U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2009–5248 

Prepared in cooperation with the Kentucky Division of Water 

The Water Availability Tool for Environmental Resources 

(WATER): A Water-Budget Modeling Approach for 

Managing Water-Supply Resources in Non-Karst Areas of 

Kentucky (Phase I)—Data Processing and Model Structure 

Documentation 
Cover: Screen-capture images of model output from the Water Availability Tool for Environmental Resources (WATER), Phase I, 

application.
The Water Availability Tool for 

Environmental Resources (WATER): 

A Water-Budget Modeling Approach for 

Managing Water-Supply Resources in 

Kentucky—Phase I: Data Processing, 

Model Development, and Application to 

Non-Karst Areas 

By Tanja N. Williamson, Kenneth R. Odom, Jeremy K. Newson, Aimee C. Downs, 

Hugh L. Nelson Jr., Peter J. Cinotto, and Mark A. Ayers 

Prepared in cooperation with the Kentucky Division of Water 

Scientific Investigations Report 2009–5248 

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: 2009

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U.S. Government.

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

reproduce any copyrighted materials contained within this report.

Suggested citation:

Williamson, T.N., Odom, K.R., Newson, J.K., Downs, A.C., Nelson Jr., H.L., Cinotto, P.J., and Ayers, M.A., 2009, The 

Water Availability Tool for Environmental Resources (WATER)—A water-budget modeling approach for managing 

water-supply resources in Kentucky—Phase I—Data processing, model development, and application to non-karst 

areas: U.S. Geological Survey Scientific Investigations Report 2009–5248, 34 p. 
iii

Contents

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

Introduction.....................................................................................................................................................1

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

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

Study Area, Data Sources, and Model Development and Calibration..................................................4

Selection of Non-Karst Study-Area and Calibration Basins .........................................................4

Data Sources.........................................................................................................................................4

Model Development and Calibration Methods................................................................................4

Structure and Output of WATER..................................................................................................................5

Data Processing and Organization ...........................................................................................................10

Stream-Channel Initiation and Width..............................................................................................10

Soil-Data Aggregation .......................................................................................................................15

Withdrawal and Discharge Data......................................................................................................16

Topographic Wetness Index Data....................................................................................................16

Overview of the WATER Histogram Builder ..........................................................................16

Climate Data.........................................................................................................................................17

Statistical Evaluation and Calibration of WATER....................................................................................17

Tested Scenarios ................................................................................................................................17

Model Results......................................................................................................................................19

Precipitation Data Source........................................................................................................19

Rooting Depth.............................................................................................................................19

Hydraulic Conductivity..............................................................................................................26

Scaling Parameter.....................................................................................................................26

Model Applications and Limitations .........................................................................................................26

Summary........................................................................................................................................................32

References Cited..........................................................................................................................................32

Figures

1. Map showing Water Availability Tool for Environmental Resources (WATER) 

Phase-I development and calibration basins and study area, with hydrologic 

unit code six (HUC-6) basins shown for reference .................................................................3

2. Computer screen shot showing graphic user interface for WATER....................................6

3. Directory structure of WATER ....................................................................................................7

4. Diagram showing data framework of WATER..........................................................................8

5. Diagram showing Fortran TOPMODEL code structure used by WATER.............................9

6. Schematic showing soil layers and relation to WATER .......................................................15

7. Computer screen shot showing example hydrograph and flow-duration curve 

produced by WATER for calibration basin 03282040.............................................................18
iv

8–18. Graphs showing:

8. Bias of WATER calibration scenarios.............................................................................20

9. Root mean square error of WATER calibration scenarios..........................................21

10. Correlation coefficients of WATER calibration scenarios ..........................................22

11. Nash-Sutcliffe efficiency of WATER calibration scenarios........................................23

12. Comparison of precipitation sources and discharge for calibration 

basin 03282040....................................................................................................................24

13. Comparison of rooting depths and discharge for calibration basin 03282040 ........25

14. Statistical summary of scaling parameter (m) calibration scenarios ......................27

15. Comparison of scaling parameter (m) scenarios and discharge for calibration 

basin 03282040....................................................................................................................28

16. Statistical summary of WATER estimates for 30- and 40-centimeter 

rooting-depth scenarios ...................................................................................................29

17. Comparison of 30- and 40-centimeter rooting-depth scenarios using 0.1 m

for calibration basin 03282040 .........................................................................................30

18. Statistical summary of WATER estimates for calibration basins in order 

of basin area.......................................................................................................................31 

Tables

1. Data sources .................................................................................................................................5

2. Basin characteristics: attributes, data sources, and processing summary.....................11

3. Calibration basins used for statistical evaluation and calibration of WATER ..................19

4. Matrix of scenarios tested for WATER efficiency and calibration.....................................19 
v

Conversion Factors and Abbreviations

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

acre 0.4047 hectare (ha)

square kilometer (km2

) 0.3861 square mile (mi2)

Volume

million gallons (Mgal) 3,785 cubic meter (m3

)

Flow rate

million gallons per day (Mgal/d) 0.04381 cubic meter per second (m3

/s)

Hydraulic conductivity

inch per hour (in/hr) 7.057 micrometer per sec (μm/sec)

micrometer per second (μm/s) 0.1417 inch per hour (in/hr)

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 the North American Datum of 1983 (NAD 83)

Abbreviations used in the text

ASCII American Standard Code for Information Exchange

COOP National Weather Service Cooperative Network

DEM Digital elevation model

Ef Nash-Sutcliffe efficiency

GUI Graphic user interface

HB Histogram builder

KDOW Kentucky Division of Water

KPDES Kentucky Point Discharge Elimination System 

Ksat Saturated hydraulic conductivity

NEXRAD Next Generation Radar

NLCD National Land Cover Database
vi

NOAA National Oceanic and Atmospheric Administration

NWS National Weather Service

RMSE Root mean square error or standard deviation

SSURGO Soil Survey Geographic Database

STATSGO State Survey Geographic Database

TWI Topographic wetness index (also known as the compound topographic 

 index or wetness index)

USDA U.S. Department of Agriculture

USGS U.S. Geological Survey

WATER Water Availability Tool for Environmental Resources 
Abstract 

The Water Availability Tool for Environmental Resources 

(WATER) was developed in cooperation with the Kentucky 

Division of Water to provide a consistent and defensible 

method of estimating streamflow and water availability in 

ungaged basins. WATER is process oriented; it is based on the 

TOPMODEL code and incorporates historical water-use data 

together with physiographic data that quantitatively describe 

topography and soil-water storage. The result is a user-friendly 

decision tool that can estimate water availability in non-karst 

areas of Kentucky without additional data or processing. 

The model runs on a daily time step, and critical source data 

include a historical record of daily temperature and precipitation, digital elevation models (DEMs), the Soil Survey 

Geographic Database (SSURGO), and historical records of 

water discharges and withdrawals. The model was calibrated 

and statistically evaluated for 12 basins by comparing the estimated discharge to that observed at U.S. Geological Survey 

streamflow-gaging stations. When statistically evaluated over 

a 2,119-day time period, the discharge estimates showed a 

bias of -0.29 to 0.42, a root mean square error of 1.66 to 5.06, 

a correlation of 0.54 to 0.85, and a Nash-Sutcliffe Efficiency 

of 0.26 to 0.72. The parameter and input modifications that 

most significantly improved the accuracy and precision of 

streamflow-discharge estimates were the addition of Next 

Generation radar (NEXRAD) precipitation data, a rooting 

depth of 30 centimeters, and a TOPMODEL scaling parameter 

(m) derived directly from SSURGO data that was multiplied 

by an adjustment factor of 0.10. No site-specific optimization 

was used. 

Introduction

A detailed water-budget analysis is a critical starting 

point for developing realistic targets for within-basin and 

basin-to-basin water-supply development. In compliance 

with the Kentucky Department of Environmental Protection 

Management Plan, the U.S. Geological Survey (USGS), in 

cooperation with the Kentucky Division of Water (KDOW), 

directed the development of a computerized water-budgeting 

tool, eventually planned for statewide implementation, to aid 

water-allocation decisions. 

Some of the criteria for development were that the 

approach

• be successful in various land covers and physiographic 

terranes, 

• require minimal training and data interaction by users, 

and

• be built around a database that could be easily updated. 

The outcome is the Water Availability Tool for Environmental Resources (WATER), a process-oriented, data-driven 

model that provides a better understanding of streamflow 

and water balance than was previously available for ungaged 

basins in Kentucky. 

For planning purposes, the model development was 

divided into two implementation phases: the first phase 

(Phase I) included basic model development and calibration for flows in non-karst basins only, and the second phase 

(Phase II) incorporated additional model development to 

account for flows in karstic basins, enhanced parameter 

The Water Availability Tool for Environmental Resources 

(WATER): A Water-Budget Modeling Approach for 

Managing Water-Supply Resources in Non-Karst Areas 

of Kentucky (Phase I)—Data Processing and Model 

Structure Documentation 

By Tanja N. Williamson, Kenneth R. Odom, Jeremy K. Newson, Aimee C. Downs, Hugh L. Nelson, Peter J. 

Cinotto, Mark A. Ayers 
2 The Water Availability Tool for Environmental Resources (WATER): Phase I—Data Processing and Documentation

regionalization techniques, and more diverse graphical user 

interface (GUI) functionality. The outcome of Phase I was a 

Water-Availability Toolbox, later renamed the Water Availability Tool for Environmental Resources (WATER), that was 

based on a TOPMODEL approach (Beven and Kirkby, 1979; 

Wolock, 1993) but that has been implemented by incorporating recently released data for state topography, soils, and 

water use. WATER was designed to work for Hydrologic Unit 

Code 12 (HUC-12) basins (85 km2 on average in Kentucky); 

however, it has been tested in basins ranging in area from 16 

to 1,565 km2. 

WATER provides a better means than was previously 

available for assessing and predicting streamflow and water 

balance in ungaged watersheds in Kentucky. Consequently, 

the Commonwealth will now have a higher level of confidence 

in estimates of water availability and will be better able to 

identify areas where shortages are likely. Although WATER 

has been designed mainly for assessment of water budgets for 

individual basins, model outputs can be useful in stormwater 

and water-quality analyses, including hydrographs, flow-duration curves, and overland-flow distributions. 

