U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2012–5120

National Water-Quality Assessment Program

Comparison of TOPMODEL Streamflow Simulations 

Using NEXRAD-Based and Measured Rainfall Data, 

McTier Creek Watershed, South Carolina Daily mean flow, in cubic feet per second

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2007 2008 2009

J J A S O N D J F M A M J J A S O N D J F M A M J J A S O

Comparison of TOPMODEL Streamflow 

Simulations Using NEXRAD-Based and 

Measured Rainfall Data, McTier Creek 

Watershed, South Carolina

By Toby D. Feaster, Nancy E. Westcott, Robert J.M. Hudson, Paul A. Conrads, and 

Paul M. Bradley

National Water-Quality Assessment Program

Scientific Investigations Report 2012–5120

U.S. Department of the Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2012

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Suggested citation:

Feaster, T.D., Westcott, N.E., Hudson, R.J.M., Conrads, P..A., and Bradley P.M., 2012, Comparison of TOPMODEL 

streamflow simulations using NEXRAD-based and measured rainfall data, McTier Creek watershed, South Carolina: 

U.S. Geological Survey Scientific Investigations Report 2012–5120, 33 p.
iii

Foreword

The U.S. Geological Survey (USGS) is committed to providing the Nation with reliable scientific 

information that helps to enhance and protect the overall quality of life and that facilitates effective management of water, biological, energy, and mineral resources (http://www.usgs.gov/). 

Information on the Nation’s water resources is critical to ensuring long-term availability of water 

that is safe for drinking and recreation and is suitable for industry, irrigation, and fish and wildlife. Population growth and increasing demands for water make the availability of that water, 

measured in terms of quantity and quality, even more essential to the long-term sustainability of 

our communities and ecosystems.

The USGS implemented the National Water-Quality Assessment (NAWQA) Program in 1991 to 

support national, regional, State, and local information needs and decisions related to waterquality management and policy (http://water.usgs.gov/nawqa). The NAWQA Program is designed 

to answer: What is the quality of our Nation’s streams and groundwater? How are conditions 

changing over time? How do natural features and human activities affect the quality of streams 

and groundwater, and where are those effects most pronounced? By combining information on 

water chemistry, physical characteristics, stream habitat, and aquatic life, the NAWQA Program 

aims to provide science-based insights for current and emerging water issues and priorities. From 

1991 to 2001, the NAWQA Program completed interdisciplinary assessments and established a 

baseline understanding of water-quality conditions in 51 of the Nation’s river basins and aquifers, 

referred to as Study Units (http://water.usgs.gov/nawqa/studies/study_units.html).

National and regional assessments are ongoing in the second decade (2001–2012) of the 

NAWQA Program as 42 of the 51 Study Units are selectively reassessed. These assessments 

extend the findings in the Study Units by determining water-quality status and trends at sites 

that have been consistently monitored for more than a decade, and filling critical gaps in 

characterizing the quality of surface water and groundwater. For example, increased emphasis 

has been placed on assessing the quality of source water and finished water associated with 

many of the Nation’s largest community water systems. During the second decade, NAWQA is 

addressing five national priority topics that build an understanding of how natural features and 

human activities affect water quality, and establish links between sources of contaminants, 

the transport of those contaminants through the hydrologic system, and the potential effects of 

contaminants on humans and aquatic ecosystems. Included are studies on the fate of agricultural chemicals, effects of urbanization on stream ecosystems, bioaccumulation of mercury in 

stream ecosystems, effects of nutrient enrichment on aquatic ecosystems, and transport of 

contaminants to public-supply wells. In addition, national syntheses of information on pesticides, volatile organic compounds (VOCs), nutrients, trace elements, and aquatic ecology 

are continuing.

The USGS aims to disseminate credible, timely, and relevant science information to address 

practical and effective water-resource management and strategies that protect and restore 

water quality. We hope this NAWQA publication will provide you with insights and information to meet your needs, and will foster increased citizen awareness and involvement in the 

protection and restoration of our Nation’s waters.
iv

The USGS recognizes that a national assessment by a single program cannot address all waterresource issues of interest. External coordination at all levels is critical for cost-effective management, regulation, and conservation of our Nation’s water resources. The NAWQA Program, 

therefore, depends on advice and information from other agencies—Federal, State, regional, 

interstate, Tribal, and local—as well as nongovernmental organizations, industry, academia, and 

other stakeholder groups. Your assistance and suggestions are greatly appreciated.

 

 William H. Werkheiser

USGS Associate Director for Water
v

Contents

Foreword ........................................................................................................................................................iii

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

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

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

Previous Studies ...................................................................................................................................2

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

Rainfall Data Comparisons...........................................................................................................................5

Measured Rainfall Data from NWS COOP Stations........................................................................5

Gridded Multisensor Precipitation Estimates ..................................................................................5

Measured Rainfall and NEXRAD-Based Estimated Rainfall Comparisons Approach ..............7

Rainfall Comparison Statistics...................................................................................................7

Rainfall Comparison in McTier Creek Subwatersheds...................................................................8

TOPMODEL Application..............................................................................................................................13

TOPMODEL Code Correction ............................................................................................................14

TOPMODEL Streamflow Simulations........................................................................................................15

Effects of TOPMODEL Code Correction ..........................................................................................16

Streamflow Simulations Using NEXRAD-Based Rainfall Data....................................................16

Monetta Streamflow .................................................................................................................17

New Holland Streamflow .........................................................................................................17

Comparison of NWS COOP and NEXRAD-Based Rainfall Data Inputs for the Monetta 

and New Holland Watershed TOPMODEL Simulations ..................................................20

Recalibration of TOPMODEL Using NEXRAD-Based Rainfall Data............................................21

Monetta and New Holland Streamflow Simulations ...........................................................22

Streamflow Simulations in McTier Creek Subwatersheds..........................................................23

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

References Cited..........................................................................................................................................31

Figures

1. Map showing location of the McTier Creek watershed, Aiken County, 

South Carolina...............................................................................................................................4

2. Map showing National Weather Service Cooperative meteorological stations

near the McTier Creek watershed, South Carolina, included in this investigation

and NEXRAD-based grid locations............................................................................................6

3. Graphs showing residuals of NEXRAD-based and measured rainfall at National

Weather Service Cooperative meteorological station locations near the McTier

Creek watershed, South Carolina ..............................................................................................9

4. Graphs showing NEXRAD-based and measured rainfall at National Weather

Service Cooperative meteorological station locations near the McTier Creek

watershed, South Carolina........................................................................................................10

5. Map showing McTier Creek subwatersheds, Aiken County, South Carolina...................11

6. Graph showing single-mass curves of NEXRAD-based daily rainfall for McTier

Creek subwatersheds MA01 to MA12 for June 13, 2007, to September 30, 2009 ............12
vi

7. Graph showing single-mass curves of cumulative rainfall at National Weather

Service Cooperative meteorological stations near McTier Creek watershed for

concurrent periods of record from June 13, 2007, to September 30, 2009........................12

8. Graph showing single-mass curves of cumulative NEXRAD-based rainfall at

National Weather Service Cooperative meteorological station locations near the

McTier Creek watershed for periods concurrent with the concurrent NWS COOP

rainfall data from June 13, 2007, to September 30, 2009 ......................................................13

9. Diagram showing definition of selected water-source variables from TOPMODEL.......14

10. Graphs showing single-mass curves of simulated and observed daily mean flow

at McTier Creek watershed near Monetta and New Holland for June 13, 2007, to

September 30, 2009.....................................................................................................................18

11. Graphs showing TOPMODEL-C/C and TOPMODEL-C/N streamflow simulations in

relation to observed streamflow for daily mean flows, flow-duration curves, and

single-mass curves at McTier Creek near Monetta for June 13, 2007, to

September 30, 2009.....................................................................................................................18

12. Graphs showing TOPMODEL-C/C and TOPMODEL-C/N streamflow simulations in

relation to observed streamflow for daily mean flows, flow-duration curves, and

single-mass curves at McTier Creek near New Holland for June 13, 2007, to

September 30, 2009.....................................................................................................................20

13. Graph showing cumulative total daily measured rainfall for the McTier Creek

watershed in relation to total daily NEXRAD-based rainfall for Monetta and 

New Holland ................................................................................................................................21

14. Graphs showing TOPMODEL-C/C and TOPMODEL-N/N streamflow simulations in

relation to daily mean flows, flow-duration curves, and single-mass curves at

McTier Creek near Monetta for June 13, 2007, to September 30, 2009 .............................24

15. Graphs showing TOPMODEL-C/C and TOPMODEL-N/N streamflow simulations in

relation to observed streamflow for daily mean flows, flow-duration curves, and

single-mass curves at McTier Creek near New Holland for June 13, 2007, to

September 30, 2009.....................................................................................................................24

16. Graphs showing TOPMODEL-C/C streamflow simulations of daily mean

streamflow for June 13, 2007, to September 30, 2009, at McTier Creek near

Monetta and the sum of the simulated daily mean streamflow for subwatersheds

MA01 to MA06, and McTier Creek near New Holland and the sum of the simulated

daily mean streamflow for subwatersheds MA01 to MA12 ................................................25

17. Graphs showing TOPMODEL-N/N streamflow simulations for June 13, 2007, to

September 30, 2009, at McTier Creek near Monetta and the sum of simulated daily

mean streamflow for subwatersheds MA01 to MA06, and McTier Creek near New

Holland and the sum of simulated daily mean streamflow for subwatersheds

MA01 to MA12.............................................................................................................................25

18. Graphs showing TOPMODEL-C/C and TOPMODEL-N/N simulated daily mean

streamflow at McTier Creek subwatersheds MA01 to MA12 for June 13, 2007, to

September 30, 2009.....................................................................................................................26

19. Map showing South Carolina precipitation, 1971–2000 .......................................................29
vii

Tables

1. National Weather Service Cooperative meteorological stations near the McTier

Creek watershed, South Carolina, used in this investigation................................................5

2. Comparison statistics with respect to days with non-zero rainfall for measured

and NEXRAD-based estimated daily rainfall for selected National Weather

Service Cooperative stations near the McTier Creek watershed from June 13,

2007, to September 30, 2009 ........................................................................................................8

3. Parameter values used for the TOPMODEL calibration for McTier Creek near

Monetta and for McTier Creek near New Holland for June 13, 2007, to 

September 30, 2009.....................................................................................................................15

4. Station number and name, period of record used in the model simulations, and

model simulation period reference name for the McTier Creek watershed, 

South Carolina.............................................................................................................................16

5. Goodness-of-fit statistics at McTier Creek near Monetta and McTier Creek near

New Holland from Feaster and others (2010) and from the corrected TOPMODEL

source code simulations for June 13, 2007, to September 30, 2009 ...................................17

6. Goodness-of-fit statistics for McTier Creek near Monetta for the June 13, 2007, to

September 30, 2009 simulation .................................................................................................19

7. Goodness-of-fit statistics for McTier Creek near New Holland for the June 13,

2007, to September 30, 2009 simulation...................................................................................19

8. Goodness-of-fit statistics for the McTier Creek near Monetta TOPMODEL

simulations for June 13, 2007, to September 30, 2009...........................................................22

9. Goodness-of-fit statistics for the McTier Creek near New Holland TOPMODEL

simulations for June 13, 2007, to September 30, 2009...........................................................23

10. McTier Creek subwatershed area and percent difference between

TOPMODEL-C/C and TOPMODEL-N/N simulated total volume of daily mean

streamflow...................................................................................................................................28

11. Total NEXRAD-based rainfall by subwatershed and differences from total

NEXRAD-based rainfall and total NWS COOP rainfall for McTier Creek near 

New Holland ................................................................................................................................28
viii

Conversion Factors and Abbreviations

SI to Inch/Pound

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

hectare (ha) 2.471 acre

square kilometer (km2) 247.1 acre

square kilometer (km2) 0.3861 square mile (mi2)

Volume

cubic meter (m3) 35.31 cubic foot (ft3)

Flow rate

meter per second (m/s) 3.281 foot per second (ft/s) 

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

Hydraulic conductivity

meter per day (m/d) 3.281 foot per day (ft/d) 

Hydraulic gradient

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

Inch/Pound to SI

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

acre 0.004047 square kilometer (km2)

square mile (mi2) 2.590 square kilometer (km2) 

Volume

cubic foot (ft3) 0.02832 cubic meter (m3) 

Flow rate

cubic foot per second (ft3/s) 0.02832 cubic meter per second (m3/s)

cubic foot per second per square 

mile [(ft3/s)/mi2]

0.01093 cubic meter per second per 

square kilometer [(m3/s)/km2]

inch per hour (in/h) 0.0254 cubic meter per hour (m3/h)

Hydraulic conductivity

foot per day (ft/d) 0.3048 meter per day (m/d)
ix

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F=(1.8×°C)+32

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows:

°C=(°F–32)/1.8

Horizontal coordinate information is referenced to North American Datum of 1927 (NAD 27) or 

North American Datum of 1983 (NAD 83).

Vertical coordinate information is referenced to the North American Veritcal Datum of 1988 

(NAVD 88) or National Geodetic Vertical Datum of 1929 (NGVD 29).

