005................................................................................7

5. (A) Orthophotograph, (B) 6.1-meter by 6.1-meter digital elevation model, and 

(C) 1.5-meter by 1.5-meter digital elevation model of the Tar River floodplain near 

Tarboro in Edgecombe County, North Carolina .......................................................................9

6. (A) Hill shade digital elevation model of the area near the confluence of Crisp Creek 

with Conetoe Creek in North Carolina, showing the inundated area (B) before 

and (C) after hydroconditioning................................................................................................10 
iv

7. Example of spurious spike in the elevation surface .............................................................11

8. Overbank cross sections used in the Rocky Mount hydraulic model................................14

9. Simulated water-surface profiles for Little Fishing Creek near White Oak 

streamgage (site 9) in the Tar River basin, North Carolina..................................................15

10. Relation of water-surface elevation at the Tar River at Tarboro (site 12) to peak flow 

at Swift Creek at Hilliardston (site 7), North Carolina...........................................................16

11. Measured and simulated stage-discharge relations for Swift Creek at Hilliardston 

(site 7) in the Tar River basin, North Carolina ........................................................................19

12. (A) Land-surface elevation, (B) estimated water-surface elevation, and 

(C) inundated area for the Tarboro model ..............................................................................20

13. Inundation map showing topographic relief for the Tar River at Rocky Mount 

at NC 97 (site 6) for a water-surface elevation at this site of 38.6 meters above 

North American Vertical Datum of 1988..................................................................................23

14. Inundation map showing topographic relief and transportation networks for the 

Tar River at Rocky Mount at NC 97 (site 6) for a water-surface elevation at this site 

of 38.6 meters above North American Vertical Datum of 1988............................................24

15. Inundation map showing orthoimagery for the Tar River at Rocky Mount at NC 97 

(site 6) for a water-surface elevation at this site of 38.6 meters above North 

American Vertical Datum of 1988.............................................................................................25

16. Estimated floodwater depth for the water-surface elevation of 7.9 meters above 

North American Vertical Datum of 1988 in the Tar River basin at Greenville, 

North Carolina .............................................................................................................................26

17. (A) Relation of measured water level and simulated discharge and (B) simulated 

hydrograph for the Tar River at Tarboro, North Carolina, during Hurricane Floyd 

flood, September 5–30, 1999 .....................................................................................................28

Tables

1. Study sites in the Tar River basin of North Carolina ...............................................................6

2. LiDAR quality-control statistics for the Tar River basin, North Carolina .............................8

3. Summary of hydraulic models developed for the Tar River basin study sites in 

North Carolina .............................................................................................................................12

4. Summary of hydraulic model performance for sites in the Tar River basin of 

North Carolina .............................................................................................................................18

5. Inundation-map libraries for the Tar River basin in North Carolina ...................................23
v

Conversion Factors

SI to Inch/Pound

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft)

kilometer (km) 0.6214 mile (mi)

Area

square meter (m2) 10.76 square foot (ft2)

square kilometer (km2) 0.3861 square mile (mi2)

Volume

cubic meter (m3) 264.2 gallon (gal)

cubic meter (m3) 0.0002642 million gallons (Mgal)

cubic hectometer (hm3) 810.7 acre-foot (acre-ft)

Flow rate

cubic meter per second (m3/s) 35.31 cubic foot per second (ft3/s)

cubic meter per day (m3/d) 35.31 cubic foot per day (ft3/d)

cubic meter per second (m3/s) 22.83 million gallons per day (Mgal/d)

Hydraulic gradient

meter per kilometer (m/km) 5.27983 foot per mile (ft/mi)

Vertical coordinate information is referenced to the National American Vertical Datum of 1988 

(NAVD 88).

Horizontal coordinate information (latitude and longitude) is referenced to the North American 

Datum of 1983 (NAD 83).

Altitude, as used in this report, refers to distance above the vertical datum.
vi

Acronyms used in this report:

AVHRR	 advanced very high resolution radiometer

DEM	 digital elevation model

DFIRM	 Digital Flood Insurance Rate Map

DGPS	 differential global positioning system

FEMA	 Federal Emergency Management Agency

FMP	 Floodplain Mapping Program

GIS	 geographic information system

HEC–RAS	 Hydraulic Engineering Center–River Analysis System

HU	 hydrologic unit

LiDAR	 light detection and ranging

NCCGIA	 North Carolina Center for Geographic Information and Analysis

NCDEM	 North Carolina Division of Emergency Management

NCGS	 North Carolina Geodetic Survey

NWS	 National Weather Service

QC	 quality control

SAR	 synthetic aperture radar

TIN	 triangulated irregular network

USGS	 U.S. Geological Survey
North Carolina Floodplain Mapping Program

LiDAR-Derived Flood-Inundation Maps for Real-Time 

Flood-Mapping Applications, Tar River Basin, 

North Carolina

By Jerad D. Bales, Chad R. Wagner, Kirsten C. Tighe, and Silvia Terziotti

Abstract

Flood-inundation maps were created for selected 

streamgage sites in the North Carolina Tar River basin. Light 

detection and ranging (LiDAR) data with a vertical accuracy 

of about 20 centimeters, provided by the Floodplain Mapping 

Information System of the North Carolina Floodplain Mapping Program, were processed to produce topographic data 

for the inundation maps. Bare-earth mass point LiDAR data 

were reprocessed into a digital elevation model with regularly 

spaced 1.5-meter by 1.5-meter cells. A tool was developed as 

part of this project to connect flow paths, or streams, that were 

inappropriately disconnected in the digital elevation model by 

such features as a bridge or road crossing.

The Hydraulic Engineering Center–River Analysis 

System (HEC–RAS) model, developed by the U.S. Army 

Corps of Engineers, was used for hydraulic modeling at each 

of the study sites. Eleven individual hydraulic models were 

developed for the Tar River basin sites. Seven models were 

developed for reaches with a single gage, and four models 

were developed for reaches of the Tar River main stem that 

receive flow from major gaged tributaries, or reaches in which 

multiple gages were near one another. Combined, the Tar 

River hydraulic models included 272 kilometers of streams 

in the basin, including about 162 kilometers on the Tar River 

main stem.

The hydraulic models were calibrated to the most current 

stage-discharge relations at 11 long-term streamgages where 

rating curves were available. Medium- to high-flow discharge 

measurements were made at some of the sites without rating 

curves, and high-water marks from Hurricanes Fran and Floyd 

were available for high-stage calibration. Simulated rating 

curves matched measured curves over the full range of flows. 

Differences between measured and simulated water levels for 

a specified flow were no more than 0.44 meter and typically 

were less.

The calibrated models were used to generate a set of 

water-surface profiles for each of the 11 modeled reaches 

at 0.305-meter increments for water levels ranging from 

bankfull to approximately the highest recorded water level at 

the downstream-most gage in each modeled reach. Inundated 

areas were identified by subtracting the water-surface 

elevation in each 1.5-meter by 1.5-meter grid cell from the 

land-surface elevation in the cell through an automated routine 

that was developed to identify all inundated cells hydraulically 

connected to the cell at the downstream-most gage in the 

model domain. 

Inundation maps showing transportation networks and 

orthoimagery were prepared for display on the Internet. 

These maps also are linked to the U.S. Geological Survey 

North Carolina Water Science Center real-time streamflow 

website. Hence, a user can determine the near real-time stage 

and water-surface elevation at a U.S. Geological Survey 

streamgage site in the Tar River basin and link directly to 

the flood-inundation maps for a depiction of the estimated 

inundated area at the current water level.

Although the flood-inundation maps represent distinct 

boundaries of inundated areas, some uncertainties are 

associated with these maps. These are uncertainties in the 

topographic data for the hydraulic model computational grid 

and inundation maps, effective friction values (Manning’s n), 

model-validation data, and forecast hydrographs, if used. 

The Tar River flood-inundation maps were developed by 

using a steady-flow hydraulic model. This assumption clearly 

has less of an effect on inundation maps produced for low 

flows than for high flows when it typically takes more time 

to inundate areas. A flood in which water levels peak and fall 

slowly most likely will result in more inundation than a similar 

flood in which water levels peak and fall quickly. Limitations 

associated with the steady-flow assumption for hydraulic 

modeling vary from site to site. 

The one-dimensional modeling approach used in this 

study resulted in good agreement between measurements and 
simulations. The one-dimensional approach is reasonable for 

a prismatic channel in a relatively narrow floodplain but may 

not be appropriate for sinuous rivers with several tributaries 

in broad floodplains. Uncertainty in the flood-inundation 

polygons increases with distance from the main channel for 

which water-surface slopes are simulated. Two-dimensional 

models are increasingly used for simulating floodplain 

inundation because of the variability in topography across the 

floodplain, particularly in wide floodplains with numerous 

tributaries.

Introduction

Floods affect all parts of North Carolina. Of the 50 states, 

North Carolina had the ninth highest total flood damages 

(measured in 1995 dollars) during 1983–2003, and the twelfth 

highest damages per capita (Pielke and others, 2002; University Corporation for Atmospheric Research, 2006). 

A few major floods in North Carolina have been associated with stalled frontal systems, such as the widespread and 

damaging flood that occurred in a 16-county area of western 

North Carolina in November 1977 (Zembrzuski and others, 

1991). Most North Carolina floods with exceedance levels 

in excess of about 10 years, however, typically result from 

relatively short-duration, locally intense rainfall or from 

rainfall associated with tropical cyclones. Flash floods from 

locally intense rainfall most commonly occur in the Blue 

Ridge Physiographic Province and in large urban areas across 

the State (for example, Mason and Caldwell, 1993; Eddins and 

Zembrzuski, 1998; Robinson and others, 1998). 

Tropical cyclones and the remnants of tropical 

cyclones have caused flooding in all parts of North Carolina 

(Zembrzuski and others, 1991). Tropical cyclones generated 

devastating floods in the Blue Ridge Physiographic Province 

in 1916, 1928, 1940, and most recently in 2004. Within a 

period of less than 2 weeks in September 2004, Hurricanes 

Frances and Ivan produced back-to-back floods having 

exceedance levels of 100 years or greater (U.S. Geological 

Survey, 2006d). Maximum water levels established during 

the 1940 flood were exceeded in many locations during 2004, 

although maximum water levels established along parts of the 

French Broad River during the 1916 flood were not exceeded 

in 2004. The Pigeon River basin was affected particularly by 

the storms; flood peaks at Canton for both Frances and Ivan 

were at the 500-year exceedance level (based on an analysis of 

flood peaks through 1996; Pope and others, 2001).

Widespread and destructive floods produced by rainfall 

from tropical cyclones also can occur in the Piedmont and 

Coastal Plain of North Carolina. The 1916 and 1940 floods 

occurred not only in the Blue Ridge but also extended into the 

Piedmont Physiographic Province. Because of the remnants 

of a tropical cyclone that traveled from extreme northwestern 

North Carolina into the Sand Hills and on into the extreme 

northeastern part of North Carolina, the 1940 flood included 

the Coastal Plain as well (Zembrzuski and others, 1991). 

Tropical cyclones in 1954 and 1955 also produced widespread 

flooding in the Coastal Plain and, to a lesser extent, the 

Piedmont, but tropical cyclones produced relatively little 

regional flooding in these provinces between 1956 and 1995 

when tropical cyclone activity was somewhat low (Bales 

and others, 2000). A period of more intense tropical cyclone 

activity began in 1996 and continued through at least 2005. 

Hurricane Fran in 1996 produced fairly widespread flooding 

in the Neuse, Tar, and Cape Fear River basins (Bales and 

Childress, 1996), with floods at the 100-year exceedance level 

in several locations. 

Flooding from Hurricanes Dennis and Floyd in 1999 was 

among the most destructive and widespread in North Carolina 

in the last 100 years (Bales and others, 2000). Flooding at the 

500-year exceedance level occurred in all of the major river 

basins draining to Pamlico Sound (Bales, 2003), and 66 of 

the 100 North Carolina counties were declared disaster areas. 

Damages from Hurricane Floyd were estimated to be in excess 

of $3.5 billion, including the destruction of more than 4,100 

uninsured or underinsured homes (North Carolina Floodplain 

Mapping Program, 2006).

Flooding from Hurricanes Fran, Dennis, Floyd, Frances, 

and Ivan clearly demonstrated a growing demand and need for 

more and better flood information and flood forecasts. Floodinformation products are needed that (1) are available for more 

locations, (2) provide mapped information on inundated areas 

rather than on river stage only, and (3) interface with a full 

suite of other flood-related products, such as Digital Flood 

Insurance Rate Maps (DFIRMs) and flood-forecast products 

produced by the National Weather Service (NWS). Such 

information must be readily available for both governmental 

officials and the citizens of North Carolina in an easily 

understood format. 

Based on experiences from Hurricane Floyd flooding, the 

Governor of North Carolina and the North Carolina General 

Assembly assigned the North Carolina Floodplain Mapping 

Program (FMP) in 2000 the task of improving flood information and flood forecasting in the State. The primary goal of 

the long-term effort is to provide emergency managers and the 

public with more accessible, informative, timely, and accurate 

flood information and flood-forecast products for more 

locations. The FMP, which is in the Office of Geospatial and 

Technology Management in the North Carolina Division of 

Emergency Management (NCDEM), is working cooperatively 

with other offices in NCDEM, the North Carolina Center for 

Geographic Information and Analysis (NCCGIA), other State 

and local agencies, the NWS, the Federal Emergency Management Agency (FEMA), and the U.S. Geological Survey 

(USGS) to develop and demonstrate technology to improve 

flood forecasts and information dissemination. 

