FLOOD-FREQUENCY RELATIONS FOR URBAN STREAMS 

IN GEORGIA—1994 UPDATE

U.S. GEOLOGICAL SURVEY

Prepared in cooperation with the

GEORGIA DEPARTMENT OF TRANSPORTATION

DEKALB COUNTY

CONSOLIDATED GOVERNMENT OF COLUMBUS

CITY OF ALBANY

CITY OF MOULTRIE

CITY OF THOMASVILLE

CITY OF VALDOSTA

Water-Resources Investigations Report 95-4017
FLOOD-FREQUENCY RELATIONS FOR URBAN STREAMS IN 

GEORGIA—1994 UPDATE

By Ernest J. Inman

____________________________________________________________________________________

U.S. GEOLOGICAL SURVEY

Water-Resources Investigations Report 95-4017

Prepared in cooperation with the

GEORGIA DEPARTMENT OF TRANSPORTATION

DEKALB COUNTY

CONSOLIDATED GOVERNMENT OF COLUMBUS

CITY OF ALBANY

CITY OF MOULTRIE

CITY OF THOMASVILLE

CITY OF VALDOSTA

Atlanta, Georgia

1995
U.S. DEPARTMENT OF THE INTERIOR

BRUCE BABBITT, Secretary

U.S. GEOLOGICAL SURVEY

Gordon P. Eaton, Director

Any use of trade, product, or firm names is for descriptive purposes only

and does not imply endorsement by the U.S. Government

For additional information, please write to:

District Chief

U.S. Geological Survey

3039 Amwiler Road

Peachtree Business Center

Suite 130

Atlanta, GA 30360

Copies of this report can be purchased from:

U.S. Geological Survey

Earth Science Information Center

Open File Reports Section

Box 25286, MS 517

Denver Federal Center

Denver, CO 80225
iii

CONTENTS

Abstract 1

Introduction 1

Purpose and scope 1

Previous studies 2

Acknowledgments 2

Site selection 2

Data collection and processing 3

Current data 3

Long-term rainfall and daily pan-evaporation data 5

Flood-frequency relations 5

Description of rainfall-runoff model 5

Calibration 6

Verification 7

Flood-frequency analysis 10

Regional regression analysis 13

 Regional flood-frequency estimating equations 16

Testing of regression equations 16

Bias 16

Sensitivity 16

Standard error of prediction 18

Use of flood-frequency relations 18

Summary 18

Selected references 18
iv

ILLUSTRATIONS

Figure 1. Map showing rural flood-frequency regions in Georgia, cities where gaging stations are used 

in this study, and number of gages in each city 4

2-5. Maps showing regional rural flood-frequency boundaries and cities and number of

urban sites in northwest Georgia

2. Northwest Georgia 19

3. Northeast Georgia 20

4. Southwest Georgia 21

5. Southeast Georgia 22

TABLES

Table 1. Gaging stations in the Statewide urban study, by city, 1994 24

2. National Weather Service long-term rainfall stations used in Statewide urban study, 1994 5

3. National Weather Service daily pan- evaporation stations used in Statewide urban study, 1994 5

4. Infiltration, soil-moisture accounting, and surface-runoff routing parameters for the U.S. Geological 

Survey rainfall-runoff model (RRM) 6

5. Optimized rainfall-runoff model parameter values for each study site, by city 8

6. U.S. Geological Survey rainfall-runoff model (RRM) split-sample test results for peak discharges for 

six selected sites 10

7. Flood-frequency data from long-term synthesis for Albany, Athens, Atlanta, Augusta, Columbus, 

Moultrie, Rome, Savannah, Thomasville, and Valdosta stations 11

9. Regional flood-frequency relations for rural streams in Georgia 13

8. Basin characteristics for Statewide urban study sites and estimated peak discharges for equivalent rural 

basins 14

10. Estimated effective record lengths for 2- to 500-year recurrence intervals 16

11. Regional flood-frequency equations for urban streams in Georgia 17

12. Sensitivity of computed peak discharges to errors in independent variables in the 2-, 25-, and 100-year 

estimating equations 18
v

CONVERSION FACTORS AND VERTICAL DATUM

CONVERSION FACTORS

Length

Multiply by To obtain

inch (in.) 25.4 millimeter

foot (ft) 0.3048 meter

mile (mi) 1.609 kilometer

Area

square mile (mi2) 2.590 square kilometer

Flow

cubic foot per second (ft3/s) 0.02832 cubic meter per second

VERTICAL DATUM

Sea Level: In this report, “sea level” refers to the National Geodetic Vertical Datum of 1929 (NGVD of 1929) - a 

geodetic datum derived from a general adjustment of the first-order level nets of both the United States and Canada, 

formerly called “Sea Level Datum of 1929.”

1

FLOOD-FREQUENCY RELATIONS

FOR URBAN STREAMS

IN GEORGIA—1994 UPDATE

By Ernest J. Inman

ABSTRACT

A statewide study of flood magnitude and frequency 

in urban areas of Georgia was made to develop methods 

of estimating flood characteristics at ungaged urban 

sites. A knowledge of the magnitude and frequency of 

floods is needed for the design of highway drainage 

structures, establishing flood-insurance rates, and other 

uses by urban planners and engineers.

A U.S. Geological Survey rainfall-runoff model was 

calibrated for 65 urban drainage basins ranging in size 

from 0.04 to 19.1 square miles in 10 urban areas of 

Georgia. Rainfall-runoff data were collected for a 

period of 5 to 7 years at each station beginning in 1973 

in Metropolitan Atlanta and ending in 1993 in 

Thomasville, Ga. Calibrated models were used to 

synthesize long-term annual flood peak discharges for 

these basins from existing long-term rainfall records. 

The 2- to 500-year flood-frequency estimates were 

developed for each basin by fitting a Pearson Type III 

frequency distribution curve to the logarithms of these 

annual peak discharges.

Multiple-regression analyses were used to define 

relations between the station flood-frequency data and 

several physical basin characteristics, of which drainage 

area and total impervious area were the most 

statistically significant. Using these regression 

equations and basin characteristics, the magnitude and 

frequency of floods at ungaged urban basins can be 

estimated throughout Georgia.

INTRODUCTION

A knowledge of flood characteristics of streams is 

essential for the design of roadway drainage structures, 

establishing flood-insurance rates, and for other uses by 

urban planners and engineers. Because urbanization can 

produce significant changes in the flood-frequency 

characteristics of streams, natural (rural) basin floodfrequency relations are not applicable to urban streams. 

Beginning in 1973 in Metropolitan Atlanta (Inman, 

1983) and ending in 1986 in Athens, Augusta, 

Columbus, Rome, and Savannah (Inman, 1988), 

rainfall-runoff data were collected at 45 stations. These 

data were used to calibrate a model to produce a report 

for statewide use.

Recognizing the need for additional reliable urban 

peak-flood data and improved equations for estimating 

floods, the U.S. Geological Survey (USGS), in cooperation with the city governments of Albany, Moultrie, 

Thomasville, and Valdosta, Ga., initiated floodfrequency studies in 1986 to supplement data from 

earlier studies (Inman, 1983) and to update floodfrequency relations (Inman, 1988). The earlier studies 

(Inman, 1983, 1988) were conducted under cooperative 

agreements with between the USGS and the Georgia 

Department of Transportation, the Consolidated 

Government of Columbus, Ga., and DeKalb County, 

Ga.

Purpose and Scope

This report describes the results of a study to 

develop regression equations for estimating the 

magnitude and frequency of floods for urban streams 

Statewide. Recognizing the need for additional 

observed urban data in south Georgia, 20 basins were 

selected in four urban areas in south Georgia to 

supplement data from 45 basins used in earlier studies 

(table 1, in back of this report). Two basins were 

selected in Albany, four in Moultrie, six in Thomasville, 

and eight in Valdosta. Data from at least 40 floods per 

basin were used to calibrate a USGS rainfall-runoff 

model (RRM), as described by Bergmann, Inman, and 

Lumb (U.S. Geological Survey, written commun., 

1990).

After the RRM was successfully calibrated for each 

drainage basin, long-term rainfall data from a nearby 

National Weather Service (NWS) station were used to 

synthesize about 60 to 90 years of annual peaks 

depending on the length of the long-term rainfall. These 

synthesized peaks were used to develop flood-frequency 

relations at each basin. Forty-five additional floodfrequency relations from the earlier urban studies were 

also used in the Statewide analysis. The final step in 

analyzing these data was to develop regression 

equations that can be used to estimate the magnitude 

and frequency of floods at ungaged urban sites 

throughout the State.
2

Previous Studies

Many urban flood-frequency studies have been 

undertaken in the State of Georgia and Metropolitan 

Atlanta, however, none have been based on the number 

of stations and the amount of observed data contained in 

this report. A few of the more prominent ones are listed 

in this section.

Lumb (1975), in his report, “UROS4: Urban Flood 

Simulation Model, Part 1, Documentation and Users 

Manual”, explained how the UROS4 model was used to 

simulate an annual series of flood peaks and perform a 

flood-frequency analysis at a selected point. James and 

Lumb (1975) applied the UROS4 model to eight 

watersheds in DeKalb County, Ga., with limited 

observed data for verification.

Golden (1977) presented flood-frequency relations 

for urban streams in Metropolitan Atlanta based on the 

technique used by Sauer (1974) for Oklahoma, which 

used the natural flood-frequency and rainfall-frequency 

characteristics of the local area. Sauer (1974) adjusted 

natural flood-frequency relations to urban conditions by 

using local rainfall-frequency characteristics, the 

percentage of impervious area in the basin, and the 

percentage of the basin served by storm sewers. Price 

(1979) used the same technique on a Statewide basis. 

Jones (1978) presented simplified equations that can be 

used on small watersheds (less than 200 acres) to 

estimate peak discharges in DeKalb County.

 Lichty and Liscum (1978) described a procedure for 

computing estimates of 2- through 100-year floods that 

incorporates a rainfall information-transfer mechanism 

in the form of three maps, and a generalized definition 

of synthetic T-year flood potential as a function of fitted 

rainfall-runoff model parameters. Impervious area was 

incorporated in the T-year flood equations to account 

for urban development. This procedure is applicable for 

most of the Eastern United States.

An updated method for estimating the magnitude 

and frequency of floods on small streams in the Atlanta 

Metropolitan area was presented by Inman (1983). This 

method was based on observed peak-discharge data 

from 19 stations, which were used to calibrate a USGS 

rainfall-runoff model (Dawdy and others, 1972). The 

model was then used to synthesize long-term annual 

peak discharges for these 19 basins. The 2- to 100-year 

flood estimates were developed for the 19 basins from 

these synthetic, long-term peak discharges by fitting a 

Pearson Type III frequency distribution curve to the 

logarithms of the annual peak discharges. Multipleregression analyses were used to define relations 

between the flood-frequency station data and certain 

physical characteristics of the basin, of which drainage 

area, main-channel slope, and measured total 

impervious area were found to be statistically 

significant. These relations were used to estimate the 

magnitude and frequency of floods at ungaged basins in 

the Atlanta Metropolitan area.

Regression equations and several other methods of 

estimating flood-frequency for urban watersheds on a 

nationwide basis were presented by Sauer and others 

(1983). Five basins from the Atlanta area were used in 

that analysis.