Elevation, as used in this report, refers to distance above the vertical datum.
Abbreviations

a.m. before midday

EE estimation efficiency

EST Eastern Standard Time

GBMM Grid-based mercury model

GIS Geographic information system

HADS Hydrometeorological Automated Data System

m scaling parameter

MA01–MA12 Model assessment points

MAE mean absolute error

Monetta Station 02172300, McTier Creek near Monetta, SC

MPE multisensor precipitation estimates

NEXRAD Next generation weather radar

New Holland Station 02172305, McTier Creek near New Holland, SC 

NAWQA Natural Water-Quality Assessment Program

NOAA National Oceanic and Atmospheric Administration

NSE Nash-Sutcliffe coefficient of efficiency

NWS National Weather Service

NWS COOP National Weather Service cooperative station

PEST Parameter estimation program

r Pearson’s correlation coefficient

RMSD root mean square difference

RMSE root mean square error

SC South Carolina

SWAT Soil and Water Assessment Tool

TOPMODEL Topography-based hydrological model

TOPMODEL-C/C TOPMODEL calibration and simulations done using measured rainfall

TOPMODEL-C/N TOPMODEL calibration using measured rainfall and simulations using 

NEXRAD-based rainfall

TOPMODEL-N/N TOPMODEL calibration and simulations done using NEXRAD-based rainfall

USGS U.S. Geological Survey

WSR-88D Weather surveillance radar–1988 Doppler system
Comparison of TOPMODEL Streamflow Simulations 

Using NEXRAD-Based and Measured Rainfall Data, 

McTier Creek Watershed, South Carolina 

By Toby D. Feaster1, Nancy E. Westcott2, Robert J.M. Hudson3, Paul A. Conrads1, and Paul M. Bradley1

1U.S. Geological Survey.

2Illinois State Water Survey, University of Illinois at Urbana-Champaign.

3University of Illinois at Urbana-Champaign.

Abstract

Rainfall is an important forcing function in most watershed models. As part of a previous investigation to assess 

interactions among hydrologic, geochemical, and ecological 

processes that affect fish-tissue mercury concentrations in the 

Edisto River Basin, the topography-based hydrological model 

(TOPMODEL) was applied in the McTier Creek watershed in 

Aiken County, South Carolina. Measured rainfall data from 

six National Weather Service (NWS) Cooperative (COOP) 

stations surrounding the McTier Creek watershed were used 

to calibrate the McTier Creek TOPMODEL. Since the 1990s, 

the next generation weather radar (NEXRAD) has provided 

rainfall estimates at a finer spatial and temporal resolution than 

the NWS COOP network. For this investigation, NEXRADbased rainfall data were generated at the NWS COOP stations 

and compared with measured rainfall data for the period 

June 13, 2007, to September 30, 2009. Likewise, these 

NEXRAD-based rainfall data were used with TOPMODEL to 

simulate streamflow in the McTier Creek watershed and then 

compared with the simulations made using measured rainfall 

data. NEXRAD-based rainfall data for non-zero rainfall days 

were lower than measured rainfall data at all six NWS COOP 

locations. The total number of concurrent days for which 

both measured and NEXRAD-based data were available at 

the COOP stations ranged from 501 to 833, the number of 

non-zero days ranged from 139 to 209, and the total difference 

in rainfall ranged from –1.3 to –21.6 inches. 

With the calibrated TOPMODEL, simulations using 

NEXRAD-based rainfall data and those using measured 

rainfall data produce similar results with respect to matching 

the timing and shape of the hydrographs. Comparison of the 

bias, which is the mean of the residuals between observed and 

simulated streamflow, however, reveals that simulations using 

NEXRAD-based rainfall tended to underpredict streamflow 

overall. Given that the total NEXRAD-based rainfall data for 

the simulation period is lower than the total measured rainfall 

at the NWS COOP locations, this bias would be expected. 

Therefore, to better assess the use of NEXRAD-based rainfall 

estimates as compared to NWS COOP rainfall data on the 

hydrologic simulations, TOPMODEL was recalibrated and 

updated simulations were made using the NEXRAD-based 

rainfall data. Comparisons of observed and simulated streamflow show that the TOPMODEL results using measured 

rainfall data and NEXRAD-based rainfall are comparable. 

Nonetheless, TOPMODEL simulations using NEXRAD-based 

rainfall still tended to underpredict total streamflow volume, 

although the magnitude of differences were similar to the 

simulations using measured rainfall.

The McTier Creek watershed was subdivided into 

12 subwatersheds and NEXRAD-based rainfall data were 

generated for each subwatershed. Simulations of streamflow 

were generated for each subwatershed using NEXRAD-based 

rainfall and compared with subwatershed simulations using 

measured rainfall data, which unlike the NEXRAD-based 

rainfall were the same data for all subwatersheds (derived 

from a weighted average of the six NWS COOP stations 

surrounding the basin). For the two simulations, subwatershed streamflow were summed and compared to streamflow 

simulations at two U.S. Geological Survey streamgages. The 

percentage differences at the gage near Monetta, South Carolina, were the same for simulations using measured rainfall 

data and NEXRAD-based rainfall. At the gage near New 

Holland, South Carolina, the percentage differences using 

the NEXRAD-based rainfall were twice as much as those 

using the measured rainfall. Single-mass curve comparisons 

showed an increase in the total volume of rainfall from north 

to south. Similar comparisons of the measured rainfall at the 

NWS COOP stations showed similar percentage differences, 

but the NEXRAD-based rainfall variations occurred over a 

much smaller distance than the measured rainfall. Nonetheless, it was concluded that in some cases, using NEXRADbased rainfall data in TOPMODEL streamflow simulations 

may provide an effective alternative to using measured 

rainfall data. For this investigation, however, TOPMODEL 

streamflow simulations using NEXRAD-based rainfall data 
2 Comparison of TOPMODEL Streamflow Simulations Using NEXRAD-Based and Measured Rainfall Data

for both calibration and simulations did not show significant 

improvements with respect to matching observed streamflow 

over simulations generated using measured rainfall data.

Introduction

Rainfall is an essential forcing function for hydrologic 

models. Traditionally, rainfall data collected at specific gages, 

often on a 24-hour basis, have been used for hydrologic model 

inputs (Beven, 2001). To help compensate for the temporal 

and spatial variability that naturally occurs in rainfall, 

numerous techniques are available for integrating or weighting 

data from various gages in and (or) around a modeled watershed (Shepard, 1968; Maidment, 1993; Bedient and others, 

2008). In the fall of 1990, the first weather surveillance radar1988 Doppler (WSR–88D) system was installed near Oklahoma City, Oklahoma (Crum and Alberty, 1993). Since that 

time, the next generation weather radar (NEXRAD) network 

of WSR–88D units has provided rainfall estimates that have 

finer spatial and temporal resolution than the National Weather 

Service (NWS) Cooperative (COOP) stations. The WSR–88D 

precipitation estimation consists of several processing stages 

(Hardegree and others, 2008). Stage I occurs at the individual 

radar site producing spatial rainfall estimates for a single radar 

domain. Stages II and III involve, respectively, multisensory 

bias adjustment and creation of a multiradar mosaic of precipitation estimates for areas with overlapping radar coverage. 

In Stage IV, the multisensor estimates undergo an additional 

process that involves the mosaicking of the estimates from all 

the NWS River Forecast Centers (Habib and others, 2009). 

Although the technology continues to improve, comparisons 

have shown that uncertainties associated with the NEXRADbased data should be considered (National Climatic Data 

Center, 1996; Seo and Breidenbach, 2002; Jayakrishnan and 

others, 2004; Moon and others, 2004; Hardegree and others, 

2008; and Young and Brunsell, 2008).

Two hydrologic models were developed for the McTier 

Creek watershed in Aiken County, South Carolina (Feaster 

and others, 2010) to advance understanding of the fate and 

transport of mercury in stream ecosystems and to expand the 

understanding of hydrologic, geochemical, and ecological 

processes within the Edisto River Basin that affect fish-tissue 

mercury concentrations (Bradley and others, 2011). The 

two models are the topography-based hydrological model 

(TOPMODEL) (Kennen and others, 2008) and the grid-based 

mercury model (GBMM) (Dai and others, 2005; Tetra Tech, 

2006). The rainfall data used for the models were obtained 

from six National Weather Service (NWS) Cooperative 

(COOP) stations. In the current follow-up assessment of the 

water balance for the McTier Creek watershed, NEXRADbased data were compiled for the 6 NWS COOP stations and 

for 12 subbasins in the McTier Creek watershed. Hereafter, 

the NWS COOP measured rainfall data will be referred 

to as measured rainfall. This research effort is part of the 

U.S. Geological Survey (USGS) National Water-Quality 

Assessment (NAWQA) Program.

Purpose and Scope

The purpose of this report is to document the results of 

a study to compare measured rainfall data at NWS COOP 

stations with estimated NEXRAD-based rainfall data for a 

specific small watershed. The report also documents the evaluation of using different rainfall data on simulated streamflow 

using TOPMODEL as described by Feaster and others (2010). 

The evaluation of simulated streamflow includes documentation of the recalibration of TOPMODEL using the NEXRADbased rainfall inputs. The scope of the report includes the 

McTier Creek watershed located in Aiken County, S.C., and 

areas gaged by six NWS COOP stations surrounding the 

McTier Creek watershed located in Saluda, Lexington, and 

Edgefield Counties, S.C. 

An important part of the USGS mission is to provide 

scientific information for the effective water-resources 

management of the Nation (U.S. Geological Survey, 2007). To 

assess the quantity and quality of the Nation’s surface water, 

the USGS collects hydrologic and water-quality data from 

rivers, lakes, and estuaries by using standardized methods 

and maintains the data in a national database. These data are 

analyzed and used for hydrologic simulation models to enhance 

the understanding of the dynamics of hydrologic systems. 

The techniques presented in this report demonstrate selected 

approaches for comparing hydrologic datasets and for evaluating the effect on simulated output from hydrologic models.

Previous Studies

Numerous investigations have compared measured 

rainfall data, which occurred over areas much larger than the 

current investigation, to estimates obtained from the NWS 

radar system. The National Climatic Data Center (1996) 

compared NEXRAD-estimated storm-total precipitation with 

measured precipitation for five events: Missouri-Kansas April 

1994, Southeast Texas October 1994, Florida November 

1994, Louisiana-Mississippi-Alabama May 1995, and South 

Carolina-North Carolina-Georgia August 1995. The five 

events were chosen on the basis of both their extensive 

and damaging nature and the availability of one or more 

NEXRAD sites with suitable areal coverage. For 80 percent 

of the 220 raingage stations included in the National Climatic 

Data Center study (1996), the NEXRAD estimates were too 

low, sometimes by a factor of 2 to 3. As a result of the study, 

precipitation processing parameters were changed at some 

sites in an attempt to improve the rainfall estimates.

Smith and others (1996) compared about 1 year of 

WSR–88D hourly precipitation estimates with raingage data 

from the Southern Plains of Texas and Oklahoma. The study 

focused on systematic biases that affect the radar estimates. 

Numerous issues were discussed with respect to biases relating 

to distance from the radar. Within a 40-kilometer (km) range, 

bias due to reflectivity observations at high elevation angles 

caused significant underestimation of rainfall. Beyond 100 km, 
Introduction 3

considerable underestimation of precipitation occurred. It was 

noted that spatial analysis of heavy rainfall showed advantages 

of radar over the raingage network. 

Mizzell (1999) presents findings from a comparison of 

WSR–88D rainfall estimates with measured rainfall data in 

Lexington County, S.C., which forms the northern border 

of Aiken County, S.C., the county in which the McTier 

Creek watershed being analyzed in the current investigation 

resides. Mizzell (1999) compared radar estimates from the 

NWS WSR–88D radar located at the Columbia Airport in 

Lexington, S.C., with measured rainfall at 72 raingages. The 

study focused on seven precipitation events, which occurred 

between September 1997 and September 1998, that covered 

a variety of storm types, such as convective storms, tropical 

systems, and stratiform events. Results showed that radar 

estimates consistently underestimated measured rainfall, 

regardless of the storm type.

Jayakrishnan and others (2004) compared measured 

rainfall with WSR–88D Stage III precipitation data over the 

Texas-Gulf basin at 545 raingages for the period 1995 to 1999. 

The results showed that underestimation bias occurred at the 

majority of the stations analyzed. The study found that, in 

general, the radar estimates showed improvement over the 

years of the study, which was consistent with the ongoing 

improvement in developments of processing algorithms for the 

radar estimates. The paper also provides a thorough discussion 

of previous studies that readers are encouraged to review for 

additional information.

Young and Brunsell (2008) compared NEXRAD and 

multisensor precipitation estimates (MPE) in the Missouri 

River Basin with NWS COOP rainfall data. The raingage 

network was independent of the data used to develop the 

NEXRAD products and, therefore, provided an independent 

assessment of the NEXRAD data, which covered the period 

1998 to 2004. The overall bias for NEXRAD data was 

–39 percent for the cold season and –32 percent for the warm 

season. As was noted by Jayakrishnan and others (2004), the 

NEXRAD estimates showed improvement over the period 

of the study with the warm season bias decreasing from 

–44 percent in 1998 to –15 percent in 2004.

In addition to numerous studies comparing NEXRADbased rainfall data with measured rainfall data, many studies 

have compared the differences between simulations of 

streamflow using raingage data and NEXRAD-based data 

as rainfall inputs to hydrologic models. Moon and others 

(2004) performed such a comparison using the Soil and Water 

Assessment Tool (SWAT) in the Trinity River Basin of Texas. 

Regression analysis was used to compare the raingage and 

NEXRAD data at six raingages used in the investigation. The 

coefficient of determination (R2) for the six gages ranged 

from 0.43 to 0.80 with the Nash-Sutcliffe coefficient of 

efficiency (NSE) ranging from 0.28 to 0.77. For comparisons 

of simulated streamflow using NEXRAD-based rainfall with 

streamflow simulations using measured rainfall data, the NSE 

ranged from 0.57 to 0.82 and 0.48 to 0.78, respectively. In 

general, SWAT-NEXRAD simulations overpredicted high-flow 

events and underpredicted low-flow events. Nonetheless, the 

authors concluded that NEXRAD-based rainfall data were a 

good alternative to measured rainfall data.