One of the primary roles of the USGS in this multiagency 

effort is to develop and demonstrate the technology for 

producing detailed flood-inundation maps. These maps can be 

used in conjunction with USGS real-time streamgage data and 

a flood-inundation mapping and alert network being developed 

 LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina
by the FMP and NCCGIA to depict current areas of inundation and to provide estimates of areas that are expected, based 

on NWS flood forecasts, to become inundated hours or even 

days into the future. The maps also can be used separately as 

stand-alone products to provide emergency managers and the 

public with detailed estimates of flood inundation over a range 

of river stages.

Purpose and Scope

The purpose of this report is to document methods 

of the USGS, in cooperation with the North Carolina 

Floodplain Mapping Program, to create flood-inundation 

maps. Maps of selected streamgage sites in the Tar River 

basin, North Carolina (fig. 1), were created to demonstrate 

flood-inundation mapping technology. Maps were produced 

for water levels ranging from approximately bankfull to the 

maximum observed water level at each site. The hydrologic 

and topographic data used to create the maps are described 

herein, as are the methods for processing raw topographic 

data. Hydraulic modeling methods and results are described, 

and uncertainties associated with the inundation maps are 

discussed. An example of the metadata for one set of inundation maps is provided in Appendix 1.

Study Area

The Tar-Pamlico River basin is one of four major river 

basins that are contained entirely within North Carolina. The 

basin, as identified by the State of North Carolina, includes 

five 8-digit hydrologic units (HUs; fig. 1). This study includes 

only the part of the basin upstream from Washington, or HUs 

Figure 1. Selected study sites in the Tar-Pamlico River basin, North Carolina.

Introduction 

!(

!(

!(

!(

Washington Greenville

3

7818’20” 7717’19” 7616’18”

366’4”

355’3”

Piedmont

Coastal

Plain

Blue Ridge

GA SC

TN

VA

0 15 30 45 60 KILOMETERS

0 10 20 30 40 MILES

LOCATION OF THE TAR RIVER BASIN AND PHYSIOGRAPHIC

PROVINCES IN NORTH CAROLINA 

Tar

R.

19

17 18

16

15

14

13

6

7

5

10

4

9

2

1

12

11 8

Rocky Mount

Tarboro

03020101

03020102

03020103

03020104

03020105

03020105

Hydrologic unit code boundary

Stage-only site

Continuous discharge site

8-digit hydrologic unit codes

 in the Tar-Pamlico basin

EXPLANATION

Pamlico

Sound

Albemarle Sound

Atlantic

Ocean

Virginia

North Carolina
03020101, 03020102, and 03020103. The drainage area of the 

basin at Washington is about 8,300 square kilometers (km2). 

In 2000, the population of the basin was 393,217, a 

density of 48 people per square kilometer (people/km2) in 

contrast to a statewide density of 59 people/km2 (North 

Carolina Division of Water Quality, 2004). Major cities in the 

basin include Rocky Mount and Greenville. Population density 

in 2000 for HU 03020103, which includes the city of Greenville, was 68 people/km2

. Population in Greenville increased 

8.4 percent between April 2000 and July 2003, nearly twice 

the population-growth rate for the entire State during the 

same period (U.S. Census Bureau, 2006a). Rocky Mount had 

essentially no population change during the same period (U.S. 

Census Bureau, 2006b). Population in the western part of the 

basin, which is within commuting distance of the Research 

Triangle (Raleigh-Durham-Chapel Hill area), is expected to 

increase by almost 50 percent between the years 2000 and 

2020, and the population in the Greenville area is expected to 

grow about 40 percent during the same period (North Carolina 

Division of Water Quality, 2004).

About 7 percent of the land in the basin is classified as 

urban or developed. Almost two-thirds of the land cover in HU 

03020102 is forest. The lowest percentage of forest land cover 

is in HU 03020103, which is entirely in the Coastal Plain; this 

HU also has the highest percentage of cropland (40 percent) 

in the basin (North Carolina Division of Water Quality, 2004). 

In March 2003, 13 registered poultry operations and 101 

registered swine operations were located in the basin (North 

Carolina Division of Water Quality, 2004). Most of the poultry 

operations were in the western part of the basin, west of Rocky 

Mount. Swine operations were distributed relatively uniformly 

except in the extreme western part of the basin where no 

registered swine operations were located.

Average annual precipitation in the basin ranges from 

115.6 centimeters (cm) at Oxford, in the western end of the 

basin, to 125.5 cm at Washington (State Climate Office of 

North Carolina, 2006). Highest monthly rainfall typically 

occurs during the summer, although the seasonal difference is 

greater in the eastern part of the basin than in the western part. 

Average annual evapotranspiration ranges from about 81 cm 

in the western part of the basin to about 86 cm in the eastern 

part (Mason and Jackson, 1986). Mean monthly streamflow 

is strongly seasonal, ranging from 33 cubic meters per second 

(m3/s) in October to 125 m3/s in March at Tar River at Tarboro 

(site 12, fig. 1), which is typical of other locations throughout 

the basin. 

Streamflow data for the Tar River at Tarboro extends back 

to 1897 (fig. 2), and the period of record at this gage is among 

the longest in the Nation. The peak flow from Hurricane Floyd 

in 1999 was by far the greatest during more than 100 years 

Figure 2. Annual flood peaks for the Tar River at Tarboro, North Carolina, 1897–2000 and 1906–2005.

 LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina

1897 1902 1907 1912 1917 1922 1927 1932 1937 1942 1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002

2,200

2,000

1,800

1,600

1,400

1,200

1,000

800

600

400

200

0

ANNUAL PEAK FLOW, IN CUBIC METERS PER SECOND

5-year recurrence

interval flood flow 2-year recurrence

interval flood flow

25-year recurrence interval flood flow

10-year recurrence interval flood flow
of record (fig. 2). Other than the Hurricane Floyd flood, only 

one flood has been greater than the 10-year exceedance level 

since 1941 (fig. 2). During the 33-year period between 1908 

and 1940, however, five floods were at the 10-year exceedance 

level or greater.

During 1960–2005, the highest annual peak flow (or 

annual flood) on the main stem Tar River at Tarboro (site 12, 

fig. 1) and on a tributary (site 9, fig. 1 (Little Fishing Creek 

near White Oak)) occurred in all months of the year except 

December (fig. 3). (The common period of record for the 

two gages is 1960–2005.) More than 55 percent of the annual 

floods at Tarboro, which has a drainage area of 5,653 km2, 

occurred during the winter months of January–March, 

however, compared to 38 percent at Little Fishing Creek, 

which has a drainage area of 458 km2 (table 1). During the 

summer months of June–August, 23 percent of the annual 

floods occurred at Little Fishing Creek compared to 9 percent 

at Tarboro. This is likely because the smaller basin is more 

readily affected by convective storms that occur in the summer. 

Figure 3. Annual flood peaks, in percent by month, for Tar River at Tarboro and Little Fishing Creek near 

White Oak, North Carolina, 1960–2005.

All of the annual floods with an exceedance level greater 

than 10 years at the Tar River at Tarboro occurred during the 

months of May–October (fig. 4), although most of the annual 

floods occurred during the winter months (fig. 3). Of the 10 

largest annual floods at Tarboro, 7 floods occurred during 

the months of July–October, and 4 occurred in September 

alone. All seven of these floods were associated with tropical 

cyclones, except perhaps for the 1919 flood (National Oceanic 

and Atmospheric Administration, 2006). Most of the annual 

floods at the 2- to 5-year exceedance levels, however, occurred 

during the winter.

Introduction 

Little Fishing Creek near White Oak (site 9)

Tar River at Tarboro (site 12)

JAN. FEB. MAR. APR. MAY JUNE JULY AUG. SEPT. OCT. NOV. DEC.

25

20

15

10

5

0

PERCENT OF ANNUAL FLOOD PEAKS
Table 1. Study sites in the Tar River basin of North Carolina.

[NAD 83, North American Datum of 1983; km2, square kilometers; m, meter; NAVD 88, North American Vertical Datum of 1988; m3/s, cubic meters per second; S, stage; Q, discharge; RF, precipitation; NA, not applicable; SR, secondary road; ND, not determined. Station numbers in bold type are National Weather Service Flood Forecast locations]Site no. 

(fig. 1)

USGS 

station 

no.

Site name County Latitude (NAD 83) Longitude (NAD 83) Drainage area (km2

)

Data 

type

Beginning 

of 

record

Maximum 

recorded elevation 

at gage (m above 

NAVD 88) and datea

Maximum 

recorded streamflow at gage 

(m3/s) and datea

1 02081500 Tar River near Tar River Granville 36°11'39" 78°34'59" 432 S, Q 10/1939 94.63 (9/6/1996) 564.1 (9/6/1996)

2 02081747 Tar River at U.S. 401 at Louisburg Franklin 36°05'35" 78°17'46" 1,105 S, Q, RF 10/1963 61.52 (9/17/1999) 671.8 (9/17/1999)

3 02082506 Tar River below Tar River Reservoir near Rocky Mount Nash 35°54'02" 77°51'56" 2,012 S, Q 8/1972 35.90 (9/16/1999) 830.5 (9/16/1999)4 0208250885 Tar River at U.S. 301 Bypass at Rocky Mount Nash 35°55'34" 77°49'50" 2,038 S, RF 5/2003 27.79 (8/17/2004) NA5 02082576 Stony Creek at Winstead Avenue at Rocky Mount Nash 35°58'06" 77°50'59" 290 S 7/2003 30.38 (9/1/2004) NA

6 02082585 Tar River at NC 97 at Rocky Mount Edgecombe 35°57'17" 77°47'14" 2,396 S, Q, RF 8/1976 25.75 (9/17/1999) 966.5 (9/17/1999)

7 02082770 Swift Creek at Hilliardston Nash 36°06'44" 77°55'12" 430 S, Q 7/1963 45.95 (9/17/1999) 651.9 (9/17/1999)8 0208281175 Swift Creek at NC 97 near Leggett Edgecombe 35°58'49" 77°35'40" 681 S, RF 1/2003 15.08 (4/11,12/2003) NA9 02082950 Little Fishing Creek near White Oak Halifax 36°11'00" 77°52'34" 458 S, Q 10/1959 44.58 (9/16/1999) 878.7 (9/16/1999)

10 02083000 Fishing Creek near Enfield Edgecombe 36°09'02" 77°41'35" 1,362 S, Q 10/1923 28.93 (9/18/1999) 853.2 (9/18/1999)

11 0208331077 Fishing Creek at NC 97 near Leggett Edgecombe 36°00'30" 77°31'33" 1,963 S 1/2003 14.93 (9/23/2003) NA

12 02083500 Tar River at Tarboro Edgecombe 35°53'40" 77°31'59" 5,653 S, Q 7/1896 15.50 (9/19/1999) 2,001 (9/19/1999)

13 02083640 Town Creek at U.S. 258 near Pinetops Edgecombe 35°47'53" 77°35'29" 492 S, RF 7/2003 7.90 (9/20/2003) NA14 02083893 Tar River at U.S. 264 Bypass near Rock Springs Pitt 35°38'43" 77°25'22" 6,788 S 6/2003 4.62 (8/20/2004) NA

15 02084000 Tar River at Greenville Pitt 35°37'00" 77°22'22" 6,889 S, Q, RF 5/1997 7.98 (9/21/1999) 2,069 (9/21/1999)

16 02084160 Chicod Creek at SR 1760 near Simpson Pitt 35°33'42" 77°13'51" 116 S, Q 10/1975 6.21 (9/18/1999) ND17 02084173 Tar River at SR 1565 near Grimesland Pitt 35°34'26" 77°10'33" 7,402 S 5/2003 1.26 (9/18/2003) NA18 0208436195 Tranters Creek at SR 1403 near Washington Beaufort 35°33'47" 77°05'10" 637 S, RF 6/2003 1.52 (9/18/2003) NA19b 02084472b Pamlico River at Washington Beaufort 35°32'36" 77°03'43" 8,288 S, Q 10/1999 2.14 (9/21/1999) 2,353 (9/21/1999) aRecords through September 30, 2005.

bFlood-inundation maps were not created for this site.

 LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina
Data

One of the keys for the development of reliable floodinundation maps is the availability of detailed topographic data 

of known quality. The topographic data that were processed 

for hydraulic modeling and the creation of inundation maps 

are described here, as are the hydrologic data that were used in 

model calibration and testing.

Hydrologic Data

The Tar River basin hydrologic network consists of 19 

streamgages (fig. 1; table 1). All of the gages are equipped 

with satellite radio transmitters that allow data to be accessed 

routinely on the Internet within an hour of collection and 

within about 15 minutes during floods. Five of the sites are 

NWS flood-forecast data sites (National Weather Service, 

2006). Flood-inundation maps were created for 18 of these 

sites; the exception was the Pamlico River at Washington 

(site 19), where flooding can result from storm surge, upland 

flooding, or a combination of conditions.