WATER packages several data-analysis functions into 

one GUI. In the Phase I version of WATER, the Fortran 

TOPMODEL code has been encapsulated into a Java GUI that 

accesses an extensive database of basin characteristics, as well 

as other background data. The result is a versatile water-quantification program that meets the water-availability assessment 

needs of KDOW. WATER has also been designed so that it can 

be expanded for other regulation-related computations including, but not limited to, sediment and nutrient loads, as well as 

flows that are necessary for ecological viability. 

Purpose and Scope

This report details the process-oriented approach of 

WATER in terms of data available for the Commonwealth 

of Kentucky. WATER was developed in two phases; the first 

phase is the subject of this report and focused on the design 

of WATER and implementation of the tool for two non-karst 

areas of Kentucky, including the Southwestern and Central 

Appalachians and Western Allegheny Plateau Ecoregions 

(Woods and others, 2002). For Phase I, basin characteristics 

were aggregated for 500 HUC-12 basins in the non-karst areas 

of Kentucky (fig. 1). The second phase will involve preparation of WATER for the remaining 782 HUC-12 basins of 

Kentucky and will include development of a model to address 

flow in karst-area basins and improve GUI functionality. 

Basic model development, including data and program 

structure, preprocessing decisions and statistical sampling of 

spatial-data layers, and selection of development and calibration basins are illustrated and explained. Finally, the Phase I 

WATER calibration process for non-karst basins in Kentucky 

is explained. This report is intended as a technical manual for 

the Phase I version of WATER, to explain the data decisions, 

processing steps, and limitations that model users will need to 

know for successful application of WATER. 

Previous Studies

WATER is built upon a physically based hydrologic 

model that simulates the variable-source-area concept of 

streamflow and is an extension of the TOPMODEL code 

described in Wolock (1993). The TOPMODEL code was 

originally developed by Beven and Kirkby (1979); however, 

many researchers have extensively modified the TOPMODEL 

code, and numerous versions now exist in several program 

languages (for example, Robson and others, 1992; Romanowicz, 1997). TOPMODEL has been used to study a variety 

of hydrologic research topics, including topographic effects 

on water quality (Wolock, 1988; Wolock and others, 1989, 

1990), topographic effects on streamflow (Beven and Wood, 

1983; Beven and others, 1984; Kirkby, 1986), spatial-scale 

effects on hydrologic processes (Sivapalan and others, 1987, 

1990; Beven and others, 1988; Wood and others, 1988, 1990; 

Famiglietti and Wood, 1991; Famiglietti, 1992), and the geomorphic evolution of basins (Ijjász-Vásquez and others, 1992). 

TOPMODEL has also been used for estimating regional-scale 

variability in hydrologic properties in the United States (for 

example, Wolock, 2003), flood frequency (Beven, 1986a 

and b), effects of climate change on hydrologic processes 

(Wolock and Hornberger, 1991), carbon budgets (Band and 

others, 1991), base-flow residence times (Vitvar and others, 

2002), and ecological-flow factors (Kennen and others, 2008). 

Finally, TOPMODEL has been used to reveal interactions 

among variables in model-parameter calibration (Hornberger 

and others, 1985; Wolock, 1988; Wolock and McCabe, 1995), 

including an understanding of how input data must change 

with a change in digital-data resolution (Brassington and 

Richards, 1998). 
0 25 50 75 100 MILES0 25 50 75 100 KILOMETERS

Base from U.S. Geological Survey digital data, 1983, 1:100,000Lambert Conformal Conic projectionStandard parallels 37oN and 39oN, central meridian 85o45'W

K

by Kent

03406500

03251200

0323725

03281100

03282040

07024000

03282500

03280700

03611260

03611800

GREEN

KENTUCKY

LICKING

UPPER CUMBERLAND

LOWER OHIO - SALT

LOWER OHIO

LOWER CUMBERLAND HATCHIE - OBION

LOWER 

TENNESSEE

MIDDLE 

 - RACO

MIDDLE OHIO

 - LITTLE MIAMI

LOWER 

MISSISSIPPI

 - MEMPHIS

88°30'0"W 87°0'0"W 85°30'0"W 84°0'0"W 

39°0'0"N

37°30'0"N

36°0'0"N EXPLANATION

Calibration basins; numberis U.S. Geological Surveystreamflow-gaging station associated with basinDevelopment basinsKentucky HUC-6 basins

Phase I study area Karst areas of Kentucky

Figure 1. Water Availability Tool for Environmental Resources (WATER) Phase-I development and calibration basins and study area, with hydrologbasins shown for reference. 
4 The Water Availability Tool for Environmental Resources (WATER): Phase I—Data Processing and Documentation

WATER is dependent on input that incorporates climatic, topographic, and pedogenic data. Wolock and McCabe 

(1999) showed that an accurate precipitation record was the 

most significant requirement for a successful hydrologicresponse model; soil-moisture storage, derived from pedogenic data, was identified as an additional critical variable. 

WATER provides a historical climate record that extends from 

1948 through 2006; however, the period 2000–06 includes 

NEXRAD precipitation data. Over and others (2007) showed 

that NEXRAD data were consistently within ±10 percent 

of tipping-bucket rain gage estimates, with the NEXRAD 

data showing relatively fewer large precipitation values and 

more small precipitation values. This difference in recorded 

precipitation is largely owing to the area sampled by each: 

0.03 m2 by the rain gage and 16 km2 by the radar. Disposition 

of precipitation into soil storage, overland flow, and base flow 

has historically been estimated by using soil parameters from 

a combination of State Soil Geographic Database laboratory 

data (STATSGO; http://soils.usda.gov/survey/geography/

statsgo/) and manual estimation (for example, Brassington 

and Richards, 1998; Wolock, 2003; Kennen and others, 2008); 

these soil parameters include available water-holding capacity, field capacity, porosity, soil thickness, saturated hydraulic 

conductivity, the conductivity multiplier, and the scaling 

parameter. However, Williamson and Odom (2007) showed 

that the Soil Survey Geographic Database (SSURGO; http://

soils.usda.gov/survey/geography/ssurgo/) included data at 

a resolution that was more appropriate for analysis of small 

basins and that yielded better results without the subjectivity 

of manual estimation. 

Currently (2009), WATER, or some variant of the current 

tool, is being used in applied research for estimating water 

availability in Kentucky and Alabama, as well as generating 

load-duration curves for use in developing total maximum 

daily loads (TMDLs). Additionally, WATER is being used to 

evaluate the effects of hydrologic factors on ecological conditions within a fluvial system (Gary R. Buell, U.S. Geological 

Survey, oral commun., 2009), conduct flood assessments in 

small watersheds in Indiana (Scott E. Morlock, U.S. Geological Survey, written commun., 2009), and assess the hydrologic 

controls on the transport of mercury species in New York 

and South Carolina (Douglas A. Burns and Toby D. Feaster, 

U.S. Geological Survey, written commun., 2009). 

Study Area, Data Sources, and Model 

Development and Calibration

The development and calibration of WATER included 

only basins from non-karst areas of Kentucky in order to focus 

on hydrologic processes that operate as a function of topography and soil characteristics. The Phase I version of WATER 

includes an historical climate record together with a database 

of physical parameters for basins in the non-karst areas of 

Kentucky. However, the spatial-data layers are available for 

the entire Commonwealth. 

Selection of Non-Karst Study-Area and 

Calibration Basins

Twenty-two gaged basins in the non-karst areas of the 

Commonwealth, ranging in size from 16 to 1,565 km2 (fig. 1), 

were used to develop the model, and 12 of these basins were 

used for calibration of the model. These 22 basins were chosen 

based on 

• physiographic region,

• availability of a USGS streamflow-gaging station for 

the 2000–05 period,

• comprehensive information on withdrawals and 

discharges for the basin,

• a known absence of flow regulation, and

• a non-urban environment. 

These 22 basins were used to design the data structure 

of WATER, select appropriate data sources and preprocessing 

methods for spatial-data layers, and identify potential sources 

of error. Only 12 of these basins were used as calibration 

basins (fig. 1), described below in “Statistical Evaluation and 

Calibration of WATER,” because the discharge record coincided with NEXRAD precipitation data that were available 

for Kentucky for the January 2000–August 2006 time period; 

these more accurate precipitation data were not available until 

much of WATER had already been developed. 

Data Sources

To develop an approach that could be applied across the 

Commonwealth, including contrasting physiographic terranes, 

rigorous data processing was applied to several model inputdata sources (table 1). Each data layer, discussed below in 

detail, was processed and statistically sampled for each basin 

using ArcGIS. 

Model Development and Calibration Methods

Statistical evaluation of the TOPMODEL code and its 

individual components has been documented in numerous 

studies (for example, Wolock, 1993) and, therefore, rigorous 

statistical analysis of the variable source area concept and 

the basic TOPMODEL code is not repeated here. Statistical 

evaluation of WATER, as reported in the following sections, 

is limited to the comparison of the estimated discharge to that 

observed at the respective USGS streamflow-gaging station. 

WATER was originally developed by visual evaluation 

of hydrographs and flow-duration curves of observed and 

modeled data for the 22 gaged watersheds. Although this 

process resulted in a functional model, the model could not be 
Structure and Output of WATER 5

statistically evaluated because of questions about the accuracy 

of the precipitation data. After the NEXRAD precipitation data 

became available, the 12 calibration basins for which discharge data were available for the same time period were used 

to statistically evaluate and calibrate the model by employing 

four statistics that are commonly used in hydrologic-modeling 

studies (for example Wolock and McCabe, 1999; Martin and 

others, 2000): 

• Bias ( ) x y

n

∑ i i − (1)

• Root Mean Square Error ( ) y x

n

∑ i i − 2

 (2)

• Correlation ( )( )

( ) ( )

x x y y

x x y y

i i

i i

∑ − −

∑ − − ∑

2 2

 (3)

• Nash-Sutcliffe Efficiency 1

2

− 2

∑ −

∑ −

( )

( )

y x

x x

i i

i

 (4)

where 

xi is observed mean discharge at the USGS 

streamflow-gaging station for an individual 

day, 

yi is modeled discharge for an individual day, 

x is mean observed discharge for the period of 

record, 

y is mean modeled discharge for the period of 

record, and 

n is the number of observations. 