A method for estimating the magnitude and 

frequency of floods for urban streams on a statewide 

basis for Georgia was presented by Inman (1988). This 

method was based on observed data from 45 stations, 

which were used to calibrate a USGS rainfall-runoff 

model (Dawdy and others, 1972). The model was then 

used to synthesize long-term peak discharges for these 

basins. The 2- to 100-year peak discharge estimates 

were developed for each basin from these synthetic, 

long-term annual peak discharge records and by fitting a 

Pearson Type III frequency distribution curve to the 

logarithms of the annual peak discharges. Multipleregression analyses were used to define relations 

between the flood-frequency station data and certain 

physical characteristics of the basin, of which drainage 

area, equivalent rural discharge, and measured total 

impervious area were found to be statistically 

significant. These relations were used to estimate the 

magnitude and frequency of floods at ungaged basins in 

urban areas on a statewide basis for Georgia.

Acknowledgments

The author wishes to acknowledge Thomas N. Debo, 

Georgia Institute of Technology, City Planning 

Department, Atlanta, Ga., for his assistance in the 

selection of sites and in the determination of impervious 

area for basins in this study. Long-term rainfall and 

daily pan-evaporation records were obtained from the 

U.S. Department of Commerce, National Weather 

Service (NWS), Asheville, N.C.

SITE SELECTION

Extensive field reconnaissance was conducted at 

about 300 sites and 65 basins were selected for this 

study. A broad range in drainage area, main-channel 

slope, and main-channel length was considered. 

Suitability for rain-gage location, hydraulic 

characteristics at the gaging site, absence of significant 

permanent surface storage, and land use also were 

factors involved in the selection process. One of the 

most important factors considered was land-use 

stability. Thomas N. Debo, Georgia Institute of 

Technology, City Planning Department, Atlanta, Ga., 

consulted with all city and county planners in the 

metropolitan areas involved in this study, and based on 

their data and general knowledge of the areas, 

determined the stability of developed areas. This 

information was presented on color-coded city and 

county maps as being either stable, fairly stable, or 

unstable.
3

The next step in this study was a field 

reconnaissance of selected basins in areas designated as 

stable and fairly stable. Many of these basins were 

excluded because their hydraulic characteristics were 

not suitable for indirect computations of peak discharge 

or because they contained no suitable location for a rain 

gage. The remaining basins were roughly delineated on 

USGS 7 1/2-minute topographic maps, and approximate 

drainage areas, main-channel slopes, and lengths were 

determined. From this information, about 100 sites 

were selected to provide broad ranges in drainage area, 

main-channel slope, and main-channel length.

Sixty-five urban basins were selected for study from 

about 100 deemed suitable. These generally were the 

basins with the best hydraulic characteristics for indirect 

computations of peak discharge and the most suitable 

rain-gage locations. The selected basins provide suitable 

distributions of drainage area, main-channel slope, and 

main-channel length. The locations of cities with gages 

and the number of gages in each city are shown in figure 

1. Further information on the rural hydrologic regions 

shown in figure 1 can be obtained from Stamey and 

Hess (1993).

DATA COLLECTION

AND PROCESSING

Digital recorders were used to collect stage and 

rainfall at 5-minute intervals in each basin. The 

recording stage gage for most basins was housed on top 

of an 18-in. vertical corrugated metal-pipe stilling well 

in the upstream approach section. Each stilling well had 

two 2-in. intakes near the base and 1/2-in. diameter 

holes drilled about every 6 in. above ground level to 

flood stage. Several of the stage gages also were housed 

on top of 3-in. galvanized pipe attached to the end of an 

upstream wing wall. All stilling wells were flushed 

after every flood event and intakes were cleaned during 

every inspection trip.

Each site had at least one rain gage, generally 

located near the stage gage. Rain gage recorders were 

housed on top of 8-ft collector wells made from 3-in. 

galvanized pipe. Collector wells of this size will hold 

about 11 in. of rainfall. A drain plug near the bottom of 

the collector well was used to drain the well during each 

inspection trip.

Crest-stage gages also were installed at each site, 

with at least one in the upstream approach section and 

one at the downstream end of the culvert. The fall in 

water surface elevation through the culverts obtained 

from these crest-stage gage relations and the culvert 

geometry were used to compute a theoretical stagedischarge relation described by Bodhaine (1968). All 

theoretical stage-discharge relations were verified by 

current-meter measurements. 

The crest-stage relations also served other purposes. 

A plot of upstream crest-gage stage and downstream 

crest-gage stage was established for each site. These 

relations should remain fairly site-consistent or the 

reason for the inconsistency must be determined. These 

plots were used primarily on culverts having backwater 

control. For example, an accumulation of debris at a 

culvert entrance which could produce excessive fall, or 

a blockage downstream that would greatly reduce 

normal fall, could be detected from these crest-stage 

relations. For culverts with inlet or outlet control, the 

crest-stage relations are not consistent, but for large 

blockages, some indication of the problem might be 

evident. Many times city and county highway 

maintenance crews would remove debris from culverts 

between gage servicing trips. When this occurred, 

outliers from the crest-stage relations were the only 

evidence of blockage. Records of storm events that 

were influenced by blockages were not used in model 

calibration. At most sites, the stage at the recording gage 

was lower than the stage at the upstream crest-stage 

gage. This probably was caused by drawdown of the 

intakes rather than by intake lag, as can be demonstrated 

by the equation in Buchanan and Somers (1968, p. 13).

A relation between upstream crest-gage stage and 

recorder stage was established to enable plotting of the 

theoretical discharge computations, as described above, 

and the recorder stage. Thus, digital tapes could be 

processed without having to make a shift correction for 

each tape. The upstream crest-gage stage and the 

recorder stage relation also would indicate any problem 

with the stage hydrograph, such as a hanging float, a 

float tape that jumped the splines, or intakes clogged 

with sediment.

Current Data

All flood events with complete rain and stage data 

and without culvert blockages were processed and 

loaded into USGS computer storage on a near-current 

basis. Generally, five to eight storm events were 

processed annually for each site. Unit-rainfall, unitdischarge, and daily rainfall data were then retrieved 

and the unit data were plotted against time. The unitdata hydrographs were used to (1) visually edit data, 

allowing a bad punch by the recorder or a misread 

punch by the electronic-tape transmitter to be detected 

easily; (2) detect partially clogged rain-gage intakes or 

hanging floats; (3) serve as the basis for estimating the 

rising limb of a storm hydrograph if the stilling well 

intakes were out of the water at the beginning of a rise; 

(4) estimate the falling limb in the event that the intakes 

became partially clogged with sediment on the 

recession; and (5) estimate the routing parameters in the 

RRM. After editing and estimations were completed, 

the data were reloaded into USGS computer storage.
4

Figure 1. Rural flood-frequency regions in Georgia, cities where gaging stations are used in this study, 

and number of gages in each city.
5

Daily pan-evaporation data are needed to calibrate 

the RRM. Such data were available for Athens and 

Savannah from nearby NWS stations. However, there 

were no NWS evaporation stations available near 

Albany, Atlanta, Augusta, Columbus, Moultrie, 

Thomasville and Valdosta. Evaporation maps presented 

by Kohler and others (1959) were used as a guide to 

select the appropriate NWS evaporation station.

The University of Georgia Plant Science farm 

evaporation station data at Watkinsville were used to 

calibrate sites in Atlanta, Augusta, and Athens. Data 

from the Calhoun Experiment station were used for 

Rome, the Byron Experiment station for Columbus, and 

the Savannah Airport station for Savannah. Data from a 

NWS station near Tifton was used to calibrate sites in 

Albany, Moultrie, Thomasville, and Valdosta. 

Long-Term Rainfall and

Daily Pan-Evaporation Data

Long-term rainfall and daily pan-evaporation data 

are required for flood-peak simulation, as described 

later in this report. Daily rainfall records from six NWS 

stations were obtained from NWS (1948-87) 

publications and loaded into USGS computer storage 

(table 2). About four to eight rainfall events per year 

were selected based on hydrologic judgement and by 

scanning the daily rainfall totals. The dates of 

significant rainstorms since 1948 were obtained from 

hourly data in NWS publications (1948-87). For periods 

before 1948, the daily charts were obtained from the 

NWS for all daily rainfall events of 1-in. or more per 

day. The selected storm-rainfall data were coded at 5-

minute intervals and loaded into USGS computer 

storage.

Table 2.—National Weather Service long-term rainfall 

stations used in Statewide urban study, 1994

[station number is based on latitude and longitude]

Station name Period of record

(in water years)1/ Station number

 Atlanta 1898-1981 333900084260050

 Augusta 1902-1973 332200081580050

 Chattanooga 1901-1973 350200085120001

 Macon 1900-1973 324200083390050

 Savannah 1898-1987 320800081120050

 Thomasville-Coolidge 1906-1933

1941-1973

304800083540050

1/Water year is the 12-month period beginning October 1 and ending 

September 30, and is designated in the calendar year in which it 

ends.

Daily pan-evaporation data were obtained from 

NWS publications for the five stations either at or near 

the cities where the rainfall-runoff data were collected 

(table 3). The observed record from each evaporation 

station was used to synthesize harmonic average 

evaporation data for periods prior to the observed record 

by use of USGS computer program H266 (Carrigan and 

others, 1977). 

Table 3.—National Weather Service daily panevaporation stations used in Statewide urban study, 1994

[station number is based on latitude and longitude]

 Location 

 (near)

Synthetic 

period of 

record

(in water 

years)1/

1/Water year is the 12-month period beginning October 1 and ending 

September 30, and is designated in the calendar year in which it ends.

Observed 

period of 

record

(in water 

years)1/

Station number

Ailey 1898-1946 1947-1981 321100082340050

Athens 1898-1939 1940-1992 335500083210050

Experiment 1897-1935 1936-1981 331600084170050

Rome 1898-1944 1945-1986 342100085100050

Tifton 1898-1936 1937-1993 312800083310050

FLOOD-FREQUENCY RELATIONS

Several phases of data analysis are required to 

develop and test equations used to estimate peak 

discharges for various recurrence intervals. The first 

step is to calibrate and verify the RRM. The second step 

is to analyze the frequency characteristics of peakdischarge simulations from the RRM. Next, regression 

analyses are performed to relate flood-frequency 

estimates to basin characteristics. The final phase of the 

data analysis is statistical testing of the derived 

regression equations. 

Description of Rainfall-Runoff Model

Program RRM, a lumped-parameter rainfall-runoff 

model, is described in detail by Bergmann, Inman, and 

Lumb (USGS, written commun., 1990). The original 

version of the rainfall-runoff model was described in 

detail by Dawdy and others (1972). Revisions to the 

original computer code were presented by Carrigan 

(1973). The model has three basic components: soilmoisture accounting, infiltration, and surface-runoff 

routing. Provisions for accounting for nonpervious 

areas were included in the code. Eleven parameters are 

used in the three basic components, and are listed and 

defined in table 4.
6

Table 4.— Infiltration, soil-moisture accounting, and surface-runoff routing parameters for the U.S. Geological 

Survey rainfall-runoff model (RRM)

[--, dimensionless parameter; RRM from Bergman, Inman, and Lumb (U.S. Geological Survey, written commun., 

1990)]

Parameter 

identifier code

Units Infiltration, soil-moisture accounting, and surface-runoff routing parameters

 PSP inches combined effects of soil-moisture content and suction at the wetting front for 

soil moisture at field capacity

 RGF -- ratio of PSP for soil moisture at wilting point to that at field capacity

 KSAT inches per hour minimum saturated value of hydraulic conductivity used to determine soilinfiltration rates

 TIA -- ratio of total impervious area to total basin area

 BMSM inches soil moisture-storage volume at field capacity

 EVC -- coefficient to convert pan evaporation to potential evapotranspiration values

 DRN inches per hour constant drainage rate for redistribution of soil moisture

 RR -- proportion of daily rainfall that infiltrates the soil

 KSW hours time characteristic for linear reservoir storage

 TC minutes time base of the triangular translation hydrograph

 TP/TC -- ratio of time to peak to base length of the triangular translation hydrograph

The soil-moisture accounting component determines 

the effect of antecedent conditions on infiltration, and is 

based on daily rainfall and evaporation. Four model 

parameters (BMSM, EVC, DRN, and RR), as described 

in table 4, are used in simulating continuous antecedent 

soil moisture.