Water level is measured continuously at all of the sites 

using methods described by Buchanan and Somers (1982) 

and Kennedy (1990). Continuous records of streamflow are 

computed at 11 of the 19 sites. Streamflow measurement and 

computation methods are documented by Rantz and others 

(1982), Morlock and others (2002), Simpson (2002), and 

Oberg and others (2005). Water-surface elevations are referenced to North American Vertical Datum of 1988 (NAVD 88).

Discrete discharge measurements were made at sites 

for which long-term streamflow records were unavailable 

(table 1). These measurements were made during periods of 

moderate to high flow and were used for model calibration. 

High-water marks measured following Hurricane Fran and 

Hurricane Floyd floods also were used as data for model 

calibration (U.S. Army Corps of Engineers, 1997; Federal 

Emergency Management Agency, 2000).

Topographic Data Derived from Light Detection 

and Ranging (LiDAR) Systems

The topographic-data source and quality-control 

(QC) measures performed on the data are discussed herein. 

Procedures used by the USGS to prepare the data for use in 

the hydraulic models and for development of inundation maps 

also are discussed.

Sources of LiDAR Data

The development of DFIRMS requires accurate landsurface elevation data. LiDAR systems were used to collect 

elevation data and the horizontal position of each elevation 

Figure 4. Annual flood peaks, by month, and recurrence intervals at the Tar River at Tarboro, North Carolina, 

1897–1900 and 1906–2005.

Data 

2- to less than 5-year recurrence interval

5- to less than 10-year recurrence interval

10- to greater than 500-year recurrence

 interval

JAN. FEB. MAR. APR. MAY JUNE JULY AUG. SEPT. OCT. NOV. DEC.

10

9

8

7

6

5

4

3

2

1

0

NUMBER OF ANNUAL FLOOD PEAKS
data point for the Tar River basin during January–March 2001 

(North Carolina Floodplain Mapping Program, 2003). Raw 

data subsequently were processed to remove LiDAR returns 

from such objects as trees, buildings, and other structures. 

The resulting product, called bare-earth mass points data, is 

available online from the FMP in 3,048-meter (m) by 3,048-m 

tiles (North Carolina Floodplain Mapping Program, 2003). 

Horizontal position is referenced to the North Carolina State 

Plane coordinate system, North American Datum of 1983 

(NAD 83), and vertical position is referenced to NAVD 88. 

Accuracy of Original LiDAR Data

The North Carolina Geodetic Survey (NCGS) collected 

QC information for the LiDAR bare-earth mass points data 

(North Carolina Geodetic Survey, 2002). Ground surveys 

were made by NCGS personnel for at least 100 points in each 

county in which LiDAR data were collected. Survey points 

were distributed among five different land-cover classes 

(grass, weeds or crops, scrub, forest, and developed), and the 

percentage of the survey points for each of the five land-cover 

classes was approximately proportional to the percentage of 

each land-cover class in the county. The percentage (5 percent) 

of the land-survey points that had the greatest error between 

the LiDAR data and land-survey positions were removed from 

the QC data set, and the statistics on the remaining points 

were computed (table 2). Statistics were computed both for 

the entire county and for the portion of the county within the 

Tar River basin; table 2 contains statistics for the part of each 

county that lies within the basin. The root mean square error 

(the difference between control-point elevations and LiDAR 

elevations) for all of the counties in the Tar River basin was 

less than 20 cm, which is the vertical accuracy required by the 

FMP for acceptance of LiDAR data measured in Coastal Plain 

counties. The vertical accuracy required for counties in the 

Piedmont is 25 cm. 

The absolute maximum difference between LiDARderived land-surface elevations and ground-survey elevations 

was about 40 cm for the Tar River basin counties, although 

the absolute maximum difference in most counties was less 

than 30 cm. More than 50 percent of the LiDAR-derived 

elevations at the ground-control points were within 7 cm of the 

actual elevation for Tar River basin counties in which USGS 

streamgages are located (table 2). In general, LiDAR-derived 

elevations tended to be higher than actual land-surface elevations. Although no real pattern was obvious in the relation 

between land cover and the maximum error, the largest error 

was associated with scrub or forested lands, and the smallest 

error was associated with grass or developed land cover.

Conversion of LiDAR Data to Digital Elevation 

Model 

Bare-earth mass point data received from the FMP 

were a collection of irregularly spaced points. These data 

were reprocessed into a digital elevation model (DEM) with 

regularly spaced, 1.5-m by 1.5-m cells. The DEM was created 

by first generating a triangulated irregular network (TIN). A 

TIN maintains the exact horizontal and vertical positions of 

the source data at the vertices of each triangle in the TIN, thus 

maintaining the integrity of the original data. Other horizontal 

and vertical positions can be interpolated along the edges and 

faces of the triangles in the TIN. A representation of major 

streams (known as break lines) provided by the FMP was used 

to guide the interpolation along the edges of the triangles. 

LiDAR-derived surfaces stored as 6.1-m by 6.1-m cell DEMs 

also are available from the FMP, although 

this resolution did not adequately capture 

the variations in topography that were 

needed to generate the flood-inundation 

maps at a resolution consistent with the 

resolution of the LiDAR data (for example, 

fig. 5).

The bare-earth mass points data 

were reprocessed to make use of all of the 

individual LiDAR points available. The 

TIN was interpolated to a finer resolution 

than FMP specifications require for 

production of DFIRMs to preserve as many 

original individual points as possible in the 

development of the DEM. This approach 

is similar in concept to using the TIN 

directly for elevation data; but because the 

TIN processes very slowly in a geographic 

information system (GIS) environment, a 

finer resolution DEM that is very nearly the 

same as the TIN but processes more quickly 

was created.

Table 2. LiDAR quality-control statistics for the Tar River basin, North Carolina 

(summarized from North Carolina Geodetic Survey, 2002).

[USGS, U.S. Geological Survey]

Countya

Error statistics, in centimeters Number 

of USGS 

streamgages, 

by county, in the 

Tar River basin

Root mean 

square error Mean errorb Median errorb

Edgecombe 17.4 -2.2 -3.8 6

Franklin 16.5 -6.1 -6.7 1

Granville 13.3 -1.4 -1.9 1

Halifax 14.7 6.9 6.5 1

Martin 14.4 -2.6 -1.3 0

Nash 14.2 1.2 0.8 4

Pitt 11.7 -4.6 -6.3 4

Vance 17.9 -6.3 -7.4 0

Warren 14.4 0.7 -0.6 0

Wilson 19.3 -13.7 -14.3 0

a

Statistics for Beaufort County were unavailable.

b

Negative values indicate that LiDAR values exceed ground-survey values.

   LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina
Hydroconditioning of LiDAR Surfaces

The LiDAR return signal is based on the reflection of 

the laser from a solid surface. Hence, when a road crosses 

a stream, the LiDAR-derived DEM maintains the elevation 

of the road as the land-surface elevation, resulting in a 

discontinuous stream channel (fig. 6). The road as represented 

by the DEM becomes, in effect, a dam on the stream through 

which flow cannot pass. When calculating inundation areas 

from downstream to upstream locations, it is important for the 

flow path (stream channel) to be continuous along streambeds 

and low-lying areas. A tool, which is an ARC-Info script, was 

developed as part of this study to connect flow paths that were 

inappropriately disconnected by such features as bridges or 

road crossings. This tool uses the DEM, the road network, 

and the stream network to automatically (1) identify road and 

stream intersections, (2) identify the lowest elevation on the 

DEM within a specified distance (or buffer) on both sides 

of a road and stream intersection, (3) create a segment that 

connects these two low points, (4) assign this created segment 

the lowest elevation value within the buffer, and (5) integrate 

the segment back into the DEM so that a flow path is created 

through the road, connecting the upstream and downstream 

water segments (fig. 6). This then allows proper representation 

of inundated areas. This hydroconditioning process is not 

related to hydraulic modeling of flows through bridges or 

culverts, which is discussed later.

Figure 5. (A) Orthophotograph, (B) 6.1-meter by 6.1-meter digital elevation model, and (C) 1.5-

meter by 1.5-meter digital elevation model of the Tar River floodplain near Tarboro in Edgecombe 

County, North Carolina. (A vertical exaggeration of 5 was applied to display the hillshade 

surfaces.)

Data 

A. B.

C.
Figure 6. (A) Hill shade digital elevation model of the area near the confluence of Crisp Creek with Conetoe Creek in North 

Carolina, showing the inundated area (B) before and (C) after hydroconditioning. (Red arrows indicate direction of streamflow.)

10 LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina

A.

B. C.
Quality Assurance

Following reprocessing of the LiDAR data and 

hydroconditioning of the DEM, all elevation surfaces in areas 

that potentially could be inundated were carefully reviewed. 

Two steps were taken to identify potential problems with the 

DEM. First, the maximum and minimum elevations within 

each 3,048-m by 3,048-m tile were computed. If values were 

unexpectedly high or low, the tile was examined visually 

for problems (for example, fig. 7). Second, after inundation 

polygons were developed, all inundated areas were examined 

for the presence of islands and to ensure that hydroconditioning was complete. When islands were identified in the DEM, 

orthophotographs were examined to determine if the islands 

could be documented in the orthophotographs. Land-surface 

elevations that were in error and affected the inundation 

mapping results were smoothed by assigning an elevation 

value that was equal to that of surrounding points. 

Development of Flood-Inundation Maps

Flood-inundation maps were created for each of 18 

streamgage sites in the Tar River basin by simulating the 

water-surface elevations along a stream reach that includes 

a streamgage and then combining this elevation with the 

topographic data to create a two-dimensional mapped water 

surface, or flood polygon. The hydraulic modeling for 

simulating the water-surface profiles is documented in this 

section, as is the approach used to create the flood polygons. 

Uncertainties associated with the approach and with the final 

results also are presented.

Hydraulic Modeling

The one-dimensional, step-backwater model, Hydraulic 

Engineering Center–River Analysis System (HEC–RAS), 

was used for hydraulic modeling at 

each of the study sites (Hydrologic 

Engineering Center, 2002). Version 3.1.3, released in May 2005 

(Hydrologic Engineering Center, 

2006), was used for this application. 

HEC–RAS is widely used for 

simulating steady-flow watersurface profiles in stream reaches 

and for one-dimensional hydraulic 

analysis at bridge crossings. The 

one-dimensional energy equation 

is solved within HEC–RAS for 

determination of water-surface 

profiles. The momentum equation 

can be included in the solution 

for situations in which the watersurface profile changes markedly 

with distance, such as at hydraulic 

jumps, bridges and culverts, and 

stream junctions. The effects of 

obstructions, such as bridges, 

culverts, weirs, and structures in 

the floodplain, are included in the 

hydraulic computations (Hydrologic 

Engineering Center, 2002). 

Unsteady-flow simulations also can 

be performed using HEC–RAS. 

Eleven individual hydraulic 

models were developed for the Tar 

River basin sites (table 3). Seven 

models were developed for reaches 

with a single streamgage, and four 

models were developed for reaches 

of the Tar River main stem that 

receive flow from major gaged 

tributaries, or reaches in which 

multiple gages are near one another. 

Figure 7. Example of spurious spike in the elevation surface.

Development of Flood-Inundation Maps 11
Table 3. Summary of hydraulic models developed for the Tar River basin study sites in North Carolina.

[m, meter; NAVD 88, North American Vertical Datum of 1988; MRC, measured stage-discharge rating curve; HWM, high-water mark; QM, measured discharge; —, no information available for calibration]

Model name Streams in model

Site 

number 

(fig. 1)

Model 

reach 

length 

(m)

Number 

of cross 

sections in 

model

Average 

bed slope 

over reach

Number of 

bridges in 

reach

Range in 

Manning’s n

for reach

Range in simulated 

water-surface elevations at gage site 

(m above NAVD 88)

Model 

calibration 

method

Tar River Tar River 1 10,660 33 0.00088 2 0.060 – 0.19 88.09 – 95.56 MRCLouisburg Tar River 2 9,145 31 0.00023 2 0.060 – 0.20 55.78 – 62.48 MRCRocky Mount

Tar River 3, 4, 6 19,777 43 0.00051 12 0.035 – 6.77 29.57 – 36.42a MRC, HWMStony Creek 5 9,405 77 0.00055 8 0.045 – 0.20 28.83 – 34.91 HWM, QM

Hilliardston Swift Creek 7 10,644 41 0.00049 3 0.075 – 0.40 41.15 – 46.48 MRCLittle Fishing Little Fishing Creek 9 11,278 40 0.00039 3 0.060 – 0.39 36.88 – 45.11 MRCEnfield Fishing Creek 10 13,564 35 0.00032 3 0.060 – 0.64 24.84 – 29.26 MRCTarboro

Swift Creek 8 11,674 28 0.00047 2 0.065 – 0.38 14.11 – 18.33

—

Fishing Creek 11 18,471 39 0.00027 2 0.059 – 0.40 12.83 – 17.74 

—

Tar River 12 55,848 83 0.00018 7 0.030 – 1.00 8.38 – 16.00 MRC

Pinetops Town Creek 13 18,322 41 0.00031 3 0.028 – 0.20 9.75 – 17.22 HWM, QMGreenville Tar River 14, 15 40,397 55 0.00011 6 0.055 – 6.05 2.64 – 8.83b MRC, HWMGrimesland

Chicod Creek 16 11,668 60 0.00071 3 0.058 – 2.96 1.27 – 6.22 MRCTar River 17 13,863 30 0.00013 1 0.045 – 2.74 -0.31 – 5.79 HWM

Washington

Tranters Creek 18 11,887 24 -0.00016 1 0.047 – 0.35 -0.15 – 2.74 HWM, QMTar River / Pamlico River 19 5,820 13 0.00037 1 0.041 – 0.40 -0.17 – 2.05 MRC

aReferenced to site 3. bReferenced to site 14.