Values of bias and root mean square error (RMSE, also known 

as standard deviation) that are closer to zero indicate better 

agreement between observed and model-estimated flow values. Correlation and Nash-Sutcliffe Efficiency (Ef ) values that 

are closer to 1 indicate better model results; an Ef = 0 indicates 

that the model-flow estimates are no more accurate than using 

a mean-flow value, and an Ef < 0 indicates that the mean-flow 

value is more accurate than the model results (Nash and Sutcliffe, 1970; McCuen and others, 2006). 

Structure and Output of WATER 

WATER incorporates TOPMODEL Fortran code that has 

been successfully used to assess other hydrologic systems in 

the United States (for example, Kennen and others, 2008). 

However, the Java-based GUI (fig. 2) and data structure were 

developed for Phase I of the Kentucky model to create a userfriendly environment. The Phase I version of WATER uses a 

specific directory structure (fig. 3) that organizes preprocessed 

data (described below) for the encapsulated Fortran TOPMODEL code (figs. 4 and 5); these data include historical 

climate data, basin characteristics, and USGS streamflow data 

(for development and calibration). Consequently, no further 

data or preprocessing are required from the user to make the 

model run; because of this simplification,WATER has been run 

on a computer with as little as 512 MB of RAM and a 1-GHz 

processor. 

Table 1. Data sources. 

[WATER, Water Availability Tool for Environmental Resources] 

Data source Contribution to WATER

National Weather Service COOP TD3200 stations Temperature and precipitation data for 1948–2006

National Weather Service River Forecast Centers Operational 

Forecast System NEXRAD (Next Generation radar) mean areal 

precipitation

Precipitation data for 2000–06

9.14-meter Digital Elevation Model of Kentucky (U.S. Geological 

Survey, 2008)

Basin areas, area of the stream channel, topographic wetness index

SSURGO (Soil Survey Geographic Database; U.S. Department of 

Agriculture, 2007)

Hydrologic soil variables and TOPMODEL specific variables

2001 NLCD (National Land-Cover Database; U.S. Geological 

Survey, 1992) 

Impervious areas

Kentucky Division of Water (written commun., 2007) Water withdrawals, lake areas

Kentucky Pollutant Discharge Elimination System (written 

commun., 2007)

Water discharges
Figure 2. Graphic user interface for Water Availability Tool for Environmental Resources (WATER). 
Study Area, Data Sources, and Model Development and Calibration 7

C drive

TOPMODEL

calibration

[USGS Streamflow Gage IDs]

input database

Basin_characteristics.mdb

exe

kyTOPMODELnew.exe

lib

KentuckyClimate.jar

HUC12

BasinID

basinatn.txt

bclim.txt

basinchar.txt

topout.txt

USGS_ Gages

flow

dv{Gage ID}.txt

jcommon-1.0.10.jar

jfreechart-1.0.7.jar

swing-layout-1.0.3.jar

HUC10

HUC8

basinatn.txt

bclim.txt

basinchar.txt

topout.txt

- Folder that contains data required for TOPMODEL portion of WATER

- WATER must be located on the C drive in order to function

- For each calibration basin

 - Historical climate record

 - Topographic Wetness Index ASCII

 - Storage of model input and output

- Discharge record for each calibration basin

- Basin characteristics for all basins

}

- Library of JAVA modules used in WATER }

- Fortran TOPMODEL code }

- For each HUC-12

 - Historical climate record

 - Topographic Wetness Index ASCII } - Storage of model input and output

}

}- Inventory of smaller basins for routing and identification

KentuckyTOPMODEL.jar - JAVA module that activates WATER

Figure 3. Directory structure of Water Availability Tool for Environmental Resources (WATER). 
8 The Water Availability Tool for Environmental Resources (WATER): Phase I—Data Processing and Documentation

Basin

Characteristics

MS Access

Database

Digital

Elevation

Model

Soil Survey

Geographic

Database

(SSURGO)

Kentucky

Division of 

Water

Kentucky

Point Discharge

Elimination

System

National 

Land Cover

Database

Topographic

Wetness

Index

Histogram

Builder

Climate

Generator

and

NEXRAD

basinatn.txt

(ASCII file 

accessed

by program)

bclim.txt

(climate file 

accessed

by program)

Water Availability Tool for Environmental Resources

Graphic User Interface

Select Basin

Run

WATER

TOPMODEL

Baseflow

Evapotranspiration

Infiltration exceedance

 overland flow

Saturation overland flow

Recharge

Runoff

Manually Select Output

Flow statistics

Hydrograph

Flow duration curve

Water budget

Flow separation

 EXPLANATION

ET - Evapotranspiration

Data

Decision

Process

 Database

 External process 

 Output

Figure 4. Data framework of Water Availability Tool for Environmental Resources (WATER). 
Study Area, Data Sources, and Model Development and Calibration 9

Figure 5. Fortran TOPMODEL code structure used by Water Availability Tool for Environmental Resources (WATER). 

 EXPLANATION

ET - Evapotranspiration

 Connector

 Decision

 Process

 Data

 Predefined process/data

Water Year

If Precipitation = 

Potential ET

If Precipitation < 

Potential ET

Yes

Yes

Pervious Surface

Runoff

Impervious

Surface Runoff

Volumetric

Increment of 

Infiltration

Local Deficit

Saturation

Overland Flow

Evaporation From

Soil Root Zone

Base Flow

Read in Basin

Climate Files

Read in Basin

Characteristics

Routing/Delay of

Flow

Output to 

topout.txt

bclim.txt

No

No

End of Time

Series

Yes

No

Yes

No

End of 

Hourly time-step

Convert Time-step

to Hourly

Yes

No

Recharge

End of

Topographic Wetness 

Index File

Read In

Topographic 

Wetness Index

ASCII

Do 

Mass Balance 

Over Topographic 

Wetness Indexes

Infiltration Excess 

Green-Ampt Model

Over Log-Normal 

Distribution of 

Hydraulic Conductivity
10 The Water Availability Tool for Environmental Resources (WATER): Phase I—Data Processing and Documentation

To run the model, the user activates WATER, and using 

the GUI (fig. 2) selects the basin of interest, chooses either 

a single basin or a nested-basin run, and selects Run TOPMODEL. A nested-basin run includes output for all upstream 

contributing HUC-12s. When a HUC-12 basin is selected and 

run in nested mode, the program writes the selected HUC-12 

and all upstream HUC-12s to an external file called UpstreamHucList.{initially selected HUC-12}. A TOPMODEL execution loop is then set up according to the number of HUC-12s 

in this external file. Each TOPMODEL loop processes one 

HUC-12 from the external file, and the resulting estimates of 

streamflow are saved to an internal data object. After the TOPMODEL loop has completed and the estimates of streamflow 

have been stored, a method named SimpleRoute is called. SimpleRoute sums the hydrographs from all HUC-12s; delays or 

attenuations in flows are not accounted for. Because this model 

is based on daily flows, errors owing to traveltimes of less 

than 24 hours are thought to be minimal. Traveltimes greater 

than 24 hours may introduce more error. For this reason, this 

version of WATER sums flow only for HUC-12 basins that are 

nested within a single, larger, HUC-8 basin and will not sum 

flow for a HUC-12 sequence that crosses a HUC-8 boundary. 

If the drainage area is split among multiple HUC-8s, the flow 

estimates will include flow derived only from HUC-12s in the 

most downstream HUC-8; no flow from upstream HUC-8s 

will be included. 

Currently (2009), standard output from the model provides graphic and tabular data, including

• hydrographs, 

• flow-duration curves, 

• annual and monthly water budgets, and

• climatic histories. 

In addition, there is the option of simulating changes in 

climate or water use by altering the input file in the GUI Data

menu; any changes that are made to this input file are saved 

in the basinchar.txt file in the input folder for the basin. The 

basinchar.txt file can be renamed for future reference; otherwise, it will be overwritten the next time WATER is run for 

the specified basin. Daily data for each parameter calculated 

by the TOPMODEL portion of the program are saved in the 

topout.txt file that is saved in the input folder for the basin. 

This file can also be renamed for future reference, or it can be 

imported into a spreadsheet program for further calculations; 

otherwise, this file will be overwritten the next time the program is run for this basin. Additional functionality is shown in 

the existing GUI; however, these functions (including period, 

statistics, and sediment) are not active in the Phase I application but will be developed in Phase II. 

Data Processing and Organization

This initial development of WATER focused on identifying, obtaining, and processing the required background data 

that make the model scientifically and programatically function. The Basin Characteristics database includes most of the 

input data for WATER; table 2 summarizes each parameter and 

its source. All data layers were mapped and rasterized in the 

Kentucky single-plane projection using ArcMap 8.2; the uniform grid cell of each raster layer was 9.14 m (30 ft). Several 

of the attributes come from source data, listed in table 1, that 

then required some form of GIS preprocessing. In addition to 

the Basin Characteristics database, the TOPMODEL portion 

of WATER also requires a histogram of the topographic wetness index (TWI), as well as a climatic record for each basin. 

These processes are summarized below.

Stream-Channel Initiation and Width

Because WATER was developed to analyze basins from 

physiographically diverse terranes, hydrologic parameters 

such as stream-channel width and the drainage area required 

to initiate channelized streamflow had to be uniformly defined. 

To estimate stream-cell area, the stream network was defined 

by applying the following rules to DEMs derived from 

1:24,000 topographic maps: 

1. Streams were defined as 1-cell wide for the entire 

network. 

2. Streams were initiated after accumulating a total 

of 0.25 km2 (3,000 9.14-m cells) upstream area by 

using the Arc Hydro Flow Accumulation tool (with a 

single-flow direction algorithm). 