The infiltration component of the model uses unit 

rainfall data, and the output from the soil-moisture 

accounting component that indicates the soil moisture 

content at the beginning of the storm rainfall, to 

compute infiltration losses. Four parameters (PSP, RGF, 

KSAT, and TIA), as described in table 4, are used with 

the modified Philip (1954) infiltration equation.

The surface runoff or routing component 

(parameters KSW, TC, and TP/TC (table 4)) is based on 

a modification of the Clark (1945) form of the 

instantaneous unit-hydrograph procedure. The routing 

component was modified, as described by Carrigan 

(1973), to incorporate a triangularly shaped translation 

hydrograph as an internal feature of the computer 

program rather than as an externally developed timearea histogram. This modification simplified the 

calibration procedure and allows separation of 

compound peaks, a feature that provides the model user 

with more events to use in calibration. Mitchell (1972) 

described the triangular representation of the translation 

hydrograph as a sufficiently accurate assumption for 

most drainage areas.

Calibration

An average of about 45 flood events per station was 

initially available for model calibration. The data to be 

used to fit model parameters were reviewed before 

beginning the calibration process. The review was 

made to identify gross violations of assumptions 

implicit in the RRM. The most evident assumption 

violation is that rainfall is uniform over the basin during 

periods of runoff simulation. The uniform rainfall 

assumption is almost never met by nature; therefore, an 

“averaging effect” is assumed to apply to the parameter 

fitting process. The error from the averaging 

consideration is likely to be within the range of model 

and data errors if there are a sufficient number (more 

than 30) of flood-events in the data set. Events that 

indicate a gross discrepancy between observed rainfall 

and runoff (such as total rainfall less than total runoff) 

were discarded because these events can influence the 

fitting of parameters, at best, and did not help in the 

fitting process. A secondary rain gage near or in the 

basin being modeled was used to verify the uniformity 

of rainfalls. Ideally, discarding events based on 

nonrepresentative rainfall should be done one time, 

prior to any parameter fitting, to avoid a tendency of 

merely trying to reduce errors that are inherent in the 

modeling process.
7

Defining events and sub-events are part of the initial 

data-review effort. The beginning and ending time as 

well as the initial base flow must be determined for each 

event and sub-event. Usually, there is little question as 

to where to begin an event or its value of base flow 

when no sub-event is involved; however, determining 

base flows when sub-events are involved is far more 

difficult. It was decided not to sub-divide an event with 

regard to runoff volume when the base flow is highly 

questionable although the sub-event could be defined 

for peak-flow simulation without great error from a 

questionable base-flow value. The ending time of an 

event or sub-event is much more subjective. RRM does 

not include any secondary flow component and base 

flow is assumed constant; therefore, attempts are made 

to balance the influence of increased base flow against 

some remaining surface flow when selecting the time to 

end an event. After determining rainfall uniformity and 

defining events and sub-events, the next step in 

calibration is to determine starting values and limits on 

the parameters listed in table 4. 

A range for KSAT (table 4) of 0.05 to 0.40 was 

obtained from Chow (1964). A starting value of 0.15 

was used for KSAT. The range and starting values of 

the other soil-moisture accounting and infiltration 

parameters RR, BMSM, RGF, and PSP (table 4) are 

obtained from Inman (1988). EVC (table 4) is obtained 

from NWS Technical Paper 37 (Kohler and others, 

1959). TIA (table 4) is determined by a grid-overlay 

method from aerial photography (U.S. Department of 

Agriculture, Agricultural Stabilization and Conservation 

Service, 1992). (See TIA discussion in “Regression 

Analysis” section in this report). A lower limit of -25 

percent of the computed value and an upper limit of +15 

percent of the computed value are applied as limits to 

this parameter.

A sensitivity analysis of all parameters in the RRM 

was done for three basins in region 2 of Georgia for the 

small streams rural study (Golden and Price, 1976). 

Although DRN (table 4) is included in RRM as a 

parameter, model results are insensitive even to large 

changes in DRN. For all cities in this Statewide study, a 

value of 1.00 is used and held constant for DRN, as used 

by Alley and Smith (1982).

The starting values for the routing parameters KSW 

and TC (table 4) are obtained from plots of the 

discharge hydrographs and the rainfall hyetographs. 

Initial estimates of KSW and TC can be made 

completely from rainfall-runoff plots. KSW is the 

difference in the time of the discharge at the inflection 

point divided by three, and the time of the discharge at 

the inflection point. TC is estimated from flood events 

with intense short-duration rainfall and is defined as the 

time, in minutes, from the end of rainfall excess to the 

inflection point. The ratio TP/TC (table 4), was fixed at 

0.50, as suggested by Mitchell (1972).

After determining starting values and limits for 

parameters, RRM calibration can be started. Model 

calibration is the process of determining a set of 

parameter values that can produce RRM simulations 

which best duplicate observed events. Further 

information on RRM calibration may be obtained from 

Bergmann, Inman, and Lumb (USGS, written commun., 

1990). Because there is no provision in the RRM to 

account for storage, the routing parameters must be 

optimized and adjusted manually to reproduce the 

observed peak discharges. The higher peak discharges 

are given much more emphasis than lower peaks on the 

final runs of this phase of optimization because the 

calibrated RRM models are used to simulate relatively 

large events (annual peak discharges). The final 

optimized parameter values for the models are listed in 

table 5. The 45 stations from the earlier Statewide study 

by Inman (1988), are recalibrated using RRM and the 

new parameter values are also listed in table 5. 

Observed versus simulated plots of the final optimized 

runs of volumes and peak discharges are plotted and the 

slopes of the best-fit line are between 0.95 and 1.05; 

thereby assuring that no bias exists. 

The RRM computer program uses input from only 

one rain gage in each basin. Eight of the larger basins 

(greater than 3 square miles) in the Atlanta area, that 

were included in this study, had two or more rain gages. 

The daily rainfall from the additional gages is combined 

into one daily record by applying coefficients, as 

suggested by Thiessen (1911), to each rain-gage record. 

Unit rainfall was combined into one record by a method 

described by Inman (1988). Thiessen (1911) 

coefficients are determined for each rain-gage record, 

and a total Thiessen weighted rainfall for the resulting 

flood is computed. A ratio of the Thiessen weighted 

total rainfall to the total rainfall at the gage having the 

largest Thiessen coefficient, is multiplied by each 5-

minute increment of rainfall at the gage having the 

largest Thiessen weight to provide one record of 

weighted-unit rainfall. This method of combining unit 

rainfall is used to maintain the integrity of the individual 

increments. Weighting unit rainfall in the same manner 

as daily rainfall tends to have a smoothing effect on the 

incremental rainfall; and therefore, is not used. 

Verification

Verification is the procedure where estimates of the 

dependent variables computed by the calibrated RRM 

are compared to observed data different than the 

observed data used for calibration. The RRM 

parameters are considered acceptable (verified) if the 

mean square error obtained during the verification 

process falls within preselected acceptable values. The 

use of part of the data from a basin for calibration, and a 

different part for verification, is referred to as splitsample testing and is the primary basis to assess the 

accuracy of the RRM for purposes of prediction. 
8

Table 5.--Optimized rainfall-runoff model parameter values for each study site, by city

[RRM, rainfall-runoff model; parameters are defined in table 4; parameters DRN and TP/TC are assigned fixed values of 1.00 

and 0.50, respectively, for all stations and not optimized; parameter EVC is assigned a fixed value of 0.77 for the Savannah

area, and 0.75 for all other areas and not optimized; SE, standard error of estimate of calibration results, based on the meansquare difference of logs of observed and synthesized peaks]

Station

number

RRM infiltration, soil-moisture accounting, and surface-runoff routing parameters SE,