Sexton and others (2010) compared results from using 

measured raingage data and various NEXRAD MPE precipitation datasets (non-corrected, bias corrected, and inverse 

distance weighted corrected) on SWAT model simulations in 

a small Northeastern watershed. The SWAT was calibrated 

using each source of precipitation data, and streamflow simulations were compared to measured streamflow data. The NSE 

for calibration and validation simulations using measured 

rainfall ranged from 0.42 to 0.73, and R2 values ranged from 

0.42 to 0.75. For the simulations using NEXRAD rainfall 

data, NSE values ranged from 0.46 to 0.76, and R2 ranged 

from 0.47 to 0.76. The study showed that for watersheds with 

no raingages in the watershed, the proximity of raingages 

outside the watershed and the direction of storm patterns can 

be important. Given these conditions, the conclusion was 

that NEXRAD data can be a good alternative to measured 

rainfall data. 

Study Area

McTier Creek is a small headwater stream located in 

the Edisto River Basin and is a tributary to the South Fork 

Edisto River (fig. 1). The entire McTier Creek watershed 

encompasses about 38 square miles (mi2), is designated by 

the 12-digit hydrologic unit code 030502040102, and lies 

completely in the County of Aiken, S.C. (Eidson and others, 

2005). The study area encompasses about 31 mi2.

McTier Creek lies within the inland part of the Coastal 

Plain Physiographic Province known as the Sand Hills 

(Griffith and others, 2002; fig. 1). Some studies integrate 

the Sand Hills within a broader area referred to as the inner 

or upper Coastal Plain (Bloxham, 1976). The headwaters of 

the McTier Creek watershed are located near the Fall Line 

(fig. 1), which is the name given to the boundary between 

the Piedmont and Coastal Plain Physiographic Provinces. In 

general, this boundary is characterized by a series of rapids 

or falls where the streams cascade off the more resistant 

rocks of the Piedmont into the deeper valleys worn into the 

softer sandy sediments of the Coastal Plain (Cooke, 1936). 

Commonly, the headwaters of watersheds located just below 

the Fall Line transition from characteristics similar to Piedmont streams to characteristics of the Coastal Plain downstream. In the upper part of the McTier Creek watershed, the 

channel is characterized by rock outcrops, a characteristic of 

many Piedmont streams. 

As typically defined, precipitation occurs in a variety of 

forms such as hail, rain, freezing rain, sleet, or snow. In the 

South Carolina Coastal Plain environment, the majority of 

precipitation is in the form of rain. During the period covered 

in this report, no major frozen precipitation events were noted. 

Thus, in this report, precipitation is often being referred to 

simply as rainfall.
4 Comparison of TOPMODEL Streamflow Simulations Using NEXRAD-Based and Measured Rainfall Data

Figure 1. Location of the McTier Creek watershed, Aiken County, South Carolina (modfied from 

Feaster and others, 2010).

02172305

81°36' 81°32'

33°52'

33°48'

33°44'

Watershed

boundary

Fall Line

SOUTH

ATLANTIC

OCEAN

GEORGIA

McTier Creek

watershed

Blue Ridge

Piedmont

Lower

Coastal Plain

Sand Hills

Fall

Line

Upper

Coastal Plain

NORTH CAROLINA

CAROLINA

Base from National Hydrography Dataset and digital line graphs, 1:100,000

ESRI data and maps, 2006

Albers equal area projection; central meridian-96 00 00; datum NAD 83

South

McTier

Gully

Creek

Creek

Fork Edisto

River

20

SALUDA

AIKEN

U.S. Geological Survey streamgaging

station and number

EXPLANATION

02172305

02172300

0 0.5 1 2 KILOMETERS

0 0.5 1 2 MILES
Rainfall Data Comparisons 5

Rainfall Data Comparisons

A number of works reporting comparisons between 

NEXRAD-based rainfall estimates and measured rainfall data 

have been reported for large watersheds (Jayakrishnan and 

others, 2004; Moon and others, 2004). The compilation of the 

NEXRAD-based data for the McTier Creek watershed allows 

for a comparison with measured rainfall data at NWS COOP 

stations at a small watershed. In this report, comparisons are 

made between the NEXRAD-based and measured rainfall 

data at the NWS COOP stations and at locations within 

the McTier Creek watershed. Additionally, the NEXRADbased data are incorporated in the TOPMODEL developed 

by Feaster and others (2010), and resultant streamflow 

simulations are compared to simulations that use measured 

rainfall data.

Measured Rainfall Data from NWS COOP 

Stations

Feaster and others (2010) describe the calibration of 

the TOPMODEL hydrologic model for the McTier Creek 

watershed (fig. 1). The meteorological input data (rainfall and 

average temperature) for that model were based on available 

data collected daily from six stations that are part of the 

NWS COOP station network and are located near the McTier 

Creek watershed (table 1; fig. 2). Although observations at 

NWS COOP stations are typically made by volunteers, the 

observing equipment is calibrated and maintained by NWS 

personnel (National Climatic Data Center, 2006). At the NWS 

COOP stations used in this comparison, observations were 

recorded daily at either 7:00 or 8:00 a.m. The six meteorological datasets were aggregated using inverse distance 

weighting based on the distance from each NWS COOP 

station to the center of the McTier Creek watershed (fig. 2; 

Feaster and others, 2010).

Gridded Multisensor Precipitation Estimates

Daily gridded (approximately 4- by 4-km grid cells) MPE 

were obtained from the National Oceanic and Atmospheric 

Administration (NOAA) National Center for Environmental 

Prediction (NCEP) for 2007 to 2009. These 24-hour accumulated estimates are valid at 7 a.m. Eastern Standard Time. 

Multisensor precipitation estimates for the study region are a 

composite of three 10-centimeter NWS WSR–88D NEXRAD 

radars adjusted with Hydrometeorological Automated Data 

System (HADS) raingage observations. The gridded values 

were computed using the stage III/IV MPE algorithm (Seo and 

Breidenbach, 2002) as implemented by NOAA in February 

2002. During 2003, a further improvement was made to the 

stage III/IV MPE algorithm with the elimination of a truncation error that particularly affected stratiform precipitation 

estimates (Fulton and others, 2003). 

The McTier Creek watershed is located within about 

18–45 miles of the Columbia, S.C., NEXRAD radar (KCAE) 

and within about 69–102 miles of the Charleston, S.C., 

radar (KCLX). Based on the distance from the radar to the 

watershed, biases due to radar beam elevation effects should 

be minimal (Westcott, 2009), and because of the gently 

rolling topography, terrain blockage also should be minimal. 

The precipitation estimates are based on reflectivity values 

from the lowest available beam elevation at each pixel 

(Seo and Breidenbach, 2002) with the assumption that the 

mean bias in the radar data has been removed by the MPE 

algorithm (Fulton and others, 1998; Seo and Breidenbach, 

2002). Near realtime adjustments to the data are made using 

approximately 25 HADS precipitation gages located within 

the KCAE radar region (http://www.nws.noaa.gov/oh/hads/

accessed on March 7, 2011). These gages typically are tipping 

bucket gages that are accessed remotely by satellite telemetry. 

Adjustment by HADS gages to the radar data is performed 

after the hourly data from individual radars are mosaicked to 

the national grid (Young and Brunsell, 2008). By using the 

radar measurements to capture the spatial variability and by 

also using the measured precipitation data to reduce inherent 

radar biases, the final gridded MPE precipitation product 

is considered an improvement over estimates provided 

by radar or gages alone. In this report to distinguish raw 

NEXRAD data from NEXRAD data that have been processed 

using procedures like those described previously, the 

NEXRAD rainfall data will be referred to as NEXRAD-based 

rainfall data.

Table 1. National Weather Service Cooperative meteorological 

stations near the McTier Creek watershed, South Carolina, used 

in this investigation.

[°, degrees; ', minutes]

Meteorological station Latitude 

(north)

Longitude 

(west) Data available

Number Name

380074 Aiken 5SE 33°30' 81°42' Rainfall, 

temperature

380506 Batesburg 33°54' 81°32' Rainfall

382712 Edgefield 3 NNE 33°50' 81°55' Rainfall

384607 Johnston 4 SW 33°47' 81°51' Rainfall, 

temperature

386775 Pelion 4 NW 33°43' 81°16' Rainfall, 

temperature

387631 Saluda 34°00' 81°46' Rainfall, 

temperature
6 Comparison of TOPMODEL Streamflow Simulations Using NEXRAD-Based and Measured Rainfall Data

Figure 2. National Weather Service Cooperative (NWS COOP) meteorological stations near the McTier 

Creek watershed, South Carolina, included in this investigation and NEXRAD-based grid locations.

Saluda

Batesburg

Aiken 5SE

Pelion 4NW

Johnston 4SW

Edgefield 3NNE

Watershed

boundary

02172305

20.36

19.17

18.85

18.22

14.68

9.02

1.9

81°45' 81°30' 81°15'

34°

33°45'

33°30'

SALUDA

AIKEN

LEXINGTON 

EDGEFIELD

RICHLAND

ORANGEBURG

NEWBERRY

Base from ESRI® Data & Maps, 2010

Albers Equal-Area Conic projection

Standard parallels 29 30 N and 45 30 N

Central meridian -96 00 00

BARNWELL

0

0 3 6 12 KILOMETERS

3 6 12 MILES

EXPLANATION

NEXRAD grid

U.S. Geological Survey gaging

station and number

National Weather Service cooperative stations

Precipitation and temperature collection

Precipitation collection

Note: distances on map are in miles

02172305
Rainfall Data Comparisons 7

Measured Rainfall and NEXRAD-Based 

Estimated Rainfall Comparisons Approach

In recent years, progress has been made in the collection 

and processing of NEXRAD-based data to help address some 

of the biases associated with those data (Nelson and others, 

2010). Measured rainfall at gaging stations also are not 

completely error free. Factors such as poor gage location or 

changing conditions near the gage, wind-induced issues, and 

human error are just some of the areas where uncertainty could 

be introduced into the measured data (Young and Brunsell, 

2008). In addition, there is substantial disparity in the scale 

of the NEXRAD-based data and the measured rainfall data. 

The typical NWS COOP raingage has an 8-inch opening. The 

NEXRAD-based estimates are from an approximately 4- by 

4-km grid in which the NWS COOP gage resides. However, 

because both estimates are 24-hour values and are spatially 

colocated, it is expected that the precipitation accumulations 

measured by both approaches are similar. Regardless, if 

similar patterns are detected at a number of locations, useful 

insights can be obtained.

In this study, both graphical and analytical methods were 

used to compare the measured rainfall and NEXRAD-based 

estimated rainfall at the NWS COOP station locations listed in 

table 1 for June 13, 2007, to September 30, 2009. This was the 

same period for which TOPMODEL simulations were generated at USGS gaging stations 02172300, McTier Creek near 

Monetta, SC, and 02172305, McTier Creek near New Holland, 

SC, as documented in Feaster and others (2010). Hereafter, 

USGS gaging stations 02172300 and 02172305 will be referred 

to as Monetta and New Holland, respectively (fig. 1). The 

measured data represent the daily accumulation of precipitation at the NWS COOP station locations, and the NEXRADbased data represent the estimated data from the grid cells 

that contain the NWS COOP station locations. Descriptive 

statistics were computed using only days when the rainfall was 

not zero for either the measured or the NEXRAD-based data 

(conditional statistics) (Jayakrishnan and others, 2004). 

Using the conditional data (days for which non-zero 

accumulations were reported for both the NEXRAD-based 

and measured data), the measured 24-hour rainfall at the NWS 

COOP stations and the concurrent NEXRAD-based estimates 

were compared using the following descriptive statistics: 

(1) total difference in rainfall, (2) estimation bias, (3) estimation efficiency, and (4) root mean square difference. The total 

difference in rainfall is simply the difference between the total 

of the NEXRAD-based estimates minus the total measured 

rainfall from the NWS COOP station for the assessment period.

Total difference in rainfall (inches) = 

NEXRAD-based total – measured total (1)

Estimation bias is the normalized difference between the total 

NEXRAD-based estimates and total measured rainfall for the 

comparison period and, therefore, is a comparison of total 

rainfall volume. A negative estimation bias indicates that the 

total NEXRAD-based rainfall is less than the total measured 

rainfall.

Estimation bias (percent) = 

 100 × (NEXRAD-based total – 

measured total)/measured total

(2)

Estimation efficiency (EE) is the same as the NSE (Nash 

and Sutcliffe, 1970), which is widely used to assess agreement between measured and modeled timeseries data such 

as streamflow (Feaster and others, 2010). For the NEXRADbased and measured rainfall comparisons, the estimation 

efficiency is computed as

EE

R W

R R

i i

i

n

i m

i

n = −

−

−

=

=

∑

∑

1

2

1

2

1

( )

( ) , (3)

where

n is the number of days of comparison;

Ri is the measured rainfall for day i;

Wi is the NEXRAD-based rainfall for the day i; 

and

Rm is the mean measured rainfall for all days.

The root mean squared difference (RMSD) between the 

measured rainfall and the NEXRAD-based estimated rainfall 

is computed as

RMSD

R W

n

i i = ∑( ) − 2

, (4)

where the variables are as previously defined. 

A lower RMSD indicates better agreement between 

the data being compared; however, the RMSD is sensitive 

to outliers because the differences in the data are squared 

(Janssen and Heuberger, 1993).

Rainfall Comparison Statistics

Comparison statistics for the measured data at the NWS 

COOP stations and the NEXRAD-based data estimated for the 

grid locations containing the NWS COOP stations indicate that 

the NEXRAD-based estimates were less than the measured 

rainfall at every location (table 2). The total difference in rainfall ranged from –1.3 inches at Batesburg to –21.6 inches at 

Pelion 4 NW. The estimation bias ranged from –1.6 percent at 

Batesburg to –24.1 percent at Edgefield 3 NNE. Jayakrishnan 

and others (2004) reported similar estimation bias in an 

investigation over a much larger area in the Texas-Gulf Basin. 