These two flow-accumulation criteria area were established 

to include first-order, perennial, channelized drainages while 

excluding small, ephemeral, hillslope systems. Streams were 

defined as 1-cell wide to remove the highest values of the TWI 

from the distribution of TWI values in a given basin while still 

maintaining a representative riparian zone to serve as a variable source area for streamflow. Although this stream-network 

definition is representative of most HUC-12 streams (the basin 

size for which WATER was developed), utilization of WATER 

in larger or smaller basins may require adjustment of this 

parameter to accurately estimate stream response. 
Table 2. Basin characteristics: attributes, data sources, and processing summary. —Continued[km2, square kilometers; m, meter; cm, centimeter; mm, millimeter; μm, micrometer; in, inch; hr, hour; s, second; %, percent; Mgal, million gallons; yr, year; d, day; conmult, co

evapotranspiration; K , Saturated hydraulic conductivity; m, scaling parameter; DEM, Digital Elevation Model; KDOW, Kentucky Division of Water; KPDES, Kentucky Pollu

sat

System; NLCD, National Land Cover Database; SSURGO, Soil Survey Geographic Database; USDA, U.S. Department of Agriculture; USGS, U.S. Geological Survey. Note: Partifacts of FORTRAN program.]

Basin property Title in Basin Characteristics database Title in program Units in program Example or default value Data source Processing sp

Total area

Lake areaStream cell areaTotal cells

Saturated hydraulic 

conductivity

Soil thicknessField capacity

Water holding 

capacity

totalArea

lakeArea

# of stream cells

totalCells

permeability

soilDepth

fieldCapacity

watholCapacity

totalarea

alake

streamCells

totalCells

Ksat

ztot and soildepin

thfc and fieldcap

whcin

km2

km2

Unitless Unitless

in/hr 

in

Decimal

Decimal

200.2061

0.04

26431

3763302

1.818932

51.18267

0.205983

0.128884

USGS 9.14-m 

DEM

Kentucky Division 

of Water, Dam 

Safety

USGS 9.14-m 

DEM

USGS 9.14-m 

DEM

SSURGO

SSURGO

SSURGO

SSURGO

For calibration, basins were de

location using the flow accuotherwise, HUC-12 basin ar

Only included lakes larger than 

Single cell-width channel deriv

Flow Direction and Flow AcArcMap. Streams initiated anumber of cells are then con

stream cells

stream area = × t

total cells

Total cell count for basin.

Values for soil properties were 

cess for the thickness where 

the SSURGO database. Repfor each soil map unit were rdrainage basin. 

Thickness for which K > 1 μm

sat

cm. Representative values as unit were rasterized and sam

Values for soil properties were 

where K > 1 μm/s in Micro

sat

values as reported for each sand sampled by drainage bas

Values for soil properties were 

where K > 1 μm/s in Micro

sat

values as reported for each sand sampled by drainage bascm/cm to %.
Table 2. Basin characteristics: attributes, data sources, and processing summary. —Continued [km2, square kilometers; m, meter; cm, centimeter; mm, millimeter; μm, micrometer; in, inch; hr, hour; s, second; %, percent; Mgal, million gallons; yr, year; d, day; conmult, 

evapotranspiration; K , Saturated hydraulic conductivity; m, scaling parameter; DEM, Digital Elevation Model; KDOW, Kentucky Division of Water; KPDES, Kentucky Poll

sat System; NLCD, National Land Cover Database; SSURGO, Soil Survey Geographic Database; USDA, U.S. Department of Agriculture; USGS, U.S. Geological Survey. Note: 

artifacts of FORTRAN program.] Basin property Title in Basin Characteristics database Title in program Units in program Example or default value Data source Processing s

Porosity Percent impervious Percent road impervious Latitude Site ID Effective impervious Conductivity multiplier Percent macropore flow mGroundwater 

withdrawal

porosity

perImpervious

perRoadImpervious

latitude

siteID

effImpervious

conductMultiplier

perMacroporeFlow

scalingParameter

groundwaterWithdrawal

poros

perimp

perroad

xlat

staid

Effimp

conmult

pmac

szm

Gw

Decimal

%%

Decimal

degrees

NA

%

Unitless

%

mm

Mgal/yr

0.322057

1.130.58

37.42

03282040

0.7

3.38956

0.2

124.46

69.93

SSURGO

NLCD 2001NLCD 2001

National Map 2001

NWISNWIS

User defined

SSURGO

User defined

SSURGO

KDOW

Values for soil properties wer

where K > 1 μm/s in Mic

sat

values as reported for each and sampled by drainage b

Imperviousness layer. Imperviousness layer clipped 

tion layer. 

Latitude at visual center of ea

basin.

HUC-12 (Michael Griffin, US

Center, oral commun., 200tion number for calibration 

Held constant. Used to decrea

is not roads. 

K − high sat surface 

conmult =

K − low 

sat "bottom"

Values for soil properties wer

where K > 1 μm/sec in M

sat

tive values as reported for eized and sampled by draina

Constant 

lnconmult 

f = soil thickness 

porosity − field capacity 

m = f 

Values for soil properties wer

where K > 1 μm/s in Mic

sat

values as reported for each ized and sampled by drainacalibrated uniformly for all 

Monthly averages of water-wConverted to Mgal/yr as 1/0.0
Table 2. Basin characteristics: attributes, data sources, and processing summary. —Continued[km2, square kilometers; m, meter; cm, centimeter; mm, millimeter; μm, micrometer; in, inch; hr, hour; s, second; %, percent; Mgal, million gallons; yr, year; d, day; conmult, co

evapotranspiration; Ksat, Saturated hydraulic conductivity; m, scaling parameter; DEM, Digital Elevation Model; KDOW, Kentucky Division of Water; KPDES, Kentucky Pollu

System; NLCD, National Land Cover Database; SSURGO, Soil Survey Geographic Database; USDA, U.S. Department of Agriculture; USGS, U.S. Geological Survey. Note: Partifacts of FORTRAN program.]

Basin property Title in Basin Characteristics database Title in program Units in program Example or default value Data source Processing sp

Surface-water 

withdrawal

surfacewaterWithdrawal Sw Mgal/yr 218.21 KDOW Monthly averages of water-wit

Converted to Mgal/yr as 1/0.00

Surface-water 

discharge

surfacewaterDischarge sdisch Mgal/yr 178.83 KPDES Monthly averages of water-dis

Converted to Mgal/yr as 1/0.00

Change impervious 

surface

flagImpSurfaceHistory iischg Unitless 0 User defined Changing to 1 employs algorith

surface TR-55 curve number

Topographic wetness 

index adjustment

wiAdjustment Atn_adj Unitless 1 User defined A multiplier that alters the topo

gram distribution. Held cons

Celerity wave 

velocity

celerityWave Subv km/d 20 User defined Not currently being used; comm

Depth of root zone rootZoneDepth Zroot m 0.3 Hendrick and 

Pregitzer, 1996

Representative depth of plant r

literature search. Held const

Impervious runoff 

constant

imperviousRunoffDelay imp_const Unitless 0.1 User defined Held constant; must be > 0. De

from non-road impervious a

TR55 curve number imperviousCurveNumber imp_cn Unitless 98 User defined Held constant. Runoff from im

after the USDA (1986) methsheds. Because a conservativarea is being used, this “urbaall basins. 

Uplake area uplakeArea uplake km2 2.221391 10-m DEM Area upstream of lake outlets—

This value was determined utool in ArcHydro. 

Lake delay lakeDelay Rip_decay Day 1.5 User defined Coefficient for delaying water 

the uplake area is temporarilstream: 1 = no delay and 2 = delivered in equal incrementmust be ≥ 1; there is no uppe

Evapotranspiration 

adjustment

etExponent Et_exp Unitless 0 User defined 

labile calculated maximum 

soil ETET 

 = ×

Held constant at 0; calculated v

Return flow returnFlowFlag iretflow Unitless 1 User defined Held constant; a value of 1 allo

channel. 

ET ET 
Table 2. Basin characteristics: attributes, data sources, and processing summary. —Continued [km2, square kilometers; m, meter; cm, centimeter; mm, millimeter; μm, micrometer; in, inch; hr, hour; s, second; %, percent; Mgal, million gallons; yr, year; d, day; conmult, co

evapotranspiration; Ksat, Saturated hydraulic conductivity; m, scaling parameter; DEM, Digital Elevation Model; KDOW, Kentucky Division of Water; KPDES, Kentucky Pollu

System; NLCD, National Land Cover Database; SSURGO, Soil Survey Geographic Database; USDA, U.S. Department of Agriculture; USGS, U.S. Geological Survey. Note: Partifacts of FORTRAN program.] Basin property Title in Basin Characteristics database Title in program Units in program Example or default value Data source Processing sp

Hourly precipitation data hourlyPrecipFileLocation hrlyppt Varies ‘None’ Varies Currently unused, but provides 

plications that may need mo(either using an index station the basin). Description None User defined Necessary text for internal mod

Impervious scenario flag Imp_Flag IFlag Unitless 0 User defined Placeholder for future upgrade 

of changes in impervious suno scenario has been createdhas been created and will be folder for processing by TOP

Withdrawal scenario flag Withd_Flag WFlag Unitless 0 User defined Placeholder for future upgrade 

withdrawals. 0 signifies that ated. 1 signifies that scenario be stored in the specific HUC TOPMODEL at runtime. Discharge scenario flag Disch_Flag DFlag Unitless 0 User defined Placeholder for future upgrade discharges. 0 signifies that nated. 1 signifies that scenario be stored in the specific HUC TOPMODEL at runtime. Climate scenario flag Climate_Flag CFlag Unitless 0 User defined Placeholder for future upgrade 

changes in temperature or prno scenario has been createdhas been created and will be folder for processing by TOP
Study Area, Data Sources, and Model Development and Calibration    ﻿  

Soil-Data Aggregation

Soil data for WATER were downloaded during February 

and March 2008 from the Soil Survey Geographic Database 

(SSURGO; U.S. Department of Agriculture, 2007). Spatial 

data were compiled in a single geodatabase by using ArcMap 8.2. SSURGO representative values of soil parameters 

were aggregated by using a series of queries in Microsoft 

Access; soil parameters of interest included

• porosity (saturated water content),

• field capacity (1/3 bar water content),

• available water-holding capacity,

• saturated hydraulic conductivity (Ksat; listed as permeability in the Basin Characteristics database), and

• soil thickness. 