in

PSP KSAT RGF BMSM RR KSW TC TIA percent

Albany

02352605 2.75 0.175 29.6 5.85 0.880 1.80 82.0 28.8 28.4

02352964 2.99 .241 13.0 11.7 .700 1.50 100 11.0 35.5

Athens

02217505 1.53 .150 28.2 2.29 .947 .678 63 40.2 20.6

02217506 2.51 .146 37.8 3.27 .950 .330 22 31.0 29.7

02217730 2.45 .125 26.2 2.20 .950 .780 26 35.6 22.4

02217750 .71 .1435 10.2 3.28 .948 .500 35 38.1 22.5

02217905 2.99 .173 22.8 3.94 .90 .22 15 61.6 23.2

02217990 .70 .091 37.7 3.01 .950 .600 36 33.7 22.0

Atlanta

02203820 1.01 .143 23.1 7.70 .918 2.00 210 30.5 29.7

02203835 1.95 .121 12.8 3.20 .831 1.00 90 25.6 26.7

02203845 1.47 .139 10.0 2.83 .948 .45 70 30.6 26.1

02203850 1.32 .115 10.0 6.85 .900 1.44 193 28.2 19.3

02203870 2.49 .108 7.9 2.41 .950 1.35 120 25.8 26.0

02203884 1.13 .165 10.0 5.00 .900 .74 116 26.7 29.3

02336080 .65 .114 32.5 2.57 .942 3.50 475 31.4 29.1

02336090 .63 .180 28.0 6.30 .903 .45 45 19.0 36.9

02336102 .96 .103 47.3 2.24 .958 .64 156 27.2 24.4

02336150 .90 .100 14.4 2.06 .950 1.85 200 24.1 26.2

02336180 1.02 .081 18.4 5.69 .940 2.92 540 25.9 25.9

02336200 1.26 .137 7.3 6.75 .922 0.64 48 32.3 23.5

02336238 1.83 .117 31.4 11.30 .930 0.45 35 33.6 26.0

02336325 .81 .112 25.0 3.56 .903 1.10 56 42.0 21.2

02336690 .93 .104 21.2 4.11 .949 .78 42 20.3 26.3

02336697 .97 .086 36.3 4.46 .947 .50 30 19.1 26.2

02336700 2.23 .114 10.3 6.43 .949 .80 40 28.3 28.1

02336705 .97 .098 23.6 5.92 .926 1.15 130 29.5 28.3

02337081 1.16 .210 15.3 5.74 .915 .60 35 28.6 23.7

Augusta

02196570 1.52 .097 40.0 2.09 .944 .92 51 19.9 35.5

02196605 1.69 .198 24.6 2.26 .736 .40 24.5 28.1 22.9

02196725 1.18 .195 34.2 4.39 .851 2.30 110.0 42.4 28.5

02196730 1.78 .245 22.3 2.27 .925 3.10 190.0 33.4 25.9

02196760 1.35 .250 39.9 3.60 .806 .65 74.2 23.0 27.9

02196850 1.17 .080 11.2 4.96 .752 .32 10.0 28.5 27.6
9

Columbus

02341542 2.00 0.150 8.3 3.33 0.800 4.20 150 0.98 37.8

02341544 1.18 .140 38.9 6.95 .950 .83 30 17.6 24.2

02341546 1.20 .200 10.0 5.90 .900 1.00 75 16.0 30.0

02341548 1.57 .214 10.0 5.02 .950 .95 92 16.8 28.8

Moultrie

02318565 2.34 .310 32.0 14.3 .833 1.03 54 23.2 20.3

02327202 1.02 .066 21.3 3.28 .882 .91 68 33.9 25.4

02327203 2.01 .193 12.7 5.20 .892 .78 49 21.0 28.1

02327204 .71 .089 33.8 3.97 .784 1.33 79 22.3 20.9

Rome

02395990 .981 .167 26.7 5.31 .950 .90 50 16.6 27.5

02396290 2.514 .206 39.6 4.52 .825 1.20 80 6.6 30.4

02396510 .23 .071 36.0 15.0 .911 .55 45 17.4 32.5

02396515 1.16 .125 38.6 4.60 .946 .85 125 18.3 26.6

02396550 1.44 .071 26.6 4.60 .911 .40 11 18.8 27.2

02396680 .58 .074 23.4 5.74 .947 1.50 100 22.1 27.6

Savannah

02203541 2.19 .266 18.0 7.18 .702 .90 69 59.5 23.6

02203542 .80 .070 27.0 2.90 .913 4.00 250 19.5 30.0

02203543 2.87 .202 25.9 8.83 .936 2.25 150 29.7 24.4

02203544 1.04 .155 24.2 7.80 .945 .85 65 25.9 28.3

Thomasville

02326182 1.15 .356 9.39 3.01 .834 .14 13 16.4 22.1

02327467 .87 .119 39.5 3.87 .816 1.50 110 20.9 29.3

02327468 1.28 .107 30.6 8.23 .83 1.84 113 25.6 27.4

02327471 3.00 .444 39.8 14.9 .862 .37 22 42.4 22.3

02327473 1.53 .165 31.0 5.16 .890 .70 35 26.6 33.1

02327474 1.76 .112 32.5 7.03 .744 .43 33 6.1 33.4

Valdosta

02317564 2.17 .114 37.9 4.79 .818 2.85 155 22.3 21.9

02317566 .84 .121 40.0 3.98 .943 5.51 337 20.4 28.7

023177551 1.18 .070 39.5 4.72 .827 1.00 75 20.7 33.7

023177553 .91 .066 23.7 5.20 .916 1.40 73 28.7 25.8

023177554 1.50 .168 39.8 4.13 .793 1.25 65 29.8 28.2

023177556 .90 .110 25.6 3.90 .936 .42 35 11.8 20.4

023177557 1.35 .162 16.0 6.82 .949 1.24 75 27.1 21.2

023177558 2.87 .225 22.3 9.73 .743 1.00 40 34.4 26.9

Table 5.--Optimized rainfall-runoff model parameter values for each study site, by city

[RRM, rainfall-runoff model; parameters are defined in table 4; parameters DRN and TP/TC are assigned fixed values of 1.00 

and 0.50, respectively, for all stations and not optimized; parameter EVC is assigned a fixed value of 0.77 for the Savannah

area, and 0.75 for all other areas and not optimized; SE, standard error of estimate of calibration results, based on the meansquare difference of logs of observed and synthesized peaks]

Station

number

RRM infiltration, soil-moisture accounting, and surface-runoff routing parameters SE,

in

PSP KSAT RGF BMSM RR KSW TC TIA percent
10

The RRM was verified at six of the Atlanta area sites 

by split-sample testing. Flood events at each site were 

divided into two samples. The flood events were 

arranged in descending order according to peak 

magnitude. The odd-numbered events made up the first 

sample and the even-numbered events the second 

sample. RRM was recalibrated using only the flood 

events in the first sample. The computed peak 

discharges for the second sample were compared with 

the observed data, and the standard error of estimate was 

computed. The results (table 6) were considered to be 

acceptable (within about 30 percent) and additional 

split-sample testing was not necessary.

Table 6.—U.S. Geological Survey rainfall-runoff 

model (RRM) split-sample test results for peak 

discharges for six selected sites

[RRM from Bergman, Inman, and Lumb, U.S. 

Geological Survey, written commun., 1990]

Station

number

Standard error of estimate of calibration and 

verification results for peak discharges (in percent)

Calibration

(all events)

Calibration

(odd-numbered

events)

Verification

(even numbered

events)

02336080 ± 25 ± 27 ± 27

02336238 ± 26 ± 25 ± 27

02336325 ± 26 ± 30 ± 26

02336690 ± 23 ± 25 ± 22

02336697 ± 33 ± 21 ± 34

02336705 ± 22 ± 22 ± 30

Flood-Frequency Analysis

The calibrated RRM is run with NWS long-term 

precipitation (table 2) and evaporation data (table 3) to 

simulate annual peaks for each of the 65 stations used in 

the study. Because Atlanta, Augusta, Savannah, and 

Thomasville have long-term rainfall stations located in 

or near each city, the long-term rainfall data are used 

directly with the nearby rainfall-runoff sites. Macon is 

the only NWS station close to Columbus with a long 

enough period of record to be used to simulate annual 

peaks. Thomasville-Coolidge is the only NWS station 

close to Albany, Moultrie, and Valdosta with a long 

enough period of record to be used to simulate annual 

peaks. The sites in Rome and Athens have two longterm stations, each for use in peak simulation. Rainfallfrequency isopluvial maps prepared by the NWS (1961) 

are used as a guide in selecting weighting values for 

each of the long-term stations. For Rome, a weight of 

0.60 is given to frequency curves generated from 

Atlanta long-term rainfall record, and a weight of 0.40 is 

given to frequency curves generated from Chattanooga 

long-term rainfall record. For Athens, a weight of 0.50 

was given to frequency curves generated from Atlanta 

long-term rainfall record, and a weight of 0.50 to 

frequency curves generated from Augusta long-term 

rainfall record. 

The Pearson Type III frequency distribution is fit to 

the logarithms of the annual peak discharges at each site 

in accordance with “Guidelines for Determining Flood 

Flow Frequency”, Bulletin 17B (Interagency Advisory 

Committee on Water Data (IACWD), 1982) 

recommendations. These recommendations include the 

proper handling of low and high outliers. Frequency 

curves for flood peaks simulated by the RRM represent 

an “as is” storage condition that may be present at 

upstream roadway embankments with culverts of 

limited capacity, or minor flood plain storage. 

Skew coefficients are computed directly from the 

simulated data. No attempt was made to adjust the skew 

coefficients of the frequency curves, because the data 

did not meet the criteria specified in the IACWD (1982). 

The generalized skew-coefficient map in IACWD 

(1982), used in the adjustment computations, is for rural 

watersheds; and therefore, is not applicable to the 

simulated urban flood peaks.

Twenty-one of the 65 sites had 10 or more years of 

observed record. However, no attempt was made to 

combine the observed flood-frequency data with the 

simulated flood-frequency data because: 

• the 100-year flood from 19 of the 21 stations 

is less than the 100-year flood from the 

simulated data;

• the 100-year flood from 10 of the 21 stations 

is less than the equivalent rural 100-year 

flood—estimated using equations from 

Stamey and Hess (1993); and

• at 64 of the 65 stations, the two highest 

simulated peaks occurred well before the 

observed record began.

It was therefore concluded that the period of 

observed record for the 65 stations in this study was a 

relatively dry period. Flood-frequency data from the 

log-Pearson Type III frequency analyses for selected 

recurrence intervals are shown in table 7.
11

Table 7.—Flood-frequency data from long-term synthesis for Albany, Athens, Atlanta, Augusta, Columbus, 

Moultrie, Rome, Savannah, Thomasville, and Valdosta stations

Station

number

Drainage

area 

(in square

 miles)

Peak-discharge data, in cubic feet per second, for indicated recurrence interval, in years 