For that investigation, estimation bias indicated underestimation of rainfall at 88 percent of the COOP locations. Another 

study, which included 72 raingages in nearby Lexington 
8 Comparison of TOPMODEL Streamflow Simulations Using NEXRAD-Based and Measured Rainfall Data

County, S.C., and compared seven rainfall events covering a 

variety of storm types, showed that NEXRAD-based estimates 

consistently underestimated measured rainfall regardless of the 

storm type (Mizzell, 1999).

Residuals, which were computed for each day by 

subtracting the measured rainfall value from the NEXRADbased estimated rainfall value, do not appear to indicate a 

geographical pattern at the six NWS COOP station locations 

(figs. 3 and 4). For example, the Saluda, Edgefield 3 NNE, 

and Johnston 4 SW locations are all in a quadrant northwest 

of the McTier Creek watershed (fig. 2). Johnston 4 SW and 

Saluda have the highest estimation efficiency and lowest 

RMSD of the six locations, whereas Edgefield 3 NNE has 

the lowest estimation efficiency and highest RMSD. It is 

interesting that Batesburg and Saluda, the two locations 

farthest north of the McTier Creek watershed, have the lowest 

estimation bias; however, Edgefield 3 NNE and Pelion 4 NW, 

which are located the farthest west and east of the watershed, 

have similar estimation bias at –24.1 and –21.3 percent, 

respectively, the highest of all the stations.

Residuals at each location are fairly consistent throughout 

the comparison period with a few exceptions (fig. 3). For the 

Edgefield 3 NNE location, residual scatter tended to increase 

slightly during the later part of the comparison period. At the 

Johnston 4 SW location, an increase in the negative residuals 

is noted in the later part of the record. The residuals for the 

Pelion 4 NW location are the most consistent over the entire 

comparison period. 

In addition to the residuals, scatter plots of the data 

were generated, including a line of equal value to help 

distinguish estimation bias. As noted earlier, the Edgefield 3 

NNE and Pelion 4 NW locations had the largest estimation 

biases (fig. 4). The plots indicate an underestimation bias 

by NEXRAD-based estimates during large rain events at all 

locations and is especially evident at the Edgefield 3 NNE 

location. In addition, the plots show that the majority of 

rainfall events for the comparison period were less than 1 inch. 

Thus, calibration of the NEXRAD-based system is likely to 

be heavily weighted toward “normal” rainfall events, creating 

greater uncertainty in NEXRAD-based estimates for the 

infrequent, larger rainfall events. This situation is consistent 

with Westcott and others (2008) who found that larger events 

were underestimated by the NEXRAD-based estimates in the 

central Midwest.

Rainfall Comparison in McTier Creek 

Subwatersheds

One of the potential benefits of using NEXRAD-based 

rainfall data is the ability to generate a unique set of input rainfall data for subsections of a watershed under consideration. 

In Feaster and others (2010), the McTier Creek watershed 

was subdivided into 12 subwatersheds, and streamflow was 

simulated for each subwatershed for the period June 13, 

2007, to September 30, 2009 (fig. 5). To derive NEXRADbased daily precipitation for the subwatersheds, the Thiessen 

polygon algorithm was applied in ArcGIS to the center points 

of each NEXRAD-based grid cell, and the resultant polygons 

were mapped onto the 12 subwatersheds within the McTier 

Creek Basin above New Holland. Single-mass curves of the 

NEXRAD-based rainfall data were generated for the subwatersheds (fig. 6). The plot shows that the NEXRAD-based 

rainfall was similar for all subwatersheds through the summer 

of 2008. Subsequently, the NEXRAD-based rainfall volumes 

begin to deviate among the subwatersheds and tend to increase 

from MA01 to MA12. For the period June 13, 2007, to 

September 30, 2009, the total volume of rainfall at MA12 was 

about 15 percent greater than the total volume at MA01.

As a comparison of the variability in the NEXRAD-based 

rainfall data for the 12 subwatersheds, single-mass curves of 

measured rainfall were generated for the concurrent period of 

record from June 13, 2007, to September 30, 2009, at the six 

NWS COOP stations (table 1; fig. 7). The record at Aiken 5SE 

Table 2. Comparison statistics with respect to days with non-zero rainfall for measured and NEXRAD-based estimated daily rainfall for 

selected National Weather Service Cooperative stations near the McTier Creek watershed from June 13, 2007, to September 30, 2009.

Cooperative station

Total 

number of 

concurrent 

days

Number of 

non-zero 

days

Total 

measured 

rainfall 

(inches)

Total difference 

in rainfall 

(inches)

Estimation 

bias 

(percent)

Estimation 

efficiency

Root mean 

square 

difference 

Number Name (inches)

380074 Aiken 5SE 501 139 62.3 –10.8 –17.3 0.53 0.38

380506 Batesburg 798 178 82.8 –1.3 –1.6 0.50 0.38

382712 Edgefield 3 NNE 754 144 85.6 –20.6 –24.1 0.42 0.58

384607 Johnston 4 SW 768 201 92.6 –16.6 –17.9 0.70 0.31

386775 Pelion 4 NW 822 209 101.4 –21.6 –21.3 0.59 0.33

387631 Saluda 833 209 92.4 –6.3 –6.9 0.68 0.30
Rainfall Data Comparisons 9

January

2007

July

2007

February

2008

August

2008

March

2009

September

2009

January

2007

July

2007

February

2008

August

2008

March

2009

September

2009

Johnston 4 SW

3

2

1

0

–1

–2

–3

–4

Aiken 5SE

Saluda

3

2

1

0

–1

–2

–3

–4

Batesburg

Pelion 4 NW

3

2

1

0

–1

–2

–3

–4

Edgefield 3 NNE

Residual (NEXRAD-based rainfall minus measured rainfall), in inches

Figure 3. Residuals of NEXRAD-based and measured rainfall at National Weather Service Cooperative (NWS COOP) meteorological 

station locations near the McTier Creek watershed, South Carolina.
10 Comparison of TOPMODEL Streamflow Simulations Using NEXRAD-Based and Measured Rainfall Data

0 1 2 3 4 5 0 1 2 3 4 5

Johnston 4 SW

5

4

3

2

1

0

5

4

3

2

1

0

5

4

3

2

1

0

Aiken 5SE

Saluda Batesburg

Pelion 4 NW Edgefield 3 NNE

NEXRAD based rainfall, in inches

Measured rainfall, in inches

Figure 4. NEXRAD-based and measured rainfall at National Weather Service Cooperative (NWS COOP) meteorological station

locations near the McTier Creek watershed, South Carolina.
Rainfall Data Comparisons 11

Figure 5. McTier Creek subwatersheds, Aiken County, South Carolina (modified from Feaster and others, 2010).

MA10

MA11

MA09 MA08

MA04

MA02

MA07

MA05

MA03

MA01

MA06

MA12

81°39' 81°36' 81°33'

33°48'

33°45'

0 0.5 1 2 MILES

0 0.5 1 2 KILOMETERS

EXPLANATION

NEXRAD grid

Watershed boundary

Subwatershed boundary

Subwatershed descriptors

Base From National Hydrography Dataset, 1:100,000

ESRI® Data & Maps, 2010

Universal Transverse Mercator projection, zone 17

AIKEN

SALUDA

LEXINGTON

MA12
12 Comparison of TOPMODEL Streamflow Simulations Using NEXRAD-Based and Measured Rainfall Data Cumulative concurrent rainfall at NWS COOP station locations, in inches

100

90

80

70

60

50

40

30

20

10

0

Jan. Apr. July Oct. Jan. Apr. July Oct. Jan. July Jan.

2010

Apr. Oct.

Pelion 4NW

Johnston 4SW

Aiken 5SE

Batesburg

Saluda

Edgefield 3NNE

EXPLANATION

2007 2008 2009

Figure 6. Single-mass curves 

of NEXRAD-based daily rainfall 

for McTier Creek subwatersheds 

MA01 to MA12 for June 13, 2007, 

to September 30, 2009. (Locations 

are shown in fig. 5.)

Figure 7. Single-mass curves 

of cumulative rainfall at National 

Weather Service Cooperative 

(NWS COOP) meteorological 

stations near McTier Creek 

watershed for concurrent periods 

of record from June 13, 2007, to 

September 30, 2009.

Cumulative NEXRAD-based rainfall, in inches

100

90

80

70

60

50

40

30

20

10

0

MA01

MA02

MA03

MA04

MA05

MA06

MA07

MA08

MA09

MA10

MA11

MA12

Apr. Aug.

2007

Nov. Feb. June Sept. Dec. Mar. July

2009

Oct. Jan.

2010

Subwatershed descriptors

EXPLANATION

2008
TOPMODEL Application 13

ended on October 31, 2008, and therefore is concurrent only to 

that time. For the concurrent period at the other five stations, 

Pelion 4NW had the largest total volume of rainfall, and Batesburg had the smallest volume of rainfall with the total concurrent rainfall at Pelion 4 NW being about 25 percent greater than 

the total concurrent rainfall at Batesburg. The Johnston 4SW 

total concurrent rainfall was about 13 percent larger than the 

total concurrent rainfall at Batesburg. As with the NEXRADbased rainfall for the subwatersheds, the total volume of rainfall 

for the concurrent period at the NWS COOP stations appears 

to increase from north to south. This seems to indicate that 

the pattern of increasing total NEXRAD-based rainfall from 

subwatersheds MA01 to MA12 rainfall is reasonable although 

the magnitude of that difference (15 percent) occurs over a 

much smaller distance than that of the NWS COOP stations 

(figs. 2, 6, and 7). That is, the distance between NWS COOP 

stations Saluda and Aiken 5SE is approximately 35 miles 

(fig. 2) and the difference in total rainfall for the concurrent 

period at those stations is about 23 percent (fig. 7). However, 

the distance from the northern end of the McTier Creek watershed to the southern end is approximately 9 miles (fig. 2) with 

the difference in total rainfall between subwatersheds MA01 to 

MA12, as noted above, being about 15 percent (fig. 7).

One additional comparison was made to assess the 

variability of the NEXRAD-based rainfall as compared 

to the variability of the NWS COOP measured rainfall at 

the NWS COOP station locations. Single-mass curves of 

NEXRAD-based rainfall were generated from NEXRAD grid 

cells in which the NWS COOP rainfall stations are located. 

For comparisons with the NWS COOP rainfall data as shown 

in figure 7, only NEXRAD-based rainfall that were concurrent 

with the NWS COOP data were included (fig. 8). For that 

period, Aiken 5SE had the largest total for the NEXRADbased rainfall data and Johnston 4 SW had the smallest total 

with the difference being about 11 percent. The difference is 

similar to the 13 percent difference between the total NWS 

COOP rainfall data at Johnston 4 SW and Batesburg and also 

is similar to the 15 percent variability noted for the NEXRADbased rainfall data at subwatersheds MA01 and MA12. It 

should once again be noted that the areal distance of the 

McTier Creek watershed compared to the distance between the 

NWS COOP stations is much different. It seems reasonable 

to assume that rainfall over a smaller area would tend to be 

more similar than the rainfall over a larger area, thus these 

differences and (or) similarities are noteworthy.

TOPMODEL Application

The topography-based hydrological model 

(TOPMODEL) is a physically based watershed model that 

simulates streamflow by using the variable source-area 

concept of streamflow generation (fig. 9). TOPMODEL is 

a semidistributed model that uses a topographic index to 

Note: These NEXRAD-based

rainfall are concurrent with the

NWS COOP rainfall that were at

all NWS COOP station locations

Concurrent cumulative NEXRAD-based rainfall at NWS COOP

station locations, in inches

90

80

70

60

50

40

30

20

10

0

Jan. Apr. July Oct. July

For the concurrent period from June 13, 2007, to September 30, 2009

Jan. Apr. Oct. Jan. July Jan.

2010

Apr. Oct.

Pelion 4NW

Johnston 4SW

Aiken 5SE

Batesburg

Saluda

Edgefield 3NNE

EXPLANATION

2007 2008 2009

Figure 8. Single-mass curves of 

cumulative NEXRAD-based rainfall 

at National Weather Service 

Cooperative (NWS COOP) 

meteorological station locations 

near the McTier Creek watershed 

for periods concurrent with the 

concurrent NWS COOP rainfall 

data from June 13, 2007, to 

September 30, 2009.
14 Comparison of TOPMODEL Streamflow Simulations Using NEXRAD-Based and Measured Rainfall Data

Precipitation (ppt)

Water table

Open-water

bodies (qsrip)

Impervious flow (qimp)

Infiltration-excess

overland flow (qinf)

Evapotranspiration (pet)

Total flow in stream (qpred)

Land surface

Stream

Potential regions for

overland saturation flow (qof)

and return flow (qret)

Subsurface flow (qb)

(includes traditional base

flow and subsurface

storm flow)

group hydrologically similar areas of a watershed. In the 

variable source-area concept, saturated land-surface areas are 

sources of streamflow during precipitation events in several 

ways. Saturation overland flow (also called Dunne overland 

flow) is generated if the subsurface is not transmissive and 

if slopes are gentle and convergent (Dunne and Black, 1970; 

Wolock, 1993). Saturation overland flow can arise from direct 

precipitation on the saturated land-surface areas or from return 

flow of subsurface water to the surface in the saturated areas. 

Subsurface stormwater flow is generated if the near-surface 

soil zone is very transmissive (large saturated hydraulic 

conductivity) and if gravitational gradients (slopes) are steep. 