For compilation of this database, soil thickness was 

defined as the portion of the soil for which the representative 

Ksat > 1 μm/s. This is equivalent to choosing all soil layers for 

which Ksat is “moderately high or higher” as defined by the 

Natural Resources Conservation Service (U.S. Department of 

Agriculture, 1993). The decision to use layers with Ksat > 1 

μm/s was based on two criteria: (1) elimination of soil layers 

defined as restrictive in the SSURGO database and (2) field 

observations that a portion of the subsoil remained moist 

regardless of the moisture condition of the overlying soil, thus 

indicating that these subsoil layers are not involved in the 

daily water processes simulated by the TOPMODEL portion 

of WATER (fig. 6). Consequently, the first query run on the 

SSURGO data eliminated all soil layers that did not meet this 

criterion. The remaining soil layers were used to determine 

the soil thickness: this is the sum of each of the layers for 

which Ksat > 1 μm/s. In Kentucky, 472 of 5,766 (8 percent) of 

the soil-mapping units included a horizon with Ksat > 1 μm/s 

that was bounded above by a layer with Ksat ≤ 1 μm/s; this 

amounted to 7 percent of the total area. In these cases, the 

bounded layers were included in the soil thickness; this decision will be reevaluated in later versions of the model. 

Using only soil layers for which Ksat > 1 μm/s, a soilmapping-unit value was calculated for each of the five soil 

parameters. A thickness-weighted mean of each soil parameter 

was calculated for each soil component by using the thickness of individual soil layers. A component-weighted mean of 

each soil parameter was then calculated by using the relative 

percentages of individual soil components that were reported 

for each soil-mapping unit. 

In addition to the soil parameters listed above, two 

calculated soil parameters—the conductivity multiplier 

(conmult) and the scaling parameter (m)—were calculated 

from SSURGO data (U.S. Department of Agriculture, 2007). 

Although this procedure ignores the effect of macropores on 

Ksat, it is consistent with the findings of Brassington and Richards (1998), who reported that when using DEM rasters on the 

order of 20 m, a laboratory determined Ksat and correspondingly small scaling-parameter value produced the most accurate results. In most previous TOPMODEL-based research, 

these two soil parameters have been altered to optimize the 

discharge estimate (for example, Wolock, 1993; Kennen and 

others, 2008). For WATER, these parameters were calculated 

from SSURGO data and functions that describe how soil water 

relates to overland flow and base flow (Beven, 1984). This 

process included a series of queries and calculations that was 

run for each soil-mapping unit: 

1. In most cases, high and low Ksat values were reported 

in SSURGO, in addition to the representative value. 

The highest Ksat value for the soil-mapping unit was 

identified from the Ksat-high data for the surface 

layer. The lowest Ksat value for the soil-mapping unit 

was identified from the Ksat-low data for the bottom

layer (as defined by the soil thickness). 

Figure 6. Schematic of 

soil layers and relation 

to Water Availability 

Tool for Environmental 

Resources (WATER). 

H1, Ksat > 1μm/s

H2, Ksat > 1μm/s

H3, Ksat < 1μm/s

H4, Ksat > 1μm/s

In 8 percent of the soil components, a 

bounded layer with Ksat > 1 μm/s was 

included in the soil-parameter calculations.

Soil thickness was defined as the portion of 

the soil for which Ksat > 1 μm/s because these 

layers are involved in the daily hydrologic 

processes simulated by WATER. These layers 

(in this example, H1 and H2, as designated in 

SSURGO) were then used to calculate the 

required soil parameters.

This layer would not be included in the soilparameter calculations.

}

Plant canopy

Plant roots

Soil surface
16 The Water Availability Tool for Environmental Resources (WATER): Phase I—Data Processing and Documentation

2. The conductivity multiplier for each soil-mapping 

unit was calculated as follows: 

f conmult

soil thickness = ln

(6)

where 

 Ksat-highsurface is the high Ksat value for the surface layer and 

 Ksat-lowbottom is the low Ksat value for the bottommost soil 

layer for which the representative 

Ksat > 1 μm/s. 

3. The scaling parameter (m) was then calculated for 

each soil-mapping unit: 

where 

m = porosity field capacity −

f

(7)

and (8)

The component-weighted mean that was calculated 

for each SSURGO soil-mapping unit was then joined to 

the SSURGO spatial data by using the unique identifiers of 

the soil-mapping-unit polygons. These polygons, attributed 

with an individual soil parameter, were then converted to a 

9.14-m raster; this resulted in a total of seven rasters (porosity, field capacity, available water-holding capacity, Ksat, soil 

thickness, conmult, m). For each of these soil parameters, the 

mean soil-parameter value for the basin (calibration basins or 

HUC-12) was calculated by using ArcMap. During development of WATER, the calculated m value was divided by 25 

to improve the output based on visual inspection of estimated 

hydrographs; this is the development scaling parameter (md). 

Once NEXRAD data became available, this parameter was 

calibrated uniformly using the 12 calibration basins. 

Withdrawal and Discharge Data

Withdrawal and discharge data were provided by the 

KDOW. Withdrawal data from KDOW permits were averaged 

for a period from 2002 to 2006 (Rita Hockensmith, Kentucky 

Division of Water, Water Availability and Use Branch, written 

commun., 2007). Discharge data from the Kentucky Pollutant 

Discharge Elimination System (KPDES) were averaged for a 

period from 1989 to 2004 (Vickie Prather, Kentucky Division 

of Water, Kentucky Pollutant Discharge Elimination System 

Branch, written commun., 2005). All discharge categories 

were included except for the KPDES stormwater runoff 

waste code; this runoff is accounted for elsewhere in the 

TOPMODEL portion of WATER. Median monthly discharge 

was averaged over the specified time period and summed 

(for HUC-12 or calibration basins) to attain a representative 

annual value. Data were compiled and averaged using SAS 

and Microsoft Excel. For each basin, the sum of surface-water 

withdrawals and discharges, as well as groundwater withdrawals and discharges, is reported in the Basin Characteristics

database. 

Topographic Wetness Index Data

The topographic wetness index (TWI) is defined as 

TWI = A



















 ln tanb (9)

where 

A is upslope contributing area per unit contour 

width (meters) and 

β is local slope (degrees). 

The TWI, sometimes referred to as the wetness index or 

compound topographic index, is used to describe how water 

accumulates in the drainage basin based on a DEM (Quinn 

and others, 1997). This relation is the basis of TOPMODEL, 

and it illustrates the model assumption that flow of subsurface 

water reflects surface topography. However, the functionality of TOPMODEL, and consequently, WATER, is derived 

from how the model includes the TWI information. Instead of 

basing computations on each cell in a basin individually, the 

cells are distributed in a histogram, and each bin of cells from 

the histogram (histogram interval) is dealt with as a group 

based on the mean value of that bin. All of the cells from each 

bin are treated the same way for all future calculations on 

the principle that cells with a similar TWI will have a similar 

hydrologic response (Beven and Kirkby, 1979). 

For WATER, the TWI raster was processed from the 

original DEM; the stream network defined previously (with 

a 9.14-m width and 0.25-km2

 flow accumulation) was used 

to remove stream-cell TWI values because these high values 

do not reflect hillslope processes (Quinn and others, 1997). 

The resulting raster was then exported as an ASCII file and 

processed with the WATER Histogram Builder. 

Overview of the WATER Histogram Builder 

Because it is necessary to process numerous TWI rasters 

in an objective, consistent, and efficient manner, the Histogram Builder (HB) was developed for use in the preprocessing 

of data for WATER. The HB requires a TWI raster that has 

been converted to an ASCII format. The ASCII file includes 

one TWI value for each cell of the DEM, and the cell width 

can be variable depending on the cell size of the DEM from 

which the TWI values were derived. The user must specify 

the number of histogram bins desired in the final output file; 

30-bin histograms were used for development of WATER. 

The HB operates in batch mode to allow the user to process a large number of TWI ASCII files from a single execution of the program. After starting the program, the user must 

select a batch-list file that contains a listing of all the TWI 

ASCII files to be processed. The user must then select a directory where the TWI ASCII files reside. The filenames in the 

batch list must correspond exactly to the TWI ASCII filenames 

and include the filename extensions; incorrect or incomplete 

filenames will result in termination of the HB. An output 

directory must also be selected to store the output files. Upon 

"bottom"

surface

lowK

highK conmult = sat −

sat −
Statistical Evaluation and Calibration of WATER 17

successful completion of these preconditions, the user presses 

the START button to begin processing. 

The processing starts by reading the entire ASCII file 

to get the total, minimum, and maximum TWI values. These 

minimum and maximum values, along with the user-specified 

number of bins, are used to determine the low and high value 

of each individual bin for a set of equal-width bins. The count 

for each bin is also divided by the total number of TWI values 

to determine the fraction of the watershed that each bin represents. A second read through the entire original file puts each 

individual TWI value into the bin whose low and high values 

bracket that specific TWI value. For each bin, TWI values 

are summed, and the sums are divided by the counts to get 

the mean TWI value for each bin. Finally, the output data are 

written to an ASCII text file called basinatn.txt file for use in 

WATER. The first line of the output file contains the number 

of bins requested by the user. The reported value on this line 

may be smaller than the user-specified value if one or more of 

the bins is empty after the processing of the ASCII file. Each 

of the remaining lines in the text file represents one histogram 

bin. The first value on the line is the mean TWI value for 

the histogram bin, and the second value is the fraction of the 

watershed represented by that bin. The output file is then ready 

for use in WATER; no further processing is necessary. 