2-year 5-year 10-year 25-year 50-year 100-year 200-year 500-year

Albany

02352605 0.16 38.5 58.1 71.4 88.5 101 114 127 144

02352964 .05 7.2 12.7 16.9 22.8 27.7 32.8 38.4 46.3

Athens

02217505 1.44 488 770 973 1,250 1,460 1,690 1,920 2,250

02217506 .19 79.2 130 168 223 268 317 370 446

02217730 .30 116 176 220 278 322 368 416 484

02217750 .35 180 280 349 440 509 580 653 752

02217905 .42 335 500 616 770 889 1,010 1,140 1,320

02217990 .30 152 228 283 355 410 467 527 610

Atlanta

02203820 8.67 1,310 2,050 2,560 3,210 3,700 4,190 4,670 5,320

02203835 3.43 996 1,610 2,020 2,540 2,920 3,300 3,670 4,150

02203845 .84 411 646 801 995 1,130 1,270 1,400 1,580

02203850 7.50 1,660 2,500 3,040 3,710 4,190 4,660 5,120 5,720

02203870 3.68 900 1,380 1,710 2,140 2,450 2,770 3,080 3,510

02203884 1.88 652 1,020 1,270 1,580 1,800 2,020 2,240 2,510

02336080 19.10 2,060 3,010 3,680 4,550 5,230 5,930 6,650 7,640

02336090 .32 149 243 304 376 426 473 517 572

02336102 2.19 592 932 1,170 1,460 1,680 1,900 2,120 2,420

02336150 5.29 1,120 1,640 1,990 2,410 2,720 3,030 3,330 3,730

02336180 11.00 1,210 1,800 2,200 2,720 3,110 3,510 3,910 4,460

02336200 .98 502 768 942 1,160 1,310 1,460 1,610 1,800

02336238 .92 416 675 859 1,100 1,290 1,480 1,670 1,940

02336325 1.35 532 789 957 1,170 1,320 1,480 1,630 1,830

02336690 .52 220 335 415 521 602 684 769 885

02336697 .21 106 165 207 262 305 349 395 458

02336700 .79 316 494 611 758 864 967 1,069 1,200

02336705 8.80 2,350 3,600 4,410 5,390 6,090 6,750 7,390 8,210

02337081 .88 360 583 729 909 1,040 1,160 1,280 1,420

Augusta

02196570 0.66 177 280 359 474 570 676 792 964

02196605 1.67 640 1,050 1,390 1,900 2,340 2,850 3,420 4,300

02196725 1.44 141 230 301 407 499 602 718 893

02196730 4.06 353 575 759 1,040 1,280 1,550 1,870 2,350

02196760 1.56 256 456 629 902 1,150 1,440 1,770 2290

02196850 .30 264 386 470 581 666 753 842 965
Columbus

02341542 6.54 624 1,050 1,380 1,850 2,230 2,630 3,070 3,700

02341544 1.58 483 824 1,080 1,420 1,680 1,960 2,240 2,640

02341546 .26 78.6 126 162 212 254 298 346 416

02341548 1.42 358 599 784 1,040 1,260 1,480 1,720 2,070

Moultrie

02318565 .27 79.4 120 148 184 210 237 264 300

02327202 .48 280 377 440 520 579 638 697 776

02327203 .39 180 262 318 389 444 499 555 632

02327204 1.65 668 944 1,110 1,310 1,450 1,580 1,700 1,860

Rome

02395990 .37 107 174 216 268 305 340 373 415

02396290 .62 62.5 123 172 242 301 364 432 528

02396510 .04 25.6 37.5 45.4 55.4 62.7 69.8 76.8 86.2

02396515 .29 66.6 107 135 170 197 222 249 283

02396550 .19 120 184 228 282 324 365 408 463

02396680 1.31 394 563 675 814 917 1,020 1,120 1,260

Savannah

02203541 .24 108 158 193 240 276 313 351 404

02203542 1.27 237 365 456 577 672 770 872 1,010

02203543 .95 170 284 373 498 601 711 830 1,000

02203544 .18 85.1 135 168 207 235 261 286 318

Thomasville

02326182 .12 147 210 252 308 350 392 436 495

02327467 1.07 336 489 586 702 784 863 939 1,040

02327468 2.90 790 1,190 1,440 1,750 1,980 2,190 2,400 2,670

02327471 .21 117 168 202 246 278 311 345 390

02327473 1.04 531 780 951 1,170 1,340 1,510 1,680 1,920

02327474 .12 62.6 100 125 156 178 199 219 245

Valdosta

02317564 1.27 204 318 397 498 575 653 731 836

02317566 3.81 422 644 800 1,000 1,160 1,320 1,490 1,710

023177551 .16 70.1 100 120 144 162 179 196 219

023177553 .99 456 616 720 848 942 1,030 1,130 1,250

023177554 2.66 790 1,220 1,510 1,890 2,170 2,460 2,740 3,130

023177556 .16 128 180 213 254 283 312 341 378

023177557 .55 221 314 375 451 506 560 614 686

023177558 1.18 383 575 706 875 1,000 1,130 1,260 1,440

Table 7.—Flood-frequency data from long-term synthesis for Albany, Athens, Atlanta, Augusta, Columbus, 

Moultrie, Rome, Savannah, Thomasville, and Valdosta stations

Station

number

Drainage

area 

(in square

 miles)

Peak-discharge data, in cubic feet per second, for indicated recurrence interval, in years 

2-year 5-year 10-year 25-year 50-year 100-year 200-year 500-year
13

Regional Regression Analysis

So that flood magnitude and frequency could be 

estimated for ungaged sites, the 2-, 5-, 10-, 25-, 50-, 

100-, 200-, and 500-year floods, obtained from the 65 

urban basins in the study, are related to the basin 

characteristics of their origin. This is done by the 

generalized least-squares (GLS) regression method 

(Johnston, 1972). Further information on GLS 

applications can be obtained from Stedinger and Tasker 

(1985), Tasker and others (1986), Livingston and 

Minges (1987), and Tasker and Stedinger (1989). 

GLS is used in this study because simulated rainfallrunoff records generated from the same historical 

rainfall and evaporation data are highly correlated and 

because GLS reduces the weight given to sites having 

high correlation. GLS should also be used (1) if the siteto-site variances of the streamflow characteristics are 

not similar, and (2) for stations with different record 

lengths. In this study, all flood-frequency estimates are 

based on about the same length of rainfall record, so 

little was gained by the GLS analysis from this aspect. 

Regression equations provide a mathematical 

relation between response variables (2- to 500-year 

flood peaks) and explanatory variables (basin 

characteristics). All variables are transformed into 

logarithms before analysis to insure a linear-regression 

model and to achieve equal variance about the 

regression line throughout the range of explanatory 

variables (Riggs, 1968). In the analyses performed, 95-

percent confidence limits are used to evaluate the 

statistical significance of independent variables. 

Basin characteristics used in this analysis are defined 

below and individual station data are shown in table 8. 

Drainage area (A).—Area of the basin, in square 

miles, planimetered from USGS 7 1/2-minute topographic maps. All basin boundaries were field checked.

Channel slope (S).—The main-channel slope, in feet 

per mile (ft/mi), as determined from topographic maps. 

The main channel slope was computed as the difference 

in elevation, in feet, at the 10- and 85-percent points 

divided by the length, in miles, between the two points.

Channel length (L).—The length of the main 

channel, in miles, as measured from the gaging station 

upstream along the channel to the basin divide. 

L/(S0.5).—A ratio, with L and S defined above.

Total impervious area (TIA).--The percentage of 

drainage area that is impervious to infiltration of 

rainfall. This parameter is determined from aerial 

photography (U.S. Department of Agriculture, 

Agricultural Stabilization and Conservation Service) by 

use of a grid-overlay method. According to Cochran 

(1963), a minimum of 200 points, or grid intersections, 

per area or subbasin can provide a confidence level of 

0.10. Three counts of at least 200 points per subbasin 

were obtained and the results averaged for the final 

value of total impervious area. On several of the larger 

basins in the Atlanta area, where some development 

occurred during the period of data collection, this 

parameter was determined from aerial photographs 

made near the beginning of data collection, and then 

averaged with the values obtained from aerial 

photographs made near the end of data collection.

Rural regression discharge (RQT).—The peak 

discharge, in cubic feet per second (ft3

/s), for an 

equivalent rural drainage basin in the same hydrologic 

region as the urban basin, and for recurrence interval T. 

The equivalent rural discharges were computed from 

regression equations by Stamey and Hess (1993). The 

equations for computing RQt

 are given in table 9. 

Table 9.—Regional flood-frequency relations for rural 

streams in Georgia

Flood

discharge, 

QT, for

T-year

recurrence

interval

Flood-frequency relations for indicated regions (fig. 1) in 

the form QT=aAb

, where A is the drainage area, in square 

miles, and a and b are as presented below

1 2 3 4

 RQ2 207A0.654 182A0.622 76A0.620 142A0.591

 RQ5 357A0.632 311A0.616 133A0.620 288A0.589

 RQ10 482A0.619 411A0.613 176A0.621 410A0.591

 RQ25 666A0.605 552A0.610 237A0.623 591A0.595

 RQ50 827A0.595 669A0.607 287A0.625 748A0.599

 RQ100 1,010A0.584 794A0.605 340A0.627 926A0.602

 RQ200 1.220A0.575 931A0.603 396A0.629 1,1200.606

 RQ500 1,530A0.563 1,130A0.601 474A0.632 1,4200.611

Preliminary regression analyses were performed 

using procedures defined by SAS Institute, Inc., (1989). 

The two specific SAS analyses performed were (1) 

REG-estimate parameters within confidence limits, and 

(2) GLM-plots of predicted and observed peak 

discharges, and plots of residuals as a function of the 

significant variables. Additional information on the 

methods is available from the SAS Institute, Inc., 

(1989). The preliminary regression results indicates that 

the most significant variables are drainage area, total 

impervious area, and rural regression discharge.

The preliminary results, also, indicated that the 

residuals for the Rome sites were consistently negative, 

meaning that the observed was less than the predicted. 

Therefore, a qualitative variable was created to account 

for the apparent bias in the Rome sites. This logtransformed qualitative variable (QV) is one (1) if the 

site is in Rome, and zero (0) otherwise. The preliminary 

and final equations were rewritten by adjusting the 

constant and producing a set of equations without the 

qualitative variable for Rome only.
Table 8.—Basin characteristics for Statewide urban study sites and estimated peak discharges for equivalent rural 

basins

[A, drainage area, in square miles; L, channel length, in miles; S, channel slope, in feet per mile; L/S0.5, a ratio, where L and S 

have been previously defined; TIA, area that is impervious to infiltration of rainfall, in percent; R, flood-frequency region where 

the basin is located (Stamey and Hess, 1993); RQ2~500, peak discharge, in cubic feet per second, for an equivalent rural drainage 

basin in the same hydrologic area as the urban basin, and for 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals 

using the Stamey and Hess (1993) equation]

Station

number

Basin characteristics Estimated rural peak discharges

A L S L/(S0.5) TIA R RQ2 RQ5 RQ10 RQ25 RQ50 RQ100 RQ200 RQ500

Albany

02352605 0.16 0.58 33.3 0.10 28.8 3 24 43 56 76 91 108 125 149

02352964 .05 .36 22.5 .08 11.1 3 11 20 26 35 42 49 57 68

Athens

02217505 1.44 1.89 91.6 .20 40.2 2 228 389 514 690 835 909 1,160 1,410

02217506 .19 .75 214 .05 31.0 2 65 112 148 200 244 291 342 416

02217730 .30 .70 106 .80 35.6 2 86 148 196 265 322 383 450 548

02217750 .35 .90 122 .08 38.1 2 95 163 216 291 354 421 494 601

02217905 .42 .76 158 .07 61.6 2 106 182 241 325 395 470 552 671

02217990 .30 1.04 102 .10 33.7 2 86 148 196 265 322 383 450 548

Atlanta

02203820 8.67 7.58 28.0 1.43 30.5 2 697 1,180 1,540 2,060 2,480 2,930 3,420 4,140

02203835 3.43 2.66 61.0 .34 25.6 2 392 664 875 1,170 1,410 1,670 1,960 2,370

02203845 .84 1.93 67.6 .24 30.6 2 163 279 369 496 602 715 838 1,020

02203850 7.50 5.91 34.8 1.00 28.2 2 637 1,080 1,410 1,890 2,270 2,690 3,140 3,800

02203870 3.68 3.95 37.5 .64 25.8 2 409 669 914 1,220 1,480 1,750 2,040 2,470

02203884 1.88 2.22 74.1 .26 26.7 2 269 459 605 811 981 1,160 1,360 1,650

02336080 19.0 7.43 16.0 1.86 31.4 1 1,420 2,300 2,990 3,970 4,780 5,650 6,650 8,050

02336090 .32 1.12 129 .10 19.0 1 98 174 238 334 420 520 634 806

02336102 2.19 2.50 62.8 .32 27.2 1 346 586 783 1,070 1,320 1,600 1,910 2,380

02336150 5.29 5.06 25.8 1.00 24.1 1 615 1,020 1,350 1,830 2,230 2,670 3,180 3,910

02336180 11.0 9.03 19.0 2.07 25.9 1 993 1,620 2,130 2,840 3,440 4,100 4,840 5,900