12  LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina
Model domains were established so that model boundaries did not extend beyond any major changes in the stream 

relative to conditions at the gage. Inundation maps were not 

developed for the entire model domain for the Tarboro and 

Washington models because of uncertainties near model 

boundaries. 

The FMP provided the USGS with selected HEC–RAS 

models that were constructed by contractors for the creation 

of DFIRMs. The FMP provided the Rocky Mount model, the 

Hilliardston model, the Enfield model, the Tarboro Tar River 

reach model, the Greenville model, and the Grimesland Tar 

River reach model (table 3). These models were tested by the 

USGS, and the models performed satisfactorily for flows in 

excess of approximately the 100-year exceedance level, or 

flows primarily used in the development of DFIRMs. The 

models generally did not perform adequately for development 

of flood-inundation maps at flows ranging from bankfull to 

about the 100-year flow. Hence, all of these models required 

additional development, calibration, and testing to simulate 

water levels with sufficient accuracy for inundation mapping. 

Geometry Data 

Bathymetric and topographic data used to develop the 

inundation models were derived from existing FMP models, 

LiDAR data, field surveys, and interpolation of measured 

information. The hydraulic models provided by the FMP were 

inspected to determine if additional bathymetric data were 

required, and the Enfield model was the only FMP model for 

which additional field surveys were needed. Bathymetric data 

for the models developed entirely by the USGS were collected 

by USGS personnel from bridges and manned boats by 

using graduated survey rods in shallow streams and acoustic 

instruments in deeper creeks and rivers. A differential global 

positioning system (DGPS) was used to establish horizontal 

stationing. 

The overbank topography for the FMP models, which 

was derived from a 6.1-m by 6.1-m DEM, was checked and 

compared to the 1.5-m by 1.5-m DEM developed by the 

USGS. Significant differences between the two DEMs were 

resolved by replacing the coarse-resolution DEM with the 

fine-resolution DEM. Overbank topography for the remaining 

models was developed from the 1.5-m by 1.5-m DEM.

Overbank cross sections for use in HEC–RAS generally 

are approximately perpendicular to the stream at the point 

the cross section intersects the stream, and each cross section 

intersects the main channel only once. In addition, two crosssection lines cannot intersect each other. Many of the streams 

in the study area are quite sinuous, so great care was required 

to develop overbank cross sections that met these requirements. Even so, there were cases, such as near the confluence 

of a tributary with the main channel, that it was not possible 

for the overbank cross section to be perpendicular to both the 

main channel and the tributary (for example, fig. 8). Although 

overbank cross-section locations were delineated manually for 

this study, the process has since been automated.

The geometry of structures, such as bridges and roadway 

elevations, in the FMP models was reviewed, potential 

problems were identified, and structural geometry was verified 

by land surveys as needed. Bridge and roadway geometries for 

the models created entirely by the USGS were determined by 

survey crews using an electronic total station supplemented 

with North Carolina Department of Transportation bridge 

plans, when available. 

Combined, the Tar River hydraulic models included 

272 km of streams in the basin, including about 162 km on 

the Tar River main stem (table 3). Cross-section density in 

the models was about one cross section per 400 m of stream 

length, with generally higher densities on tributary streams 

than on the main stem. The models also included the effects of 

59 bridges on flow in the basin. Bridges can have a noticeable 

effect on water levels under certain conditions (for example, 

the water-surface profile at the Ward Road bridge across Little 

Fishing Creek in reference to a water-surface elevation of 

43.43 m at the streamgage, fig. 9).

Development of Flood-Inundation Maps 13
Figure 8. Overbank cross sections used in the Rocky Mount hydraulic model.

14  LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina

#

#

#

#

7751’ 7748’

3557’

3554’

Stony Creek at Winstead Ave.

at Rocky Mount

(site 5)

Tar River at NC 97

at Rocky Mount

(site 6)

Tar River at US 301 Bypass

at Rocky Mount

(site 4)

Tar River below Tar River Reservoir

near Rocky Mount

(site 3)

0 6,000 12,000 18,000 24,000 FEET

0 1,500 3,000 4,500 6,000 METERS

EXPLANATION

Overbank cross-section lines

Streamgage

Cross section

A

B

A B
Downstream Boundary Conditions—Nontidal 

Sites 

Downstream boundary conditions for all models except 

the Grimesland and Washington models were established 

using the normal depth condition (Hydrologic Engineering 

Center, 2002, p. 7–4) with a friction slope estimated from 

the streambed slope through the reach. The normal depth is 

calculated by using the Manning equation with user-provided 

data for slope, geometry, and Manning’s n. The Pinetops 

model boundary was extended downstream to include the 

Tar River, which affects flow at the Town Creek site under 

high-flow conditions. 

Downstream Boundary Conditions—Tidal Sites 

A normal-depth downstream boundary condition was 

not appropriate for the Grimesland and Washington models 

because the simulated flows can be affected by tides or 

backwater from the Pamlico River. Therefore, the approach 

for the Washington model was as follows. Stage and an index 

velocity were measured continuously at site 19 (fig. 1, Pamlico 

River at Washington), and a continuous record of discharge 

was computed. Records from this site were examined to 

identify dates of high ebb flow. The measured stage and 

computed discharge for these dates then were used to construct 

a stage-discharge rating for downstream (ebb) flows. This 

rating provides a reasonable estimate of the relation between 

stage and ebb flow at site 19 for the purposes of constructing a 

steady-flow model. A simple stage-discharge rating cannot be 

developed, however, over the full range of tidal conditions that 

occur at site 19.

The approach for developing the downstream boundary 

condition for the Grimesland model was different from the 

Washington model because discharge records were not available for site 17 (fig. 1, Tar River at SR 1565 near Grimesland). 

A stage-discharge rating for site 17 was established by 

successively interpolating known stages at sites 15 (Tar River 

at Greenville) and 19 (Pamlico River at Washington) to site 17, 

based on the water-surface slope between these two gages, for 

ebb-flow conditions at site 19. This estimated water-surface 

elevation at site 17 was then associated with the discharge at 

site 15 for a series of high ebb-flow conditions to establish a 

stage-discharge rating for the downstream boundary condition. 

Upstream Boundary Conditions—Gaged Sites

Steady flow was the upstream boundary condition for 

all of the models. The full range of flows for the downstream 

rating curve was used as the upstream boundary condition for 

Figure 9. Simulated water-surface profiles for Little Fishing Creek near White Oak streamgage (site 9) in the 

Tar River basin, North Carolina.

Development of Flood-Inundation Maps 15

7,500 6,500 5,500 4,500 3,500 2,500 1,500 500 0 500 1,500 2,500 3,500 4,500

DISTANCE UPSTREAM FROM STREAMGAGE,

IN METERS

DISTANCE DOWNSTREAM FROM STREAMGAGE, IN METERS

32

34

36

38

40

42

44

46

48

50

ELEVATION, IN METERS

Streambed

Location of gage

45.10 meters

43.43 meters

41.76 meters

40.10 meters

38.90 meters

36.90 meters

Water-surface profiles for streamgage

 water-surface elevations of

Ward Road bridge

Secondary Road

1337 bridge Ringwood Road

bridge
all models that were calibrated by using downstream rating 

curves (table 3). Upstream flows for model calibration at other 

sites were based on either estimated flows at which high-water 

marks were established or flows at which discharge measurements were made. Upstream boundary conditions for development of water-surface profiles for inundation mapping were 

developed from the downstream rating curve or a synthetic 

rating curve as described below. Upstream flows were incrementally selected to provide a 0.305-m change in water level 

at the gage over a range of flows from approximately bankfull 

to the maximum recorded water level.

Upstream Boundary Conditions—Ungaged Sites 

Synthetic stage-discharge relations were developed for 

sites 8, 11, 13, and 18 for which measured rating curves were 

not available (table 1). Synthetic rating curves were required to 

estimate the flows associated with the measured downstream 

discharge (sites 8 and 11) and high-water marks (sites 13 

and 18) to which the models at these sites were calibrated. 

Flow and water level at these sites also can be affected by 

backwater from the Tar River, so this factor was considered 

in the development of the synthetic ratings. The approach for 

developing the synthetic rating curves was as follows, using 

site 8 (Swift Creek at NC 97 near Leggett) as an example. 

A streamgage with long-term records in a basin with 

similar hydrologic characteristics as site 8 was identified. In 

this case, site 7 (fig. 1; table 1, Swift Creek at Hilliardston) 

was used. In addition, because site 8 can be affected by Tar 

River backwater, the nearest downstream Tar River streamgage 

was identified, which was site 12 (fig. 1; table 1, Tar River at 

Tarboro). A set of recent mid- to high-flow peak discharges 

(daily mean flow) was identified and compiled for site 7. The 

corresponding peak water-surface elevations at site 12 then 

were compiled, and a relation between peak flow at site 7 and 

peak water-surface elevation at site 12 was developed (fig. 10). 

The correlation coefficient for the relation between flow at 

site 7 and water-surface elevation at site 12 was 0.95, and the 

correlation coefficients for similar relations for the other three 

sites (11, 13, and 18) ranged from 0.82 to 0.99. The relation 

between flow at site 7 and water-surface elevation at site 12 

was then used to estimate the flow at site 8 for a specified 

water-surface elevation in the Tar River at site 12. This was 

done by multiplying the discharge at site 7 by the ratio of the 

drainage area at site 8 to site 7. 

Figure 10. Relation of water-surface elevation at the Tar River at Tarboro (site 12) to peak flow at Swift Creek at 

Hilliardston (site 7), North Carolina.

16  LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina

1,000

100

10

1 10 100

DISCHARGE AT SWIFT CREEK AT HILLIARDSTON, IN CUBIC METERS PER SECOND

WATER-SURFACE ELEVATION AT TAR RIVER AT TARBORO, IN METERS ABOVE NAVD 88
Peak flows for a large regional flood occur earlier at 

site 8 than at site 12 because the drainage area at site 8 is 

substantially less than at site 12. Yet, the analysis described 

above assumes that the peaks on the tributary (site 8) and the 

Tar River (site 12) occur on the same day. A routing factor 

was therefore applied to the peak flow determined for site 8 to 

account for the fact that site 8 typically peaks before site 12. 

The routing factor was determined by computing the sum of 

the peak discharges for site 6 (the Tar River streamgage just 

upstream from site 12) and the gaged tributaries draining to 

the Tar River between sites 6 and 12. The ratio of this sum to 

the peak flow at site 12 was then the routing factor. Hence, for 

a given water-surface elevation at site 12, the flow at site 8 was 

(1) calculated from the relation of water-surface elevation at 

site 12 to flow at site 7 and adjusted for drainage area at site 8, 

as described above, and (2) multiplied by the routing factor 

to account for differences in peak arrival times at site 12. The 

discharge-water-surface elevation pairs determined in this 

manner then constituted the synthetic rating curve at site 8. 

A routing factor also was required for the Pinetops model 

(site 13).

The upstream boundary conditions for the Grimesland 

model were established in a slightly different manner. The two 

upstream boundaries for the Grimesland model are Chicod 

Creek and the Tar River. The drainage area at the Grimesland 

gage (site 17) is 6.9 percent greater than at the Greenville 

gage (site 15). Approximately 53 percent of the 6.9-percent 

drainage area increase is attributed to Chicod Creek, so inflow 

from Chicod Creek was equal to the measured flow at Greenville multiplied by 0.037 (53 percent of 6.9 percent). The 

flow in the Tar River at the upstream end of the Grimesland 

model was equal to the flow at Greenville multiplied by 1.032 

(47 percent of 6.9 percent plus the flow at Greenville). The 

estimated water-surface elevation at site 17 (described above) 

was then associated with the computed discharge at site 17 for 

a series of high ebb-flow conditions to establish the synthetic 

stage-discharge rating for the upstream boundary condition.

Calibration and Performance 

The hydraulic models were calibrated to the most current 

stage-discharge relations at 11 long-term streamgages where 

rating curves have been developed (table 3). Of the 19 gaging 

stations, however, 8 did not have established rating curves 

because the gages were installed as part of this project to 

collect only stage data. Medium- to high-flow discharge 

measurements were made at several of the eight sites (table 3), 

and high-water marks from Hurricanes Fran and Floyd were 

available for high-stage calibration (U.S. Army Corps of 

Engineers, 1997; Federal Emergency Management Agency, 

2000). These data were not sufficient, however, to calibrate the 

models over the full range of flows at the eight sites for which 

no rating curves were available. 