Climate Data

Originally, both temperature and precipitation input data 

for WATER were derived by using the Kentucky Watershed 

Modeling Information Portal climate-data generator (http://

technology.ky.gov/gis/kwmip/). This climate-data generator 

computes daily climate data (daily precipitation in inches and 

maximum/minimum temperature in degrees Fahrenheit) for 

each day of a requested time period. The climate-data generator employs an inverse distance-weighting approach coupled 

with an elevation adjustment (after Hay and others, 2002) 

using X-Y-Z (latitude-longitude-elevation) data from 243 

climate stations (National Weather Service COOP TD3200 

stations) in and near Kentucky; this creates an irregular 

spatial precision of approximately 400 km2

. The model was 

optimized by using shuffled complex evolution given userassigned weighting to the inverse distance versus the elevation 

approach. All interpolated data were estimated and stored on 

a 1-km grid—one raster for each variable for each day of the 

1948–2006 time period. These daily mean rasters were then 

sampled for each basin (HUC-12 or calibration basin) and 

these data were aggregated in the bclim.txt text file. 

After the initial development of WATER, it was evident 

that the precipitation data developed by means of the climate 

generator were at too coarse of a resolution to properly calibrate the model. The observed discharge record from USGS 

streamflow-gaging stations, located within calibration basins, 

indicated storm events that did not correspond with the modeled flow events in either timing or magnitude, although the 

general size of peaks and duration of events was similar. The 

Next Generation radar (NEXRAD) Stage III data for 2000–08 

were acquired from the NOAA National Weather Service, 

Ohio River Forecast Center (Ray Davis, written commun., 

2008). These data were provided as daily totals summed from 

hourly rasters that had a spatial resolution of 4 km for the 

entire study area. These daily totals were averaged by basin 

(calibration basin or HUC-12) and aggregated into a single 

precipitation record for each basin. These NEXRAD precipitation values were then merged with the previously calculated 

temperature values to produce a more accurate climatic record 

for the 2000–06 time period. Consequently, this 2000–06 time 

period was used for the statistical evaluation and calibration of 

WATER. 

Statistical Evaluation and Calibration 

of WATER

During initial development of WATER, evaluation of 

the general accuracy of the model output was based on visual 

inspection of hydrographs and flow-duration curves (fig. 7) 

for the 2000–05 time period. However, once the NEXRAD 

data were acquired and processed, statistical evaluation and 

calibration of WATER were possible. Statistical evaluation and 

calibration were done for the 12 non-karst calibration basins

(fig. 1 and table 3) that were part of the initial model development and for which there were USGS streamflow-gaging 

records that continued through August 8, 2006. 

Tested Scenarios

For each basin, modeled discharge was compared to 

observed discharge for a series of scenarios (table 4). First, 

the two precipitation sources were compared to quantify the 

significance of the new NEXRAD data. Second, rooting depth 

was manipulated because this value affects the evapotranspiration calculations in the model; during model development, 

changes in this parameter visibly affected both the peak and 

the duration of storm events. WATER was initially developed 

with a 20-cm rooting depth, but it was hypothesized that the 

NEXRAD precipitation data might document localized storm 

events that previously were missed. Consequently, rooting depths of 20 cm, 30 cm, and 40 cm were tested. Further 

scenarios included the manipulation of Ksat, a parameter used 

by previous researchers to improve TOPMODEL estimations 

(for example, Wolock, 1993); this soil parameter was tested 

with factors of 0.5, 1.5, and 2 times the value calculated by 

using the SSURGO database (U.S. Department of Agriculture, 

2007) and compared to output when the calculated value from 

SSURGO was used directly (that is, a factor of 1). For each 

of these scenarios, all other soil parameters were calculated 

directly from the SSURGO; the scaling parameter development value (md

) was the SSURGO value divided by 25. 
18 The Water Availability Tool for Environmental Resources (WATER): Phase I—Data Processing and Documentation

Yellow indicates that the model

 underestimates the magnitude

 of flow relative to the observed

 discharge

Green indicates that the model

overestimates the magnitude

 of flow relative to the observed

 discharge

Figure 7. Example hydrograph and flow-duration curve produced by Water Availability Tool for 

Environmental Resources (WATER) for calibration basin 03282040. 
Statistical Evaluation and Calibration of WATER 19

Table 3. Calibration basins used for statistical evaluation and calibration of WATER. 

[WATER, Water Availability Tool for Environmental Resources; USGS, U.S. Geological Survey; km2, square kilometers; HUC, hydrologic unit 

code] 

USGS site 

identification Site name Basin area (km2

) HUC-12 Comments

03207965 Grapevine Creek near Phyllis, Ky. 16.80 050702020204 None

03210000 Johns Creek near Meta, Ky. 146.01 050702030301 None

03237255 Kinniconick Creek below Trace Creek at Tannery, Ky. 554.02 050902010103 None

03251200 North Fork Licking River near Mt. Olivet, Ky. 583.25 051001011001 None

03280700 Cutshin Creek at Wooton, Ky. 158.31 051002020102 None

03281100 Goose Creek at Manchester, Ky. 422.69 051002030104 None

03282040 Sturgeon Creek at Crestmont, Ky. 200.54 051002040101 None

03282500 Red River near Hazel Green, Ky. 170.69 051002040201 None

03406500 Rockcastle River at Billows, Ky. 1,564.48 051301020302 None

03611260 Massac Creek near Paducah, Ky. 26.97 051402060301 None

03611800 Bayou Creek near Heath, Ky. 16.96 051402060701 None

07024000 Bayou de Chien near Clinton, Ky. 178.00 080102010104 Tile drained

Table 4. Matrix of scenarios tested for WATER efficiency and 

calibration. 

[WATER, Water Availability Tool for Environmental Resources; 

COOP, National Weather Service COOP TD3200 stations; NEXRAD, Next 

Generation radar; Ksat, saturated hydraulic conductivity; m, scaling parameter]

Root depth 

(centimeters)

Model parameter

COOP NEXRAD 0.5 Ksat 1.5 Ksat 2 Ksat m

20 • • • • •

30 • •

40 • •

Model Results

Each of the tested scenarios was evaluated on the basis of 

a combination of four statistics: bias, RMSE, correlation, and 

Ef. Scenarios were tested in each of the 12 calibration basins

for the January 1, 2000, to August 8, 2006, time period; one 

year of data are used to equilibrate the model, so discharge 

estimates were statistically evaluated for the period from 

December 31, 2000, to August 8, 2006 (a total of 2,119 days). 

Because the statistics varied between basins, final model selection was based on the consistency of each statistic for a given 

scenario; for example, a scenario that produced all Ef > 0.25 

was preferable to a scenario that produced an Ef = 0.8 in one 

basin but an Ef < 0 in another basin. 

Precipitation Data Source

The precipitation sources (NWS COOP and NEXRAD) 

were compared by using the 20-cm rooting depth; only the 

precipitation data were changed (figs. 8a–11a). The bias of 

the model output from the NWS COOP network was inconsistent and ranged from 0.02 to 0.85. The NEXRAD model 

output showed a smaller bias (-0.26-0.48); the only basin with 

a negative bias is tile drained (USGS ID 07024000) and also 

showed a negative bias in all other NEXRAD scenarios. The 

other statistics showed no consistent differences between the 

two models; however, visual inspection of the hydrographs 

from the NEXRAD scenarios confirmed a better relation 

between precipitation events and peak discharges (fig. 12). 

Consequently, the NEXRAD data (for 2000–06) were used for 

all subsequent models. 

Rooting Depth

WATER was originally developed with a 20-cm rooting 

depth on the basis of visual inspection of the hydrograph and 

the goal of balancing low flows with seasonal evapotranspiration patterns. However, with the incorporation of NEXRAD 

data, the relation between precipitation and discharge events 

became more precise, thus the soil storage and evapotranspiration relation changed. Rooting depths of 20 cm, 30 cm, and 

40 cm were compared by using model scenarios that included 

NEXRAD precipitation data (figs. 8a–11a and 13). There was 

little difference in RMSE or correlation between the scenarios; 

however, the Ef

 was consistently better for discharge estimates in combination with the 30-cm or 40-cm rooting depth 

compared to the 20-cm rooting depth. The mean model bias 

decreased as the rooting depth increased; however, the bias 

range was smallest for the 20-cm rooting depth and largest 

for the 40-cm rooting depth. The 40-cm rooting depth also 

resulted in several basins where the discharge estimates were 

negatively biased, not just the tile-drained basin (USGS ID 

07024000). Consequently, the 30-cm rooting depth was used 

during the calibration of the m parameter, and the 40-cm rooting depth was not tested again until the final m value had been 

determined. 
20 The Water Availability Tool for Environmental Resources (WATER): Phase I—Data Processing and Documentation

-0.5

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20cmCOOPmd 20cmNEXRADmd 30cmNEXRADmd 40cmNEXRADmd

Bias

Scenarios

a. Precipitation and rooting depth

-0.5

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SSURGO Ksat 0.5 Ksat 1.5 Ksat 2.0 Ksat

Bias

20-cm rooting depth, md, NEXRAD Scenarios

b. Hydraulic conductivity

03207965

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U.S. Geological Survey

 site identification Scenario parameters

20cm 20-centimeter rooting depth

30cm 30-centimeter rooting depth

40cm 40-centimeter rooting depth

COOP National Weather Service

 Cooperative Network

NEXRAD Next Generation Radar

03282040

03282500

03406500

03611260

03611800

07024000

2.0 2.0 x SSURGO calculated value

1.5 1.5 x SSURGO calculated value

0.5 0.5 x SSURGO calculated value

SSURGO State Soil Survey Geographic database

Ksat Saturated hydraulic conductivity

md Model-development scaling parameter

EXPLANATION

Figure 8. Bias of Water Availability Tool for Environmental Resources (WATER) calibration scenarios. 
Statistical Evaluation and Calibration of WATER 21