02336200 .98 1.47 94.5 .15 32.3 1 204 352 476 658 817 998 1,210 1,510

02336238 .92 1.60 106 .16 33.6 1 196 339 458 633 787 962 1,163 1,460

02336325 1.35 2.14 53.8 .29 42.0 1 252 432 580 799 989 1,200 1,450 1,810

02336690 .52 1.22 90.7 .13 20.3 1 135 236 322 448 560 689 838 1,060

02336697 .21 1.09 136 .09 19.1 1 75 133 183 259 327 406 497 635

02336700 .79 1.46 75.8 .17 28.3 1 177 308 417 577 719 880 1,070 1,340

02336705 8.80 4.95 33.7 .85 29.5 1 858 1,410 1,850 2,480 3,020 3,600 4,260 5,200

02337081 .88 1.43 86.9 .15 28.6 1 190 329 445 616 766 937 1,130 1,420

Augusta

02196570 0.66 1.67 96.0 0.17 19.9 2 140 241 319 428 520 617 725 880

02196605 1.67 1.86 117 0.17 28.1 2 250 427 563 755 913 1,080 1,270 1,540

02196725 1.44 2.67 118 0.25 42.4 3 95.0 167 22t 297 360 427 498 597

02196730 4.06 3.97 80.6 0.42 33.4 3 181 317 420 567 689 819 956 1150

02196760 1.56 2.07 111 0.20 23.0 3 100 175 232 313 379 449 524 628

02196850 0.30 1.06 239 0.07 28.5 2 86 148 196 265 322 383 450 548
15

Columbus

02341542 6.54 4.96 36.8 0.82 .98 2 585 989 1,300 1,740 2,090 2,470 2,890 3,490

02341544 1.58 2.20 69.7 .26 17.6 2 242 412 544 730 883 1,050 1,230 1,490

02341546 .26 1.06 81.8 .12 16.0 2 79 136 180 243 295 352 413 503

02341548 1.42 2.23 60.5 .29 16.8 2 226 386 510 684 828 982 1,150 1,400

Moultrie

02318565 .27 .80 60.3 .10 23.2 4 66 133 189 271 341 421 507 638

02327202 .48 1.02 45.6 .15 33.9 4 92 187 266 382 482 595 718 907

02327203 .39 .74 48.7 .11 21.0 4 80 163 231 332 419 517 623 786

02327204 1.65 2.10 25.4 .42 22.3 4 191 387 551 796 1,010 1,250 1,520 1,930

Rome

02395990 .37 .82 76.4 .08 16.6 1 108 190 260 365 458 565 689 874

02396290 .62 .97 98.6 .10 6.6 1 151 264 359 499 622 764 927 1,170

02396510 .04 .34 772 .01 17.4 1 26 49 69 99 127 161 200 260

02396515 .29 .72 257 .06 18.3 1 92 163 224 315 396 491 599 762

02396550 .19 .70 345 .04 18.8 1 70 125 172 244 308 383 470 601

02396680 1.31 2.41 55.2 .32 22.1 1 247 423 570 784 971 1,180 1,420 1,780

Savannah

02203541 .24 .84 17.5 .20 59.5 3 31 55 73 97 118 139 161 192

02203542 1.27 2.02 13.6 .55 19.5 3 88 154 204 275 333 395 460 551

02203543 .95 1.78 13.0 .49 29.7 3 74 129 170 230 278 329 383 459

02203544 .18 .51 29.9 .09 25.9 3 26 46 61 81 98 116 135 160

Thomasville

02326182 .12 .46 89.7 .05 16.4 4 41 83 117 167 210 258 310 389

02327467 1.07 1.65 31.5 .29 20.9 4 145 295 420 605 766 948 1,150 1,450

02327468 2.90 3.05 24.9 .61 25.6 4 268 542 774 1,120 1,420 1,770 2,150 2,740

02327471 .21 .60 82.2 .07 42.4 4 56 115 163 234 294 361 435 547

02327473 1.04 1.21 60.6 .16 26.6 4 145 295 420 605 766 948 1,150 1,450

02327474 .12 .60 110 .06 6.1 4 41 83 117 167 210 258 310 389

Valdosta

02317564 1.27 1.70 11.8 .49 22.3 3 88 154 204 275 333 395 460 551

02317566 3.81 3.69 9.39 1.20 20.4 3 174 305 404 545 662 787 918 1,100

023177551 .16 .74 23.4 .15 20.7 4 48 98 139 199 250 307 369 463

023177553 .99 1.52 26.3 .30 28.7 4 141 286 408 587 744 920 1,110 1,410

023177554 2.66 3.03 19.4 .69 29.8 4 253 512 731 1,060 1,340 1,670 2,030 2,580

023177556 .16 .49 40.8 .08 11.8 4 48 98 139 199 250 307 369 463

023177557 .55 .89 47.9 .13 27.1 4 100 203 288 414 523 646 780 985

023177558 1.18 1.52 41.2 .24 34.4 4 157 317 452 652 826 1,020 1,240 1,570

Table 8.—Basin characteristics for Statewide urban study sites and estimated peak discharges for equivalent rural 

basins

[A, drainage area, in square miles; L, channel length, in miles; S, channel slope, in feet per mile; L/S0.5, a ratio, where L and S 

have been previously defined; TIA, area that is impervious to infiltration of rainfall, in percent; R, flood-frequency region where 

the basin is located (Stamey and Hess, 1993); RQ2~500, peak discharge, in cubic feet per second, for an equivalent rural drainage 

basin in the same hydrologic area as the urban basin, and for 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals 

using the Stamey and Hess (1993) equation]

Station

number

Basin characteristics Estimated rural peak discharges

A L S L/(S0.5) TIA R RQ2 RQ5 RQ10 RQ25 RQ50 RQ100 RQ200 RQ500
16

 Regional Flood-Frequency

Estimating Equations

Because the synthetic annual peak series exhibit 

strong correlations between sites based on a common 

rainfall record, ordinary least-squares (OLS) regression 

analysis is not an efficient method of estimating 

regression coefficients and their standard errors. 

Stedinger and Tasker (1985) and Tasker and Stedinger 

(1989) described a generalized least-squares (GLS) 

regression method that accounts for correlation in the 

dependent variables.

Application of the GLS regression method to urban 

streams in Georgia requires regional estimates of the 

standard deviations of the synthetic series of annual 

peaks at each site; estimates of the cross correlation 

coefficients of the annual peaks at each pair of sites; and 

an estimate of the effective record length at each site. 

The standard deviation of annual peaks were estimated 

from a regional regression of sample standard 

deviations of annual peaks against RQT, DA, and TIA. 

Cross-correlations of annual peaks are based on average 

cross correlations for sites based on a common rainfall 

record. These correlations are estimated to be 0.9 for 

sites within the same city. 

For sites in different cities, the correlation between 

sites was estimated to be 0.0 except for sites in Atlanta, 

Rome, and Athens. The correlations between sites in 

Atlanta and sites in Rome are estimated as 0.45 because 

the Rome peak discharges were, in part, based on the 

Atlanta rainfall record. The correlations between sites 

in Atlanta and Athens and in Athens and Augusta are 

also estimated to be 0.45 because the Athens peak 

discharges are based on the Atlanta and Augusta rainfall 

records. Estimates of the effective record length for the 

synthetic record were computed based on methods 

described by Lichty and Liscum (1978). These 

estimated effective record lengths vary with recurrence 

interval as shown in table 10. 

Table 10.—Estimated effective record lengths for 2- 

to 500-year recurrence intervals

Recurrence interval

(in years)

Effective record lengths

(in years)

 2 5

 5 9

 10 14

 25 19

 50 21

 100 21

 200 21

 500 21

The updated regional equations for the logtransformed model parameters for each recurrence 

interval are given in table 11. Because RQT is highly 

correlated with DA and is a function of only DA, the 

updated estimating equations can be expressed as 

functions of DA and TIA only, for each region. An 

example of the originally developed equation, with A, 

TIA, RQT, and QV for Rome is, UQ10 = 1.59A0.15

TIA0.21 RQ10

0.90 QV-0.21. This equation can be 

expressed as functions of A and TIA only, by adjusting 

the constant and exponent of A to give an equation of 

the form UQ10 = 249A0.70 TIA0.21. The Rome equations 

should be used only in the immediate Rome area, 

otherwise, in region 1, use region 1 equations. The 

equations in table 11 supersede the equations in the 

previous report “Flood-Frequency Relations for Urban 

Streams in Georgia” (Inman, 1988), and the equations in 

the report “Flood-Frequency Relations for Urban 

Streams in Metropolitan Atlanta, Georgia” (Inman, 

1983).

Testing of Regression Equations

Two tests or evaluations generally are required to 

establish the soundness of regression equations. These 

tests are Bias and Sensitivity, as explained below. 

Bias

Two tests for bias are performed, one for variable 

bias and the other for geographical bias. The variablebias tests are made by plotting the residuals (difference 

between observed and predicted floods) against each of 

the independent variables for all stations. The plots are 

made during the preliminary OLS regression analysis. 

These plots were visually inspected to determine 

whether there was a consistent over-prediction or underprediction within the range of any of the independent 

variables. These plots also verified the linearity 

assumptions of the equations. On the basis of visual 

inspection of the plots, the equations are free of variable 

bias throughout the range of independent variables. 

Geographical bias is tested by determining the 

number of positive and negative residuals at sites in a 

city. Although some cities do have a majority of 

negative or positive residuals, the Wilcoxin Signed 

Ranks test, as described by Tasker (1982), when applied 

to the residuals in each of the 10 cities, indicates that the 

estimated peak discharges are not biased. 

Sensitivity

The second test analyzes the sensitivity of 2-, 25-, 

and 100-year computed discharges to errors in the two 

independent variables in the estimating equations. The 

test results (table 12) are computed by using a constant 

value for all independent variables except the one being 

tested for sensitivity. The sensitivity of the region 1 

equations is the only one tested because TIA has the 

same exponent in each region, and the exponent for A 

changes by only a small amount. 
17

Table 11.—Regional flood-frequency equations for urban streams in Georgia[UQT, peak discharge for an urban drainage basin, in cubic feet per second; A, drainage area, in square miles; TIA, area that is imperrainfall, in percent; ±, plus-minus]

UQT

recurrence

(in years)

interval

Flood-frequency

estimating

equations

(region 1)

Average standard

error of 

prediction

(in percent)

Flood-frequency

estimating

equations

(Rome)

Average

error of

standard

prediction

(in percent)

Flood-frequency

estimating

equations

(region 2)

Averagestandard

error of

prediction

(in percent)

Flood-frequency

estimating

equations

(region 3)

Average

error of

standard

prediction

(in percent)

F

2 167A0.73 T1A031 ±34 107A0.73T1A0.31 ±40 145A0.70 TIA0.31 ±35 54.6A0.69 TIA0.31 ±34 1 5 301A0.71 TIA0.26 ±31 183A0.71T1A0.26 ±36 258A0.69 TIA0.26 ±31 99.7A0.69 TIA0.26 ±31 2

10 405A0.70 TIA0.21 ±31 249A0.70T1A021 ±35 35 lA0.70 T1A0.21 ±31 164 A0.71 T1A0.21 ±32 325 527A0.70 TIA0.20 ±29 316A0.70T1A0.20 ±33 452A0.70 T1A0.20 ±29 226 A0.71 T1A0.20 ±30 450 643A0.69 TIA0.18 ±28 379A0.69 TIA0.18 ±33 548A0.70 TIA0.18 ±29 288 A0.72 TIA0.18 ±30 5

100 762A0.69 TIA0.17 ±28 440A0.69 TIA0.17 ±33 644A0.70 TIA0.17 ±29 355 A0.72 TIA0.17 ±30 7200 892A0.68 TIA0.16 ±28 505A0.68 TIA0.16 ±34 747A0.70 TIA0.16 ±28 428 A0.72 TIA0.16 ±30 8500 1063A0.68 TIA0.14 ±28 589A0.68 TIA0.14 ±34 888A0.70 TIA0.14 ±28 531 A0.72 TIA0.14 ±30 10
18

Table 12.—Sensitivity of computed peak discharges to 

errors in independent variables in the 2-, 25-, and 100-

year estimating equations

[A, drainage area, in square miles; TIA, area that is impervious 

to infiltration of rainfall, in percent]

Percent error 

in 

independent 

variable

Independent variables

Percent error in 

computed

2-yr flood

Percent error in 

computed

25-yr flood

Percent error in 

computed

100-yr flood

A TIA A TIA A TIA

+50 +34.4 +13.4 +32.8 +8.5 +32.3 +7.1

+25 +17.7 +7.1 +16.9 +4.6 +16.7 +3.8

+10 + 7.2 +3.1 +7.0 +2.0 +6.8 +1.6

 -10 -7.3 -3.2 -7.1 -2.1 -7.0 -1.8

 -25 -19.1 -8.3 -18.2 -5.6 -18.0 -4.8

 -50 -39.8 -19.3 -38.5 12.9 -38.0 -11.1

Standard Error of Prediction

One measure of how good the GLS regression 

model is for prediction is the average standard error of 

prediction (table 12) which is the error expected two 

thirds of the time when averaged over watersheds 

similar to those used in the analysis. For further 

information on this statistic, refer to Stedinger and 

Tasker (1985).