Whipkey (1965) defined subsurface stormwater flow as 

underground stormwater flow that reaches the stream channel 

without entering the groundwater storage zone. 

The version of TOPMODEL used by Feaster and others 

(2010) also was used by Kennen and others (2008) in New 

Jersey and by Williamson and others (2009) in Kentucky. 

For a more in-depth discussion of the mathematical underpinnings of TOPMODEL, see Beven and Kirkby (1979), 

Wolock (1993), Beven (1997), Hornberger and others (1998), 

or Beven (2001). Feaster and others (2010) describe the 

calibration of TOPMODEL for the McTier Creek watershed 

(fig. 1) in considerable detail. Except as noted, the same 

calibration parameter set (table 3) was used to generate 

simulations described below.

TOPMODEL Code Correction

Prior to this investigation, a detailed assessment of the 

water balance for the McTier Creek simulations identified a 

single sign error in the TOPMODEL source code that caused 

an error in the intra-timestep water balance calculations. The 

identified error was not expected to significantly alter previous 

TOPMODEL streamflow results as much as it affected the 

partitioning of water between internal compartments. To verify 

the assumed minimal impact on the streamflow simulations, 

the corrected source code was recompiled (Kenneth Odom, 

U.S. Geological Survey, written commun., 2011), and the 

corrected TOPMODEL was then used to simulate streamflow 

at Monetta and New Holland for the period June 13, 2007, 

to September 30, 2009. Results from these comparisons are 

discussed in the following sections.

Figure 9. Definition of selected water-source variables from TOPMODEL (modified from Wolock, 1993).
TOPMODEL Streamflow Simulations 15

TOPMODEL Streamflow Simulations

Feaster and others (2010) described the calibration 

of TOPMODEL for the McTier Creek watershed (fig. 1). In 

the McTier Creek watershed, two USGS streamflow gages 

were available for the comparison of measured daily mean 

flows with simulated daily mean flows: (1) Monetta and 

(2) New Holland (table 4). In this investigation, TOPMODEL 

was used to assess the difference in the streamflow simulations using NWS COOP measured and NEXRAD-based 

rainfall data. 

The same goodness-of-fit statistics used by Feaster and 

others (2010) were used in this report to compare various 

simulations. Those statistics were (1) the Nash-Sutcliffe coefficient of model-fit efficiency index (NSE) (Nash and Sutcliffe, 

1970), (2) Pearson’s correlation coefficient (r), (3) the bias, 

(4) the root mean square error (RMSE), and (5) the mean 

absolute error (MAE). 

The Nash-Sutcliffe coefficient of model-fit efficiency, 

NSE, is calculated as 

NSE

Qo Qo Qo Qs

Qo Qo

i i i

i

n

i

n

i

i

n =

− − −

−

= =

=

∑ ∑

∑

( ) ( )

( )

2 2

1 1

2

1

, (5)

where

Qoi is the observed flow for time step i,

Qo is the mean observed flow for the simulation 

period,

Qsi is the simulated flow for time step i, and

n is the number of time steps in the simulation 

period.

A NSE value of 1.0 would indicate a perfect fit between the 

observed and simulated data, and a value of zero or less would 

indicate that using the mean of the observed data would be a 

better predictor than the model (Krause and others, 2005). 

Pearson’s correlation coefficient, r, is calculated as

r

Qo Qo Qs Qs

Qo Qo Qs Qs

i i

i

n

i i

i

n

i

n

=

− −

− − 



 





=

= =

∑

∑ ∑

( )( )

( ) ( )

1

2 2

1 1

1

2 , (6)

where Qs is the mean simulated flow for the simulation period, 

and other variables are as previously defined.

Pearson’s r is one of the most commonly used measures 

of correlation and is called the linear correlation coefficient 

because it measures the linear association between two 

datasets (Helsel and Hirsch, 1995). If the data lie exactly along 

a straight line with positive slope, then r is 1.

The bias is calculated as

bias

Qs Qoi

n

i

i

n

=

− =

∑1

, (7)

where the variables are as previously defined.

The bias is the mean of the residuals between the simulated 

and observed data. The bias indicates whether the model is, on 

average, over- or underpredicting the value being assessed. 

Bias also can be presented in a percentage form (Moriasi 

and others, 2007) as

PBIAS

Qsi Qoi

Qoi

i

n

i

n =

− 























=

=

∑

∑

1

1

*100 , (8)

where PBIAS is percent bias and measures the average tendency of the simulated data to be larger or smaller than the 

observed data and expressed as a percentage, and the other 

Table 3. Parameter values used for the TOPMODEL calibration 

for McTier Creek near Monetta and for McTier Creek near New 

Holland for June 13, 2007, to September 30, 2009 (Feaster and 

others, 2010).

Model parameter Monetta New 

Holland

Total area (square kilometers) 40.46 79.41

Lake area (square kilometers) 0.63 1.22

Stream area (square kilometers) 0.47 0.91

Saturated conductivity (inches 

per hour)

6.57 7.17

Soil depth (inches) 74.0 74.2

Field capacity (unitless) 0.18 0.18

Water holding capacity (unitless) 0.094 0.094

Porosity (unitless) 0.39 0.39

Percent impervious 1.5 1.3

Percent road impervious 1.0 0.9

Latitude (decimal degree) 33.754 33.718

Effective impervious (decimal percent)0.8 0.8

Conductivity multiplier (unitless) 2.9 2.9

Percent macropore (decimal percent) 0.5 0.5

Scaling parameter, m (millimeters) 47.0 45.0

Depth of root zone (meters) 1.9 1.9

Impervious runoff constant (unitless) 0.10 0.10

TR55 curve number (unitless) 98 98

Uplake area (square kilometers) 34.0 59.0

Lake delay (unitless) 1.2 1.2
16 Comparison of TOPMODEL Streamflow Simulations Using NEXRAD-Based and Measured Rainfall Data

variables are as previously defined. The optimal value of 

PBIAS is zero, with low-magnitude values indicating accurate model simulation. In this formulation of PBIAS, positive 

values indicate model overestimation and negative values 

indicate model underestimation.

Although the bias is a useful statistic, it can conceal large 

absolute differences between the observed and simulated data. 

That is, a bias of zero only indicates that the model is equally 

over- and underpredicting, but provides no information on 

the magnitude of the over- or underpredictions. One approach 

to assessing such differences is by computing the variance, 

which is the average square of the residuals (Norman and 

Streiner, 1997). Because the variance is not in the units of the 

original data, however, it is difficult to interpret. Alternatively, 

the square root of the variance (RMSE) is in the units of the 

original data. The RMSE represents the mean of the absolute 

distance between the observed and simulated values. A 

lower RMSE indicates a better fit between the observed and 

simulated data. The RMSE is calculated as

RMSE Qs Qo

n

i i = ∑( ) − 2

, (9)

where the variables are as previously defined.

Janssen and Heuberger (1993) noted that the RMSE 

also is sensitive to outliers because the differences between 

observed and simulated data are squared. The mean absolute 

error (MAE) is less sensitive to outliers. The MAE is similar 

to the bias except that it is the mean of the absolute value of 

the residuals as opposed to the mean of the actual residuals. 

Thus, the MAE provides the average of the magnitude of the 

residuals. In general, the RMSE can be expected to be greater 

than or equal to MAE for the range of most values. The degree 

to which the RMSE exceeds the MAE provides an indication 

of the extent to which outliers exist in the data (Legates and 

McCabe, 1999). The MAE is calculated as

MAE

Qs Qo

n

i i

i

n

=

− =

∑| |

1

. (10)

In addition to the goodness-of-fit statistics, single-mass 

curves for the assessment periods also were used for comparison of various simulations. The single-mass curve presents 

the cumulative daily mean flow and, therefore, represents the 

Table 4. Station number and name, period of record used in the model simulations (Feaster and others, 2010), and model simulation 

period reference name for the McTier Creek watershed, South Carolina.

Station 

number Station name Station reference name

Drainage area, 

in square miles

Period of record used 

in the model simulation

02172300 McTier Creek near Monetta, SC Monetta 15.6 June 13, 2007–September 30, 2009

02172305 McTier Creek near New Holland, SC New Holland 30.7 June 13, 2007–September 30, 2009

cumulative volume of daily mean flow for the period being 

analyzed. A substantial change in the slope of the single-mass 

curve indicates changes in the hydrologic regime. Thus, a 

steep slope would indicate a wet period, whereas a flat slope 

would indicate a dry period. 

Effects of TOPMODEL Code Correction

Using the parameter sets and environmental forcings 

used in Feaster and others (2010), simulations were generated 

with the corrected TOPMODEL code. The goodness-of-fit 

statistics indicate that the simulations generated using the 

corrected TOPMODEL code more closely match the observed 

data than those generated using the original code (table 5). 

The RMSE decreased by 9 percent at Monetta and 5 percent 

at New Holland although certainly not enough to invalidate 

the previous results. In contrast, the effect of the code 

correction on the total streamflow volume was minimal; the 

difference in volume at Monetta and New Holland was –0.3 

and –0.4 percent, respectively (fig. 10A and B), and the two 

simulated single-mass curves were indistinguishable. The 

simulations in the sections of this report comparing measured 

and NEXRAD-based rainfall data were all generated using the 

corrected TOPMODEL code.

Streamflow Simulations Using NEXRAD-Based 

Rainfall Data

Using the updated TOPMODEL code and the calibration 

parameters from Feaster and others (2010) (table 3), NEXRADbased rainfall data generated for the Monetta and New Holland 

watersheds were used to simulate streamflow for June 13, 

2007, to September 30, 2009, which was the period for which 

concurrent observed daily mean streamflow data were available 

at both streamgages. The simulations were then compared with 

those generated using measured rainfall data. The simulations 

were made with the TOPMODEL that had been calibrated 

using NWS COOP rainfall data. Consequently, the streamflow 

simulations for which TOPMODEL was calibrated using NWS 

COOP rainfall data but for which streamflow was generated 

using NEXRAD-based rainfall data will hereafter be referred 

to as TOPMODEL-C/N. The streamflow simulations for which 

TOPMODEL was calibrated using NWS COOP rainfall data 

and for which streamflow was generated using NWS COOP 

data will be referred to as TOPMODEL-C/C.
TOPMODEL Streamflow Simulations 17

Monetta Streamflow

Using the calibration parameters described in Feaster 

and others (2010), TOPMODEL streamflow simulations 

using NEXRAD-based rainfall data were generated for June 

13, 2007, to September 30, 2009, at the Monetta watershed 

(table 3). Goodness-of-fit statistics were computed from 

comparisons of TOPMODEL-C/C and TOPMODEL-C/N 

streamflow simulations with observed streamflow. Graphical 

comparisons of the streamflow simulations and observed 

streamflow also were made (fig. 11). During comparisons of 

the daily mean flows, it was observed that the TOPMODELC/N streamflow simulations showed a significant rainfall event 

on March 21, 2009, which was not seen in the TOPMODELC/C streamflow simulations nor was it seen in the observed 

streamflow data. A review of the NEXRAD-based rainfall for 

Monetta and New Holland indicated that 2.06 and 2.15 inches 

of rain, respectively, were estimated for March 21, 2009. No 

rainfall was estimated for the several days before or after that 

date. Based on reviews of the measured rainfall data and the 

observed streamflow data for Monetta and New Holland, it was 

concluded that the NEXRAD-based rainfall value for March 

21, 2009, was erroneous and, therefore, was set to zero. This 

disparity between measured data and NEXRAD-based modeled 

precipitation estimates illustrates a substantial concern when 

using atmospheric phenomenon to estimate precipitation at the 

land surface. Several of the goodness-of-fit statistics are based 

on the squared differences between simulated and observed 

data, and having an erroneous value of this magnitude would 

significantly alter those statistics.

Goodness-of-fit statistics RMSE, MAE, r, and NSE 

indicate that the TOPMODEL-C/N streamflow simulations 

provide similar results with respect to matching the observed 

streamflow data as do the TOPMODEL-C/C streamflow 

simulations (table 6). However, the bias of –1.03 cubic 

feet per second (ft3/s) (PBIAS of –11.0 percent) for the 

TOPMODEL-C/N streamflow simulations indicate that 

overall the model underpredicts the observed streamflow. This 

underprediction also is evident in the mean and median for 

the TOPMODEL-C/N streamflow simulations. Based on the 

previous comparisons of NWS COOP and NEXRAD-based 

rainfall at the NWS COOP station locations (table 2), underprediction of streamflow would be expected. Comparison 

plots of the TOPMODEL-C/C streamflow simulations and 

the TOPMODEL-C/N streamflow simulations, as well as the 

observed streamflow, also indicate this bias, which is most 

clearly seen in the comparison of the single-mass curves 

(fig. 11A, B, and C).

New Holland Streamflow

For New Holland, the calibration parameters described 

in Feaster and others (2010) were used in TOPMODEL as 

well as the NEXRAD-based rainfall to simulate streamflow 

for June 13, 2007, to September 30, 2009 (table 3). As was 

the case for Monetta, the RMSE, MAE, r, and NSE statistics 

indicate that the TOPMODEL-C/C and TOPMODEL-C/N 

streamflow simulations provide similar results with respect to 

matching the observed streamflow data (table 7). However, as 

Table 5. Goodness-of-fit statistics at McTier Creek near Monetta and McTier Creek near New Holland from Feaster and others (2010) 

and from the corrected TOPMODEL source code simulations for June 13, 2007, to September 30, 2009.