0

2

4

6

8

10

12

14

Root Mean Square Error

Scenarios

a. Precipitation and rooting depth

20cmCOOPmd 20cmNEXRADmd 30cmNEXRADmd 40cmNEXRADmd

0

2

4

6

8

10

12

14

Root Mean Square Error

b. Hydraulic conductivity

SSURGO Ksat 0.5 Ksat 1.5 Ksat 2.0 Ksat

20-cm rooting depth, md, NEXRAD Scenarios

03207965

03210000

03237255

03251200

03280700

03281100

U.S. Geological Survey

 site identification Scenario parameters

20cm 20-centimeter rooting depth

30cm 30-centimeter rooting depth

40cm 40-centimeter rooting depth

COOP National Weather Service

 Cooperative Network

NEXRAD Next Generation Radar

03282040

03282500

03406500

03611260

03611800

07024000

2.0 2.0 x SSURGO calculated value

1.5 1.5 x SSURGO calculated value

0.5 0.5 x SSURGO calculated value

SSURGO State Soil Survey Geographic database

Ksat Saturated hydraulic conductivity

md Model-development scaling parameter

EXPLANATION

Figure 9. Root mean square error of Water Availability Tool for Environmental Resources (WATER) calibration scenarios. 
22 The Water Availability Tool for Environmental Resources (WATER): Phase I—Data Processing and Documentation

20cmCOOPmd 20cmNEXRADmd 30cmNEXRADmd 40cmNEXRADmd

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Correlation Coefficient

Scenarios

a. Precipitation and rooting depth

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Correlation Coefficient

b. Hydraulic conductivity

SSURGO Ksat 0.5 Ksat 1.5 Ksat 2.0 Ksat

20-cm rooting depth, md, NEXRAD Scenarios

03207965

03210000

03237255

03251200

03280700

03281100

U.S. Geological Survey

 site identification Scenario parameters

20cm 20-centimeter rooting depth

30cm 30-centimeter rooting depth

40cm 40-centimeter rooting depth

COOP National Weather Service

 Cooperative Network

NEXRAD Next Generation Radar 

03282040

03282500

03406500

03611260

03611800

07024000

2.0 2.0 x SSURGO calculated value

1.5 1.5 x SSURGO calculated value

0.5 0.5 x SSURGO calculated value

SSURGO State Soil Survey Geographic database

Ksat Saturated hydraulic conductivity

md Model-development scaling parameter

EXPLANATION

Figure 10. Correlation coefficients of Water Availability Tool for Environmental Resources (WATER) calibration scenarios. 
Statistical Evaluation and Calibration of WATER 23

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

20cmCOOPmd 20cmNEXRADmd 30cmNEXRADmd 40cmNEXRADmd

Scenarios

a. Precipitation and rooting depth

-1.0

-0.8

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-0.4

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0.0

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0.8

1.0

SSURGO Ksat 0.5 Ksat 1.5 Ksat 2.0 Ksat

20-cm rooting depth, md, NEXRAD Scenarios

b. Hydraulic conductivity

Nash-Sutcliffe Efficiency Nash-Sutcliffe Efficiency

03207965

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U.S. Geological Survey

 site identification Scenario parameters

20cm 20-centimeter rooting depth

30cm 30-centimeter rooting depth

40cm 40-centimeter rooting depth

COOP National Weather Service

 Cooperative Network

NEXRAD Next Generation Radar 

03282040

03282500

03406500

03611260

03611800

07024000

2.0 2.0 x SSURGO calculated value

1.5 1.5 x SSURGO calculated value

0.5 0.5 x SSURGO calculated value

SSURGO State Soil Survey Geographic database

Ksat Saturated hydraulic conductivity

md Model-development scaling parameter

EXPLANATION

Figure 11. Nash-Sutcliffe efficiency of Water Availability Tool for Environmental Resources (WATER) calibration scenarios. 
0 

102030405060

1-Apr

11-Apr

21-Apr

1-May

11-May

21-May

31-May

10-Jun

20-Jun

30-Jun

10-Jul

20-Jul

30-Jul

9-Aug

19-Aug

29-Aug

8-Sep

2003

NWS Cooperative Network precipitationNEXRAD precipitationObserved dischargeNWS Cooperative Network dischargeNEXRAD discharge

DISCHARGE, IN MILLIMETERS

Figure 12. Comparison of precipitation sources and discharge for calibration basin 03282040. 
0 

102030405060

1-Apr

11-Apr

21-Apr

1-May

11-May

21-May

31-May

10-Jun

20-Jun

30-Jun

10-Jul

20-Jul

30-Jul

9-Aug

19-Aug

29-Aug

8-Sep

2003

NEXRAD precipitationObserved discharge40-cm rooting depth30-cm rooting depth20-cm rooting depth

DISCHARGE, IN MILLIMETERS

Figure 13. Comparison of rooting depths and discharge for calibration basin 03282040. 
26 The Water Availability Tool for Environmental Resources (WATER): Phase I—Data Processing and Documentation

Hydraulic Conductivity

The hydraulic conductivity (Ksat) was evaluated in models that used NEXRAD precipitation data, a 20-cm rooting 

depth, and md (figs. 8b–11b). For each individual watershed, 

there was little difference in any of the statistics between 

the 0.5 Ksat, 1.5 Ksat, or 2 Ksat scenarios as compared with the 

original scenario that used the Ksat calculated directly from 

SSURGO. Consequently, these transformations were eliminated from the model input. 

Scaling Parameter

During development of WATER, visual inspection of 

estimated hydrographs indicated the need to adjust m; therefore, a development scaling parameter (md

) was used that 

equaled 1/25 (0.39) the calculated value from SSURGO. Once 

NEXRAD data became available, this parameter was calibrated uniformly using the 12 calibration basins. The scaling factor (m) was evaluated in models that used NEXRAD 

precipitation data, a 30-cm rooting depth, and a Ksat calculated 

directly from SSURGO. A model simulation using the development value md

 was compared to discharge estimates using 

different factors of the SSURGO-calculated m, including 1, 

0.5, 0.25, 0.10, and 0.05 (fig. 14). All four statistics illustrated 

that the 1, 0.5, and 0.25 m scenarios were poor, and the modeled hydrographs show that these event peaks were too low 

and the recession after a peak too slow (fig. 15); consequently, 

these transformations were eliminated as a model input. 

Model output from the 0.10 m scenario resulted in all positive Ef

 values, in contrast to model output from the original 

md scenario. Although the 0.05 m scenario hydrograph was 

as good or better for some of the calibration basins (fig. 15), 

the statistical analysis showed that this transformation created 

inconsistent results among the basins, including a negative 

Ef value for calibration basin 03281100. Consequently, the 

0.10 m transformation (m0.10) was determined to be the best 

model parameter. 

Based on the similarity of each of the four statistics 

(figs. 8a–11a), the 30-cm and 40-cm rooting depth scenarios 

were repeated with the m0.10 value in combination with 

NEXRAD precipitation. There was little statistical difference 

between these two scenarios (fig. 16); however, the hydrographs showed that streamflow-event peaks were more accurate with the 30-cm-rooting-depth scenario (fig. 17). 

WATER was designed for use at a HUC-12 basin size. In 

Kentucky, these basins range from 8.5 to 91.6 km2; the mean 

area of the 1,283 HUC-12 basins is 84.9 km2. The calibration 

basins ranged in size from 16.8 to 1,564.5 km2; however, the 

statistical analysis shows that the results are the most accurate 

for those basins in the 26.9- to 422.7-km2

 range (fig. 18). 

In summary, WATER successfully estimated longterm flow for twelve HUC-12 basins in non-karst areas of 

Kentucky, with a mean bias of 0.10±0.18, a mean RMSE of 

2.47±0.98, a mean correlation of 0.73±0.10, and a mean Ef

 of 

0.51±0.16. This statistical evaluation of the model indicates 

that WATER successfully estimates long-term flow in the nonkarst areas of Kentucky. 

Model Applications and Limitations

Many methods for simulating streamflow (both hydrologic and hydraulic) are readily available; selection and 

application of the correct model is generally based on the 

intended use of the model, limitations of available data, and, 

often, the experience of the user (see http://water.usgs.gov/

software/ or http://smig.usgs.gov/SMIC/model_pages/ for 

model summaries). Where greater precision is required for 

a specific purpose and experienced personnel are available 

to set up the model, hydraulic-routing models and (or) some 

combination of hydraulic and hydrologic models may be 

preferable; for example, BRANCH (Schaffranek and others, 

1981). Where streamflow and water quality are the primary 

focus, other models incorporate hydrology and hydraulics 

(for example, HSPF; Bicknell and others, 1997). WATER is 

a regional-scale hydrologic model that will optimally function in smaller, unregulated, upland watersheds where precise 

channel-geometry data are not available or are unfeasible to 

collect. Users of WATER should be aware of the limitations 

regarding a regional hydrologic model including the temporal 

nature of surface-water model data (for example, withdrawal 

and discharge data). However, these limitations are offset by 

the benefits of preprocessing the large amounts of complex 

digital data (for example, topographic and soil parameters), 

as well as decisions about model parameters, that have been 

incorporated by WATER, resulting in a more user-friendly, 

enhanced hydrologic-modeling tool. 