Use of Flood-Frequency Relations

Flood-peak discharges at specific recurrence 

intervals can be estimated by locating the drainage basin 

in one of the hydrologic regions (figs. 2-5), determining 

drainage area, impervious area, and using the 

appropriate equation from table 11. The ranges of basin 

variables listed below should not be exceeded.

A comparison with the equivalent rural peak 

discharge also is helpful for small values of total 

impervious area. If the equivalent rural peak discharge 

exceeds the peak computed from the urban equations, 

then use the equivalent rural peak discharge. In the 

immediate Rome area, the urban equations may very 

well compute peak discharges that are less than the 

equivalent rural peak discharges. It is left to the 

discretion of the user, based on their hydrologic 

judgement and knowledge of the area, to decide which 

computed peak discharge to use. The user is also 

cautioned that the equations presented in this report are 

applicable only to basins having insignificant surface 

storage, and insignificant embankment storage.

The ranges of basin variables used in the estimating 

equations presented in this report are listed below.

Variable Minimum Maximum Units

A

TIA

0.04

1.00

19.1

62

in square miles

in percent

SUMMARY

Rainfall-runoff data were collected at 65 urban 

basins in 10 urban areas of Georgia ranging in size from 

0.04 to 19.1 square miles and in total impervious area 

from about 1 to 62 percent. Extensive field reconnaissance was required to select the 65 basins used in 

this study. Many sites were inspected for possible use. 

A range in drainage area, main channel slope, and 

channel length also were considered. Another very 

important factor was land-use stability. Each site has a 

stage gage and at least one rain gage equipped with 

digital recorders with 5-minute punch intervals. All 

flood events with complete rain and stage data and 

without culvert blockages were processed and loaded 

into U.S. Geological Survey (USGS) computer storage. 

The USGS rainfall-runoff model (RRM) is calibrated 

for the 65 basins, and verified by split-sample testing at 

six basins. 

After the RRM is successfully calibrated, long-term 

rainfall and daily pan-evaporation data from the 

appropriate U.S. Department of Commerce, National 

Weather Service (NWS) stations are used to synthesize 

about 60 to 90 years of annual peak-discharge data. The 

synthesized peaks are used to develop flood-frequency 

relations at each site. GLS multiple-regression analysis 

is used to define relations between the flood-frequency 

data and selected basin characteristics, of which 

drainage area, measured total impervious area, and rural 

regression discharge are statistically significant. Tests 

indicate that the equations are not parametrically or 

geographically biased. Estimates of magnitude and 

frequency of urban peak discharges at ungaged sites 

throughout Georgia can be determined for the 2- to 500-

year recurrence intervals by using these equations. 

Average standard errors of prediction of the five 

regional equations ranged from ± 40 percent at the 2-

year recurrence interval to ± 28 percent at the 500-year 

recurrence interval. The use of the regional equations is 

limited to basins within the range of physical 

characteristics listed.

SELECTED REFERENCES

Alley, W.M., and Smith, P.E., 1982, User’s guide for 

distributed routing rainfall-runoff model version II: 

U.S. Geological Survey Open-File Report 82-344, 201 

p.

Bodhaine, G.L., 1968, Measurement of peak discharge at 

culverts by indirect methods: U.S. Geological Survey 

Techniques of Water-Resources Investigations, book 3, 

chap. A3, 60 p.

Buchanan, T.J., and Somers, W.P., 1968, Stage 

measurement at gaging stations: U.S. Geological 

Survey Techniques of Water-Resources Investigations, 

book 3, chap. A7, 28 p.

Carrigan, P.H., Jr., 1973, Calibration of U.S. Geological 

rainfall/runoff model for peak flow synthesis—natural 

basins: U.S. Geological Survey unnumbered report, 

NTIS PB-226-217, 109 p.
19

Figure 2. Regional rural flood-frequency boundaries and cities and number of

urban sites in northwest Georgia.
20

Figure 3. Regional rural flood-frequency boundaries and cities and number of urban sites in

northeast Georgia.
21

Figure 4. Regional rural flood-frequency boundaries and cities and number of urban

sites in southwest Georgia.
22

Figure 5. Regional rural flood-frequency boundaries and cities and number of urban sites

in southeast Georgia.
23

SELECTED REFERENCES—Continued

Carrigan, P.H., Jr., Dempster, G.R., Jr., and Bower, D.E., 

1977, User’s guide for U.S. Geological Survey rainfallrunoff models—revision of Open-File Report 77-33: 

U.S. Geological Survey Open-File Report 77-884, 260 

p.

Chow, V.T., 1964, Handbook of applied hydrology: New 

York, John Wiley, p. 12-1—12-30.

Clark, C.O., 1945, Storage and the unit hydrograph: 

American Society of Civil Engineers Transactions, v. 

110, p. 1419-1388.

Cochran, W.G., 1963, Sampling techniques: New York, 

John Wiley, p. 71-86.

Dawdy, D.R., Lichty, R.W., and Bergmann, J.M., 1972, A 

rainfall-runoff simulation model for estimation of flood 

peaks for small drainage basins: U.S. Geological 

Survey Professional Paper 506-B, 28 p.

Golden, H.G., 1977, Preliminary flood-frequency relations 

for urban streams in Metropolitan Atlanta, Georgia: 

U.S. Geological Survey Open-File Report 77-57, 16 p.

Golden, H.G., and Price, McGlone, 1976, Flood-frequency 

analysis for small natural streams in Georgia: U.S. 

Geological Survey Open-File Report 76-511, 75 p.

Inman, E.J., 1983, Flood-frequency relations for urban 

streams in metropolitan Atlanta, Georgia: U.S. 

Geological Survey Water-Resources Investigations 

Report 83-4203, 38 p.

___ 1988, Flood-frequency relations for urban streams in 

Georgia: U.S. Geological Survey Water-Resources 

Investigations Report 88-4085, 36 p.

Interagency Advisory Committee on Water Data, 1982, 

Guidelines for determining flood-flow frequency, 

revised 1981: Washington, D.C., Bulletin 17B, 183 p.

James, L.D., and Lumb, A.M., 1975, UROS4: Urban flood 

simulation model, Part 2. Application to selected 

DeKalb County watersheds: Atlanta, Ga., Georgia 

Institute of Technology, School of Civil Engineering, 

223 p.

Johnston, John, 1972, Econometric methods (2nd ed.): 

New York, McGraw Hill, 437 p.

Jones, K.R., 1978, Determination of the effects of 

urbanization on expected peak flows from small 

watersheds in DeKalb County, Georgia, unpublished

MSCE thesis: Atlanta, Ga., Georgia Institute of 

Technology, School of Civil Engineering, 132 p.

Kohler, M.A., Nordensen, T.J., and Baker, D.R., 1959, 

Evaporation maps for the United States: Asheville, 

N.C., U.S. Department of Commerce, National 

Weather Service Technical Paper 37, 13 p.

Lichty, R.W., and Liscum, Fred, 1978, A rainfall-runoff 

modeling procedure for improving estimates of T-year 

annual floods for small drainage basins: U.S. 

Geological Survey Water-Resources Investigations 

Report 78-7, 44 p.

Livingston, R.K., and Minges, D.R., 1987, Techniques for 

estimating regional flood characteristics of small rural 

watersheds in the plains region of eastern Colorado: 

U.S. Geological Survey Water-Resources Investigations Report 87-4094, 72 p.

Lumb, A.M., 1975, UROS4: Urban flood simulation 

model, part 1—documentation and user manual: 

Atlanta, Ga., Georgia Institute of Technology, School 

of Civil Engineering, 214 p.

Mitchell, W.D., 1972, Model hydrographs: U.S. Geological 

Survey Water-Supply Paper 2005, 85 p.

Philip, J.R., 1954, An infiltration equation with physical 

significance in Proceedings: Soil Science Society of 

America, v. 77, p. 153-157.

Price, McGlone, 1979, Floods in Georgia, magnitude and 

frequency: U.S. Geological Survey Water-Resources 

Investigations Report 78-137, 269 p.

Riggs, H.C., 1968, Some statistical tools in hydrology: 

U.S. Geological Survey Techniques of WaterResources Investigations, book 4, chap. A1, 39 p.

SAS Institute, Inc., 1989, SAS user’s guide: Statistics, 583 

p.

Sauer, V.B., 1974, An approach to estimating flood 

frequency for urban areas in Oklahoma: U.S. 

Geological Survey Water-Resources Investigations 

Report 23-74, 10 p.

Sauer, V.B., Thomas, W.O., Jr., Stricker, V.A., and Wilson, 

K.V., 1983, Flood characteristics of urban watersheds 

in the United States—techniques for estimating 

magnitude and frequency of urban floods: U.S. 

Geological Survey Water-Supply Paper 2207.

Stamey, T.C., and Hess, G.W., 1993, Techniques for 

estimating magnitude and frequency of floods in rural 

basins of Georgia: U.S. Geological Survey WaterResources Investigations Report 93-4016, 75 p.

Stedinger, J.R., and Tasker, G.D., 1985, Regional 

hydrologic analysis 1—ordinary, weighted, and 

generalized least squares compared: American 

Geophysical Union, Water Resources Research, v. 21, 

no. 9, p. 1421-1432.

Tasker, G.D., 1982, Simplified Testing of Hydrologic 

Regression Regions in Proceedings: American Society 

of Civil Engineers, v. 108, p. 1218-1222.

Tasker, G.D., Eychaner, J.H., and Stedinger, J.R., 1986, 

Application of generalized least squares in regional 

hydrologic regression analysis: U.S. Geological 

Survey Water-Supply Paper 2310.

Tasker, G.D., and Stedinger, J.R., 1989, An operational 

GLS model for hydrologic regression: Journal of 

Hydrology, v. 111, p. 361-375.

Thiessen, A.H., 1911, Precipitation for large areas: 

Monthly Weather Review, v. 39, p. 1082-1084.

U.S. Department of Agriculture, 1990, Aerial photographs 

of Georgia: Salt Lake City, Utah, Agricultural 

Stabilization and Conservation Service, variously sized.

U.S. Department of Commerce, 1948-92, Climato-logical 

data, monthly publications for Georgia: Asheville, 

N.C., National Oceanic and Atmospheric 

Administration, National Climatic Data Center, 

variously paged.