[ft3/s, cubic foot per second; RMSE, root mean square error; MAE, mean absolute error; r, Pearson’s correlation coefficient; NSE, Nash-Sutcliffe coefficient of 

model-fit efficiency; NC, not computed]

Statistic

McTier Creek near Monetta McTier Creek near New Holland

Observed

TOPMODEL 

Simulated 

(Feaster and 

others, 2010)

TOPMODEL 

Simulated 

(using updated 

source code)

Observed

TOPMODEL 

Simulated 

(Feaster and 

others, 2010)

TOPMODEL 

Simulated 

(using updated 

source code)

Maximum (ft3/s) 123 67 80 163 130 159

Mean (ft3/s) 10.4 10.7 10.6 21.8 21.6 21.5

Median (ft3/s) 7.5 7.3 7.2 17.0 14.9 14.6

Minimum (ft3/s) 1.8 2.0 1.6 2.6 5.0 4.08

Standard deviation (ft3/s) 12.0 9.0 9.9 19.9 17.1 18.8

Bias (ft3/s) 0.27 0.24 0.15 0.07

PBIAS (percent) NC 2.2 NC 0.31

RMSE (ft3/s) 7.6 6.9 11.7 11.1

MAE (ft3/s) 4.2 3.9 7.2 6.80

r 0.78 0.82 0.80 0.83

NSE 0.61 0.67 0.64 0.67
18 Comparison of TOPMODEL Streamflow Simulations Using NEXRAD-Based and Measured Rainfall Data

Figure 10. Single-mass curves of simulated and observed daily 

mean flow at McTier Creek watershed near A, Monetta and 

B, New Holland for June 13, 2007, to September 30, 2009.

Figure 11. TOPMODEL-C/C and TOPMODEL-C/N streamflow 

simulations in relation to observed streamflow for A, daily mean 

flows, B, flow-duration curves, and C, single-mass curves at 

McTier Creek near Monetta (station 02172300) for June 13, 2007, 

to September 30, 2009. NWS COOP is National Weather Service 

Cooperative meteorological station.

Cumulative daily mean flow, in cubic feet per second

10,000

9,000

8,000

7,000

6,000

5,000

4,000

3,000

2,000

1,000

0

Observed

TOPMODEL (Feaster and others, 2010)

TOPMODEL (updated source code)

EXPLANATION

Observed

TOPMODEL (Feaster and others, 2010)

TOPMODEL (updated source code)

EXPLANATION

A

20,000

18,000

16,000

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

B

2007 2008 2009

J J A S O N D J F M A M J J A S O N D J F M A M J J A S O

Cumulative daily mean flow, in cubic feet per second Daily mean flow, in cubic feet per second

140

130

120

110

100

90

80

70

60

50

40

30

20

10

0

1,000

100

10

1

10,000

9,000

8,000

7,000

6,000

5,000

4,000

3,000

2,000

1,000

0

0 10 20 30 40 50

Percent of time indicated flow was equaled or exceeded

60 70 80 90 100

A. Daily mean flows

B. Flow-duration curves

C. Single-mass curves

2007 2008 2009

J J A S O N D J F M A M J J A S O N D J F M A M J J A S O

2007 2008 2009

J J A S O N D J F M A M J J A S O N D J F M A M J J A S O

Observed

TOPMODEL (NWS COOP)

TOPMODEL (NEXRAD)

EXPLANATION

Observed

TOPMODEL (NWS COOP)

TOPMODEL (NEXRAD)

EXPLANATION

Observed

TOPMODEL (NWS COOP)

TOPMODEL (NEXRAD)

EXPLANATION
TOPMODEL Streamflow Simulations 19

Table 6. Goodness-of-fit statistics for McTier Creek near Monetta for the June 13, 2007, to 

September 30, 2009 simulation.

[ft3/s, cubic foot per second; RMSE, root mean square error; MAE, mean absolute error; r, Pearson’s correlation 

coefficient; NSE, Nash-Sutcliffe coefficient of model-fit efficiency]

Statistic Observed

TOPMODEL-C/C 

(Calibrated and 

simulated using 

measured rainfall)

TOPMODEL-C/N 

(Calibrated using 

measured rainfall 

and simulated using 

NEXRAD-based 

rainfall)

Maximum (ft3/s) 123 80 88

Mean (ft3/s) 10.4 10.6 9.4

Median (ft3/s) 7.5 7.2 5.7

Minimum (ft3/s) 1.8 1.6 1.5

Standard deviation (ft3/s) 12.0 9.9 9.6

Bias (ft3/s) 0.24 –1.03

PBIAS (percent) 2.2 –11.0

RMSE (ft3/s) 6.9 6.6

MAE (ft3/s) 3.9 3.5

r 0.82 0.84

NSE 0.67 0.70

Table 7. Goodness-of-fit statistics for McTier Creek near New Holland for the June 13, 2007, to 

September 30, 2009 simulation.

[ft3/s, cubic foot per second; RMSE, root mean square error; MAE, mean absolute error; r, Pearson’s correlation 

coefficient; NSE, Nash-Sutcliffe coefficient of model-fit efficiency]

Statistic Observed

TOPMODEL-C/C 

(Calibrated and 

simulated using 

measured rainfall)

TOPMODEL-C/N 

(Calibrated using 

measured rainfall 

and simulated using 

NEXRAD-based 

rainfall)

Maximum (ft3/s) 163 159 179

Mean (ft3/s) 21.8 21.5 20.5

Median (ft3/s) 17.0 14.6 12.9

Minimum (ft3/s) 2.6 4.1 3.6

Standard deviation (ft3/s) 19.9 18.8 20.1

Bias (ft3/s) 0.07 –0.94

PBIAS (percent) 0.31 –4.6

RMSE (ft3/s) 11.1 11.2

MAE (ft3/s) 6.8 6.9

r 0.82 0.84

NSE 0.67 0.67
20 Comparison of TOPMODEL Streamflow Simulations Using NEXRAD-Based and Measured Rainfall Data

also was the case with Monetta, a bias of –0.94 ft3/s (PBIAS 

of –4.6 percent) for the TOPMODEL-C/N streamflow simulations indicate that overall the model underpredicts streamflow, 

which also is indicated in the mean and median for the 

TOPMODEL-C/N streamflow simulations. Comparison plots 

of the TOPMODEL-C/C and TOPMODEL-C/N streamflow simulations, as well as the observed streamflow data, 

confirmed this bias (fig. 12A, B, and C).

Comparison of NWS COOP and NEXRAD-Based 

Rainfall Data Inputs for the Monetta 

and New Holland Watershed TOPMODEL 

Simulations

A comparison of the cumulative NWS COOP rainfall 

data used in the TOPMODEL calibration for the McTier 

Creek watershed with the NEXRAD-based rainfall data for 

the Monetta and New Holland watersheds shows similar 

differences in the rainfall volumes as previously noted 

(fig. 13). For the period June 13, 2007, to September 30, 

2009, the total volume for the NWS COOP rainfall was 

94.8 inches compared to 84.5 and 88.6 inches for the 

NEXRAD-based rainfall at Monetta and New Holland, 

respectively. This denotes an estimation bias of –10.9 and 

–6.5 percent, respectively, for the NEXRAD-based data, 

which is in the range of values for comparisons at the NWS 

COOP stations (table 2) and is similar to the PBIAS for the 

TOPMODEL-C/N streamflow simulations at Monetta and 

New Holland (tables 6 and 7).

The actual number of days of zero and non-zero rainfall 

were compared for the NWS COOP and NEXRAD-based rainfall for the Monetta and New Holland watersheds. For the 841 

days in the period June 13, 2007, to September 30, 2009, there 

were 344 non-zero days of measured rainfall and 263 non-zero 

days of estimated NEXRAD-based rainfall at Monetta. The 

total amount of NEXRAD-based rainfall that occurred on 

days for which no measured rainfall occurred was 0.82 inches; 

however, the total amount of measured rainfall that occurred 

on days for which the NEXRAD-based rainfall was zero was 

4.23 inches. Because the same NWS COOP rainfall data were 

used for both Monetta and New Holland, the number of zero 

and non-zero days of rainfall are the same as noted above. For 

the NEXRAD-based rainfall for the New Holland watershed, 

there were 268 non-zero days of estimated rainfall. The total 

amount of NEXRAD-based rainfall that occurred on days 

for which no measured rainfall occurred was 0.97 inches; 

however, the total amount of measured rainfall that occurred 

on days for which the NEXRAD-based rainfall was zero was 

4.08 inches. Part of the disparity between the NEXRAD-based 

rainfall and the NWS COOP measured rainfall may be the 

additional days for which rainfall was recorded at the NWS 

COOP stations but for which zero rainfall was indicated in the 

NEXRAD-based data. 

Figure 12. TOPMODEL-C/C and TOPMODEL-C/N streamflow 

simulations in relation to observed streamflow for A, daily mean 

flows, B, flow-duration curves, and C, single-mass curves at 

McTier Creek near New Holland (station 02172305) for June 13, 

2007, to September 30, 2009. NWS COOP is National Weather 

Service Cooperative meteorological station. Cumulative daily mean flow, in cubic feet per second Daily mean flow, in cubic feet per second

240

220

200

180

160

140

120

100

80

60

40

20

0

1,000

100

10

1

Observed

TOPMODEL (NWS COOP)

TOPMODEL (NEXRAD)

EXPLANATION

Observed

TOPMODEL (NWS COOP)

TOPMODEL (NEXRAD)

EXPLANATION

20,000

18,000

16,000

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

2007 2008 2009

0 10 20 30 40 50

Percent of time indicated flow was equaled or exceeded

60 70 80 90 100

A. Daily mean flows

B. Flow-duration curves

C. Single-mass curves

J J A S O N D J F M A M J J A S O N D J F M A M J J A S O

2007 2008 2009

J J A S O N D J F M A M J J A S O N D J F M A M J J A S O

Observed

TOPMODEL (NWS COOP)

TOPMODEL (NEXRAD)

EXPLANATION
TOPMODEL Streamflow Simulations 21

estimates, TOPMODEL was recalibrated using NEXRADbased rainfall as was done in the calibration using measured 

rainfall data. The TOPMODEL simulations for which 

NEXRAD-based rainfall were used to calibrate the model and 

were used for streamflow simulations will hereafter be referred 

to as TOPMODEL-N/N.

Similar to the TOPMODEL calibration documented in 

Feaster and others (2010), the parameter estimation program 

PEST was used to assist in the recalibration of the model 

parameters for which measured values were not available 

(Doherty, 2005) for the TOPMODEL setup using NEXRADbased rainfall. The PEST program is a nonlinear parameter 

estimator that adjusts model parameters until the fit between 

model estimates and measured observations are optimized 

by using a weighted least squares scheme. From the PEST 

analyses based on sensitivity assessments and similarity of 

the values of variables from this recalibration as compared 

to the original calibration of the McTier Creek TOPMODEL, 

only the decay or scaling parameter (m) was adjusted for 

the recalibration with the NEXRAD-based rainfall. For 

TOPMODEL, m is arguably the single most important variable 

in determining the fit of simulated data to the observed data. 

Beven (2001) states that a physical interpretation of the decay 

parameter m is that it controls the effective depth or active 

storage of the catchment soil profile. A larger value of m effectively increases the active storage of the soil profile, whereas a 

small value generates a shallow effective soil with pronounced 

transmissivity decay.

Monetta (NEXRAD-based)

New Holland (NEXRAD-based)

McTier Creek watershed (NWS COOP)

EXPLANATION

Cumulative rainfall, in inches

100

90

80

70

60

50

40

30

20

10

0

Mar. May July

2007

Sept. Nov. Jan. Mar. May July Sept. Nov. Jan. Mar. May July Sept. Nov.

2008 2009

Recalibration of TOPMODEL Using NEXRADBased Rainfall Data

Because rainfall is one of the main forcing functions in 

watershed models, model calibration based on minimizing 

differences between observed and simulated streamflow data 

depends on the particular rainfall data used for the calibration. Specifically, the model calibration process can indirectly 

account for some amount of error in the model input data. 

Assuming relatively stationary conditions and a robust model, 

calibration can be assumed to provide satisfactory results 

outside the calibration period under similar meteorological 

conditions. Where a watershed model has been calibrated to 

use measured rainfall data, however, a comparison of model 

simulations based on the substitution of the measured rainfall 

with NEXRAD-based rainfall, or any other estimated rainfall 

data for that matter, which have been shown to have a bias with 

respect to the measured data, may not provide an impartial 

comparison. Because the comparison of the measured and 

NEXRAD-based rainfall at the NWS COOP stations indicated 

a consistent negative NEXRAD-based bias at all stations 

(table 2), replacing the measured data with the NEXRAD-based 

rainfall in TOPMODEL would be expected to produce degraded 

simulations of streamflow, which was the case, as indicated by 

the negative bias of –1.03 and –0.94 ft3/s for Monetta and New 

Holland, respectively (tables 6 and 7). Thus, as a final assessment of the potential results of using NEXRAD-based rainfall 

Figure 13. Cumulative total daily 

measured rainfall for the McTier Creek 

watershed in relation to total daily 

NEXRAD-based rainfall for Monetta 

and New Holland. NWS COOP is 

National Weather Service Cooperative 

meteorological station.
22 Comparison of TOPMODEL Streamflow Simulations Using NEXRAD-Based and Measured Rainfall Data

Monetta and New Holland Streamflow 

Simulations

The calibration at Monetta and New Holland using 

NEXRAD-based rainfall resulted in the m value being 

adjusted from 47.0 to 49.0 and 45.0 to 46.0, respectively. All 

other calibration parameters were kept the same as those listed 

in table 3. The same goodness-of-fit statistics and plots previously presented also were generated to compare TOPMODELN/N streamflow simulations of daily mean streamflow to 

observed daily mean streamflow. For Monetta and New 

Holland, the comparisons were made using the period June 13, 

2007, to September 30, 2009 (tables 8 and 9; figs. 14A–C and 

15A–C, respectively). For both locations, descriptive statistics 

(maximum, mean, median, minimum, and standard deviation) 

for streamflow data from the TOPMODEL-C/C simulated 

streamflow tended to more closely match those statistics for 

the observed streamflow data than did the TOPMODEL-N/N 

simulated streamflow but only slightly more (tables 8 and 

9). On the other hand, most of the goodness-of-fit statistics (RMSE, MAE, r, and E) were slightly better for the 

TOPMODEL-N/N streamflow simulations. The exception 

was estimation bias for the TOPMODEL-N/N streamflow 

simulations at Monetta and New Holland, which was –0.24 

and –0.18 ft3/s, respectively. Thus, by adjusting the scaling 

parameter, the simulations using the NEXRAD-based rainfall 

may be made comparable to those using the measured rainfall 

data, with respect to the timing and shape of the hydrographs. 