Sites 4 and 5 did not have measured rating curves, but 

these sites were within the Rocky Mount model (table 3), 

which included two other sites that did have measured rating 

curves. Calibration of the Rocky Mount model, then, primarily 

focused on matching measured and simulated stage-discharge 

ratings at sites 3 and 6. Limited discharge measurements and 

high-water marks subsequently were used to check model 

performance at site 4, which is physically between sites 3 and 

6, and site 5. A similar approach was used for the Tarboro, 

Greenville, and Grimesland models. These models each 

contained one site with a measured stage-discharge relation 

(site 12 for the Tarboro model, site 15 for the Greenville 

model, and site 16 for the Grimesland model). These sites 

were the focus of the respective model calibrations. Performance of the Greenville and Grimesland models subsequently 

was tested at the two sites without stage-discharge relations 

(sites 14 and 17 for Greenville and Grimesland, respectively) 

using data from high-water marks.

Model calibration was accomplished by adjusting 

Manning’s n values and, in some cases, channel cross sections and slope. Manning’s n values varied among the cross 

sections; the highest values were in the floodplains (table 3). 

Extremely high Manning’s n values were required on the outer 

edges of the floodplains in order to achieve calibration over 

the full range of flows for some of the models provided by the 

FMP. It is likely that these high values were needed because 

the spacing between channel cross sections in the FMP models 

was much greater than in the USGS models and because the 

FMP models were constructed for the highest flows, whereas 

the USGS models were constructed for a greater range of 

flows.

Models were calibrated for water levels ranging from 

approximately bankfull at the streamgage to flow at the 

500-year exceedance level or to Hurricane Floyd peak stages, 

whichever was greater. The mean difference between the 

lowest and highest simulated water level was 5.7 m for the 

11 models (table 3). The correlation coefficients between 

measured and simulated rating curves at 9 of the 11 sites with 

measured rating curves were 0.99 (table 4), and simulated 

rating curves matched measured curves over the full range of 

flows (fig. 11). Differences between measured and simulated 

water levels for a specified flow were no more than 0.44 m 

and typically were less. Differences between measured and 

simulated water levels for models calibrated to high-water 

marks were less than 0.25 m. These results demonstrate that 

the 11 Tar River models are capable of simulating accurate 

water levels over a wide range of flows in the Tar River and 

tributary streams.

Development of Flood-Inundation Maps 17
Table 4. Summary of hydraulic model performance for sites in the Tar River basin of North Carolina.

[m, meter; —, no data]

Model

Site 

no. 

(fig. 1)

Calibration results

High-water marks Field measurements Measured stage-discharge rating curve

Number Errora

(m) Number Errora(m)

Correlation 

coefficient

Range in errorb

(m)

Tar River 1 — — — — 0.9997 -0.41 – 0.06

Louisburg 2 — — — — 0.9924 -0.44 – 0.38 

Rocky Mount

3 — — — — 0.9999 -0.34 – 0.36

4 — — — — — —

5 2 -0.20, 0.14 1 0.15 m — —

6 — — — — 0.9990 -0.19 – 0.23

Hilliardston 7 — — — — 0.9770 -0.37 – 0.51

Little Fishing 9 — — — — 0.9936 -0.30 – 0.31 

Enfield 10 — — — — 0.9618 -0.35 – 0.37

Tarboro

8 — — — — — —

11 — — — — — —

12 — — — — 0.9994 -0.04 – 0.30

Pinetops 13 1 -0.12 1 0.3 m — —

Greenville

14 — — — — — —

15 — — — — 0.9981 -0.28 – 0.18

Grimesland

16 — — — — 0.9962 -0.13 – 0.13

17 1 0.12 5 -0.13 – 0.27 m — —

Washington

18 1 0.23 — — — —

19 — — — — 0.9954 -0.20 – 0.23

a

Measured water level minus simulated water level.

b

For a given discharge, water level from rating curve minus simulated water level.

18 LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina
Flood-Inundation Maps

A set of water-surface profiles was generated for each 

of the 11 modeled reaches. The water-surface profiles were 

generated at 0.305-m increments for water levels ranging from 

bankfull to approximately the highest recorded water level at 

the downstream-most gage in each modeled reach. For a given 

water-surface elevation at the downstream-most gage in the 

modeled reach, a water-surface elevation was assigned to each 

cross section in the reach (for example, fig. 8) at the point the 

cross section intersected the main stem of the modeled reach. 

The water surface was assumed to be level across the entire 

cross section, which is consistent with the one-dimensional 

modeling approach. Water-surface elevations between cross 

sections were estimated by using a spline interpolation 

(fig. 12).

Inundated areas were identified by subtracting the 

water-surface elevation in each 1.5-m by 1.5-m grid cell from 

the land-surface elevation in the cell (fig. 12). Because all 

inundated cells were not necessarily hydraulically connected 

to the flooded river or stream, an automated procedure was 

developed to identify all inundated cells that were hydraulically connected to the cell at the downstream-most gage in the 

model domain. All other inundated cells were deleted from 

the map layer. This entire process was repeated for each of 

the 0.305-m increment water-surface profiles in each model 

domain, creating 11 sets of inundation-map libraries (table 5). 

The number of inundation maps per modeled reach ranged 

from 10 to 28 and was a function of the range in water levels 

at the site. An example of metadata for the inundation maps is 

given in the appendix of this report.

The inundation polygons can be merged with a variety 

of other geospatial data to provide useful information for 

addressing flood mitigation or planning emergency response. 

For example, the inundation polygon can be combined with a 

topographic relief image to show the river channel through the 

inundated area and to depict inundation in relation to various 

topographic features in the area (fig. 13). Care must be taken 

in interpreting this type of image, however. In some cases, 

bridges appear to be inundated; for example, note the red 

circle in figure 13. In fact, this particular case is an example 

of hydroconditioning of the DEM, as discussed previously, 

in that the bridge is artificially cut so that the segments of the 

river upstream and downstream from the bridge are hydraulically connected. The yellow circle in figure 13 shows an area 

in which the road is actually inundated. This problem can be 

resolved by using the hydroconditioned DEM for mapping the 

inundation and then reintroducing the preconditioned DEM for 

depiction of potential transportation disruptions.

Figure 11. Measured and simulated stage-discharge relations for Swift Creek at Hilliardston (site 7) in the 

Tar River basin, North Carolina.

Development of Flood-Inundation Maps 19

37

35

33

31

29

27

25

WATER-SURFACE ELEVATION, IN METERS

DISCHARGE, IN CUBIC METERS PER SECOND

0 100 200 300 400 500 600 700 800 900 1,000

Measured rating curve

DFIRM model

USGS model

Bankfull elevation
Figure 12A. Land-surface elevation for the Tarboro model.

20 LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina

#

#

#

Swift Creek

at NC 97

near Leggett

(site 8)

Fishing Creek

at NC 97

near Leggett

(site 11)

Tar River

at Tarboro

(site 12)

Swift

Creek

Creek

Fishing

River

Tar

0 6,000 12,000 18,000 24,000 FEET

0 1,500 3,000 4,500 6,000 METERS

Value Land-surface elevation, in meters

EXPLANATION

High : 40.3

Low : 2.7

Streamgage

7736’ 7733’ 7730’

36

3557’

3554’
Figure 12B. Estimated water-surface elevation for the Tarboro model.

Development of Flood-Inundation Maps  21

#

#

#

Swift Creek

at NC 97

near Leggett

(site 8)

Fishing Creek

at NC 97

near Leggett

(site 11)

Tar River

at Tarboro

(site 12)

Swift

Creek

Creek

Fishing

River

Tar

0 6,000 12,000 18,000 24,000 FEET

0 1,500 3,000 4,500 6,000 METERS

Value Water-surface elevation at 14.6 meters

EXPLANATION

High : 19.6

Low : 13.6

Streamgage

7736’ 7733’ 7730’

36

3557’

3554’
Figure 12C. Inundated area for the Tarboro model.

22  LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina

#

#

#

Swift Creek at 

NC 97 near Leggett

(site 8)

Fishing Creek

at NC 97

near Leggett

(site 11)

Tar River

at Tarboro

(site 12)

Swift

Creek

Creek

Fishing

River

Tar

0 6,000 12,000 18,000 24,000 FEET

0 1,500 3,000 4,500 6,000 METERS

Inundated areas

EXPLANATION

Streamgage name and number

7736’ 7733’ 7730’

36

3557’

3554’

12
Table 5. Inundation-map libraries for the Tar River basin in North Carolina.

[m, meter; NAVD 88, North American Vertical Datum of 1988]

Model Sites 

(fig. 1)

Number of 

maps for 

model domain

Range in water-surface elevation for 

map library Site to which 

water levels are 

referenced Minimum 

(m above NAVD 88)

Maximum 

(m above NAVD 88)

Tar River 1 25 88.1 95.4 1

Louisburg 2 23 55.8 62.5 2

Rocky Mount 3, 4, 5, and 6 23 29.6 36.3 3

Hilliardston 7 18 41.1 46.3 7

Little Fishing 9 28 36.9 45.1 9

Enfield 10 15 24.8 29.1 10

Tarboro 8, 11, and 12 26 8.4 16.0 12

Pinetops 13 25 9.8 17.1 13

Greenville 14 and 15 21 1.8 7.9 15

Grimesland 16 and 17 21 -0.3 5.8 17

Washington 18 and 19 10 -0.2 2.6 18

Figure 13. Inundation map showing topographic relief for the Tar River at NC 97 at Rocky Mount (site 6) for a 

water-surface elevation at this site of 38.6 meters above North American Vertical Datum of 1988.

Development of Flood-Inundation Maps  23

7749’48” 7748’36” 7747’24” 7746’12”

3558’48”

3558’12”

3557’36”

3557’

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

0 500 1,000 1,500 2,000 METERS

EXPLANATION

Streamgage

Inundation

Hydroconditioning
Transportation networks can be superimposed on the 

shaded relief map and inundation polygon to better identify 

roads affected by floodwaters (fig. 14). The use of orthoimagery with the inundation maps provides further information on 

flooded or potentially flooded structures, agricultural lands, 

and other areas of interest (fig. 15). Finally, the water-surface 

elevations throughout the inundated area can be combined 

with land-surface elevation data to depict estimated water 

depth (fig. 16).

Low-resolution inundation maps showing transportation 

networks and orthoimagery have been prepared for display on 

the Internet (U.S. Geological Survey, 2006c). These maps also 

are linked to USGS real-time streamflow in the National Water 

Information System database (for example, U.S. Geological 

Survey, 2006b). Hence, a user can determine the near real-time 

stage and water-surface elevation at a USGS streamgage site 

in the Tar River basin and link directly to the flood-inundation 

maps to obtain a depiction of the estimated inundated area at 

the current water level.

Uncertainty Associated with Inundation Maps

Although the flood-inundation maps represent the 

boundaries of inundated areas with a distinct line, some 

uncertainty is associated with these maps. Because of this 

uncertainty, flood boundaries more reasonably could be 

replaced with zones depicting probability of flooding (Brown 

and Damery, 2002). Moreover, uncertainty varies, depending 

on the scale at which the inundation modeling is conducted 

(Horritt and Bates, 2001). There are a number of methods for 

estimating uncertainties associated with hydraulic modeling 

and inundation mapping, but data required to apply these 

methods are seldom available (Beven, 2006), which is true in 

this case. Nevertheless, uncertainties in the Tar River basin 

inundation maps can be discussed in a qualitative manner, 

which is the purpose of this section. 

Figure 14. Inundation map showing topographic relief and transportation networks for the Tar River at NC 97 at Rocky 

Mount (site 6) for a water-surface elevation at this site of 38.6 meters above North American Vertical Datum of 1988. 

(Transportation networks from North Carolina Department of Transportation, 2006)

24  LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina

EXPLANATION

Streamgage

#

7749’48” 7748’36” 7747’24” 7746’12”

3558’48”

3558’12”

3557’36”

3557’

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

0 500 1,000 1,500 2,000 METERS
Figure 15. Inundation map showing orthoimagery for the Tar River at NC 97 at Rocky Mount (site 6) for a watersurface elevation at this site of 38.6 meters above North American Vertical Datum of 1988. (Orthoimagery from the 

U.S. Geological Survey, 2006a)

Development of Flood-Inundation Maps  25

EXPLANATION

Streamgage

7749’48” 7748’36” 7747’24”

3558’48”

3558’12”

3557’36”

3557’

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

0 500 1,000 1,500 2,000 METERS
Data for Model Development and Calibration 

Flood-inundation models require three types of 

data—(1) topographic data for the hydraulic model 

computational grid and the inundation maps; (2) effective 

friction values (Manning’s n) for each computational segment 

(one-dimensional model) or cell (two-dimensional model); 

and (3) model-validation data of some type (Bates, 2004), and 

each of these data types has uncertainties. Models applied in 

near real time also require a forecast hydrograph.

As previously noted, the root mean square error in the 

Tar River basin land-surface elevation data was about 0.2 m. 

Land-surface elevations, or topography, is the dominant 

influence on the location of the simulated shoreline of an 

inundated area. Other studies have demonstrated that lowresolution one-dimensional hydraulic modeling combined with 

high-resolution topographic data gives a good representation 

of shoreline (Horritt and Bates, 2001), which is the reason 

the original LiDAR data were reprocessed to provide 1.5-m 

horizontal resolution topographic data. 