___ 1961, Rainfall-frequency atlas of the United States: 

Asheville, N.C., National Weather Service Technical 

Paper 40, 115 p.
24

Table 1.—Gaging stations in the Statewide urban study, by city, 1994

 Station

number1/ Station name Location

Albany

02352605 Flint River tributary 1, at Albany Lat 31°32'52", long 84°09'28", Dougherty County, at 

culvert on Emily Avenue, at Albany

02352964 Percosin Creek tributary, at Albany Lat 31°35'47", long 84°14'03", Dougherty County, at 

culvert on Dean’s Road, at Albany

Athens

02217505 Brooklyn Creek, at Athens Lat 33°56'32", long 83°24'07", Clarke County, at 

culvert on Dudley Drive, at Athens

02217506 Brooklyn Creek tributary, at Athens Lat 33°56'26", long 83°23'48", Clarke County, at 

culvert on McWhorter Road, at Athens

02217730 Tributary of North Oconee River tributary no. 2, at 

Athens

Lat 33°58'16", long 83°23'59", Clarke County, at 

culvert on U.S. Highway 29, at Athens

02217750 North Oconee River tributary, at Athens Lat 33°58'11", long 83°23'14", Clarke County, at 

culvert on Barber Street, at Athens

02217905 Tanyard Creek, at Athens Lat 33°57'05", long 83°22'42", Clarke County, at 

culvert on Baxter Street, at Athens

02217990 Cedar Creek tributary, near Whitehall Lat 33°55'02", long 83°20'05", Clarke County, at 

culvert on Forest Road, near Whitehall

Atlanta

02203820 Sugar Creek, near Atlanta Lat 33°41'41", long 84°18'15", DeKalb County, at 

culvert on Clifton Church Road, near Atlanta

02203835 Shoal Creek, near Atlanta Lat 33°44'48", long 84°16'50", DeKalb County, at 

culvert on Line Street, near Atlanta

02203845 Shoal Creek tributary, near Atlanta Lat 33°43'05", long 84°15'45", DeKalb County, at 

culvert on Glendale Drive near Atlanta

02203850 Shoal Creek, near Atlanta Lat 33°42'36", long 84°15'57", DeKalb County, at 

culvert on Rainbow Drive, near Atlanta

02203870 Cobbs Creek, near Atlanta Lat 33°43'44", long 84°14'17", DeKalb County, at 

culvert on Snapfinger Road, near Atlanta

02203884 Conley Creek, near Forest Park Lat 33°38'08", long 84°20'38", Clayton County, at 

culvert on Rock Cut Road, near Forest Park

02336080 North Fork Peachtree Creek, near Chamblee Lat 33°51'43", long 84°17'13", DeKalb County, at 

culvert on Shallowford Road, near Chamblee

02336090 North Fork Peachtree Creek tributary, near Chamblee Lat 33°50'53", long 84°17'57", DeKalb County, at 

culvert on Meadowcliff Drive, near Chamblee

02336102 North Fork Peachtree Creek tributary, near Atlanta Lat 33°51'20", long 84°19'19", DeKalb County, at 

culvert on Drew Valley Road, near Atlanta

02336150 South Fork Peachtree Creek, at Clarkston Lat 33°48'51", long 84°14'38", DeKalb County, at 

culvert on Montreal Road, at Clarkston

02336180 South Fork Peachtree Creek, near Decatur Lat 33°48'20", long 84°17'52", DeKalb County, at 

bridge on Wi11ivee Drive near Decatur
25

Atlanta—Continued

02336200 South Fork Peachtree Creek, tributary, at Decatur Lat 33°47'21", long 84°17'50", DeKalb County, at 

culvert on Scott Boulevard, at Decatur

02336238 South Fork Peachtree Creek tributary, near Atlanta Lat 33°47'11", long 84°20'29", DeKalb County, at 

culvert on East Rock Springs Road, near Atlanta

02336325 Nancy Creek tributary, near Chamblee Lat 33°54'22", long 84°18'21", DeKalb County, at 

culvert on Plantation Lane, near Chamblee

02336690 South Utoy Creek tributary no. 2, at East Point Lat 33°42'09", long 84°26'57", Fulton County, at 

culvert on Fort Valley Drive, at East Point

02336697 South Utoy Creek tributary no. 1, at East Point Lat 33°41'51", long 84°27'33", Fulton County, at 

culvert on Woodberry Avenue, at East Point

02336700 South Utoy Creek tributary, at East Point Lat 33°41'25", long 84°28'05", Fulton County, at 

culvert on Headland Drive, at East Point

02336705 South Utoy Creek, at Atlanta Lat 33°42'57", long 84°28'4l", Fulton County, at 

culvert on Adams Drive, at Atlanta

02337081 Camp Creek, at College Park Lat 33°39'39", long 84°27'44", Fulton County, at 

culvert on Park Terrace, at College Park

Augusta

02196570 Raes Creek tributary no. 2, at Augusta Lat 33°32'19", long 82°02'34", Richmond County, at 

culvert on Skinner Mill Road at junction with 

Boy Scout Road, at Augusta

02196605 Raes Creek tributary no. 1, at Augusta Lat 33°29'36", long 82°02'17", Richmond County, at 

culvert on Boy Scout Road, at Augusta

02196725 Oates Creek, at Augusta Lat 33°27'19", long 82°02'23", Richmond County, at 

culvert on White Road, at Augusta

02196730 Oates Creek at Old Savannah Road, at Augusta Lat 33°26'39", long 8l°59'39", Richmond County, at 

culvert on Old Savannah Road, at Augusta

02196760 Rocky Creek tributary, at Augusta Lat 33°27'07", long 82°02'57", Richmond County, at 

culvert on U.S. Highways 78 and 278, at 

Augusta

02196850 Butler Creek tributary, at Augusta Lat 33°25'00", long 82°04'41", Richmond County, at 

culvert on Meadowbrook Drive, at Augusta

Columbus

02341542 Flat Rock Creek, at Columbus Lat 32°32'57", long 84°53'07", Muscogee County, at 

bridge on Warm Springs Road, at Columbus

0234l544 Mill Branch, at Columbus Lat 32°28'19", long 84°53'58", Muscogee County, at 

culvert on Chalbena Road, at Columbus

02341546 Bull Creek tributary, at Columbus Lat 32°28'38", long 84°55'36", Muscogee County, at 

culvert on Woodland Drive, at Columbus

02341548 Lindsey Creek tributary, at Columbus Lat 32°31'33", long 84°56'21", Muscogee County, at 

culvert on Canberra Avenue, at Columbus

Table 1.—Gaging stations in the Statewide urban study, by city, 1994--Continued

 Station

number1/ Station name Location
26

Moultrie

02318565 Okapilco Creek tributary, at Moultrie Lat 31°10'12", long 83°46'40", Colquitt County, at 

culvert on Southeast 10th Street, at Moultrie

02327202 Ochlockonee River tributary, at Moultrie Lat 31°10'25", long 83°48'03", Colquitt County, at 

culvert on Southwest 11th Street, at Moultrie

02327203 Tributary to Ochlockonee River tributary, at Moultrie Lat 31°09'54", long 83°47'35", Colquitt County, at 

culvert on Southwest 4th Street, at Moultrie

02327204 Ochlockonee River tributary, at Moultrie Lat 31°09'38", long 83°48'11", Colquitt County, at 

culvert on West Boulevard, at Moultrie

Rome

02395990 Etowah River tributary, near Rome Lat 34°16'02", long 85°08'18", Floyd County, at 

culvert on Atteiram Road, near Rome

02396290 Silver Creek tributary no. 1, near Rome Lat 34°10'24", long 85°09'21", Floyd County, at 

culvert on Silver Creek Road, near Rome

02396510 Silver Creek tributary no. 2 at Lindale Road, near 

Rome

Lat 34°12'56", long 85°10'09", Floyd County, at 

culvert on Lindale Road, near Rome

02396515 Silver Creek tributary no. 2 at U.S. Highways 27 and 411, 

near Rome

Lat 34°13'08", long 85°10'27", Floyd County, at 

culvert on U.S. Highways 27 and 411, at junction 

with Old Lindale Road, near Rome

02396550 Silver Creek tributary no. 3, at Rome Lat 34°13'26", long 85°09'14", Floyd County, at 

culvert on U.S. Highway 27, 0.4 mile north of U.S. 

Highway 411 interchange, at Rome

02396680 Horseleg Creek, at Rome Lat 34°16'03", long 85°13'29", Floyd County, at 

culvert on Castlewood Drive, at Rome

Savannah

02203541 Harmon Canal tributary, at Savannah Lat 32°00'02", long 81°06'49", Chatham County, at 

culvert on Hodgson Memorial Drive, at Savannah

02203542 Harmon Canal, near Savannah Lat 32°00'00", long 81°07'45", Chatham County, at 

culvert on Perimeter Road, within the limits of 

Hunter Army Airfield, 50 feet upstream from 

Montgomery Cross Road, near Savannah

02203543 Wilshire Canal, near Savannah Lat 31°59'27", long 81°08'15", Chatham County, at 

culvert on Tibet Avenue, near Savannah

02203544 Wilshire Canal tributary, near Savannah Lat 31°58'25", long 81°08'20", Chatham County, at 

culvert on Windsor Road, near Savannah

Thomasville

02326182 Olive Creek tributary, at Thomasville Lat 30°49'51", long 83°57'51", Thomas County, at 

culvert on Baybrook Street, at Thomasville

02327467 Oquina Creek, at Thomasville Lat 30°50'12", long 83°59'38", Thomas County, at 

culvert on Wolf Street, at Thomasville

Table 1.—Gaging stations in the Statewide urban study, by city, 1994--Continued

 Station

number1/ Station name Location
27

Thomasville--Continued

02327468 Oquina Creek, at Cairo Road at Thomasville Lat 30°51'02", long 84°00'10", Thomas County, at 

culvert on Cairo Road (Highway 84 West), at 

Thomasville

02327471 Bruces Branch, at Thomasville Lat 30°50'39", long 83°58'36", Thomas County, at 

culvert on North Hansell Street, at Thomasville

02327473 Bruces Branch at Walcott Street, at Thomasville Lat 30°51'07", long 83°58'42", Thomas County, at 

culvert on Walcott Street, at Thomasville

02327474 Bruces Branch tributary, at Thomasville Lat 30°51'20", long 83°58'18", Thomas County, at 

culvert on Fontaine Drive, at Thomasville

Valdosta

02317564 Dukes Bay Canal, at Valdosta Lat 30°49'13", long 83°16'20", Lowndes County, at 

culvert on South Patterson Street at intersection with 

State Route 94, at Valdosta

02317566 Dukes Bay Canal at Industrial Boulevard, at Valdosta Lat 30°48'34", long 83°15'43", Lowndes County, at 

culvert on Industrial Boulevard, at Valdosta

023177551 Sugar Creek tributary, at Valdosta Lat 30°49'51", long 83°18'27", Lowndes County, at 

culvert on Hyde Park Road, at Valdosta

023177553 Onemile Branch, at Valdosta Lat 30°50'58", long 83°16'38", Lowndes County, at 

culvert on Vallotton Street, at Valdosta

023177554 Onemile Branch, at Wainwright Drive at Valdosta Lat 30°50'34", long 83°18'04", Lowndes County, at 

culvert on Wainwright Drive, at Valdosta

023177556 Twomile Branch, at Valdosta Lat 30°52'09", long 83°16'43", Lowndes County, at 

culvert on Bemiss Road, at Valdosta

023177557 Twomile Branch at University Drive, at Valdosta Lat 30°52'05", long 83°17'04", Lowndes County, at 

culvert on University Drive, at Valdosta

023177558 Twomile Branch at Oak Street, at Valdosta Lat 30°51'48", long 83°17'32", Lowndes County, at 

culvert on Oak Street, at Valdosta

1/U.S. Geological Survey downstream order number.

Table 1.—Gaging stations in the Statewide urban study, by city, 1994--Continued

 Station

number1/ Station name Location