Overall, however, the simulations using NEXRAD-based 

rainfall data still tended to slightly underpredict the total 

volume, as indicated by the negative bias. Nonetheless, as the 

PBIAS indicates, the magnitude of the differences between 

the TOPMODEL-N/N and TOPMODEL-C/N streamflow 

simulations are similar (tables 8 and 9). In addition, figure 15A

shows that at New Holland, the TOPMODEL-N/N streamflow 

simulations tended to better represent the high-flow events, 

which also was the case for some of the high-flow events at 

Monetta (fig. 14A).

Moriasi and others (2007) did a thorough literature 

review on various watershed model applications in an effort 

to develop guidelines for quantifying the accuracy of such 

models. General guidelines were developed on the basis of 

several goodness-of-fit statistics, including NSE and PBIAS, 

in addition to graphical assessments. Based on their guidelines 

for streamflow simulation models using a monthly timestep, 

including the NSE and PBIAS statistics, the TOPMODELC/C and TOPMODEL-N/C simulations merit a performance 

rating of “good” based on the NSE (0.65<NSE<0.75) and 

“very good” based on the PBIAS (PBIAS <±10). Although 

their recommendations are based on simulations using a 

monthly timestep, they found that model simulations were 

typically poorer for shorter timesteps compared to longer 

timesteps. Thus, the performance ratings applied to the 

TOPMODEL simulations, which used a daily timestep, are 

likely conservative.

Table 8. Goodness-of-fit statistics for the McTier Creek near Monetta TOPMODEL simulations for 

June 13, 2007, to September 30, 2009.

[ft3/s, cubic foot per second; RMSE, root mean square error; MAE, mean absolute error; r, Pearson’s correlation 

coefficient; NSE, Nash-Sutcliffe coefficient of model-fit efficiency]

Statistic Observed

TOPMODEL-C/C 

(Calibrated and 

simulated using 

measured rainfall)

TOPMODEL-C/N 

(Calibrated using 

measured rainfall 

but simulated using 

NEXRAD-based 

rainfall)

TOPMODEL-N/N 

(Calibrated and 

simulated using 

NEXRAD-based 

rainfall)

Maximum (ft3/s) 123 80 88 87

Mean (ft3/s) 10.4 10.6 9.4 10.2

Median (ft3/s) 7.5 7.2 5.7 6.8

Minimum (ft3/s) 1.8 1.6 1.5 2.2

Standard deviation (ft3/s) 12.0 9.9 9.6 9.5

Bias (ft3/s) 0.24 –1.03 –0.24

PBIAS (percent) 2.2 –11.0 –2.3

RMSE (ft3/s) 6.9 6.6 6.4

MAE (ft3/s) 3.9 3.5 3.5

r 0.82 0.84 0.85

NSE 0.67 0.70 0.71
TOPMODEL Streamflow Simulations 23

Streamflow Simulations in McTier Creek 

Subwatersheds

One of the benefits of a watershed model such as 

TOPMODEL is the ability to simulate streamflow for 

subwatersheds using the model parameters from a calibrated 

watershed model. If the subwatershed basin characteristics 

are similar to those of the calibrated watershed, it is reasonable to assume the parameters from the calibrated watershed 

will provide acceptable streamflow simulations at ungaged 

locations. In Feaster and others (2010), the McTier Creek 

watershed was subdivided into 12 subwatersheds, and streamflow was simulated for each subwatershed for the period 

June 13, 2007, to September 30, 2009, using the calibrated 

TOPMODEL parameters from Monetta and New Holland as 

well as soil and watershed characteristics from the subwatersheds (fig. 5). As a result, the subwatershed models represented separate, individual applications of TOPMODEL. For 

those simulations, the same measured rainfall data were used 

for all subwatersheds as were used for the Monetta and New 

Holland watersheds.

The individual subwatersheds were not gaged, and 

therefore, no continuous streamflow record was available 

to compare with the simulated streamflow. Nonetheless, a 

comparison of the simulated streamflow for each subwatershed from the TOPMODEL-C/C and TOPMODEL-N/N 

models was made to assess the similarity in the simulated 

hydrographs. As in Feaster and others (2010), streamflow 

simulations for subwatersheds also were summed and 

compared with streamflow simulations for the Monetta and 

New Holland watersheds to assess overall reasonableness of 

subwatershed streamflow simulations.

TOPMODEL-C/C and TOPMODEL-N/N streamflow 

simulations were generated for subwatersheds MA01 to 

MA12. The streamflow simulations from subwatersheds 

MA01 to MA06 were summed and compared to the 

TOPMODEL streamflow simulation at the Monetta gage. 

The streamflow simulations for subwatersheds MA01 to 

MA12 were summed and compared to the TOPMODEL simulation at the New Holland gage. For the TOPMODEL-C/C 

streamflow simulations, the total volume of the streamflow 

simulated for the subwatersheds was about 1 and 3 percent 

greater than the total volume of the simulations at Monetta 

and New Holland (figs. 16A and B), respectively. For the 

TOPMODEL-N/N streamflow simulations, the total streamflow volume for the subwatersheds was about 1 and 6 percent 

greater than the total streamflow volume of the TOPMODEL 

simulations at Monetta and New Holland (figs. 17A and B), 

respectively. 

Plots showing TOPMODEL-C/C and TOPMODEL-N/N 

streamflow simulations for each subwatershed were generated 

for the period June 13, 2007, to September 30, 2009 (fig. 18). 

The plots indicate that the hydrograph shapes tend to be similar 

with varying deviations in magnitude for particular events. 

The percent difference for the total streamflow volume for the 

period simulated at each subwatershed was computed (table 10). 

Table 9. Goodness-of-fit statistics for the McTier Creek near New Holland TOPMODEL simulations for 

June 13, 2007, to September 30, 2009.

[ft3/s, cubic foot per second; RMSE, root mean square error; MAE, mean absolute error; r, Pearson’s correlation 

coefficient; NSE, Nash-Sutcliffe coefficient of model-fit efficiency]

Statistic Observed

TOPMODEL-C/C 

(Calibrated and 

simulated using 

measured rainfall)

TOPMODEL-C/N 

(Calibrated using 

measured rainfall 

but simulated using 

NEXRAD-based 

rainfall)

TOPMODEL-N/N 

(Calibrated and 

simulated using 

NEXRAD-based 

rainfall)

Maximum (ft3/s) 163 159 179 177

Mean (ft3/s) 21.8 21.5 20.5 21.2

Median (ft3/s) 17.0 14.6 12.9 13.8

Minimum (ft3/s) 2.6 4.1 3.6 4.7

Standard deviation (ft3/s) 19.9 18.8 20.1 19.7

Bias (ft3/s) 0.07 –0.94 –0.18

PBIAS (percent) 0.31 –4.6 –0.87

RMSE (ft3/s) 11.1 11.2 10.9

MAE (ft3/s) 6.8 6.9 6.6

r 0.82 0.84 0.85

NSE 0.67 0.67 0.68
24 Comparison of TOPMODEL Streamflow Simulations Using NEXRAD-Based and Measured Rainfall Data

Figure 14. TOPMODEL-C/C and TOPMODEL-N/N streamflow 

simulations in relation to A, daily mean flows, B, flow-duration 

curves, and C, single-mass curves at McTier Creek near Monetta 

(station 02172300) for June 13, 2007, to September 30, 2009. NWS 

COOP is National Weather Service Cooperative meteorological 

station.

Figure 15. TOPMODEL-C/C and TOPMODEL-N/N streamflow 

simulations in relation to observed streamflow for A, daily mean 

flows, B, flow-duration curves, and C, single-mass curves at 

McTier Creek near New Holland (station 02172305) for June 13, 

2007, to September 30, 2009. NWS COOP is National Weather 

Service Cooperative meteorological station.

Cumulative daily mean flow, in cubic feet per second Daily mean flow, in cubic feet per second

140

130

120

110

100

90

80

70

60

50

40

30

20

10

0

A. Daily mean flows

1,000

100

10

1

B. Flow-duration curves

10,000

9,000

8,000

7,000

6,000

5,000

4,000

3,000

2,000

1,000

0

C. Single-mass curves

0 10 20 30 40 50

Percent of time indicated flow was equaled or exceeded

60 70 80 90 100

2007 2008 2009

J J A S O N D J F M A M J J A S O N D J F M A M J J A S O

2007 2008 2009

J J A S O N D J F M A M J J A S O N D J F M A M J J A S O

Observed

TOPMODEL (NWS COOP)

TOPMODEL (NEXRAD)

EXPLANATION

Observed

TOPMODEL (NWS COOP)

TOPMODEL (NEXRAD)

EXPLANATION

Observed

TOPMODEL (NWS COOP)

TOPMODEL (NEXRAD)

EXPLANATION

Daily mean flow, in cubic feet per second

A. Daily mean flows

B. Flow-duration curves

C. Single-mass curves

Observed

TOPMODEL (NWS COOP)

TOPMODEL (NEXRAD)

EXPLANATION

Observed

TOPMODEL (NWS COOP)

TOPMODEL (NEXRAD)

EXPLANATION

Observed

TOPMODEL (NWS COOP)

TOPMODEL (NEXRAD)

Cumulative daily mean flow, in cubic feet per second

240

220

200

180

160

140

120

100

80

60

40

20

0

1,000

100

10

1

20,000

18,000

16,000

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

0 10 20 30 40 50

Percent of time indicated flow was equaled or exceeded

60 70 80 90 100

2007 2008 2009

J J A S O N D J F M A M J J A S O N D J F M A M J J A S O

2007 2008 2009

J J A S O N D J F M A M J J A S O N D J F M A M J J A S O
TOPMODEL Streamflow Simulations 25

Figure 16. TOPMODEL-C/C streamflow simulations of daily mean 

streamflow for June 13, 2007, to September 30, 2009, at A, McTier 

Creek near Monetta (station 02172300) and the sum of the simulated daily mean streamflow for subwatersheds MA01 to MA06, 

and B, McTier Creek near New Holland (station 02172305) and the 

sum of the simulated daily mean streamflow for subwatersheds 

MA01 to MA12.

Figure 17. TOPMODEL-N/N streamflow simulations for June 

13, 2007, to September 30, 2009, at A, McTier Creek near Monetta 

(station 02172300) and the sum of simulated daily mean streamflow 

for subwatersheds MA01 to MA06, and B, McTier Creek near New 

Holland (station 02172305) and the sum of simulated daily mean 

streamflow for subwatersheds MA01 to MA12. NWS COOP is 

National Weather Service Cooperative meteorological station.

Daily mean flow, in cubic feet per second

New Holland

Sum of subwatersheds MA01–MA12

EXPLANATION

Monetta

Sum of subwatersheds MA01–MA06

EXPLANATION

A. McTier Creek near Monetta

200

180

160

140

120

100

80

60

40

20

0

200

180

160

140

120

100

80

60

40

20

0

B. McTier Creek near New Holland

2007

May Aug. Nov. Feb. May Aug. Nov. Feb. May Aug. Nov.

2008 2009

Daily mean flow, in cubic feet per second

New Holland

Sum of subwatersheds MA01–MA12

EXPLANATION

Monetta

Sum of subwatersheds MA01–MA06

EXPLANATION

A. McTier Creek near Monetta

200

180

160

140

120

100

80

60

40

20

0

200

180

160

140

120

100

80

60

40

20

0

B. McTier Creek near New Holland

2007

May Aug. Nov. Feb. May Aug. Nov. Feb. May Aug. Nov.

2008 2009
26 Comparison of TOPMODEL Streamflow Simulations Using NEXRAD-Based and Measured Rainfall Data

Figure 18. TOPMODEL-C/C and TOPMODEL-N/N simulated daily mean streamflow at McTier Creek subwatersheds MA01 to MA12 for 

June 13, 2007, to September 30, 2009.

2007

June Aug. Oct. Dec. Feb. Apr. June Aug. Oct. Dec. Feb. Apr. June Aug. Oct.

2008 2009 2007

June Aug. Oct. Dec. Feb. Apr. June Aug. Oct. Dec. Feb. Apr. June Aug. Oct.

2008 2009

MA01

40

35

30

25

20

15

10

5

0

40

35

30

25

20

15

10

5

0

40

35

30

25

20

15

10

5

0

MA02

MA03 MA04

MA05 MA06

Daily mean flow, in cubic feet per second

Figure 18. 

TOPMODEL (NWS COOP)

TOPMODEL (NEXRAD)

EXPLANATION
TOPMODEL Streamflow Simulations 27

Figure 18. Graphs showing TOPMODEL-C/C and TOPMODEL-N/N simulated daily mean streamflow at McTier Creek subwatersheds 

MA01 to MA12 for June 13, 2007, to September 30, 2009.—Continued

2007

June Aug. Oct. Dec. Feb. Apr. June Aug. Oct. Dec. Feb. Apr. June Aug. Oct.