WATER, and the underlying TOPMODEL code, provide 

a comprehensive picture of basin processes, including water 

budget, streamflow, and slope processes in basins of varying 

size and with a database that can readily be aggregated for an 

extensive geographic area. Given the wide range of potential 

models and modeling applications, the WATER application 

was written in such a way that it may be upgraded or combined with models that use basin processes to estimate water 

quality and ecosystem needs. This ability to adapt the model 

code is required as new uses for the model become apparent and the availability of input data improves. For example, 

NOAA NEXRAD precipitation data have been incorporated 

into the current WATER application to improve precision 

and accuracy over previous versions of TOPMODEL. Recent 

advances in LIDAR (light detection and ranging) are among 

the more promising improvements that could potentially 

build increased precision and accuracy into future versions of 

WATER by greatly improving topographic resolution. 
-1.6-1.1-0.6-0.1

0.40.9

m

0.5m

0.25m

0.10m

0.05mmd

Bias

30-cm rooting depth, NEXRAD Scenarios

a. Bias

012345678

Root Mean Square Error

m

0.5m

0.25m

0.10m

30-cm rooting depth, NEXRAD Sc

-0.2

Correlation Coefficient

c. Correlation coefficient

m

0.5m

0.25m

0.10m

0.05mmd

30-cm rooting depth, NEXRAD Scenarios

00.2

0.40.6

0.81

m

0.5m

0.25m

0.10m

-1 30-cm rooting depth, NEXRAD Sc-0.8-0.6-0.4-0.2

00.20.40.60.81

Nash-Sutcliffe Efficiency

b. Root mean square errord. Nash-Sutcliffe efficiency

032079650321000003237255032512000328070003281100

U.S. Geological Survey

 site identification Scenario parameters

30-cm 30-centimeter rooting depthNEXRAD Next Generation Radar0.5 0.5 x SSURGO calculated value0.25 0.25 x SSURGO calculated value0.10 0.10 x SSURGO calculated value

032820400328250003406500036112600361180007024000

0.05 0.05 x SSURGO calculated valueSSURGO State Soil Survey Geographic database

m Scaling parametermd Model-development scaling parameter

EXPLANATION

Figure 14. Statistical summary of scaling parameter (m) calibration scenarios. 
0 

102030405060

1-Apr

11-Apr

21-Apr

1-May

11-May

21-May

31-May

10-Jun

20-Jun

30-Jun

10-Jul

20-Jul

30-Jul

9-Aug

19-Aug

29-Aug

8-Sep

2003

NEXRAD precipitationObserved discharge

m (mm) as calculated from 

SSURGO

0.5 m

0.25 m

0.1 m

0.05 m

DISCHARGE, IN MILLIMETERS

Figure 15. Comparison of scaling parameter (m) scenarios and discharge for calibration basin 03282040. 
- 0.5- 0.4- 0.3- 0.2- 0.1

0

0.10.20.30.40.5

03207965

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03237255

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03281100

03282040

03282500

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Bias

m0.10, NEXRAD scenarios

a. Bias

30-centimeter rooting depth40-centimeter rooting depth

03611260

07024000

0

0.10.20.30.40.50.60.70.80.9

03207965

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Correlation

c. Correlation

0

0.10.20.30.40.50.60.70.80.9

03207965

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03237255

03251200

03280700

03281100

03282040

03282500

d. Nash-Sutcliffe efficiency

0123456

03207965

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03251200

03280700

03281100

03282040

03282500

Root Mean Square Error

b. Root mean square error

Nash-Sutcliffe Efficiency

NEXRAD Next Generation Radar

m Scaling parameter

03207965 U.S. Geological Survey site identification

EXPLANATION

Figure 16. Statistical summary of Water Availability Tool for Environmental Resources (WATER) estimates for 30- and 40-centimeter rooting-depth 
0 

102030405060

1 -Apr

11 -Apr

21 -Apr

1 -May

11 -May

21 -May

31 -May

10 -Jun

20 -Jun

30 -Jun

10 -Jul

20 -Jul

30 -Jul

9 -Aug

19 -Aug

29 -Aug

8 -Sep

2003

NEXRAD precipitatObserved discharg

30-cm rooting dept40-cm rooting dept

DISCHARGE, IN MILLIMETERS

Figure 17. Comparison of 30- and 40-centimeter rooting-depth scenarios using 0.1 m for calibration basin 03282040. 
03207965

03611800

03611260

03210000

03280700

03282500

07024000

03282040

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03237255

03251200

03406500

-0.4-0.3-0.2-0.1

0

0.10.20.30.40.5

Bias

30-cm rooting depth, m0.10, NEXRAD Scenario

a. Bias

USGS site ID

30-cm rooting depth, m0.10, NEX

17

17

27

146

158

171

178

200 4

0123456 b. Root mean square error

Calibration basin area (km

30-cm rooting depth, m0.10, NEX

0

0.10.20.30.40.50.60.70.8 d. Nash-Sutcliffe efficiency

Correlation Coefficient

Nash-Sutcliffe Efficiency Root Mean Square Error

0

0.10.20.30.40.50.60.70.80.9 c. Correlation coefficient

30-cm rooting depth, m0.10, NEXRAD Scenario

032079650321000003237255032512000328070003281100

U.S. Geological Survey 

site identification Scenario parameters

30-cm 30-centimeter rooting depthCOOP National Weather Service Cooperative Network NEXRAD Next Generation Radarm Scaling parameter

032820400328250003406500036112600361180007024000

EXPLANATION

Figure 18. Statistical summary of Water Availability Tool for Environmental Resources (WATER) estimates for calibration basins in order of basi
32 The Water Availability Tool for Environmental Resources (WATER): Phase I—Data Processing and Documentation

Summary

The Water Availability Tool for Environmental Resources 

(WATER) was developed in phases to model ungaged streams 

in Kentucky; Phase I, discussed herein, modeled non-karst 

areas of Kentucky. WATER provides hydrographs, flow-duration curves, and a separation of flow components—together 

with a 58-year (1948–2006) climatic record—that can be used 

in to help make water-management decisions. The model is 

firmly based in topographic, pedogenic, and anthropogenic 

water-use data; it requires no additional input from the user. 

Consequently, it is easy to use, requires little training, and 

provides consistent and defensible analyses. 

The model has been statistically tested and calibrated for 

12 gaged basins over the 2000–06 time period. (Evaluation 

statistics were bias, root mean square error, correlation, and 

Nash-Sutcliffe efficiency.) This statistical evaluation showed 

that the use of Next Generation radar (NEXRAD) precipitation 

data, as opposed to precipitation data of coarser spatial resolution, is critical to accurately estimating discharge events. This 

statistical evaluation also showed that multiple physiographic 

terranes can be modeled accurately and that these discharge 

estimates are the most consistent when a 30-centimeter rooting 

depth is used together with a Soil Survey Geographic Database (SSURGO) derived scaling factor of 0.10 m. Although 

the statistics indicate that localized optimization of the model 

could improve discharge estimates, for example in cases of 

tile-drained landscapes, WATER successfully performs with 

these model inputs across the entire study region—the nonkarst areas of Kentucky. 

References Cited

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Lammers, Richard, Dungan, Jennifer, and Nemani, Ramakrishna, 1991, Forest ecosystem processes at the watershed 

scale—Basis for distributed simulation: Ecological Modelling, v. 56, p. 171–196.

Beven, K.J., 1984, Infiltration into a class of vertically nonuniform soils: Hydrological Sciences Journal, v. 29, no. 4, 

p. 425–434. 

Beven, K.J., 1986a, Runoff production and flood frequency in 

catchments of order n—An alternative approach, in Gupta, 

V.K., Rodriguez-Iturbe, I., and Wood, E.F., eds., Scale problems in hydrology: Dordrecht, The Netherlands, D. Reidel 

Publishing Company, p. 107–131.

Beven, K.J., 1986b, Hillslope runoff processes and flood 

frequency characteristics, in Abrahams, A.D., ed., Hillslope 

processes: London, Allen and Unwin, p. 187–202.

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hydrological heterogeneity—Catchment morphology and 

catchment response: Journal of Hydrology, v. 100, nos. 1–3, 

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Bicknell, B.R., Imhoff, J.C., Kittle, J.L., Jr., Donigian, A.S., 

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Research Laboratory, EPA/600/R–97/080, 755 p.

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between model predictions, parameters and DTM scales for 

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and macroscale models of water and energy balance: Princeton, N.J., Princeton University, Ph.D. dissertation, 207 p.

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simulations: Journal of Hydrometeorology, v. 3, no. 5, 

p. 571–590. 

Hendrick, R.L, and Pregitzer, K.S., 1996, Temporal and depthrelated patterns of fine root dynamics in northern hardwood 

forests: Journal of Ecology, v. 84, no. 2, p. 167–176. 

Hornberger, G.M., Beven, K.J., Cosby, B.J., and Sappington, 

D.E., 1985, Shenandoah watershed study—Calibration of 

a topography-based, variable contributing area hydrological model to a small forested catchment: Water Resources 

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Ijjász-Vásquez, E.J., Bras, R.L., and Moglen, G.E., 1992, 

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Kennen, J.G., Kauffman, L.J., Ayers, M.A., Wolock, D.M., 

and Colarullo, S.J., 2008, Use of an integrated flow model 

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no 10, p. 57–76. 

Kirkby, M., 1986, A runoff simulation model based on hillslope topography, in Gupta, V.K., Rodriguez-Iturbe, I., and 

Wood, E.F., eds., Scale problems in hydrology: Dordrecht, 

The Netherlands, D. Reidel Publishing Company, p. 39–56.

Martin, G.R., Zarriello, P.J., and Shipp, A.A., 2000, Hydrologic and water-quality characterization and modeling of 

the Chenoweth Run Basin, Jefferson County, Kentucky: 

U.S. Geological Survey Water-Resources Investigations 

Report 00 –4239, 197 p. 

McCuen, R.H., Knight, Z., and Cutter, A.G., 2006, Evaluation 

of the Nash-Sutcliffe efficiency index: Journal of Hydrologic Engineering, v. 11, p. 597– 602. 

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through conceptual models, Part 1—A discussion of principles: Journal of Hydrology, v. 10, no. 3, p. 282–290. 

Over, T.M., Murphy, E.A., Ortel, T.W., and Ishii, A.L., 2007, 

Comparisons between NEXRAD radar and tipping-bucket 

gage rainfall data—A case study for DuPage County, 

Illinois, in Kabbes, K.C., ed., Restoring our natural habitat—Proceedings of the World Environmental and Water 

Resources Congress 2007, Tampa, Fla., May 15–19, 2007: 

Reston, Va., American Society of Civil Engineers, 14 p. 

Quinn, P.F., Beven, K.J., and Lamb, R., 1997, The ln(a/tanβ) 

index—How to calculate it and how to use it within the 

TOPMODEL framework, in Beven, K.J., ed., Distributed 

hydrological modeling—Applications of the TOPMODEL 

concept: Chichester, England, Wiley, p. 31–52. 

Robson, Alice, Beven, Keith, and Neal, Colin, 1992, Towards 

identifying sources of subsurface flow—A comparison of 

components identified by a physically based runoff model 

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