Inundation maps for selected flows produced by using the 

1.5-m grid cells for the Tar River at Tarboro were compared 

with maps produced by using the 6.1-m horizontal resolution 

cells. Inundated areas for streamflow at the 100-year exceedance level differed markedly for the two DEMs; the fineresolution topography showed a much larger inundated area 

than the coarser resolution topography. Further analysis indicated that the 6.1-m grid cell topography depicted a roadway 

as a barrier to flow, whereas the 1.5-m grid cell topography 

depicted flow over the roadway. Ground surveys subsequently 

confirmed that the 1.5-m cell topography properly represented 

the roadway.

Friction values, or Manning’s n, are effective in that they 

account for the effects of variable cross sections, nonuniform 

Figure 16. Estimated floodwater depth for the water-surface elevation of 7.9 meters above North American 

Vertical Datum of 1988 in the Tar River basin at Greenville, North Carolina.

26  LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina

#

#

Tar River at

Greenville

(site 15)

Tar River at US 264 Bypass

near Rock Springs

(site 14)

Depth, in meters

0 - 1

1 - 3

3 - 5

5 - 7

7 - 16

0 6,000 12,000 18,000 24,000 FEET

EXPLANATION 0 1,500 3,000 4,500 6,000 METERS

Streamgage

7730’ 7727’ 7724’ 7721’

3542’

3539’

3536’
slope, vegetation, and structures at the scale of the distance 

between model cross sections. Distributed data throughout 

the floodplain are seldom available as a basis for estimating 

friction values for the entire model domain. Studies also 

have shown that many different model-parameter sets can 

perform equally well in HEC–RAS (Pappenberger and others, 

2005a), which represents uncertainty in the model, regardless 

of the goodness of fit between measured data and simulated 

values. Moreover, the Manning formula was developed for 

uniform flow, which is not present during floodflows, and the 

equation is dimensionally nonhomogeneous. Hence, many 

of the uncertainties in one-dimensional hydraulic models are 

included in the Manning n value. Extreme care must be used 

when developing these models to ensure that the Manning n

value is not used to compensate for errors in other aspects of 

the model in order to achieve model calibration.

The HEC–RAS model can be calibrated adequately at 

a single point by using a time series of stage and discharge 

(Horritt and Bates, 2002). The models, however, are used to 

simulate an inundated area, so calibration at a single point is 

not really an adequate calibration. Moreover, the extent of an 

inundated area alone may not be sufficient to evaluate model 

performance, particularly for flat floodplains (Hesselink and 

others, 2003).

Adequate calibration data are seldom available for 

inundation models. Although the models are used to provide 

estimates of the shoreline throughout the inundated area, the 

models typically are calibrated measurements and simulations 

at a single, or perhaps a few locations. In addition, streamgage 

data can be in error by as much as 20 percent or more during 

extreme floods, so improved methods for monitoring floodflows are needed (Bates and others, 2006). An ideal data set 

for flood-inundation model calibration and validation would 

include spatially detailed data for the inundation shoreline 

and, if the models are used to provide estimates of inundation 

depth, data for water depth throughout the model domain. 

Several approaches have been attempted to obtain 

inundation model-validation data sets. Puech and Raclot 

(2002) developed a unique approach to estimate inundation 

water depth from aerial photographs, but the approach likely 

is not applicable to North Carolina floodplains because the 

floodplain must be broad and divided by numerous natural 

elements, such as dikes and levees. Other remote-sensing 

approaches include the following:

SPOT satellite and RADARSAT images were used 

to classify inundation areas data (Toyra and others, 

2002). Accuracies increased from 70 to 80 percent, and 

one method alone increased accuracy to greater than 

90 percent with combined use of the two images. Pixel 

size was 10–20 m. 

Successive LANDSAT Thematic mapper images with 

resolutions of 30 m by 30 m were used to develop a 

model of flood growth in a 600-km-long river, but 

problems with water turbidity, dense riparian vegetation, and cloud cover were common (Overton, 2005). 

•

•

LANDSAT images also were used to develop an inundation map of the Tar River in Pitt County following 

Hurricane Floyd (Wang and others, 2002), but images 

were not available until 9 days after the flood crested.

Advanced very high resolution radiometer (AVHRR) 

data were used to delineate flood inundation during the 

major floods of 1988, 1995, and 1999 in Bangladesh. 

AVHRR resolution, however, is 1.1 km and is available 

only during cloud-free periods, and ground control 

points are needed to correct for geometric distortion 

(Islam and Sado, 2002). 

RADARSAT synthetic aperture radar (SAR) was 

used to map inundation in the Roanoke River floodplain of North Carolina. Mapping was approximately 

94 percent accurate and was 90 percent accurate when 

the water level was between minus 10 cm and plus 

10 cm of the ground surface (Townsend, 2001). SAR 

can be used regardless of cloud cover or vegetation 

conditions, but data are available only at 7- to 10-day 

intervals. Bates and others (2006) collected four SAR 

images from an aircraft during a flood event on River 

Severn in England. Resolution of the images was 

1.2 m, and streamgage data also were available. This 

is, perhaps, the most extensive flood-verification data 

set available. 

Steady-Flow Versus Unsteady-Flow Modeling 

The Tar River flood-inundation maps were developed 

by using a steady-flow hydraulic model. This means, for 

example, that if an inundation map were developed for the Tar 

River at Tarboro (site 12, fig. 1) for a flow of 1,000 m3/s, it 

could be assumed that the flow at Tarboro would be a constant 

1,000 m3/s for a sufficient period to allow all land areas that 

could be flooded at that flow to become inundated. This 

assumption clearly has less of an effect on inundation maps 

produced for lower flows than for higher flows, because more 

time typically is required to inundate areas at high flows than 

at low flows. Moreover, two floods at a given location having 

the same peak flow can have distinctly different inundation 

patterns. A flood in which water levels rise slowly to the peak 

and then fall slowly likely will result in more inundation than 

a flood with the same peak flow but in which water levels rise 

and fall quickly. 

The Tarboro model (table 3) was used to investigate the 

effects of unsteady-flow simulations (Hydrologic Engineering 

Center, 1993) on estimates of flood inundation by simulating 

the Hurricane Floyd flood flows during September 1999 

(fig. 17). The relation between the measured water level and 

simulated discharge at the site during the flood is shown in 

figure 17A. The arrows on the curve indicate rising or falling 

stage. For example, four different water levels were associated 

with a flow of 360 m3/s (fig. 17A) occurring on September 9 

during a rising water level, September 14 during falling stage, 

•

•

Development of Flood-Inundation Maps  27
Figure 17. (A) Relation of measured water level and simulated discharge and (B) simulated hydrograph for the Tar 

River at Tarboro, North Carolina, during Hurricane Floyd flood, September 5–30, 1999.

28 LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina

0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000

DISCHARGE, IN CUBIC METERS PER SECOND

W

ATER-SURFACE ELEV

ATION, IN METERS ABOVE NA

VD 88

F R

A.

R

F

B.

16

14

12

10

8

6

4

2

0

DISCHARGE, IN CUBIC METERS PER SECOND

2,000

1,800

1,600

1,400

1,200

1,000

800

600

400

200

0

5 10 15

SEPTEMBER 1999

20 25 30

Rising water levels

Declining water levels

Declining water levels Rising water levels

Direction of water-level change
September 17 during rising stage, and on September 27 during 

falling stage (fig. 17B). Water-surface elevations on these 

dates were 9.71, 10.55, 9.90, and 11.45 m above NAVD 88, 

respectively, a range of about 1.7 m. Hence, an inundation map 

for a flow of 360 m3/s could have been selected from among 

seven different maps (9.7, 10.0, 10.3, and so on to 11.3 m 

above NAVD 88). 

The peak flow for a given exceedance level seldom 

occurs during steady-flow conditions, yet DFIRMs and 

inundation maps typically are created by assuming steady-flow 

conditions. Failure to attain steady-flow results in variable 

inundation extents for a single river flow (or stage), because 

a stable distribution of water in the floodplain is not attained. 

Additional research is needed on the relation between attainment of steady flow and the lateral distribution of water in 

the floodplain. Hence, the use of inundation maps to estimate 

inundation that may occur during, for example, a 50-year 

flood must be done with recognition of the uncertainties in the 

maps. 

The Hurricane Floyd simulated hydrograph also 

demonstrates that flows of distinctly different magnitudes can 

occur for a given water level (fig. 17B). As water levels rose, 

a flow of 1,460 m3/s occurred at a water-surface elevation of 

13.7 m (R in fig. 17) on September 19, whereas the flow at the 

same water-surface elevation during falling water levels was 

723 m3/s on September 25 (F in fig. 17), or less than half the 

flow at the same water level. The Tarboro hydraulic model was 

applied to the unsteady flows during September 5–30, 1999, 

to estimate inundated areas. According to the simulations, 

the inundated areas for the two water-surface elevations of 

13.7 m differed by more than 70,000 square meters (m2), with 

more area inundated on September 25 (falling water level), 

despite the fact that the flow was less than half the flow at the 

same water level on the rising side of the hydrograph. These 

limitations associated with the steady-flow assumption for 

hydraulic modeling vary from site to site. 

The alternative to using the steady-flow assumption and 

developing inundation-map libraries, as was done for this 

project, is to estimate inundated areas in real time during or 

immediately prior to a flood so that particular characteristics 

of the rainfall and flood hydrograph are well represented in 

hydraulic modeling. Research applications have demonstrated 

that it is possible to produce reasonable inundation maps 

from medium range weather forecasts (Pappenberger and 

others, 2005b) or from NWS estimates of rainfall derived 

from NEXRAD weather radar (Whiteaker and others, 2006). 

The limitation of this approach is that the models must be 

operating in real time for each event and that results must be 

distributed quickly to emergency management officials and all 

other interested parties. Moreover, there is some uncertainty 

in the forecast flows in the reach for which the inundation 

modeling is being conducted (Bates, 2004). The infrastructure 

and funding for such a system is not in place for the Tar River 

basin, nor is it available for most locations in the United 

States. (Fort Collins, Colorado, is a notable exception; Fort 

Collins Office of Emergency Management, 2006.) Hence, the 

steady-flow assumption, map-library approach was used in 

this study. Further studies are needed to document any benefits 

of the real-time system over the map-library approach and to 

demonstrate the conditions for which each approach is most 

applicable.

One-Dimensional Versus Two-Dimensional 

Modeling 

The one-dimensional modeling approach used in this 

study resulted in good agreement between measured and 

simulated stage-discharge ratings at sites where these ratings 

were available, and good agreement between measured and 

simulated stage and discharge observations at other sites. 

One-dimensional models are relatively easy to construct, and 

simulations can be made quickly. One-dimensional hydraulic 

models are based on the assumption that the hydraulic 

variables (water-surface elevation, water-surface slope, 

velocity, and cross-sectional flow area) are uniform across a 

transect and that primary variations in these variables are from 

upstream to downstream. This assumption is reasonable for a 

prismatic channel in a relatively narrow floodplain. 

The limitations of the one-dimensional approach can 

be seen, however, in section A–B in figure 8. Water levels 

are assumed to be constant across cross section A–B, but the 

cross section intersects streams other than the Tar River at five 

locations, where according to the one-dimensional assumption, 

water levels are the same as in the Tar River. It is possible to 

build branching one-dimensional models, as was done in the 

Rocky Mount model, which included Stony Creek and the 

Tar River, to partially avoid the problems associated with the 

one-dimensional assumption. It is not possible, however, to 

avoid the problems altogether, as even Stony Creek cross sections intersect multiple streams in some locations (fig. 8). The 

conclusion from this discussion, then, is that uncertainty in the 

flood-inundation polygons increases with distance from the 

main channel for which water-surface slopes were simulated, 

particularly in broad floodplains with numerous tributaries. 

Two-dimensional models are being used increasingly 

for simulation of floodplain inundation because of variability 

in topography across the floodplain, particularly in wide 

floodplains with numerous tributaries. The results of previous 

studies have demonstrated that correct simulation of water 

storage in a floodplain near a channel is important for predicting flood-wave timing, and the increased lateral resolution 

of a two-dimensional model allows proper simulation of this 

process (Horritt and Bates, 2001). Raster models, which use 

a one-dimensional representation of channel flow linked to a 

simple model of flow between grids of cells on the floodplain, 

have been used to improve spatial resolution of flood inundation without significantly increasing computational requirements of the hydraulic model (for example Bates and De Roo 

(2000)). A coupled one-dimensional and two-dimensional 

model was used to develop a library of flood-inundation 

polygons for the Blue River, Missouri, where backwater from 

Development of Flood-Inundation Maps  29
the Missouri River affects flows in the Blue River (Kelly and 

Rydlund, 2006). Two-dimensional models are particularly 

appropriate for simulating a flood hydrograph. Jones and 

others (2002) used a fine-scale two-dimensional model to 

simulate the movement of a NWS forecast flood hydrograph 

through the Snoqualmie River valley in Washington to produce 

forecast inundation along a 28-km reach of the river. 