2008 2009 2007

June Aug. Oct. Dec. Feb. Apr. June Aug. Oct. Dec. Feb. Apr. June Aug. Oct.

2008 2009

MA07

40

35

30

25

20

15

10

5

0

40

35

30

25

20

15

10

5

0

40

35

30

25

20

15

10

5

0

MA08

MA09 MA10

MA11 MA12

Daily mean flow, in cubic feet per second

Figure 18. cont. 

TOPMODEL (NWS COOP)

TOPMODEL (NEXRAD)

EXPLANATION
28 Comparison of TOPMODEL Streamflow Simulations Using NEXRAD-Based and Measured Rainfall Data

Table 10. McTier Creek subwatershed area and percent difference between TOPMODEL-C/C and TOPMODEL-N/N simulated total 

volume of daily mean streamflow.

[mi2, square mile; C/C, TOPMODEL calibration and simulations done using measured rainfall; N/N, TOPMODEL calibration and simulations done using 

NEXRAD-based rainfall]

McTier Creek Subwatersheds

MA01 MA02 MA03 MA04 MA05 MA06 MA07 MA08 MA09 MA10 MA11 MA12

Area (mi2) 2.66 6.75 1.46 1.63 0.81 2.31 4.88 2.58 2.40 1.67 0.91 2.59

Percent 

difference

21.8 6.9 0.4 7.1 4.3 0.5 –6.4 –7.9 –10.0 –6.1 –19.3 –12.7

Table 11. Total NEXRAD-based rainfall by subwatershed and differences from total NEXRAD-based rainfall and total NWS COOP 

rainfall for McTier Creek near New Holland.

[in., inch]

McTier Creek Subwatersheds

MA01 MA02 MA03 MA04 MA05 MA06 MA07 MA08 MA09 MA10 MA11 MA12

Total NEXRADbased daily rainfall 

(in.)

82.2 83.8 86.2 85.0 87.5 86.5 93.2 93.7 93.3 90.9 88.6 94.6

Difference from total 

NEXRAD-based 

rainfall at New 

Holland (in.)

–6.4 –4.8 –2.4 –3.6 –1.1 –2.1 4.6 5.1 4.7 2.3 0.0 6.0

Difference from 

total NWS COOP 

rainfall at New 

Holland (in.)

–12.5 –10.9 –8.6 –9.8 –7.2 –8.3 –1.5 –1.0 –1.4 –3.9 –6.1 –0.2

the case for measured data. The data in table 10, however, 

are presented without taking the absolute value in order to 

illustrate the direction and magnitude of differences between 

TOPMODEL-C/C and TOPMODEL-N/N streamflow simulations. The percentage differences for subwatersheds MA01 to 

MA06 are all positive, indicating the total TOPMODEL-C/C 

simulated streamflow is larger than the total TOPMODELN/N simulated streamflow. For subwatersheds MA07 to 

MA12, the opposite is true with the total volume from the 

TOPMODEL-N/N streamflow simulations being greater than 

the TOPMODEL-C/C streamflow simulations. Weighting the 

percentage differences by area and then summing the values 

results in an average percentage difference of –0.5 percent.

The differences in the total NEXRAD-based rainfall for 

the McTier Creek watershed at New Holland (88.6 inches) for 

the period June 13, 2007, to September 30, 2009, and the total 

NEXRAD-based rainfall for each subwatershed for the same 

period are given in table 11. Also listed are the differences in 

the total NWS COOP rainfall at New Holland (94.7 inches) 

and the total NEXRAD-based rainfall for each subwatershed. 

The percent difference is computed as the absolute value 

of the difference in the total streamflow volume from the 

TOPMODEL-C/C streamflow simulations and TOPMODELN/N streamflow simulations divided by the average of the 

two total streamflow volumes and that quantity is multiplied 

by 100: 

PercentDifference

x x

x x = −

 +



 





× | | 1 2

1 2

2

100 , (11)

where

x1 is the total simulated flow using measured 

rainfall data, and

x2 is the total simulated flow using NEXRADbased rainfall data.

The absolute value of the difference is taken here because 

the values being compared are both model estimates and, 

therefore, neither is considered to be more “correct,” as is 
TOPMODEL Streamflow Simulations 29

The largest difference occurred at MA01 with the total 

NEXRAD-based rainfall being 12.5 inches less than the total 

NWS COOP rainfall. Consequently, it is reasonable that the 

largest percentage difference in total volume of daily mean 

streamflow between the TOPMODEL-C/C and TOPMODELN/N simulations also would occur at MA01 (table 10). On the 

other hand, the smallest percentage difference in total volume 

of daily mean streamflow between the TOPMODEL-C/C and 

TOPMODEL-N/N simulations occurred at MA03 (table 10), 

yet the smallest difference in total rainfall between the NWS 

COOP rainfall for New Holland and the NEXRAD-based rainfall for the subwatersheds occurred at MA12 (table 11). Other 

factors that influence differences in the streamflow simulations 

for the whole watershed as compared to the subwatersheds are 

the soil properties included in TOPMODEL. For simulations 

at New Holland, the soil properties are a weighted-average of 

those properties across the whole watershed, whereas for the 

subwatersheds, the soil properties are a weighted-average just 

for the subwatershed. Consequently, these differences along 

with the differences in rainfall input also are influencing the 

differences in streamflow simulations between the subwatersheds and the simulations for the whole watershed.

As noted earlier, the NEXRAD-based rainfall amounts 

for the subwatersheds tend to increase in a southeasterly direction (fig. 6). A similar southeasterly increase also is noticeable 

in the cumulative rainfall amounts for the measured stations 

(figs. 2 and 7); however, a map of the total annual precipitation for South Carolina based on the period 1971–2000 shows 

that the expected range of rainfall in the area of the McTier 

Creek watershed is about 8 inches, which is slightly smaller 

than the range of 12.4 inches between the largest and smallest 

total NEXRAD-based rainfall for the subwatersheds (fig. 19; 

table 11). Whether these differences are due to variations 

related to the periods being compared or is an issue with the 

NEXRAD-based rainfall is unknown.

0 20 40 60 80 MILES

0 20 40 60 80 KILOMETERS

88

80

72

64

56

48

40

Rainfall, in inches

EXPLANATION

Figure 19.

ATLANTIC

OCEAN

NORTH CAROLINA

GEORGIA

N

Figure 19. South Carolina precipitation, 1971–2000 (from Wachob and others, 2009).
30 Comparison of TOPMODEL Streamflow Simulations Using NEXRAD-Based and Measured Rainfall Data

Summary and Conclusions

The decision to use NEXRAD-based rainfall data or 

measured rainfall data in watershed model simulations may be 

influenced by several factors, such as the number of measured 

rainfall stations in or around the study watershed, the available 

rainfall data for the period of interest, the confidence in the 

accuracy of both the NEXRAD-based and measured data, and 

geographical considerations. The simple fact that NEXRADbased data can be generated for a watershed at a finer spatial 

resolution than the measured data does not necessarily 

guarantee better results with respect to simulating streamflow. 

The comparisons in this report also show that although differences in the NEXRAD-based and measured rainfall data exist, 

calibrating a hydrologic model with the rainfall data that will 

be used in streamflow simulations generated with that model is 

important and may result in simulations that are overall similar 

with respect to magnitude of the goodness-of-fit statistics used 

to assess those simulations.

Measured rainfall data were compared with NEXRADbased rainfall data at six NWS COOP stations that surround 

the McTier Creek watershed. The comparison, using rainfall 

data for non-zero days at the NWS COOP station locations, 

indicated that the NEXRAD-based data were lower at all six 

locations with the total difference in rainfall ranging from 

–1.3 inches at the Batesburg COOP station to –21.6 inches 

at the Pelion COOP station. The estimation bias, which is a 

comparison of the total volume of rainfall for the comparison 

period, ranged from –1.6 percent at the Batesburg COOP 

station to –24.1 percent at the Edgefield 3 NNE COOP station. 

These findings are comparable with results reported for larger 

watersheds or coverage areas.

A comparison of the number of days with zero and 

non-zero rainfall for the NWS COOP and NEXRAD-based 

rainfall for the Monetta and New Holland watersheds 

indicated that the NEXRAD-based data had fewer days of 

non-zero rainfall. For the period June 13, 2007, to September 

30, 2009 (841 days), the number of non-zero days in the 

NEXRAD-based data at Monetta and New Holland was 263 

and 268, respectively. For the same period, the NWS COOP 

measured data had 344 days of non-zero rainfall. Thus, at 

Monetta, there were 31 percent more days for which rainfall 

was recorded than was indicated in the NEXRAD-based 

estimates and for New Holland, there were 28 percent more 

days for which rainfall was recorded than was indicated in the 

NEXRAD-based estimates. In terms of total rainfall, for the 

days for which no rainfall was noted in the NEXRAD-based 

estimates, there was an additional 4.23 and 4.08 inches of 

recorded rainfall at the NWS COOP stations for Monetta and 

New Holland, respectively. 

Using the calibrated watershed model (TOPMODEL) 

for McTier Creek, streamflow simulations were made using 

NEXRAD-based rainfall data and then compared with simulations using measured rainfall data at USGS stations 02172300, 

McTier Creek near Monetta, SC, and 02172305, McTier 

Creek near New Holland, SC. Several of the goodness-of-fit 

statistics that provide numerical comparisons of the timing 

and shape of simulated and observed streamflow hydrographs 

indicated similar results from the streamflow simulations using 

measured and NEXRAD-based rainfall data. However, bias, 

which is the average of the residuals between the simulated 

and observed streamflow data, showed that streamflow 

simulations using the NEXRAD-based rainfall had a negative bias of –1.03 and –0.94 ft3/s, respectively, compared to a 

bias of 0.07 and 0.24 ft3/s, respectively, using the measured 

rainfall data. The equivalent percentage biases (PBIAS) were 

–11.0 and –4.6 percent, respectively, and 0.31 and 2.2 percent, 

respectively. Given that the comparisons of the measured 

and NEXRAD-based rainfall at the NWS COOP stations had 

previously showed a negative bias in the NEXRAD-based 

data, these result were expected. 

As an additional comparison of TOPMODEL simulations using the measured and NEXRAD-based rainfall, 

TOPMODEL was recalibrated using NEXRAD-based rainfall 

(TOPMODEL-N/N). Although still negative, the recalibration reduced the bias at Monetta and New Holland to –0.24 

and –0.18 ft3/s, respectively. Other goodness-of-fit statistics 

showed similar results as those from the TOPMODEL 

simulations using measured rainfall data. The comparisons 

of TOPMODEL simulations using measured and NEXRADbased rainfall for the McTier Creek watershed indicate that 

both rainfall datasets provide reasonable results with respect to 

matching the measured streamflow. 

One of the perceived benefits of NEXRAD-based rainfall 

is the ability to generate a finer-resolution set of input rainfall 

data for subwatersheds in the simulated watershed. In order 

to evaluate this benefit, McTier Creek was divided into 12 

subwatersheds, and streamflow simulations were generated 

for each subwatershed using both measured rainfall data and 

NEXRAD-based rainfall. The streamflow simulations from the 

subwatersheds upstream from the Monetta gage were summed 

and compared to the TOPMODEL streamflow simulation at 

the Monetta gage. The same was done for the subwatersheds 

upstream from the New Holland gage. For the TOPMODEL 

simulations using measured rainfall, the total streamflow 

volume for the subwatersheds was about 1 and 3 percent 

greater than the total simulated streamflow volumes at the 

Monetta and New Holland gages, respectively. Using the 

NEXRAD-based rainfall, the total volumes were about 1 and 

6 percent greater, respectively. 

A comparison of the variability in the NEXRAD-based 

rainfall data for the 12 subwatersheds also was made using 

single-mass curves. The results showed that the total volume 

of rainfall increased by about 15 percent from subwatershed 

MA01 to subwatershed MA12. A similar comparison was 

made of the variability of the total rainfall volume at the 

six NWS COOP stations. The Pelion station had the largest 

volume and the Batesburg station had the smallest with about 

a 25 percent difference between the two. The difference in 

the total measured rainfall between the Johnston 4SW station 

and the Batesburg station was about 10 percent. As was true 
References Cited 31

with the NEXRAD-based rainfall data, the total volume of 

the measured rainfall data for the comparison period appeared 

to increase from north to south although the magnitude of 

the NEXRAD-based difference (15 percent) occurred over 

a much smaller distance than the NWS COOP stations. The 

distance between the most northern and most southern located 

NWS COOP stations is about 35 miles, whereas the distance 

from the northern part of the McTier Creek watershed to the 

southern part is about 9 miles. 

Regarding subwatershed simulations, NEXRAD-based 

estimates provide the opportunity for finer spatial resolution 

input rainfall data over an area were the rainfall data are 

known to vary. In smaller watersheds, this variability is likely 

to be within the uncertainty of the watershed model simulations. Nonetheless, where measured rainfall data are limited, 

NEXRAD-based multisensory precipitation data may provide 

an effective alternative.

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Manuscript approved June 14, 2012

For more information about this publication, contact:

Toby Feaster, Hydrologist

USGS South Carolina Water Science Center

Clemson Field Office

405 College Ave., Suite 200

Clemson, SC 29631

phone: (864) 656-6747

e-mail: tfeaster@usgs.gov

Or visit the South Carolina Water Science Center

Web site at: http://sc.water.usgs.gov

Edited by Kay P. Naugle

Illustrations by Jeffrey L. Corbett

Layout by Mike G. Deacon

Cover design concept by James R. Douglas

A PDF version of this publication is available online at

http://pubs.usgs.gov/sir/2011/5180

Feaster and others—Comparison of TOPMODEL Streamflow Simulations Using NEXRAD-Based and Measured Rainfall Data—Scientific Investigations Report 2012–5120