The raw LiDAR data that were processed to provide 

topographic information for the Tar River inundation modeling 

also can be processed to provide other information that could 

be useful for two-dimensional modeling. For example, Cobby 

and others (2001) used LiDAR data to not only delineate the 

floodplain topography for a two-dimensional model but also 

to estimate vegetation height, which typically would be used 

in a flood-inundation model to determine friction coefficients. 

Different algorithms are used for different types of vegetation.

Summary and Conclusions

Flooding from Hurricane Floyd and other recent hurricanes in North Carolina clearly demonstrated that there is a 

growing need for more and better flood information, including 

products that (1) are available for more locations, (2) provide 

mapped information showing actual or predicted inundated 

areas, and (3) interface with a full suite of other flood-related 

products, such as DFIRMs and flood-forecast products 

produced by the NWS. In 2000, the FMP began a multiagency 

program to improve the flood information and floodforecasting system for the State, with a long-term goal of 

providing emergency managers and the public more accessible, informative, timely, and accurate flood information and 

flood-forecast products for more locations. One of the primary 

roles of the USGS in this effort is to develop and demonstrate 

the technology for producing detailed flood-inundation maps 

for the Tar River basin. The maps can be used in conjunction 

with real-time streamgage measurements to depict current 

flooding and to provide estimates based on NWS flood 

forecasts of areas that are expected to become inundated.

Data from 19 streamgages in the Tar River basin 

upstream from (and including) Washington were used in 

this study. Five of the sites are NWS flood-forecast points. 

Continuous streamflow data were available from 11 of the 

sites, and continuous stage data were available from the other 

8 sites. High-water mark and discharge-measurement data also 

were available for model development and testing. 

LiDAR data with a vertical accuracy of about 20 cm 

were processed to produce topographic data for the inundation 

maps. Bare-earth mass point LiDAR data received from the 

FMP were reprocessed into a digital elevation model (DEM) 

with regularly spaced, 1.5-m by 1.5-m cells. A tool was 

developed as part of this project to connect flow paths (or 

streams) that were inappropriately disconnected in the DEM 

by such features as a bridge or road crossing. This process is 

known as hydroconditioning.

The HEC–RAS model was used for hydraulic modeling. 

Eleven individual hydraulic models were developed for 

the Tar River basin sites. Seven models were developed for 

reaches with a single gage, and four models were developed 

for reaches of the Tar River main stem that receive flow from 

major gaged tributaries, or reaches in which multiple gages are 

near one another. Bathymetric and topographic data used to 

develop the inundation models were derived from the existing 

FMP models, LiDAR data, field surveys, and interpolation of 

measured data. 

Overbank cross sections for use in HEC–RAS generally 

are approximately perpendicular to the stream at the point 

the cross section intersects the stream, and each cross section 

intersects the main channel only once. In addition, two 

cross-section lines cannot intersect each other. Many of the 

streams in the study area are quite sinuous, so great care was 

required to develop overbank cross sections that met these 

requirements. Even so, there were cases, such as near the 

confluence of a tributary with the main channel, that it was not 

possible for the overbank cross section to be perpendicular to 

both the main channel and the tributary. Although overbank 

cross-section locations were delineated manually for this 

study, the process has since been automated. 

Combined, the Tar River hydraulic models included 

272 km of streams in the basin, including about 162 km on 

the Tar River main stem. Cross-section density in the models 

was about one cross section per 400 m of stream length, with 

generally higher densities on tributary streams than on the 

main stem. The models also included the effects of 59 bridges 

on flow in the basin. 

The hydraulic models were calibrated to the most current 

stage-discharge relations at 11 long-term streamgages where 

rating curves were available. Medium- to high-flow discharge 

measurements were made at some of the sites without rating 

curves, and high-water marks from Hurricanes Fran and Floyd 

flooding were available for high-stage calibration. Simulated 

rating curves matched measured curves over the full range 

of flows. Differences between measured and simulated 

water levels for a specified flow were no more than 0.44 m 

and typically were less. Differences between measured and 

simulated water levels for models calibrated to high-water 

marks were less than 0.25 m. These results demonstrate that 

the 11 Tar River models are capable of simulating accurate 

water levels over a wide range of steady flows in the Tar River 

and tributary streams. 

The calibrated models were used to generate a set of 

water-surface profiles for each of the 11 modeled reaches. 

The profiles were generated at 0.305-m increments for water 

levels ranging from bankfull to approximately the highest 

recorded water level at the downstream-most gage in each 

modeled reach. Inundated areas were identified by subtracting 

the water-surface elevation in each 1.5-m by 1.5-m grid cell 

from the land-surface elevation in the cell. An application was 

developed to identify all inundated cells that were hydraulically connected to the cell at the downstream-most gage in the 

model domain. All other inundated cells were deleted from the 

30 LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina
map layer. This entire process was to create 11 sets of 

inundation-map libraries for the Tar River basin.

Low-resolution inundation maps that include transportation networks and orthoimagery are accessible on the Internet 

through the USGS North Carolina Water Science Center’s 

website. These maps also are linked to the USGS National 

Water Information System website. Hence, a user can determine the near real-time stage and water-surface elevation at a 

USGS streamgage site in the Tar River basin and link directly 

to the flood-inundation maps to obtain a depiction of the 

estimated inundated area at the current water level.

Although the flood-inundation maps represent the 

boundaries of inundated areas with a distinct line, there is 

some uncertainty associated with these maps. There are a 

number of methods for estimating uncertainties associated 

with hydraulic modeling and inundation mapping, but data 

required to apply these methods are seldom available, which 

is true in this case. Flood-inundation models require at least 

three types of data, all of which contribute to model uncertainty. These are (1) topographic data for the hydraulic model 

computational grid and the inundation maps, (2) effective 

friction values (Manning’s n) for each computational segment 

(one-dimensional model) or cell (two-dimensional model), and 

(3) model-validation data of some type. Uncertainties exist in 

each of these data types. Models applied in near real-time also 

require a forecast hydrograph.

Land-surface elevation, or topography, is the dominant 

influence on the location of the simulated shoreline of the 

inundated area. Results of other studies have demonstrated that 

low-resolution one-dimensional hydraulic modeling combined 

with high-resolution topographic data provide good representation of shoreline, which is the reason the original LiDAR 

data were reprocessed to provide 1.5-m horizontal resolution 

topographic data in this study. 

The HEC–RAS model, which was used in this study, can 

be adequately calibrated at a point by using a time series of 

stage and discharge. The model, however, is used to simulate 

inundation area, so calibration at a single point is not really an 

adequate calibration, but adequate calibration data are seldom 

available for inundation models. In addition, streamgage 

data can be in error by as much as 20 percent or more during 

extreme floods; thus, improved methods for monitoring 

floodflows also are needed. An ideal data set for floodinundation model calibration and validation would include 

spatially detailed data on the inundation shoreline and, if the 

models were used to provide estimates of inundation depth, 

data on water depth throughout the model domain. Several 

remote-sensing techniques have been used with mixed success 

in other studies to provide detailed inundation data.

The Tar River flood-inundation maps were developed by 

using a steady-flow hydraulic model. Two floods at a given 

location having the same peak flow could have distinctly 

different inundation patterns. A flood in which water levels 

rise slowly to the peak and then fall slowly likely will result 

in more inundation than a flood having the same peak flow 

but water levels that rise and fall quickly. Simulations using 

the Tarboro model and the Hurricane Floyd data at Tarboro 

demonstrated that flows of distinctly different magnitudes 

can occur for a given water level. According to the model 

simulations, the inundated areas for the two occurrences of a 

water-surface elevation of 13.7 m at Tarboro differed by more 

than 70,000 m2. These limitations associated with the steadyflow assumption for hydraulic modeling vary from site to site. 

The alternative to using the steady-flow assumption and 

developing inundation-map libraries, as was done for this 

study, is to estimate inundated areas in real time during or 

immediately prior to a flood so that particular characteristics 

of the rainfall and flood hydrograph are well represented in the 

hydraulic modeling. Results of research studies have demonstrated that this approach is feasible. The limitations of this 

approach are that the models must be operating in real time for 

each event, and results must be distributed quickly to emergency management officials and all other interested parties. 

Moreover, there will be some uncertainty in the forecast flows 

to the reach for which inundation modeling is to be conducted. 

The infrastructure and funding for such a system are not in 

place for the Tar River basin or currently for most locations in 

the United States. Further studies are needed to investigate the 

benefits of the real-time system and the map-library approach 

and to demonstrate the conditions for which each approach is 

most applicable.

The one-dimensional modeling approach used in this 

study resulted in good agreement between measured and 

simulated stage-discharge ratings. One-dimensional hydraulic 

models, however, are based on the assumption that the 

hydraulic variables are uniform across a transect. This assumption is reasonable for a prismatic channel in a relatively narrow 

floodplain but may not be appropriate in broad floodplains 

for sinuous rivers with several tributaries. Uncertainty in the 

flood-inundation polygons increases with distance from the 

main channel for which water-surface slopes are simulated. 

Two-dimensional models increasingly are used for simulation 

of floodplain inundation because of the variability in topography across the floodplain, particularly in wide floodplains with 

numerous tributaries.

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34  LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina
Appendix

Example of metadata for the flood-inundation maps.
36  LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina

Identification_Information:

 Citation:

 Citation_Information:

 Originator: Jerad Bales

 Originator: Kirsten Cassingham

 Publication_Date: Unpublished Material

 Title: Flood Inundation area of US Geological Survey Tar River near Tar River; 02081500

 Geospatial_Data_Presentation_Form: vector digital data

 Online_Linkage: 

 Description:

 Abstract:

 Flood inundation maps were created for selected streamgage sites in the Tar River basin, North Carolina. LiDAR data from the North Carolina 

Floodplain Mapping Program’s Floodplain Mapping Information System, available in 10,000-foot by 10,000-foot tiles, were processed to produce 

topographic data for the inundation maps. Based on root mean square errors between LiDAR data and ground surveys, vertical accuracy of 

the LiDAR data was about 20 cm. Bare earth mass point LiDAR re-processed into a digital elevation model with regularly spaced, 1.5-meter) 

by 1.5-meter cells. A tool was developed as part of this project to connect flow paths (or streams) that were inappropriately disconnected in 

the digital elevation model by some feature such as a bridge or road crossing.

 The HEC-RAS model was used for hydraulic modeling at each of the study sites. Eleven individual hydraulic models were developed for the 

Tar River basin sites. Seven models were developed for reaches with a single gage, and four models were developed for reaches of the Tar 

River mainstem that receive flow from major gaged tributaries, or reaches in which there were multiple gages near one another. 

 Combined, the Tar River hydraulic models included 272 kilometers of streams in the basin, including about 162 kilometers on the Tar River 

mainstem. Cross-section density in the models was about one cross section per 400 meters of stream length, and the models included the 

effects of 59 bridges on flow in the basin. 

 The hydraulic models were calibrated to the most current stage-discharge relationship at eleven long-term streamgages where rating curves 

were available. Medium to high-flow discharge measurements were made at some of the sites without rating curves, and high-water marks 

from Hurricanes Fran and Floyd were available for high stage calibration. Simulated rating curves matched measured curves over the full 

range of flows. Differences between measured and simulated water levels for a specified flow were no more than 0.44 m, and typically less.

 The calibrated models were used to generate a set of water-surface profiles for each of the 11 modeled reaches at 0.305-meter increments 

for water levels ranging from bankfull to approximately the highest recorded water level at the downstream-most gage in the each modeled 

reach. Inundated areas were identified by subtracting the water-surface elevation in each 1.6-meter by 1.6-meter grid cell from the land-surface elevation in the cell through an application that was developed to identify all inundated cells hydraulically connected to the cell at the 

downstream-most gage in the model domain. 

 

 Although the flood inundation maps represent the boundaries of inundated areas with a distinct line on a map, there is some uncertainty 

associated with these maps. These are uncertainties in topographic data for the hydraulic model computational grid and the inundation maps, 

effective friction values (Manning’s n), model validation data of some type, and forecast hydrographs, if used. 

 The Tar River flood inundation maps were developed by using a steady-flow hydraulic model. This assumption clearly has less of an effect on 

inundation maps produced for lower flows than for higher flows because more time typically is required to inundate areas at high flows than at 

low flows. A flood for which water levels rise slowly to the peak, and then fall slowly will most likely result in more inundation than a flood with 

the same peak flow, but which rises and falls very quickly. Limitations associated with the steady-flow assumption for hydraulic modeling will 

vary from site to site. 

 The one-dimensional modeling approach used in this study resulted in good agreement between measurements and simulations. The onedimensional approach is reasonable for a prismatic channel in a relatively narrow floodplain, but may not be appropriate in broad floodplains 

for sinuous rivers with several tributaries. Uncertainty in the flood inundation polygons increases with distance from the main channel for 

which water-surface slopes were simulated. Two-dimensional models are finding increasing use for simulation of floodplain inundation 

because of the variability in topography across the floodplain, particularly in wide floodplains with numerous tributaries.
 Purpose:

 The existing flood information and flood forecasting system has served the State of North Carolina and the Nation well. However, as indicated 

by experiences during Hurricanes Fran and Floyd, as well as other flood events across the State and Nation, there is a growing demand and 

need for more and better flood information and flood forecasts. The objective of this project is to develop an enhanced capability for (1) 

providing flood and flood-related information to emergency managers and the public and (2) producing flood forecasts. The initial phase of the 

project is focused on the Tar River Basin. The procedures and technology developed in this demonstration study will then be applied throughout the State.

 The primary goal of the project is to provide more accessible, informative, timely, and accurate flood information and flood forecast products 

and for more locations. The project will focus on six specific themes:

 “ Risk Assessment

 “ Improved Weather Information

 “ Real-Time Flood Information and Inundation Mapping

 “ Enhanced Flood Forecasting

 “ Flood Forecast Mapping

 “ Information Delivery System-Integration with NC FMP Information Technology System

 Key elements of this project are consistent with those proposed nationally under the NWS AHPS program and the USGS National Streamflow 

Information Program, as well as the North Carolina Floodplain Mapping Program. This project will ensure that the necessary flood information 

and forecast capabilities are available in the very near future for North Carolina.

 Supplemental_Information:

 Eighteen USGS streamgages located in the Tar River Basin were chosen; 

 02081500 Tar River nr Tar River

 02081747 Tar River at US 401 ner Louisburg

 02082506 Tar River below Tar River Reservoir ner Rocky Mount

 0208250885Tar River at US 301 Bypass ner Rocky Mount

 02082576 Stony Creek at Winstead Ave. at Rock mt

 02082585 Tar River at NC97 at Rocky Mount

 02082770 Swift Creek at Hilliardston

 020828117 Little Fishing Creek nr White Oak

 02082950 Fishing Creek nr Enfield

 02083000 Fishing Creek at NC97

 020833107 Tar River at Tarboro

 02083500 Town Creek at US258 nr Pinetops

 02083640 Conetoe Creek nr Bethel

 02083893 Tar River at US264 Bypass near Greenville

 02084000 Tar River at Greenville

 02084160 Chcod Creek nr Simpson

 02084173 Tar River at SR 1565 nr Grimesland

 020873619 Tranters Creek at SR1567 nr Washington

 Gages that were in appropriate hydrologic proximity were combined for continuous modeling. 

 PROCESSING STEPS: 

 The eighteen gages were processed using Arc Macro Language (aml) programs. They were used to automate and document the steps for 

further processing of other sites and review. Below are the general steps to produce inundation layers at half foot increments. 

 1. Locate the XY coordinates for the gage on your line work. Distances from the latitude and longitude were provided by the Hydrologic 

Engineering Centers River Analysis System (HEC-RAS) model. Each point is a measured distance from the gage with an associated elevation 

of inundation at half foot increments. 

 2. A line coverage is then digitized perpendicular to the stream which also crosses the points of elevation. These arcs are then used to 

interpolate inundated surfaces using TOPOGRID (contour, xyzlimits). 

 3. A “depth” grid is generated by subtraction of the surface (inundated surfaces at half increments) from the digital elevation model. This 

grid provides a grid of water depth above sea level. 

Appendix  37
 4. REGIONGROUP was used to select the connecting cells on 4 sides. This grid is used as a mask to give the “depth” grid lateral definition. 

 5. Grids at each inundation level are then merged to one grid. This grid is then converted to a polygon coverage or shapefile. The values 

of inundated elevations and depths are then joined to the associated table. 

 DISCLAIMER:

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

this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in 

ARC/INFO format, this metadata file may include some ARC/INFO-specific terminology.

 Time_Period_of_Content:

 Time_Period_Information:

 Single_Date/Time:

 Calendar_Date: unknown

 Currentness_Reference: publication date

 Status:

 Progress: Complete

 Maintenance_and_Update_Frequency: None planned

 Spatial_Domain:

 Bounding_Coordinates:

 West_Bounding_Coordinate: -78.649111

 East_Bounding_Coordinate: -78.569046

 North_Bounding_Coordinate: 36.228350

 South_Bounding_Coordinate: 36.168764

 Keywords:

 Theme:

 Theme_Keyword_Thesaurus: None

 Theme_Keyword: Flood

 Theme_Keyword: Inundation

 Theme_Keyword: floodzones

 Theme_Keyword: LIdar derived

 Place:

 Place_Keyword_Thesaurus: None

 Place_Keyword: US

 Place_Keyword: North Carolina

 Place_Keyword: Tar River Basin

 Place_Keyword: Rocky Mount

 Access_Constraints:

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

 Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well 

as in ARC/INFO format, this metadata file may include some ARC/INFO-specific terminology.

 Use_Constraints:

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

 Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well 

as in ARC/INFO format, this metadata file may include some ARC/INFO-specific terminology.

 Point_of_Contact:

 Contact_Information:

 Contact_Person_Primary:

 Contact_Person: Jerad Bales

38 LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina
 Contact_Organization: USGS

 Contact_Position: Hydrologist

 Contact_Voice_Telephone: 919-571-4000

 Contact_Facsimile_Telephone: 919-5714041

 Native_Data_Set_Environment: Microsoft Windows XP Version 5.1 (Build 2600) Service Pack 2; ESRI ArcCatalog 9.0.0.535

Data_Quality_Information:

 Lineage:

 Process_Step:

 Process_Description: Metadata imported.

 Process_Step:

 Process_Description: Metadata imported.

 Source_Used_Citation_Abbreviation: C:\DOCUME~1\kmcassin\LOCALS~1\Temp\xml4C2E.tmp

 Process_Step:

 Process_Description: Metadata imported.

 Source_Used_Citation_Abbreviation: C:\DOCUME~1\kmcassin\LOCALS~1\Temp\xml25B4.tmp

 Process_Step:

 Process_Description: Dataset copied.

 Source_Used_Citation_Abbreviation: l:\tarriver_tarriver\cf-grids\fl1500

Spatial_Data_Organization_Information:

 Direct_Spatial_Reference_Method: Vector

 Point_and_Vector_Object_Information:

 SDTS_Terms_Description:

 SDTS_Point_and_Vector_Object_Type: Complete chain

 Point_and_Vector_Object_Count: 861239

 SDTS_Terms_Description:

 SDTS_Point_and_Vector_Object_Type: Label point

 Point_and_Vector_Object_Count: 386978

 SDTS_Terms_Description:

 SDTS_Point_and_Vector_Object_Type: GT-polygon composed of chains

 Point_and_Vector_Object_Count: 386978

 SDTS_Terms_Description:

 SDTS_Point_and_Vector_Object_Type: Point

 Point_and_Vector_Object_Count: 4

 SDTS_Terms_Description:

 SDTS_Point_and_Vector_Object_Type: Composite object

 Point_and_Vector_Object_Count: 51

Spatial_Reference_Information:

 Horizontal_Coordinate_System_Definition:

 Planar:

 Map_Projection:

 Map_Projection_Name: Lambert Conformal Conic

 Lambert_Conformal_Conic:

 Standard_Parallel: 34.333333

 Standard_Parallel: 36.166667

 Longitude_of_Central_Meridian: -79.000000

 Latitude_of_Projection_Origin: 33.750000

 False_Easting: 2000000.002617

 False_Northing: 0.000000

 Planar_Coordinate_Information:

 Planar_Coordinate_Encoding_Method: coordinate pair

 Coordinate_Representation:

 Abscissa_Resolution: 0.000064

Appendix  39
 Ordinate_Resolution: 0.000064

 Planar_Distance_Units: survey feet

 Geodetic_Model:

 Horizontal_Datum_Name: North American Datum of 1983

 Ellipsoid_Name: Geodetic Reference System 80

 Semi-major_Axis: 6378137.000000

 Denominator_of_Flattening_Ratio: 298.257222

Entity_and_Attribute_Information:

 Detailed_Description:

 Entity_Type:

 Entity_Type_Label: fl1500.pat

 Entity_Type_Definition: Regions dissolved into grid-code values

 Attribute:

 Attribute_Label: FID

 Attribute_Definition: Internal feature number.

 Attribute_Definition_Source: ESRI

 Attribute_Domain_Values:

 Unrepresentable_Domain: Sequential unique whole numbers that are automatically generated.

 Attribute:

 Attribute_Label: Shape

 Attribute_Definition: Feature geometry.

 Attribute_Definition_Source: ESRI

 Attribute_Domain_Values:

 Unrepresentable_Domain: Coordinates defining the features.

 Attribute:

 Attribute_Label: AREA

 Attribute_Definition: Area of feature in internal units squared.

 Attribute_Definition_Source: ESRI

 Attribute_Domain_Values:

 Unrepresentable_Domain: Positive real numbers that are automatically generated.

 Attribute:

 Attribute_Label: PERIMETER

 Attribute_Definition: Perimeter of feature in internal units.

 Attribute_Definition_Source: ESRI

 Attribute_Domain_Values:

 Unrepresentable_Domain: Positive real numbers that are automatically generated.

 Attribute:

 Attribute_Label: FL1500#

 Attribute_Definition: Internal feature number.

 Attribute_Definition_Source: ESRI

 Attribute_Domain_Values:

 Unrepresentable_Domain: Sequential unique whole numbers that are automatically generated.

 Attribute:

 Attribute_Label: FL1500-ID

 Attribute_Definition: User-defined feature number.

 Attribute_Definition_Source: ESRI

 Attribute:

 Attribute_Label: GRID-CODE

 Attribute_Definition: Each number starting with 1 represents half foot increments in flooding areas.

 Attribute:

 Attribute_Label: ELEV-MSL

 Attribute_Definition: Elevation at mean sea level.

40 LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina
 Attribute:

 Attribute_Label: SURF-RANGE

 Attribute_Definition: Range at half foot values of surface water at mean sea level.

 Attribute:

 Attribute:

 Detailed_Description:

 Entity_Type:

 Entity_Type_Label: fl1500.patsub

 Entity_Type_Definition: Attribute table

 Attribute:

 Attribute_Label: FID

 Attribute_Definition: Internal feature number.

 Attribute_Definition_Source: ESRI

 Attribute_Domain_Values:

 Unrepresentable_Domain: Sequential unique whole numbers that are automatically generated.

 Attribute:

 Attribute_Label: Shape

 Attribute_Definition: Feature geometry.

 Attribute_Definition_Source: ESRI

 Attribute_Domain_Values:

 Unrepresentable_Domain: Coordinates defining the features.

 Attribute:

 Attribute_Label: AREA

 Attribute_Definition: Area of feature in internal units squared.

 Attribute_Definition_Source: ESRI

 Attribute_Domain_Values:

 Unrepresentable_Domain: Positive real numbers that are automatically generated.

 Attribute:

 Attribute_Label: PERIMETER

 Attribute_Definition: Perimeter of feature in internal units.

 Attribute_Definition_Source: ESRI

 Attribute_Domain_Values:

 Unrepresentable_Domain: Positive real numbers that are automatically generated.

 Attribute:

 Attribute_Label: SUB#

 Attribute_Definition: Internal feature number.

 Attribute_Definition_Source: ESRI

 Attribute_Domain_Values:

 Unrepresentable_Domain: Sequential unique whole numbers that are automatically generated.

 Attribute:

 Attribute_Label: SUB-ID

 Attribute_Definition: User-defined feature number.

 Attribute_Definition_Source: ESRI

 Attribute:

 Attribute_Label: GRID-CODE

Distribution_Information:

 Resource_Description: Downloadable Data

 Standard_Order_Process:

 Digital_Form:

 Digital_Transfer_Information:

 Transfer_Size: 202.097

Appendix  41
Metadata_Reference_Information:

 Metadata_Date: 20061027

 Metadata_Contact:

 Contact_Information:

 Contact_Organization_Primary:

 Contact_Organization: US Geological Survey, WRD

 Contact_Person: Kirsten C. Tighe

 Contact_Address:

 Address_Type: mailing and physical address

 Address: 3916 Sunset Ridge Rd.

 City: Raleigh

 State_or_Province: North Carolina

 Postal_Code: 27609

 Contact_Voice_Telephone: 919-571-4050

 Contact_Facsimile_Telephone: 919-571-4041

 Metadata_Standard_Name: FGDC Content Standards for Digital Geospatial Metadata

 Metadata_Standard_Version: FGDC-STD-001-1998

 Metadata_Time_Convention: local time

 Metadata_Extensions:

 Online_Linkage: http://www.esri.com/metadata/esriprof80.html

 Profile_Name: ESRI Metadata Profile

 Metadata_Extensions:

 Online_Linkage: http://www.esri.com/metadata/esriprof80.html

 Profile_Name: ESRI Metadata Profile

 Metadata_Extensions:

 Online_Linkage: http://www.esri.com/metadata/esriprof80.html

 Profile_Name: ESRI Metadata Profile

 Metadata_Extensions:

 Online_Linkage: http://www.esri.com/metadata/esriprof80.html

 Profile_Name: ESRI Metadata Profile

42  LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina
Prepared by:

U.S. Geological Survey

Enterprise Publishing Network

Raleigh Publishing Service Center

3916 Sunset Ridge Road

Raleigh, NC 27607

A PDF version of this publication is available online at URL 

	 http://pubs.water.usgs.gov/sir2007–5032/
Printed on recycled paper

Bales and others—LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, NC USGS SIR 2007–5032